The following lists only preprints without a corresponding final revised paper.
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04 Jun 2025
NAAC (v1.0): A Seamless Two-Decade Cross-Scale Simulation from the North American Atlantic Coast to Tidal Wetlands Using the 3D Unstructured-grid Model SCHISM (v5.11.0)
Xun Cai, Qubin Qin, Linlin Cui, Xiucheng Yang, Yinglong Joseph Zhang, and Jian Shen
EGUsphere, https://doi.org/10.5194/egusphere-2025-593, https://doi.org/10.5194/egusphere-2025-593, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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We presented NAAC, a high-resolution, two-decade simulation of coastal hydrodynamics using the 3D unstructured-grid model SCHISM (v5.11.0). This model seamlessly integrates simulations from the North American Atlantic coastal ocean to tidal tributaries and wetlands. By bridging the gap between large-scale regional ocean models and fine-scale shallow water systems and intertidal zones, this work helps fill observational gaps and provides valuable insights into studies like saltwater intrusion.
03 Jun 2025
SWIIFT v0.10: a numerical model of wave-induced sea ice breakup based on an energy criterion
Nicolas Guillaume Alexandre Mokus, Véronique Dansereau, Guillaume Boutin, Jean-Pierre Auclair, and Alexandre Tlili
EGUsphere, https://doi.org/10.5194/egusphere-2025-1831, https://doi.org/10.5194/egusphere-2025-1831, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Arctic sea ice recedes and it is more exposed to waves. Waves can then fracture continuous pack ice into floes, which are more mobile and easier to melt. The fracture process itself is not well understood, because of harsh field conditions. We propose a novel sea ice fracture criterion incorporated into a numerical model that simulates wave propagation. This criterion can be compared to existing ones. We relate our results to laboratory experiments, and find qualitative agreement.
03 Jun 2025
Machine learning significantly improves the simulation of hourly-to-yearly scale cloud nuclei concentration and radiative forcing in polluted atmosphere
Jingye Ren, Songjian Zou, Honghao Xu, Guiquan Liu, Zhe Wang, Anran Zhang, Chuanfeng Zhao, Min Hu, Dongjie Shang, Lizi Tang, Ru-Jin Huang, Yele Sun, and Fang Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1483, https://doi.org/10.5194/egusphere-2025-1483, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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In this study, a new framework of cloud condensation nuclei (CCN) prediction in polluted region has been developed and it achieves well prediction of hourly-to-yearly scale across North China Plain. The study reveals a significant long-term decreasing trend of CCN concentration at typical supersaturations due to a rapid reduction in aerosol concentrations from 2014 to 2018. This improvement of our new model would be helpful to aerosols climate effect assessment in models.
03 Jun 2025
Examining Spin-Up Behaviour within WRF Dynamical Downscaling Applications
Megan S. Mallard, Tanya Spero, Jared Bowden, Jeff Willison, Christopher G. Nolte, and Anna M. Jalowska
EGUsphere, https://doi.org/10.5194/egusphere-2025-2352, https://doi.org/10.5194/egusphere-2025-2352, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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“Spin-up” is time needed for a model’s result to become effectively free of influence from initial conditions, and it is usually excluded from analysis. Here, spin-up is examined by comparing one decadal simulation to another initialized 20 years prior, in order to determine when their solutions converge. Differences lessen over the first fall and winter, but re-emerge over the following spring and summer, suggesting that at least 1 annual cycle is needed to spin up regional climate simulations.
03 Jun 2025
Development of the global maize production model MATCRO-Maize version 1.0
Marin Nagata, Astrid Yusara, Tomomichi Kato, and Yuji Masutomi
EGUsphere, https://doi.org/10.5194/egusphere-2025-1885, https://doi.org/10.5194/egusphere-2025-1885, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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We developed maize version of process-based crop model coupled with a land surface model (MATCRO). It extends the original MATCRO-Rice by incorporating C4 photosynthesis and maize-specific parameters. The model was validated using field data from four sites and global yield data from FAOSTAT. MATCRO-Maize captured the interannual yield variability in global and county-level yield data, demonstrating its potential for climate impact assessments on maize production.
03 Jun 2025
Modeling Supercritical CO2 Flow and Mineralization in Reactive Host Rocks with PFLOTRAN v7.0
Michael Nole, Katherine Muller, Glenn Hammond, Xiaoliang He, and Peter Lichtner
EGUsphere, https://doi.org/10.5194/egusphere-2025-1343, https://doi.org/10.5194/egusphere-2025-1343, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Subsurface injection of carbon dioxide (CO2) can be used for a variety of purposes including geologic carbon storage and enhanced oil recovery. Recently, CO2 injection into reactive host rocks has been explored as a way to transform CO2 into dense solid minerals. We present a simulation framework for modeling flow of CO2 due to injection and subsequent reactions that take place to mineralize CO2.
02 Jun 2025
Best practices in software development for robust and reproducible geoscientific models based on insights from the Global Carbon Project models
Konstantin Gregor, Benjamin F. Meyer, Tillmann Gaida, Victor Justo Vasquez, Karina Bett-Williams, Matthew Forrest, João P. Darela-Filho, Sam Rabin, Marcos Longo, Joe R. Melton, Johan Nord, Peter Anthoni, Vladislav Bastrikov, Thomas Colligan, Christine Delire, Michael C. Dietze, George Hurtt, Akihiko Ito, Lasse T. Keetz, Jürgen Knauer, Johannes Köster, Tzu-Shun Lin, Lei Ma, Marie Minvielle, Stefan Olin, Sebastian Ostberg, Hao Shi, Reiner Schnur, Urs Schönenberger, Qing Sun, Peter E. Thornton, and Anja Rammig
EGUsphere, https://doi.org/10.5194/egusphere-2025-1733, https://doi.org/10.5194/egusphere-2025-1733, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Geoscientific models are crucial for understanding Earth’s processes. However, they sometimes do not adhere to highest software quality standards, and scientific results are often hard to reproduce due to the complexity of the workflows. Here we gather the expertise of 20 modeling groups and software engineers to define best practices for making geoscientific models maintainable, usable, and reproducible. We conclude with an open-source example serving as a reference for modeling communities.
02 Jun 2025
Implementing Riverine Biogeochemical Inputs in ECCO-Darwin: a Critical Step Forward for a Pioneering Data-Assimilative Global-Ocean Biogeochemistry Model
Raphaël Savelli, Dustin Carroll, Dimitris Menemenlis, Jonathan Lauderdale, Clément Bertin, Stephanie Dutkiewicz, Manfredi Manizza, Anthony Bloom, Karel Castro-Morales, Charles E. Miller, Marc Simard, Kevin W. Bowman, and Hong Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1707, https://doi.org/10.5194/egusphere-2025-1707, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Accounting for carbon and nutrients in rivers is essential for resolving carbon dioxide (CO2) exchanges between the ocean and the atmosphere. In this study, we add the effect of present-day rivers to a pioneering global-ocean biogeochemistry model. This study highlights the challenge for global ocean numerical models to cover the complexity of the flow of water and carbon across the Land-to-Ocean Aquatic Continuum.
02 Jun 2025
An Emulator-Based Modelling Framework for Studying Astronomical Controls on Ocean Anoxia with an Application on the Devonian
Loïc Sablon, Pierre Maffre, Yves Goddéris, Paul J. Valdes, Justin Gérard, Jarno J. C. Huygh, Anne-Christine Da Silva, and Michel Crucifix
EGUsphere, https://doi.org/10.5194/egusphere-2025-1696, https://doi.org/10.5194/egusphere-2025-1696, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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We propose an innovative climate modelling framework that combines statistical methods with climate simulations to study Earth's environmental systems. The model captures how orbital changes and carbon dioxide levels influence climate atmospheric dynamics, offering a detailed and efficient way to explore long-term processes. This tool provides new opportunities to investigate Earth's climate history and its implications for future changes.
02 Jun 2025
The microbial community model MCoM 1.0: A scalable framework for modelling phototroph-heterotrophic interactions in diverse microbial communities
Leonhard Lücken, Michael J. Follows, Jason G. Bragg, and Sinikka T. Lennartz
EGUsphere, https://doi.org/10.5194/egusphere-2025-2227, https://doi.org/10.5194/egusphere-2025-2227, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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The Microbial Community Model (MCoM) is a flexible biogeochemical modeling framework which resolves a rich set of interactions between photosynthetic and heterotrophic microbes, including cross-feeding, metabolite exchange, and nutrient recycling. As such, it allows to assess community-level effects on elemental turnover emerging from microbial interactions. Its scalability allows to represent both simple pairwise interactions and large, diverse communities.
02 Jun 2025
A simple step heating approach for wall surface temperature estimation in the SOlar and LongWave Environmental Irradiance Geometry (SOLWEIG) model
Nils Wallenberg, Björn Holmer, Fredrik Lindberg, Jessika Lönn, Erik Maesel, and David Rayner
EGUsphere, https://doi.org/10.5194/egusphere-2025-2093, https://doi.org/10.5194/egusphere-2025-2093, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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This work presents a method to calculate wall surface temperatures in complex urban areas using a step heating equation based on air temperature and net radiation at the wall surface. Our results show that the step heating approach is a fast and accurate, comparable to other more complex methods. This method can potentially be applied in different areas of interest where wall surface temperatures are important, e.g. modeling of thermal comfort, building energy and urban energy balance.
02 Jun 2025
Scenario set-up and the new CMIP6-based climate-related forcings provided within the third round of the Inter-Sectoral Model Intercomparison Project (ISIMIP3b, group I and II)
Katja Frieler, Stefan Lange, Jacob Schewe, Matthias Mengel, Simon Treu, Christian Otto, Jan Volkholz, Christopher P. O. Reyer, Stefanie Heinicke, Colin Jones, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Ryan Heneghan, Derek P. Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Dánnell Quesada Chacón, Kerry Emanuel, Chia-Ying Lee, Suzana J. Camargo, Jonas Jägermeyr, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Lisa Novak, Inga J. Sauer, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, Michel Bechtold, Robert Reinecke, Inge de Graaf, Jed O. Kaplan, Alexander Koch, and Matthieu Lengaigne
EGUsphere, https://doi.org/10.5194/egusphere-2025-2103, https://doi.org/10.5194/egusphere-2025-2103, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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This paper describes the experiments and data sets necessary to run historic and future impact projections, and the underlying assumptions of future climate change as defined by the 3rd round of the ISIMIP Project (Inter-sectoral Impactmodel Intercomparison Project, isimip.org). ISIMIP provides a framework for cross-sectorally consistent climate impact simulations to contribute to a comprehensive and consistent picture of the world under different climate-change scenarios.
02 Jun 2025
An endogenous modelling framework of dietary behavioural change in the fully coupled human-climate FRIDA v2.1 model
Jefferson K. Rajah, Benjamin Blanz, Birgit Kopainsky, and William Schoenberg
EGUsphere, https://doi.org/10.5194/egusphere-2025-2260, https://doi.org/10.5194/egusphere-2025-2260, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Climate models often exclude human behaviour. We introduce a model that includes economic, social, and environmental factors that influence dietary choices. This helps us understand how behaviour shifts impact future emissions and climate conditions. By considering a range of plausible behaviours, we provide a more accurate picture of potential outcomes, improving representations in climate models.
02 Jun 2025
Advanced modeling of gas chemistry and aerosol dynamics with SSH-aerosol v2.0
Karine Sartelet, Zhizhao Wang, Youngseob Kim, Victor Lannuque, and Florian Couvidat
EGUsphere, https://doi.org/10.5194/egusphere-2025-2191, https://doi.org/10.5194/egusphere-2025-2191, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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SSH-aerosol v2 simulates the evolution of primary and secondary pollutants via gas-phase chemistry, aerosol dynamics (including ultrafine particles), and intra-particle reactions. It uses a sectional approach for size and composition, includes a wall-loss module, and links gas-phase mechanisms of different complexity to secondary organic aerosol formation. Representation of particle phase composition allows viscosity and non-ideality to be taken into account.
02 Jun 2025
Numerical strategies for representing Richards' equation and its couplings in snowpack models
Kévin Fourteau, Julien Brondex, Clément Cancès, and Marie Dumont
EGUsphere, https://doi.org/10.5194/egusphere-2025-444, https://doi.org/10.5194/egusphere-2025-444, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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The percolation of liquid water down snowpacks is a complex phenomenon, and its representation can sometimes be complicated for snowpack models. The goal of this article is to transpose some state-of-the-art strategies used for modeling liquid percolation in other media (such as rocks or soil) into snowpack models. With this, snowpack models can be made more efficient, requiring less time and power to perform their computation.
02 Jun 2025
Validation of climate mitigation pathways
Pascal Weigmann, Rahel Mandaroux, Fabrice Lécuyer, Anne Merfort, Tabea Dorndorf, Johanna Hoppe, Jarusch Müßel, Robert Pietzcker, Oliver Richters, Lavinia Baumstark, Elmar Kriegler, Nico Bauer, Falk Benke, Chen Chris Gong, and Gunnar Luderer
EGUsphere, https://doi.org/10.5194/egusphere-2025-2284, https://doi.org/10.5194/egusphere-2025-2284, 2025
Preprint under review for GMD (discussion: open, 1 comment)
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We present the Potsdam Integrated Assessment Modeling validation tool, piamValidation, an open-source R package for validating IAM scenarios. The tool enables structured comparison of IAM outputs with historical data, feasibility constraints, and alternative scenarios or models. Designed as a community resource, validation configuration files can serve as a knowledge sharing platform. The main objective is to improve the credibility of IAMs by promoting standardized validation practices.
30 May 2025
Exploiting Physics-Based Machine Learning to Quantify Geodynamic Effects – Insights from the Alpine Region
Denise Degen, Ajay Kumar, Magdalena Scheck-Wenderoth, and Mauro Cacace
EGUsphere, https://doi.org/10.5194/egusphere-2025-1925, https://doi.org/10.5194/egusphere-2025-1925, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Geodynamical simulations cover a wide spatial and temporal range and are crucial to understand and assess the evolution of the Earth system. To enable computationally efficient modeling approaches that can account for potentially unknown subsurface properties, we present a surrogate modeling technique. This technique combines physics-based and machine-learning techniques to enable reliable predictions of geodynamical applications, as we illustrate for the case study of the Alpine Region.
28 May 2025
Global ocean and sea ice variability simulated in eddy-permitting climate models
Yushi Morioka, Eric Maisonnave, Sébastien Masson, Clement Rousset, Luis Kornblueh, Marco Giorgetta, Masami Nonaka, and Swadhin K. Behera
EGUsphere, https://doi.org/10.5194/egusphere-2025-2258, https://doi.org/10.5194/egusphere-2025-2258, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Ocean mesoscale eddies, which have a horizontal scale with an order of 100 km, play a prominent role in global ocean heat transport that regulates Earth climate. Here we newly develop an eddy-permitting climate model to demonstrate that the increased ocean model resolution improves representation of air-sea interaction in the western and eastern boundary current regions, while the improved sea ice model physics benefit realistic simulation of sea ice variability.
28 May 2025
A barycenter-based approach for the multi-model ensembling of subseasonal forecasts
Camille Le Coz, Alexis Tantet, Rémi Flamary, and Riwal Plougonven
EGUsphere, https://doi.org/10.5194/egusphere-2025-1330, https://doi.org/10.5194/egusphere-2025-1330, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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We reformulate multi-model ensembles by treating ensemble forecasts as discrete probability distributions and combining them using barycenters. We compare the L2 barycenter (equivalent to pooling) with the Wasserstein barycenter (more precisely its Gaussian approximation). Both have the same ensemble mean but differ in how they represent forecasts uncertainty. In terms of Continuous Ranked Probability Score, the Wasserstein barycenter outperforms more often while performing similarly on average.
28 May 2025
A Transformer-based agent model of GEOS-Chem v14.2.2 for informative prediction of PM2.5 and O3 levels to future emission scenarios: TGEOS v1.0
Dehao Li, Jianbing Jin, Guoqiang Wang, Mijie Pang, and Hong Liao
EGUsphere, https://doi.org/10.5194/egusphere-2025-2186, https://doi.org/10.5194/egusphere-2025-2186, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Efficient air quality modeling in future emission scenarios is vital for air pollution policies. Restricted by model structure, previous methods are computationally expensive or focus on single target. Thus, based on advanced machine learning framework "Transformer", this study introduces a rapid GEOS-Chem proxy model "TGEOS v1.0". Its predictions are similar to the GEOS-Chem v14.2.2 output. It can also predict the probability distributions of PM2.5 and O3 under various emission scenarios.
28 May 2025
A new efficiency metric for the spatial evaluation and inter-comparison of climate and geoscientific model output
Andreas Karpasitis, Panos Hadjinicolaou, and George Zittis
EGUsphere, https://doi.org/10.5194/egusphere-2025-1471, https://doi.org/10.5194/egusphere-2025-1471, 2025
Preprint under review for GMD (discussion: open, 2 comments)
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The use of models to understand Earth's climate is essential, but evaluating how well these models reproduce real-world patterns remains a challenge. In this study, the Modified Spatial Efficiency metric was introduced to improve model assessment. Our results show that this metric reliably captures spatial patterns under diverse conditions and aligns well with our intuition. This advancement can help researchers better compare climate models and improve predictions of environmental changes.
28 May 2025
PHOREAU v1.0: a new process-based model to predict forest functioning, from tree ecophysiology to forest dynamics and biogeography
Tanguy Postic, François de Coligny, Isabelle Chuine, Louis Devresse, Daniel Berveiller, Hervé Cochard, Matthias Cuntz, Nicolas Delpierre, Émilie Joetzjer, Jean-Marc Limousin, Jean-Marc Ourcival, François Pimont, Julien Ruffault, Guillaume Simioni, Nicolas K. Martin-StPaul, and Xavier Morin
EGUsphere, https://doi.org/10.5194/egusphere-2025-2110, https://doi.org/10.5194/egusphere-2025-2110, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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PHOREAU is a forest dynamic model that links plant traits with water use, growth, and climate responses to explore how species diversity affects productivity and resilience. Validated across European forests, PHOREAU simulates how tree communities function under drought and warming. Our findings support the use of trait-based modeling to guide forest adaptation strategies under future climate scenarios.
28 May 2025
The Met Office Unified Model Global Atmosphere 8.0 and JULES Global Land 9.0 configurations
Martin Richard Willett, Melissa Brooks, Andrew Bushell, Paul Earnshaw, Samantha Smith, Lorenzo Tomassini, Martin Best, Ian Boutle, Jennifer Brooke, John M. Edwards, Kalli Furtado, Catherine Hardacre, Andrew J. Hartley, Alan Hewitt, Ben Johnson, Adrian Lock, Andy Malcolm, Jane Mulcahy, Eike Müller, Heather Rumbold, Gabriel G. Rooney, Alistair Sellar, Masashi Ujiie, Annelize van Niekerk, Andy Wiltshire, and Michael Whitall
EGUsphere, https://doi.org/10.5194/egusphere-2025-1829, https://doi.org/10.5194/egusphere-2025-1829, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Global Atmosphere (GA) configurations of the Unified Model (UM) and Global Land (GL) configurations of JULES are developed for use in any global atmospheric modelling application. We describe a recent iteration of these configurations, GA8GL9, which includes improvements to the represenation of convection and other physical processes. GA8GL9 is used for operational weather prediction in the UK and forms the basis for the next GA and GL configuration.
27 May 2025
The Atlantic Ocean's Decadal Variability in mid-Holocene Simulations using Shannon's Entropy
Iuri Gorenstein, Ilana Wainer, Francesco S. R. Pausata, Luciana F. Prado, Pedro L. S. Dias, Allegra N. LeGrande, Clay R. Tabor, and William R. Peltier
EGUsphere, https://doi.org/10.5194/egusphere-2025-921, https://doi.org/10.5194/egusphere-2025-921, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Using a new approach based on information theory we study climate variability in the tropical and South Atlantic by examining broad patterns in ocean and rainfall data at decadal scales. Four climate models under mid‐Holocene and pre‐industrial conditions show that shifts in vegetation and dust yield varied weather responses. Our findings indicate that incorporating large-scale patterns provides a framework for understanding long-term climate behavior, offering insights for improved predictions.
27 May 2025
HOPE: An Arbitrary-Order Non-Oscillatory Finite-Volume Shallow Water Dynamical Core with Automatic Differentiation
Lilong Zhou and Wei Xue
EGUsphere, https://doi.org/10.5194/egusphere-2025-1889, https://doi.org/10.5194/egusphere-2025-1889, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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This study develops a novel physics-based weather prediction model using artificial intelligence development platforms, achieving high accuracy while maintaining strict physical conservation laws. Our algorithms are optimized for modern super computers, enabling efficient large-scale weather simulations. A key innovation is the model's inherent differentiable nature, allowing seamless integration with AI systems to enhance predictive capabilities through machine learning techniques.
27 May 2025
Attention-Driven and Multi-Scale Feature Integrated Approach for Earth Surface Temperature Data Reconstruction
Minghui Zhang, Yunjie Chen, Fan Yang, and Zhengkun Qin
EGUsphere, https://doi.org/10.5194/egusphere-2025-1980, https://doi.org/10.5194/egusphere-2025-1980, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Considering the key role of high-resolution surface observation temperature data in the study of surface atmospheric temperature in ocean regions, we propose a new two-stage deep learning model. The model is used to fill ocean surface temperature data missing from satellite observations due to the orbital clearance of polar satellites.
26 May 2025
Recovery of stratigraphic data with associated uncertainties from drillhole databases using litho2strat 1.0
Vitaliy Ogarko and Mark Jessell
EGUsphere, https://doi.org/10.5194/egusphere-2025-1294, https://doi.org/10.5194/egusphere-2025-1294, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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We developed a new method to reconstruct underground rock layers from drillhole data, using an advanced algorithm to ensure geologically realistic results. By combining data from multiple drillholes, our approach reduces uncertainty and improves accuracy. Tested on South Australian data, it successfully predicted stratigraphy and highlighted ways to enhance data quality. This innovation makes geological analysis more reliable, aiding exploration and resource management.
26 May 2025
Contribution of physical latent knowledge to the emulation of an atmospheric physics model: a study based on the LMDZ Atmospheric General Circulation Model
Ségolène Crossouard, Soulivanh Thao, Thomas Dubos, Masa Kageyama, Mathieu Vrac, and Yann Meurdesoif
EGUsphere, https://doi.org/10.5194/egusphere-2025-1418, https://doi.org/10.5194/egusphere-2025-1418, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Current atmospheric models are limited by the computational time required for physical processes, known as physical parameterizations. To address this, we developed neural network-based emulators to replace these parameterizations in the IPSL climate model, using a simplified aquaplanet setup. We found that incorporating some physical knowledge, such as latent variables, into the learning process can improve predictions.
26 May 2025
Simulating the recent drought-induced mortality of European beech (Fagus sylvatica L.) and Norway spruce (Picea abies L.) in German forests
Gina Marano, Ulrike Hiltner, Nikolai Knapp, and Harald Bugmann
EGUsphere, https://doi.org/10.5194/egusphere-2025-1534, https://doi.org/10.5194/egusphere-2025-1534, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Drought is reshaping Europe’s forests. Using an uncalibrated process-based model across hundreds of German sites, we identified key drivers of tree mortality in European beech and Norway spruce forests. Our model captured both the timing and extent of mortality. A new bark beetle module improved predictions for spruce. High soil water capacity and heterogeneous soils reduced drought impacts. These findings offer new insights to anticipate forest responses in a warming, drying climate.
26 May 2025
QuadTune version 1: A regional tuner for global atmospheric models
Vincent Larson, Zhun Guo, Benjamin Stephens, Colin Zarzycki, Gerhard Dikta, Yun Qian, and Shaocheng Xie
EGUsphere, https://doi.org/10.5194/egusphere-2025-1593, https://doi.org/10.5194/egusphere-2025-1593, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Global models of the atmosphere contain errors that lead to inaccurate simulations. A software tool ("QuadTune") is presented that attempts to mitigate some of the inaccuracies. It also displays diagnostic plots that provide hints about where the errors might lie in the model.
26 May 2025
Smoothing and spatial verification of global fields
Gregor Skok and Katarina Kosovelj
External preprint server, https://doi.org/10.48550/arXiv.2412.00936, https://doi.org/10.48550/arXiv.2412.00936, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Forecast verification is essential for improving weather prediction models but faces challenges with traditionally used metrics. New spatial verification metrics like the Fraction Skill Score (FSS) perform better but are difficult to use in a global domain due to large computational cost. We introduce two new global smoothing methodologies that can used with smoothing-based metrics in a global domain. We demonstrate their effectiveness with an analysis of global precipitation forecasts.
26 May 2025
Quantifying Coupling Errors in Atmosphere-Ocean-Sea Ice Models: A Study of Iterative and Non-Iterative Approaches in the EC-Earth AOSCM
Valentina Schüller, Florian Lemarié, Philipp Birken, and Eric Blayo
EGUsphere, https://doi.org/10.5194/egusphere-2025-1342, https://doi.org/10.5194/egusphere-2025-1342, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Earth system models consist of many components, coupled in time and space. Standard coupling algorithms introduce a numerical error, which one can compute with iterative coupling methods. We use such a method for the EC-Earth AOSCM, which models a single vertical column of the atmosphere, ocean, and sea ice. We find that coupling errors in the atmosphere and at the ice surface can be substantial and that discontinuous physics parameterizations lead to convergence issues of the iteration.
21 May 2025
Hybrid Lake Model (HyLake) v1.0: unifying deep learning and physical principles for simulating lake-atmosphere interactions
Yuan He and Xiaofan Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1983, https://doi.org/10.5194/egusphere-2025-1983, 2025
Preprint under review for GMD (discussion: open, 1 comment)
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This study introduces HyLake, a hybrid lake model that embeds a deep-learning surrogate for the water temperature module within a process-based backbone. HyLake simulates lake surface temperature and the latent and sensible heat fluxes in Lake Taihu more accurately than traditional process-based models and other hybrid experiments across different forcing datasets. The proposed coupling strategy provides a reliable tool for quantifying the impacts of climate change on aquatic ecosystems.
21 May 2025
Impact of Satellite-Based Ice Surface Temperature Initialization on Arctic Winter Forecasts Using the Korean Integrated Model
Eui-Jong Kang, Byung-Ju Sohn, Wonho Kim, Young-Chan Noh, Shihye Lee, In-Hyuk Kwon, and Hwan-Jin Song
EGUsphere, https://doi.org/10.5194/egusphere-2025-2071, https://doi.org/10.5194/egusphere-2025-2071, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Accurate ice skin temperature (IST) is crucial for reliable Arctic weather forecasts. In this study, we assess the impact of realistic IST initialization using satellite-based data within the Korean Integrated Model. Results show that, while this reduces random errors by 3–5 %, its benefits are constrained by inherent model bias. These findings highlight the need to jointly improve IST initialization and model physics, offering guidance for future updates in KIM and other forecasting systems.
21 May 2025
DSCALE v0.1 – an open-source algorithm for downscaling regional and global mitigation pathways to the country level
Fabio Sferra, Bas van Ruijven, Keywan Riahi, Philip Hackstock, Florian Maczek, Jarmo Kikstra, and Reinhard Haas
EGUsphere, https://doi.org/10.5194/egusphere-2025-121, https://doi.org/10.5194/egusphere-2025-121, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Assessments of future emissions and the effectiveness of climate policies are usually performed with Integrated Assessment Models (IAMs). Bringing together insights from IAMs with information at the country level has remained difficult, as these models provide results for a limited number of regions. This paper presents DSCALE, a novel algorithm designed to downscale regional IAMs outcomes to the country level and shows results for a current policy and a 1.5C scenario from the NGFS 2023 project.
21 May 2025
A Climate Intervention Dynamical Emulator (CIDER) for Scenario Space Exploration
Jared Farley, Douglas G. MacMartin, Daniele Visioni, Ben Kravitz, Ewa Bednarz, Alistair Duffey, and Matthew Henry
EGUsphere, https://doi.org/10.5194/egusphere-2025-1830, https://doi.org/10.5194/egusphere-2025-1830, 2025
Preprint under review for GMD (discussion: open, 1 comment)
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As the climate changes, many are studying sunlight reflection as a potential method of cooling. Such climate intervention could be deployed in many possible ways, including in scenarios where not every actor agrees on the strategy of cooling. These scenarios are so diverse that to explore all of them using earth system models proves to be too costly. In this paper, we develop a simplified climate model that allows users to easily explore climate intervention scenarios of their choice.
21 May 2025
Runoff Evaluation in an Earth System Land Model for Permafrost Regions
Xiang Huang, Yu Zhang, Bo Gao, Charles J. Abolt, Ryan L. Crumley, Cansu Demir, Richard P. Fiorella, Bob Busey, Bob Bolton, Scott L. Painter, and Katrina E. Bennett
EGUsphere, https://doi.org/10.5194/egusphere-2025-1753, https://doi.org/10.5194/egusphere-2025-1753, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Predicting hydrological runoff in Arctic permafrost regions is difficult due to limited observations and complex terrain. We used a detailed physics-based model to improve runoff estimates in a Earth system land model. Our method improved runoff accuracy and worked well across two different Arctic regions. This helps make climate models more reliable for understanding water flow in permafrost areas under a changing climate.
21 May 2025
Implementation of a dry surface layer soil resistance in two contrasting semi-arid sites with SURFEX-ISBA V9.0
Belén Martí, Jannis Groh, Guylaine Canut, and Aaron Boone
EGUsphere, https://doi.org/10.5194/egusphere-2025-1783, https://doi.org/10.5194/egusphere-2025-1783, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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The characterization of vegetation at two sites proved insufficient to simulate adequately the evapotranspiration. A dry surface layer was implemented in the land surface model SURFEX-ISBA v9.0. It is compared to simulations without a soil resistance. The application to an alfalfa site and a natural grass site in semiarid conditions results in an improvement in the estimation of the latent heat flux. The surface energy budget and the soil and vegetation characteristics are explored in detail.
21 May 2025
Configuring parallel use of custom ArcGIS toolboxes in a Linux high-performance computing environment
Jeremy Baynes, Jacob Tafrate, Donald Ebert, and Steven Lennartz
EGUsphere, https://doi.org/10.5194/egusphere-2025-1558, https://doi.org/10.5194/egusphere-2025-1558, 2025
Preprint under review for GMD (discussion: open, 2 comments)
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This work describes our efforts of using Esri ArcGIS Toolboxes for large-scale geospatial processing in a high-performance computing (HPC) environment. Our custom ArcGIS toolbox was useful for automating workflows and our Agency’s HPC system offered massive parallel processing. However, these two products were not immediately compatible. Here we describe our solution and demonstrate its utility by performing an assessment of riparian land cover proportions across the conterminous United States.
21 May 2025
A Novel Method for Sea Surface Temperature Prediction using a Featural Granularity-Based ConvLSTM Model of Data-Knowledge-Driven
Mengmeng Cao, Kebiao Mao, Yibo Yan, Sayed Bateni, and Zhonghua Guo
EGUsphere, https://doi.org/10.5194/egusphere-2025-239, https://doi.org/10.5194/egusphere-2025-239, 2025
Preprint under review for GMD (discussion: open, 2 comments)
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We proposed a novel SST prediction model based on granular computing and a data-knowledge-driven ConvLSTM framework. Validation against observations and cross-comparisons with baseline models demonstrate that our approach generates consistent and more accurate regional SST predictions, making it highly promising for medium- and long-term monthly SST forecasts.
20 May 2025
Evaluating the Impact of Task Aggregation in Workflows with Shared Resource Environments: use case for the MONARCH application
Manuel G. Marciani, Miguel Castrillo, Gladys Utrera, Mario C. Acosta, Bruno P. Kinoshita, and Francisco Doblas-Reyes
EGUsphere, https://doi.org/10.5194/egusphere-2025-1104, https://doi.org/10.5194/egusphere-2025-1104, 2025
Preprint under review for GMD (discussion: open, 4 comments)
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Earth System Model simulations are executed with workflows in congested HPC resources. These workflows could be made of thousands of tasks, which, if naively submitted to be executed, might add overheads due to queueing for resources. In this paper we explored a technique of aggregating tasks into a single submission. We related it to a key factor used by the software in charge of the scheduling. We find that this simple technique can reduce up to 7 % of the time spent in queue.
19 May 2025
An information-theoretic approach to obtain ensemble averages from Earth system models
Carlos A. Sierra and Estefanía Muñoz
EGUsphere, https://doi.org/10.5194/egusphere-2025-1640, https://doi.org/10.5194/egusphere-2025-1640, 2025
Preprint under review for GMD (discussion: open, 1 comment)
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We propose an approach to obtain weights for calculating averages of variables from Earth system models (ESM) based on concepts from information theory. It quantifies a relative distance between model output and reality, even though it is impossible to know the absolute distance from model predictions to reality. The relative ranking among models is based on concepts of model selection and multi-model averages previously developed for simple statistical models, but adapted here for ESMs.
19 May 2025
GUST1.0: A GPU-accelerated 3D Urban Surface Temperature Model
Shuo-Jun Mei, Guanwen Chen, Jian Hang, and Ting Sun
EGUsphere, https://doi.org/10.5194/egusphere-2025-1485, https://doi.org/10.5194/egusphere-2025-1485, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Cities face growing heat challenges due to dense buildings, but predicting surface temperatures is complex because sunlight, airflow, and heat radiation interact. By simulating how sunlight bounces between structures and how heat transfers through materials, we accurately predicted temperatures on roofs, roads, and walls. The model successfully handled intricate city layouts thanks to GPU speed. By revealing which heat matters most, we aim to guide smarter city designs for a warming climate.
16 May 2025
Data clustering to optimise the representativity of observational data in air quality data assimilation: a case study with EURAD-IM (version 5.9.1 DA)
Alexander Hermanns, Anne Caroline Lange, Julia Kowalski, Hendrik Fuchs, and Philipp Franke
EGUsphere, https://doi.org/10.5194/egusphere-2025-450, https://doi.org/10.5194/egusphere-2025-450, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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For air quality analyses, data assimilation models split available data into assimilation and validation data sets. The former is used to generate the analysis, the latter to verify the simulations. A preprocessor classifying the observations by the data characteristics is developed based on clustering algorithms. The assimilation and validation data sets are compiled by equally allocating data of each cluster. The resulting improvement of the analysis is evaluated with EURAD-IM.
15 May 2025
All-sky AMSU-A radiance data assimilation using the gain-form of Local Ensemble Transform Kalman filter within MPAS-JEDI-2.1.0: implementation, tuning, and evaluation
Tao Sun, Jonathan J. Guerrette, Zhiquan Liu, Junmei Ban, Byoung-Joo Jung, Ivette Hernández Baños, and Chris Snyder
EGUsphere, https://doi.org/10.5194/egusphere-2025-2079, https://doi.org/10.5194/egusphere-2025-2079, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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We evaluated a new ensemble data assimilation system that uses satellite observations in all weather conditions for global weather forecasts. The results show that including cloud- and precipitation-affected satellite data improves forecasts of moisture, wind, and clouds, especially in the tropics. This work highlights the potential of this new ensemble data assimilation system to enhance global weather forecasts.
15 May 2025
Sensitivity of a Sahelian groundwater-based agroforestry system to tree density and water availability using the land surface model ORCHIDEE (r7949)
Espoir Koudjo Gaglo, Emeline Chaste, Sebastiaan Luyssaert, Olivier Roupsard, Christophe Jourdan, Sidy Sow, Nadeige Vandewalle, Frédéric Do, Daouda Ngom, and Aude Valade
EGUsphere, https://doi.org/10.5194/egusphere-2025-1102, https://doi.org/10.5194/egusphere-2025-1102, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Agroforestry in the Sahel help store carbon and support food production, but land surface models struggle to capture their dynamics. We adapted the ORCHIDEE model to simulate Faidherbia albida, a tree that taps deep groundwater. This work highlights the need to integrate deep water uptake in land surface models for groundwater-dependent ecosystems, as it could enhance predictions, helping to sustain agroforestry in a changing climate.
15 May 2025
Optimizing physical scheme selection in RegCM5 for improved air–sea fluxes over Southeast Asia
Quentin Desmet, Marine Herrmann, and Thanh Ngo-Duc
EGUsphere, https://doi.org/10.5194/egusphere-2025-1579, https://doi.org/10.5194/egusphere-2025-1579, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Climate model performance at the air–sea interface has long been overlooked across the Southeast Asian seas. We thus assess various regional model physics configurations in this regard. Finding one optimal configuration is challenging: reliable rainfall rarely coincides with correct radiative heating. Simulations of rainfall however yield more dissensus, suggesting that this variable should be prioritized, for which the best results are obtained with the cumulus convection scheme of Tiedtke.
15 May 2025
MET-AICE v1.0: an operational data-driven sea ice prediction system for the European Arctic
Cyril Palerme, Johannes Röhrs, Thomas Lavergne, Jozef Rusin, Are Frode Kvanum, Atle Macdonald Sørensen, Arne Melsom, Julien Brajard, Martina Idžanović, Marina Durán Moro, and Malte Müller
EGUsphere, https://doi.org/10.5194/egusphere-2025-2001, https://doi.org/10.5194/egusphere-2025-2001, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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We present MET-AICE, a sea ice prediction system based on artificial intelligence techniques that has been running operationally since March 2024. The forecasts are produced daily and provide sea ice concentration predictions for the next 10 days. We evaluate the MET-AICE forecasts from the first year of operation, and we compare them to forecasts produced by a physically-based model (Barents-2.5km). We show that MET-AICE is skillful and provides more accurate forecasts than Barents-2.5km.
15 May 2025
The Atmospheric Potential Oxygen forward Model Intercomparison Project (APO-MIP1): Evaluating simulated atmospheric transport of air-sea gas exchange tracers and APO flux products
Yuming Jin, Britton B. Stephens, Matthew C. Long, Naveen Chandra, Frédéric Chevallier, Joram J. D. Hooghiem, Ingrid T. Luijkx, Shamil Maksyutov, Eric J. Morgan, Yosuke Niwa, Prabir K. Patra, Christian Rödenbeck, and Jesse Vance
EGUsphere, https://doi.org/10.5194/egusphere-2025-1736, https://doi.org/10.5194/egusphere-2025-1736, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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We carry out a comprehensive atmospheric transport model (ATM) intercomparison project. This project aims to evaluate errors in ATMs and three air-sea O2 exchange products by comparing model simulations with observations collected from surface stations, ships, and aircraft. We also present a model evaluation framework to independently quantify transport-related and flux-related biases that contribute to model-observation discrepancies in atmospheric tracer distributions.
15 May 2025
Modeling Indian Ocean circulation to study marine debris dispersion: insights into high-resolution and Stokes drift effects with Symphonie 3.6.6
Lisa Weiss, Marine Herrmann, Patrick Marsaleix, Matthieu Bompoil, and Christophe Maes
EGUsphere, https://doi.org/10.5194/egusphere-2025-1918, https://doi.org/10.5194/egusphere-2025-1918, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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We developed a high-resolution ocean model to study the dispersion of marine debris across the Indian Ocean, from small coastal scales to the open sea. Our results show that both model resolution and the effect of wind-driven surface waves play a key role in shaping ocean circulation, seasonal energy budgets and floating debris trajectories. High-resolution currents and wave forcing increase the spread and distance traveled by drifting material, especially during monsoon periods.
13 May 2025
Evaluation of a coupled ocean and sea-ice model (MOM6-NEP10k) over the Bering Sea and its sensitivity to turbulence decay scales
Vivek Seelanki, Wei Cheng, Phyllis J. Stabeno, Albert J. Hermann, Elizabeth J. Drenkard, Charles A. Stock, and Katherine Hedstrom
EGUsphere, https://doi.org/10.5194/egusphere-2025-1229, https://doi.org/10.5194/egusphere-2025-1229, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Both physical and ecosystem properties of the ocean are rapidly changing. These changes anticipating ecosystem responses to environmental change and effectively managing marine. The model-based predictions and their performance in the historical states of the ocean must be carefully evaluated against observations. In this study a coupled ocean and sea-ice simulation during 1993–2018 using observations. We focus on the Bering Sea shelf, which is the largest productive ecosystem in the U.S.
12 May 2025
Global atmospheric hydrogen chemistry and long-term source-sink budget simulation with the EMAC v2.55 model
Nic Surawski, Benedikt Steil, Christoph Brühl, Sergey Gromov, Klaus Klingmüller, Anna Martin, Andrea Pozzer, and Jos Lelieveld
EGUsphere, https://doi.org/10.5194/egusphere-2025-1559, https://doi.org/10.5194/egusphere-2025-1559, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Hydrogen usage will likely increase to achieve net zero emission targets. We undertook calculations with an Earth system model using a high performance computer to explore hydrogen atmospheric dynamics. Simulations with the EMAC model yielded highly accurate results at global scale. Correctly representing hydroxyl radicals in the model is a critical requirement for predicting hydrogen concentrations well. Our hydrogen budget is also in very good agreement with bottom-up literature estimates.
12 May 2025
Multigrid Beta Filter for Faster Computation of Ensemble Covariance Localization
Sho Yokota, Miodrag Rancic, Ting Lei, R. James Purser, and Manuel S. F. V Pondeca
External preprint server, https://doi.org/10.22541/essoar.172499883.39847608/v2, https://doi.org/10.22541/essoar.172499883.39847608/v2, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Covariance localization to mitigate sampling error of ensemble-based forecast error covariances is one of the main parts of the calculation in ensemble-variational data assimilation for the atmosphere. This study clarifies that the multigrid beta filter-based localization makes it several times faster than the conventional recursive filter-based one without significantly changing the analysis if a coarser filter grid is applied and filters except for the coarsest resolution are omitted.
09 May 2025
The Chemical Mechanism Integrator Cminor v1.0: A Stand-Alone Fortran Environment for the Particle-Based Simulation of Chemical Multiphase Mechanisms
Levin Rug, Willi Schimmel, Fabian Hoffmann, and Oswald Knoth
EGUsphere, https://doi.org/10.5194/egusphere-2025-380, https://doi.org/10.5194/egusphere-2025-380, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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We present the Chemical Mechanism Integrator (Cminor) v1.0, a tool to predict concentrations of chemical compounds undergoing arbitrary reactions. Cminor is an advanced, open-source solver to model either combustion chemistry, or atmospheric chemistry and its direct influence on condensation of cloud droplets and the subsequent processing of aerosol. It uses the superdroplet idea, making it particularly feasible for coupling with such models, which is part of future work.
09 May 2025
The SAPRC Atmospheric Chemical Mechanism Generation System (MechGen)
William P. L. Carter, Jia Jiang, Zhizhao Wang, and Kelley C. Barsanti
EGUsphere, https://doi.org/10.5194/egusphere-2025-1183, https://doi.org/10.5194/egusphere-2025-1183, 2025
Preprint under review for GMD (discussion: open, 2 comments)
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The SAPRC Atmospheric Chemical Mechanism Generation System (MechGen) generates explicit chemical reaction mechanisms for organic compounds. MechGen has been used for decades in the development of the widely-used SAPRC mechanisms. This manuscript, detailing the software system, and a companion manuscript, detailing the chemical basis, represent the first complete documentation of MechGen. This manuscript includes examples and instructions for generating explicit and reduced mechanisms.
08 May 2025
Datasets and protocols for including anomalous freshwater from melting ice sheets in climate simulations
Gavin A. Schmidt, Kenneth D. Mankoff, Jonathan L. Bamber, Dustin Carroll, David M. Chandler, Violaine Coulon, Benjamin J. Davison, Matthew H. England, Paul R. Holland, Nicolas C. Jourdain, Qian Li, Juliana M. Marson, Pierre Mathiot, Clive R. McMahon, Twila A. Moon, Ruth Mottram, Sophie Nowicki, Anne Olivé Abelló, Andrew G. Pauling, Thomas Rackow, and Damien Ringeisen
EGUsphere, https://doi.org/10.5194/egusphere-2025-1940, https://doi.org/10.5194/egusphere-2025-1940, 2025
Preprint under review for GMD (discussion: open, 1 comment)
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The impact of increasing mass loss from the Greenland and Antarctic ice sheets has not so far been included in historical climate model simulations. This paper describes the protocols and data available for modeling groups to add this anomalous freshwater to their ocean modules to better represent the impacts of these fluxes on ocean circulation, sea ice, salinity and sea level.
08 May 2025
A Bayesian statistical method to estimate the climatology of extreme temperature under multiple scenarios: the ANKIALE package
Yoann Robin, Mathieu Vrac, Aurélien Ribes, Occitane Barbaux, and Philippe Naveau
EGUsphere, https://doi.org/10.5194/egusphere-2025-1121, https://doi.org/10.5194/egusphere-2025-1121, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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We describe an improved method and the associated free licensed package ANKIALE (ANalysis of Klimate with bayesian Inference: AppLication to extreme Events) for estimating the statistics of temperature extremes. This method uses climate model simulations (including multiple scenarios simultaneously) to provide a prior of the real-world changes, constrained by the observations. The method and the tool are illustrated via an application to temperature over Europe until 2100, for four scenarios.
05 May 2025
Development of UI-WRF-Chem (v1.0) for the MAIA satellite mission: case demonstration
Huanxin Zhang, Jun Wang, Nathan Janechek, Cui Ge, Meng Zhou, Lorena Castro García, Tong Sha, Yanyu Wang, Weizhi Deng, Zhixin Xue, Chengzhe Li, Lakhima Chutia, Yi Wang, Sebastian Val, James L. McDuffie, Sina Hasheminassab, Scott E. Gluck, David J. Diner, Peter R. Colarco, and Arlindo M. da Silva
EGUsphere, https://doi.org/10.5194/egusphere-2025-1360, https://doi.org/10.5194/egusphere-2025-1360, 2025
Preprint under review for GMD (discussion: open, 2 comments)
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We present here the development of the Unified Inputs (of initial and boundary conditions) for WRF-Chem (UI-WRF-Chem) framework to support the Multi-Angle Imager for Aerosols (MAIA) satellite mission. Some of the major updates include improving dust size distribution in the chemical boundary conditions, updating land surface properties using timely satellite data and improvement of soil NOx emissions. We demonstrate subsequent model improvement over several of the MAIA target areas.
05 May 2025
Bakaano-Hydro (v1.1). A distributed hydrology-guided deep learning model for streamflow prediction
Confidence Duku
EGUsphere, https://doi.org/10.5194/egusphere-2025-1633, https://doi.org/10.5194/egusphere-2025-1633, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Reliable streamflow prediction is vital for managing floods, droughts, and water resources, yet remains challenging due to data limitations and complex hydrological processes. Traditional models require intensive calibration, while many machine learning methods lack physical realism. Bakaano-Hydro integrates physical hydrology with machine learning to improve interpretability, generalizability, and performance, offering a robust approach for streamflow prediction in data-scarce regions.
05 May 2025
A double-box model for aircraft exhaust plumes based on the MADE3 aerosol microphysics (MADE3 v4.0)
Monica Sharma, Mattia Righi, Johannes Hendricks, Anja Schmidt, Daniel Sauer, and Volker Grewe
EGUsphere, https://doi.org/10.5194/egusphere-2025-1137, https://doi.org/10.5194/egusphere-2025-1137, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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A plume model is developed to simulate aerosol microphysics in a dispersing aircraft plume, including interactions between ice crystals and aerosols in vortex regime. Compared to an instantaneous dispersion approach, the plume approach estimates 15 % lower aviation aerosol number concentrations, due to more efficient coagulation at plume scale. The model is sensitive to background conditions and initialization parameters, such as ice crystal number concentration and fuel sulfur content.
05 May 2025
Predicting oceanic Lagrangian trajectories with hybrid space-time CNN architecture
Lorenzo Della Cioppa and Bruno Buongiorno Nardelli
EGUsphere, https://doi.org/10.5194/egusphere-2025-1136, https://doi.org/10.5194/egusphere-2025-1136, 2025
Preprint under review for GMD (discussion: open, 1 comment)
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Forecasting of particles trajectories transported by ocean currents is of great importance for research and operational tasks. Even with satellite observations data or numerical simulations, the problem challenging. In this paper a neural network approach is proposed which is capable of learning from observed trajectories and corresponding data observed from satellites to generate predictions. The network is trained and validated on synthetic data, but it is easily applicable in the real-world.
30 Apr 2025
Adjoint-Based Simultaneous State and Parameter Estimation in an Arctic Sea Ice-Ocean Model using MITgcm (c63m)
Guokun Lyu, Longjiang Mu, Armin Koehl, Ruibo Lei, Xi Liang, and Chuanyu Liu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-189, https://doi.org/10.5194/gmd-2024-189, 2025
Preprint under review for GMD (discussion: open, 1 comment)
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In the sea ice-ocean models, errors in the parameters and missing spatiotemporal variations contribute to the deviations between the simulations and the observations. We extended an adjoint method to optimize spatiotemporally varying parameters together with the atmosphere forcing and the initial conditions using satellite and in-situ observations. Seasonally, this scheme demonstrates a more prominent advantage in mid-autumn and show great potential for accurately reproducing the Arctic changes.
29 Apr 2025
Development of a High-Resolution Coupled SHiELD-MOM6 Model. Part I – Model Overview, Coupling Technique, and Validation in a Regional Setup
Joseph Mouallem, Kun Gao, Brandon G. Reichl, Lauren Chilutti, Lucas Harris, Rusty Benson, Niki Zadeh, Jing Chen, Jan-Huey Chen, and Cheng Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1690, https://doi.org/10.5194/egusphere-2025-1690, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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We introduce a new high-resolution model that couple the atmosphere and ocean to better simulate extreme weather events. It combines GFDL’s advanced atmospheric and ocean models with a powerful coupling system that allows robust and efficient two-way interactions. Simulations show the model accurately captures hurricane behavior and its impact on the ocean. It also runs efficiently on supercomputers. This model is a key step toward improving extreme weather forecast.
29 Apr 2025
Modelling stratospheric composition for the Copernicus Atmosphere Monitoring Service: multi-species evaluation of IFS-COMPO Cy49R1
Simon Chabrillat, Samuel Rémy, Quentin Errera, Vincent Huijnen, Christine Bingen, Jonas Debosscher, François Hendrick, Swen Metzger, Adrien Mora, Daniele Minganti, Marc Op de beek, Léa Reisenfeld, Jason E. Williams, Henk Eskes, and Johannes Flemming
EGUsphere, https://doi.org/10.5194/egusphere-2025-1327, https://doi.org/10.5194/egusphere-2025-1327, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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We document the forecasts of the composition of the stratosphere by the Copernicus Atmosphere Monitoring Service. The model's predictions are compared with satellite measurements over a recent period, during polar ozone depletion events, and after the Mount Pinatubo volcanic eruption. The system performs well for sulfate aerosols, ozone and several other key gases but not as well for several nitrogen-containing gases. Chemical processes in aerosols and polar clouds should be improved.
28 Apr 2025
Urban Weather Modeling using WRF: Linking Physical Assumptions, Code Implementation, and Observational Needs
Parag Joshi, Tzu-Shun Lin, Cenlin He, and Katia Lamer
EGUsphere, https://doi.org/10.5194/egusphere-2025-1751, https://doi.org/10.5194/egusphere-2025-1751, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Study revisits urban representation (using canopy models & bulk parameterization) in the Weather Research & Forecasting model. We propose methods to identify evaluable parameters via field measurements and found inconsistencies between UCM physics and code implementation. Simulations reveal small errors can significantly impact outputs, highlighting the need for precise physics implementation.
25 Apr 2025
Data-Driven Estimation of the hydrologic response via Generalized Additive Models
Quentin Duchemin, Maria Grazia Zanoni, Marius G. Floriancic, Hansjörg Seybold, Guillaume Obozinski, James W. Kirchner, and Paolo Benettin
EGUsphere, https://doi.org/10.5194/egusphere-2025-1591, https://doi.org/10.5194/egusphere-2025-1591, 2025
Preprint under review for GMD (discussion: open, 1 comment)
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We introduce GAMCR, a data-driven model that estimates how catchments respond to individual precipitation events. We validate GAMCR on synthetic data and demonstrate its ability to investigate the characteristic runoff responses from real-world hydrologic series. GAMCR provides new data-driven opportunities to understand and compare hydrological behavior across different catchments worldwide.
25 Apr 2025
Description and evaluation of airborne microplastics in the United Kingdom Earth System Model (UKESM1.1) using GLOMAP-mode
Cameron McErlich, Felix Goddard, Alex Aves, Catherine Hardacre, Nikolaos Evangeliou, and Laura E. Revell
EGUsphere, https://doi.org/10.5194/egusphere-2025-1575, https://doi.org/10.5194/egusphere-2025-1575, 2025
Preprint under review for GMD (discussion: open, 1 comment)
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Airborne microplastics are a new air pollutant but are not yet included in most global models. We add them to the UK Earth System Model to show how they move, change, and are removed from air. Smaller microplastics persist for longer and can travel further, even to Antarctica. While their current role in air pollution is small, their presence is expected to grow in future. This work offers a framework to assess future impacts of microplastics on air quality and climate.
25 Apr 2025
A method for assessing model extensions: Application to modelling winter precipitation with a microscale obstacle-resolving meteorological model (MITRAS v4.0)
Karolin Sarah Samsel, Marita Boettcher, David Grawe, K. Heinke Schlünzen, and Kevin Sieck
EGUsphere, https://doi.org/10.5194/egusphere-2024-2464, https://doi.org/10.5194/egusphere-2024-2464, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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A microscale, obstacle-resolving meteorological model has been extended with a snow cover and precipitation scheme making it the first model of its kind that includes rain and snow. The model allows first estimates on the influence of different city characteristics on precipitation heterogeneities. The performance of the model extension is assessed by comparing the results of different model versions. For the comparisons, threshold values were derived based on computational accuracy.
24 Apr 2025
Numerical simulations of ocean surface waves along the Australian coast with a focus on the Great Barrier Reef
Xianghui Dong, Qingxiang Liu, Stefan Zieger, Alberto Alberello, Ali Abdolali, Jian Sun, Kejian Wu, and Alexander V. Babanin
EGUsphere, https://doi.org/10.5194/egusphere-2025-698, https://doi.org/10.5194/egusphere-2025-698, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Ocean surface wave research is vital for coastal management, marine ecology, and ocean engineering. This study simulates waves along the Australian coast using advanced physical and numerical schemes. Model verification with altimeter and buoy data shows good performance. A two-step parameterization improves accuracy in the complex Great Barrier Reef. This study will help us better understand coastal wave climates and assess sea states, enabling us to better develop, protect, and use the sea.
24 Apr 2025
REMO2020: a modernized modular regional climate model
Joni-Pekka Pietikäinen, Kevin Sieck, Lars Buntemeyer, Thomas Frisius, Christine Nam, Peter Hoffmann, Christina Pop, Diana Rechid, and Daniela Jacob
EGUsphere, https://doi.org/10.5194/egusphere-2025-1586, https://doi.org/10.5194/egusphere-2025-1586, 2025
Preprint under review for GMD (discussion: open, 2 comments)
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This paper introduces REMO2020, a modernized version of the well-known and widely used REMO regional climate model. We demonstrate why REMO2020 will be our new model version for future dynamical downscaling activities. It outperforms our previous model version in many analyzed areas and is the biggest update to REMO so far. It also supports climate service needs based developments through new more modular structure.
23 Apr 2025
Improvement of near-surface wind speed modeling through refined aerodynamic roughness length in built-up regions: implementation and validation in the Weather Research and Forecasting (WRF) model version 4.0
Jiamin Wang, Kun Yang, Jiarui Liu, Xu Zhou, Xiaogang Ma, Wenjun Tang, Ling Yuan, and Zuhuan Ren
EGUsphere, https://doi.org/10.5194/egusphere-2025-1513, https://doi.org/10.5194/egusphere-2025-1513, 2025
Preprint under review for GMD (discussion: open, 5 comments)
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Aerodynamic roughness length (z0) is a key parameter determining wind profiles in models, but most models neglect the urban effects. We proposed a low-cost method to estimate z0 at weather stations in built-up areas across China, and then developed a z0 dataset. Tests in the Weather Research and Forecasting model show that it significantly improves the simulation accuracy of wind speed at both 10-m and 100-m heights, supporting urban planning, air quality management, and wind energy projects.
22 Apr 2025
Comparison of simulations from a state-of-the-art dynamic global vegetation model (LPJ-GUESS ) driven by low- and high-resolution climate data
Dmitry Otryakhin, David Martín Belda, and Almut Arneth
EGUsphere, https://doi.org/10.5194/egusphere-2025-1401, https://doi.org/10.5194/egusphere-2025-1401, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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We developed a methodology for comparison of simulation results by a dynamic global vegetation model (DGVM). Using this methodology, we reveal systematic differences between high- and low-resolution DGVM simulations caused by under-representation of climate variability in the low-resolution data and poor representation of shore lines and inland water bodies. In a study area covering European Union, the differences in aggregated output variables were found to be 2 %–10 %.
22 Apr 2025
A computationally efficient method to model Stratospheric Aerosol Injection experiments
Jasper de Jong, Daniel Pflüger, Simone Lingbeek, Claudia E. Wieners, Michiel L. J. Baatsen, and René R. Wijngaard
External preprint server, https://doi.org/10.22541/essoar.174273333.31930996/v1, https://doi.org/10.22541/essoar.174273333.31930996/v1, 2025
Preprint under review for GMD (discussion: open, 2 comments)
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Injection of reflective sulphate aerosols high in the atmosphere is a proposed method to mitigate global warming. Climate simulations with injection are more expensive than standard future projections. We propose a method that dynamically scales the forcing fields based on pre-existing full-complexity data. This opens up possibilities for ensemble generation, new scenarios and higher resolution runs. We show that our method works for multiple model versions, injection scenarios and resolutions.
16 Apr 2025
Machine learning-driven characterization and prescription of aerosol optical properties for atmospheric models
Nilton Évora do Rosário, Karla M. Longo, Pedro H. Toso, Saulo R. Freitas, Marcia A. Yamasoe, Luiz Flávio Rodrigues, Otavio Medeiros, Haroldo Campos Velho, Isilda da Cunha Menezes, and Ana Isabel Miranda
EGUsphere, https://doi.org/10.5194/egusphere-2025-454, https://doi.org/10.5194/egusphere-2025-454, 2025
Preprint under review for GMD (discussion: open, 1 comment)
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The present article focuses on the topic of observations to constrain aerosol optical properties in climate models . We combine a machine learning approach (based on clustering), used to identify and characterize aerosol optical regimes, with another machine learning technique (Random Forest), used to train the prescription of the identified optical regimes from a mixture of columnar mass density of different aerosol-types.
15 Apr 2025
Linear Meta-Model optimization for regional climate models (LiMMo version 1.0)
Sergei Petrov, Andreas Will, and Beate Geyer
EGUsphere, https://doi.org/10.5194/egusphere-2025-710, https://doi.org/10.5194/egusphere-2025-710, 2025
Preprint under review for GMD (discussion: open, 2 comments)
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This study introduces a new method that helps improve the accuracy of climate models by automatically selecting the best parameters to match real-world observations. Instead of manually adjusting many parameters, the method uses a mathematical tool to find the most appropriate settings for the model. It can be especially helpful for researchers who introduce new physical parameters into climate models to assess their impact on model results and optimize them along with the old ones.
15 Apr 2025
SERGHEI v2.1: a Lagrangian Model for Passive Particle Transport using a 2D Shallow Water Model (SERGHEI-LPT)
Pablo Vallés, Mario Morales-Hernández, Volker Roeber, Pilar García-Navarro, and Daniel Caviedes-Voullième
EGUsphere, https://doi.org/10.5194/egusphere-2025-722, https://doi.org/10.5194/egusphere-2025-722, 2025
Preprint under review for GMD (discussion: open, 3 comments)
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This study presents a computational model for passive particle transport in water. Their trajectories depend on advection and turbulence, represented by a random-walk model. Three numerical methods are compared to estimate their trajectory, evaluating accuracy and computational cost. Tests show that the Euler method offers the best balance. Finally, a rainfall event in a catchment is simulated to validate the model’s performance over irregular terrain.
14 Apr 2025
An example of how data quality hinders progress: translating the latest findings on the regulation of leaf senescence timing in trees into the DP3 model (v1.0)
Michael Meier, Christof Bigler, and Isabelle Chuine
EGUsphere, https://doi.org/10.5194/egusphere-2025-460, https://doi.org/10.5194/egusphere-2025-460, 2025
Preprint under review for GMD (discussion: open, 1 comment)
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Formulated according to the leaf development process, the DP3 model of leaf coloring considerably contrasts previous models and allows to set up new hypotheses, e.g. on aging versus stress caused color changes. The DP3 model was as accurate as previous models and a comparison to the constant simulation of the mean date of leaf coloring indicated that noisy leaf coloring data forced the models to resort to this mean, which hinders model evaluation.
14 Apr 2025
Implementing a process-based representation of soil water movement in a second-generation dynamic vegetation model: application to dryland ecosystems (LPJ-GUESS-RE v1.0)
Wim Verbruggen, David Wårlind, Stéphanie Horion, Félicien Meunier, Hans Verbeeck, and Guy Schurgers
EGUsphere, https://doi.org/10.5194/egusphere-2025-1259, https://doi.org/10.5194/egusphere-2025-1259, 2025
Preprint under review for GMD (discussion: open, 1 comment)
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We improved the representation of soil water movement in a state-of-the-art dynamic vegetation model. This is especially important for dry ecosystems, as they are often driven by changes in soil water availability. We showed that this update resulted in a generally better match with observations, and that the updated model is more sensitive to soil texture. This updated model will help scientists to better understand the future of dry ecosystems under climate change.
14 Apr 2025
Optimizing Precipitation Parameterizations in Regional Climate Model (RegCM5): A Case Study of the Upper Blue Nile Basin (UBNB)
Eatemad Keshta, Doaa Amin, Ashraf M. ElMoustafa, and Mohamed A. Gad
EGUsphere, https://doi.org/10.5194/egusphere-2025-532, https://doi.org/10.5194/egusphere-2025-532, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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RegCM5 reasonably succeeded to simulate the precipitation over the UBNB using the Emanuel with NoTo. Exploring the better performance of NoTo than SUBEX is considered one of the research novelty. We recommend using the new dynamic core option of MOLOCH non-hydrostatic in future research to improve the precipitation simulation and be applicable to study the impact of the land use change, due to the construction of Dams impounding reservoirs along the basin, on the spatiotemporal rainfall pattern.
14 Apr 2025
Meteorological Landscape of Tropical Cyclone
Pascal Oettli, Keita Tokuda, Yusuke Imoto, and Shunji Kotsuki
EGUsphere, https://doi.org/10.5194/egusphere-2025-1458, https://doi.org/10.5194/egusphere-2025-1458, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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A tropical cyclone is a circular air movement that emerges over warm waters of the tropical ocean and its movement is guided by complex interactions between the ocean and the atmosphere. To better understand this complexity, we adopted ideas and techniques from biology and bioinformatics, to have a fresh look at this matter. This led to the creation of "MeteoScape," a tool that calculates the probability of paths for tropical cyclones can take and visualize them in an understandable way.
14 Apr 2025
Comparison of calibration methods of a PICO basal ice shelf melt module implemented in the GRISLI v2.0 ice sheet model
Maxence Menthon, Pepijn Bakker, Aurélien Quiquet, Didier M. Roche, and Ronja Reese
EGUsphere, https://doi.org/10.5194/egusphere-2025-777, https://doi.org/10.5194/egusphere-2025-777, 2025
Preprint under review for GMD (discussion: open, 2 comments)
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The ice-ocean interaction is a large source of uncertainty in future projections of the Antarctic ice sheet. Here we implement a basal ice shelf melt module (PICO) in a ice sheet model (GRISLI) and test six simple statistical methods to calibrate this module. We show that calculating the mean absolute error of bins best fits the observational datasets under multiple conditions. We also assess the impact of the module implementation and calibration choice on future projections until 2300.
11 Apr 2025
PyESPERv1.01.01: A Python implementation of empirical seawater property estimation routines (ESPERs)
Larissa Marie Dias and Brendan Rae Carter
EGUsphere, https://doi.org/10.5194/egusphere-2025-458, https://doi.org/10.5194/egusphere-2025-458, 2025
Preprint under review for GMD (discussion: open, 7 comments)
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The increasing availability of oceanographic physical and chemical data necessitates accompanying methods for optimizing use of this data. This project produced algorithms (PyESPERs) for estimating biogeochemical seawater properties in Python, a freely available coding language. These algorithms were based on Empirical Seawater Property Estimation Routines (ESPERs), which were originally written in the proprietary MATLAB coding language and can be used in studies of marine carbonate chemistry.
10 Apr 2025
CoCoMET v1.0: A Unified Open-Source Toolkit for Atmospheric Object Tracking and Analysis
Travis Hahn, Hershel Weiner, Calvin Brooks, Jie Xi Li, Siddhant Gupta, and Dié Wang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1328, https://doi.org/10.5194/egusphere-2025-1328, 2025
Preprint under review for GMD (discussion: open, 2 comments)
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Understanding how clouds evolve is important for improving weather predictions, but existing tools for tracking cloud changes are complex and difficult to compare. To address this, we developed the Community Cloud Model Evaluation Toolkit (CoCoMET) that makes it easier to analyze clouds in both models and observations. By simplifying data processing, standardizing results, and introducing new analysis features, CoCoMET helps researchers better evaluate cloud behavior and improve models.
10 Apr 2025
DRIVE v1.0: A data-driven framework to estimate road transport emissions and temporal profiles
Daniel Kühbacher, Jia Chen, Patrick Aigner, Mario Ilic, Ingrid Super, and Hugo Denier van der Gon
EGUsphere, https://doi.org/10.5194/egusphere-2025-753, https://doi.org/10.5194/egusphere-2025-753, 2025
Preprint under review for GMD (discussion: open, 3 comments)
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We present DRIVE v1.0, a data-driven framework to estimate road transport emissions, their temporal profiles, and the associated uncertainties. The method was applied to the city of Munich, where we present bottom-up emission estimates for the years 2019 to 2022. The estimates are compared against official municipal reports as well as national and European downscaled inventories.
09 Apr 2025
On stabilisation of compositional density jumps in compressible mantle convection simulations
Paul J. Tackley
EGUsphere, https://doi.org/10.5194/egusphere-2025-1543, https://doi.org/10.5194/egusphere-2025-1543, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Large density jumps in numerical simulations of solid Earth dynamics can cause numerical oscillations. An effective method to prevent these at a free surface already exists. Here this is tested for compositional layers deeper in the mantle. The stabilisation method works effectively if density gradients due purely to compositional gradients are used, but produces severe artefacts if total density is used.
08 Apr 2025
Veris: Fast & Efficient Sea-Ice Modeling in Python with GPU Acceleration
Jan P. Gärtner, Martin Losch, Markus Jochum, and Roman Nuterman
External preprint server, https://doi.org/10.22541/essoar.173940251.11733929/v1, https://doi.org/10.22541/essoar.173940251.11733929/v1, 2025
Preprint under review for GMD (discussion: open, 1 comment)
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Climate simulations help us understand the Earth systems and inform climate policies. These complex models require advanced programming and significant energy, as they run on large grids over long timescales. A key component of a climate model is its sea ice component. We present a sea ice model that simplifies development while maintaining high performance. By utilizing GPUs, our model can replace dozens to hundreds of CPUs, drastically reducing the energy usage of running climate simulations.
07 Apr 2025
A Deep-learning Framework for Retrieving Tropical Cyclone Intensity and Structure from Gridded Climate Data (TCNN V1.0)
Minh-Khanh Luong and Chanh Kieu
EGUsphere, https://doi.org/10.5194/egusphere-2025-1074, https://doi.org/10.5194/egusphere-2025-1074, 2025
Preprint under review for GMD (discussion: open, 1 comment)
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This work presents a deep learning (DL) model to retrieve tropical cyclone (TC) information from gridded data, a critical task for forecasting or downscaling TC intensity from climate outputs. Our DL model shows good capability for retrieving TC intensity/size when applied to climate data at 0.5-degree resolution. However, the model performance strongly depends on sampling methods, underscoring the complexities of applying DL models to new TC data. Potential improvements are also discussed.
07 Apr 2025
TECO-CNP Sv1.0: A coupled carbon-nitrogen-phosphorus model with data assimilation for subtropical forests
Fangxiu Wan, Chenyu Bian, Ensheng Weng, Yiqi Luo, and Jianyang Xia
EGUsphere, https://doi.org/10.5194/egusphere-2025-1243, https://doi.org/10.5194/egusphere-2025-1243, 2025
Preprint under review for GMD (discussion: open, 1 comment)
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We developed an improved model that captures how nutrients, especially phosphorus, influence carbon cycle in subtropical forest. By combining biogeochemical cycling with advanced data analysis techniques, our model creates a powerful tool for parameter optimization and reliable predictions. Using field observations from a phosphorus-limited forest, we validated that this integrated approach provides more accurate estimates, offering better support for climate-related decision making.
07 Apr 2025
Further Evaluating the Generalized Itô Correction for Accelerating Convergence of Stochastic Parameterizations with Colored Noise
William Johns, Lidong Fang, Huan Lei, and Panos Stinis
EGUsphere, https://doi.org/10.5194/egusphere-2025-765, https://doi.org/10.5194/egusphere-2025-765, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Colored noise processes can be used to imitate processes that are two small to include fully in a model. The naïve introduction of a colored noise process to a numerical algorithm can lead to unrealistic outputs. This is remedied by the introduction of the recently introduced the Generalized Ito Correction (GIC). We demonstrate the effectiveness of GIC to improve results at a low cost on two models from the atmosphere modeling literature for a range of colored noise processes.
07 Apr 2025
Modelling Microplastic Dynamics in Estuaries: A Comprehensive Review, Challenges and Recommendations
Betty John Kaimathuruthy, Isabel Jalón-Rojas, and Damien Sous
EGUsphere, https://doi.org/10.5194/egusphere-2025-529, https://doi.org/10.5194/egusphere-2025-529, 2025
Preprint under review for GMD (discussion: final response, 5 comments)
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Studies on plastic pollution have emerged as a rapidly growing field of research. Modelling microplastic transport in estuaries stems from their complex hydrodynamics and diverse particle behaviours affecting the dispersion and retention of microplastics. Our paper reviews key modelling approaches applied in estuaries analyzing their setups and parameterizations. We provide recommendations and future directions to improve the accuracy and modelling strategies for estuarine microplastic research.
04 Apr 2025
TOAR-classifier v2: A data-driven classification tool for global air quality stations
Ramiyou Karim Mache, Sabine Schröder, Michael Langguth, Ankit Patnala, and Martin G. Schultz
EGUsphere, https://doi.org/10.5194/egusphere-2025-1399, https://doi.org/10.5194/egusphere-2025-1399, 2025
Preprint under review for GMD (discussion: open, 4 comments)
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The TOAR-classifier model is a data-driven tool that allows for an objective classification of air quality measuring stations as urban, rural, or suburban. Such classification is important in the analysis of air pollutant trends and regional signatures. The model is employed in the second Tropospheric Ozone Assessment Report but can also be used in other research work.
02 Apr 2025
A Python library for solving ice sheet modeling problems using Physics Informed Neural Networks, PINNICLE v1.0
Gong Cheng, Mansa Krishna, and Mathieu Morlighem
EGUsphere, https://doi.org/10.5194/egusphere-2025-1188, https://doi.org/10.5194/egusphere-2025-1188, 2025
Revised manuscript under review for GMD (discussion: final response, 7 comments)
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Predicting ice sheet contributions to sea level rise is challenging due to limited data and uncertainties in key processes. Traditional models require complex methods that lack flexibility. We developed PINNICLE, an open-source Python library that integrates machine learning with physical laws to improve ice sheet modeling. By combining data and physics, PINNICLE enhances predictions and adaptability, providing a powerful tool for climate research and sea level rise projections.
02 Apr 2025
The ISIMIP Groundwater Sector: A Framework for Ensemble Modeling of Global Change Impacts on Groundwater
Robert Reinecke, Annemarie Bäthge, Ricarda Dietrich, Sebastian Gnann, Simon N. Gosling, Danielle Grogan, Andreas Hartmann, Stefan Kollet, Rohini Kumar, Richard Lammers, Sida Liu, Yan Liu, Nils Moosdorf, Bibi Naz, Sara Nazari, Chibuike Orazulike, Yadu Pokhrel, Jacob Schewe, Mikhail Smilovic, Maryna Strokal, Yoshihide Wada, Shan Zuidema, and Inge de Graaf
EGUsphere, https://doi.org/10.5194/egusphere-2025-1181, https://doi.org/10.5194/egusphere-2025-1181, 2025
Preprint under review for GMD (discussion: final response, 2 comments)
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Here we describe a collaborative effort to improve predictions of how climate change will affect groundwater. The ISIMIP groundwater sector combines multiple global groundwater models to capture a range of possible outcomes and reduce uncertainty. Initial comparisons reveal significant differences between models in key metrics like water table depth and recharge rates, highlighting the need for structured model intercomparisons.
01 Apr 2025
OpenBench: a land models evaluation system
Zhongwang Wei, Qingchen Xu, Fan Bai, Xionghui Xu, Zixin Wei, Wenzong Dong, Hongbin Liang, Nan Wei, Xingjie Lu, Lu Li, Shupeng Zhang, Hua Yuan, Laibo Liu, and Yongjiu Dai
EGUsphere, https://doi.org/10.5194/egusphere-2025-1380, https://doi.org/10.5194/egusphere-2025-1380, 2025
Preprint under review for GMD (discussion: final response, 2 comments)
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Land surface models are used for simulating earth's surface interacts with the atmosphere. As models grow more complex and detailed, researchers need better tools to evaluate their performance. OpenBench, a new software system that makes evaluation process more comprehensive and efficient. It stands out by incorporating various factors and working with data at any scale which enabling scientists to incorporate new types of models and measurements as our understanding of Earth’s systems evolves.
01 Apr 2025
rsofun v5.0: A model-data integration framework for simulating ecosystem processes
Josefa Arán Paredes, Koen Hufkens, Mayeul Marcadella, Fabian Bernhard, and Benjamin D. Stocker
External preprint server, https://doi.org/10.1101/2023.11.24.568574, https://doi.org/10.1101/2023.11.24.568574, 2025
Preprint under review for GMD (discussion: final response, 10 comments)
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Mechanistic vegetation models serve to estimate terrestrial carbon fluxes and climate impacts on ecosystems across diverse conditions. Here we present the {rsofun} R package, providing an implementation of a model for site-scale ecosystem photosynthesis including functions for Bayesian model-data integration. The package {rsofun} lowers the bar of entry to ecosystem modelling and model-data integration and serves as an open-access resource for model development and dissemination.
01 Apr 2025
Matter (v1): An open-source MPM solver for granular matter
Lars Blatny and Johan Gaume
EGUsphere, https://doi.org/10.5194/egusphere-2025-1157, https://doi.org/10.5194/egusphere-2025-1157, 2025
Preprint under review for GMD (discussion: final response, 2 comments)
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Matter is a new computer model that simulates granular media like sand, snow, and soil. These materials can behave like both solids and fluids, making their modeling difficult. Matter addresses this with a unified framework, using a numerical solver called MPM. Able to capture cohesion, density variations and complex terrains, it’s particularly relevant for snow avalanches or landslides. Matter runs efficiently on standard computers, making advanced simulations more accessible.
01 Apr 2025
Development of a Model Framework for Terrestrial Carbon Flux Prediction: the Regional Carbon and Climate Analytics Tool (RCCAT) Applied to Non-tidal Wetlands
Ashley Brereton, Zelalem Mekonnen, Bhavna Arora, William Riley, Kunxiaojia Yuan, Yi Xu, Yu Zhang, Qing Zhu, Tyler Anthony, and Adina Paytan
EGUsphere, https://doi.org/10.5194/egusphere-2025-361, https://doi.org/10.5194/egusphere-2025-361, 2025
Preprint under review for GMD (discussion: final response, 2 comments)
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Wetlands absorb carbon dioxide (CO2), helping slow climate change, but they also release methane, a potent warming gas. We developed a collection of AI-based models to estimate magnitudes of CO2 and methane exchanged between the land and the atmosphere, for wetlands on a regional scale. This approach helps to inform land-use planning, restoration, and greenhouse gas accounting, while also creating a foundation for future advancements in prediction accuracy.
31 Mar 2025
The tracer nudging method for correcting and preventing uneven tracer distributions in geodynamical models
Paul James Tackley
EGUsphere, https://doi.org/10.5194/egusphere-2025-1354, https://doi.org/10.5194/egusphere-2025-1354, 2025
Preprint under review for GMD (discussion: final response, 2 comments)
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Tracers are commonly used in geodynamical models to track composition, but a common problem is that over time, gaps in the tracer distribution can develop, as well as bunches. Here a method to correct such problems is presented and tested. The method perturbs or “nudges” the positions of tracers in such a way as to close gaps and eliminate bunching. Test results show that this tracer nudging method is highly effective. The computational cost is small.
31 Mar 2025
PortUrb: A Performance Portable, High-Order, Moist Atmospheric Large Eddy Simulation Model with Variable-Friction Immersed Boundaries
Matthew Norman, Muralikrishnan Gopalakrishnan Meena, Kalyan Gottiparthi, Nicholson Koukpaizan, and Stephen Nichols
EGUsphere, https://doi.org/10.5194/egusphere-2025-1135, https://doi.org/10.5194/egusphere-2025-1135, 2025
Preprint under review for GMD (discussion: open, 8 comments)
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A new code, portUrb, is described and validated. portUrb is an atmospheric simulation code for turbulent boundary layer including flow through urban areas. The model is coded with an emphasis on robustness, simplicity, readability, portable performance on Graphics Processing Units (GPUs), and rapid prototyping of surrogate models through an ensemble capability where many different configurations can be run simultaneously to explore parameter choices.
31 Mar 2025
Replicability in Earth System Models
Kai Rasmus Keller, Marta Alerany Solé, and Mario Acosta
EGUsphere, https://doi.org/10.5194/egusphere-2025-1367, https://doi.org/10.5194/egusphere-2025-1367, 2025
Preprint under review for GMD (discussion: final response, 2 comments)
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Can we be sure that different computing environments, that should not change the model climate, indeed leave the climate unaltered? In this article, we present a novel methodology that answers whether two model climates are statistically the same. Besides a new methodology, able to detect significant differences between two model climates 60 % more accurately than a similar recent state-of-the-art method, we also provide an analysis on what actually constitutes a different climate.
28 Mar 2025
Comparative Analysis of Continuous and Reinitialized Dynamical Downscaling in the North Atlantic and Surrounding Continents
Brieuc Thomas, Jose Carlos Fernández-Alvarez, Xurxo Costoya, Maite deCastro, Raquel Nieto, David Carvalho, Luis Gimeno, and Moncho Gómez-Gesteira
EGUsphere, https://doi.org/10.5194/egusphere-2025-1339, https://doi.org/10.5194/egusphere-2025-1339, 2025
Preprint under review for GMD (discussion: open, 1 comment)
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Understanding climate change is crucial, but global models lack fine detail for local assessments. Regional climate models improve accuracy by simulating climate at higher resolution. This study compares two approaches: one continuous and one resetting daily to reduce errors and speed up processing. Both perform well and similarly, but the reinitialized method is 30 times more efficient. Its lower cost makes it a promising option for high-resolution climate modelling and regional predictions.
28 Mar 2025
Impact of topography and meteorological forcing on snow simulation in the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC)
Libo Wang, Lawrence Mudryk, Joe R. Melton, Colleen Mortimer, Jason Cole, Gesa Meyer, Paul Bartlett, and Mickaël Lalande
EGUsphere, https://doi.org/10.5194/egusphere-2025-1264, https://doi.org/10.5194/egusphere-2025-1264, 2025
Preprint under review for GMD (discussion: final response, 3 comments)
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This study shows that an alternate snow cover fraction (SCF) parameterization significantly improves SCF simulated in the CLASSIC model in mountainous areas for all three choices of meteorological datasets. Annual mean bias, unbiased root mean squared area, and correlation improve by 75 %, 32 %, and 7 % when evaluated with MODIS SCF observations over the Northern Hemisphere. We also link relative biases in the meteorological forcing data to differences in simulated snow water equivalent and SCF.
27 Mar 2025
Automatic Optical Depth Parametrization in Radiative Transfer Model RTTOV v13 via LASSO-Induced Sparsity for Satellite Data Assimilation
Franklin Vargas Jiménez and Juan Carlos De los Reyes
EGUsphere, https://doi.org/10.5194/egusphere-2025-950, https://doi.org/10.5194/egusphere-2025-950, 2025
Preprint under review for GMD (discussion: open, 1 comment)
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Our research improves satellite-based weather prediction by making complex models faster and more efficient. We developed a method that automatically selects key atmospheric factors, reducing computational costs without losing accuracy. This advancement helps meteorologists analyze satellite data more quickly and effectively, leading to better forecasts and a deeper understanding of atmospheric conditions.
27 Mar 2025
Bias Correcting Regional Scale Earth Systems Model Projections: Novel Approach using Empirical Mode Decomposition
Arkaprabha Ganguli, Jeremy Feinstein, Ibraheem Raji, Akintomide Akinsanola, Connor Aghili, Chunyong Jung, Jordan Branham, Tom Wall, Whitney Huang, and Rao Kotamarthi
EGUsphere, https://doi.org/10.5194/egusphere-2025-1112, https://doi.org/10.5194/egusphere-2025-1112, 2025
Preprint under review for GMD (discussion: open, 7 comments)
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This study introduces a timescale-aware bias-correction framework to enhance Earth system model assessments, vital for the geoscience community. By decomposing model outputs into oscillatory components, we preserve critical information across various timescales, ensuring more reliable projections. This improved reliability supports strategic decisions in sectors such as agriculture, water resources, and disaster preparedness.
27 Mar 2025
DNS (v1.0): An open source ray-tracing tool for space geodetic techniques
Florian Zus, Kyriakos Balidakis, Ali Hasan Dogan, Rohith Thundathil, Galina Dick, and Jens Wickert
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-237, https://doi.org/10.5194/gmd-2024-237, 2025
Revised manuscript accepted for GMD (discussion: final response, 10 comments)
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Atmospheric signal propagation effects are one of the largest error sources in the analysis of space geodetic techniques. Inaccuracies in the modelling map into errors in positioning, navigation and timing. We describe the open source ray tracing tool DNS and show the two outstanding features of this tool compared to previous model developments: it can handle both the troposphere and the ionosphere and it does so efficiently. This makes the tool perfectly suited for geoscientific applications.
27 Mar 2025
MLUCM BEP+BEM: An offline one-dimensional Multi-Layer Urban Canopy Model based on the BEP+BEM Scheme
Gianluca Pappaccogli, Andrea Zonato, Alberto Martilli, Riccardo Buccolieri, and Piero Lionello
EGUsphere, https://doi.org/10.5194/egusphere-2025-219, https://doi.org/10.5194/egusphere-2025-219, 2025
Preprint under review for GMD (discussion: final response, 11 comments)
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We present the MLUCM BEP+BEM model that bridges mesoscale and microscale phenomena within the urban canopy, capturing scale interactions and feedback. The accuracy and low computational cost of this one-dimensional model makes it ideal for offline climate projections to assess urban climate impacts under different emission scenarios. The model's features allow analyzing urban overheating, energy demands, and evaluating the efficiency of strategies like green/cool roofs, and photovoltaic panels.
26 Mar 2025
The ACCESS-CM2 climate model with a higher resolution ocean-sea ice component (1/4°)
Wilma G. C. Huneke, Andy McC. Hogg, Martin Dix, Daohua Bi, Arnold Sullivan, Shayne McGregor, Chiara Holgate, Siobhan P. O'Farrell, and Micael J. T. Oliveira
EGUsphere, https://doi.org/10.5194/egusphere-2025-1006, https://doi.org/10.5194/egusphere-2025-1006, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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A new configuration of the Australian Community Climate and Earth System Simulator coupled model, ACCESS-CM2, with a higher resolution ocean-sea ice component is introduced. The new version of the coupled climate model was designed to better capture smaller-scale ocean motions. While this configuration improves the representation of many aspects of the climate system, some biases from the existing lower-resolution version persist.
26 Mar 2025
Towards an integrated inventory of anthropogenic emissions for China
Yijuan Zhang, Guy Brasseur, Maria Kanakidou, Claire Granier, Nikos Daskalakis, Alexandros Panagiotis Poulidis, Kun Qu, and Mihalis Vrekoussis
EGUsphere, https://doi.org/10.5194/egusphere-2025-268, https://doi.org/10.5194/egusphere-2025-268, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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A new inventory of anthropogenic emissions, the China INtegrated Emission Inventory (CINEI), was developed in this study to better represent emission sectors, chemical speciation and spatiotemporal variations in China. Compared to simulations driven by global inventories, CINEI demonstrated better numerical modeling performance in ozone and its precursors (nitrogen dioxide and carbon monoxide). This study provides valuable insights for designing ozone mitigation strategies.
26 Mar 2025
Increasing Resolution and Accuracy in Sub-Seasonal Forecasting through 3D U-Net: the Western US
Jihun Ryu, Hisu Kim, Shih-Yu Simon Wang, and Jin-Ho Yoon
EGUsphere, https://doi.org/10.5194/egusphere-2025-308, https://doi.org/10.5194/egusphere-2025-308, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Using a neural network model, county-level weather forecasts was achieved in the Western U.S. By combining traditional forecasting data with actual weather observations, the AI system achieved better temperature predictions at local scales. While showed promise for temperature forecasting, it still had difficulty accurately predicting extreme rainfall events. The research advances weather forecasting capabilities, potentially helping communities prepare for severe weather conditions.
25 Mar 2025
Numerical simulation of nitrous oxide over Asia using regional climate-chemistry-ecology coupling model RegCM-Chem-YIBs
Xin Zeng, Tijian Wang, Congwu Huang, Bingliang Zhuang, Shu Li, Mengmeng Li, Min Xie, Qian Zhang, and Nanhong Xie
EGUsphere, https://doi.org/10.5194/egusphere-2025-608, https://doi.org/10.5194/egusphere-2025-608, 2025
Preprint under review for GMD (discussion: open, 1 comment)
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In this study, we enhanced the regional climate-chemistry-ecology model to reveal the seasonal and spatial variations of N2O levels. The lowest concentration was recorded in June (334.01 ppb), while the highest occurred in December (335.42 ppb). Certain regions, such as the North China Plain and the Ganges Basin, exhibited higher nitrous oxide levels. We also gained deeper insights into the complex interactions between N2O emissions and atmospheric processes.
25 Mar 2025
Computation of Self-recruitment in Fish Larvae using Forward- and Backward-in-Time Particle Tracking in a Lagrangian Model (SWIM-v2.0) of the Simulated Circulation of Lake Erie (AEM3D-v1.1.2)
Wei Shi, Leon Boegman, Josef Ackerman, Shiliang Shan, and Yingming Zhao
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-215, https://doi.org/10.5194/gmd-2024-215, 2025
Revised manuscript under review for GMD (discussion: final response, 7 comments)
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Self-recruitment of a population at a given larval settlement location is dependent on larval production from each source location, independent of larval recruits at the settlement location. An arbitrary choice of the number of larvae released from each source location in forward tracking is found to cause ambiguous self-recruitment. In contrast, we found that an arbitrary choice of the number of larvae released from the settlement location in backtracking leads to unambiguous self-recruitment.
24 Mar 2025
UpsFrac v1.0: An open-source software for integrating modelling and upscaling permeability for fractured porous rocks
Tao Chen, Honghao Sheng, Yu Zhang, and Fengxin Kang
EGUsphere, https://doi.org/10.5194/egusphere-2025-36, https://doi.org/10.5194/egusphere-2025-36, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Understanding fluid and its coupled processes in fractured rocks is vital for groundwater and geothermal energy management. We developed UpsFrac, a user-friendly software that integrates two essential processes: modeling discrete fractures in rocks and upscaling equivalent permeability across scales. This open-source tool empowers scientists to better predict fluid movement in complex fractured rocks, enabling more precise and efficient assessments of groundwater and geothermal resources.
24 Mar 2025
CROMES v1.0: A flexible CROp Model Emulator Suite for climate impact assessment
Christian Folberth, Artem Baklanov, Nikolay Khabarov, Thomas Oberleitner, Juraj Balkovič, and Rastislav Skalský
EGUsphere, https://doi.org/10.5194/egusphere-2025-862, https://doi.org/10.5194/egusphere-2025-862, 2025
Preprint under review for GMD (discussion: open, 1 comment)
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Global gridded crop models (GGCMs) are important tools in agricultural climate impact assessments but computationally costly. An emergent approach to derive crop productivity estimates similar to those from GGCMs are emulators that mimic the original model, but typically with considerable bias. Here we present a modelling package that trains emulators with very high accuracy and high computational gain, providing a basis for more comprehensive scenario assessments.
24 Mar 2025
SnapWave: fast, implicit wave transformation from offshore to nearshore
Dano Roelvink, Maarten van Ormondt, Johan Reyns, and Marlies van der Lugt
EGUsphere, https://doi.org/10.5194/egusphere-2025-492, https://doi.org/10.5194/egusphere-2025-492, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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Existing wave models are often quite heavy for coastal applications. The SnapWave model simulates wave refraction (turning towards the coast), shoaling (steepening up) and dissipation (loss of energy by friction and wave breaking), and it uses an efficient computational mesh that you can refine where you need it. In the paper we show that the model can reproduce time series of waves anywhere in the world, using a depth map and wave data from a global model (ERA5) or a local wave buoy.
21 Mar 2025
Spatially varying parameters improve carbon cycle modeling in the Amazon rainforest with ORCHIDEE r8849
Lei Zhu, Philippe Ciais, Yitong Yao, Daniel Goll, Sebastiaan Luyssaert, Isabel Martínez Cano, Arthur Fendrich, Laurent Li, Hui Yang, Sassan Saatchi, and Wei Li
EGUsphere, https://doi.org/10.5194/egusphere-2025-397, https://doi.org/10.5194/egusphere-2025-397, 2025
Revised manuscript under review for GMD (discussion: final response, 8 comments)
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This study enhances the accuracy of modeling the carbon dynamics of Amazon rainforest by optimizing key model parameters based on satellite data. Using spatially varying parameters for tree mortality and photosynthesis, we improved predictions of biomass, productivity, and tree mortality. Our findings highlight the critical role of wood density and water availability in forest processes, offering insights to refine global carbon cycle models.
21 Mar 2025
Developing an eco-physiological process-based model of soybean growth and yield (MATCRO-Soy v.1): Model calibration and evaluation
Astrid Yusara, Tomomichi Kato, Elizabeth A. Ainsworth, Rafael Battisti, Etsushi Kumagai, Satoshi Nakano, Yushan Wu, Yutaka Tsusumi-Morita, Kazuhiko Kobayashi, and Yuji Masutomi
EGUsphere, https://doi.org/10.5194/egusphere-2025-453, https://doi.org/10.5194/egusphere-2025-453, 2025
Preprint under review for GMD (discussion: open, 3 comments)
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We developed a soybean model, an ecosystem model for crop yield (namely MATCRO-Soy), integrating crop response toward climate variable. It offers a detailed yield estimation. Parameter tuning in the model used literature and field experiments. The model shows a moderate correlation with observed yields at the global, national, and grid levels. Development of MATCRO-Soy enhances crop modeling diversity approaches, particularly in climate change impact studies.
20 Mar 2025
A Python diagnostics package for evaluation of MJO-Teleconnections in S2S forecast systems
Cristiana Stan, Saisri Kollapaneni, Andrea Jenney, Jiabao Wang, Zheng Wu, Cheng Zheng, Hyemi Kim, Chaim Garfinkel, and Ayush Singh
EGUsphere, https://doi.org/10.5194/egusphere-2025-1142, https://doi.org/10.5194/egusphere-2025-1142, 2025
Preprint under review for GMD (discussion: open, 1 comment)
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The MJO-Teleconnections diagnostics package is an open-source Python software package to be used for evaluation of MJO-Teleconnections predicted by subseasonal-to-seasonal (S2S) forecast systems.
20 Mar 2025
DINO: A Diabatic Model of Pole-to-Pole Ocean Dynamics to Assess Subgrid Parameterizations across Horizontal Scales
David Kamm, Julie Deshayes, and Gurvan Madec
EGUsphere, https://doi.org/10.5194/egusphere-2025-1100, https://doi.org/10.5194/egusphere-2025-1100, 2025
Preprint under review for GMD (discussion: final response, 2 comments)
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We propose an idealized model of pole-to-pole ocean dynamics designed as a testbed for eddy parameterizations across a range of horizontal scales. While computationally affordable, it is able to capture key metrics of the climate system. By comparing simulations at low, intermediate and high horizontal resolution, we demonstrate its utility for evaluating eddy parameterizations, both in terms of their effect on the mean-state and by diagnosing the unresolved eddy fluxes they aim to represent.
20 Mar 2025
ISEFlow v1.0: A Flow-Based Neural Network Emulator for Improved Sea Level Projections and Uncertainty Quantification
Peter Van Katwyk, Baylor Fox-Kemper, Sophie Nowicki, Hélène Seroussi, and Karianne J. Bergen
EGUsphere, https://doi.org/10.5194/egusphere-2025-870, https://doi.org/10.5194/egusphere-2025-870, 2025
Preprint under review for GMD (discussion: open, 6 comments)
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We present ISEFlow, a machine learning emulator that predicts how the melting of the Antarctic and Greenland ice sheets will contribute to sea level. ISEFlow is fast and accurate, allowing scientists to explore different climate scenarios with greater confidence. ISEFlow distinguishes between high and low emissions scenarios, helping us understand how today’s choices impact future sea levels. ISEFlow supports more reliable climate predictions and helps scientists study the future of ice sheets.
20 Mar 2025
Improved vapor pressure predictions using group contribution-assisted graph convolutional neural networks (GC2NN)
Matteo Krüger, Tommaso Galeazzo, Ivan Eremets, Bertil Schmidt, Ulrich Pöschl, Manabu Shiraiwa, and Thomas Berkemeier
EGUsphere, https://doi.org/10.5194/egusphere-2025-1191, https://doi.org/10.5194/egusphere-2025-1191, 2025
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This work uses machine learning to predict saturation vapor pressures of atmospherically-relevant organic compounds, crucial for partitioning of secondary organic aerosol (SOA). We introduce a new method using graph convolutional neural networks, in which molecular graphs enable the model to capture molecular connectivity better than with non-structural embeddings. The method shows strong agreement with experimentally determined vapor pressures, and outperforms existing estimation methods.
20 Mar 2025
SWAT+MODFLOW: A New Hydrologic Model for Simulating Surface-Subsurface Flow in Managed Watersheds
Ryan Bailey, Salam Abbas, Jeffrey Arnold, and Michael White
EGUsphere, https://doi.org/10.5194/egusphere-2025-300, https://doi.org/10.5194/egusphere-2025-300, 2025
Revised manuscript accepted for GMD (discussion: final response, 5 comments)
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Water managers often make use of computer models to assess a region’s water supply under future conditions and management scenarios. This article introduces a new computer model that combines a land surface model (SWAT+) and a groundwater model (MODFLOW) and shows how it can be applied to managed, irrigated watersheds. This new model can be used for regions that rely on both surface water and groundwater for drinking water, agriculture, and industry.
17 Mar 2025
The Process and Value of Reprogramming a Legacy Global Hydrological Model
Emmanuel Nyenah, Petra Döll, Martina Flörke, Leon Mühlenbruch, Lasse Nissen, and Robert Reinecke
EGUsphere, https://doi.org/10.5194/egusphere-2025-1096, https://doi.org/10.5194/egusphere-2025-1096, 2025
Preprint under review for GMD (discussion: open, 2 comments)
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We reprogrammed the latest WaterGAP model (2.2e) to create a sustainable global hydrological model. By utilizing best software practices like modular design, version control, and clear documentation, the new WaterGAP supports collaboration across teams. It can be easily understood, applied, and enhanced by both novice and experienced modellers. Additionally, we share the reprogramming process to assist in the reprogramming of other large geoscientific research software.
17 Mar 2025
ROCKE-3D 2.0: An updated general circulation model for simulating the climates of rocky planets
Kostas Tsigaridis, Andrew S. Ackerman, Igor Aleinov, Mark A. Chandler, Thomas L. Clune, Christopher M. Colose, Anthony D. Del Genio, Maxwell Kelley, Nancy Y. Kiang, Anthony Leboissetier, Jan P. Perlwitz, Reto A. Ruedy, Gary L. Russell, Linda E. Sohl, Michael J. Way, and Eric T. Wolf
EGUsphere, https://doi.org/10.5194/egusphere-2025-925, https://doi.org/10.5194/egusphere-2025-925, 2025
Preprint under review for GMD (discussion: final response, 5 comments)
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We present the second generation of ROCKE-3D, a generalized 3-dimensional model for use in Solar System and exoplanetary simulations of rocky planet climates. We quantify how the different component choices affect model results, and discuss strengths and limitations of using each component, together with how one can select which component to use. ROCKE-3D is publicly available and tutorial sessions are available for the community, greatly facilitating its use by any interested group.
17 Mar 2025
Deep learning representation of the aerosol size distribution
Donifan Barahona, Katherine Breen, Karoline Block, and Anton Darmenov
EGUsphere, https://doi.org/10.5194/egusphere-2025-482, https://doi.org/10.5194/egusphere-2025-482, 2025
Preprint under review for GMD (discussion: final response, 4 comments)
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Particulate matter impacts Earth's radiation, clouds, and human health, but modeling their size is challenging due to computational and observational limits. We developed a machine learning model to predict aerosol size distributions, which accurately replicates advanced models and field measurements.
17 Mar 2025
On the proper use of temperature screen-level measurements in weather forecasting models over mountains
Danaé Préaux, Ingrid Dombrowski-Etchevers, Isabelle Gouttevin, and Yann Seity
EGUsphere, https://doi.org/10.5194/egusphere-2025-708, https://doi.org/10.5194/egusphere-2025-708, 2025
Preprint under review for GMD (discussion: final response, 2 comments)
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Structural inhomogeneities of the valleys and mountains observational network contribute to the misrepresentation of near-surface air temperature and should be considered both when evaluating the model performances and in assimilation.
14 Mar 2025
A REtrieval Method for optical and physical Aerosol Properties in the stratosphere (REMAPv1)
Andrin Jörimann, Timofei Sukhodolov, Beiping Luo, Gabriel Chiodo, Graham Mann, and Thomas Peter
EGUsphere, https://doi.org/10.5194/egusphere-2025-145, https://doi.org/10.5194/egusphere-2025-145, 2025
Preprint under review for GMD (discussion: final response, 2 comments)
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Aerosol particles in the stratosphere affect our climate. Climate models therefore need an accurate description of their properties and evolution. Satellites measure how strongly aerosol particles extinguish light passing through the stratosphere. We describe a method to use such aerosol extinction data to retrieve the number and sizes of the aerosol particles and calculate their optical effects. The resulting data sets for models are validated against ground-based and balloon observations.
14 Mar 2025
The sensitivity of EC-Earth3 decadal predictions to the choice of volcanic forcing dataset: Insights for the next major eruption
Roberto Bilbao, Thomas J. Aubry, Matthew Toohey, and Pablo Ortega
EGUsphere, https://doi.org/10.5194/egusphere-2025-609, https://doi.org/10.5194/egusphere-2025-609, 2025
Preprint under review for GMD (discussion: final response, 4 comments)
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Large volcanic eruptions are unpredictable and can have significant climatic impacts. If one occurs, operational decadal forecasts will become invalid and must be rerun including the volcanic forcing. By analyzing the climate response in EC-Earth3 retrospective predictions, we show that idealised forcings produced with two simple models could be used in operational decadal forecasts to account for the radiative impacts of the next major volcanic eruption.
14 Mar 2025
The Lagrangian moisture source and transport diagnostic WaterSip V3.2
Harald Sodemann
EGUsphere, https://doi.org/10.5194/egusphere-2025-574, https://doi.org/10.5194/egusphere-2025-574, 2025
Preprint under review for GMD (discussion: final response, 7 comments)
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The WaterSip software locates regions where precipitation comes from. WaterSip evaluates of the water budget of the air masses, providing information on the conditions during evaporation, transport, and arrival at the target area. WaterSip can be easily configured and writes gridded output files. Guidance is given on where uncertainties arise using a case study, and best practices are recommended. This manuscript supports the comparison of different methods to find precipitation sources.
14 Mar 2025
Constraining CMIP6 sea ice simulations with ICESat-2
Alek Aaron Petty, Christopher Cardinale, and Madison Smith
EGUsphere, https://doi.org/10.5194/egusphere-2025-766, https://doi.org/10.5194/egusphere-2025-766, 2025
Revised manuscript under review for GMD (discussion: final response, 5 comments)
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We put global climate models to the test against NASA’s ICESat-2 satellite to see how well they simulate global sea ice cover. By adding fancy laser data from ICESat-2, we can better assess how well the models are performing compared to the standard assessments of sea ice area. Overall the models do a good job but there’s room for improvement, especially across the Southern Ocean. We should think a bit more about sea ice density if we want more reliable freeboard comparisons.
14 Mar 2025
psit 1.0: A System to Compress Lagrangian Flows
Alexander Pietak, Langwen Huang, Luigi Fusco, Michael Sprenger, Sebastian Schemm, and Torsten Hoefler
EGUsphere, https://doi.org/10.5194/egusphere-2025-793, https://doi.org/10.5194/egusphere-2025-793, 2025
Preprint under review for GMD (discussion: open, 0 comments)
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As meteorological models grow in complexity, the volume of output data increases, making compression increasingly desirable. However, no specialized methods currently exist for compressing data in the Lagrangian frame. To address this gap, we developed psit, a pipeline for the lossy compression of Lagrangian flow data. In most cases, psit achieves performance that is equivalent or superior to non specialized alternatives, with compression errors behaving similar to measurement inaccuracies.
13 Mar 2025
METEORv1.0.1: A novel framework for emulating multi-timescale regional climate responses
Marit Sandstad, Norman Julius Steinert, Susanne Baur, and Benjamin Mark Sanderson
EGUsphere, https://doi.org/10.5194/egusphere-2025-1038, https://doi.org/10.5194/egusphere-2025-1038, 2025
Preprint under review for GMD (discussion: final response, 2 comments)
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In this article we present METEORv1.0.0, a climate model emulator, that can be trained on full spacially resolved and widely available climate model data to reproduce climate variables, and make predictions from unseen emission trajectories. The methodology which consists of identifying patterns associated with various timescales of impact for one or more forcers using idealised experiments and anomaly calculations. Results for precipitation and temperature show good model performance.
13 Mar 2025
HAPI2LIBIS (v1.0): A new tool for flexible high resolution radiative transfer computations with libRadtran (version 2.0.5)
Antti Kukkurainen, Antti Mikkonen, Antti Arola, Antti Lipponen, Ville Kolehmainen, and Neus Sabater
EGUsphere, https://doi.org/10.5194/egusphere-2025-220, https://doi.org/10.5194/egusphere-2025-220, 2025
Preprint under review for GMD (discussion: open, 2 comments)
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HAPI2LIBIS is a new software tool that enhances the capabilities of the radiative transfer model libRadtran. It simplifies high wavelength resolution simulations by using up-to-date molecular data from the HITRAN database and streamlining computations. This tool helps researchers analyze how gases interact with radiation in the Earth's atmosphere and surface, improving atmospheric studies and satellite observations, and making detailed modeling more accurate and accessible.
12 Mar 2025
SMASH v1.0: A Differentiable and Regionalizable High-Resolution Hydrological Modeling and Data Assimilation Framework
François Colleoni, Ngo Nghi Truyen Huynh, Pierre-André Garambois, Maxime Jay-Allemand, Didier Organde, Benjamin Renard, Thomas De Fournas, Apolline El Baz, Julie Demargne, and Pierre Javelle
EGUsphere, https://doi.org/10.5194/egusphere-2025-690, https://doi.org/10.5194/egusphere-2025-690, 2025
Preprint under review for GMD (discussion: final response, 4 comments)
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We present smash, an open-source framework for high-resolution hydrological modeling and data assimilation. It combines process-based models with neural networks for regionalization, enabling accurate simulations from catchment to country scale. With an efficient, differentiable solver, smash supports large-scale calibration and parallel computing. Tested on open datasets, it shows strong performance in river flow prediction, making it a valuable tool for research and operational use.
12 Mar 2025
A first calibration of the JULES-crop version 7.4 for rice using the novel O3-FACE experiment in China
Beiyao Xu, Steven Dobbie, Huiyi Yang, Lianxin Yang, Yu Jiang, Andrew Challinor, Karina Williams, Yunxia Wang, and Tijian Wang
EGUsphere, https://doi.org/10.5194/egusphere-2024-4077, https://doi.org/10.5194/egusphere-2024-4077, 2025
Preprint under review for GMD (discussion: open, 2 comments)
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Ozone (O3) pollution harms rice production and threatens food security. To understand these impacts, we calibrated a crop model using unique data from experiments where rice was grown in open fields under controlled O3 exposure (free air). This is the first time such data has been used to improve a model’s ability to predict how rice responds to O3 pollution. Our work provides a more accurate tool to study O3’s effects and guide strategies to protect agriculture.
12 Mar 2025
Assessment of gap-filling techniques applied to satellite phytoplankton composition products for the Atlantic Ocean
Ehsan Mehdipour, Hongyan Xi, Alexander Barth, Aida Alvera-Azcárate, Adalbert Wilhelm, and Astrid Bracher
EGUsphere, https://doi.org/10.5194/egusphere-2025-112, https://doi.org/10.5194/egusphere-2025-112, 2025
Preprint under review for GMD (discussion: open, 4 comments)
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Phytoplankton are vital for marine ecosystems and nutrient cycling, detectable by optical satellites. Data gaps caused by clouds and other non-optimal conditions limit comprehensive analyses like trend monitoring. This study evaluated DINCAE and DINEOF gap-filling methods for reconstructing chlorophyll-a datasets, including total chlorophyll-a and five major phytoplankton groups. Both methods showed robust reconstruction capabilities, aiding pattern detection and long-term ocean colour analysis.
07 Mar 2025
HydroBlocks-MSSUBv0.1: A Multiscale Approach for Simulating Lateral Subsurface Flow Dynamics in Land Surface Models
Daniel Guyumus, Laura Torres-Rojas, Luiz Bacelar, Chengcheng Xu, and Nathaniel Chaney
EGUsphere, https://doi.org/10.5194/egusphere-2025-563, https://doi.org/10.5194/egusphere-2025-563, 2025
Preprint under review for GMD (discussion: final response, 3 comments)
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This study explores a new tiling scheme within the HydroBlocks Land Surface Model to represent local, regional and intermediate subsurface flow. Using high-resolution environmental data, the scheme defines parameterized flow units, enabling water and energy flux simulations. Compared against a benchmark simulation, the multiscale scheme demonstrates strong agreement in spatial mean, standard deviation, and temporal variability, showcasing its potential for large-scale hydrological simulation.
07 Mar 2025
Offline Fennel: A High-Performance and Computationally Efficient Biogeochemical Model within the Regional Ocean Modeling System (ROMS)
Júlia Crespin, Jordi Solé, and Miquel Canals
EGUsphere, https://doi.org/10.5194/egusphere-2025-865, https://doi.org/10.5194/egusphere-2025-865, 2025
Revised manuscript under review for GMD (discussion: final response, 4 comments)
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This study presents the Offline Fennel model, a tool designed to simulate ocean biogeochemical processes efficiently. By using existing hydrodynamic data, the model significantly reduces computation time from 6 hours to just 30 minutes. We tested its accuracy in the Northern Gulf of Mexico and found it closely matches physical-biogeochemical coupled simulations. This model allows researchers to conduct more tests and simulations without the need for extensive computational resources.
28 Feb 2025
SanDyPALM v1.0: Static and Dynamic Drivers for the PALM-4U Model to Facilitate Realistic Urban Microclimate Simulations
Julian Vogel, Sebastian Stadler, Ganesh Chockalingam, Afshin Afshari, Johanna Henning, and Matthias Winkler
EGUsphere, https://doi.org/10.5194/egusphere-2025-144, https://doi.org/10.5194/egusphere-2025-144, 2025
Revised manuscript under review for GMD (discussion: final response, 6 comments)
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This study presents a toolkit to simplify input data creation for the urban microclimate model PALM-4U. It introduces novel methods to automate the use of open data sources. Our analysis of four test cases created from different geographic data sources shows variations in temperature, humidity, and wind speed, influenced by data quality. Validation indicates that the automated methods yield results comparable to expert-driven approaches, facilitating user-friendly urban climate modeling.
28 Feb 2025
High-resolution mapping of urban NO2 concentrations using Retina v2: a case study on data assimilation of surface and satellite observations in Madrid
Bas Mijling, Henk Eskes, Sascha Hofmann, Pau Moreno, David García Falin, and María Encarnación de Vega Pastor
EGUsphere, https://doi.org/10.5194/egusphere-2025-202, https://doi.org/10.5194/egusphere-2025-202, 2025
Preprint under review for GMD (discussion: final response, 4 comments)
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Given the serious health risks of urban air pollution, monitoring local pollution levels is crucial. The Retina v2 algorithm creates high-resolution pollution maps by integrating satellite and local measurements with an air quality model. Easily portable to other cities, it balances accuracy with low computational demands, matching or outperforming complex dispersion models and data-heavy machine learning. Satellite data proves especially valuable in cities with sparse or no monitoring networks.
28 Feb 2025
Generalized local fractions – a method for the calculation of sensitivities to emissions from multiple sources for chemically active species, illustrated using the EMEP MSC-W model (rv5.5)
Peter Wind and Willem van Caspel
EGUsphere, https://doi.org/10.5194/egusphere-2024-3571, https://doi.org/10.5194/egusphere-2024-3571, 2025
Revised manuscript under review for GMD (discussion: final response, 4 comments)
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This paper presents a numerical method to assess the origin of air pollution. Combined with a numerical air pollution transport and chemistry model, it can follow the contributions from a large number of emission sources. The result is a series of maps that give the relative contributions from for example all European countries at each point.
28 Feb 2025
Assessment of machine learning-based approaches to improve sub-seasonal to seasonal forecasting of precipitation in Senegal
Dioumacor Faye, Felipe M. de Andrade, Roberto Suárez-Moreno, Dahirou Wane, Michaela I. Hegglin, Abdou L. Dieng, François Kaly, Redouane Lguensat, and Amadou T. Gaye
EGUsphere, https://doi.org/10.5194/egusphere-2024-4040, https://doi.org/10.5194/egusphere-2024-4040, 2025
Preprint under review for GMD (discussion: open, 2 comments)
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This study evaluates machine learning (ML) methods to improve subseasonal-to-seasonal (S2S) rainfall forecasts in Senegal during the West African monsoon. Using high-resolution precipitation data and atmospheric-oceanic reanalysis, we show that ML models like ridge regression outperform traditional climate models. These methods enhance prediction accuracy and efficiency, offering valuable tools for climate risk management and water resource planning.
27 Feb 2025
Soil Parameterization in Land Surface Models Drives Large Discrepancies in Soil Moisture Predictions Across Hydrologically Complex regions of the Contiguous United States
Kachinga Silwimba, Alejandro N. Flores, Irene Cionni, Sharon A. Billings, Pamela L. Sullivan, Hoori Ajami, Daniel R. Hirmas, and Li Li
EGUsphere, https://doi.org/10.5194/egusphere-2025-713, https://doi.org/10.5194/egusphere-2025-713, 2025
Preprint under review for GMD (discussion: final response, 7 comments)
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This study evaluates the influence of soil hydraulic parameterizations on soil moisture simulations in CLM5 across the CONUS (1980–2010) using Empirical Orthogonal Function (EOF) analysis. Results reveal significant regional discrepancies, particularly in the Great Plains, where parameter uncertainty drives biases in soil moisture variability. Comparisons with ERA5-Land highlight seasonal mismatches, underscoring the need for improved soil parameterization to enhance land surface model accuracy.
26 Feb 2025
Stabilized two-phase material point method for hydromechanical coupling problems in solid-fluid porous media
Xiong Tang, Wei Liu, Siming He, Lei Zhu, Michel Jaboyedoff, Huanhuan Zhang, Yuqing Sun, and Zenan Huo
EGUsphere, https://doi.org/10.5194/egusphere-2025-707, https://doi.org/10.5194/egusphere-2025-707, 2025
Revised manuscript accepted for GMD (discussion: final response, 4 comments)
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This manuscript presents a numerical model about an explicit stabilized coupled two phase material point method. The novelty lies in the employment of stabilized techniques including the strain smoothing method and the multi-field variational principleto to mitigate the spurious pore pressure and maintain the numerical stability. The proposed formulation provides an effective and reliable approach for simulating solid-fluid porous media under static and dynamic conditions.
26 Feb 2025
Sentinel-1 SAR-based Globally Distributed Co-Seismic Landslide Detection by Deep Neural Networks
Lorenzo Nava, Alessandro Mondini, Kushanav Bhuyan, Chengyong Fang, Oriol Monserrat, Alessandro Novellino, and Filippo Catani
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-230, https://doi.org/10.5194/gmd-2024-230, 2025
Preprint under review for GMD (discussion: open, 3 comments)
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This paper presents a new framework for landslide detection using radar and deep learning, informed by data from 73000 landslides across diverse regions in the world. The method showed high accuracy and rapid response potential regardless of weather and illumination conditions. By overcoming the limits of optical satellite imagery, it offers a powerful tool for global landslide detection, benefiting disaster management and advancing methods for monitoring hazardous terrains.
25 Feb 2025
Improving wheat phenology and yield forecasting with a deep learning-enhanced WOFOST model under extreme weather conditions
Jinhui Zheng, Le Yu, Zhenrong Du, and Liujun Xiao
EGUsphere, https://doi.org/10.5194/egusphere-2024-4010, https://doi.org/10.5194/egusphere-2024-4010, 2025
Preprint under review for GMD (discussion: final response, 3 comments)
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This study integrates the extreme weather index and deep learning algorithms with the World Food Studies Simulation Model (WOFOST), proposing the WOFOST-EW model. WOFOST-EW significantly improves the simulation of winter wheat growth under extreme weather conditions, providing more accurate predictions of phenology and yield. As extreme weather events become more frequent, WOFOST-EW provides a key tool for agricultural development.
25 Feb 2025
Benchmarking and evaluating the NASA Land Information System (version 7.5.2) coupled with the refactored Noah-MP land surface model (version 5.0)
Cenlin He, Tzu-Shun Lin, David M. Mocko, Ronnie Abolafia-Rosenzweig, Jerry W. Wegiel, and Sujay V. Kumar
EGUsphere, https://doi.org/10.5194/egusphere-2024-4176, https://doi.org/10.5194/egusphere-2024-4176, 2025
Preprint under review for GMD (discussion: final response, 2 comments)
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This study integrates the refactored community Noah-MP version 5.0 model with the NASA Land Information System (LIS) version 7.5.2 to streamline the synchronization, development, and maintenance of Noah-MP within LIS and to enhance their interoperability and applicability. The model benchmarking and evaluation results reveal key model strengths and weaknesses in simulating land surface quantities and show implications for future model improvements.
24 Feb 2025
Implementation of a dry deposition module (DEPAC v3.11) in a large eddy simulation code (DALES v4.4)
Leon Geers, Ruud Janssen, Gudrun Thorkelsdottir, Jordi Vilà-Guerau de Arellano, and Martijn Schaap
EGUsphere, https://doi.org/10.5194/egusphere-2025-426, https://doi.org/10.5194/egusphere-2025-426, 2025
Revised manuscript under review for GMD (discussion: final response, 4 comments)
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High-resolution data on reactive nitrogen deposition are needed to inform cost-effective policies. Here, we describe the implementation of a dry deposition module into a large eddy simulation code. With this model, we are able to represent the turbulent exchange of tracers at the hectometer resolution. The model calculates the dispersion and deposition of NOx and NH3 in great spatial detail, clearly showing the influence of local land use patterns.
21 Feb 2025
Simple Eulerian-Lagrangian approach to solve equations for sinking particulate organic matter in the ocean
Vladimir Maderich, Igor Brovchenko, Kateryna Kovalets, Seongbong Seo, and Kyeong Ok Kim
EGUsphere, https://doi.org/10.5194/egusphere-2025-491, https://doi.org/10.5194/egusphere-2025-491, 2025
Revised manuscript under review for GMD (discussion: final response, 7 comments)
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We have developed a new simple Eulerian-Lagrangian approach to solve equations for sinking particulate organic matter in the ocean. We rely on the known parameterizations, but our approach to solving the problem differs, allowing the algorithm to be incorporated into biogeochemical global ocean models with relative ease. New analytical and numerical solutions confirmed that feedback between degradation rate and sinking velocity significantly changes particulate matter fluxes.
21 Feb 2025
Accurate and fast prediction of radioactive pollution by Kriging coupled with Auto-Associative Models
Raphaël Périllat, Sylvain Girard, and Irène Korsakissok
EGUsphere, https://doi.org/10.5194/egusphere-2024-3838, https://doi.org/10.5194/egusphere-2024-3838, 2025
Revised manuscript under review for GMD (discussion: final response, 4 comments)
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We developed a method to improve decision-making during nuclear crises by predicting the spread of radiation more efficiently. Existing approaches are often too slow, especially when analyzing complex data like radiation maps. Our method combines techniques to simplify these maps and predict them quickly using statistical tools. This approach could help authorities respond faster and more accurately in emergencies, reducing risks to the population and the environment.
19 Feb 2025
nextGEMS: entering the era of kilometer-scale Earth system modeling
Hans Segura, Xabier Pedruzo-Bagazgoitia, Philipp Weiss, Sebastian K. Müller, Thomas Rackow, Junhong Lee, Edgar Dolores-Tesillos, Imme Benedict, Matthias Aengenheyster, Razvan Aguridan, Gabriele Arduini, Alexander J. Baker, Jiawei Bao, Swantje Bastin, Eulàlia Baulenas, Tobias Becker, Sebastian Beyer, Hendryk Bockelmann, Nils Brüggemann, Lukas Brunner, Suvarchal K. Cheedela, Sushant Das, Jasper Denissen, Ian Dragaud, Piotr Dziekan, Madeleine Ekblom, Jan Frederik Engels, Monika Esch, Richard Forbes, Claudia Frauen, Lilli Freischem, Diego García-Maroto, Philipp Geier, Paul Gierz, Álvaro González-Cervera, Katherine Grayson, Matthew Griffith, Oliver Gutjahr, Helmuth Haak, Ioan Hadade, Kerstin Haslehner, Shabeh ul Hasson, Jan Hegewald, Lukas Kluft, Aleksei Koldunov, Nikolay Koldunov, Tobias Kölling, Shunya Koseki, Sergey Kosukhin, Josh Kousal, Peter Kuma, Arjun U. Kumar, Rumeng Li, Nicolas Maury, Maximilian Meindl, Sebastian Milinski, Kristian Mogensen, Bimochan Niraula, Jakub Nowak, Divya Sri Praturi, Ulrike Proske, Dian Putrasahan, René Redler, David Santuy, Domokos Sármány, Reiner Schnur, Patrick Scholz, Dmitry Sidorenko, Dorian Spät, Birgit Sützl, Daisuke Takasuka, Adrian Tompkins, Alejandro Uribe, Mirco Valentini, Menno Veerman, Aiko Voigt, Sarah Warnau, Fabian Wachsmann, Marta Wacławczyk, Nils Wedi, Karl-Hermann Wieners, Jonathan Wille, Marius Winkler, Yuting Wu, Florian Ziemen, Janos Zimmermann, Frida A.-M. Bender, Dragana Bojovic, Sandrine Bony, Simona Bordoni, Patrice Brehmer, Marcus Dengler, Emanuel Dutra, Saliou Faye, Erich Fischer, Chiel van Heerwaarden, Cathy Hohenegger, Heikki Järvinen, Markus Jochum, Thomas Jung, Johann H. Jungclaus, Noel S. Keenlyside, Daniel Klocke, Heike Konow, Martina Klose, Szymon Malinowski, Olivia Martius, Thorsten Mauritsen, Juan Pedro Mellado, Theresa Mieslinger, Elsa Mohino, Hanna Pawłowska, Karsten Peters-von Gehlen, Abdoulaye Sarré, Pajam Sobhani, Philip Stier, Lauri Tuppi, Pier Luigi Vidale, Irina Sandu, and Bjorn Stevens
EGUsphere, https://doi.org/10.5194/egusphere-2025-509, https://doi.org/10.5194/egusphere-2025-509, 2025
Preprint under review for GMD (discussion: open, 1 comment)
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The nextGEMS project developed two Earth system models that resolve processes of the order of 10 km, giving more fidelity to the representation of local phenomena, globally. In its fourth cycle, nextGEMS performed simulations with coupled ocean, land, and atmosphere over the 2020–2049 period under the SSP3-7.0 scenario. Here, we provide an overview of nextGEMS, insights into the model development, and the realism of multi-decadal, kilometer-scale simulations.
19 Feb 2025
A New Hybrid Particle-Puff Approach to Atmospheric Dispersion Modelling, Implemented in the Danish Emergency Response Model of the Atmosphere (DERMA)
Kasper Skjold Tølløse and Jens Havskov Sørensen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-173, https://doi.org/10.5194/gmd-2024-173, 2025
Revised manuscript under review for GMD (discussion: final response, 4 comments)
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In this study, we improve the short-scale dispersion modelling capabilities of the Danish Emergency Response Model of the Atmosphere (DERMA) by developing and implementing a new hybrid particle-puff description of turbulent diffusion, as well as updating a few other parameterizations in the model. The new model is evaluated against data from three different tracer gas experiments, and the promising results are an important first step towards using DERMA also for short-range dispersion modelling.
18 Feb 2025
Improving Fine Mineral Dust Representation from the Surface to the Column in GEOS-Chem 14.4.1
Dandan Zhang, Randall V. Martin, Xuan Liu, Aaron van Donkelaar, Christopher R. Oxford, Yanshun Li, Jun Meng, Danny M. Leung, Jasper F. Kok, Longlei Li, Haihui Zhu, Jay R. Turner, Yu Yan, Michael Brauer, Yinon Rudich, and Eli Windwer
EGUsphere, https://doi.org/10.5194/egusphere-2025-438, https://doi.org/10.5194/egusphere-2025-438, 2025
Revised manuscript under review for GMD (discussion: final response, 4 comments)
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This study develops the fine mineral dust simulation in GEOS-Chem by: 1) implementing a new dust emission scheme with further refinements; 2) revisiting the size distribution of emitted dust; 3) explicitly tracking fine dust for emission, transport and deposition in 4 size bins; 4) updating the parametrization for below-cloud scavenging. All revisions significantly reduce the overestimation of surface fine dust from 73% to 21% while retaining comparable skill in representing columnar abundance.
17 Feb 2025
NutGEnIE 1.0: nutrient cycle extensions to the cGEnIE Earth system model to examine the long-term influence of nutrients on oceanic primary production
David A. Stappard, Jamie D. Wilson, Andrew Yool, and Toby Tyrrell
EGUsphere, https://doi.org/10.5194/egusphere-2025-436, https://doi.org/10.5194/egusphere-2025-436, 2025
Preprint under review for GMD (discussion: final response, 4 comments)
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This research explores nutrient limitations in oceanic primary production. While traditional experiments identify the immediate limiting nutrient at specific locations, this study aims to identify the ultimate limiting nutrient (ULN), which governs long-term productivity. A mathematical model incorporating nitrogen, phosphorus, and iron nutrient cycles is used. The model's results are compared with ocean observational data to assess its effectiveness in investigating the ULN.
17 Feb 2025
COSP-RTTOV-1.0: Flexible radiation diagnostics to enable new science applications in model evaluation, climate change detection, and satellite mission design
Jonah K. Shaw, Dustin J. Swales, Sergio DeSouza-Machado, David D. Turner, Jennifer E. Kay, and David P. Schneider
EGUsphere, https://doi.org/10.5194/egusphere-2025-169, https://doi.org/10.5194/egusphere-2025-169, 2025
Revised manuscript accepted for GMD (discussion: final response, 3 comments)
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Satellites have observed earth's emission of infrared radiation since the 1970s. Because infrared wavelengths interact with the atmosphere in distinct ways, these observations contain information about the earth and atmosphere. We present a tool that runs alongside global climate models and produces output that can be directly compared with satellite measurements of infrared radiation. We then use this tool for climate model evaluation, climate change detection, and satellite mission design.
14 Feb 2025
Evaluation of Ozone and its Precursors using the Multi-Scale Infrastructure for Chemistry and Aerosols Version 0 (MUSICAv0) during the Michigan-Ontario Ozone Source Experiment (MOOSE)
Noribeth Mariscal, Louisa K. Emmons, Duseong S. Jo, Ying Xiong, Laura M. Judd, Scott J. Janz, Jiajue Chai, and Yaoxian Huang
EGUsphere, https://doi.org/10.5194/egusphere-2025-228, https://doi.org/10.5194/egusphere-2025-228, 2025
Preprint under review for GMD (discussion: final response, 2 comments)
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The distribution of ozone (O3) and its precursors (NOx, VOCs) is explored using the chemistry-climate model, MUSICAv0, and evaluated using measurements from the Michigan-Ontario Ozone Source Experiment. A custom grid of ~7 km was created over Michigan. A sector-based diurnal cycle for anthropogenic nitric oxide was included in the model. This work shows that grid resolution played a more important role for O3 precursors, and the diurnal cycle significantly impacted nighttime O3 formation.
13 Feb 2025
EcoPro-LSTM𝑣0: A Memory-based Machine Learning Approach to Predicting Ecosystem Dynamics across Time Scales in Mediterranean Environments
Mitra Cattry, Wenli Zhao, Juan Nathaniel, Jinghao Qiu, Yao Zhang, and Pierre Gentine
EGUsphere, https://doi.org/10.5194/egusphere-2024-3726, https://doi.org/10.5194/egusphere-2024-3726, 2025
Preprint under review for GMD (discussion: open, 4 comments)
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Climate change alters Mediterranean biota, affecting how they absorb and store carbon. These associated impacts arise from short- and long-term effects of rainfall, temperature, and other atmospheric forcings, which existing tools struggle to capture. This study presents a memory-integrated model combining high- and low-resolution data to track daily ecosystem responses. By analyzing past conditions, we show how earlier conditions shape plant carbon uptake and improve predictions.
12 Feb 2025
The discontinuous Galerkin coastal and estuarine modelling system (DGCEMS v1.0.0): a three-dimensional, mode-nonsplit, implicit-explicit Runge–Kutta hydrostatic model
Zereng Chen, Qinghe Zhang, Guoquan Ran, and Yang Nie
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-240, https://doi.org/10.5194/gmd-2024-240, 2025
Preprint under review for GMD (discussion: open, 8 comments)
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This study presents the development of a novel three-dimensional discontinuous Galerkin coastal and estuarine modelling system, DGCEMS. The model have low spurious mixing, a second-order convergence of surface water elevation, horizontal velocity and tracer field. It has the capability to simulate salt-freshwater interactions in the presence of wetting and drying boundaries.
12 Feb 2025
Mitigating Hail Overforecasting in the 2-Moment Milbrandt-Yau Microphysics Scheme (v2.25.2_beta_04) in WRF (v4.5.1) by Incorporating the Graupel Spongy Wet Growth Process (MY2_GSWG v1.0)
Shaofeng Hua, Gang Chen, Baojun Chen, Mingshan Li, and Xin Xu
EGUsphere, https://doi.org/10.5194/egusphere-2024-3834, https://doi.org/10.5194/egusphere-2024-3834, 2025
Revised manuscript accepted for GMD (discussion: final response, 4 comments)
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Hail forecasting using numerical models remains a challenge. In this study, we found that the commonly used graupel-to-hail conversion parameterization method led to hail overforecasting in heavy rainfall cases where no hail was observed. By incorporating the spongy wet growth process, we successfully mitigated hail overforecasting. The modified scheme also produced hail in real hail events. This research contributes to a better understanding of hail formation.
12 Feb 2025
Implementation of Water Tracers in the Met Office Unified Model
Alison J. McLaren, Louise C. Sime, Simon Wilson, Jeff Ridley, Qinggang Gao, Merve Gorguner, Giorgia Line, Martin Werner, and Paul Valdes
EGUsphere, https://doi.org/10.5194/egusphere-2024-3824, https://doi.org/10.5194/egusphere-2024-3824, 2025
Preprint under review for GMD (discussion: final response, 3 comments)
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We describe a new development in a state-of-the-art computer atmosphere model, which follows the movement of the model’s water. This provides an efficient way to track all the model’s rain and snow back to the average location of the evaporative source as shown in a present-day simulation. The new scheme can be used in simulations of the future to predict how the sources of regional rain or snowfall may change due to human actions, providing useful information for water management purposes.
11 Feb 2025
Impact of spatial resolution on CMIP6-driven Mediterranean climate simulations: a focus on precipitation distribution over Italy
Maria Vittoria Struglia, Alessandro Anav, Marta Antonelli, Sandro Calmanti, Franco Catalano, Alessandro Dell'Aquila, Emanuela Pichelli, and Giovanna Pisacane
EGUsphere, https://doi.org/10.5194/egusphere-2025-387, https://doi.org/10.5194/egusphere-2025-387, 2025
Revised manuscript under review for GMD (discussion: final response, 4 comments)
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We present the results of downscaling global climate projections for the Mediterranean and Italian regions aiming to produce high-resolution climate information for the assessment of climate change signals, focusing on extreme events. A general warming is foreseen by the end of century with a mean precipitation reduction accompanied, over Italian Peninsula, by a strong increase in the intensity of extreme precipitation events, particularly relevant for the high emissions scenario during autumn
10 Feb 2025
Calibrating the GAMIL3-1° climate model using a derivative-free optimization method
Wenjun Liang, Simon Frederick Barnard Tett, Lijuan Li, Coralia Cartis, Danya Xu, and Wenjie Dong
EGUsphere, https://doi.org/10.5194/egusphere-2024-3770, https://doi.org/10.5194/egusphere-2024-3770, 2025
Preprint under review for GMD (discussion: open, 2 comments)
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Predicting climate accurately is challenging due to uncertainties in model parameters. This study introduced an automated approach to refine key parameters, focusing on processes like cloud formation and atmospheric circulation. Testing adjustments to 10 and 20 parameters improved the model’s accuracy and stability, reducing errors in long-term simulations. This faster, more reliable method enhances climate models, supporting better future predictions and aiding global decision-making.
07 Feb 2025
The Detection and Attribution Model Intercomparison Project (DAMIP v2.0) contribution to CMIP7
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
EGUsphere, https://doi.org/10.5194/egusphere-2024-4086, https://doi.org/10.5194/egusphere-2024-4086, 2025
Revised manuscript accepted for GMD (discussion: final response, 6 comments)
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Climate model simulations of the response to human and natural influences together, natural climate influences alone, and greenhouse gases alone, among others, are key to quantifying human influence on the climate. The last set of such coordinated simulations underpinned key findings in the last Intergovernmental Panel on Climate Change (IPCC) report. Here we propose a new set of such simulations to be used in the next generation of attribution studies, and to underpin the next IPCC report.
07 Feb 2025
PALACE v1.0: Paranal Airglow Line And Continuum Emission model
Stefan Noll, Carsten Schmidt, Patrick Hannawald, Wolfgang Kausch, and Stefan Kimeswenger
EGUsphere, https://doi.org/10.5194/egusphere-2024-3512, https://doi.org/10.5194/egusphere-2024-3512, 2025
Revised manuscript accepted for GMD (discussion: final response, 4 comments)
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Non-thermal emission from chemical reactions in the Earth's middle und upper atmosphere strongly contributes to the brightness of the night sky below about 2.3 µm. The new Paranal Airglow Line and Continuum Emission model calculates the emission spectrum and its variability with an unprecedented accuracy. Relying on a large spectroscopic data set from astronomical spectrographs and theoretical molecular/atomic data, it is valuable for airglow research and astronomical observatories.
06 Feb 2025
The next generation sea-ice model neXtSIM, version 2
Einar Ólason, Guillaume Boutin, Timothy Williams, Anton Korosov, Heather Regan, Jonathan Rheinlænder, Pierre Rampal, Daniela Flocco, Abdoulaye Samaké, Richard Davy, Timothy Spain, and Sean Chua
EGUsphere, https://doi.org/10.5194/egusphere-2024-3521, https://doi.org/10.5194/egusphere-2024-3521, 2025
Preprint under review for GMD (discussion: open, 1 comment)
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This paper introduces a new version of the neXtSIM sea-ice model. NeXtSIM is unique among sea-ice models in how it represents sea-ice dynamics, focusing on features such as cracks and ridges and how these impact interactions between the atmosphere and ocean where sea ice is present. The new version introduces some physical parameterisations and model options detailed and explained in the paper. Following the paper's publication, the neXtSIM code will be released publicly for the first time.
06 Feb 2025
Statistical summaries for streamed data from climate simulations: One-pass algorithms (v0.6.2)
Katherine Grayson, Stephan Thober, Aleksander Lacima-Nadolnik, Ehsan Sharifi, Llorenç Lledó, and Francisco Doblas-Reyes
EGUsphere, https://doi.org/10.5194/egusphere-2025-28, https://doi.org/10.5194/egusphere-2025-28, 2025
Revised manuscript under review for GMD (discussion: final response, 7 comments)
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To provide the most accurate climate adaptation information, climate models are being run with finer grid resolution, resulting in larger data output. This paper presents intelligent data reduction algorithms that act on streamed data, a novel way of processing climate data as soon as it is produced. Using these algorithms to calculate statistics, we show that the accuracy provided is well within acceptable bounds while still providing memory savings that bypass unfeasible storage requirements.
06 Feb 2025
An extension of the WeatherBench 2 to binary hydroclimatic forecasts
Tongtiegang Zhao, Qiang Li, Tongbi Tu, and Xiaohong Chen
EGUsphere, https://doi.org/10.5194/egusphere-2025-3, https://doi.org/10.5194/egusphere-2025-3, 2025
Revised manuscript under review for GMD (discussion: final response, 8 comments)
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The recent WeatherBench 2 provides a versatile framework for the verification of deterministic and ensemble forecasts. In this paper, we present an explicit extension to binary forecasts of hydroclimatic extremes. Sixteen verification metrics for binary forecasts are employed and scorecards are generated to showcase the predictive performance. The extension facilitates more comprehensive comparisons of hydroclimatic forecasts and provides useful information for forecast applications.
06 Feb 2025
r.avaflow v4, a multi-purpose landslide simulation framework
Martin Mergili, Hanna Pfeffer, Andreas Kellerer-Pirklbauer, Christian Zangerl, and Shiva Prasad Pudasaini
EGUsphere, https://doi.org/10.5194/egusphere-2025-213, https://doi.org/10.5194/egusphere-2025-213, 2025
Preprint under review for GMD (discussion: open, 1 comment)
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We present a new version of the landslide model r.avaflow. It includes a model where different materials move on top of each other instead of mixing; a model supporting the entire range from block sliding to flowing; a model for slow-moving processes; and an interface for virtual reality visualization. Based on the results for four case studies we conclude that, at the moment, our enhancements are very useful for visualization of landslides for awareness building and environmental education.
06 Feb 2025
CRITER 1.0: A coarse reconstruction with iterative refinement network for sparse spatio-temporal satellite data
Matjaž Zupančič Muc, Vitjan Zavrtanik, Alexander Barth, Aida Alvera-Azcarate, Matjaž Ličer, and Matej Kristan
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-208, https://doi.org/10.5194/gmd-2024-208, 2025
Preprint under review for GMD (discussion: open, 4 comments)
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Accurate sea surface temperature data (SST) is crucial for weather forecasting and climate modeling, but satellite observations are often incomplete. We developed a new method called CRITER, which uses machine learning to fill in the gaps in SST data. Our two-stage approach reconstructs large-scale patterns and refines details. Tested on Mediterranean, Adriatic, and Atlantic seas data, CRITER outperforms current methods, reducing errors by up to 44 %.
04 Feb 2025
Implementation of an intermediate complexity snow-physics scheme (ISBA-Explicit Snow) into a sea-ice model (SI3): 1D thermodynamic coupling and validation
Théo Brivoal, Virginie Guemas, Martin Vancoppenolle, Clément Rousset, and Bertrand Decharme
EGUsphere, https://doi.org/10.5194/egusphere-2024-3220, https://doi.org/10.5194/egusphere-2024-3220, 2025
Preprint under review for GMD (discussion: final response, 2 comments)
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Snow in polar regions is key to sea ice formation and the Earth's climate, but current climate models simplify snow cover on sea ice. This study integrates an intermediate complexity snow-physics scheme into a sea-ice model designed for climate applications. We show that modelling the temporal changes in properties such as the density and thermal conductivity of the snow layers leads to a more accurate representation of heat transfer between the underlying sea ice and the atmosphere.
03 Feb 2025
Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application
Carolina Natel, David Martin Belda, Peter Anthoni, Neele Haß, Sam Rabin, and Almut Arneth
EGUsphere, https://doi.org/10.5194/egusphere-2024-4064, https://doi.org/10.5194/egusphere-2024-4064, 2025
Revised manuscript accepted for GMD (discussion: final response, 9 comments)
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Complex models predict forest carbon responses to future climate change but are slow and computationally intensive, limiting large-scale analyses. We used machine learning to accelerate predictions from the LPJ-GUESS vegetation model. Our emulators, based on random forests and neural networks, achieved 97 % faster simulations. This approach enables rapid exploration of climate mitigation strategies and supports informed policy decisions.
03 Feb 2025
Love number computation within the Ice-sheet and Sea-level System Model (ISSM v4.24)
Lambert Caron, Erik Ivins, Eric Larour, Surendra Adhikari, and Laurent Metivier
EGUsphere, https://doi.org/10.5194/egusphere-2024-3414, https://doi.org/10.5194/egusphere-2024-3414, 2025
Preprint under review for GMD (discussion: open, 1 comment)
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Presented here is a new model of the solid-Earth response to tides and mass changes in ice sheets, oceans, and groundwater, in of terms of gravity change and bedrock motion. The model is capable simulating mantle deformation including elasticity, transient and steady-state viscous flow. We detail our approach to numerical optimization, and report the accuracy of results with respect to community benchmarks. The resulting coupled system features kilometer-scale resolution and fast computation.
30 Jan 2025
Towards the Assimilation of Atmospheric CO2 Concentration Data in a Land Surface Model using Adjoint-free Variational Methods
Simon Beylat, Nina Raoult, Cédric Bacour, Natalie Douglas, Tristan Quaife, Vladislav Bastrikov, Peter Julien Rayner, and Philippe Peylin
EGUsphere, https://doi.org/10.5194/egusphere-2025-109, https://doi.org/10.5194/egusphere-2025-109, 2025
Revised manuscript under review for GMD (discussion: final response, 6 comments)
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Land surface models are important tools for understanding and predicting the land components of the carbon cycle. Atmospheric CO2 concentration data is a valuable source of information that can be used to improve the accuracy of these models. In this study, we present a statistical method named 4DEnVar to calibrate parameters of a land surface model using this data. We show that this method is easy to implement and more efficient and accurate than traditional methods.
29 Jan 2025
A framework for three-dimensional dynamic modeling of mountain glaciers in the Community Ice Sheet Model (CISM v2.2)
Samar Minallah, William Lipscomb, Gunter Leguy, and Harry Zekollari
EGUsphere, https://doi.org/10.5194/egusphere-2024-4152, https://doi.org/10.5194/egusphere-2024-4152, 2025
Preprint under review for GMD (discussion: final response, 2 comments)
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We implemented a new modeling framework within an Earth system model to study the evolution of mountain glaciers under different climate scenarios and applied it to the European Alps. Alpine glaciers will lose a large volume fraction under current temperatures, with near complete ice loss under warmer scenarios. This is the first use of a 3D, higher-order ice flow model for regional-scale glacier simulations that will enable assessments of coupled land ice and Earth system processes.
28 Jan 2025
FjordRPM v1.0: a reduced-physics model for efficient simulation of glacial fjords
Donald Alexander Slater, Eleanor Johnstone, Martim Mas e Braga, Neil Fraser, Tom Cowton, and Mark Inall
EGUsphere, https://doi.org/10.5194/egusphere-2024-3934, https://doi.org/10.5194/egusphere-2024-3934, 2025
Revised manuscript under review for GMD (discussion: final response, 4 comments)
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Glacial fjords connect ice sheets to the ocean, controlling heat delivery to glaciers, which impacts ice sheet melt, and freshwater discharge to the ocean, affecting ocean circulation. However, their dynamics are not captured in large-scale climate models. We designed a simplified, computationally efficient model – FjordRPM – which accurately captures key fjord processes. It has direct applications for improving projections of ice melt, ocean circulation and sea-level rise.
27 Jan 2025
age_flow_line-1.0: a fast and accurate numerical age model for a pseudo-steady flow tube of an ice sheet
Frédéric Parrenin, Ailsa Chung, and Carlos Martín
EGUsphere, https://doi.org/10.5194/egusphere-2024-3411, https://doi.org/10.5194/egusphere-2024-3411, 2025
Revised manuscript under review for GMD (discussion: final response, 5 comments)
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We developed a new numerical age solver for a pseudo-steady flow tube of an ice sheet. Thanks to a new coordinate system which tracks the trajectories and a change of the time variable, our scheme combines the advantages of Eulerian and Lagrangian schemes: no numerical diffusion and no dilution of tracers. Our model is so fast that it is easy to optimize its parameters. Our model is made available to the ice sheet community as an easy to use open-source software coded in python.
24 Jan 2025
Comprehensive evaluation of iAMAS (v1.0) in simulating Antarctic meteorological fields with observations and reanalysis
Qike Yang, Chun Zhao, Jiawang Feng, Gudongze Li, Jun Gu, Zihan Xia, Mingyue Xu, and Zining Yang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-229, https://doi.org/10.5194/gmd-2024-229, 2025
Revised manuscript under review for GMD (discussion: final response, 8 comments)
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This study presents the first comprehensive evaluation of unstructured meshes using the iAMAS model over Antarctica, encompassing both surface and upper-level meteorological fields. Comparison with ERA5 and observational data reveals that the iAMAS model performs well in simulating the Antarctic atmosphere; iAMAS demonstrates comparable, and in some cases superior, performance in simulating temperature and wind speed in East Antarctica when compared to ERA5.
24 Jan 2025
BOSSE v1.0: the Biodiversity Observing System Simulation Experiment
Javier Pacheco-Labrador, Ulisse Gomarasca, Daniel E. Pabon-Moreno, Wantong Li, Mirco Migliavacca, Martin Jung, and Gregory Duveiller
EGUsphere, https://doi.org/10.5194/egusphere-2025-318, https://doi.org/10.5194/egusphere-2025-318, 2025
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Measuring biodiversity is necessary to assess its loss, evolution, and role in ecosystem functions. Satellites image the whole terrestrial surface and could cost-efficiently map plant diversity globally. However, developing the necessary methods lacks consistent and sufficient field measurements. Thus, we propose using a simulation tool that generates virtual ecosystems, with species properties and functions varying in response to meteorology and the respective remote sensing imagery.
24 Jan 2025
ClimLoco1.0: CLimate variable confidence Interval of Multivariate Linear Observational COnstraint
Valentin Portmann, Marie Chavent, and Didier Swingedouw
EGUsphere, https://doi.org/10.5194/egusphere-2025-62, https://doi.org/10.5194/egusphere-2025-62, 2025
Preprint under review for GMD (discussion: final response, 2 comments)
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Future climate is very uncertain due to the large dispersion in projections from numerical models. Observational constraints (OCs) decrease this uncertainty using real-world observations. The article proposes a new rigorous statistical OC model that provides updated estimates of confidence intervals as used in IPCC reports. It allows the use of multiple observations at the same time, and proposes an innovative and proper illustration of this OC approach.
24 Jan 2025
Evaluating the performance of CE-QUAL-W2 version 4.5 sediment diagenesis model
Manuel Almeida and Pedro Coelho
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-202, https://doi.org/10.5194/gmd-2024-202, 2025
Preprint under review for GMD (discussion: open, 2 comments)
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This study aims to assess the capabilities of the advanced CE-QUAL-W2 v4.5 sediment diagenesis model, focusing on its application to a reservoir in Portugal over a six-year period (2016–2021). Our findings indicate that the model performs very well in simulating dissolved oxygen profiles, nutrient concentrations, and organic matter levels.
23 Jan 2025
A component based modular treatment of the soil-plant-atmosphere continuum: the GEOSPACE framework (v.1.2.9)
Concetta D'Amato, Niccolò Tubini, and Riccardo Rigon
EGUsphere, https://doi.org/10.5194/egusphere-2024-4128, https://doi.org/10.5194/egusphere-2024-4128, 2025
Revised manuscript under review for GMD (discussion: final response, 7 comments)
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This paper presents GEOSPACE and its 1D implementation: an open-source tool for simulating soil-plant-atmosphere continuum (SPAC) interactions. Using object-oriented programming, GEOSPACE modularizes SPAC processes, focusing on infiltration, evapotranspiration, and root water uptake. The 1D deployment integrates plant transpiration, soil evaporation, and root growth, providing a flexible and validated framework for ecohydrological modeling and applications.
23 Jan 2025
Representing high-latitude deep carbon in the pre-industrial state of the ORCHIDEE-MICT land surface model (r8704)
Yi Xi, Philippe Ciais, Dan Zhu, Chunjing Qiu, Yuan Zhang, Shushi Peng, Gustaf Hugelius, Simon P. K. Bowring, Daniel S. Goll, and Ying-Ping Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-206, https://doi.org/10.5194/gmd-2024-206, 2025
Revised manuscript under review for GMD (discussion: final response, 9 comments)
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Including high-latitude deep carbon is critical for projecting future soil carbon emissions, yet it’s absent in most land surface models. Here we propose a new carbon accumulation protocol by integrating deep carbon from Yedoma deposits and representing the observed history of peat carbon formation in ORCHIDEE-MICT. Our results show an additional 157 PgC in present-day Yedoma deposits and a 1–5 m shallower peat depth, 43 % less passive soil carbon in peatlands against the convention protocol.
23 Jan 2025
FACA v1 – Fully Automated Co-Alignment of UAV Point Clouds
Nick Schüßler, Jewgenij Torizin, Claudia Gunkel, Karsten Schütze, Lars Tiepolt, Dirk Kuhn, Michael Fuchs, and Steffen Prüfer
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-209, https://doi.org/10.5194/gmd-2024-209, 2025
Preprint under review for GMD (discussion: final response, 2 comments)
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FACA – Fully Automated Co-Alignment – is a tool designed to generate co-aligned point clouds. We aim to accelerate the application of the co-alignment method and achieve fast results with evolving temporal data and minimal site-specific preparation. FACA offers multiple ways to interact with the workflow, enabling new users to quickly generate initial results through the custom interface, as well as integration into larger automated workflows via the command line. Test datasets are provided.
23 Jan 2025
Implementation of solar UV and energetic particle precipitation within the LINOZ scheme in ICON-ART
Maryam Ramezani Ziarani, Miriam Sinnhuber, Thomas Reddmann, Bernd Funke, Stefan Bender, and Michael Prather
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-227, https://doi.org/10.5194/gmd-2024-227, 2025
Preprint under review for GMD (discussion: final response, 12 comments)
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Our study aims to present a new method for incorporating top-down solar forcing into stratospheric ozone relying on linearized ozone scheme. The addition of geomagnetic forcing led to significant ozone losses in the polar upper stratosphere of both hemispheres due to the catalytic cycles involving NOy. In addition to the particle precipitation effect, accounting for solar UV variability in the ICON-ART model leads to the changes in ozone in the tropical stratosphere.
23 Jan 2025
Direct assimilation of ground-based microwave radiometer observations with machine learning bias correction based on developments of RTTOV-gb v1.0 and WRFDA v4.5
Qing Zheng, Wei Sun, Zhiquan Liu, Jiajia Mao, Jieying He, Jian Li, and Xingwen Jiang
EGUsphere, https://doi.org/10.5194/egusphere-2025-12, https://doi.org/10.5194/egusphere-2025-12, 2025
Revised manuscript under review for GMD (discussion: final response, 8 comments)
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Ground-based microwave radiometers (MWRs) offer high temporal resolution observations with strong sensitivity to the lower atmosphere, making them valuable for data assimilation. However, their assimilation has traditionally focused on retrieved profiles. This study implemented the direct assimilation of brightness temperatures from MWRs with a machine learning-based bias correction scheme. The results show improvements in the low-level atmospheric structure and precipitation predictions.
23 Jan 2025
Projecting management-relevant change of undeveloped coastal barriers with the Mesoscale Explicit Ecogeomorphic Barrier model (MEEB) v1.0
Ian R. B. Reeves, Andrew D. Ashton, Erika E. Lentz, Christopher R. Sherwood, Davina L. Passeri, and Sara L. Zeigler
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-232, https://doi.org/10.5194/gmd-2024-232, 2025
Preprint under review for GMD (discussion: open, 7 comments)
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We describe a new model of coastal barrier ecogeomorphic change that operates over spatiotemporal scales congruous with effective management practices, incorporates key ecogeomorphic feedbacks, and provides probabilistic projections. The model skillfully captures important barrier dynamics through robust data integration and calibration of relatively simple model parameterizations, and can be used to understand and predict when, where, and how barriers evolve to inform decision-making processes.
21 Jan 2025
pyVPRM: A next-generation Vegetation Photosynthesis and Respiration Model for the post-MODIS era
Theo Glauch, Julia Marshall, Christoph Gerbig, Santiago Botía, Michał Gałkowski, Sanam N. Vardag, and André Butz
EGUsphere, https://doi.org/10.5194/egusphere-2024-3692, https://doi.org/10.5194/egusphere-2024-3692, 2025
Revised manuscript accepted for GMD (discussion: final response, 6 comments)
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The Vegetation Photosynthesis and Respiration Model (VPRM) estimates carbon exchange between the atmosphere and biosphere by modeling gross primary production and respiration using satellite data and weather variables. Our new version, pyVPRM, supports diverse satellite products like Sentinel-2, MODIS, VIIRS and new land cover maps, enabling high spatial and temporal resolution. This improves flux estimates, especially in complex landscapes, and ensures continuity as MODIS nears decommissioning.
17 Jan 2025
Fitting the junction model and other parameterizations for the unsaturated soil hydraulic conductivity curve: KRIAfitter version 1.0
Gerrit Huibert de Rooij
EGUsphere, https://doi.org/10.5194/egusphere-2024-3487, https://doi.org/10.5194/egusphere-2024-3487, 2025
Preprint under review for GMD (discussion: open, 1 comment)
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Water flows ever more slowly in soil when the soil dries out. This can be described by the conductivity curve that accounts for water filling up small spaces, sticking to grains if films, and water vapour diffusion. This paper introduces a relatively simple model for this curve that needs one parameter less then most others. It works well for most soils, but some need the extra parameter. The paper also presents a computer program to determine the parameter values of this and other models.
17 Jan 2025
FLAML version 2.3.3 model-based assessment of gross primary productivity at forest, grassland, and cropland ecosystem sites
Jie Lai, Yuan Zhang, Anzhi Wang, Wenli Fei, Yiwei Diao, Rongping Li, and Jiabin Wu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-169, https://doi.org/10.5194/gmd-2024-169, 2025
Revised manuscript accepted for GMD (discussion: closed, 15 comments)
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In this study, a new model called FLAML-LUE was created by combining the FLAML model with LUE-based models, the latter provides the key variables of vegetation growth for modeling. These models utilize the Fast Lightweight Automated Machine Learning (FLAML) framework, using variables of LUE models, to investigate the potential of estimating site-scale GPP.
17 Jan 2025
The glacial systems model (GSM) Version 24G
Lev Tarasov, Benoit S. Lecavalier, Kevin Hank, and David Pollard
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-175, https://doi.org/10.5194/gmd-2024-175, 2025
Preprint under review for GMD (discussion: final response, 2 comments)
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We document the glacial system model (GSM), a 3D glaciological ice sheet systems model specifically designed for large ensemble modelling in glacial cycle contexts. The model is distinguished by the breadth of relevant processes represented for this context. This ranges from meltwater surface drainage with proglacial lake formation to state-of-the-art subglacial sediment production/transport/deposition. The other key distinguishing design feature is attention to addressing process uncertainties.
16 Jan 2025
Optimizing output operations in high-resolution climate models through dynamic scheduling
Dong Wang and Xiaomeng Huang
EGUsphere, https://doi.org/10.5194/egusphere-2024-3533, https://doi.org/10.5194/egusphere-2024-3533, 2025
Revised manuscript under review for GMD (discussion: final response, 4 comments)
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This study presents a method to enhance data output efficiency in high-resolution climate models by redistributing workloads and allowing lighter tasks to temporarily store data. We use smaller communication groups and I/O aggregation for efficient data writing. A reinforcement learning agent optimizes the approach based on performance data from two models, suggesting a promising strategy to reduce data output overhead and improve model performance.
16 Jan 2025
Comparing an idealized deterministic-stochastic model (SUP model, version 1) of the tide-and-wind driven sea surface currents in the Gulf of Trieste to HF Radar observations
Sofia Flora, Laura Ursella, and Achim Wirth
EGUsphere, https://doi.org/10.5194/egusphere-2024-3391, https://doi.org/10.5194/egusphere-2024-3391, 2025
Revised manuscript accepted for GMD (discussion: final response, 3 comments)
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We developed a hierarchy of idealized deterministic-stochastic models to simulate the sea surface currents in the Gulf of Trieste. They include tide-and-wind driven sea surface current components, resolving the slowly varying part of the flow and a stochastic signal, representing the fast-varying small-scale dynamics. The comparison with High Frequency Radar observations shows that the non-Gaussian stochastic model captures the essential dynamics and permits to mimic the observed fat-tailed PDF.
15 Jan 2025
Combining empirical and mechanistic understanding of spruce bark beetle outbreak dynamics in the LPJ-GUESS (v4.1, r13130) vegetation model
Fredrik Lagergren, Anna Maria Jönsson, Mats Lindeskog, and Thomas A. M. Pugh
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-239, https://doi.org/10.5194/gmd-2024-239, 2025
Preprint under review for GMD (discussion: final response, 3 comments)
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The European spruce bark beetle (SBB) has, in recent years, been the most important disturbance agent in many European forests. We implemented a SBB module in a dynamic vegetation model and calibrated it against observations from Sweden, Switzerland, Austria and France. The start and duration of outbreaks triggered by storm damage and the increased damage driven by recent warm and dry periods were reasonably well simulated, although the spread was reflected in uncertain parameter estimates.
15 Jan 2025
Fluid flow channeling and mass transport with discontinuous porosity distribution
Simon Boisserée, Evangelos Moulas, and Markus Bachmayr
External preprint server, https://doi.org/10.48550/arXiv.2411.14211, https://doi.org/10.48550/arXiv.2411.14211, 2025
Preprint under review for GMD (discussion: final response, 2 comments)
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Understanding porous fluid flow is key for many geology applications. Traditional methods cannot resolve cases with sharp discontinuities in hydraulic/mechanical properties across those layers. Here we present a new space-time method that can handle such discontinuities. This approach is coupled with trace element transport. Our study reveals that the layering of rocks significantly influences the formation of fluid-rich channels and the material distribution adjacent to discontinuities.
15 Jan 2025
Advanced climate model evaluation with ESMValTool v2.11.0 using parallel, out-of-core, and distributed computing
Manuel Schlund, Bouwe Andela, Jörg Benke, Ruth Comer, Birgit Hassler, Emma Hogan, Peter Kalverla, Axel Lauer, Bill Little, Saskia Loosveldt Tomas, Francesco Nattino, Patrick Peglar, Valeriu Predoi, Stef Smeets, Stephen Worsley, Martin Yeo, and Klaus Zimmermann
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-236, https://doi.org/10.5194/gmd-2024-236, 2025
Revised manuscript accepted for GMD (discussion: closed, 6 comments)
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The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth system models. Here, we describe recent significant improvements of ESMValTool’s computational efficiency including parallel, out-of-core, and distributed computing. Evaluations with the enhanced version of ESMValTool are faster, use less computational resources, and can handle input data larger than the available memory.
14 Jan 2025
JuWavelet – Continuous Wavelet Transform and Stockwell-transform for gravity wave analysis
Jörn Ungermann and Robert Reichert
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-207, https://doi.org/10.5194/gmd-2024-207, 2025
Preprint under review for GMD (discussion: final response, 2 comments)
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This paper describes the software package JuWavelet, which implements the continuous wavelet transform, which is a popular tool in the Geosciences to analyse wave-like phenomena. The code implements the transform in 1-D, 2-D, and 3-D for both analysis and synthesis, which closes a gap in available open-source software. The mathematics behind the transformation are given and several examples showcase the capabilities of the software.
14 Jan 2025
The Coupled Model Intercomparison Project (CMIP): Reviewing project history, evolution, infrastructure and implementation
Paul J. Durack, Karl E. Taylor, Peter J. Gleckler, Gerald A. Meehl, Bryan N. Lawrence, Curt Covey, Ronald J. Stouffer, Guillaume Levavasseur, Atef Ben-Nasser, Sebastien Denvil, Martina Stockhause, Jonathan M. Gregory, Martin Juckes, Sasha K. Ames, Fabrizio Antonio, David C. Bader, John P. Dunne, Daniel Ellis, Veronika Eyring, Sandro L. Fiore, Sylvie Joussaume, Philip Kershaw, Jean-Francois Lamarque, Michael Lautenschlager, Jiwoo Lee, Chris F. Mauzey, Matthew Mizielinski, Paola Nassisi, Alessandra Nuzzo, Eleanor O’Rourke, Jeffrey Painter, Gerald L. Potter, Sven Rodriguez, and Dean N. Williams
EGUsphere, https://doi.org/10.5194/egusphere-2024-3729, https://doi.org/10.5194/egusphere-2024-3729, 2025
Preprint under review for GMD (discussion: final response, 9 comments)
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CMIP6 was the most expansive and ambitious Model Intercomparison Project (MIP), the latest in a history, extending four decades. CMIP engaged a growing community focused on improving climate understanding, and quantifying and attributing observed climate change being experienced today. The project's profound impact is due to the combining the latest climate science and technology, enabling the latest-generation climate simulations and increasing community attention in every successive phase.
09 Jan 2025
Features of mid- and high-latitude low-level clouds and their relation to strong aerosol effects in the Energy Exascale Earth System Model version 2 (E3SMv2)
Hui Wan, Abhishek Yenpure, Berk Geveci, Richard C. Easter, Philip J. Rasch, Kai Zhang, and Xubin Zeng
EGUsphere, https://doi.org/10.5194/egusphere-2024-4020, https://doi.org/10.5194/egusphere-2024-4020, 2025
Revised manuscript under review for GMD (discussion: final response, 4 comments)
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In E3SMv2 and many other global climate models, the simulated anthropogenic aerosol influence on the Earth's energy balance is sensitive to the presence of clouds with very low droplet number concentrations. Numerical experiments conducted in this study suggest that mid- and high-latitude low-level stratus occurring under weak turbulence is an important cloud regime for understanding the causes of very low cloud droplet number concentrations in global climate simulations.
08 Jan 2025
Modelling extensive green roof CO2 exchanges in the TEB urban canopy model
Aurélien Mirebeau, Cécile de Munck, Bertrand Bonan, Christine Delire, Aude Lemonsu, Valéry Masson, and Stephan Weber
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-233, https://doi.org/10.5194/gmd-2024-233, 2025
Revised manuscript accepted for GMD (discussion: final response, 4 comments)
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The greening of cities is recommended to limit the effects of climate change. In particular, green roofs can provide numerous environmental benefits, such as urban cooling, water retention and carbon sequestration. The aim of this research is to develop a new module for calculating green roof CO2 fluxes within a model that can already simulate hydrological and thermal processes of such roofs. The calibration and evaluation of this module take advantage of long term experimental data.
07 Jan 2025
Interactive coupling of a Greenland ice sheet model in NorESM2
Heiko Goelzer, Petra M. Langebroek, Andreas Born, Stefan Hofer, Konstanze Haubner, Michele Petrini, Gunter Leguy, William H. Lipscomb, and Katherine Thayer-Calder
EGUsphere, https://doi.org/10.5194/egusphere-2024-3045, https://doi.org/10.5194/egusphere-2024-3045, 2025
Preprint under review for GMD (discussion: final response, 3 comments)
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On the backdrop of observed accelerating ice sheet mass loss over the last few decades, there is growing interest in the role of ice sheet changes in global climate projections. In this regard, we have coupled an Earth system model with an ice sheet model and have produced an initial set of climate projections including an interactive coupling with a dynamic Greenland ice sheet.
06 Jan 2025
Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research
Sebastian H. M. Hickman, Makoto Kelp, Paul T. Griffiths, Kelsey Doerksen, Kazuyuki Miyazaki, Elyse A. Pennington, Gerbrand Koren, Fernando Iglesias-Suarez, Martin G. Schultz, Kai-Lan Chang, Owen R. Cooper, Alexander T. Archibald, Roberto Sommariva, David Carlson, Hantao Wang, J. Jason West, and Zhenze Liu
EGUsphere, https://doi.org/10.5194/egusphere-2024-3739, https://doi.org/10.5194/egusphere-2024-3739, 2025
Preprint under review for GMD (discussion: final response, 2 comments)
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Machine learning is being more widely used across environmental and climate science. This work reviews the use of machine learning in tropospheric ozone research, focusing on three main application areas in which significant progress has been made. Common challenges in using machine learning across the three areas are highlighted, and future directions for the field are indicated.
06 Jan 2025
Optimized step size control within the Rosenbrock solvers for stiff chemical ODE systems in KPP version 2.2.3_rs4
Raphael Dreger, Timo Kirfel, Andrea Pozzer, Simon Rosanka, Rolf Sander, and Domenico Taraborrelli
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-166, https://doi.org/10.5194/gmd-2024-166, 2025
Revised manuscript accepted for GMD (discussion: closed, 4 comments)
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Model simulations are essentials for understanding the interactions between atmospheric composition and weather. However, models including chemistry are very slow. Hence, any computation speedup of such models is important for advancing the understanding of interactions within the Earth System. In this study we analysed and optimized the time stepping for chemistry calculations. Our results show that atmospheric chemistry models could be run notably faster without any loss in the accuracy.
03 Jan 2025
A new set of indicators for model evaluation complementing to FAIRMODE’s MQO
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, and Enrico Pisoni
EGUsphere, https://doi.org/10.5194/egusphere-2024-3690, https://doi.org/10.5194/egusphere-2024-3690, 2025
Revised manuscript accepted for GMD (discussion: final response, 9 comments)
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We assess the relevance and utility indicators developed within FAIRMODE by evaluating 9 CAMS models in calculated air pollutant values. For NO2, the results highlight difficulties at traffic stations. For PM2.5 and PM10 the bias and Winter-Summer gradients reveal issues. O3 evaluation shows that e.g. seasonal gradients are useful. Overall, the indicators provide valuable insights into model limitations, yet there is a need to reconsider the strictness of some indicators for certain pollutants.
03 Jan 2025
A high-resolution physical-biogeochemical model for marine resource applications in the Northern Indian Ocean (MOM6-COBALT-IND12 v1.0)
Enhui Liao, Laure Resplandy, Fan Yang, Yangyang Zhao, Sam Ditkovsky, Manon Malsang, Jenna Pearson, Andrew C. Ross, Robert Hallberg, and Charles Stock
EGUsphere, https://doi.org/10.5194/egusphere-2024-3646, https://doi.org/10.5194/egusphere-2024-3646, 2025
Preprint under review for GMD (discussion: open, 1 comment)
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We introduce a regional ocean model of the northern Indian Ocean, a region central to the livelihoods and economies of countries that comprise about one-third of the world’s population. The model successfully represents the key physical and biogeochemical features of the region and is well suited for physical and biogeochemical studies on timescales ranging from weeks to decades, in addition to supporting marine resource applications and management in the northern Indian Ocean.
03 Jan 2025
The Development and Application of an Arctic Sea Ice Emulator v.1
Sian Megan Chilcott and Malte Meinshausen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-203, https://doi.org/10.5194/gmd-2024-203, 2025
Revised manuscript accepted for GMD (discussion: closed, 7 comments)
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Climate models are expensive to run and often underestimate how sensitive Arctic sea ice is to climate change. To address this, we developed a simple model that emulates the response of sea ice to global warming. We find the remaining carbon dioxide (CO2) emissions that will avoid a seasonally ice-free Arctic Ocean is lower than previous estimates of 821 Gigatonnes of CO2. Our model also provides insights into the future of winter sea ice, examining a larger ensemble than previously possible.
20 Dec 2024
An evolving Coupled Model Intercomparison Project phase 7 (CMIP7) and Fast Track in support of future climate assessment
John Patrick Dunne, Helene T. Hewitt, Julie Arblaster, Frédéric Bonou, Olivier Boucher, Tereza Cavazos, Paul J. Durack, Birgit Hassler, Martin Juckes, Tomoki Miyakawa, Matthew Mizielinski, Vaishali Naik, Zebedee Nicholls, Eleanor O’Rourke, Robert Pincus, Benjamin M. Sanderson, Isla R. Simpson, and Karl E. Taylor
EGUsphere, https://doi.org/10.5194/egusphere-2024-3874, https://doi.org/10.5194/egusphere-2024-3874, 2024
Revised manuscript under review for GMD (discussion: final response, 10 comments)
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This manuscript provides the motivation and experimental design for the seventh phase of the Coupled Model Intercomparison Project (CMIP7) to coordinate community based efforts to answer key and timely climate science questions and facilitate delivery of relevant multi-model simulations for: prediction and projection, characterization, attribution and process understanding; vulnerability, impacts and adaptations analysis; national and international climate assessments; and society at large.
20 Dec 2024
ISWFM-NSCS v2.0: advancing the internal solitary wave forecasting model with background currents and horizontally inhomogeneous stratifications
Yankun Gong, Xueen Chen, Jiexin Xu, Zhiwu Chen, Qingyou He, Ruixiang Zhao, Xiao-Hua Zhu, and Shuqun Cai
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-165, https://doi.org/10.5194/gmd-2024-165, 2024
Preprint under review for GMD (discussion: final response, 11 comments)
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A new internal solitary wave forecasting (ISW) model in the northern South China Sea (ISWFM-NSCS v2.0) improves ISW predictions by incorporating background currents and inhomogeneous stratifications. Additionally, viscosity and diffusivity coefficients are optimized to maintain stable stratifications, extending the forecasting period. Sensitivity experiments illustrate that ISWFM-NSCS v2.0 significantly enhances predictions of various wave properties.
19 Dec 2024
Development of the global hydro-economic model (ECHO-Global version 1.0) for assessing the performance of water management options
Taher Kahil, Safa Baccour, Julian Joseph, Reetik Sahu, Peter Burek, Jia Yi Ng, Samar Asad, Dor Fridman, Jose Albiac, Frank A. Ward, and Yoshihide Wada
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-238, https://doi.org/10.5194/gmd-2024-238, 2024
Preprint under review for GMD (discussion: final response, 4 comments)
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This study presents the development of the global version of the ECHO hydro-economic model for assessing the economic and environmental performance of water management options. This improved version covers a large number of basins worldwide, includes a detailed representation of irrigated agriculture, and accounts for economic benefits and costs of water use. Results of this study demonstrates the capacity of the model to address emerging water-related research and practical questions.
19 Dec 2024
The updated Multi-Model Large Ensemble Archive and the Climate Variability Diagnostics Package: New tools for the study of climate variability and change
Nicola Maher, Adam S. Phillips, Clara Deser, Robert C. Jnglin Wills, Flavio Lehner, John Fasullo, Julie M. Caron, Lukas Brunner, and Urs Beyerle
EGUsphere, https://doi.org/10.5194/egusphere-2024-3684, https://doi.org/10.5194/egusphere-2024-3684, 2024
Preprint under review for GMD (discussion: final response, 13 comments)
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We present a new multi-model large ensemble archive (MMLEAv2) and introduce the newly updated Climate Variability Diagnostics Package version 6 (CVDPv6), which is designed specifically for use with large ensembles. For highly variable quantities, we demonstrate that a model might evaluate poorly or favourably compared to the single realisation of the world that the observations represent, highlighting the need for large ensembles for model evaluation.
19 Dec 2024
A modular wind profile retrieval software for heterogeneous Doppler lidar measurements
Anselm Erdmann and Philipp Gasch
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-222, https://doi.org/10.5194/gmd-2024-222, 2024
Preprint under review for GMD (discussion: open, 4 comments)
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A new software for the calculation of quality controlled wind profiles from heterogeneous Doppler lidar measurements is presented. The processing is designed modularly. A provided standard processing chain is validated using radiosondes for three common Doppler lidar types at different locations.
19 Dec 2024
Process-based modeling framework for sustainable irrigation management at the regional scale: Integrating rice production, water use, and greenhouse gas emissions
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-212, https://doi.org/10.5194/gmd-2024-212, 2024
Revised manuscript accepted for GMD (discussion: closed, 6 comments)
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This study proposed an advancing framework for modeling regional rice production, water use, and greenhouse gas emissions. The framework integrated a process-based soil-crop model with key physiological effects, a novel model upscaling method, and the NSGA-II multi-objective optimization algorithm at a parallel computing platform. The framework provides a valuable tool for irrigation optimization to deliver co-benefits of ensuring food production, reducing water use and greenhouse gas emissions.
19 Dec 2024
Coupling the TKE-ACM2 Planetary Boundary Layer Scheme with the Building Effect Parameterization Model
Wanliang Zhang, Chao Ren, Edward Yan Yung Ng, Michael Mau Fung Wong, and Jimmy Chi Hung Fung
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-205, https://doi.org/10.5194/gmd-2024-205, 2024
Revised manuscript under review for GMD (discussion: final response, 4 comments)
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This study focuses on improving the accuracy of numerical weather prediction (NWP) model particularly in urbanized areas. We coupled a recently validated boundary layer model with a building effect model within an NWP. Validation has been performed under idealized atmospheric conditions by benchmarking the coupled model with a fine-scale numerical model. Subsequently, the improvements and limitations are investigated aided by observations in real case simulations.
19 Dec 2024
A Bayesian framework for inferring regional and global change from stratigraphic proxy records (StratMC v1.0)
Stacey Edmonsond and Blake Dyer
EGUsphere, https://doi.org/10.5194/egusphere-2024-2579, https://doi.org/10.5194/egusphere-2024-2579, 2024
Revised manuscript accepted for GMD (discussion: final response, 3 comments)
Short summary
Executive editor
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The chemistry of sedimentary rocks is used to reconstruct past changes in Earth’s climate and biogeochemical cycles. Reconstructing global change requires merging stratigraphic proxy records from many locations, each of which may be incomplete, time-uncertain, and influenced by both global and local processes. StratMC uses Bayesian modeling to see through this complexity, building more accurate and testable reconstructions of global change from stratigraphic data.
Executive editor
This paper represents a major step forward in understanding Earth history proxy records and how to model and correlate records, as illustrated by examples in the paper. The work presented here should have direct implications in the field of reconstructing Earth history from paleo proxy records but also beyond with a wide range of possible applications.
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18 Dec 2024
PALM-SLUrb v24.04: A single-layer urban canopy model for the PALM model system – Model description and first evaluation
Sasu Karttunen, Matthias Sühring, Ewan O'Connor, and Leena Järvi
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-235, https://doi.org/10.5194/gmd-2024-235, 2024
Revised manuscript under review for GMD (discussion: final response, 4 comments)
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This paper presents PALM-SLUrb, a single-layer urban canopy model for the PALM system, designed to simulate urban-atmosphere interactions without resolving flow around individual buildings. The model is described in detail and evaluated against grid-resolved urban canopy simulations, demonstrating its ability to model urban surfaces accurately. By bridging the gap between computational efficiency and physical detail, PALM-SLUrb broadens PALM's potential for urban climate research.
18 Dec 2024
A regional physical-biogeochemical ocean model for marine resource applications in the Northeast Pacific (MOM6-COBALT-NEP10k v1.0)
Elizabeth J. Drenkard, Charles A. Stock, Andrew C. Ross, Yi-Cheng Teng, Theresa Morrison, Wei Cheng, Alistair Adcroft, Enrique Curchitser, Raphael Dussin, Robert Hallberg, Claudine Hauri, Katherine Hedstrom, Albert Hermann, Michael G. Jacox, Kelly A. Kearney, Remi Pages, Darren J. Pilcher, Mercedes Pozo Buil, Vivek Seelanki, and Niki Zadeh
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-195, https://doi.org/10.5194/gmd-2024-195, 2024
Revised manuscript accepted for GMD (discussion: closed, 4 comments)
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We made a new regional ocean model to assist fisheries and ecosystem managers make decisions in the Northeast Pacific Ocean (NEP). We found that the model did well simulating past ocean conditions like temperature, and nutrient and oxygen levels, and can even reproduce metrics used by and important to ecosystem managers.
17 Dec 2024
A trait-based model to describe plant community dynamics in managed grasslands (GrasslandTraitSim.jl v1.0.0)
Felix Nößler, Thibault Moulin, Oksana Buzhdygan, Britta Tietjen, and Felix May
EGUsphere, https://doi.org/10.5194/egusphere-2024-3798, https://doi.org/10.5194/egusphere-2024-3798, 2024
Revised manuscript under review for GMD (discussion: final response, 4 comments)
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To predict the response of grassland plant communities to management and climate change, we developed the computer model GrasslandTraitSim.jl. Unlike other models, it uses measurable plant traits such as height, leaf thinness, and root structure as inputs, rather than hard-to-measure species data. This allows realistic simulation of many species. The model tracks daily changes in above- and below-ground biomass, plant height, and soil water, linking plant community composition to biomass supply.
17 Dec 2024
Modelling framework for asynchronous land-atmosphere coupling using NASA GISS ModelE and LPJ-LMfire: Design, Application and Evaluation for the 2.5ka period
Ram Singh, Alexander Koch, Allegra N. LeGrande, Kostas Tsigaridis, Riovie D. Ramos, Francis Ludlow, Igor Aleinov, Reto Ruedy, and Jed O. Kaplan
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-219, https://doi.org/10.5194/gmd-2024-219, 2024
Preprint under review for GMD (discussion: open, 1 comment)
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This study presents and demonstrates an experimental framework for asynchronous land-atmosphere coupling using the NASA GISS ModelE and LPJ-LMfire models for the 2.5ka period. This framework addresses the limitation of NASA ModelE, which does not have a fully dynamic vegetation model component. It also shows the role of model performance metrics, such as model bias and variability, and the simulated climate is evaluated against the multi-proxy paleoclimate reconstructions for the 2.5ka climate.
13 Dec 2024
Impacts of CICE sea ice model and ERA atmosphere on an Antarctic MetROMS ocean model, MetROMS-UHel-v1.0
Cecilia Äijälä, Yafei Nie, Lucía Gutiérrez-Loza, Chiara De Falco, Siv Kari Lauvset, Bin Cheng, David A. Bailey, and Petteri Uotila
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-213, https://doi.org/10.5194/gmd-2024-213, 2024
Revised manuscript accepted for GMD (discussion: closed, 3 comments)
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The sea ice around Antarctica has experienced record lows in recent years. To understand these changes, models are needed. MetROMS-UHel is a new version of an ocean–sea ice model with updated sea ice code and the atmospheric data. We investigate the effect of our updates on different variables with a focus on sea ice and show an improved sea ice representation as compared with observations.
12 Dec 2024
Copernicus Atmosphere Monitoring Service – Regional Air Quality Production System v1.0
Augustin Colette, Gaëlle Collin, François Besson, Etienne Blot, Vincent Guidard, Frederik Meleux, Adrien Royer, Valentin Petiot, Claire Miller, Oihana Fermond, Alizé Jeant, Mario Adani, Joaquim Arteta, Anna Benedictow, Robert Bergström, Dene Bowdalo, Jorgen Brandt, Gino Briganti, Ana C. Carvalho, Jesper Heile Christensen, Florian Couvidat, Ilia D’Elia, Massimo D’Isidoro, Hugo Denier van der Gon, Gaël Descombes, Enza Di Tomaso, John Douros, Jeronimo Escribano, Henk Eskes, Hilde Fagerli, Yalda Fatahi, Johannes Flemming, Elmar Friese, Lise Frohn, Michael Gauss, Camilla Geels, Guido Guarnieri, Marc Guevara, Antoine Guion, Jonathan Guth, Risto Hänninen, Kaj Hansen, Ulas Im, Ruud Janssen, Marine Jeoffrion, Mathieu Joly, Luke Jones, Oriol Jorba, Evgeni Kadantsev, Michael Kahnert, Jacek W. Kaminski, Rostislav Kouznetsov, Richard Kranenburg, Jeroen Kuenen, Anne Caroline Lange, Joachim Langner, Victor Lannuque, Francesca Macchia, Astrid Manders, Mihaela Mircea, Agnes Nyiri, Miriam Olid, Carlos Pérez García-Pando, Yuliia Palamarchuk, Antonio Piersanti, Blandine Raux, Miha Razinger, Lennard Robertson, Arjo Segers, Martijn Schaap, Pilvi Siljamo, David Simpson, Mikhail Sofiev, Anders Stangel, Joanna Struzewska, Carles Tena, Renske Timmermans, Thanos Tsikerdekis, Svetlana Tsyro, Svyatoslav Tyuryakov, Anthony Ung, Andreas Uppstu, Alvaro Valdebenito, Peter van Velthoven, Lina Vitali, Zhuyun Ye, Vincent-Henri Peuch, and Laurence Rouïl
EGUsphere, https://doi.org/10.5194/egusphere-2024-3744, https://doi.org/10.5194/egusphere-2024-3744, 2024
Revised manuscript under review for GMD (discussion: final response, 10 comments)
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The Copernicus Atmosphere Monitoring Service – Regional Production delivers daily forecasts, analyses, and reanalyses of air quality in Europe. The Service relies on a distributed modelling production by eleven leading European modelling teams following stringent requirements with an operational design which has no equivalent in the world. All the products are full, free, open and quality assured and disseminated with a high level of reliability.
10 Dec 2024
Least travel time ray tracer, version Two (LTT v2) adapted to the grid geometry of the OpenIFS atmospheric model
Maksym Vasiuta, Angel Navarro Trastoy, Sanam Motlaghzadeh, Lauri Tuppi, Torsten Mayer-Gürr, and Heikki Järvinen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-136, https://doi.org/10.5194/gmd-2024-136, 2024
Revised manuscript accepted for GMD (discussion: closed, 4 comments)
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Propagation of electromagnetic signals in the Earth's neutral atmosphere inflicts errors in space geodetic observations. To model these errors as accurately as possible, it is necessary to use a signal ray tracing algorithm which is informed of the state of the atmosphere. We developed such algorithm and tested it by modelling errors in GNSS network observations. Our algorithm's main advantage is loss-less utilization of atmospheric information provided by numerical weather prediction models.
10 Dec 2024
A dynamical process-based model for quantifying global agricultural ammonia emissions – AMmonia–CLIMate v1.0 (AMCLIM v1.0) – Part 2: livestock farming
Jize Jiang, David S. Stevenson, Aimable Uwizeye, Giuseppe Tempio, Alessandra Falcucci, Flavia Casu, and Mark A. Sutton
EGUsphere, https://doi.org/10.5194/egusphere-2024-3803, https://doi.org/10.5194/egusphere-2024-3803, 2024
Revised manuscript accepted for GMD (discussion: final response, 4 comments)
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A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from livestock farming. It is estimated that about 30 % of excreted N from livestock was lost due to NH3 emissions from housing, manure management and land application of manure. High NH3 volatilization often occurred in hot regions, while poor management practices also result in significant N losses through NH3 emissions.
10 Dec 2024
A Flexible ROMS-based Hybrid Coupled Model for ENSO Studies–Model Formulation and Performance Evaluation
Yang Yu, Yin-Nan Li, Rong-Hua Zhang, Shu-Hua Chen, Yu-Heng Tseng, Wenzhe Zhang, and Hongna Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-187, https://doi.org/10.5194/gmd-2024-187, 2024
Revised manuscript under review for GMD (discussion: final response, 4 comments)
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In this paper, we develop a new flexible Hybrid Coupled Model (HCM) by incorporating the Regional Ocean Modeling System (ROMS) with a statistical atmospheric model. The model performance is evaluated for its ability to simulate El Niño and Southern Oscillation (ENSO)-related processes. The newly developed HCMROMS is expected to become an effective modeling tool for studying the multi-scale and multi-sphere interactions associated with ENSO in the tropical Pacific.
09 Dec 2024
GEOCLIM7, an Earth System Model for multi-million years evolution of the geochemical cycles and climate
Pierre Maffre, Yves Goddéris, Guillaume Le Hir, Élise Nardin, Anta-Clarisse Sarr, and Yannick Donnadieu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-220, https://doi.org/10.5194/gmd-2024-220, 2024
Preprint under review for GMD (discussion: final response, 3 comments)
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A new version (v7) of the numerical model GEOCLIM is presented here. GEOCLIM models the evolution of ocean and atmosphere chemical composition on multi-million years timescale, including carbon and oxygen cycles, CO2 and climate. GEOCLIM is associated to a climate model, and a new procedure to link the climate model to GEOCLIM is presented here. GEOCLIM is applied here to investigate the evolution of ocean oxygenation following Earth's orbital parameter variations, around 94 million years ago.
09 Dec 2024
asQ: parallel-in-time finite element simulations using ParaDiag for geoscientific models and beyond
Joshua Hope-Collins, Abdalaziz Hamdan, Werner Bauer, Lawrence Mitchell, and Colin Cotter
External preprint server, https://doi.org/https://doi.org/10.48550/arXiv.2409.18792, https://doi.org/https://doi.org/10.48550/arXiv.2409.18792, 2024
Revised manuscript accepted for GMD (discussion: final response, 11 comments)
Short summary
Executive editor
Short summary
Effectively using modern supercomputers requires massively parallel algorithms. Time-parallel algorithms calculate the system state (e.g. the atmosphere) at multiple times simultaneously and have exciting potential, but are tricky to implement and still require development. We have developed software to simplify implementing and testing the ParaDiag algorithm on supercomputers. We show that for some atmospheric problems it can enable faster or more accurate solutions than traditional techniques.
Executive editor
Parallelization is important for speeding up complex geoscientific
models. In addition to spatial parallelization, several parallel-in-time
(PinT) methods have been developed. This paper introduces the reader to
PinT methods for hyperbolic and geophysical models, and it presents the
asQ library which facilitates the implementation of
diagonalization-based (ParaDiag) methods.
09 Dec 2024
Computationally efficient subglacial drainage modelling using Gaussian Process emulators: GlaDS-GP v1.0
Tim Hill, Derek Bingham, Gwenn E. Flowers, and Matthew J. Hoffman
External preprint server, https://doi.org/10.22541/essoar.172736254.41350153/v2, https://doi.org/10.22541/essoar.172736254.41350153/v2, 2024
Revised manuscript accepted for GMD (discussion: final response, 6 comments)
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Subglacial drainage models represent water flow beneath glaciers and ice sheets. Here, we train fast statistical models called Gaussian Process emulators to accelerate subglacial drainage modelling by ~1000 times. We use the fast emulator predictions to show that three of the model parameters are responsible for >90 % of the variance in model outputs. The fast GP emulators will enable future uncertainty quantification and calibration of these models.
05 Dec 2024
Models of buoyancy-driven dykes using continuum plasticity and fracture mechanics: a comparison
Yuan Li, Timothy Davis, Adina E. Pusok, and Richard F. Katz
EGUsphere, https://doi.org/10.5194/egusphere-2024-3504, https://doi.org/10.5194/egusphere-2024-3504, 2024
Preprint under review for GMD (discussion: final response, 6 comments)
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Magmatic dykes transport magma to the Earth's surface, sometimes causing eruptions. We advanced a model of dyking, treating it as plastic deformation in a porous medium, unlike the classic model that treats dykes as fractures in elastic solids. Comparing the two, we found the plastic model aligns with the fracture model in dyke speed and energy consumption, despite quantitative differences. This new method could be a powerful tool for understanding volcanic processes during tectonic activity.
04 Dec 2024
Assessment of the accuracy in UV index modelling using the UVIOS2 system during the UVC-III campaign
Ilias Fountoulakis, Kyriaki Papachristopoulou, Stelios Kazadzis, Gregor Hülsen, Julian Gröbner, Ioannis-Panagiotis Raptis, Dimitra Kouklaki, Akriti Masoom, Charalampos Kontoes, and Christos S. Zerefos
EGUsphere, https://doi.org/10.5194/egusphere-2024-2964, https://doi.org/10.5194/egusphere-2024-2964, 2024
Preprint under review for GMD (discussion: final response, 4 comments)
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The UVIOS2 model has been evaluated at Davos, Switzerland during the UVCIII campaign. The accuracy in the modelled UV indices has been assessed for different combinations of model inputs. A good overall agreement between UVIOS2 and the world reference spectroradiometer QASUME was found (average ratio of ~1 between the modelled and measured UV index), although the variability in the ratio can be large under cloudy conditions.
03 Dec 2024
A new parameterisation for homogeneous ice nucleation driven by highly variable dynamical forcings
Alena Kosareva, Stamen Dolaptchiev, Peter Spichtinger, and Ulrich Achatz
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-193, https://doi.org/10.5194/gmd-2024-193, 2024
Revised manuscript under review for GMD (discussion: final response, 3 comments)
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This study improves how we predict ice formation in clouds by accounting for variable ice sizes and different weather conditions. Using simulations, we developed a more accurate method that works efficiently, making it suitable for application in weather and climate prediction models. The new approach is numerically verified and provides precise predictions of ice formation events and reliable estimates of key parameters.
03 Dec 2024
Carbon dioxide plume dispersion simulated at hectometer scale using DALES: model formulation and observational evaluation
Arseniy Karagodin-Doyennel, Fredrik Jansson, Bart van Stratum, Hugo Denier van der Gon, Jordi Vilà-Guerau de Arellano, and Sander Houweling
EGUsphere, https://doi.org/10.5194/egusphere-2024-3721, https://doi.org/10.5194/egusphere-2024-3721, 2024
Revised manuscript accepted for GMD (discussion: final response, 4 comments)
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We introduce a new simulation platform based on the Dutch Large-Eddy Simulation (DALES) to simulate carbon dioxide (CO2) emissions and their dispersion in the turbulent environments with hectometer resolution. This model incorporates both anthropogenic emission inventory and ecosystem exchanges. Simulation results for the main urban area in the Netherlands demonstrate a strong potential of DALES to enhance CO2 emission modeling, which is important for refining their reduction strategies.
27 Nov 2024
An evaluation of the regional distribution and wet deposition of secondary inorganic aerosols and their gaseous precursors in IFS-COMPO cycle 49R1
Jason Williams, Swen Metzer, Samuel Remy, Vincent Huijnen, and Johannes Flemming
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-188, https://doi.org/10.5194/gmd-2024-188, 2024
Preprint under review for GMD (discussion: final response, 2 comments)
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One of the main constituents of Particulate Matter at the surface are Secondary Inorganic Aerosols (SIA) which are influenced by both anthropogenic emissions and the acidity of clouds and aerosols. This study shows improvements in introduced into the IFS-COMPO simulating the surface concentrations of SIA and the resulting changes in the total wet deposition for Europe, the US and South-East Asia.
27 Nov 2024
Improving the fine structure of intense rainfall forecast by a designed adversarial generation network
Zuliang Fang, Qi Zhong, Haoming Chen, Xiuming Wang, Zhicha Zhang, and Hongli Liang
EGUsphere, https://doi.org/10.5194/egusphere-2024-2888, https://doi.org/10.5194/egusphere-2024-2888, 2024
Preprint under review for GMD (discussion: final response, 6 comments)
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We developed a deep learning model based on Generative Adversarial Networks (GANs) to improve rainfall forecasts in northern China. Traditional models struggle with accuracy, especially for heavy rain. Our model merges data from multiple forecasts, capturing detailed rainfall patterns and offering more reliable short-term predictions.
26 Nov 2024
A Time-Dependent Three-Dimensional Magnetopause Model Based on Quasi-elastodynamic Theory
Yaxin Gu, Yi Wang, Fengsi Wei, Xueshang Feng, Andrey Samsonov, Xiaojian Song, Boyi Wang, Pingbing Zuo, Chaowei Jiang, Yalan Chen, Xiaojun Xu, and Zhilu Zhou
EGUsphere, https://doi.org/10.5194/egusphere-2024-3012, https://doi.org/10.5194/egusphere-2024-3012, 2024
Revised manuscript accepted for GMD (discussion: final response, 6 comments)
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This study presents the POS model, the first time-dependent three-dimensional magnetopause model. The POS model captures the real-time movement and shape of the magnetopause with superior accuracy. Its concise formulation and fast computational speed make it suitable for future onboard satellite deployment, enhancing space weather forecasting capabilities and offering new methodologies for magnetopause modeling on other planets.
20 Nov 2024
Data-Informed Inversion Model (DIIM): a framework to retrieve marine optical constituents in the BOUSSOLE site using a three-stream irradiance model
Carlos Enmanuel Soto López, Fabio Anselmi, Mirna Gharbi Dit Kacem, and Paolo Lazzari
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-174, https://doi.org/10.5194/gmd-2024-174, 2024
Preprint under review for GMD (discussion: final response, 5 comments)
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Our goal was to use an analytical expression to estimate the density of optical constituents, allowing us to have an interpretable formulation consistent with the laws of physics. We focused on a probabilistic approach, optimizing the model and retrieving quantities with their respective uncertainty. Considering future application to Big Data, we also explored a Neural Network based method, retrieving computationally efficient estimates, maintaining consistency with the analytical expression.
20 Nov 2024
FastCTM (v1.0): Atmospheric chemical transport modelling with a principle-informed neural network for air quality simulations
Baolei Lyu, Ran Huang, Xinlu Wang, Weiguo Wang, and Yongtao Hu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-198, https://doi.org/10.5194/gmd-2024-198, 2024
Preprint under review for GMD (discussion: final response, 4 comments)
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FastCTM is a neural network model to simulate key criteria air pollution levels, offering an efficient alternative to traditional chemical transport models. Its structure is informed by the physical and chemical principles of the atmosphere, allowing it to learn and replicate complex atmospheric processes. FastCTM demonstrated matching accuracy to traditional models with less computational demand. It also provides analysis of how different atmospheric processes contribute to air quality changes.
20 Nov 2024
Development of the CMA-GFS-AERO 4D-Var assimilation system v1.0 – Part 1: System description
Yongzhu Liu, Xiaoye Zhang, Wei Han, Chao Wang, Wenxing Jia, Deying Wang, Zhaorong Zhuang, and Xueshun Shen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-148, https://doi.org/10.5194/gmd-2024-148, 2024
Revised manuscript accepted for GMD (discussion: closed, 10 comments)
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In order to investigate the feedbacks of chemical data assimilation on meteorological forecasts, we developed a strongly coupled chemistry meteorology four-dimensional variational (4D-Var) assimilation system, CMA-GFS-AERO 4D-Var, based on the framework of the incremental analysis scheme of the CMA-GFS operational global numerical weather model. The results show that assimilating BC observations can generate analysis increments not only for BC but also for atmospheric variables.
19 Nov 2024
Atmospheric moisture tracking with WAM2layers v3
Peter Kalverla, Imme Benedict, Chris Weijenborg, and Ruud J. van der Ent
EGUsphere, https://doi.org/10.5194/egusphere-2024-3401, https://doi.org/10.5194/egusphere-2024-3401, 2024
Revised manuscript accepted for GMD (discussion: final response, 5 comments)
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We introduce a new version of WAM2layers, a computer program that tracks how the weather brings water from one place to another. It uses data from weather and climate models, whose resolution is steadily increasing. Processing the latest data became a challenge, and the updates presented here ensure that WAM2layers runs smoothly again. We also made it easier to use the program and to understand its source code. This makes it more transparent and reliable, and easier to maintain.
19 Nov 2024
DECIPHeR-GW v1: A coupled hydrological model with improved representation of surface-groundwater interactions
Yanchen Zheng, Gemma Coxon, Mostaquimur Rahman, Ross Woods, Saskia Salwey, Youtong Rong, and Doris Wendt
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-211, https://doi.org/10.5194/gmd-2024-211, 2024
Revised manuscript accepted for GMD (discussion: closed, 4 comments)
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Groundwater is vital for people and ecosystems, but most physical models lack surface-groundwater interactions representation, leading to inaccurate streamflow predictions in groundwater-rich areas. This study presents DECIPHeR-GW v1, which links surface and groundwater systems to improve predictions of streamflow and groundwater levels. Tested across England and Wales, DECIPHeR-GW shows high accuracy, especially in south east England, making it a valuable tool for large-scale water management.
19 Nov 2024
The third Met Office Unified Model-JULES Regional Atmosphere and Land Configuration, RAL3
Mike Bush, David L. A. Flack, Huw W. Lewis, Sylvia I. Bohnenstengel, Chris J. Short, Charmaine Franklin, Adrian P. Lock, Martin Best, Paul Field, Anne McCabe, Kwinten Van Weverberg, Segolene Berthou, Ian Boutle, Jennifer K. Brooke, Seb Cole, Shaun Cooper, Gareth Dow, John Edwards, Anke Finnenkoetter, Kalli Furtado, Kate Halladay, Kirsty Hanley, Margaret A. Hendry, Adrian Hill, Aravindakshan Jayakumar, Richard W. Jones, Humphrey Lean, Joshua C. K. Lee, Andy Malcolm, Marion Mittermaier, Saji Mohandas, Stuart Moore, Cyril Morcrette, Rachel North, Aurore Porson, Susan Rennie, Nigel Roberts, Belinda Roux, Claudio Sanchez, Chun-Hsu Su, Simon Tucker, Simon Vosper, David Walters, James Warner, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Michael Whitall, Keith D. Williams, and Hugh Zhang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-201, https://doi.org/10.5194/gmd-2024-201, 2024
Revised manuscript accepted for GMD (discussion: closed, 4 comments)
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RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator. The third version of the Regional Atmosphere and Land (RAL3) science configuration for kilometre and sub-km scale modelling represents a major advance compared to previous versions (RAL2) by delivering a common science definition for applications in tropical and mid-latitude regions. RAL3 has more realistic precipitation distributions and improved representation of clouds and visibility.
18 Nov 2024
Hunga Tonga-Hunga Ha’apai Volcano Impact Model Observation Comparison (HTHH-MOC) Project: Experiment Protocol and Model Descriptions
Yunqian Zhu, Hideharu Akiyoshi, Valentina Aquila, Elisabeth Asher, Ewa M. Bednarz, Slimane Bekki, Christoph Brühl, Amy H. Butler, Parker Case, Simon Chabrillat, Gabriel Chiodo, Margot Clyne, Lola Falletti, Peter R. Colarco, Eric Fleming, Andrin Jörimann, Mahesh Kovilakam, Gerbrand Koren, Ales Kuchar, Nicolas Lebas, Qing Liang, Cheng-Cheng Liu, Graham Mann, Michael Manyin, Marion Marchand, Olaf Morgenstern, Paul Newman, Luke D. Oman, Freja F. Østerstrøm, Yifeng Peng, David Plummer, Ilaria Quaglia, William Randel, Samuel Rémy, Takashi Sekiya, Stephen Steenrod, Timofei Sukhodolov, Simone Tilmes, Kostas Tsigaridis, Rei Ueyama, Daniele Visioni, Xinyue Wang, Shingo Watanabe, Yousuke Yamashita, Pengfei Yu, Wandi Yu, Jun Zhang, and Zhihong Zhuo
EGUsphere, https://doi.org/10.5194/egusphere-2024-3412, https://doi.org/10.5194/egusphere-2024-3412, 2024
Revised manuscript under review for GMD (discussion: final response, 10 comments)
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To understand the climate impact of the 2022 Hunga volcanic eruption, we developed a climate model-observation comparison project. The paper describes the protocols and models that participate in the experiments. We designed several experiments to achieve our goal of this activity: 1. evaluate the climate model performance; 2. understand the Earth system responses to this eruption.
15 Nov 2024
RiverBedDynamics v1.0: A Landlab component for computing two-dimensional sediment transport and river bed evolution
Angel D. Monsalve, Samuel R. Anderson, Nicole M. Gasparini, and Elowyn M. Yager
EGUsphere, https://doi.org/10.5194/egusphere-2024-3390, https://doi.org/10.5194/egusphere-2024-3390, 2024
Revised manuscript accepted for GMD (discussion: final response, 4 comments)
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Rivers shape landscapes by moving sediments and changing their beds, but existing computer models oversimplify these processes by only considering erosion. We developed new software that simulates how rivers transport sediments and change over time through both erosion and deposition. This helps scientists and engineers better predict river behavior for water management, flood prevention, and ecosystem protection.
15 Nov 2024
flat10MIP: An emissions-driven experiment to diagnose the climate response to positive, zero, and negative CO2 emissions
Benjamin Mark Sanderson, Victor Brovkin, Rosie Fisher, David Hohn, Tatiana Ilyina, Chris Jones, Torben Koenigk, Charles Koven, Hongmei Li, David Lawrence, Peter Lawrence, Spencer Liddicoat, Andrew Macdougall, Nadine Mengis, Zebedee Nicholls, Eleanor O'Rourke, Anastasia Romanou, Marit Sandstad, Jörg Schwinger, Roland Seferian, Lori Sentman, Isla Simpson, Chris Smith, Norman Steinert, Abigail Swann, Jerry Tjiputra, and Tilo Ziehn
EGUsphere, https://doi.org/10.5194/egusphere-2024-3356, https://doi.org/10.5194/egusphere-2024-3356, 2024
Revised manuscript accepted for GMD (discussion: final response, 5 comments)
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Executive editor
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This study investigates how climate models warm in response to simplified carbon emissions trajectories, refining understanding of climate reversibility and commitment. Metrics are defined for warming response to cumulative emissions and for the cessation or ramp-down to net-zero and net-negative levels. Results indicate that previous concentration-driven experiments may have overstated zero emissions commitment due to emissions rates exceeding historical levels.
Executive editor
As a core contribution to CMIP7, this paper offers an idealized yet insightful projection of climate system behavior during the net-zero transition. Its policy relevance is clear, as it effectively links human-driven emission mitigation efforts with their climatic consequences.
14 Nov 2024
Simulating the drought response of European tree species with the dynamic vegetation model LPJ-GUESS (v4.1, 97c552c5)
Benjamin Franklin Meyer, João Paulo Darela-Filho, Konstantin Gregor, Allan Buras, Qiao-Lin Gu, Andreas Krause, Daijun Liu, Phillip Papastefanou, Sijeh Asuk, Thorsten E. E. Grams, Christian S. Zang, and Anja Rammig
EGUsphere, https://doi.org/10.5194/egusphere-2024-3352, https://doi.org/10.5194/egusphere-2024-3352, 2024
Revised manuscript accepted for GMD (discussion: final response, 6 comments)
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Climate change has increased the likelihood of drought events across Europe, potentially threatening European forest carbon sink. Dynamic vegetation models with mechanistic plant hydraulic architecture are needed to model these developments. We evaluate the plant hydraulic architecture version of LPJ-GUESS and show it's capability at capturing species-specific evapotranspiration responses to drought and reproducing flux observations of both gross primary production and evapotranspiration.
14 Nov 2024
Estimation of local training data point densities to support the assessment of spatial prediction uncertainty
Fabian Lukas Schumacher, Christian Knoth, Marvin Ludwig, and Hanna Meyer
EGUsphere, https://doi.org/10.5194/egusphere-2024-2730, https://doi.org/10.5194/egusphere-2024-2730, 2024
Preprint under review for GMD (discussion: final response, 9 comments)
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Machine learning is increasingly used in environmental sciences for spatial predictions, but its effectiveness is challenged when models are applied beyond the areas they were trained on. We propose a Local Training Data Point Density (LPD) approach that considers how well a model's environment is represented by training data. This method provides a valuable tool for evaluating model applicability and uncertainties, crucial for broader scientific and practical applications.
14 Nov 2024
Intercomparison of bias correction methods for precipitation of multiple GCMs across six continents
Young Hoon Song and Eun-Sung Chung
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-177, https://doi.org/10.5194/gmd-2024-177, 2024
Preprint under review for GMD (discussion: final response, 16 comments)
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This study assessed three methods for correcting daily precipitation data: Quantile Delta Mapping, Empirical Quantile Mapping (EQM), and Detrended Quantile Mapping (DQM) using 11 GCMs. EQM performed best overall, offering reliable corrections and lower uncertainty. The best bias correction method for each grid is selected differently depending on the weighting case. The best bias correction method can vary depending on factors such as climate and terrain.
11 Nov 2024
Spy4Cast v1.0: a Python Tool for statistical seasonal forecast based on Maximum Covariance Analysis
Pablo Duran-Fonseca and Belén Rodríguez-Fonseca
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-164, https://doi.org/10.5194/gmd-2024-164, 2024
Revised manuscript under review for GMD (discussion: final response, 7 comments)
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This paper describes the first release of Spy4Cast, a python interface to run a maximum covariance analysis model to produce seasonal forecast. This API allows the user to increase automation and productivity, including determination of modes, crossvalidation hindcast and validation. It includes a visualisation module for the results as well as a preprocessing tool that can be also used for other climate variability studies.
07 Nov 2024
UA-ICON with NWP physics package (version: ua-icon-2.1): mean state and variability of the middle atmosphere
Markus Kunze, Christoph Zülicke, Tarique Adnan Siddiqui, Claudia Christine Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-191, https://doi.org/10.5194/gmd-2024-191, 2024
Revised manuscript accepted for GMD (discussion: closed, 4 comments)
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We present the Icosahedral Nonhydrostatic (ICON) general circulation model with upper atmosphere extension with the physics package for numerical weather prediction (UA-ICON(NWP)). The parameters for the gravity wave parameterizations were optimized, and realistic modelling of the thermal and dynamic state of the mesopause regions was achieved. UA-ICON(NWP) now shows a realistic frequency of major sudden stratospheric warmings and well-represented solar tides in temperature.
06 Nov 2024
Diagnosis of winter precipitation types using Spectral Bin Model (SBM): Comparison of five methods using ICE-POP 2018 field experiment data
Wonbae Bang, Jacob Carlin, Kwonil Kim, Alexander Ryzhkov, Guosheng Liu, and Gyuwon Lee
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-179, https://doi.org/10.5194/gmd-2024-179, 2024
Revised manuscript accepted for GMD (discussion: closed, 10 comments)
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Microphysics model-based diagnosis such as the spectral bin model (SBM) recently has been attempted to diagnose winter precipitation types. In this study, the accuracy of SBM-based precipitation type diagnosis is compared with other traditional methods. SBM have relatively higher accuracy about snow and wetsnow events whereas lower accuracy about rain event. When microphysics scheme in the SBM was optimized for the corresponding region, accuracy about rain events was improved.
05 Nov 2024
Impact of Multiple Radar Wind Profilers Data Assimilation on Convective Scale Short-Term Rainfall Forecasts: OSSE Studies over the Beijing-Tianjin-Hebei region
Juan Zhao, Jianping Guo, and Xiaohui Zheng
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-194, https://doi.org/10.5194/gmd-2024-194, 2024
Revised manuscript accepted for GMD (discussion: closed, 6 comments)
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A series of observing system simulation experiments are conducted to assess the impact of multiple radar wind profiler (RWP) networks on convective scale numerical weather prediction. Results from three southwest-type heavy rainfall cases in the Beijing-Tianjin-Hebei region suggest the added forecast skill of ridge and foothill networks associated with the Taihang Mountains over the existing RWP network. This research provides valuable guidance for designing optimal RWP networks in the region.
04 Nov 2024
A Novel Method for Quantifying the Contribution of Regional Transport to PM2.5 in Beijing (2013–2020): Combining Machine Learning with Concentration-Weighted Trajectory Analysis
Kang Hu, Hong Liao, Dantong Liu, Jianbing Jin, Lei Chen, Siyuan Li, Yangzhou Wu, Changhao Wu, Shitong Zhao, Xiaotong Jiang, Ping Tian, Kai Bi, Ye Wang, and Delong Zhao
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-157, https://doi.org/10.5194/gmd-2024-157, 2024
Revised manuscript accepted for GMD (discussion: closed, 7 comments)
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This study combines Machine Learning with Concentration-Weighted Trajectory Analysis to quantify regional transport PM2.5. From 2013–2020, local emissions dominated Beijing's pollution events. The Air Pollution Prevention and Control Action Plan reduced regional transport pollution, but the eastern region showed the smallest decrease. Beijing should prioritize local emission reduction while considering the east region's contributions in future strategies.
04 Nov 2024
An unconstrained formulation for complex solution phase minimization
Nicolas Riel, Boris J. P. Kaus, Albert de Montserrat, Evangelos Moulas, Eleanor C. R. Green, and Hugo Dominguez
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-197, https://doi.org/10.5194/gmd-2024-197, 2024
Preprint under review for GMD (discussion: final response, 3 comments)
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Our research focuses on improving the way we predict mineral assemblage. Current methods, while accurate, are slowed by complex calculations. We developed a new approach that simplifies these calculations and speeds them up significantly using a technique called the BFGS algorithm. This breakthrough reduces computation time by more than five times, potentially unlocking new horizons in modeling reactive magmatic systems.
04 Nov 2024
A New Reduction Model for Enhancing the Interpolation Accuracy of VMF1/VMF3 Tropospheric Products in GNSS Applications
Peng Sun, Kefei Zhang, Dantong Zhu, Dongsheng Zhao, Shuangshuang Shi, Xuexi Liu, Minghao Zhang, and Suqin Wu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-123, https://doi.org/10.5194/gmd-2024-123, 2024
Preprint under review for GMD (discussion: final response, 4 comments)
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GNSS signal is delayed when it transmits through the neutral gas. In this contribution, a new model was developed for reducing the VMF1/VMF3 grid-wise ground-surface ZHD and ZWD values to the target height to improve the ZHD and ZWD interpolation performance. Test results showed that the accuracy of the ZHD, ZWD interpolated from the VMF1/VMF3 products deduced by the new model was significantly improved compared to traditional methods.
04 Nov 2024
SISSOMA (v1): modelling marine aggregate dynamics from production to export
Andre Visser, Anton Vergod Almgren, and Athanasios Kandylas
EGUsphere, https://doi.org/10.5194/egusphere-2024-2520, https://doi.org/10.5194/egusphere-2024-2520, 2024
Revised manuscript under review for GMD (discussion: final response, 4 comments)
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Global models largely rely on empirical estimates of the rate at which this material is produced and sinks. Here we propose a mechanistic model that tries to capture the most important processes regulating the size and density of particulate organic material from when it is produced by living organisms, through its aggregation and fragmentation into particles of different size and density, degradation by microbes and eventual sinking into the ocean’s interior.
04 Nov 2024
A reach-integrated hydraulic modelling approach for large-scale and real-time inundation mapping
Robert Chlumsky, James R. Craig, and Bryan A. Tolson
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-184, https://doi.org/10.5194/gmd-2024-184, 2024
Revised manuscript accepted for GMD (discussion: closed, 4 comments)
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We aim to improve mapping of floods, and present a new method for hydraulic modelling that uses a combination of novel geospatial analysis and existing hydraulic modelling approaches. This method is wrapped into a modelling software called Blackbird. We compared Blackbird to two other existing options for flood mapping and found that the Blackbird model outperformed both. The Blackbird model has the potential to support real-time and large-scale flood mapping applications in the future.
30 Oct 2024
ICON-HAM-lite: simulating the Earth system with interactive aerosols at kilometer scales
Philipp Weiss, Ross Herbert, and Philip Stier
EGUsphere, https://doi.org/10.5194/egusphere-2024-3325, https://doi.org/10.5194/egusphere-2024-3325, 2024
Revised manuscript accepted for GMD (discussion: final response, 11 comments)
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Aerosols strongly influence Earth's climate as they interact with radiation and clouds. New Earth system models run at resolutions of a few kilometers. To simulate the Earth system with interactive aerosols, we developed a new aerosol module. It represents aerosols as an ensemble of log-normal modes with given sizes and compositions. We present a year-long simulation with four modes at a resolution of five kilometers. It captures key aerosol processes like dust storms or tropical cyclones.
30 Oct 2024
Representing Lateral Groundwater Flow from Land to River in Earth System Models
Chang Liao, Ruby Leung, Yilin Fang, Teklu Tesfa, and Robinson Negron-Juarez
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-178, https://doi.org/10.5194/gmd-2024-178, 2024
Revised manuscript accepted for GMD (discussion: closed, 4 comments)
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Understanding horizontal groundwater flow is important for understanding how water moves through the ground. Current climate models often simplify this process because they don't have detailed enough information about the land surface. Our study developed a new model that divides the land surface into hillslopes to better represent how groundwater flows. This model can help improve predictions of water availability and how it affects ecosystems.
30 Oct 2024
Low-level jets in the North and Baltic Seas: Mesoscale Model Sensitivity and Climatology
Bjarke Tobias Eisensøe Olsen, Andrea Noemi Hahmann, Nicolás González Alonso-de-Linaje, Mark Žagar, and Martin Dörenkämper
EGUsphere, https://doi.org/10.5194/egusphere-2024-3123, https://doi.org/10.5194/egusphere-2024-3123, 2024
Revised manuscript accepted for GMD (discussion: final response, 6 comments)
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Low-level jets (LLJs) are strong winds in the lower atmosphere, important for wind energy as turbines get taller. This study compares a weather model (WRF) with real data across five North and Baltic Sea sites. Adjusting the model improved accuracy over the widely-used ERA5. In key offshore regions, LLJs occur 10–15 % of the time and significantly boost wind power, especially in spring and summer, contributing up to 30 % of total capacity in some areas.
29 Oct 2024
SuCCESs – a global IAM for exploring the interactions between energy, materials, land-use and climate systems in long-term scenarios (model version 2024-10-23)
Tommi Ekholm, Nadine-Cyra Freistetter, Tuukka Mattlar, Theresa Schaber, and Aapo Rautiainen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-196, https://doi.org/10.5194/gmd-2024-196, 2024
Revised manuscript accepted for GMD (discussion: closed, 4 comments)
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SuCCESs is a model that represents energy, materials, land-use and climate change globally, and can be used to calculate long-term scenarios of these systems up to year 2100. It provides a new way to model how these systems interact, and how they together could work towards reaching global sustainability targets, for example to mitigate climate change. This paper describes how the model works and the results it can produce, and how these compare to results from other models.
29 Oct 2024
Knowledge-inspired fusion strategies for the inference of PM2.5 values with a Neural Network
Matthieu Dabrowski, José Mennesson, Jérôme Riedi, Chaabane Djeraba, and Pierre Nabat
EGUsphere, https://doi.org/10.5194/egusphere-2024-2676, https://doi.org/10.5194/egusphere-2024-2676, 2024
Revised manuscript accepted for GMD (discussion: final response, 4 comments)
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This work focuses on the prediction of aerosol concentration values at ground level, which are a strong indicator of air quality, using Artificial Neural Networks. A study of different variables and their efficiency as inputs for these models is also proposed, and reveals that the best results are obtained when using all of them. Comparison of networks architectures and information fusion methods allows the extraction of knowledge on the most efficient methods in the context of this study.
28 Oct 2024
Reducing Time and Computing Costs in EC-Earth: An Automatic Load-Balancing Approach for Coupled ESMs
Sergi Palomas, Mario C. Acosta, Gladys Utrera, and Etienne Tourigny
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-155, https://doi.org/10.5194/gmd-2024-155, 2024
Revised manuscript accepted for GMD (discussion: closed, 12 comments)
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This work presents an automatic tool to enhance the performance of climate models by optimizing how computer resources are allocated. Traditional methods are time-consuming and error-prone, often resulting in inefficient simulations. Our tool improves speed and reduces computational costs without needing expert knowledge. The tool has been tested on European climate models, making simulations up to 34 % faster while using fewer resources, helping to make climate simulations more efficient.
28 Oct 2024
BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean
Nicolette Chang, Sarah-Anne Nicholson, Marcel du Plessis, Alice D. Lebehot, Thulwaneng Mashifane, Tumelo C. Moalusi, N. Precious Mongwe, and Pedro M. S. Monteiro
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-182, https://doi.org/10.5194/gmd-2024-182, 2024
Revised manuscript under review for GMD (discussion: final response, 9 comments)
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Mesoscale features (10's to 100's of km) in the Southern Ocean (SO) are crucial for global heat and carbon transport, but often unresolved in models due to high computational costs. To address this source of uncertainty, we use a regional, NEMO model of the SO at 8 km resolution with coupled ocean, ice, and biogeochemistry, BIOPERIANT12. This serves as an experimental platform to explore physical-biogeochemical interactions, model parameters/formulations, and configuring future models.
25 Oct 2024
ROMSOC: A regional atmosphere-ocean coupled model for CPU-GPU hybrid system architectures
Gesa K. Eirund, Matthieu Leclair, Matthias Muennich, and Nicolas Gruber
EGUsphere, https://doi.org/10.5194/egusphere-2024-2922, https://doi.org/10.5194/egusphere-2024-2922, 2024
Preprint under review for GMD (discussion: final response, 6 comments)
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To realistically simulate small-scale processes in the atmosphere and ocean, such as clouds or mixing, high-resolution numerical models are needed. However, these models are computationally very demanding. Here, we present a newly developed atmosphere-ocean model, which is able to resolve most of these processes and is less expensive to run, due to its computational design. Our model can be used for a wide range of applications, as the investigation of marine heatwaves or future projections.
22 Oct 2024
Implementation and validation of a supermodelling framework into CESM version 2.1.5
William Eric Chapman, Francine Schevenhoven, Judith Berner, Noel Keenlyside, Ingo Bethke, Ping-Gin Chiu, Alok Gupta, and Jesse Nusbaumer
EGUsphere, https://doi.org/10.5194/egusphere-2024-2682, https://doi.org/10.5194/egusphere-2024-2682, 2024
Revised manuscript accepted for GMD (discussion: final response, 6 comments)
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We introduce the first state-of-the-art atmosphere-connected supermodel, where two advanced atmospheric models share information in real-time to form a new dynamical system. By synchronizing the models, particularly in storm track regions, we achieve better predictions without losing variability. This approach maintains key climate patterns and reduces bias in some variables compared to traditional models, demonstrating a useful technique for improving atmospheric simulations.
22 Oct 2024
Development and assessment of the physical-biogeochemical ocean regional model in the Northwest Pacific: NPRT v1.0 (ROMS v3.9–TOPAZ v2.0)
Daehyuk Kim, Hyun-Chae Jung, Jae-Hong Moon, and Na-Hyeon Lee
EGUsphere, https://doi.org/10.5194/egusphere-2024-1509, https://doi.org/10.5194/egusphere-2024-1509, 2024
Revised manuscript accepted for GMD (discussion: final response, 8 comments)
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Physical–biogeochemical ocean global models is difficult to analyze oceanic environmental systems. To accurately understand the physical–biogeochemical processes at the regional scale, physical and biogeochemical models were coupled at a high resolution. The results successfully simulated the seasonal variations of chlorophyll and nutrients, particularly in the marginal seas, which were not captured by global models. The model is an important tool for studying physical–biogeochemical processes.
22 Oct 2024
Towards debris flows simulation using DualSPHysics v5.2 : Internal behaviour of viscous flows and mixtures
Suzanne Lapillonne, Georgios Fourtakas, Vincent Richefeu, Guillaume Piton, and Guillaume Chambon
External preprint server, https://doi.org/10.22541/au.170628457.73131740/v2, https://doi.org/10.22541/au.170628457.73131740/v2, 2024
Preprint under review for GMD (discussion: open, 18 comments)
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Debris flows are fast flowing flows saturated with granular material. They naturally occur in steep creeks and are a threat to local communities. Scientists turn to numerical models to better understand how they behave. We investigate the accuracy of a numerical model which relies on modelling the debris flow as a mixture of a granular phase and a fluid phase. We focus on a demonstration of the capacity of the model to reliably represent the behaviour of the flow at different scales.
21 Oct 2024
InsNet-CRAFTY v1.0: Integrating institutional network dynamics powered by large language models with land use change simulation
Yongchao Zeng, Calum Brown, Mohamed Byari, Joanna Raymond, Thomas Schmitt, and Mark Rounsevell
EGUsphere, https://doi.org/10.5194/egusphere-2024-2661, https://doi.org/10.5194/egusphere-2024-2661, 2024
Revised manuscript accepted for GMD (discussion: final response, 4 comments)
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Understanding environmental policy interventions is challenging due to complex institutional actor interactions. Large language models (LLMs) offer new solutions by mimicking the actors. We present InsNet-CRAFTY v1.0, a multi-LLM-agent model coupled with a land system model, simulating competing policy priorities. The model shows how LLM agents can simulate decision-making in institutional networks, highlighting both their potential and limitations in advancing land system modelling.
21 Oct 2024
Data-driven rolling model for global wave height
Xinxin Wang, Jiuke Wang, Wenfang Lu, Changming Dong, Hao Qin, and Haoyu Jiang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-181, https://doi.org/10.5194/gmd-2024-181, 2024
Revised manuscript under review for GMD (discussion: final response, 8 comments)
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Large-scale wave modeling is essential for science and society, typically relying on resource-intensive numerical methods to simulate wave dynamics. In this study, we introduce a rolling AI-based method for modeling global significant wave height. Our model achieves accuracy comparable to traditional numerical methods while significantly improving speed, making it operable on standard laptops. This work demonstrates AI's potential to enhance the accuracy and efficiency of global wave modeling.
21 Oct 2024
Correction of Air-Sea Heat Fluxes in the NEMO Ocean General Circulation Model Using Neural Networks
Andrea Storto, Sergey Frolov, Laura Slivinski, and Chunxue Yang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-185, https://doi.org/10.5194/gmd-2024-185, 2024
Revised manuscript accepted for GMD (discussion: closed, 6 comments)
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Inaccuracies in air-sea heat fluxes severely downgrade the accuracy of ocean numerical simulations. Here, we use artificial neural networks to correct the air-sea heat fluxes as a function of oceanic and atmospheric state predictors. The correction successfully improves surface and subsurface ocean temperatures beyond the training period and in prediction experiments.
17 Oct 2024
TROLL 4.0: representing water and carbon fluxes, leaf phenology and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 1: Model description
Isabelle Maréchaux, Fabian Jörg Fischer, Sylvain Schmitt, and Jérôme Chave
EGUsphere, https://doi.org/10.5194/egusphere-2024-3104, https://doi.org/10.5194/egusphere-2024-3104, 2024
Revised manuscript accepted for GMD (discussion: final response, 6 comments)
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We describe TROLL 4.0, a simulator of forest dynamics that represents trees in a virtual space at one-meter resolution. Tree birth, growth, death and the underlying physiological processes such as carbon assimilation, water transpiration and leaf phenology depend on plant traits that are measured in the field for many individuals and species. The model is thus capable of jointly simulating forest structure, diversity and ecosystem functioning, a major challenge in modelling vegetation dynamics.
17 Oct 2024
Modifying the Abdul-Razzak & Ghan aerosol activation parameterization (version ARG2000) impacts simulated cloud radiative effects (shown in the UK Met Office Unified Model, version 13.0)
Pratapaditya Ghosh, Katherine J. Evans, Daniel P. Grosvenor, Hyun-Gyu Kang, Salil Mahajan, Min Xu, Wei Zhang, and Hamish Gordon
EGUsphere, https://doi.org/10.5194/egusphere-2024-2423, https://doi.org/10.5194/egusphere-2024-2423, 2024
Revised manuscript accepted for GMD (discussion: final response, 4 comments)
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The most popular algorithm for calculating cloud droplet number concentrations in climate models is sensitive to parameters that control simulated aerosol particle number concentrations at different sizes. We recommend small modifications to functions in the algorithm to improve its performance. Implementing our changes in the UK Met Office climate model reduced average bias in simulated global droplet number concentrations, leading to more reflected solar radiation and a net cooling effect.
17 Oct 2024
Implementation of the MOSAIC Aerosol Module (v1.0) in the Canadian Air Quality Model GEM-MACH (v3.1)
Kirill Semeniuk, Ashu Dastoor, and Alex Lupu
EGUsphere, https://doi.org/10.5194/egusphere-2024-2958, https://doi.org/10.5194/egusphere-2024-2958, 2024
Revised manuscript under review for GMD (discussion: final response, 5 comments)
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The MOSAIC inorganic aerosol sub-model has been implemented in the GEM-MACH air quality model. MOSAIC includes metal cation reactions and is a non-equilibrium, two-moment scheme that conserves aerosol number. Compared to the current aerosol sub-model, MOSAIC produces a more accurate size distribution and aerosol number concentration. It also improves the simulated nitrate and ammonium distribution. This work serves to expand the capacity of GEM-MACH for chemistry and weather coupling.
16 Oct 2024
The Spatio-Temporal Visualization Tool HMMLVis in Renewable Energy Applications
Rainer Wöß, Katerina Hlavácková-Schindler, Irene Schicker, Petrina Papazek, and Claudia Plant
EGUsphere, https://doi.org/10.5194/egusphere-2024-3126, https://doi.org/10.5194/egusphere-2024-3126, 2024
Preprint under review for GMD (discussion: open, 1 comment)
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HMMLVis is a causal inference, easy-to-use visualization software. It can be applied in any scientific discipline exploring time series and their relationships. The tool uses heterogeneous Granger causality. The tool is demonstrated on different types of applications related to meteorological events in a renewable energy, air pollution, and the EUMETNET postprocessing benchmark data. We believe HMMVis will serve climatologists or meteorologists as an interpretable causal visualization tool.
16 Oct 2024
LISFLOOD-FP 8.2: GPU-accelerated multiwavelet discontinuous Galerkin solver with dynamic resolution adaptivity for rapid, multiscale flood simulation
Alovya Chowdhury and Georges Kesserwani
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-152, https://doi.org/10.5194/gmd-2024-152, 2024
Preprint under review for GMD (discussion: final response, 3 comments)
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LISFLOOD-FP 8.2 is a framework for running real-world simulations of rapid, multiscale floods driven by impact events like tsunamis. It builds on the LISFLOOD-FP 8.0 and 8.1 papers published in GMD: whereas LISFLOOD-FP 8.0 focussed on GPU-parallelisation, and LISFLOOD-FP 8.1 focussed on static mesh adaptivity of (multi)wavelets, LISFLOOD-FP 8.2 combines GPU-parallelisation with multiwavelet dynamic mesh adaptivity to drastically reduce simulation runtimes, achieving up to a 4.5-fold speedup.
16 Oct 2024
FZStats v1.0: a raster statistics toolbox for simultaneous management of spatial stratified heterogeneity and positional dependence in Python
Na Ren, Daojun Zhang, and Qiuming Cheng
EGUsphere, https://doi.org/10.5194/egusphere-2024-2461, https://doi.org/10.5194/egusphere-2024-2461, 2024
Revised manuscript under review for GMD (discussion: final response, 4 comments)
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While Focal Statistics and Zonal Statistics deal with Spatial Position Dependence (SPD) and Spatial Stratified Heterogeneity (SSH) separately, the developed Focal-Zonal Mixed Statistics can handle both simultaneously. This new tool has the potential to become a general statistics tool. The integrated FZStats v1.0 toolbox in this study includes all three models mentioned above, providing new methodological support for understanding and addressing spatial statistical issues.
15 Oct 2024
Ensemble data assimilation to diagnose AI-based weather prediction model: A case with ClimaX version 0.3.1
Shunji Kotsuki, Kenta Shiraishi, and Atsushi Okazaki
External preprint server, https://doi.org/10.48550/arXiv.2407.17781, https://doi.org/10.48550/arXiv.2407.17781, 2024
Revised manuscript under review for GMD (discussion: final response, 14 comments)
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Artificial intelligence (AI) is playing a bigger role in weather forecasting, often competing with physical models. However, combining AI models with data assimilation, a process that improves weather forecasts by incorporating observation data, is still relatively unexplored. This study explored coupling ensemble data assimilation with an AI weather prediction model ClimaX, succeeded in employing weather forecasts stably by applying techniques conventionally used for physical models.
14 Oct 2024
Updates to the Met Office’s global ocean-sea ice forecasting system including model and data assimilation changes
Davi Mignac, Jennifer Waters, Daniel J. Lea, Matthew J. Martin, James While, Anthony T. Weaver, Arthur Vidard, Catherine Guiavarc’h, Dave Storkey, David Ford, Edward W. Blockley, Jonathan Baker, Keith Haines, Martin R. Price, Michael J. Bell, and Richard Renshaw
EGUsphere, https://doi.org/10.5194/egusphere-2024-3143, https://doi.org/10.5194/egusphere-2024-3143, 2024
Revised manuscript accepted for GMD (discussion: final response, 4 comments)
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We describe major improvements of the Met Office's global ocean-sea ice forecasting system. The models and the way observations are used to improve the forecasts were changed, which led to a significant error reduction of 1-day forecasts. The new system performance in past conditions, where sub-surface observations are scarce, was improved with more consistent ocean heat content estimates. The new system will be of better use for climate studies and will provide improved forecasts for end users.
14 Oct 2024
UFS-RAQMS Global Atmospheric Composition Model: TROPOMI CO Column Assimilation
Maggie Bruckner, R. Bradley Pierce, Allen Lenzen, Glenn Diskin, Josh DiGangi, Martine De Maziere, Nicholas Jones, and Maria Makarova
EGUsphere, https://doi.org/10.5194/egusphere-2024-2501, https://doi.org/10.5194/egusphere-2024-2501, 2024
Preprint under review for GMD (discussion: final response, 2 comments)
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UFS-RAQMS incorporates the Real-time Air Quality Modeling System (RAQMS) stratosphere/troposphere chemistry into the existing NOAA Global Ensemble Forecast System (GEFS-Aerosol) version of NOAA's Unified Forecast System (UFS). Chemical data assimilation using TROPOMI CO column observations is conducted during the July-August-September 2019 period. Comparison of CO column with independent measurements shows a systematic low bias in biomass burning CO emissions without assimilation.
11 Oct 2024
Potential based Thermodynamics with Consistent Conservative Cascade Transport for Implicit Large Eddy Simulation: PTerodaC3TILES version 1.0
John Thuburn
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-153, https://doi.org/10.5194/gmd-2024-153, 2024
Revised manuscript accepted for GMD (discussion: closed, 6 comments)
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A new computational fluid dynamics code for simulating the atmospheric boundary layer and convection is presented. Moist thermodynamics is formulated via thermodynamic potentials, avoiding inconsistencies that can be introduced with conventional approaches. Numerical methods typical of weather and climate models are used, with no explicit subgrid scheme. Results highlight some advantages (e.g., large time steps) and disadvantages (e.g., weak vertical fluxes near the surface) of this approach.
11 Oct 2024
A Flexible Snow Model (FSM 2.1.0) including a forest canopy
Richard Essery, Giulia Mazzotti, Sarah Barr, Tobias Jonas, Tristan Quaife, and Nick Rutter
EGUsphere, https://doi.org/10.5194/egusphere-2024-2546, https://doi.org/10.5194/egusphere-2024-2546, 2024
Revised manuscript accepted for GMD (discussion: final response, 5 comments)
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How forests influence accumulation and melt of snow on the ground is of long-standing interest, but uncertainty remains in how best to model forest snow processes. We developed the Flexible Snow Model version 2 to quantify these uncertainties. In a first model demonstration, how unloading of intercepted snow from the forest canopy is represented is responsible for the largest uncertainty. Global mapping of forest distribution is also likely to be a large source of uncertainty in existing models.
10 Oct 2024
GPTCast: a weather language model for precipitation nowcasting
Gabriele Franch, Elena Tomasi, Rishabh Wanjari, Virginia Poli, Chiara Cardinali, Pier Paolo Alberoni, and Marco Cristoforetti
External preprint server, https://doi.org/10.48550/arXiv.2407.02089, https://doi.org/10.48550/arXiv.2407.02089, 2024
Revised manuscript under review for GMD (discussion: final response, 6 comments)
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Our research introduces GPTCast, a novel method for very short-term precipitation forecasting using radar data. By applying advanced machine learning techniques inspired by large language models, we developed a system that generates accurate and realistic weather predictions. We trained the model using six years of radar data from Northern Italy, demonstrating its superior performance over leading ensemble extrapolation methods.
10 Oct 2024
TROLL 4.0: representing water and carbon fluxes, leaf phenology, and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 2: Model evaluation for two Amazonian sites
Sylvain Schmitt, Fabian Fischer, James Ball, Nicolas Barbier, Marion Boisseaux, Damien Bonal, Benoit Burban, Xiuzhi Chen, Géraldine Derroire, Jeremy Lichstein, Daniela Nemetschek, Natalia Restrepo-Coupe, Scott Saleska, Giacomo Sellan, Philippe Verley, Grégoire Vincent, Camille Ziegler, Jérôme Chave, and Isabelle Maréchaux
EGUsphere, https://doi.org/10.5194/egusphere-2024-3106, https://doi.org/10.5194/egusphere-2024-3106, 2024
Revised manuscript accepted for GMD (discussion: final response, 7 comments)
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We evaluate the capability of TROLL 4.0, a simulator of forest dynamics, to represent tropical forest structure, diversity and functioning in two Amazonian forests. Evaluation data include forest inventories, carbon and water fluxes between the forest and the atmosphere, and leaf area and canopy height from remote-sensing products. The model realistically predicts the structure and composition, and the seasonality of carbon and water fluxes at both sites.
09 Oct 2024
Wave forecast investigations on downscaling, source terms, and tides for Aotearoa New Zealand
Rafael Santana, Richard Gorman, Emily Lane, Stuart Moore, Cyprien Bosserelle, Glen Reeve, and Christo Rautenbach
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-110, https://doi.org/10.5194/gmd-2024-110, 2024
Revised manuscript accepted for GMD (discussion: closed, 4 comments)
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This research explores improving wave forecasts in New Zealand, particularly at Banks Peninsula and Baring Head. We used detailed models, finding that forecasts at Baring Head improved significantly due to its strong tidal currents, but changes at Banks Peninsula were minimal. The study demonstrates that local conditions greatly influence the effectiveness of wave prediction models, highlighting the need for tailored approaches in coastal forecasting to enhance accuracy in the predictions.
09 Oct 2024
Using automatic calibration to improve the physics behind complex numerical models: An example from a 3D lake model using Delft3d (v6.02.10) and DYNO-PODS (v1.0)
Marina Amadori, Abolfazl Irani Rahaghi, Damien Bouffard, and Marco Toffolon
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-118, https://doi.org/10.5194/gmd-2024-118, 2024
Revised manuscript accepted for GMD (discussion: closed, 3 comments)
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Models simplify reality using assumptions, which can sometimes introduce flaws and affect their accuracy. Properly calibrating model parameters is essential, and although automated tools can speed up this process, they may occasionally produce incorrect values due to inconsistencies in the model. We demonstrate that by carefully applying automated tools, we were able to identify and correct a flaw in a widely used model for lake environments.
08 Oct 2024
Anisotropic metric-based mesh adaptation for ice flow modelling in Firedrake
Davor Dundovic, Joseph G. Wallwork, Stephan C. Kramer, Fabien Gillet-Chaulet, Regine Hock, and Matthew D. Piggott
EGUsphere, https://doi.org/10.5194/egusphere-2024-2649, https://doi.org/10.5194/egusphere-2024-2649, 2024
Revised manuscript accepted for GMD (discussion: final response, 4 comments)
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Accurate numerical studies of glaciers often require high-resolution simulations, which often prove too demanding even for modern computers. In this paper we develop a method that identifies whether different parts of a glacier require high or low resolution based on its physical features, such as its thickness and velocity. We show that by doing so we can achieve a more optimal simulation accuracy for the available computing resources compared to uniform resolution simulations.
08 Oct 2024
Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-156, https://doi.org/10.5194/gmd-2024-156, 2024
Revised manuscript accepted for GMD (discussion: closed, 5 comments)
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We developed a deep learning method to estimate CO2 emissions from power plants using satellite images. Trained and validated on simulated data, our model accurately predicts emissions despite challenges like cloud cover. When applied to real OCO3 satellite image, the results closely match reported emissions. This study shows that neural networks trained on simulations can effectively analyse real satellite data, offering a new way to monitor CO2 emissions from space.
07 Oct 2024
New submodel for emissions from Explosive Volcanic ERuptions (EVER v1.1) within the Modular Earth Submodel System (MESSy, version 2.55.1)
Matthias Kohl, Christoph Brühl, Jennifer Schallock, Holger Tost, Patrick Jöckel, Adrian Jost, Steffen Beirle, Michael Höpfner, and Andrea Pozzer
EGUsphere, https://doi.org/10.5194/egusphere-2024-2200, https://doi.org/10.5194/egusphere-2024-2200, 2024
Revised manuscript accepted for GMD (discussion: final response, 8 comments)
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SO2 from explosive volcanic eruptions reaching the stratosphere can oxidize and form sulfate aerosols, potentially persisting for several years and influencing climate and the ozone layer. We developed a new submodel for Explosive Volcanic ERuptions (EVER) that seamlessly includes stratospheric volcanic SO2 emissions in global numerical simulations based on a novel standard historical model setup. Sensitivity studies on the Nabro eruption in 2011 evaluate different emission methods.
07 Oct 2024
An Effective Communication Topology for Performance Optimization: A Case Study of the Finite Volume WAve Modeling (FVWAM)
Renbo Pang, Fujiang Yu, Yuanyong Gao, Ye Yuan, Liang Yuan, and Zhiyi Gao
EGUsphere, https://doi.org/10.5194/egusphere-2024-2515, https://doi.org/10.5194/egusphere-2024-2515, 2024
Revised manuscript accepted for GMD (discussion: final response, 4 comments)
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The application of the distributed graph communication topology in earth models has been rarely studied. We tested and compared this topology with the traditional point-to-point communication method using a global wave model. We found that this topology is more efficient. Additionally, using this topology can greatly improve the performance of the wave model and could help improve the performance of other earth models.
02 Oct 2024
Enhancing Winter Climate Simulations of the Great Lakes: Insights from a New Coupled Lake-Ice-Atmosphere (CLIAv1) Model on the Importance of Integrating 3D Hydrodynamics with a Regional Climate Model
Pengfei Xue, Chenfu Huang, Yafang Zhong, Michael Notaro, Miraj B. Kayastha, Xing Zhou, Chuyan Zhao, Christa Peters-Lidard, Carlos Cruz, and Eric Kemp
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-146, https://doi.org/10.5194/gmd-2024-146, 2024
Revised manuscript accepted for GMD (discussion: closed, 6 comments)
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This study introduces a new lake-ice-atmosphere coupled model that significantly improves winter climate simulation for the Great Lakes compared to traditional one-dimensional (1D) lake models. It better simulates both lake conditions and over-lake atmospheric conditions. More importantly, the study highlights three critical 3D lake processes—ice movement, heat transport, and turbulent mixing—as essential for accurately simulating lake-atmosphere interactions and the Great Lakes’ winter climate.
02 Oct 2024
Huge Ensembles Part II: Properties of a Huge Ensemble of Hindcasts Generated with Spherical Fourier Neural Operators
Ankur Mahesh, William Collins, Boris Bonev, Noah Brenowitz, Yair Cohen, Peter Harrington, Karthik Kashinath, Thorsten Kurth, Joshua North, Travis A. O'Brien, Michael Pritchard, David Pruitt, Mark Risser, Shashank Subramanian, and Jared Willard
External preprint server, https://doi.org/10.48550/arXiv.2408.01581, https://doi.org/10.48550/arXiv.2408.01581, 2024
Revised manuscript accepted for GMD (discussion: final response, 6 comments)
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We use machine learning to create a massive database of simulated weather extremes. This database provides a large sample size, which is essential to characterize the statistics of extreme weather events and study their physical mechanisms. Also, such large simulations can be beneficial to accurately forecast the probability of low-likelihood extreme weather.
02 Oct 2024
Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators
Ankur Mahesh, William Collins, Boris Bonev, Noah Brenowitz, Yair Cohen, Joshua Elms, Peter Harrington, Karthik Kashinath, Thorsten Kurth, Joshua North, Travis O'Brien, Michael Pritchard, David Pruitt, Mark Risser, Shashank Subramanian, and Jared Willard
External preprint server, https://doi.org/10.48550/arXiv.2408.03100, https://doi.org/10.48550/arXiv.2408.03100, 2024
Revised manuscript accepted for GMD (discussion: final response, 6 comments)
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Simulating extreme weather events in a warming world is a challenging task for current weather and climate models. These models' computational cost poses a challenge in studying low-probability extreme weather. We use machine learning to construct a new probabilistic system. We explain in-depth how we constructed this system. We present a thorough pipeline to validate our method. Our method requires fewer computational resources than existing weather and climate models.
01 Oct 2024
Optimized Dynamic Mode Decomposition for Reconstruction and Forecasting of Atmospheric Chemistry Data
Meghana Velagar, Christoph Keller, and J. Nathan Kutz
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-77, https://doi.org/10.5194/gmd-2024-77, 2024
Revised manuscript accepted for GMD (discussion: closed, 4 comments)
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We develop the data-driven method of dynamic mode decomposition for producing a robust and stable surrogate reduced order model of atmospheric chemistry dynamics. The model is computationally efficient, provides interpretable patterns of activity and produces uncertainty quantification metrics. It is ideal for forecasting of atmospheric chemistry in a computationally tractable manner.
30 Sep 2024
Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3
Anisbel Leon-Marcos, Moritz Zeising, Manuela van Pinxteren, Sebastian Zeppenfeld, Astrid Bracher, Elena Barbaro, Anja Engel, Matteo Feltracco, Ina Tegen, and Bernd Heinold
EGUsphere, https://doi.org/10.5194/egusphere-2024-2917, https://doi.org/10.5194/egusphere-2024-2917, 2024
Revised manuscript accepted for GMD (discussion: final response, 12 comments)
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This study represents the Primary marine organic aerosols (PMOA) emission, focusing on their sea-atmosphere transfer. Using the FESOM2.1-REcoM3 model, concentrations of key organic biomolecules were estimated and integrated into the ECHAM6.3–HAM2.3 aerosol-climate model. Results highlight the influence of marine biological activity and surface winds on PMOA emissions, with reasonably good agreement with observations improving aerosol representation in the Southern Oceans.
25 Sep 2024
Estimation of above- and below-ground ecosystem parameters for the DVM-DOS-TEM v0.7.0 model using MADS v1.7.3: a synthetic case study
Elchin E. Jafarov, Helene Genet, Velimir V. Vesselinov, Valeria Briones, Aiza Kabeer, Andrew L. Mullen, Benjamin Maglio, Tobey Carman, Ruth Rutter, Joy Clein, Chu-Chun Chang, Dogukan Teber, Trevor Smith, Joshua M. Rady, Christina Schädel, Jennifer D. Watts, Brendan M. Rogers, and Susan M. Natali
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-158, https://doi.org/10.5194/gmd-2024-158, 2024
Revised manuscript accepted for GMD (discussion: closed, 4 comments)
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Thawing permafrost could greatly impact global climate. Our study improves modeling of carbon cycling in Arctic ecosystems. We developed an automated method to fine-tune a model that simulates carbon and nitrogen flows, using computer-generated data. Using computer-generated data, we tested our method and found it enhances accuracy and reduces the time needed for calibration. This work helps make climate predictions more reliable in sensitive permafrost regions.
19 Sep 2024
The sensitivity of aerosol data assimilation to vertical profiles: case study of dust storm assimilation with LOTOS-EUROS v2.2
Mijie Pang, Jianbing Jin, Ting Yang, Xi Chen, Arjo Segers, Hai Xiang Lin, Hong Liao, and Wei Han
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-113, https://doi.org/10.5194/gmd-2024-113, 2024
Revised manuscript accepted for GMD (discussion: closed, 8 comments)
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Aerosol data assimilation has gained popularity as it combines the advantages of model and observation. However, few have addressed the challenges in the prior vertical structure. A variety of observations are assimilated to examine the sensitivity of assimilation to vertical structure. Results show that assimilation can optimize the dust field in general. However, if the prior introduces an incorrect structure, the assimilation can significantly deteriorate the integrity of the aerosol profile.
18 Sep 2024
GraphFlow v1.0: approximating groundwater contaminant transport with graph-based methods – an application to fault scenario selection
Léonard Moracchini, Guillaume Pirot, Kerry Bardot, Mark W. Jessell, and James L. McCallum
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-154, https://doi.org/10.5194/gmd-2024-154, 2024
Revised manuscript under review for GMD (discussion: final response, 4 comments)
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To facilitate the exploration of alternative hydrogeological scenarios, we propose to approximate costly physical simulations of contaminant transport by more affordable shortest distances computations. It enables to accept or reject scenarios within a predefined confidence interval. In particular, it can allow to estimate the probability of a fault acting as a preferential path or a barrier.
18 Sep 2024
Sunburned plankton: Ultraviolet radiation inhibition of phytoplankton photosynthesis in the Community Earth System Model version 2
Joshua Coupe, Nicole S. Lovenduski, Luise S. Gleason, Michael N. Levy, Kristen Krumhardt, Keith Lindsay, Charles Bardeen, Clay Tabor, Cheryl Harrison, Kenneth G. MacLeod, Siddhartha Mitra, and Julio Sepúlveda
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-94, https://doi.org/10.5194/gmd-2024-94, 2024
Preprint under review for GMD (discussion: open, 6 comments)
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We develop a new feature in the atmosphere and ocean components of the Community Earth System Model version 2. We have implemented ultraviolet (UV) radiation inhibition of photosynthesis of four marine phytoplankton functional groups represented in the Marine Biogeochemistry Library. The new feature is tested with varying levels of UV radiation. The new feature will enable an analysis of an asteroid impact’s effect on the ozone layer and how that affects the base of the marine food web.
11 Sep 2024
Chempath 1.0: An open-source pathway analysis program for photochemical models
Daniel Garduno Ruiz, Colin Goldblatt, and Anne-Sofie Ahm
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-163, https://doi.org/10.5194/gmd-2024-163, 2024
Revised manuscript accepted for GMD (discussion: closed, 8 comments)
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Photochemical models describe how the composition of an atmosphere changes due to chemical reactions, transport, and other processes. These models are useful for studying the composition of the Earth and other planet's atmospheres. Understanding the results of these models can be difficult. Here, we build on previous work to develop an open-source code that can identify the reaction chains (pathways) that produce the results of these models, facilitating the understanding of these results.
10 Sep 2024
The new plant functional diversity model JeDi-BACH (version 1.0) in the ICON Earth System Model (version 1.0)
Pin-Hsin Hu, Christian H. Reick, Reiner Schnur, Axel Kleidon, and Martin Claussen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-111, https://doi.org/10.5194/gmd-2024-111, 2024
Revised manuscript under review for GMD (discussion: final response, 4 comments)
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We introduce the new plant functional diversity model JeDi-BACH, a novel tool that integrates the Jena Diversity Model (JeDi) within the land component of the ICON Earth System Model. JeDi-BACH captures a richer set of plant trait variations based on environmental filtering and functional tradeoffs without a priori knowledge of the vegetation types. JeDi-BACH represents a significant advancement in modeling the complex interactions between plant functional diversity and climate.
04 Sep 2024
Evaluating Arctic Sea-Ice and Snow Thickness: A Proxy-Based Comparison of MOSAiC Data with CMIP6 Simulations
Shreya Trivedi, Imke Sievers, Marylou Athanase, Antonio Sánchez Benítez, and Tido Semmler
EGUsphere, https://doi.org/10.5194/egusphere-2024-2214, https://doi.org/10.5194/egusphere-2024-2214, 2024
Revised manuscript under review for GMD (discussion: final response, 3 comments)
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Our study introduces a new method to compare CMIP6 models' sea ice and snow simulations with in-situ (MOSAiC) measurements. We assessed models for their accuracy in replicating Arctic sea ice and snow thicknesses, using two sea-ice and atmosphere-based methods to select "proxy years." We show that the models often overestimate snow thickness and mistime sea ice cycles. Despite limitations, this approach provides a valuable tool for evaluating climate models in localized time and space.
30 Aug 2024
FLAME 1.0: a novel approach for modelling burned area in the Brazilian biomes using the Maximum Entropy concept
Maria Lucia Ferreira Barbosa, Douglas I. Kelley, Chantelle A. Burton, Igor J. M. Ferreira, Renata Moura da Veiga, Anna Bradley, Paulo Guilherme Molin, and Liana O. Anderson
EGUsphere, https://doi.org/10.5194/egusphere-2024-1775, https://doi.org/10.5194/egusphere-2024-1775, 2024
Revised manuscript accepted for GMD (discussion: final response, 3 comments)
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As fire seasons in Brazil intensify, understanding what drives these fires becomes crucial. We developed a new model, FLAME, to predict fires using environmental and human factors, while also accounting for uncertainties. We found temperature and rainfall to be key factors, with uncertainties higher in some regions. By customizing the model for different regions, we can improve fire management strategies, making FLAME a valuable tool for protecting Brazil's and other region’s landscapes.
30 Aug 2024
ML4Fire-XGBv1.0: Improving North American wildfire prediction by integrating a machine-learning fire model in a land surface model
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-151, https://doi.org/10.5194/gmd-2024-151, 2024
Revised manuscript accepted for GMD (discussion: closed, 5 comments)
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This study integrates machine learning with a land surface model to improve wildfire predictions in North America. Traditional models struggle with accurately simulating burned areas due to simplified processes. By combining the predictive power of machine learning with a land model, our hybrid framework better captures fire dynamics. This approach enhances our understanding of wildfire behavior and aids in developing more effective climate and fire management strategies.
30 Aug 2024
An improved hydro-biogeochemical model (CNMM-DNDC V6.0) for simulating dynamical forest-atmosphere exchanges of carbon and evapotranspiration at typical sites subject to subtropical and temperate monsoon climates in eastern Asia
Wei Zhang, Xunhua Zheng, Siqi Li, Shenghui Han, Chunyan Liu, Zhisheng Yao, Rui Wang, Kai Wang, Xiao Chen, Guirui Yu, Zhi Chen, Jiabing Wu, Huimin Wang, Junhua Yan, and Yong Li
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-141, https://doi.org/10.5194/gmd-2024-141, 2024
Revised manuscript not accepted (discussion: closed, 4 comments)
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Process-oriented biogeochemical models are promising tools for estimating the carbon fluxes of forest ecosystems. In this study, the hydro-biogeochemical model of CNMM-DNDC was improved by incorporating a new forest growth module derived from the Biome-BGC. The updated model was validated using the multiple-year observed carbon fluxes and showed better performance in capturing the daily dynamics and annual variations. The sensitive eco-physiological parameters were also identified.
30 Aug 2024
The Community Fire Behavior Model for coupled fire-atmosphere modeling: Implementation in the Unified Forecast System
Pedro Angel Jimenez y Munoz, Maria Frediani, Masih Eghdami, Daniel Rosen, Michael Kavulich, and Timothy W. Juliano
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-124, https://doi.org/10.5194/gmd-2024-124, 2024
Preprint under review for GMD (discussion: final response, 6 comments)
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We present the Community Fire Behavior model (CFBM) a fire behavior model designed to facilitate coupling to atmospheric models. We describe its implementation in the Unified Forecast System (UFS). Simulations of the Cameron Peak ire allowed us to verify our implementation. Our vision is to foster collaborative development in fire behavior modeling with the ultimate goal of increasing our fundamental understanding of fire science and minimizing the adverse impacts of wildland fires.
28 Aug 2024
Skillful neural network predictions of Saharan dust
Trish Ewa Nowak, Andy T. Augousti, Benno I. Simmons, and Stefan Siegert
External preprint server, https://doi.org/https://doi.org/10.48550/arXiv.2406.11754, https://doi.org/https://doi.org/10.48550/arXiv.2406.11754, 2024
Revised manuscript accepted for GMD (discussion: final response, 4 comments)
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Described here DustNet model uses advanced neural networks to accurately predict the Saharan dust transport in the atmosphere. It offers fast and precise forecasts with predictions achieved in just 2.1 seconds on a standard computer. This innovative approach outperforms traditional models, which take hours to produce a forecast and use high energy super-computers. By making high-quality dust monitoring accessible and efficient, DustNet can improve weather, climate and air quality forecasts.
28 Aug 2024
Development of an under-ice river discharge forecasting system in Delft-Flood Early Warning System (Delft-FEWS) for the Chaudière River based on a coupled hydrological-hydrodynamic modelling approach
Kh Rahat Usman, Rodolfo Alvarado Montero, Tadros Ghobrial, François Anctil, and Arnejan van Loenen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-116, https://doi.org/10.5194/gmd-2024-116, 2024
Revised manuscript under review for GMD (discussion: final response, 5 comments)
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Rivers in cold climate regions such as Canada undergo freeze up during winters which makes the estimation forecasting of under-ice discharge very challenging and uncertain since there is no reliable method other than direct measurements. The current study explored the potential of deploying a coupled modelling framework for the estimation and forecasting of this parameter. The framework showed promising potential in addressing the challenge of estimating and forecasting the under-ice discharge.
27 Aug 2024
GREAT v1.0: Global Real-time Early Assessment of Tsunamis
Usama Kadri, Ali Abdolali, and Maxim Filimonov
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-139, https://doi.org/10.5194/gmd-2024-139, 2024
Revised manuscript accepted for GMD (discussion: closed, 4 comments)
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The GREAT v1.0 software introduces a novel tsunami warning technology for global real-time analysis. It leverages acoustic signals generated by tsunamis, which propagate faster than the tsunami itself, enabling real-time detection and assessment. Integrating various models, the software provides reliable and rapid assessment, mapping risk areas, and estimating tsunami amplitude. This advancement reduces false alarms and enhances global tsunami warning systems' accuracy and efficiency.
26 Aug 2024
SynRad v1.0: A radar forward operator to generate synthetic radar return signals from volcanic ash clouds
Vishnu Nair, Anujah Mohanathan, Michael Herzog, David G. Macfarlane, and Duncan A. Robertson
EGUsphere, https://doi.org/10.5194/egusphere-2024-1835, https://doi.org/10.5194/egusphere-2024-1835, 2024
Revised manuscript accepted for GMD (discussion: final response, 5 comments)
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A numerical model which simulates the measurement processes behind ground-based radars used to detect volcanic ash clouds is introduced. Using weather radars to detect volcanic clouds is not ideal as fine ash are smaller than raindrops and remains undetected. We evaluate the performance of weather radars to study ash clouds and to identify optimal frequencies which balances the trade-off between a higher return signal and the higher path attenuation that comes at these higher frequencies.
22 Aug 2024
Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): Impact on Amazon dry-season transpiration
Carolina A. Bieri, Francina Dominguez, Gonzalo Miguez-Macho, and Ying Fan
EGUsphere, https://doi.org/10.5194/egusphere-2024-2412, https://doi.org/10.5194/egusphere-2024-2412, 2024
Revised manuscript accepted for GMD (discussion: final response, 4 comments)
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Access to deep moisture below the earth's surface is important for vegetation in areas of the Amazon where there is little precipitation for part of the year. Most existing numerical models of the earth system cannot capture where and when deep root water uptake occurs. In this study, we address this by adding a new root water uptake feature to an existing model. Adding this feature increases dry month transpiration and improves the model's simulation of the annual transpiration cycle.
22 Aug 2024
PIBM 1.0: An individual-based model for simulating phytoplankton acclimation, diversity, and evolution in the ocean
Iria Sala and Bingzhang Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-130, https://doi.org/10.5194/gmd-2024-130, 2024
Revised manuscript accepted for GMD (discussion: closed, 3 comments)
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Phytoplankton, tiny photosynthetic organisms, produce nearly half of Earth's oxygen. To analyze their physiology, diversity, and evolution in the ocean, we developed a model that treats phytoplankton as individual particles. Moreover, our model considers phytoplankton size, temperature, and light traits, and allows for mutations in phytoplankton cells. Thus, our model provides a valuable tool for advancing the study of phytoplankton physiology, diversity, and evolution.
20 Aug 2024
FINAM – is not a model (v1.0): a new Python-based model coupling framework
Sebastian Müller, Martin Lange, Thomas Fischer, Sara König, Matthias Kelbling, Jeisson Javier Leal Rojas, and Stephan Thober
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-144, https://doi.org/10.5194/gmd-2024-144, 2024
Revised manuscript accepted for GMD (discussion: closed, 8 comments)
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This study presents FINAM ("FINAM Is Not A Model"), a new coupling framework written in Python to dynamically link independently developed models. Python, as the ultimate glue language, enables the use of codes from nearly any programming language like Fortran, C++, Rust, and others. FINAM is designed to simplify the integration of various models with minimal effort, as demonstrated through various examples ranging from simple to complex systems.
19 Aug 2024
Numerical investigations on the modelling of ultrafine particles in SSH-aerosol-v1.3a: size resolution and redistribution
Oscar Jacquot and Karine Sartelet
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-150, https://doi.org/10.5194/gmd-2024-150, 2024
Revised manuscript accepted for GMD (discussion: closed, 4 comments)
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As the health impact of ultrafine particles is better understood, modeling the size distribution and the number concentration becomes increasingly important. A new analytic formulation is presented to compute coagulation partition coefficients, allowing to lower down the numerical diffusion associated to the resolution of aerosol dynamics. The significance of this effect is assessed over Greater Paris with a chemistry transport model, using different size resolution of the particle distribution.
19 Aug 2024
Model calibration and streamflow simulations for the extreme drought event of 2018 on the Rhine River Basin using WRF-Hydro 5.2.0
Andrea L. Campoverde, Uwe Ehret, Patrick Ludwig, and Joaquim G. Pinto
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-134, https://doi.org/10.5194/gmd-2024-134, 2024
Revised manuscript not accepted (discussion: closed, 6 comments)
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We looked at how well the model WRF-Hydro performed during the 2018 drought event in the River Rhine basin, even though it is typically used for floods. We used the meteorological ERA5 reanalysis dataset to simulate River Rhine’s streamflow and adjusted the model using parameters and actual discharge measurements. We focused on Lake Constance, a key part of the basin, but found issues with the model’s lake outflow simulation. By removing the lake module, we obtained more accurate results.
09 Aug 2024
Tuning a Climate Model with Machine-learning based Emulators and History Matching
Pauline Bonnet, Lorenzo Pastori, Mierk Schwabe, Marco A. Giorgetta, Fernando Iglesias-Suarez, and Veronika Eyring
EGUsphere, https://doi.org/10.5194/egusphere-2024-2508, https://doi.org/10.5194/egusphere-2024-2508, 2024
Revised manuscript accepted for GMD (discussion: final response, 4 comments)
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Tuning a climate model means adjusting uncertain parameters in the model to best match observations like the global radiation balance and cloud cover. This is usually done by running many simulations of the model with different settings, which can be time-consuming and relies heavily on expert knowledge. To make this process faster and more objective, we developed a machine learning emulator to create a large ensemble and apply a method called history matching to find the best settings.
02 Aug 2024
Wastewater matters: Incorporating wastewater reclamation into a process-based hydrological model (CWatM v1.08)
Dor Fridman, Mikhail Smilovic, Peter Burek, Sylvia Tramberend, and Taher Kahil
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-143, https://doi.org/10.5194/gmd-2024-143, 2024
Revised manuscript accepted for GMD (discussion: closed, 13 comments)
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Global hydrological models are applied at high spatial resolutions to quantify water availability and evaluate water scarcity mitigation options. Yet they mostly oversee important local processes. This paper presents and demonstrates the inclusion of wastewater treatment and reclamation into a global hydrological model. As a result model performance is improved, and models are capable to utilize treated wastewater as an alternative water source.
30 Jul 2024
Modelling Herbivory Impacts on Vegetation Structure and Productivity
Jens Krause, Peter Anthoni, Mike Harfoot, Moritz Kupisch, and Almut Arneth
EGUsphere, https://doi.org/10.5194/egusphere-2024-1646, https://doi.org/10.5194/egusphere-2024-1646, 2024
Revised manuscript under review for GMD (discussion: final response, 4 comments)
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While animal biodiversity is facing a global crisis as more and more species are becoming endangered or extinct, the role of animals for the functioning of ecosystems is still not fully understood. We contribute to bridging this gap by coupling a animal population model with a vegetation and thus enable future research in this topic.
29 Jul 2024
A Novel Model Hierarchy Isolates the Effect of Temperature-dependent Cloud Optics on Infrared Radiation
Ash Gilbert, Jennifer E. Kay, and Penny Rowe
EGUsphere, https://doi.org/10.5194/egusphere-2024-2043, https://doi.org/10.5194/egusphere-2024-2043, 2024
Preprint under review for GMD (discussion: final response, 2 comments)
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We developed a novel methodology for assessing whether a new physics parameterization should be added to a climate model based on its effect across a hierarchy of model complexities and time and spatial scales. Our study used this model hierarchy to evaluate the effect of a new cloud radiation parameterization on longwave radiation and determined that the parameterization should be added to climate radiation models, but its effect is not large enough to be a priority.
25 Jul 2024
Implementation of implicit filter for spatial spectra extraction
Kacper Nowak, Sergey Danilov, Vasco Müller, and Caili Liu
EGUsphere, https://doi.org/10.5194/egusphere-2024-1119, https://doi.org/10.5194/egusphere-2024-1119, 2024
Revised manuscript under review for GMD (discussion: final response, 3 comments)
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A new method called coarse-graining scale analysis is gaining traction as an alternative to Fourier analysis. However, it requires data to be on a regular grid. To address this, we present a high-performance Python package of coarse-graining technique using discrete Laplacians. This method can handle any mesh type and is ideal for processing output directly from unstructured-mesh models. Computation is split into preparation and solving phases, with GPU acceleration ensuring fast processing.
19 Jul 2024
PyGLDA: a fine-scale Python-based Global Land Data Assimilation system for integrating satellite gravity data into hydrological models
Fan Yang, Maike Schumacher, Leire Retegui-Schiettekatte, Albert I. J. M. van Dijk, and Ehsan Forootan
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-125, https://doi.org/10.5194/gmd-2024-125, 2024
Revised manuscript under review for GMD (discussion: final response, 8 comments)
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The satellite gravimetry can provide direct measurement of total water storage (TWS) that was never achieved before. In this study, we provide an open-source assimilation system to show how the satellite based TWS can be temporally, vertically and laterally disaggregated for constraining/validating/improving the global hydrological models. With this system, early warning and water management at a global scale would be more accurate, given the upcoming next-generation satellite gravity missions.
11 Jul 2024
A modeling System for Identification of Maize Ideotypes, optimal sowing dates and nitrogen fertilization under climate change – PREPCLIM-v1
Mihaela Caian, Catalin Lazar, Petru Neague, Antoanela Dobre, Vlad Amihaesei, Zenaida Chitu, Adrian Irasoc, Andreea Popescu, and George Cizmas
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-105, https://doi.org/10.5194/gmd-2024-105, 2024
Revised manuscript under review for GMD (discussion: final response, 11 comments)
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We present the implementation and use of a new integrated climate-phenology adaptation modeling system for climate change using CORDEX scenarios and DSSAT crop model with new developed modules for optimal agro-management and genotype identification under future climate. Optimisation is a hybrid deterministic /ML genetic algorithms method. The system is user-interactive in real time, has been implemented and tested for South Romania, is applicable for Southern-Europe and extendable for Europe.
08 Jul 2024
Broken Terrains v. 1.0: A supervised detection of fault-related lineaments on geological terrains
Michał Michalak, Christian Gerhards, and Peter Menzel
EGUsphere, https://doi.org/10.5194/egusphere-2024-2004, https://doi.org/10.5194/egusphere-2024-2004, 2024
Revised manuscript accepted for GMD (discussion: final response, 5 comments)
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This study presents a novel method for fault detection on geological terrains. Using synthetic models, we applied machine learning to classify terrain shape and nearby features. Testing on real borehole data validated its effectiveness across various fault orientations. The supervised approach represents a significant improvement over older methods that relied on simpler clustering techniques which were capable of identifying less orientations of faults.
24 Jun 2024
Evaluation of radiation schemes in the CMA-MESO model using high time-resolution radiation measurements in China: I. Long-wave radiation
Junli Yang, Weijun Quan, Li Zhang, Jianglin Hu, Qiying Chen, and Martin Wild
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-74, https://doi.org/10.5194/gmd-2024-74, 2024
Revised manuscript not accepted (discussion: closed, 12 comments)
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Due to the difficulties involved in the measurements of the Downward long-wave irradiance (DnLWI), the numerical weather prediction (NWP) models have been developed to obtain the DnLWI indirectly. In this study, a long-term high time-resolution (1 min) observational dataset of the DnLWI in China was used to evaluate the radiation scheme in the CMA-MESO model over various underlying surfaces and climate zones.
17 Jun 2024
Implementation of multi-layer snow scheme in seasonal forecast system and its impact on model climatological bias
Eunkyo Seo and Paul A. Dirmeyer
EGUsphere, https://doi.org/10.5194/egusphere-2024-1066, https://doi.org/10.5194/egusphere-2024-1066, 2024
Revised manuscript under review for GMD (discussion: final response, 4 comments)
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This study examines the impact of using a multi-layer snow scheme in seasonal forecasts. Compared to single-layer schemes, multi-layer schemes better represent snow's insulating effect, improving forecast accuracy for temperature, soil moisture, and precipitation. These enhancements lead to more realistic simulations of land-atmosphere interactions, mitigating biases and improving model performance over mid- and high-latitude regions of the Northern Hemisphere.
11 Jun 2024
Enhancing Climate Model Performance through Improving Volcanic Aerosol Representation
Ziming Ke, Qi Tang, Jean-Christoophe Golaz, Xiaohong Liu, and Hailong Wang
EGUsphere, https://doi.org/10.5194/egusphere-2024-1612, https://doi.org/10.5194/egusphere-2024-1612, 2024
Revised manuscript accepted for GMD (discussion: final response, 5 comments)
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By treating volcanic emission interactively, model results improve simulated temperature variability, showing better correlations for 1940–1959 and 1960–1979, and reveals how volcanic activity influences cloud behavior and climate.
11 Jun 2024
Comprehensive Air Quality Model With Extensions, v7.20: Formulation and Evaluation for Ozone and Particulate Matter Over the US
Christopher A. Emery, Kirk R. Baker, Gary M. Wilson, and Greg Yarwood
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-48, https://doi.org/10.5194/gmd-2024-48, 2024
Preprint withdrawn (discussion: closed, 3 comments)
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We describe the Comprehensive Air quality Model with extensions (CAMx) and evaluate a model simulation during 2016 over nine U.S. climate zones. For ozone, the model statistically replicates measured concentrations better than most other past models and applications. For small inhalable particulates, the model replicates concentrations consistent with most other past models and applications subject to common uncertainties associated with sources, weather, and chemical interactions.
11 Jun 2024
OpenMindat v1.0.0 R package: A machine interface to Mindat open data to facilitate data-intensive geoscience discoveries
Xiang Que, Jiyin Zhang, Weilin Chen, Jolyon Ralph, and Xiaogang Ma
EGUsphere, https://doi.org/10.5194/egusphere-2024-1141, https://doi.org/10.5194/egusphere-2024-1141, 2024
Revised manuscript accepted for GMD (discussion: final response, 6 comments)
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This paper describes an R package as the machine interface to the open data of Mindat.org, one of the world's most widely used databases of mineral species and their distribution. In the past decades many geoscientists have been using the Mindat data, but an open data service has never been fully established. The machine interface described in this paper will be an efficient way to meet the overwhelming data needs.
10 Jun 2024
Impact of horizontal resolution and model time step on European precipitation extremes in the OpenIFS 43r3 atmosphere model
Yingxue Liu, Joakim Kjellsson, Abhishek Savita, and Wonsun Park
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-66, https://doi.org/10.5194/gmd-2024-66, 2024
Revised manuscript under review for GMD (discussion: final response, 3 comments)
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The impact of horizontal resolution and model time step on extreme precipitation over Europe is examined in OpenIFS. We find that the biases are reduced with increasing horizontal resolution, but not with reducing time step. The large-scale precipitation is more sensitive to the horizontal resolution, however, the convective precipitation is more sensitive to the model time step. Increasing horizontal resolution is more important for extreme precipitation simulation that reducing time step.
04 Jun 2024
Improved winter conditions in SURFEX-TEB v9.0 with a multi-layer snow model and ice for road winter maintenance
Gabriel Colas, Valéry Masson, François Bouttier, Ludovic Bouilloud, Laura Pavan, and Virve Karsisto
EGUsphere, https://doi.org/10.5194/egusphere-2024-1039, https://doi.org/10.5194/egusphere-2024-1039, 2024
Revised manuscript accepted for GMD (discussion: final response, 4 comments)
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Snow-covered or icy roads increase the risk of accidents for drivers, pedestrians, and cyclists. To avoid these slippery conditions, road winter maintenance must plan their operations in advance using weather forecasts. We improved the Town Energy Balance (TEB) urban climate model to simulate the dangerous road slippery conditions in cities or in remote areas. Evaluations showed that the results are promising for using TEB to inform road winter maintenance decisions.
04 Jun 2024
Enhanced Land Subsidence Interpolation through a Hybrid Deep Convolutional Neural Network and InSAR Time Series
Zahra Azarm, Hamid Mehrabi, and Saeed Nadi
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-15, https://doi.org/10.5194/gmd-2024-15, 2024
Revised manuscript under review for GMD (discussion: final response, 8 comments)
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The article introduces a new method using deep CNN and PSInSAR to estimate land subsidence, addressing the limitations of traditional methods. It focuses on Isfahan province, demonstrating substantial improvement over traditional techniques. The deep CNN method showed a 70 % enhancement in subsidence prediction, with the study area experiencing over 38 cm of subsidence between 2014 and 2020.
30 May 2024
A Unified System for Evaluating, Ranking and Clustering in Diverse Scientific Domains
Zengyun Hu, Xi Chen, Deliang Chen, Zhuo Zhang, Qiming Zhou, and Qingxiang Li
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-82, https://doi.org/10.5194/gmd-2024-82, 2024
Preprint withdrawn (discussion: closed, 8 comments)
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ERC firstly unified the evaluating, ranking, and clustering by a simple mathematic equation based on Euclidean Distance. It provides new system to solve the evaluating, ranking, and clustering tasks in SDGs. In fact, ERC system can be applied in any scientific domain.
29 May 2024
Isogeometric analysis of the lithosphere under topographic loading: Igalith v1.0.0
Rozan Rosandi, Yudi Rosandi, and Bernd Simeon
EGUsphere, https://doi.org/10.5194/egusphere-2024-1093, https://doi.org/10.5194/egusphere-2024-1093, 2024
Revised manuscript accepted for GMD (discussion: final response, 4 comments)
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We model Earth's lithosphere as a thin elastic shell and present numerical methods of isogeometric finite element analysis to simulate its deformation in isostatic equilibrium using technologies from computer-aided design. The simulations also serve as a basis for identifying parameters of the model that are most plausible to explain observed data. This research has been done to showcase the capabilities of isogeometric analysis in solving higher-order problems in geoscientific applications.
27 May 2024
Autoencoder-based feature extraction for the automatic detection of snow avalanches in seismic data
Andri Simeon, Cristina Pérez-Guillén, Michele Volpi, Christine Seupel, and Alec van Herwijnen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-76, https://doi.org/10.5194/gmd-2024-76, 2024
Revised manuscript under review for GMD (discussion: final response, 6 comments)
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Avalanche seismic detection systems are key for forecasting, but distinguishing avalanches from other seismic sources remains challenging. We propose novel autoencoder models to automatically extract features and compare them with standard seismic attributes. These features are then used to classify avalanches and noise events. The autoencoder feature classifiers have the highest sensitivity to detect avalanches, while the standard seismic classifier performs better overall.
16 Apr 2024
The Utrecht Finite Volume Ice-Sheet Model (UFEMISM version 2.0) – part 1: description and idealised experiments
Constantijn J. Berends, Victor Azizi, Jorge Bernales, and Roderik S. W. van de Wal
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-5, https://doi.org/10.5194/gmd-2024-5, 2024
Revised manuscript accepted for GMD (discussion: closed, 4 comments)
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Ice-sheet models are computer programs that can simulate how the Greenland and Antarctic ice sheets will evolve in the future. The accuracy of these models depends on their resolution: how small the details are that the model can resolve. We have created a model with a variable resolution, which can resolve a lot of detail in areas where lots of changes happen in the ice, and less detail in areas where the ice does not move so much. This makes the model both accurate and fast.
15 Apr 2024
Application of machine learning to proximal gamma-ray and magnetic susceptibility surveys in the Maritime Antarctic: assessing the influence of periglacial processes and landforms
Danilo César de Mello, Clara Glória Oliveira Baldi, Cássio Marques Moquedace, Isabelle de Angeli Oliveira, Gustavo Vieira Veloso, Lucas Carvalho Gomes, Márcio Rocha Francelino, Carlos Ernesto Gonçalves Reynaud Schaefer, Elpídio Inácio Fernandes-Filho, Edgar Batista de Medeiros Júnior, Fabio Soares de Oliveira, José João Lelis Leal de Souza Souza, Tiago Ferreira, and José A. M. Demattê
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-2, https://doi.org/10.5194/gmd-2024-2, 2024
Preprint under review for GMD (discussion: open, 1 comment)
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The study explores Maritime Antarctica's geology, shaped by periglacial forces, using pioneering gamma-spectrometric and magnetic surveys on igneous rocks due to limited Antarctic surveys. Machine learning predicts radionuclide and magnetic content based on terrain features, linking their distribution to landscape processes, morphometrics, lithology, and pedogeomorphology. Inaccuracies arise due to complex periglacial processes and landscape complexities.
11 Apr 2024
Quantifying the Oscillatory Evolution of Simulated Boundary-Layer Cloud Fields Using Gaussian Process Regression
Gunho Oh and Philip H. Austin
EGUsphere, https://doi.org/10.5194/egusphere-2024-352, https://doi.org/10.5194/egusphere-2024-352, 2024
Revised manuscript accepted for GMD (discussion: final response, 8 comments)
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It is difficult to study the behaviour of a cloud field, due to internal fluctuations and observational noise. We perform a high-resolution simulation of the boundary-layer cloud field, and introduce numerical techniques based on machine learning algorithms to study the evolution of the cloud field, which shows a periodic behaviour. We aim to use the numerical techniques to identify underlying behaviours from noisy observations.
11 Apr 2024
A hybrid-grid global model for the estimation of atmospheric weighted mean temperature considering time-varying lapse rate in GNSS precipitable water vapor retrieval
Shaofeng Xie, Jihong Zhang, Liangke Huang, Fade Chen, Yongfeng Wu, Yijie Wang, and Lilong Liu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-21, https://doi.org/10.5194/gmd-2024-21, 2024
Revised manuscript under review for GMD (discussion: final response, 13 comments)
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We developed a new global atmospheric weighted mean temperature (Tm) model considering time-varying lapse rate. Firstly, a global multidimensional Tm lapse rate model (NGGTm-H model) was developed using the sliding window algorithm. Secondly, the daily variation characteristics of Tm and its relationships with geographical situation were investigated. Finally, a hybrid-grid global Tm model considering time-varying lapse rate (NGGTm model) was developed.
22 Mar 2024
A dynamic informed deep learning method for future estimation of laboratory stick-slip
Enjiang Yue, Mengjiao Qin, Linshu Hu, Sensen Wu, and Zhenhong Du
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-46, https://doi.org/10.5194/gmd-2024-46, 2024
Revised manuscript accepted for GMD (discussion: closed, 5 comments)
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Laboratory earthquakes is important means to understand natural earthquakes while previous work focused on transient prediction, lacking future prediction capability. We propose a method and evaluate on data from lab experiments with different slip behaviors. It outperforms state-of-the-art methods in modeling slip moments, intervals and predictions beyond trained horizons especially for challenging slip scenarios, which is crucial for quasi-periodic geophysical process like seismicity.
29 Jan 2024
A Deep Learning-Based Consistency Test Approach for Earth System Models on Heterogeneous Many-Core Systems
Yangyang Yu, Shaoqing Zhang, Haohuan Fu, Dexun Chen, Yang Gao, Xiaopei Lin, Zhao Liu, and Xiaojing Lv
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-10, https://doi.org/10.5194/gmd-2024-10, 2024
Preprint withdrawn (discussion: closed, 9 comments)
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The hardware-related perturbations caused by the heterogeneous many-core architectures can blend with software or human errors, which can affect the accuracy of the model consistency verification. We develop a deep learning-based consistency test tool for ESMs on the heterogeneous systems (ESM-DCT) and evaluate it in CESM on new Sunway system. The ESM-DCT can detect the existence of software or human errors when taking hardware-related perturbations into account.
19 Jan 2024
E3SM Chemistry Diagnostics Package (ChemDyg) Version 0.1.4
Hsiang-He Lee, Qi Tang, and Michael Prather
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-203, https://doi.org/10.5194/gmd-2023-203, 2024
Revised manuscript not accepted (discussion: closed, 4 comments)
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The E3SM Chemistry diagnostics package (ChemDyg) is a software tool, which is designed for the global climate model (E3SM) chemistry development. ChemDyg generates several diagnostic plots and tables for model-to-model and model-to-observation comparison, including 2-dimentional contour mapping plots, diurnal and annual cycle, time-series plots, and comprehensive processing tables. This paper is to introduce the details of each diagnostics set and its required input data formats in ChemDyg.
08 Jan 2024
A Study on the Transformer-CNN Imputation Method for Turbulent Heat Flux Dataset in the Qinghai-Tibet Plateau Grassland
Quanzhe Hou, Zhiqiu Gao, Zexia Duan, and Minghui Yu
EGUsphere, https://doi.org/10.5194/egusphere-2023-2685, https://doi.org/10.5194/egusphere-2023-2685, 2024
Revised manuscript accepted for GMD (discussion: final response, 4 comments)
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This study assesses turbulent heat flux data imputation at the Qinghai-Tibet Plateau using various machine learning models. The Transformer model emerged as the most effective, leading to the creation of the Transformer_CNN model, which integrates global and local attention mechanisms. Experimental results showed that Transformer_CNN surpassed other models in performance. This model was effectively used to impute the station's heat flux data from 2007 to 2016.
04 Jan 2024
Improving subseasonal forecast skill in the Norwegian Climate Prediction Model using soil moisture data assimilation
Akhilesh Sivaraman Nair, François Counillon, and Noel Keenlyside
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-217, https://doi.org/10.5194/gmd-2023-217, 2024
Publication in GMD not foreseen (discussion: closed, 9 comments)
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This study demonstrates the importance of soil moisture (SM) in subseasonal-to-seasonal predictions. To addess this, we introduce the Norwegian Climate Prediction Model Land (NorCPM-Land), a land data assimilation system developed for the NorCPM. NorCPM-Land reduces error in SM by 10.5 % by assimilating satellite SM products. Enhanced land initialisation improves predictions up to a 3.5-month lead time for SM and a 1.5-month lead time for temperature and precipitation.
04 Dec 2023
Description and validation of the ice sheet model Nix v1.0
Daniel Moreno-Parada, Alexander Robinson, Marisa Montoya, and Jorge Alvarez-Solas
EGUsphere, https://doi.org/10.5194/egusphere-2023-2690, https://doi.org/10.5194/egusphere-2023-2690, 2023
Revised manuscript accepted for GMD (discussion: final response, 4 comments)
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We introduce Nix, an ice-sheet model designed for understanding how large masses of ice behave. Nix as a computer program that simulates the movement and temperature changes in ice sheets. Nix helps us study how ice sheets respond to changes in the atmosphere and ocean. We found that how fast ice melts under the shelves and how heat is exchanged, play a role in determining the future of ice sheets. Nix is a useful tool for learning more about how climate change affects polar ice sheets.
21 Nov 2023
GHOSH v1.0.0: a novel Gauss-Hermite High-Order Sampling Hybrid filter for computationally efficient data assimilation in geosciences
Simone Spada, Anna Teruzzi, Stefano Maset, Stefano Salon, Cosimo Solidoro, and Gianpiero Cossarini
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-170, https://doi.org/10.5194/gmd-2023-170, 2023
Revised manuscript under review for GMD (discussion: final response, 7 comments)
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In geosciences, data assimilation (DA) combines modeled dynamics and observations to reduce simulation uncertainties. Uncertainties can be dynamically and effectively estimated in ensemble DA methods. With respect to current techniques, the novel GHOSH ensemble DA scheme is designed to improve accuracy by reaching a higher approximation order, without increasing computational costs, as demonstrated in idealized Lorenz96 tests and in realistic simulations of the Mediterranean Sea biogeochemistry
20 Nov 2023
Clustering analysis of very large measurement and model datasets on high performance computing platforms
Colin J. Lee, Paul A. Makar, and Joana Soares
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-185, https://doi.org/10.5194/gmd-2023-185, 2023
Publication in GMD not foreseen (discussion: closed, 5 comments)
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Clustering is an analysis technique for finding similarities within datasets. We present a new implementation of the hierarchical clustering algorithm that is able to process much larger datasets than was previously possible, by spreading the program out over many connected computers in a high-performance computing system. We show airshed maps of a high-resolution regional model output domain, and find related air pollution profiles at monitoring stations separated by thousands of kilometers.
10 Nov 2023
Updated algorithmic climate change functions (aCCF) V1.0A: Evaluation with the climate-response model AirClim V2.0
Sigrun Matthes, Simone Dietmüller, Katrin Dahlmann, Christine Frömming, Patrick Peter, Hiroshi Yamashita, Volker Grewe, Feijia Yin, and Federica Castino
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-92, https://doi.org/10.5194/gmd-2023-92, 2023
Revised manuscript not accepted (discussion: closed, 6 comments)
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Aviation aims to reduce its climate effect by identifying alternative climate-optimized aircraft trajectories. Such routing strategies requires a dedicated meteorological service in order to inform on regions of the atmosphere where aviation non-CO2 emissions have a large climate effect, e.g. by contrail formation or nitrogen-oxide (NOx)-induced ozone formation. This study presents calibration factors for individual non-CO2 effects by comparing with the climate response model AirClim.
02 Nov 2023
Simulating the variations of carbon dioxide in the global atmosphere on the hexagonal grid of DYNAMICO coupled with the LMDZ6 model
Zoé Lloret, Frédéric Chevallier, Anne Cozic, Marine Remaud, and Yann Meurdesoif
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-140, https://doi.org/10.5194/gmd-2023-140, 2023
Revised manuscript not accepted (discussion: closed, 3 comments)
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In this study, we evaluate the performance of a new model coupling, ICO, for simulating atmospheric carbon dioxide (CO2) transport. Using an unstructured grid, our model accurately captures seasonal CO2 variations at surface stations. The model exhibits comparable accuracy to a reference configuration and offers advantages in computational speed and storage. This highlights the importance of advanced modeling approaches and high-resolution grids in refining climate models.
25 Oct 2023
A deep learning method for convective weather forecasting: CNN-BiLSTM-AM (version 1.0)
Jianbin Zhang, Zhiqiu Gao, Yubin Li, and Yuncong Jiang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-187, https://doi.org/10.5194/gmd-2023-187, 2023
Preprint withdrawn (discussion: closed, 5 comments)
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This study developed a deep learning model called CNN-BiLSTM-AM for convective weather forecasting. The results showed that the CNN-BiLSTM-AM model outperformed traditional machine learning algorithms in predicting convective weather, with higher accuracy as the forecast lead time increased. When compared to subjective forecasts by forecasters, the objective approach of the CNN-BiLSTM-AM model also demonstrated advantages in various metrics.
23 Oct 2023
A close look at using national ground stations for the statistical modeling of NO2
Foeke Boersma and Meng Lu
EGUsphere, https://doi.org/10.5194/egusphere-2023-1260, https://doi.org/10.5194/egusphere-2023-1260, 2023
Revised manuscript accepted for GMD (discussion: final response, 3 comments)
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Air pollution harms health and society. Understanding and predicting it is crucial. Various models are developed to model air pollution. However, the consistency exhibited by a model in different areas is commonly neglected. Our study accounts for this and shows lower accuracy near busy roads, but higher in less populated areas. Considering location characteristics in air pollution predictions is important in comparing statistical models and understanding the health-society-space relationship.
22 Sep 2023
Parameterization and tuning of the Bay of Biscay Atlantis model v1
Ane Lopez de Gamiz-Zearra, Cecilie Hansen, Xavier Corrales, Iñaki Quincoces, Izaskun Preciado, and Eider Andonegi
EGUsphere, https://doi.org/10.5194/egusphere-2023-1368, https://doi.org/10.5194/egusphere-2023-1368, 2023
Revised manuscript accepted for GMD (discussion: final response, 10 comments)
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This paper describes the development of the first calibrated end-to-end Atlantis model for the Bay of Biscay that will help us improve the comprehension of the spatial functioning of the Bay of Biscay ecosystem and help establishing management measures of human activities. Our results highlighted the importance of lower trophic levels to the pelagic system and demonstrate the importance of having accurate and precise data for biological processes.
21 Aug 2023
Inclusion of the subgrid wake effect between turbines in the wind farm parameterization of WRF
Wei Liu, Xuefeng Yang, Shengli Chen, Shaokun Deng, Peining Yu, and Jiuxing Xing
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-174, https://doi.org/10.5194/gmd-2023-174, 2023
Revised manuscript not accepted (discussion: closed, 4 comments)
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Assessing environmental impacts of wind farms requires an accurate parameterization of wind farms in atmospheric models, which, in our study, is improved considering the wind turbine wake. Based on an engineering wake model of a turbine, a wake superposition coefficient and an angle correction coefficient are proposed, calculated and added in the model. Sensitivity experiments reveal that, with enlarged grid size and shortened turbine spacing, the new scheme shows more advantages.
16 Aug 2023
Surrogate model-based precipitation tuning for CAM5
Xianwei Wu, Liang Hu, Lanning Wang, Haitian Lu, and Juepeng Zheng
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-164, https://doi.org/10.5194/gmd-2023-164, 2023
Revised manuscript not accepted (discussion: closed, 6 comments)
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In order to build an effective surrogate model for the community atmospheric model (CAM). We present a surrogate model-based parameter tuning framework for the CAM and apply it to improve the CAM5 precipitation performance and propose a multilevel surrogate model-based optimization method. We design a nonuniform parameter parameterization scheme and integrate the parameters using a parameter smoothing scheme, and the experimental results improve in four regions.
15 Aug 2023
Deep-learning statistical downscaling of precipitation in the middle reaches of the Yellow River: A Residual in Residual Dense Block based network
He Fu, Jianing Guo, Chenguang Deng, Heng Liu, Jie Wu, Zhengguo Shi, Cailing Wang, and Xiaoning Xie
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-158, https://doi.org/10.5194/gmd-2023-158, 2023
Preprint withdrawn (discussion: closed, 7 comments)
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A Residual in Residual Dense Block based network model (RRDBNet) is designed for statistical downscaling of precipitation in the middle reaches of the Yellow River. RRDBNet has a good performance on precipitation simulations, well reproducing the spatial-temporal characteristics of high-resolution precipitation. RRDBNet has substantial improvements in extreme precipitation compared with generalized linear regression model and two deep learning-based models.
29 Jun 2023
Impacts of dynamic dust sources coupled with WRF-Chem 3.9.1 on the dust simulation over East Asia
Yu Chen, Yue Zhang, Siyu Chen, Ben Yang, Huiping Yan, Jixiang Li, Chao Zhang, Gaotong Lou, Junyan Chen, Lulu Lian, and Chuwei Liu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-81, https://doi.org/10.5194/gmd-2023-81, 2023
Revised manuscript not accepted (discussion: closed, 4 comments)
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The numerical models seriously ignoring the aeolian erosion and dust emission process on the potential sources. Six sets of dynamic dust sources were built by combine surface bareness and topographic feature. Results show that dust sources are closely related to surface exposure and topographic characteristics, which respectively control the spatial distribution and numerical value of dynamic dust sources.
27 Jun 2023
Nonparametric estimation method for river cross-sections with point cloud data from UAV photography URiver-X version 1.0 -methodology development
Taesam Lee, Jaewoo Park, Sunghyun Hwang, and Vijay Singh
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-57, https://doi.org/10.5194/gmd-2023-57, 2023
Revised manuscript not accepted (discussion: closed, 9 comments)
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The current study presents a novel method to demarcate the cross-section of a river channel using a very flexible regression model, called K-nearest neighbor local linear regression (KLR). The proposed method draws the cross-section automatically based on the point cloud data taken from unmanned aerial vehicles (UAVs). The proposed model can provide a further development of 4th industy innovation by employding the UAV-based photogrammetry.
27 Jun 2023
The CO2 and non-CO2 climate effects of individual flights: simplified estimation of CO2 equivalent emission factors
Robin N. Thor, Malte Niklaß, Katrin Dahlmann, Florian Linke, Volker Grewe, and Sigrun Matthes
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-126, https://doi.org/10.5194/gmd-2023-126, 2023
Preprint withdrawn (discussion: closed, 3 comments)
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We develop a simplied method to estimate the climate effects of single flights through CO2 and non-CO2 effects, exclusively based on the aircraft seat category as well as the origin and destination airports. The derived climate effect functions exhibit a mean relative error of only 15 % with respect to results from a climate response model. The method is designed for climate footprint assessments and covers most commerical airlines with seat capacities starting from 101 passengers.
26 Jun 2023
RICHARD 1.0 – Routine for the Isolation of Chemical Hotspots in Atmospheric Research Data
Christian Scharun, Roland Ruhnke, and Peter Braesicke
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-91, https://doi.org/10.5194/gmd-2023-91, 2023
Publication in GMD not foreseen (discussion: closed, 3 comments)
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The identification and quantification of greenhouse gas (GHG) emissions is an important task for monitoring mitigation strategies under climate change. With RICHARD 1.0, we developed a novel approach using spatiotemporal proxy data and a selection algorithm to detect GHG emission hotspots. By using a one year dataset of global climate model output we showed that RICHARD is able to determine and quantify the source strengths of GHG emission hotspots much more precisely than conventional methods.
31 May 2023
Taylor's statistical theory applied to the turbulence parameterization in the BAM-INPE global atmospheric model
Eduardo Rohde Eras, Haroldo Fraga de Campos Velho, and Paulo Yoshio Kubota
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-59, https://doi.org/10.5194/gmd-2023-59, 2023
Publication in GMD not foreseen (discussion: closed, 3 comments)
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The portion of the earth atmosphere closer to the ground is responsible for heat, moisture and mechanical energy transportation between the surface and the air through turbulence, been very important for weather forecast. Between many solutions used to model this turbulence, this is the first attempt to use one based on Taylor's statistical theory in a global atmospheric model, achieving good results for precipitation and energy transportation, specially in the Amazon basin region.
03 May 2023
STEMMUS-MODFLOW v1.0.0: Integrated Understanding of Soil Water and Groundwater Flow Processes: Case Study of the Maqu Catchment, north-eastern Tibetan Plateau
Lianyu Yu, Yijian Zeng, Huanjie Cai, Mengna Li, Yuanyuan Zha, Jicai Zeng, Hui Qian, and Zhongbo Su
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-221, https://doi.org/10.5194/gmd-2022-221, 2023
Revised manuscript not accepted (discussion: closed, 4 comments)
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We developed a coupled soil water-groundwater (SW-GW) model, which is verified as physically accurate and applicable in large-scale groundwater problems. The role of vadose zone processes, coupling approach, and spatiotemporal heterogeneity of SW-GW interactions were highlighted as essential to represent the SW-GW system. Given the relevant dataset, the developed SW-GW modeling framework has the potential to portray the processes "from bedrock to atmosphere" in a physically consistent manner.
02 May 2023
A quantitative decoupling analysis (QDA v1.0) method for assessing the contributions of meteorology, emissions, and chemistry to fine particulate pollution
Junhua Wang, Baozhu Ge, Xueshun Chen, Jie Li, Keding Lu, Yayuan Dong, Lei Kong, Zifa Wang, and Yuanhang Zhang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-22, https://doi.org/10.5194/gmd-2023-22, 2023
Revised manuscript not accepted (discussion: closed, 6 comments)
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We developed a quantitative decoupling analysis (QDA) method to quantify the contributions of emissions, meteorology, chemical reactions, and their nonlinear interactions on PM2.5. We found the effects of adverse meteorological conditions and the importance of nonlinear interactions. This method can provide valuable information for understanding of key factors to heavy pollution, but also help the modelers to find out the sources of uncertainties in numerical models.
12 Apr 2023
Novel Deep Learning Approaches for Mapping Variation of Ground Level from Spirit Level Measurements
Fawzi Zarzoura, Mosbeh Kaloop, Pijush Samui, Jong Wan Hu, Md Shayan Sabri, and Tamer ElGharbawi
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-62, https://doi.org/10.5194/gmd-2023-62, 2023
Preprint withdrawn (discussion: closed, 10 comments)
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The study aims to map variation in ground levels based on ordinary spirit levelling (SL) measurements. New machine learning techniques were developed and compared in the current study to estimate the leveling through SL measurements. The results show the developed LSTM model outperforms CNN, RNN, and BI-LSTM in modeling ground leveling in the training and testing stages. The accuracy of mapping ground levelling through the developed LSTM model is close to 99 % in terms of model error.
27 Mar 2023
Positive semi-definite variants of CBM4 and CBM05 chemistry schemes for atmospheric composition models
Risto Matias Hänninen, Rostislav Kouznetsov, and Mikhail Sofiev
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-3, https://doi.org/10.5194/gmd-2023-3, 2023
Preprint withdrawn (discussion: closed, 6 comments)
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Chemistry transport models describe the motion of particles and gases in atmosphere, containing chemistry equations that allow reaction between different species. The widely used carbon-bond chemistry schemes are originally written in a numerically problematic form that drives some concentrations to unphysical negative values. Here the chemistry equations are re-written in a form where this problem is absent, allowing an easier integration of the equations into any chemistry transport model.
22 Mar 2023
G&M3D 1.0: an Interactive framework for 3D Model Construction and Forward Calculation of Potential Fields
Kanggui Wei, Bo Chen, and Jiaxiang Peng
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-314, https://doi.org/10.5194/gmd-2022-314, 2023
Preprint withdrawn (discussion: closed, 3 comments)
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Geological model construction and forward calculation are the basis for the analysis and interpretation of geophysical data. However, open-source tools combining flexible source model construction and efficient forward calculation of the potential fields are rare. This study develops a new MATLAB-based software – G&M3D to address these issues. The real-world forward gravity modeling over a salt dome in Vinton Dome is performed to verify the correctness and practicality of the software.
17 Mar 2023
Quantitative Sub-Ice and Marine Tracing of Antarctic Sediment Provenance (TASP v0.1)
James W. Marschalek, Edward Gasson, Tina van de Flierdt, Claus-Dieter Hillenbrand, Martin J. Siegert, and Liam Holder
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-8, https://doi.org/10.5194/gmd-2023-8, 2023
Revised manuscript not accepted (discussion: closed, 8 comments)
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Ice sheet models can help predict how Antarctica’s ice sheets respond to environmental change; such models benefit from comparison to geological data. Here, we use ice sheet model results, plus other data, to predict the erosion of Antarctic debris and trace its transport to where it is deposited on the ocean floor. This allows the results of ice sheet modelling to be directly and quantitively compared to real-world data, helping to reduce uncertainty regarding Antarctic sea level contribution.
01 Mar 2023
Optimized Stochastic Representation of Soil States Model Uncertainty of WRF (v4.2) in the Ensemble Data Assimilation System
Sujeong Lim, Seon Ki Park, and Claudio Cassardo
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-28, https://doi.org/10.5194/gmd-2023-28, 2023
Revised manuscript not accepted (discussion: closed, 4 comments)
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The ensembles in the numerical weather prediction system are under-dispersed near the land surface; therefore, an inflation method is required to increase it. In this study, we perturbed soil temperature and soil moisture to represent the near-surface uncertainty. Perturbations were obtained by the optimization algorithm taking into account diurnal variations in soil states. Consequently, it indirectly inflated the temperature and water vapor mixing ratio in the planetary boundary layer.
21 Feb 2023
Assimilating the dynamic spatial gradient of a bottom-up carbon flux estimation as a unique observation in COLA (v2.0)
Zhiqiang Liu, Ning Zeng, Yun Liu, Eugenia Kalnay, Ghassem Asrar, Qixiang Cai, and Pengfei Han
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-15, https://doi.org/10.5194/gmd-2023-15, 2023
Revised manuscript not accepted (discussion: closed, 4 comments)
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We introduced a novel algorithm that assimilates a better a priori knowledge to improve the estimation of global surface carbon flux. The algorithm aims at separating the first-order systematic biases in the a priori "bottom-up" flux estimations out of the inversion framework from a comprehensive data assimilation perspective.
26 Jan 2023
Randomized Block Nonparametric Temporal Disaggregation of Hydrological Variables RB-NPD (version1.0) – model development
Taesam Lee and Taha B. M. J. Ouarda
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-274, https://doi.org/10.5194/gmd-2022-274, 2023
Publication in GMD not foreseen (discussion: closed, 2 comments)
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The current study proposed random block based nonparametric disaggregation model so that the weakness point of the existing nonparametric disaggregation models can be resolved with preserving the long-term persistence. The proposed model illustrates superior performance for disaggregating the net basin supply of the LCRR basin in the Great Lakes, which experienced the worst flood in 2011.
29 Nov 2022
Experiments with the modified Rotating Shallow Water model (modRSW, v.1.0): assessing the relevance for convective-scale data assimilation research
Thomas Kent, Luca Cantarello, Gordon Inverarity, Steven Tobias, and Onno Bokhove
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-269, https://doi.org/10.5194/gmd-2022-269, 2022
Publication in GMD not foreseen (discussion: closed, 6 comments)
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Data assimilation combines recent model forecasts and observations to estimate current atmospheric conditions for use as initial conditions for numerical weather prediction. We analyse the results of a series of data assimilation experiments using a simplified and inexpensive mathematical model of the atmosphere. We closely compare key properties of the models used by weather centres with our idealised setup, proving that it can help support operational data assimilation research.
08 Nov 2022
HiWaQ v1.0: A flexible catchment water quality assessment tool with compatibility for multiple hydrological model structures
Xiaoqiang Yang, Doerthe Tetzlaff, Chris Soulsby, and Dietrich Borchardt
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-239, https://doi.org/10.5194/gmd-2022-239, 2022
Preprint retracted (discussion: closed, 1 comment)
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We develop the catchment water quality assessment platform HiWaQ v1.0, which is compatible with multiple hydrological model structures. The nitrogen module (HiWaQ-N) and its coupling tests with two contrasting grid-based hydrological models demonstrate the robustness of the platform in estimating catchment N dynamics. With the unique design of the coupling flexibility, HiWaQ can leverage advancements in hydrological modelling and advance integrated catchment water quantity-quality assessments.
26 Sep 2022
Development of common socio-economic scenarios for climate change impact assessments in Japan
Sayaka Yoshikawa, Kiyoshi Takahashi, Wenchao Wu, Keisuke Matsuhashi, and Nobuo Mimura
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-169, https://doi.org/10.5194/gmd-2022-169, 2022
Revised manuscript not accepted (discussion: closed, 4 comments)
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Socio-economic scenarios developed worldwide require revised versions for local assessments in Japan. Moreover, global narratives may lack important region-specific drivers, national policy perspectives, and unification of government-provided data. Therefore, we present the development of several socio-economic scenarios with changes in population and land use based on the previous study as a framework for projecting climate change impacts and adaptation assessment in Japan.
09 Sep 2022
Neural networks for data assimilation of surface and upper-air data in Rio de Janeiro
Vinícius Albuquerque de Almeida, Haroldo Fraga de Campos Velho, Gutemberg Borges França, and Nelson Francisco Favilla Ebecken
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-50, https://doi.org/10.5194/gmd-2022-50, 2022
Publication in GMD not foreseen (discussion: closed, 6 comments)
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The paper focuses on data assimilation for the WRF model by employing neural network. The applied supervised ML technique was designed to emulate the 3D-Var in a regional atmospheric model. The proposed technique has the potential to significantly reduce the computational effort of data assimilation. Indeed, in the worked example the neural network scheme was more 70 times faster than 3D-Var method, with similar quality for the analysis.
08 Sep 2022
Reconstruction of past exposure to natural hazards driven by historical statistics: HANZE v2.0
Dominik Paprotny and Matthias Mengel
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-194, https://doi.org/10.5194/gmd-2022-194, 2022
Preprint withdrawn (discussion: closed, 2 comments)
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Population and economic growth over past decades have increased risk posed by natural hazards. The model presented here generates high-resolution maps of land use, population and assets (exposure) from 1870 to 2020 for 42 countries. It combines multiple methods with a large database of historical statistical data to approximate past anthropogenic environment of Europe. It enables attributing losses from past disasters to climate change by removing the influence of changes in exposure.
26 Jul 2022
PVN 1.0: using dynamic PFTs and restoration scenarios to model CO2 and CH4 emissions in peatlands
Tanya Juliette Rebecca Lippmann, Monique Heijmans, Han Dolman, Ype van der Velde, Dimmie Hendriks, and Ko van Huissteden
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-143, https://doi.org/10.5194/gmd-2022-143, 2022
Preprint withdrawn (discussion: closed, 1 comment)
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To assess the impact of vegetation on GHG fluxes in peatlands, we developed a new model, Peatland-VU-NUCOM (PVN). These results showed that plant communities impact GHG emissions, indicating that plant community re-establishment is a critical component of peatland restoration. This is the first time that a peatland emissions model investigated the role of re-introducing peat forming vegetation on GHG emissions.
20 Jul 2022
Assessment of tropospheric ozone products from CAMS reanalysis and near-real time analysis using observations over Iran
Najmeh Kaffashzadeh and Abbas Ali Aliakbari Bidokhti
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-138, https://doi.org/10.5194/gmd-2022-138, 2022
Revised manuscript not accepted (discussion: closed, 4 comments)
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Global climate chemistry models provide our best estimation of future projection of tropospheric composition. Coarse grid boxes of these models often limit their validations to a set of observations. Current generations of the models benefit from many improvements such upgrading to a finer resolution, assimilating with a wide range of observed data, or etc. This paper assesses the capability of two state-of-the-art global models in simulating tropospheric ozone using observations over Iran.
13 Jul 2022
Mapping 3D Structure of Loose Quaternary Deposits Combining Deep Learning and Multiple-point Statistics: An example in Chencun, Northern Pearl River Delta
Weisheng Hou, Hengguang Liu, Xianhe Zhang, Xiaoming Lin, Hongwei Li, Weisheng Wu, Fan Xiao, Junyi Li, and Hui Chang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-83, https://doi.org/10.5194/gmd-2022-83, 2022
Revised manuscript not accepted (discussion: closed, 6 comments)
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In this paper, a novel approach to construct the 3D Quaternary structures is proposed. Two three-dimensional model construction examples in Chencun area, Foshan City, Guangdong Province show that the algorithm proposed in this study can realize the three-dimensional reconstruction of the fine structure of Quaternary loose sediments and fracture zones.
11 Jul 2022
Data-driven Global Subseasonal Forecast Model (GSFM v1.0) for intraseasonal oscillation components
Chuhan Lu, Dingan Huang, Yichen Shen, and Fei Xin
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-146, https://doi.org/10.5194/gmd-2022-146, 2022
Preprint withdrawn (discussion: closed, 6 comments)
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As a challenge in the construction of a “seamless forecast” system, improving the prediction skills of subseasonal forecasts is a key issue for meteorologists. In this study, we developed a new subseasonal forecast model based on deep-learning. And this model performs better on the 10–30 day prediction of the intraseasonal oscillation components of meteorological elements than the CFSv2 subseasonal results to some extent.
06 Jul 2022
An Improved Method Based on VGGNet for Refined Bathymetry from Satellite Altimetry: Reducing the Errors Effectively
Xiaolun Chen, Xiaowen Luo, Ziyin Wu, Xiaoming Qin, Jihong Shang, Mingwei Wang, and Hongyang Wan
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-140, https://doi.org/10.5194/gmd-2022-140, 2022
Revised manuscript not accepted (discussion: closed, 5 comments)
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To combine the advantages of satellite altimetry-derived and multibeam sonar-derived bathymetry, we apply deep learning to perform multibeam sonar-based bathymetry correction for satellite altimetry bathymetry data. Specifically, we modify and improve a pretrained VGGNet neural network mode. Experiments show that the model can improve the precision.
02 Jun 2022
CLUMondo v2.0: Improved model by adaptive determination of conversion orders for simulating land system changes with many-to-many demand-supply relationships
Peichao Gao, Yifan Gao, Xiaodan Zhang, Sijing Ye, and Changqing Song
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-123, https://doi.org/10.5194/gmd-2022-123, 2022
Revised manuscript not accepted (discussion: closed, 4 comments)
Short summary
Short summary
We found that the featured function of CLUMondo – balancing demands and supplies in a many-to-many mode – relies on a parameter called conversion order, but the setting of this parameter should be improved. This parameter should be set manually according to the characteristics of each study area and based on expert knowledge, which is not feasible for users without understanding the whole, detailed mechanism. This problem has been addressed in this study with CLUMondo Version 2.0.
24 May 2022
An Improved Algorithm for Simulating the Surface Flow Dynamics based on the Flow-Path Network Model
Qianjiao Wu, Yumin Chen, Huaming Xie, Tong Xu, Jiayong Yu, and Ting Zhang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-92, https://doi.org/10.5194/gmd-2022-92, 2022
Preprint withdrawn (discussion: closed, 8 comments)
Short summary
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To solve the problems of accuracy and response efficiency of existing simulation methods, an improved algorithm was proposed to simulate the surface flow dynamics quickly and accurately. We considers the influence of terrain parameters on flow velocity to improve Manning’s equation for enhancing simulation accuracy. We also use CUDA to advance the efficiency. Experimental results show that it can quickly and accurately complete the multi-scale simulation and ensure simulation consistence.
23 May 2022
Global Sensitivity Analysis of the distributed hydrologic model ParFlow-CLM (V3.6.0)
Wei Qu, Heye Bogena, Christoph Schüth, Harry Vereecken, Zongmei Li, and Stephan Schulz
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-131, https://doi.org/10.5194/gmd-2022-131, 2022
Publication in GMD not foreseen (discussion: closed, 5 comments)
Short summary
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We applied the global sensitivity analysis LH-OAT to the integrated hydrology model ParFlow-CLM to investigate the sensitivity of the 12 parameters for different scenarios. And we found that the general patterns of the parameter sensitivities were consistent, however, for some parameters a significantly larger span of the sensitivities was observed, especially for the higher slope and in subarctic climatic scenarios.
19 May 2022
DFN Generator v2.0: A new tool to model the growth of large-scale natural fracture networks using fundamental geomechanics
Michael John Welch, Mikael Lüthje, and Simon John Oldfield
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-22, https://doi.org/10.5194/gmd-2022-22, 2022
Preprint withdrawn (discussion: closed, 2 comments)
Short summary
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This code can build geologically realistic models of natural fracture networks by simulating the nucleation, growth and interaction of fractures based on geomechanical principles. It uses the algorithm of Welch et al. (2020) to generate more realistic models of large fracture networks than stochastic techniques. It can build either implicit fracture models, explicit DFNs, or both, and will have applications in engineering and fluid flow modelling, as well as in understanding fracture evolution.
25 Apr 2022
Empirical Assessment of Normalized Information Flow for Quantifying Causal Contributions
Chin-Hsien Cheng and Simon Redfern
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-106, https://doi.org/10.5194/gmd-2022-106, 2022
Revised manuscript not accepted (discussion: closed, 5 comments)
Short summary
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Causality is one of the foundations of scientific understanding and progress. Statistical models extrapolate historical trends into the future through statistical tools, but may still lack insight into the physical underlying processes. We have developed a method to quantify physical causal contributions between observational time series. It plugs the gap between process-based and statistical models, providing a key to unlocking and understanding causality in Earth systems science processes.
22 Apr 2022
3D geological modelling of igneous intrusions in LoopStructural v1.4.4
Fernanda Alvarado-Neves, Laurent Ailleres, Lachlan Grose, Alexander R. Cruden, and Robin Armit
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-88, https://doi.org/10.5194/gmd-2022-88, 2022
Preprint withdrawn (discussion: closed, 4 comments)
Short summary
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We introduce a method to model igneous intrusions for 3D geological modelling. We use a parameterization of the intrusion body geometry that could be constrained using field observations. Using this parametrization, we simulate distance thresholds that represent the lateral and vertical extent of the intrusion body. We demonstrate the method with two case studies, and we present a comparison with Radial Basis Function interpolation using a case study of a sill complex located in NW Australia.
06 Apr 2022
Intercomparing radar data assimilation systems for ICE-POP 2018 snowfall cases
Ki-Hong Min, Kao-Shen Chung, Ji-Won Lee, Cheng-Rong You, and Gyuwon Lee
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-18, https://doi.org/10.5194/gmd-2022-18, 2022
Revised manuscript not accepted (discussion: closed, 12 comments)
Short summary
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LETKF underestimated the water vapor mixing ratio and temperature compared to 3DVAR due to a lack of a water vapor mixing ratio and temperature observation operator. Snowfall in GWD was less simulated in LETKF. The results signify that water vapor assimilation is important in radar DA and significantly impacts precipitation forecasts, regardless of the DA method used. Therefore, it is necessary to apply observation operators for water vapor mixing ratio and temperature in radar DA.
15 Mar 2022
The development and validation of a global 1/32° surface wave-tide-circulation coupled ocean model: FIO-COM32
Bin Xiao, Fangli Qiao, Qi Shu, Xunqiang Yin, Guansuo Wang, and Shihong Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-52, https://doi.org/10.5194/gmd-2022-52, 2022
Revised manuscript not accepted (discussion: closed, 4 comments)
Short summary
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A new global surface wave-tide-circulation coupled ocean model FIO-COM32 with resolution of 1/32° × 1/32° is developed and validated. Both the promotion of the horizontal resolution and included physical processes are proved to be important contributors to the significant improvements of FIO-COM32 simulations. It should be the time to merge these separated model components (surface wave, tidal current and ocean circulation) for new generation ocean model development.
10 Jan 2022
Evaluating dust emission model performance using dichotomous satellite observations of dust emission
Mark Hennen, Adrian Chappell, Nicholas Webb, Kerstin Schepanski, Matthew Baddock, Frank Eckardt, Tarek Kandakji, Jeff Lee, Mohamad Nobakht, and Johanna von Holdt
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-423, https://doi.org/10.5194/gmd-2021-423, 2022
Revised manuscript not accepted (discussion: closed, 4 comments)
Short summary
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We use 90,000 dust point source observations (DPS), identified in satellite imagery across 9 global dryland environments to develop a novel dust emission model performance assessment. We evaluate the albedo-based dust emission model (AEM), which agrees with dust emission observations, or lack of emission 71 % of the time. Modelled dust occurs 27 % of the time with no observation, caused mostly by the incorrect assumption of infinite sediment supply and lack of dynamic dust entrainment thresholds.
07 Jan 2022
Adaptive time step algorithms for the simulation of marine ecosystem models using the transport matrix method implementation Metos3D (v0.5.0)
Markus Pfeil and Thomas Slawig
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-392, https://doi.org/10.5194/gmd-2021-392, 2022
Revised manuscript not accepted (discussion: closed, 7 comments)
Short summary
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In investigating the global carbon cycle, shortening the runtime of the simulation of marine ecosystem models is an important issue. We present methods that automatically adjust the time step during the simulation of a steady state using transport matrices. They apply always the time step as large as possible. Two methods reduced the runtime significantly, depending on the complexity of the model. An important property was that small negative concentrations were ignored during the spin-up.
17 Nov 2021
Nonparametric-based estimation method for river cross-sections with point cloud data from UAV photography URiver-X version 1.0 -methodology development
Taesam Lee and Kiyoung Sung
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-309, https://doi.org/10.5194/gmd-2021-309, 2021
Revised manuscript not accepted (discussion: closed, 4 comments)
Short summary
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A nonparametric-based estimation technique, called the K-nearest neighbor local linear regression (KLR) model, was proposed in the current study to demarcate the cross-section of a river with a point cloud dataset from UAV photogrammetry. The results indicate that the proposed KLR model can be a suitable alternative by reproducing the critical characteristics of natural and manmade channels, including abrupt changes and small bumps, as well as the overall trapezoidal shape.
04 Nov 2021
Weaknesses in dust emission modelling hidden by tuning to dust in the atmosphere
Adrian Chappell, Nicholas Webb, Mark Hennen, Charles Zender, Philippe Ciais, Kerstin Schepanski, Brandon Edwards, Nancy Ziegler, Sandra Jones, Yves Balkanski, Daniel Tong, John Leys, Stephan Heidenreich, Robert Hynes, David Fuchs, Zhenzhong Zeng, Marie Ekström, Matthew Baddock, Jeffrey Lee, and Tarek Kandakji
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-337, https://doi.org/10.5194/gmd-2021-337, 2021
Revised manuscript not accepted (discussion: closed, 6 comments)
Short summary
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Dust emissions influence global climate while simultaneously reducing the productive potential and resilience of landscapes to climate stressors, together impacting food security and human health. Our results indicate that tuning dust emission models to dust in the atmosphere has hidden dust emission modelling weaknesses and its poor performance. Our new approach will reduce uncertainty and driven by prognostic albedo improve Earth System Models of aerosol effects on future environmental change.
22 Oct 2021
CP-DSL: Supporting Configuration and Parametrization of Ocean Models with UVic (2.9) and MITgcm (67w)
Reiner Jung, Sven Gundlach, and Wilhelm Hasselbring
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-311, https://doi.org/10.5194/gmd-2021-311, 2021
Revised manuscript not accepted (discussion: closed, 5 comments)
Short summary
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We present CP-DSL, a domain-specific language with a focus on configuration and parametrization of ocean models, which was so far not supported by domain-specific-languages. CP-DSL is designed to be model agnostic and provides a unified interface to different ocean models. We report on the DSL design, implementation, and the evaluation with scientists and research software engineers. The implementation of CP-DSL is available as open source software and a replication package is provided.
04 Oct 2021
LAPS v1.0.0: Lagrangian Advection of Particles at Sea, a Matlab program to simulate the displacement of particles in the ocean
Maxime Mouyen, Romain Plateaux, Alexander Kunz, Philippe Steer, and Laurent Longuevergne
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-233, https://doi.org/10.5194/gmd-2021-233, 2021
Preprint withdrawn (discussion: closed, 2 comments)
Short summary
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LAPS is an easy to use Matlab code that allows simulating the transport of particles in the ocean without any programming requirement. The simulation is based on publicly available ocean current velocity fields and allows to output particles spatial distribution and trajectories at time intervals defined by the user. After explaining how LAPS is working, we show a few examples of applications for studying sediment transport or plastic littering. The code is available on Github.
23 Sep 2021
CycloneDetector (v1.0) – Algorithm for detecting cyclone and anticyclone centers from mean sea level pressure layer
Martin Prantl, Michal Žák, and David Prantl
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-266, https://doi.org/10.5194/gmd-2021-266, 2021
Publication in GMD not foreseen (discussion: closed, 3 comments)
Short summary
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The purpose of our paper is to show our experiences with a new algorithm for detecting of pressure centers based only on mean sea level pressure. While other methods usually focus only on the detection of cyclones, our approach is suitable for finding anticyclones centers as well. Our method is easy to implement with only a few parameters and is based only on standard image processing algorithms. When compared to the manual analysis provided by Met Office, the agreement is around 85 % to 90 %.
01 Sep 2021
A quantitative decoupling analysis (QDA v1.0) method for the assessment of meteorological, emission and chemical contributions to fine particulate pollution
Junhua Wang, Baozhu Ge, Xueshun Chen, Jie Li, Keding Lu, Yayuan Dong, Lei Kong, Zifa Wang, and Yuanhang Zhang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-259, https://doi.org/10.5194/gmd-2021-259, 2021
Revised manuscript not accepted (discussion: closed, 5 comments)
Short summary
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This paper developed a novel quantitative decoupling analysis (QDA) method to quantify the contributions of emission, meteorology, chemical reaction, and their nonlinear interactions on PM2.5 and applied it to a pollution episode in Beijing. This method can provides the researchers and policy makers with valuable information for understanding of key factors to heavy pollution, but also help the modelers to find out the sources of uncertainties among numerical models.
21 Jul 2021
Modeling perennial bioenergy crops in the E3SM land model
Eva Sinha, Kate Calvin, Ben Bond-Lamberty, Beth Drewniak, Dan Ricciuto, Khachik Sargsyan, Yanyan Cheng, Carl Bernacchi, and Caitlin Moore
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-244, https://doi.org/10.5194/gmd-2021-244, 2021
Preprint withdrawn (discussion: closed, 5 comments)
Short summary
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Perennial bioenergy crops are not well represented in global land models, despite projected increase in their production. Our study expands Energy Exascale Earth System Model (E3SM) Land Model (ELM) to include perennial bioenergy crops and calibrates the model for miscanthus and switchgrass. The calibrated model captures the seasonality and magnitude of carbon and energy fluxes. This study provides the foundation for future research examining the impact of perennial bioenergy crop expansion.
20 Jul 2021
An improved carbon greenhouse gas simulation in GEOS-Chem version 12.1.1
Beata Bukosa, Jenny Fisher, Nicholas Deutscher, and Dylan Jones
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-173, https://doi.org/10.5194/gmd-2021-173, 2021
Revised manuscript not accepted (discussion: closed, 6 comments)
Short summary
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Human activities led to rising levels of greenhouse gases (carbon dioxide (CO2), methane (CH4), carbon monoxide (CO)) in the atmosphere, threatening our future. We use models and measurements to predict and understand the climatological impact of these gases. Here, we describe a new simulation in the GEOS-Chem model that uses a more accurate method to simulate CO2, CH4 and CO, through their chemical dependence. Relative to the original simulations our results agree better with measurements.
19 Jul 2021
Quantifying Causal Contributions in Earth Systems by Normalized Information Flow
Chin-Hsien Cheng and Simon A. T. Redfern
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-196, https://doi.org/10.5194/gmd-2021-196, 2021
Revised manuscript not accepted (discussion: closed, 8 comments)
Short summary
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Causality is one of the foundations of scientific understanding and progress. Causality, being one of the foundations of scientific understanding and progress, continues to expand its application in various research disciplines in recent years. For Earth sciences, causation is important for evaluating, constraining, and improving climate models. Here, we explore, the conditions under which information flow works best for quantifying causality and explain why it is advantageous.
13 Jul 2021
Systematic global evaluation of accuracy of seasonal climateforecasts for monthly precipitation of JMA/MRI-CPS2 bycomparing with a statistical system using climate indices
Yuji Masutomi, Toshichika Iizumi, Key Oyoshi, Nobuyuki Kayaba, Wonsik Kim, Takahiro Takimoto, and Yoshimitsu Masaki
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-131, https://doi.org/10.5194/gmd-2021-131, 2021
Revised manuscript not accepted (discussion: closed, 6 comments)
Short summary
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The accuracy of seasonal climate forecasts for monthly precipitation of JMA/MRI-CPS2, a dynamical seasonal climate forecast (SCF) system, is higher than that of statistical SCF (St-SCF) system using climate indices around the equator (10° S–10° N) even for six-month lead forecasts. On a global scale, the forecast accuracy of JMA/MRI-CPS2 is higher for one-month lead forecasts; however, St-SCFs were more accurate for forecasts more than two months in advance.
09 Jul 2021
A Norwegian Approach to Downscaling
Rasmus E. Benestad
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-176, https://doi.org/10.5194/gmd-2021-176, 2021
Revised manuscript not accepted (discussion: closed, 4 comments)
Short summary
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A Norwegian approach for deriving regional climate information through downscaling is presented. It is unique and involves a different set to techniques compared to the wider community but give more robust results. We estimate the statistical properties of daily temperature and precipitation and the results are based on large sets of simulations with global climate models.
02 Jul 2021
A Parquet Cube alternative to store gridded data for data analytics and modeling
Jean-Michel Zigna, Reda Semlal, Flavien Gouillon, Ethan Davis, Elisabeth Lambert, Frédéric Briol, Romain Prod-Homme, Sean Arms, and Lionel Zawadzki
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-138, https://doi.org/10.5194/gmd-2021-138, 2021
Preprint withdrawn (discussion: closed, 5 comments)
Short summary
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The Parquet Cube storage alternative presented here is compared with Pangeo and THREDDS platforms to access to gridded data for large scale processing and modeling. Stressing the 3 implementations through 3 data scientists' scenarii, this Parquet Cube Alternative appears to be a good candidate to share gridded data in a cloud environment and share them through different communities of users. This open source alternative can be enriched by additional services to subset, enrich or explore data.
02 Jun 2021
Sensitivity analysis of a data-driven model of ocean temperature
Rachel Furner, Peter Haynes, Dave Munday, Brooks Paige, Daniel C. Jones, and Emily Shuckburgh
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-132, https://doi.org/10.5194/gmd-2021-132, 2021
Revised manuscript not accepted (discussion: closed, 9 comments)
Short summary
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Traditional weather & climate models are built from physics-based equations, while data-driven models are built from patterns found in datasets using Machine Learning or statistics. There is growing interest in using data-driven models for weather & climate prediction, but confidence in their use depends on understanding the patterns they're finding. We look at this with a simple regression model of ocean temperature and see the patterns found by the regression model are similar to the physics.
17 May 2021
Particle dry deposition algorithms in CMAQ version 5.3: characterization of critical parameters and land use dependence using DepoBoxTool version 1.0
Qian Shu, Benjamin Murphy, Jonathan E. Pleim, Donna Schwede, Barron H. Henderson, Havala O.T. Pye, Keith Wyat Appel, Tanvir R. Khan, and Judith A. Perlinger
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-129, https://doi.org/10.5194/gmd-2021-129, 2021
Preprint withdrawn (discussion: closed, 2 comments)
Short summary
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We have bridged the gap between dry deposition measurement and modeling by rigorous use of box and regional transport models and field measurements, but more efforts are needed. This study highlights that deviation among deposition schemes is most pronounced for small and large particles. This study better links model predictions to available real-world observations and incrementally reduces uncertainties in the magnitude of loss processes important for the lifecycle of air pollutants.
20 Apr 2021
Integrating Agricultural Practices into the TRIPLEX-GHG Model v2.0 for Simulating Global Cropland Nitrous Oxide Emissions: Model Development and Evaluation
Hanxiong Song, Changhui Peng, Kerou Zhang, and Qiuan Zhu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-23, https://doi.org/10.5194/gmd-2021-23, 2021
Revised manuscript not accepted (discussion: closed, 4 comments)
Short summary
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Cropland is the major hotspot for N2O emission as affected by multiple agricultural practices. Because of the varying magnitudes of N2O emissions across observation sites and periods, it is difficult to quantify the N2O budget at a large scale. A process-based biogeochemical model, TRIPLEX-GHG, was incorporated with major agricultural practices. By comparing the modeled and measured data, we found that the TRIPLEX-GHGv2.0 is capable to provide reasonable estimations of N2O flux from cropland.
29 Mar 2021
ArcticBeach v1.0: A physics-based parameterization of pan-Arctic coastline erosion
Rebecca Rolph, Pier Paul Overduin, Thomas Ravens, Hugues Lantuit, and Moritz Langer
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-28, https://doi.org/10.5194/gmd-2021-28, 2021
Revised manuscript not accepted (discussion: closed, 8 comments)
Short summary
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Declining sea ice, larger waves, and increasing air temperatures are contributing to a rapidly eroding Arctic coastline. We simulate water levels using wind speed and direction, which are used with wave height, wave period, and sea surface temperature to drive an erosion model of a partially frozen cliff and beach. This provides a first step to include Arctic erosion in larger-scale earth system models. Simulated cumulative retreat rates agree within the same order of magnitude as observations.
17 Mar 2021
A Twenty-Year Analysis of Winds in California for Offshore Wind
Energy Production Using WRF v4.1.2
Alex Rybchuk, Mike Optis, Julie K. Lundquist, Michael Rossol, and Walt Musial
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-50, https://doi.org/10.5194/gmd-2021-50, 2021
Preprint withdrawn (discussion: closed, 4 comments)
Short summary
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We characterize the wind resource off the coast of California by conducting simulations with the Weather Research and Forecasting (WRF) model between 2000 and 2019. We compare newly simulated winds to those from the WIND Toolkit. The newly simulated winds are substantially stronger, particularly in the late summer. We also conduct a refined analysis at three areas that are being considered for commercial development, finding that stronger winds translates to substantially more power here.
15 Mar 2021
The multiple linear regression modelling algorithm ABSOLUT v1.0 for weather-based crop yield prediction and its application to Germany at district level
Tobias Conradt
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-21, https://doi.org/10.5194/gmd-2021-21, 2021
Revised manuscript not accepted (discussion: closed, 5 comments)
Short summary
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Crop yields usually depend on weather and climate. It is possible to predict yields solely based on meteorological observations, and future yield scenarios may be calculated from climate scenarios. The ABSOLUT algorithm uses regionally distributed data to auto-adapt to the individual weather-yield relations of a certain crop in its application domain. It is presented with an example for Germany where more than 75 % of the national yield variations of major crops can be explained.
08 Mar 2021
AstroGeoVis v1.0: Astronomical Visualizations and Scientific
Computing for Earth Science Education
Tihomir S. Kostadinov
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-12, https://doi.org/10.5194/gmd-2021-12, 2021
Preprint withdrawn (discussion: closed, 3 comments)
Short summary
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Here, I introduce and describe AstroGeoVis v1.0: open-source software that calculates the position of the Sun in the sky and produces astronomical visualizations relevant to the Earth and climate sciences. The code also calculates the amount of solar energy falling on a tilted flat solar panel. The code and the dynamically generated figures are intended for educational applications, in a wide variety of fields and levels; research use is also envisioned.
08 Feb 2021
Simulation study of a Squall line hailstorm using High-Resolution
GRAPES-Meso with a modified Double-Moment Microphysics
scheme
Zhe Li, Qijun Liu, Xiaomin Chen, Zhanshan Ma, Jiong Chen, and Yuan Jiang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-439, https://doi.org/10.5194/gmd-2020-439, 2021
Preprint withdrawn (discussion: closed, 3 comments)
Short summary
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Hailstorm is one of the severe disaster weathers for agricultural countries. Hail microphysics processes have been added in the double-moment microphysics scheme in the operational model GRAPES_Meso and a severe squall line hailstorm is simulated. Compared with the observation, simulation results can capture the basic character of this squall line hailstorm. Results imply the ability of high-resolution GRAPES_Meso on forecasting hailstorm.
28 Dec 2020
LPJmL-Med – Modelling the dynamics of the land-sea nutrient
transfer over the Mediterranean region–version 1: Model
description and evaluation
Mohamed Ayache, Alberte Bondeau, Rémi Pagès, Nicolas Barrier, Sebastian Ostberg, and Melika Baklouti
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-342, https://doi.org/10.5194/gmd-2020-342, 2020
Preprint withdrawn (discussion: closed, 6 comments)
Short summary
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Land forcing is reported as one of the major sources of uncertainty limiting the capacity of marine biogeochemical models. In this study, we present the first basin-wide simulation at 1/12° of water discharge as well as nitrate (NO3) and phosphate (PO4) release into the Mediterranean from basin-wide agriculture and urbanization, by using the agro-ecosystem model (LPJmL-Med). The model evaluation against observation data, and all implemented processes are described in detail in this manuscript.
22 Dec 2020
Incorporating 15N into the outputs of SMOKE version 4.6 as the emission
input dataset for CMAQ version 5.2.1 for assessing the role emission
sources plays in controlling the isotopic composition of NOx, NOy, and atmospheric nitrate
Huan Fang and Greg Michalski
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-322, https://doi.org/10.5194/gmd-2020-322, 2020
Publication in GMD not foreseen (discussion: closed, 4 comments)
Short summary
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A new emission input dataset that incorporates nitrogen isotopes has been developed to simulate isotope tracers in air pollution. The NOx emission from different sources simulated by Sparse Matrix Operator Kerner Emissions (SMOKE) were replicated using 15N. The dataset is able to predict δ15N variations in NOx that are similar to those observed in aerosol and gases in the troposphere.
16 Dec 2020
LARGE 0.2.0: 2D numerical modelling of geodynamic problems
Nicola Creati and Roberto Vidmar
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-372, https://doi.org/10.5194/gmd-2020-372, 2020
Preprint withdrawn (discussion: closed, 4 comments)
Short summary
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LARGE (Lithosphere AsthenospheRe Geodynamic Evolution) 0.2.0 is a 2D numerical geodynamic simulation software released under the MIT license. The code can operate on single and multiprocessors computer. LARGE has been written in Python, while most of simulation software are written in C or Fortran, since the language is easy to understand and write. The software provides a user friendly interface and can solve complex plate tectonics problems.
23 Nov 2020
LUCI-EntEx v1.0: A GIS-based algorithm to determine stream entry
and exit points at boundaries of any given shape
Bethanna Jackson, Rubianca Benavidez, Keith Miller, and Deborah Maxwell
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-261, https://doi.org/10.5194/gmd-2020-261, 2020
Publication in GMD not foreseen (discussion: closed, 4 comments)
Short summary
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There is an increasing will to preserve nature for its own sake and to protect its benefits for future generations. Various policies encourage more sustainable land management practices to protect rivers and lakes. Separating out broad scale from local impacts is difficult, but necessary for informed land management outcomes. We present tools automatically identifying flows of water, sediment and chemicals in and out of farms, forestry blocks, etc to enable smarter future management.
29 Oct 2020
The Effects of Ocean Surface Waves on Global Forecast
in CFS Modeling System v2.0
Ruizi Shi, Fanghua Xu, Li Liu, Zheng Fan, Hao Yu, Xiang Li, and Yunfei Zhang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-327, https://doi.org/10.5194/gmd-2020-327, 2020
Revised manuscript not accepted (discussion: closed, 7 comments)
Short summary
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To better understand the effects of surface waves, we developed a coupled global atmosphere-ocean-wave system. Processes of Langmuir circulations and sea surface momentum roughness were considered. Results from a series of 7-day forecasts show the Langmuir circulations can reduce the biases of warm sea surface temperature and shallow mixed layer in the Antarctic circumpolar current during austral summer. Whereas surface roughness enables improvements to overestimated 10-m wind and wave height.
28 Oct 2020
Evaluation and climate sensitivity of the PlaSim v.17 Earth System
Model coupled with ocean model components of different complexity
Michela Angeloni, Elisa Palazzi, and Jost von Hardenberg
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-245, https://doi.org/10.5194/gmd-2020-245, 2020
Preprint withdrawn (discussion: closed, 3 comments)
Short summary
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We compare the Planet Simulator, an Earth-system Model of Intermediate Complexity, using a 3D dynamical ocean, with two configurations using a simpler mixed-layer ocean. A tuning of oceanic parameters allows a reasonable mean climate in all cases. Model equilibrium climate sensitivity in abrupt CO2 concentration change experiments is found to be significantly affected by the sea-ice feedbacks and by the parameterization of meridional oceanic heat transport in the mixed-layer configurations.
22 Oct 2020
Strengths and weaknesses of three Machine Learning methods for
pCO2 interpolation
Jake Stamell, Rea R. Rustagi, Lucas Gloege, and Galen A. McKinley
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-311, https://doi.org/10.5194/gmd-2020-311, 2020
Revised manuscript not accepted (discussion: closed, 4 comments)
Short summary
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Using simulated surface ocean pCO2 from Earth System Models, we test three Machine Learning methods (neural network, XGBoost, random forest) to discern their ability to reconstruct global coverage from sparse observations. Synthetic data means we can train based on real-world sampling patterns and then evaluate against the known full coverage result of the original simulation. ML approaches perform best in the open ocean, but struggle in regions of low sampling. XGBoost saw the best performance.
22 Oct 2020
Parallelizing a serial code: open–source module, EZ Parallel 1.0,
and geophysics examples
Jason Louis Turner and Samuel N. Stechmann
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-257, https://doi.org/10.5194/gmd-2020-257, 2020
Revised manuscript not accepted (discussion: closed, 6 comments)
Short summary
Short summary
EZ Parallel is a Fortran Message Passing Interface library designed to allow users to easily and quickly turn their serial code into a parallel one for the purpose of obtaining simulations with higher resolutions or larger domain sizes in a shorter amount of time. In tests of the parallelized code, the strong scaling efficiency for the finite difference code is seen to be roughly 80% to 90%, which is achieved by adding roughly only 10 new lines to the serial code.
12 Oct 2020
Using a single column model (SGRIST1.0) for connecting model physics and dynamics in the Global-to-Regional Integrated forecast SysTem (GRIST-A20.8)
Xiaohan Li, Yi Zhang, Xindong Peng, and Jian Li
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-254, https://doi.org/10.5194/gmd-2020-254, 2020
Revised manuscript not accepted (discussion: closed, 4 comments)
Short summary
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This study develops a single-column model (SGRIST1.0) to bridge the coupling of physical parameterizations and a new unstructured-mesh modeling system. The physical parameterization suite is first isolated and evaluated via SGRIST1.0 to reduce the uncertainty of physics during transfer, then the validated parameterization suite is coupled to the 3D dynamical framework. The transferred package shows reasonable behavior in the full physics-dynamics interaction.
29 Sep 2020
Development and performance optimization of a parallel
computing infrastructure for an unstructured-mesh
modelling framework
Zhuang Liu, Yi Zhang, Xiaomeng Huang, Jian Li, Dong Wang, Mingqing Wang, and Xing Huang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-158, https://doi.org/10.5194/gmd-2020-158, 2020
Revised manuscript not accepted (discussion: closed, 6 comments)
Short summary
Short summary
This paper describes several techniques for the parallelization and performance optimization of
an unstructured-mesh global atmospheric model. The purpose of this research is to facilitate the rapid iterative model development. These techniques are general and can be used for other parallel modeling on unstructured meshes.
11 Sep 2020
Deep-learning based climate downscaling using the super-resolution method: a case study over the western US
Xingying Huang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-214, https://doi.org/10.5194/gmd-2020-214, 2020
Preprint withdrawn (discussion: closed, 2 comments)
02 Sep 2020
Evaluating the use of Facebook's Prophet model v0.6 in forecasting
concentrations of NO2 at single sites across the UK and in response
to the COVID-19 lockdown in Manchester, England
David Topping, David Watts, Hugh Coe, James Evans, Thomas J. Bannan, Douglas Lowe, Caroline Jay, and Jonathan W. Taylor
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-270, https://doi.org/10.5194/gmd-2020-270, 2020
Publication in GMD not foreseen (discussion: closed, 4 comments)
Short summary
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Time-series forecasting methods have often been used to mitigate some of the challenges associated with deploying chemical transport models. In this study we deploy and evaluate Facebook’s Prophetmodel v0.6 in predicting hourly concentrations of Nitrogen Dioxide [NO2]. et. Overall we find the Prophet model offers a relatively effective and simple way to make predictions about NO2 at local levels.
13 Aug 2020
Snowpack and firn densification in the Energy Exascale Earth
System Model (E3SM) (version 1.2)
Adam M. Schneider, Charles S. Zender, and Stephen F. Price
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-247, https://doi.org/10.5194/gmd-2020-247, 2020
Preprint withdrawn (discussion: closed, 6 comments)
Short summary
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We enhance the Energy Exascale Earth System Model's land
component (ELM) to better represent multi-year snow (firn) on ice sheets. Our
developments reveal ELM deficiencies regarding firn density, a fundamental
property in glaciology. To improve firn density profiles, we fine tune
ELM's snowpack parameters using statistical modeling. Our findings demonstrate
how ELM can simulate both seasonal snow and firn on ice sheets and advance a
broader effort to better predict sea level rise.
05 Aug 2020
System identification techniques for detection of teleconnections within climate models
Bethany Sutherland, Ben Kravitz, Philip J. Rasch, and Hailong Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-228, https://doi.org/10.5194/gmd-2020-228, 2020
Preprint withdrawn (discussion: closed, 4 comments)
Short summary
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Through a cascade of physical mechanisms, a change in one location can trigger a response in a different location. These responses and the mechanisms that cause them are difficult to detect. Here we propose a method, using global climate models, to detect possible relationships between changes in one region and responses throughout the globe caused by that change. A change in the Pacific ocean is used as a test case to determine the effectiveness of the method.
29 Jul 2020
Evaluation of asymmetric Oxygen Minimum Zones in the tropical
Pacific: a basin-scale OGCM-DMEC V1.0
Kai Wang, Xiujun Wang, Raghu Murtugudde, Dongxiao Zhang, and Rong-Hua Zhang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-105, https://doi.org/10.5194/gmd-2020-105, 2020
Publication in GMD not foreseen (discussion: closed, 5 comments)
Short summary
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We improve and evaluate a basin-scale model’s ability to simulate spatial distribution of mid-depth oxygen in the tropical Pacific that holds the world’s two largest Oxygen Minimum Zones (OMZs). We find that low oxygen levels in the mid-ocean are largely due to extremely weak physical mixing, but the asymmetric OMZs (i.e., larger OMZ to the north) are attributable to both physical and biological processes, i.e., weaker physical supply over 200-600 m and higher biological consumption below 600 m.
27 Jul 2020
Quasi-hydrostatic equations for climate models and the study on linear instability
Robert Nigmatulin and Xiulin Xu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-146, https://doi.org/10.5194/gmd-2020-146, 2020
Revised manuscript not accepted (discussion: closed, 27 comments)
Short summary
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We develop a 3-dimensional quasi-hydrostatic system of equations with an accurately estimated vertical velocity. Moreover, we focus on the problem of predictability of such a system of equations and the influence of different vertical velocity evaluations. It shows that the wavelengths of perturbations significantly affect stability. Thus appropriate horizontal and vertical grid sizes should be chosen for modelling. Besides, we attempt to eliminate instability by introducing pseudo-viscosities.
24 Jul 2020
Simulating interactive ice sheets in the multi-resolution AWI-ESM 1.2: A case study using SCOPE 1.0
Paul Gierz, Lars Ackermann, Christian B. Rodehacke, Uta Krebs-Kanzow, Christian Stepanek, Dirk Barbi, and Gerrit Lohmann
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-159, https://doi.org/10.5194/gmd-2020-159, 2020
Publication in GMD not foreseen (discussion: closed, 3 comments)
Short summary
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In this study, we describe the SCOPE coupler, which is used connect the ECHAM6/JSBACH/FESOM1.4 climate model to the PISM 1.1.4 ice sheet model. This system is used to simulate IPCC scenarios projected for the future, and several warm periods in the past; the mid Holocene and the Last Interglacial. Our new model allows us to simulate the ice sheet’s response to changes in the climatic conditions, providing a new avenue of investigation over the previous models, which keep the cryosphere fixed.
13 Jul 2020
Combining homogeneous and heterogeneous chemistry to model inorganic compounds concentrations in indoor environments: the H2I model (v1.0)
Eve-Agnès Fiorentino, Henri Wortham, and Karine Sartelet
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-192, https://doi.org/10.5194/gmd-2020-192, 2020
Preprint withdrawn (discussion: closed, 1 comment)
15 May 2020
The impacts of uncertainties in emissions on aerosol data assimilation and short-term PM2.5 predictions in CMAQ v5.2.1 over East Asia
Sojin Lee, Chul Han Song, Kyung Man Han, Daven K. Henze, Kyunghwa Lee, Jinhyeok Yu, Jung-Hun Woo, Jia Jung, Yunsoo Choi, Pablo E. Saide, and Gregory R. Carmichael
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-116, https://doi.org/10.5194/gmd-2020-116, 2020
Revised manuscript not accepted (discussion: closed, 8 comments)
07 May 2020
Explainable AI for Knowledge Acquisition in Hydrochemical Time Series V1.0.0
Michael C. Thrun, Alfred Ultsch, and Lutz Breuer
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-87, https://doi.org/10.5194/gmd-2020-87, 2020
Revised manuscript not accepted (discussion: closed, 3 comments)
Short summary
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We propose an explainable AI (XAI) framework for times series describing water quality & environmental parameters. The relationship between parameters is investigated by swarm based cluster analysis designed to find similar days within & dissimilar days between clusters. Resulting clusters define three states of water bodies & are visualized by a topographic map of high-dimensional structures. Rules generated by the XAI system explain clusters & improve the understanding of aquatic environments.
04 May 2020
From R-squared to coefficient of model accuracy for assessing "goodness-of-fits"
Charles Onyutha
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-51, https://doi.org/10.5194/gmd-2020-51, 2020
Revised manuscript not accepted (discussion: closed, 9 comments)
Short summary
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R2 despite its wide use in assessing model performance has several drawbacks. While taking into account the drawbacks, this paper introduces another metric (coefficient of model accuracy MCA) which is capable of assessing "goodness-of-fits". Stepwise derivation of CMA comprises an analogy to the R2. Suitability of CMA for assessing model performance was demonstrated through comparison of simulations by hydrological models calibrated using CMA and other existing objective functions.
27 Apr 2020
TraceME (v1.0) – An online Traceability analysis system for Model Evaluation on land carbon dynamics
Jian Zhou, Jianyang Xia, Ning Wei, Yufu Liu, Chenyu Bian, Yuqi Bai, and Yiqi Luo
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-76, https://doi.org/10.5194/gmd-2020-76, 2020
Revised manuscript not accepted (discussion: closed, 4 comments)
Short summary
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The increase of model complexity and data volume challenges the evaluation of Earth system models (ESMs), which mainly stems from the untraceable, unautomatic, and high computational costs. Here, we built up an online Traceability analysis system for Model Evaluation (TraceME), which is traceable, automatic and shareable. The TraceME (v1.0) can trace the structural uncertainty of simulated carbon (C) storage in ESMs and provide some new implications for the next generation of model evaluation.
06 Apr 2020
Altered sub-seasonal predictability of Community Atmosphere Model 5 (CAM5) in CESM 1.2.1 by the choices of dynamical core
Ha-Rim Kim, Baek-Min Kim, Sang-Yoon Jun, and Yong-Sang Choi
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-22, https://doi.org/10.5194/gmd-2020-22, 2020
Preprint withdrawn (discussion: closed, 3 comments)
Short summary
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Focusing on the predictability issue closely, we compare the differences in the predictive skill of two different dynamical cores adopting the same physics. We find that the predictive skills of these two cores were significantly different, raising caution about the choice of dynamical cores in the predictability studies. We believe our study initiates a new issue regarding the identification of model uncertainties in the predictability studies.
26 Mar 2020
ConvectiveFoam1.0: development and benchmarking of a infinite-Pr number solver
Sara Lenzi, Matteo Cerminara, Mattia de' Michieli Vitturi, Tomaso Esposti Ongaro, and Antonello Provenzale
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-28, https://doi.org/10.5194/gmd-2020-28, 2020
Revised manuscript not accepted (discussion: closed, 6 comments)
11 Mar 2020
Evaluation of air quality forecasting system FORAIR_IT over Europe and Italy at high resolution for year 2017
Mario Adani, Guido Guarnieri, Lina Vitali, Luisella Ciancarella, Ilaria D'Elia, Mihaela Mircea, Maurizio Gualtieri, Andrea Cappelletti, Massimo D'Isidoro, Gino Briganti, Antonio Piersanti, Milena Stracquadanio, Gaia Righini, Felicita Russo, Giuseppe Cremona, Maria Gabriella Villani, and Gabriele Zanini
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-54, https://doi.org/10.5194/gmd-2020-54, 2020
Publication in GMD not foreseen (discussion: closed, 4 comments)
Short summary
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The National Air Quality forecasting system FORAIR_IT may be considered a state of the art model, and as far as we know it is the first forecasting system at high spatial resolution proposed at Italian National level. FORAIR_IT may be a useful tool that the policy makers might use in order to apply extraordinary procedure to prevent/mitigate high levels of air pollution. Moreover general population might take advantage of FORAIR_IT to get used to the complexity of air quality issues.
05 Mar 2020
Dynamic Complex Network Analysis of PM2.5 Concentrations in the UK using Hierarchical Directed Graphs (V1.0.0)
Parya Broomandi, Xueyu Geng, Weisi Guo, Jong Ryeol Kim, Alessio Pagani, and David Topping
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-342, https://doi.org/10.5194/gmd-2019-342, 2020
Revised manuscript not accepted (discussion: closed, 4 comments)
Short summary
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As a result of our novel graph-based reduced modeling, we are able to represent high-dimensional knowledge into a causal inference and stability framework.
24 Feb 2020
Intercomparison between the Integrated Urban land Model and the
Noah Urban Canopy Model
Chunlei Meng and Junxia Dou
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-298, https://doi.org/10.5194/gmd-2019-298, 2020
Revised manuscript not accepted (discussion: closed, 5 comments)
17 Feb 2020
UFlow 1.0: A Computer Model for Projections of Urban Sprawl
André Koscianski
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-317, https://doi.org/10.5194/gmd-2019-317, 2020
Preprint withdrawn (discussion: closed, 2 comments)
Short summary
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Urban sprawl is driven by an ensemble of forces and variables that compose a complex system, difficult to predict. This paper introduces the UFlow 1.0 simulator, based on a diffusion model and a Cellular Automata structure. A procedure adjusts a matrix of coefficients, making the model sensitive to local differences of growth speed. The software can also compute reverse predictions, and the paper indicates possible adaptations with different types of input data, metrics and algorithmic rules.
03 Feb 2020
Interaction of Small-Scale Gravity Waves with the Terdiurnal Solar
Tide in the Mesosphere and Lower Thermosphere
Friederike Lilienthal, Erdal Yiğit, Nadja Samtleben, and Christoph Jacobi
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-339, https://doi.org/10.5194/gmd-2019-339, 2020
Preprint withdrawn (discussion: closed, 8 comments)
Short summary
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Gravity waves are a small-scale but prominent dynamical feature in the Earth's atmosphere. Here, we use a mechanistic nonlinear general circulation model and implement a modern whole atmosphere gravity wave parameterization. We study the response of the atmosphere on several phase speed spectra. We find a large influence of fast travelling waves on the background dynamics in the thermosphere and also a strong dependence of the amplitude of the terdiurnal solar tide, indicating wave interactions.
27 Jan 2020
GIR v1.0.0: a generalised impulse-response model for climate uncertainty and future scenario exploration
Nicholas James Leach, Zebedee Nicholls, Stuart Jenkins, Christopher J. Smith, John Lynch, Michelle Cain, Bill Wu, Junichi Tsutsui, and Myles R. Allen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-379, https://doi.org/10.5194/gmd-2019-379, 2020
Revised manuscript not accepted (discussion: closed, 8 comments)
Short summary
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GIR is a simple climate model designed to make exploration of the impact of greenhouse gas and aerosol emissions on the climate easy and understandable for its users. It uses an intuitive input and output structure, and the model is itself a set of only six equations. This lends the model to applications such as teaching, or as a lowest common denominator model between groups in large-scale climate assessments. It could also be used to investigate more complex models through emulation.
24 Jan 2020
Atmospheric aging of small-scale wood combustion emissions (model MECHA 1.0) – is it possible to distinguish causal effects from non-causal associations?
Ville Leinonen, Petri Tiitta, Olli Sippula, Hendryk Czech, Ari Leskinen, Juha Karvanen, Sini Isokääntä, and Santtu Mikkonen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-13, https://doi.org/10.5194/gmd-2020-13, 2020
Revised manuscript not accepted (discussion: closed, 7 comments)
05 Nov 2019
Enhancement and validation of a state-of-the-art global hydrological model H08 (v.bio1) to simulate second-generation herbaceous bioenergy crop yield
Zhipin Ai, Naota Hanasaki, Vera Heck, Tomoko Hasegawa, and Shinichiro Fujimori
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-277, https://doi.org/10.5194/gmd-2019-277, 2019
Revised manuscript not accepted (discussion: closed, 6 comments)
Short summary
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Reliable bioenergy crop yield simulation remains a challenge at the global scale. Here, we enhanced a state-of-the-art global hydrological model to simulate bioenergy yield. We found that unconstrained irrigation more than doubled the yield under rainfed condition, while simultaneously reducing the water-use efficiency by 29 % globally. This is the first trial to use a global hydrological model to simulate the bioenergy crop and offers an effective tool to assess the bioenergy-water relations.
17 Sep 2019
CARBON-DISC 1.0 – A coupled, process-based model of global in-stream carbon biogeochemistry
Wim Joost van Hoek, Lauriane Vilmin, Arthur H. W. Beusen, José M. Mogollón, Xiaochen Liu, Joep J. Langeveld, Alexander F. Bouwman, and Jack J. Middelburg
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-205, https://doi.org/10.5194/gmd-2019-205, 2019
Revised manuscript not accepted (discussion: closed, 4 comments)
Short summary
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In this study we present CARBON-DISC 1.0. It couples the global water balance model PCR-GLOBWB with global carbon inputs from the Integrated Model to Assess the Global Environment (IMAGE) at a 0.5° resolution and calculates gridcell-to-gridcell transport, C transformations, C emissions, C burial and primary production on a monthly timestep and without calibration.
14 Aug 2019
Spatial and Temporal Evolution of a Lightning Diagnostic in HWRF (V3.7a)
Keren Rosado, Bin Liu, Vernon Morris, Vijay Tallapragada, and Lin Zhu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-139, https://doi.org/10.5194/gmd-2019-139, 2019
Publication in GMD not foreseen (discussion: closed, 3 comments)
Short summary
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The operational Hurricane Weather Research and Forecast (HWRF) model has been used to investigate the role of lightning diagnostics in the life cycle of tropical cyclones (TC). A lightning parameterization was implemented into HWRF with the motivation of using lightning forecast as a proxy for TC intensity changes. Results from this investigation show mixed results in terms of correlating lightning forecast and TC intensity forecast.
09 Aug 2019
MetSim v2.0.0: A flexible and extensible framework for the estimation and disaggregation of meteorological data
Andrew R. Bennett, Joseph J. Hamman, and Bart Nijssen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-179, https://doi.org/10.5194/gmd-2019-179, 2019
Preprint withdrawn (discussion: closed, 2 comments)
Short summary
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MetSim is a software package for simulating meteorologic processes, and aims to be applied in the environmental and Earth sciences. It can simulate processes such as solar and thermal radiation, specific humidity, and vapor pressure across large spatial areas in an efficient manner. This paper describes the software and analyzes it's ability to be used in large simulations. We describe how MetSim can be used and provide details on the various options that are available.
09 Jul 2019
A process-based Sphagnum plant-functional-type model for implementation in the TRIFFID Dynamic Global Vegetation Model
Richard Coppell, Emanuel Gloor, and Joseph Holden
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-51, https://doi.org/10.5194/gmd-2019-51, 2019
Publication in GMD not foreseen (discussion: closed, 4 comments)
Short summary
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(1) We developed a new Sphagnum model for ecosystem exchange. (2) The model is implemented in TRIFFID which is part of the JULES land surface model. (3) Outputs compare well to empirical field data. (4) JULES can now better incorporate peatland-climate feedbacks.
02 Jul 2019
Bolchem: an On-Line Coupled Mesoscale Chemistry Model
Rita Cesari, Alberto Maurizi, Massimo D'Isidoro, Tony Christian Landi, Mihaela Mircea, Felicita Russo, Piero Malguzzi, and Francesco Tampieri
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-124, https://doi.org/10.5194/gmd-2019-124, 2019
Publication in GMD not foreseen (discussion: closed, 4 comments)
Short summary
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This work presents the on-line coupled meteorology-chemistry transport model BOLCHEM. The paper describes the meteorological and chemical modules, and presents simulation results on the European domain for one year run. For all considered pollutants (O3, NO2, PM10, PM2.5) the model performances are close to those achieved by the current state-of-the-art model system dedicated to air quality study, e.g. Copernicus CAMS products.
21 May 2019
Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model
Julien Brajard, Alberto Carrassi, Marc Bocquet, and Laurent Bertino
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-136, https://doi.org/10.5194/gmd-2019-136, 2019
Revised manuscript not accepted (discussion: closed, 8 comments)
Short summary
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We explore the possibility of combining data assimilation with machine learning. We introduce a new hybrid method for a two-fold scope: (i) emulating hidden, possibly chaotic, dynamics and (ii) predicting its future states. Numerical experiments have been carried out using the chaotic Lorenz 96 model, proving both the convergence of the hybrid method and its statistical skills including short-term forecasting and emulation of the long-term dynamics.
13 May 2019
A dual-pass carbon cycle data assimilation system to estimate surface CO2 fluxes and 3D atmospheric CO2 concentrations from spaceborne measurements of atmospheric CO2
Rui Han and Xiangjun Tian
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-54, https://doi.org/10.5194/gmd-2019-54, 2019
Preprint withdrawn (discussion: closed, 6 comments)
Short summary
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This manuscript mainly introduce a new version of the carbon cycle data assimilation system Tan-Tracker (v1), which uses a novel dual-pass assimilation framework and based on an advanced assimilation algorithm NLS-4DVar. Tan-Tracker (v1) aims to find more accurate surface CO2 flux estimates based on satellite XCO2 observations. With a more accurate surface carbon flux, Tan-Tracker (v1) can provide a new perspective on carbon budget and become a better tool for carbon cycle research.
06 May 2019
Spatio-temporal consistent bias pattern detection on MIROC5 andFGOALS-g2
Bo Cao, Ying Zhao, and Ziheng Zhou
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-107, https://doi.org/10.5194/gmd-2019-107, 2019
Revised manuscript not accepted (discussion: closed, 6 comments)
Short summary
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We propose a method to detect spatio-temporal consistent bias patterns, which are present in contiguous space with significant and coherent biases in continuous time periods, from climate model outputs. These patterns are ideal for revealing regional and seasonal characteristics of biases and worth further investigation by modelers. Experiment results on both MIROC5 and FGOALS-g2 precipitation outputs show that the proposed approach can produce some important findings.
06 May 2019
Evaluation of Unified Model Rainfall Forecasts over the Western Ghats and North East states of India
Kuldeep Sharma, Sushant Kumar, Raghavendra Ashrit, Sean Milton, Ashis K. Mitra, and Ekkattil N. Rajagopal
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-65, https://doi.org/10.5194/gmd-2019-65, 2019
Preprint withdrawn (discussion: closed, 4 comments)
Short summary
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This study is based on the long record (2007–2018) of UM model's real time rainfall forecasts over India to highlight the improved skill of forecasts over orographic regions of India. Some of these improvements are attributed to the increased horizontal and vertical resolutions as well as improved physics parameterization schemes while major credit to the substantial improvements in weather forecasting goes to the sophisticated data assimilation systems which utilizes satellite data.
13 Mar 2019
A reduced-order Kalman smoother for (paleo-)ocean state estimation: assessment and application to the LGM
Charlotte Breitkreuz, André Paul, Stefan Mulitza, Javier García-Pintado, and Michael Schulz
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-32, https://doi.org/10.5194/gmd-2019-32, 2019
Publication in GMD not foreseen (discussion: closed, 2 comments)
Short summary
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We present a technique for ocean state estimation based on the combination of a simple data assimilation method with a state reduction approach. The technique proves to be very efficient and successful in reducing the model-data misfit and reconstructing a target ocean circulation from synthetic observations. In an application to Last Glacial Maximum proxy data the model-data misfit is greatly reduced but some misfit remains. Two different ocean states are found with similar model-data misfit.
28 Feb 2019
Physical-biogeochemical regional ocean model uncertainties stemming from stochastic parameterizations and potential impact on data assimilation
Vassilios D. Vervatis, Pierre De Mey-Frémaux, Nadia Ayoub, Sarantis Sofianos, Charles-Emmanuel Testut, Marios Kailas, John Karagiorgos, and Malek Ghantous
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-31, https://doi.org/10.5194/gmd-2019-31, 2019
Revised manuscript not accepted (discussion: closed, 4 comments)
Short summary
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Our contributions were specifically targeted at the generation of ensembles, in particular (but not solely) for high-resolution ocean configurations including regional and coastal physics and biogeochemistry. The most important paradigm of this work was to adopt a balanced approach building ocean biogeochemical model ensembles and testing their relevance against observational networks monitoring upper-ocean properties, in the sense of nonzero joint probabilities.
04 Feb 2019
Semantic Description and Complete Computer Characterization of Structural Geological Models
Xianglin Zhan, Jiandong Liang, Cai Lu, and Guangmin Hu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-305, https://doi.org/10.5194/gmd-2018-305, 2019
Revised manuscript not accepted (discussion: closed, 4 comments)
Short summary
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We proposed the semantic descriptions for structural geological models in order to facilitate computer based processing of geological semantics. The semantic description is a complete representation of the structural model. And we use the multi-level heterogeneous network to be the computer characterization of the semantic description. Semantic descriptions can also be used to constrain structure modeling which forms a top-down modeling process. We validated the effectiveness with actual data.
01 Feb 2019
Mass-conserving coupling of total column CO2 (XCO2) from global to mesoscale models: Case study with CMS-Flux inversion system and WRF-Chem (v3.6.1)
Martha P. Butler, Thomas Lauvaux, Sha Feng, Junjie Liu, Kevin W. Bowman, and Kenneth J. Davis
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-342, https://doi.org/10.5194/gmd-2018-342, 2019
Revised manuscript not accepted (discussion: closed, 4 comments)
Short summary
Short summary
This paper describes a mass-conserving framework for computing time-varying lateral boundary conditions from global model carbon dioxide concentrations for introduction into the WRF-Chem regional model. The goal is to create a laboratory environment in which carbon dioxide transport uncertainties may be explored separately from inversion-derived flux uncertainties. The software is currently available on GitHub at https://github.com/psu-inversion/WRF_Boundary_Coupling.
29 Jan 2019
On fluctuating air-sea-interaction in local models: linear theory
Achim Wirth
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-300, https://doi.org/10.5194/gmd-2018-300, 2019
Revised manuscript not accepted (discussion: closed, 5 comments)
Short summary
Short summary
The dynamics of three local linear models of air-sea-interaction commonly employed in climate or ocean simulations is compared. The models differ by whether or not the ocean velocity is included in the shear calculation applied to the ocean and the atmosphere. Analytic calculations for the models with deterministic and random forcing (white and colored) are presented.The fluctuation-dissipation-relation, the fluctuation-dissipation-theorem and the fluctuation-theorem is discussed.
09 Jan 2019
Assimilation of SCATSAR Soil Wetness Index in SURFEX 8.0 to
improve weather forecasts
Stefan Schneider and Bernhard Bauer-Marschallinger
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-273, https://doi.org/10.5194/gmd-2018-273, 2019
Publication in GMD not foreseen (discussion: closed, 4 comments)
Short summary
Short summary
This paper investigates the question if satellite-measured soil moisture data are useful to improve weather forecasts. To answer this question, historical forecasts are re-computed with and without this additional data source and compared against measurements from weather stations. This test shows an positive impact of using soil moisture data which indicates that they should be used operationally in regional weather forecast models.
19 Dec 2018
Chemistry and deposition in the Model of Atmospheric composition at Global and Regional scales using Inversion Techniques for Trace gas Emissions (MAGRITTE v1.0). Part B. Dry deposition
Jean-François Müller, Trissevgeni Stavrakou, Maite Bauwens, Steven Compernolle, and Jozef Peeters
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-317, https://doi.org/10.5194/gmd-2018-317, 2018
Publication in GMD not foreseen (discussion: closed, 2 comments)
Short summary
Short summary
A new dry deposition model for gaseous species is presented. It relies on the species reactivity and water-solubility, for which a new prediction method is also presented. The deposition model parameters are adjusted based on comparisons with field data for ozone and organic compounds at numerous sites. The importance of dry deposition as a sink of oxygenated organic compounds and nitrogen oxides is demonstrated by global model simulations with the new deposition scheme.
10 Dec 2018
Optimization of the WRFV3.7 adjoint model
Qiang Cheng, Juanjuan Liu, and Bin Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-310, https://doi.org/10.5194/gmd-2018-310, 2018
Revised manuscript not accepted (discussion: closed, 5 comments)
Short summary
Short summary
Adjoint models are usually used to improve the weather forecast, but It's very time consuming. What we would like to do is determining how to significantly reduce the running cost of the adjoint model.The manuscript presented several methods. With them, we reduced the adjoint cost of the Weather Research and Forecasting plus (WRFPLUSV3.7) by almost half. Apparently, these are also productive in other applications in terms of adjoint model such as parameter estimation, singular vector etc.
30 Nov 2018
A Conceptual Framework for Integration Development of GSFLOW Model: Concerns and Issues Identified and Addressed for Model Development Efficiency
Chao Chen, Sajjad Ahmad, and Ajay Kalra
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-268, https://doi.org/10.5194/gmd-2018-268, 2018
Publication in GMD not foreseen (discussion: closed, 4 comments)
Short summary
Short summary
This study proposed a conceptual framework for development of integrated surface and groundwater flow model, GSFLOW. Study provides guidance on addressing common challenges in the model development, i.e., model conceptualization, data exchange, model calibration, and sensitivity analysis. An application of the framework demonstrated that both model development efficiency and hydrologic characterization improved. The proposed framework can be useful for other similar modeling efforts.
30 Nov 2018
SBDM v1.0: A scaling-based discretization method for the Geographical Detector Model
Xiaoyu Meng, Xin Gao, Shengyu Li, Wenjing Huang, and Jiaqiang Lei
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-274, https://doi.org/10.5194/gmd-2018-274, 2018
Preprint withdrawn (discussion: closed, 0 comments)
22 Nov 2018
Use an idealized protocol to assess the nesting procedure in regional climate
modelling
Shan Li, Laurent Li, and Hervé Le Treut
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-257, https://doi.org/10.5194/gmd-2018-257, 2018
Preprint withdrawn (discussion: closed, 7 comments)
Short summary
Short summary
Newtonian relaxation allowing RCM (regional climate model) to follow GCM (global climate model) is a widely-used technique for climate downscaling and regional weather forecasting. It is thoroughly assessed in an idealized framework for both synoptic variability and long-term mean climate. LMDz is a GCM, but it can be configured as a RCM. It thus acts as both GCM and RCM. The experimental set-up “Master versus Slave” considers GCM as the reference to assess behaviors of RCM.
22 Nov 2018
HIRHAM–NAOSIM 2.0: The upgraded version of the coupled regional atmosphere-ocean-sea ice model for Arctic climate studies
Wolfgang Dorn, Annette Rinke, Cornelia Köberle, Klaus Dethloff, and Rüdiger Gerdes
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-278, https://doi.org/10.5194/gmd-2018-278, 2018
Revised manuscript not accepted (discussion: closed, 6 comments)
Short summary
Short summary
A new version of the coupled Arctic climate model HIRHAM-NAOSIM has been designed to study interactions between atmosphere, sea ice, and ocean in the Arctic. This version utilizes upgraded, high-resolution model components and a revised coupling procedure. Simulations with the new version reveal that Arctic sea ice is thicker in all seasons and closer to observations than in the previous version. Wintertime biases in sea-ice extent and near-surface air temperatures are reduced as well.
02 Nov 2018
Model evaluation by a cloud classification based on multi-sensor
observations
Akio Hansen, Felix Ament, Verena Grützun, and Andrea Lammert
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-259, https://doi.org/10.5194/gmd-2018-259, 2018
Publication in GMD not foreseen (discussion: closed, 5 comments)
Short summary
Short summary
Clouds are responsible for large uncertainties in atmospheric models, whereby the evaluation is very challenging due to their complexity. The Cloudnet project uses multi-sensor observations to create a comprehensive Target Classification showing the cloud structure and phase, but there is no comparable model output available. The presented cloud classification algorithm generates a consistent product, which provides a comprehensive view on clouds and is used for further in-depth evaluation.
11 Oct 2018
Common metrics of calibration for continuous Gaussian data and
exceedance probabilities
Rita Glowienka-Hense, Andreas Hense, Thomas Spangehl, and Marc Schröder
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-141, https://doi.org/10.5194/gmd-2018-141, 2018
Revised manuscript not accepted (discussion: closed, 4 comments)
Short summary
Short summary
Ensemble forecast verification treats the issues of forecast errors and uncertainty estimated from ensemble spread. We suggest measures based on relative entropy. For continuous variables correlation and the mean ratio of the ensemble spread to climate variance (analysis of variance (anova)) are related to these entropies. For categorical data corresponding scores are deduced that allow the comparison with continuous data.
05 Oct 2018
Observation-based implementation of ecophysiological processes for a rubber plant functional type in the community land model (CLM4.5-rubber_v1)
Ashehad A. Ali, Yuanchao Fan, Marife D. Corre, Martyna M. Kotowska, Evelyn Hassler, Fernando E. Moyano, Christian Stiegler, Alexander Röll, Ana Meijide, Andre Ringeler, Christoph Leuschner, Tania June, Suria Tarigan, Holger Kreft, Dirk Hölscher, Chonggang Xu, Charles D. Koven, Rosie Fisher, Edzo Veldkamp, and Alexander Knohl
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-236, https://doi.org/10.5194/gmd-2018-236, 2018
Revised manuscript not accepted (discussion: closed, 4 comments)
Short summary
Short summary
We used carbon-use and water-use related datasets of small-holder rubber plantations from Jambi province, Indonesia to develop and calibrate a rubber plant functional type for the Community Land Model (CLM-rubber). Increased sensitivity of stomata to soil water stress and enhanced respiration costs enabled the model to capture the magnitude of transpiration and leaf area index. Including temporal variations in leaf life span enabled the model to better capture the seasonality of leaf litterfall.
05 Sep 2018
Impact of model resolution on Holocene climate simulations of the
Northern Hemisphere
Axel Wagner, Gerrit Lohmann, and Matthias Prange
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-172, https://doi.org/10.5194/gmd-2018-172, 2018
Publication in GMD not foreseen (discussion: closed, 6 comments)
Short summary
Short summary
This study demonstrates the dependence of simulated surface air temperatures on variations in grid resolution and resolution-dependent orography in simulations of the Mid-Holocene. A set of Mid-Holocene sensitivity experiments is carried out. The simulated Mid-Holocene temperature differences (low versus high resolution) reveal a response that regionally exceeds the Mid-Holocene to preindustrial modelled temperature anomalies, and show partly reversed signs across the same geographical regions.
09 Jul 2018
Simulation Improvements of ECHAM5-NEMO3.6 and ECHAM6-NEMO3.6 Coupled Models Compared to MPI-ESM and the Corresponding Physical Mechanisms
Shu Gui, Ruowen Yang, and Jie Cao
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-130, https://doi.org/10.5194/gmd-2018-130, 2018
Revised manuscript not accepted (discussion: closed, 21 comments)
Short summary
Short summary
In this paper, two new coupled models have been developed, both of which show substantial improvements in the model simulation compared with the MPI-ESM model that is widely used in weather forecast and atmospheric research. Inter-model comparison suggests that cumulus convection and latent heat of evaporation over the sea surface are the two major factors that shape the model error of sea surface temperature. It implies a new vision of bias origin for coupled model development practices.
02 Jul 2018
Coupling Library Jcup3: Its philosophy and application
Takashi Arakawa, Takahiro Inoue, Hisashi Yashiro, and Masaki Satoh
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-147, https://doi.org/10.5194/gmd-2018-147, 2018
Preprint withdrawn (discussion: closed, 5 comments)
Short summary
Short summary
In this paper, we discussed the design concept and implementation of a coupling software Jcup. The design concept can be summarized as dividing the function of the software into changing and not changing the values of the data and enabling users to manage and implement the function of changing the value. Based upon this concept, Jcup is constructed so that 1) remapping table is utilized as input information and 2) interpolation calculation codes can be freely implemented by users.
25 Jun 2018
Bias correction of multi-ensemble simulations from the HAPPI model intercomparison project
Fahad Saeed, Ingo Bethke, Stefan Lange, Ludwig Lierhammer, Hideo Shiogama, Dáithí A. Stone, Tim Trautmann, and Carl-Friedrich Schleussner
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-107, https://doi.org/10.5194/gmd-2018-107, 2018
Revised manuscript has not been submitted (discussion: closed, 4 comments)
21 Jun 2018
NHM-Chem, the Japan MeteorologicalAgency's regional meteorology – chemistry model (v1.0): model description and aerosol representations
Mizuo Kajino, Makoto Deushi, Tsuyoshi Thomas Sekiyama, Naga Oshima, Keiya Yumimoto, Taichu Yasumichi Tanaka, Joseph Ching, Akihiro Hashimoto, Tetsuya Yamamoto, Masaaki Ikegami, Akane Kamada, Makoto Miyashita, Yayoi Inomata, Shin-ichiro Shima, Kouji Adachi, Yuji Zaizen, Yasuhito Igarashi, Hiromasa Ueda, Takashi Maki, and Masao Mikami
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-128, https://doi.org/10.5194/gmd-2018-128, 2018
Revised manuscript not accepted (discussion: closed, 4 comments)
06 Jun 2018
A simple weather generator for applications with limited data availability: TEmpotRain 1.0 for temperatures, extraterrestrial radiation, and potential evapotranspiration
Gerrit Huibert de Rooij
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-97, https://doi.org/10.5194/gmd-2018-97, 2018
Publication in GMD not foreseen (discussion: closed, 6 comments)
Short summary
Short summary
Areas that have few or no weather stations or are subject to climate change still need weather data in order to model the demand for water, the risk of floods and droughts, etc. TEmpotRain generates rainfall, daily temperature extremes, and daily potential evaporation (from the soil) / transpiration (by plants). The physical meaning of the model parameters is clear. This allows realistic values for them to be estimated, even for hypothetical (future) climates for which data are not available.
06 Jun 2018
MOVEIM v1.0: Development of a bottom-up motor vehicular emission inventories for the urban area of Manaus in central Amazon rainforest
Paulo R. Teixeira, Saulo R. de Freitas, Francis W. Correia, and Antonio O. Manzi
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-81, https://doi.org/10.5194/gmd-2018-81, 2018
Publication in GMD not foreseen (discussion: closed, 4 comments)
Short summary
Short summary
Emissions of gases and particulates in urban areas are associated with a mixture of various sources, both natural and anthropogenic. Understanding and quantifying these emissions is necessary in studies of climate change, local air pollution issues, and weather modification. This work will also contribute to improved air quality numerical simulations, provide more accurate scenarios for policymakers and regulatory agencies to develop strategies for controlling the vehicular emissions.
02 May 2018
Marine biogeochemical cycling and climate-carbon cycle feedback simulated by
the NUIST Earth System Model: NESM-2.0.1
Yifei Dai, Long Cao, and Bin Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-68, https://doi.org/10.5194/gmd-2018-68, 2018
Revised manuscript not accepted (discussion: closed, 5 comments)
Short summary
Short summary
NESM-2.0.1 is one of the few models from China that present the ocean carbon cycle simulations. Our results demonstrate that NESM-2.0.1 does a reasonable job in simulating current-day marine ecosystems and oceanic CO2 uptake. The model also can be used as a useful tool in the investigation of feedback interactions between the ocean carbon cycle, atmospheric CO2, and climate change.
14 Mar 2018
A new tool for model assessment in the frequency domain – Spectral
Taylor Diagram : application to a global ocean general
circulation model with tides
Mabel Costa Calim, Paulo Nobre, Peter Oke, Andreas Schiller, Leo San Pedro Siqueira, and Guilherme Pimenta Castelão
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-5, https://doi.org/10.5194/gmd-2018-5, 2018
Revised manuscript not accepted (discussion: closed, 6 comments)
Short summary
Short summary
A new tool inspired on tides is introduced. The Spectral Taylor Diagram designed for evaluating and monitoring models performance in frequency domain calculates the degree of correspondence between simulated and observed fields for a given frequency (or a band of frequencies). It's a powerful tool to detect co-oscillating patterns in multi scale analysis, without using filtering techniques.
24 Jan 2018
Development of the city-scale chemistry transport model CityChem-EPISODE and its application to the city of Hamburg
Matthias Karl
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-8, https://doi.org/10.5194/gmd-2018-8, 2018
Preprint retracted (discussion: closed, 3 comments)
Short summary
Short summary
Urban air pollution issues in Europe are mainly related to the human health impacts of particulate matter and ozone. A large part of the population living in cities is exposed to ozone above the European Union air quality target. The new model CityChem-EPISODE has been developed to perform chemistry/transport simulations of multiple reactive pollutants in urban areas. The application of the model in Hamburg (Germany) in 2012 shows good performance for ozone at air quality monitoring stations.
02 Jan 2018
The Climate Generator: Stochastic climate representation for
glacial cycle integration
Mohammad Hizbul Bahar Arif, Lev Tarasov, and Tristan Hauser
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-276, https://doi.org/10.5194/gmd-2017-276, 2018
Revised manuscript has not been submitted (discussion: closed, 4 comments)
Short summary
Short summary
This study is a first step answer to the following question: Can you
use emulators (machine learning techniques) to make the output of fast
simple climate models (a 2-D energy balance model in this test case)
indistinguishable from that of a much more computationally expensive
General Circulation climate model (GCM) within the uncertainties of
GCMs? Our preliminary test of this concept for large spatio-temporal
contexts gives a positive answer.
15 Dec 2017
Performance evaluation of ROMS v3.6 on a commercial cloud system
Kwangwoog Jung, Yang-Ki Cho, and Yong-Jin Tak
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-270, https://doi.org/10.5194/gmd-2017-270, 2017
Revised manuscript not accepted (discussion: closed, 4 comments)
Short summary
Short summary
The performance of the ROMS was evaluated on Amazon Web Services for various configurations. Our study shows how numerical ocean models can be constructed and parallelised in a commercial cloud computing environment and outlines how performance similar to local high-performance computing can be achieved in commercial cloud computing environments by optimising the modelling environment. Cloud computing can be a useful tool for those who have no available computing resource.
05 Dec 2017
A 1-Dimensional Ice-Pelagic-Benthic transport model (IPBM) v0.1:
Coupled simulation of ice, water column, and sediment
biogeochemistry
Shamil Yakubov, Philip Wallhead, Elizaveta Protsenko, and Evgeniy Yakushev
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-299, https://doi.org/10.5194/gmd-2017-299, 2017
Preprint withdrawn (discussion: closed, 4 comments)
Short summary
Short summary
Aquatic biogeochemical processes can strongly interact, especially in polar regions, with processes occurring in adjacent ice and sediment layers, yet there are few modelling tools to simulate these systems in a fully coupled manner. We have developed a 1D transport model that allows simultaneous simulation of the biogeochemistry of 3 different media: ice, water, and sediments. Description of transportation processes in ice, water, and sediments for both solutes and solids was provided.
20 Nov 2017
Methods of investigating forecast error sensitivity to ensemble size in
a limited-area convection-permitting ensemble
Ross Noel Bannister, Stefano Migliorini, Alison Clare Rudd, and Laura Hart Baker
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-260, https://doi.org/10.5194/gmd-2017-260, 2017
Revised manuscript has not been submitted (discussion: closed, 7 comments)
Short summary
Short summary
An ensemble of weather forecasts (i.e. multiple forecasts) contains useful information that a traditional single forecast does not have. Most existing forecast ensembles though have few members (ensemble too small), meaning that the information that they contain is noisy. This paper shows how more ensemble members can be generated from an existing (small) ensemble, and how the value added by the extra members can be assessed in a quantitative way.
13 Nov 2017
On Quadruplet Interactions for Ocean Surface Waves
Adhi Susilo, Will Perrie, and Bash Toulany
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-256, https://doi.org/10.5194/gmd-2017-256, 2017
Preprint retracted (discussion: closed, 6 comments)
Short summary
Short summary
Solving nonlinear wave-wave interactions with Boltzmann integral requires solving the domain of the integration correctly. While we are working on finding the loci of integration, we have an idea to find the loci with different way, an explicit way. The new method shows better results than the previous one and the algorithm is easy to follow.
10 Nov 2017
Adaptation of the meteorological model Meso-NH to laboratory
experiments: implementations and validation
Jeanne Colin, Christine Lac, Valéry Massion, and Alexandre Paci
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-226, https://doi.org/10.5194/gmd-2017-226, 2017
Preprint withdrawn (discussion: closed, 5 comments)
Short summary
Short summary
The meteorological model Meso-NH is adapted in order to be run in DNS mode (Direct Numerical Simulation) to represent atmospheric flows generated in laboratory. The implementations we performed are validated against exact solutions and experimental data. Thus, Meso-NH can now be used as a complement to laboratory experiments, to complete and/or extend the data. The ability to run it in DNS also brings new prospects as it offers a new framework to test parametrizations of fine-scale processes.
06 Nov 2017
Evaluating a fire smoke simulation algorithm in the National Air Quality
Forecast Capability (NAQFC) by using multiple observation data sets during the Southeast Nexus (SENEX) field campaign
Li Pan, Hyun Cheol Kim, Pius Lee, Rick Saylor, YouHua Tang, Daniel Tong, Barry Baker, Shobha Kondragunta, Chuanyu Xu, Mark G. Ruminski, Weiwei Chen, Jeff Mcqueen, and Ivanka Stajner
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-207, https://doi.org/10.5194/gmd-2017-207, 2017
Revised manuscript not accepted (discussion: closed, 5 comments)
Short summary
Short summary
In this study, a system accounting for fire emissions in a chemical transport model is described. The focus of this work is to qualitatively evaluate the system's capability to capture fire signals identified by multiple observation data sets. We discuss how to use observational data correctly to filter out fire signals and synergistic use of multiple data sets together. We also address the limitations of each of the observation data sets and of the evaluation methods.
16 Oct 2017
Development and calibration of a global hydrological model for integrated assessment modeling
Tingju Zhu, Petra Döll, Hannes Müller Schmied, Claudia Ringler, and Mark W. Rosegrant
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-216, https://doi.org/10.5194/gmd-2017-216, 2017
Revised manuscript has not been submitted (discussion: closed, 6 comments)
Short summary
Short summary
The global hydrological model IGHM was developed to simulate water availability over global land areas month by month. The simulated water availability is for analyzing irrigation water supply and crop production in a global water and food projections model, IMPACT. Water availability simulated by another global hydrological model, WGHM, was used to determine parameter values in IGHM. This paper describes the structure of IGHM, the method of its parameter determination, and its performance.
22 Sep 2017
The degree of freedom for signal assessment of measurement networks for joint chemical state and emission analysis
Xueran Wu, Hendrik Elbern, and Birgit Jacob
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-220, https://doi.org/10.5194/gmd-2017-220, 2017
Preprint withdrawn (discussion: closed, 9 comments)
Short summary
Short summary
It is novel that the tangent linear form of the atmospheric transport model was extended by emissions under the assumption that emissions preserve the invariant diurnal profiles. Base on the Kalman smoother, the degree of freedom for signal and several metrics is derived as the criterion to evaluate the potential improvement of model states. Besides, sensitivities of observations was formulated by seeking the fastest directions of the perturbation ratio between initial states and observations.
04 Aug 2017
Importance of the advection scheme for the simulation of water isotopes over Antarctica by general circulation models: a case study with LMDZ-iso (LMDZ5a revision 1750)
Alexandre Cauquoin and Camille Risi
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-178, https://doi.org/10.5194/gmd-2017-178, 2017
Revised manuscript not accepted (discussion: closed, 5 comments)
Short summary
Short summary
AGCMs are known to have a warm and isotopically enriched bias over Antarctica. We test here the hypothesis that these biases are consequences of a too diffusive advection. We show here that a good representation of the advection, especially on the horizontal, is very important to reduce the bias in the isotopic contents of precipitation above this area and to improve the modelled water isotopes – temperature relationship, essential when using GCMs for paleoclimate applications.
13 Jul 2017
A map of global peatland distribution created using machine
learning for use in terrestrial ecosystem and earth system models
Yuanqiao Wu, Ed Chan, Joe R. Melton, and Diana L. Verseghy
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-152, https://doi.org/10.5194/gmd-2017-152, 2017
Preprint withdrawn (discussion: closed, 6 comments)
Short summary
Short summary
Peatlands are an important component of the carbon cycle that is expected to change under climate change, but accurate information on the global distribution of peatlands is presently unavailable. We use a machine-learning method to create a map of global peatland extent suitable for use in an Earth system model. For areas where data exists we find excellent agreement with observations and our method has greater skill than solely using soil datasets to estimate peatland coverage.
27 Jun 2017
ShellTrace v1.0 – A new approach for modelling growth and trace element uptake in marine bivalve shells: Model verification on pacific oyster shells (Crassostrea gigas)
Niels J. de Winter
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-137, https://doi.org/10.5194/gmd-2017-137, 2017
Revised manuscript not accepted (discussion: closed, 3 comments)
Short summary
Short summary
Bivalves grow by expanding their shells incrementally and record environmental conditions in the chemistry of their carbonate. To reconstruct these conditions, it is important to constrain the growth and trace element uptake rates in these bivalve shells. The present study models the development and chemical composition of the shells of bivalves based on XRF mapping of shell cross sections and allows changes in trace element uptake rates to be interpreted to reconstruct palaeoenvironment.
23 May 2017
Correct boundary conditions for DNS models of
nonlinear acoustic-gravity waves forced by
atmospheric pressure variations
Yuliya Kurdyaeva, Sergey Kshevetskii, Nikolay Gavrilov, and Sergey Kulichkov
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-76, https://doi.org/10.5194/gmd-2017-76, 2017
Revised manuscript not accepted (discussion: closed, 6 comments)
Short summary
Short summary
Various meteorological phenomena generate acoustic-gravity waves in the atmosphere and cause wave variations of atmospheric pressure. There are networks of microbarographs, which record pressure variations on the Earth's surface. The hydrodynamic problem of propagation of waves in the atmosphere from pressure variations on the Earth's surface is formulated. The problem wellposedness is proved. A supercomputer program for simulation of waves from pressure variations is developed and applied.
17 May 2017
Studying the Impact of Radioactive Charging on the Microphysical Evolution and Transport of Radioactive Aerosols with the TOMAS-RC v1 framework
Petros Vasilakos, Yong-Ηa Kim, Jeffrey R. Pierce, Sotira Yiacoumi, Costas Tsouris, and Athanasios Nenes
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-96, https://doi.org/10.5194/gmd-2017-96, 2017
Revised manuscript not accepted (discussion: closed, 6 comments)
Short summary
Short summary
Radioactive charging can significantly impact the way radioactive aerosols behave, and as a result their lifetime, but such effects are neglected in predictive model studies of radioactive plumes. We extend a well-established model that simulates the evolution of atmospheric particulate matter to account for radioactive charging effects in an accurate and computationally efficient way. It is shown that radioactivity can strongly impact the deposition patterns of aerosol.
15 Mar 2017
Numerical simulations of glacier evolution performed using
flow-line models of varying complexity
Antonija Rimac, Sharon van Geffen, and Johannes Oerlemans
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-67, https://doi.org/10.5194/gmd-2017-67, 2017
Revised manuscript not accepted (discussion: closed, 6 comments)
Short summary
Short summary
The main aim of this paper is to use explicit glacier flow-line models of a different complexity to analyse the glacier length and volume evolution, and to disentangle climatic signals from geometric effects. We compare length and volume evolution of a synthetically designed glaciers simulated using Full-Stokes model based on Elmer/Ice code with the results obtained using SIA model.
13 Mar 2017
Neodymium isotopes in the ocean model of the Community Earth System Model (CESM1.3)
Sifan Gu, Zhengyu Liu, Alexandra Jahn, Johannes Rempfer, Jiaxu Zhang, and Fortunat Joos
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-40, https://doi.org/10.5194/gmd-2017-40, 2017
Revised manuscript not accepted (discussion: closed, 5 comments)
Short summary
Short summary
This paper is the documentation of the implementation of neodymium (Nd) isotopes in the ocean model of CESM. Our model can simulate both Nd concentration and Nd isotope ratio in good agreement with observations. Our Nd-enabled ocean model makes it possible for direct model-data comparison in paleoceanographic studies, which can help to resolve some uncertainties and controversies in our understanding of past ocean evolution. Therefore, our model provides a useful tool for paleoclimate studies.
09 Mar 2017
An Operational Thermodynamic-Dynamic Model for the Coastal Labrador Sea Ice Melt Season
Ian D. Turnbull and Rocky S. Taylor
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-39, https://doi.org/10.5194/gmd-2017-39, 2017
Preprint withdrawn (discussion: closed, 4 comments)
Short summary
Short summary
We developed a model to forecast the timing of the seasonal break-up of coastal Labrador land-fast ice in order to aid offshore operators in the region with their planning and decision-making process. The model additionally provides shorter-term (several days) ice drift forecasts for the operators. Our model can forecast the break-up of the land-fast ice at specific locations along the Labrador coast accurately to within hours to days when initialized up to a month in advance.
23 Nov 2016
The FuGas 2.1 framework for atmosphere-ocean coupling in
geoscientific models: improving estimates of the solubilities and
fluxes of greenhouse gases and aerosols
Vasco M. N. C. S. Vieira, Pavel Jurus, Emanuela Clementi, Heidi Pettersson, and Marcos Mateus
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-273, https://doi.org/10.5194/gmd-2016-273, 2016
Revised manuscript has not been submitted (discussion: closed, 7 comments)
16 Nov 2016
An intercomparison of Large-Eddy Simulations of the Martian daytime convective boundary layer
Tanguy Bertrand, Aymeric Spiga, Scot Rafkin, Arnaud Colaitis, François Forget, and Ehouarn Millour
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-241, https://doi.org/10.5194/gmd-2016-241, 2016
Revised manuscript not accepted (discussion: closed, 4 comments)
Short summary
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We compare the results of two numerical models which simulate the Martian atmospheric turbulence in the first km above the surface, using for both similar forcings. This intercomparison is a fruitful way to evaluate the models' predictions and to indicate possible areas of improvement, thus preparing for future martian missions. Although the model predict similar evolution of the turbulence in the lower atmosphere, the intensity of the processes differ by a factor of 1.5–2.
16 Nov 2016
A multi-level canopy radiative transfer scheme for ORCHIDEE
(SVN r2566), based on a domain-averaged structure factor
Matthew J. McGrath, James Ryder, Bernard Pinty, Juliane Otto, Kim Naudts, Aude Valade, Yiying Chen, James Weedon, and Sebastiaan Luyssaert
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-280, https://doi.org/10.5194/gmd-2016-280, 2016
Revised manuscript not accepted (discussion: closed, 4 comments)
08 Nov 2016
Description and evaluation of REFIST v1.0: a regional greenhouse gas flux inversion system in Canada
Elton Chan, Douglas Chan, Misa Ishizawa, Felix Vogel, Jerome Brioude, Andy Delcloo, Yuehua Wu, and Baisuo Jin
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-213, https://doi.org/10.5194/gmd-2016-213, 2016
Revised manuscript not accepted (discussion: closed, 4 comments)
Short summary
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The main objective of this study is to examine the impacts of errors introduced by different components in our newly developed inversion system on flux estimates with a series of controlled experiments. It is very critical for any inversion system to be fully evaluated prior to applying to real observations. As demonstrated, the results can be very sensitive to the model setup and region. It is not reasonable to expect realistic results can always be obtained using the same approach.
20 Oct 2016
eWaterCycle: a hyper-resolution global hydrological model for river
discharge forecasts made from open source pre-existing components
Rolf Hut, Niels Drost, Maarten van Meersbergen, Edwin Sutanudjaja, Marc Bierkens, and Nick van de Giesen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-225, https://doi.org/10.5194/gmd-2016-225, 2016
Revised manuscript not accepted (discussion: closed, 4 comments)
Short summary
Short summary
A system that predicts the amount of water flowing in each river on earth, 9 days ahead, is build using existing parts of open source computer code build by different researchers in other projects.
The glue between all pre-existing parts are all open interfaces which means that the pieces system click together like a house of LEGOs. It is easy to remove a piece (a brick) and replace it with another, improved, piece.
The resulting predictions are available online at forecast.ewatercycle.org
29 Sep 2016
The downscaling and adjustment method ADAMONT
v1.0 for climate projections in mountainous regions
applicable to energy balance land surface models
Deborah Verfaillie, Michel Déqué, Samuel Morin, and Matthieu Lafaysse
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-168, https://doi.org/10.5194/gmd-2016-168, 2016
Revised manuscript not accepted (discussion: closed, 7 comments)
21 Sep 2016
Technical Note: Improving the computational efficiency of sparse matrix multiplication in linear atmospheric inverse problems
Vineet Yadav and Anna M. Michalak
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-204, https://doi.org/10.5194/gmd-2016-204, 2016
Revised manuscript has not been submitted (discussion: closed, 2 comments)
Short summary
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Multiplication of two matrices that consists of few non-zero entries is a fundamental operation in problems that involve estimation of greenhouse gas fluxes from atmospheric measurements. To increase computational efficiency of estimating these quantities, modification of the standard matrix multiplication algorithm for multiplying these matrices is proposed in this research.
19 Aug 2016
Development of CarbonTracker Europe-CH4 – Part 1: system set-up and sensitivity analyses
Aki Tsuruta, Tuula Aalto, Leif Backman, Janne Hakkarainen, Ingrid T. van der Laan-Luijkx, Maarten C. Krol, Renato Spahni, Sander Houweling, Marko Laine, Marcel van der Schoot, Ray Langenfelds, Raymond Ellul, and Wouter Peters
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-181, https://doi.org/10.5194/gmd-2016-181, 2016
Revised manuscript has not been submitted (discussion: closed, 6 comments)
Short summary
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In this study, we found that methane emission estimates, driven by the CTE-CH4 model, depend on model setups and inputs, especially for regional estimates. An optimal setup makes the estimates stable, but inputs, such as emission estimates from inventories, and observations, also play significant role. The results can be used for an extended analysis on relative contributions of methane emissions to atmospheric methane concentration changes in recent decades.
19 Aug 2016
Joint CO2 state and flux estimation with the 4D-Var system EURAD-IM
Johannes Klimpt, Elmar Friese, and Hendrik Elbern
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-132, https://doi.org/10.5194/gmd-2016-132, 2016
Preprint withdrawn (discussion: closed, 3 comments)
Short summary
Short summary
Atmospheric inversions optimize surface-atmosphere CO2 fluxes using CO2 concentration observations and atmospheric transport models. This study optimizes additionally the atmospheric initial concentration of CO2 jointly with the fluxes. Artificial generated observations are used to estimate limits and benefits of the used inversion method.
Uncertainty of analyzed CO2 fluxes can be reduced with the joint optimization of fluxes and the atmospheric CO2 concentration.
15 Aug 2016
Establishing relationship between measured and predicted soil water characteristics using SOILWAT model in three agro-ecological zones of Nigeria
OrevaOghene Aliku and Suarau O. Oshunsanya
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-165, https://doi.org/10.5194/gmd-2016-165, 2016
Revised manuscript not accepted (discussion: closed, 7 comments)
29 Jul 2016
Exploring global surface temperature pattern scaling methodologies and assumptions from a CMIP5 model ensemble
Cary Lynch, Corinne Hartin, Ben Bond-Lamberty, and Ben Kravitz
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-170, https://doi.org/10.5194/gmd-2016-170, 2016
Revised manuscript not accepted (discussion: closed, 6 comments)
Short summary
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Pattern scaling is used to explore uncertainty in future forcing scenarios and assess local climate sensitivity to global temperature change. This paper examines the two dominant pattern scaling methods using a multi-model ensemble with two future socio-economic storylines. We find that high latitudes show the strongest sensitivity to global temperature change and that the simple least squared regression approach to generation of patterns is a better fit to projected global temperature.
05 Jul 2016
Fundamentals of Data Assimilation
Peter Rayner, Anna M. Michalak, and Frédéric Chevallier
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-148, https://doi.org/10.5194/gmd-2016-148, 2016
Revised manuscript not accepted (discussion: closed, 5 comments)
Short summary
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Numerical models are among our most important tools for understanding and prediction. Models include quantities or equations that we cannot verify directly. We learn about these unknowns by comparing model output with observations and using some algorithm to improve the inputs. We show here that the many methods for doing this are special cases of underlying statistics. This provides a unified way of comparing and contrasting such methods.
21 Jun 2016
Microphysics parameterization sensitivity of the WRF Model version 3.1.7 to extreme precipitation: evaluation of the 1997 New Year’s flood of California
Elcin Tan
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-94, https://doi.org/10.5194/gmd-2016-94, 2016
Revised manuscript not accepted (discussion: closed, 6 comments)
Short summary
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California is vulnerable to extreme precipitation, which occurs due to atmospheric rivers. This study is an attempt to evaluate the performance of the WRF Model for the extreme precipitation event that caused the 1997 New Year’s flood in California. The results show that the accuracy of the WRF Model is much higher for the 72-hr total basin-averaged evaluations than for the hourly and point-wise comparisons. The Thompson Scheme indicates more trustworthy results than others, with a 3.1 % error.
01 Jun 2016
A robust gap-filling method for Net Ecosystem Exchange based on Cahn–Hilliard inpainting
Yufeng He and Mark Rayment
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-108, https://doi.org/10.5194/gmd-2016-108, 2016
Revised manuscript not accepted (discussion: closed, 5 comments)
Short summary
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We introduce a new method based on image inpainting to gap-filling the signal of Net Ecosystem Exchange.It is more intuitive, compact and highly comparable with a commonly-used method. Results showed a similar level of gap-filling errors between the two methods across twelve datasets. The gap-filling performance was improved from both methods when the original datasets were de-noised, implying that the noise or random structures embedded in signal determines the uncertainty level of gap-filling.
17 May 2016
AMOC-emulator M-AMOC1.0 for uncertainty assessment of future projections
Pepijn Bakker and Andreas Schmittner
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-79, https://doi.org/10.5194/gmd-2016-79, 2016
Revised manuscript not accepted (discussion: closed, 11 comments)
Short summary
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We present an AMOC-emulator framework consisting of a box model and a statistical tuning methodology that allows us to mimic the behaviour of the Atlantic Meridional Overturning Circulation (AMOC) in any complex global climate model. The simplicity of the AMOC-emulator allows us to run large numbers of simulations, test the importance of a range of uncertainties and thus provide probabilistic AMOC projections driven by future climate change including the partial melt of the Greenland Ice Sheet.
22 Mar 2016
Comparison of the glacial isostatic adjustment behaviour in glacially induced fault models
Rebekka Steffen, Holger Steffen, and Patrick Wu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-43, https://doi.org/10.5194/gmd-2016-43, 2016
Revised manuscript not accepted (discussion: closed, 4 comments)
Short summary
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We evaluate two model approaches intended to model glacially induced fault movements. We focus entirely on the glacial isostatic adjustment behaviour of those approaches and compare them with respect to displacement and stress changes. The results show that only one approach is able to model the glacial isostatic adjustment process correctly.
04 Mar 2016
Empirical Bayes approach to climate model calibration
Charles S. Jackson and Gabriel Huerta
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-20, https://doi.org/10.5194/gmd-2016-20, 2016
Preprint withdrawn (discussion: closed, 3 comments)
Short summary
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Climate data is highly correlated which can make it difficult from a statistical perspective to quantify the significance of differences that arise between a model and observations. Here we explore a common device in Bayesian inference for assessing the statistical significance of a fit between a model and data and suggest how this approach may be applied to the calibration of a climate model.
15 Feb 2016
A near-global eddy-resolving OGCM for climate studies
X. Zhang, P. R. Oke, M. Feng, M. A. Chamberlain, J. A. Church, D. Monselesan, C. Sun, R. J. Matear, A. Schiller, and R. Fiedler
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-17, https://doi.org/10.5194/gmd-2016-17, 2016
Revised manuscript not accepted (discussion: closed, 9 comments)
Short summary
Short summary
Eddy-resolving global ocean models are highly desired, but expensive to run, and also subject to many problems including drift. Here we modified a near-global eddy-resolving OGCM for climate studies with some novel strategies. We demonstrated that the historical experiment driven by Japanese atmospheric reanalysis product, didn't have significant drifts, and also provided an eddy-resolving simulation of the global ocean over 1979–2014. Our experiences can be helpful to other modelling groups.
11 Feb 2016
ClimateLearn: A machine-learning approach for climate prediction using network measures
Qing Yi Feng, Ruggero Vasile, Marc Segond, Avi Gozolchiani, Yang Wang, Markus Abel, Shilomo Havlin, Armin Bunde, and Henk A. Dijkstra
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2015-273, https://doi.org/10.5194/gmd-2015-273, 2016
Revised manuscript not accepted (discussion: closed, 4 comments)
Short summary
Short summary
We present the toolbox ClimateLearn to tackle problems in climate prediction using machine learning techniques and climate network analysis. Because spatial temporal information on climate variability can be efficiently represented by complex network measures, such data are considered here as input to the machine-learning algorithms. As an example, the toolbox is applied to the prediction of the occurrence and the development of El Niño in the equatorial Pacific.
15 Jan 2016
Spatio-temporal variability in N2O emissions from a tea-planted soil in subtropical central China
X. L. Liu, X. Q. Fu, Y. Li, J. L. Shen, Y. Wang, G. H. Zou, Y. Z. Wu, Q. M. Ma, D. Chen, C. Wang, R. L. Xiao, and J. S. Wu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2015-251, https://doi.org/10.5194/gmd-2015-251, 2016
Preprint withdrawn (discussion: closed, 7 comments)
Short summary
Short summary
We examined the spatio-temporal variability in N2O emissions from a tea-planted soil. It has two highlights: i) that the size of large static chambers used for long-term observations should be no less than 0.4m and the time interval for gas sampling should be constrained to approximately 5 days; ii) that the predictions of the spatio-temporal kriging interpolations for the total N2O were approximately 25% higher than the results in long-term observation.
10 Dec 2015
An integrated Dissolved Organic Carbon Dynamics Model (DOCDM 1.0): model development and a case study in the Alaskan Yukon River Basin
X. Lu and Q. Zhuang
Geosci. Model Dev. Discuss., 8, 10411–10454, https://doi.org/10.5194/gmdd-8-10411-2015, https://doi.org/10.5194/gmdd-8-10411-2015, 2015
Revised manuscript has not been submitted (discussion: closed, 3 comments)
13 Nov 2015
Impacts of air–sea interactions on regional air quality predictions using WRF/Chem v3.6.1 coupled with ROMS v3.7: southeastern US example
J. He, R. He, and Y. Zhang
Geosci. Model Dev. Discuss., 8, 9965–10009, https://doi.org/10.5194/gmdd-8-9965-2015, https://doi.org/10.5194/gmdd-8-9965-2015, 2015
Revised manuscript not accepted (discussion: closed, 6 comments)
Short summary
Short summary
WRF/Chem simulations are performed to understand the impacts of cumulus parameterizations and air-sea interactions on coastal air quality. The use of different cumulus parameterizations gives different vertical mixing and wet scavenging. The use of different air-sea interaction treatments also gives different predictions of O3 and PM2.5 by up to 17.3 ppb and 7.9 μg m-3, respectively. WRF/Chem-ROMS improves model predictions, illustrating the benefits and needs of using coupled atmospheric-ocean
29 Oct 2015
InMAP: a new model for air pollution interventions
C. W. Tessum, J. D. Hill, and J. D. Marshall
Geosci. Model Dev. Discuss., 8, 9281–9321, https://doi.org/10.5194/gmdd-8-9281-2015, https://doi.org/10.5194/gmdd-8-9281-2015, 2015
Revised manuscript not accepted (discussion: closed, 9 comments)
Short summary
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We develop InMAP (Intervention Model for Air Pollution), an Eulerian model which estimates changes in primary and secondary fine particle (PM2.5) concentrations attributable to annual changes in precursor emissions. InMAP uses a variable resolution grid to focus on human exposures by employing higher spatial resolution in urban areas and lower spatial resolution in rural and remote locations and in the upper atmosphere; and by directly calculating steady-state, annual average concentrations.
08 Oct 2015
The infrastructure MESSy submodels GRID (v1.0) and IMPORT (v1.0)
A. Kerkweg and P. Jöckel
Geosci. Model Dev. Discuss., 8, 8607–8633, https://doi.org/10.5194/gmdd-8-8607-2015, https://doi.org/10.5194/gmdd-8-8607-2015, 2015
Revised manuscript not accepted (discussion: closed, 7 comments)
28 Aug 2015
DasPy 1.0 – the Open Source Multivariate Land Data Assimilation Framework in combination with the Community Land Model 4.5
X. Han, X. Li, G. He, P. Kumbhar, C. Montzka, S. Kollet, T. Miyoshi, R. Rosolem, Y. Zhang, H. Vereecken, and H.-J. H. Franssen
Geosci. Model Dev. Discuss., 8, 7395–7444, https://doi.org/10.5194/gmdd-8-7395-2015, https://doi.org/10.5194/gmdd-8-7395-2015, 2015
Revised manuscript not accepted (discussion: closed, 6 comments)
Short summary
Short summary
DasPy is a ready to use open source parallel multivariate land data assimilation framework with joint state and parameter estimation using Local Ensemble Transform Kalman Filter. The Community Land Model (4.5) was integrated as model operator. The Community Microwave Emission Modelling platform, COsmic-ray Soil Moisture Interaction Code and the Two-Source Formulation were integrated as observation operators for the multivariate assimilation of soil moisture and soil temperature, respectively.
13 Aug 2015
WRF4G: WRF experiment management made simple
V. Fernández-Quiruelas, J. Fernández, A. S. Cofiño, C. Blanco, M. García-Díez, M. Magariño, L. Fita, and J. M. Gutiérrez
Geosci. Model Dev. Discuss., 8, 6551–6582, https://doi.org/10.5194/gmdd-8-6551-2015, https://doi.org/10.5194/gmdd-8-6551-2015, 2015
Revised manuscript has not been submitted (discussion: closed, 9 comments)
13 Aug 2015
DebrisInterMixing-2.3: a Finite Volume solver for three dimensional debris flow simulations based on a single calibration parameter – Part 2: Model validation
A. von Boetticher, J. M. Turowski, B. W. McArdell, D. Rickenmann, M. Hürlimann, C. Scheidl, and J. W. Kirchner
Geosci. Model Dev. Discuss., 8, 6379–6415, https://doi.org/10.5194/gmdd-8-6379-2015, https://doi.org/10.5194/gmdd-8-6379-2015, 2015
Preprint withdrawn (discussion: closed, 2 comments)
21 Jul 2015
Experiments on sensitivity of meridional circulation and ozone flux to parameterizations of orographic gravity waves and QBO phases in a general circulation model of the middle atmosphere
A. V. Koval, N. M. Gavrilov, A. I. Pogoreltsev, and E. N. Savenkova
Geosci. Model Dev. Discuss., 8, 5643–5670, https://doi.org/10.5194/gmdd-8-5643-2015, https://doi.org/10.5194/gmdd-8-5643-2015, 2015
Revised manuscript not accepted (discussion: closed, 6 comments)
Short summary
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We implemented improved parameterizations of orographic gravity wave dynamical and thermal effects and QBO flows into a general circulation model and study the sensitivity of meridional circulation and vertical velocity to the parameterizations at altitudes up to 100km. Stationary OGW effects gives changes up to 40% in the meridional velocity and associated ozone fluxes in the stratosphere. Transitions from the easterly to westerly QBO phase may alter meridional and vertical velocities by 60%.
02 Jul 2015
A simplified gross primary production and evapotranspiration model for boreal coniferous forests – is a generic calibration sufficient?
F. Minunno, M. Peltoniemi, S. Launiainen, M. Aurela, A. Lindroth, A. Lohila, I. Mammarella, K. Minkkinen, and A. Mäkelä
Geosci. Model Dev. Discuss., 8, 5089–5137, https://doi.org/10.5194/gmdd-8-5089-2015, https://doi.org/10.5194/gmdd-8-5089-2015, 2015
Revised manuscript not accepted (discussion: closed, 9 comments)
22 Jun 2015
Modelling spatial and temporal vegetation variability with the Climate Constrained Vegetation Index: evidence of CO2 fertilisation and of water stress in continental interiors
S. O. Los
Geosci. Model Dev. Discuss., 8, 4781–4821, https://doi.org/10.5194/gmdd-8-4781-2015, https://doi.org/10.5194/gmdd-8-4781-2015, 2015
Revised manuscript not accepted (discussion: closed, 7 comments)
Short summary
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A model was developed to simulate spatio-temporal variations in vegetation in response to temperature, precipitation and atmospheric CO2 levels. The model reproduced variations in vegetation well; it showed a greater response to drought stress in N Hemisphere continents than previous implementations and showed a decline in vegetation during the US dust bowl (1930s and 1950s) and the drought of the century in the Sahel (1984). Vegetation greenness increased in response to atmospheric CO2 levels.
12 Jun 2015
Importance of bitwise identical reproducibility in earth system modeling and status report
L. Liu, S. Peng, C. Zhang, R. Li, B. Wang, C. Sun, Q. Liu, L. Dong, L. Li, Y. Shi, Y. He, W. Zhao, and G. Yang
Geosci. Model Dev. Discuss., 8, 4375–4400, https://doi.org/10.5194/gmdd-8-4375-2015, https://doi.org/10.5194/gmdd-8-4375-2015, 2015
Revised manuscript has not been submitted (discussion: closed, 5 comments)
09 Mar 2015
Matching soil grid unit resolutions with polygon unit scales for DNDC modelling of regional SOC pool
H. D. Zhang, D. S. Yu, Y. L. Ni, L. M. Zhang, and X. Z. Shi
Geosci. Model Dev. Discuss., 8, 2653–2689, https://doi.org/10.5194/gmdd-8-2653-2015, https://doi.org/10.5194/gmdd-8-2653-2015, 2015
Revised manuscript not accepted (discussion: closed, 7 comments)
Short summary
Short summary
Matching soil grid unit resolution with polygon unit map scale is important to minimize uncertainty of regional soil organic carbon (SOC) pool simulation by DeNitrification–DeComposition (DNDC) process-based model as their strong influences on the uncertainty. A series of soil grid units at varying cell sizes were derived from soil polygon units. Both format soil units were used for regional SOC pool simulation with DNDC model, to determine an optimal raster resolution of grid simulation units.
04 Mar 2015
Enhancement for bitwise identical reproducibility of Earth system modeling on the C-Coupler platform
L. Liu, R. Li, C. Zhang, G. Yang, B. Wang, and L. Dong
Geosci. Model Dev. Discuss., 8, 2403–2435, https://doi.org/10.5194/gmdd-8-2403-2015, https://doi.org/10.5194/gmdd-8-2403-2015, 2015
Revised manuscript not accepted (discussion: closed, 5 comments)
18 Dec 2014
On the wind stress formulation over shallow waters in atmospheric models
P. A. Jiménez and J. Dudhia
Geosci. Model Dev. Discuss., 7, 9063–9077, https://doi.org/10.5194/gmdd-7-9063-2014, https://doi.org/10.5194/gmdd-7-9063-2014, 2014
Revised manuscript not accepted (discussion: closed, 18 comments)
Short summary
Short summary
In spite of the substantial observational evidence supporting a higher drag over shallow waters than over the open ocean, regional and global models widely use a single formulation valid for the open ocean. Results of this work indicate that adding the extra drag is necessary to reconcile model results with long term observations of the wind profile within the first 100 m of the atmosphere, being the first modeling evidence supporting the reported added drag over shallow waters.
12 Dec 2014
Intel Xeon Phi accelerated Weather Research and Forecasting (WRF) Goddard microphysics scheme
J. Mielikainen, B. Huang, and A. H.-L. Huang
Geosci. Model Dev. Discuss., 7, 8941–8973, https://doi.org/10.5194/gmdd-7-8941-2014, https://doi.org/10.5194/gmdd-7-8941-2014, 2014
Revised manuscript has not been submitted (discussion: closed, 1 comment)
22 Oct 2014
A user-friendly forest model with a multiplicative mathematical structure: a Bayesian approach to calibration
M. Bagnara, M. Van Oijen, D. Cameron, D. Gianelle, F. Magnani, and M. Sottocornola
Geosci. Model Dev. Discuss., 7, 6997–7031, https://doi.org/10.5194/gmdd-7-6997-2014, https://doi.org/10.5194/gmdd-7-6997-2014, 2014
Revised manuscript not accepted (discussion: closed, 5 comments)
17 Sep 2014
Sensitivity analysis of PBL schemes by comparing WRF model and experimental data
A. Balzarini, F. Angelini, L. Ferrero, M. Moscatelli, M. G. Perrone, G. Pirovano, G. M. Riva, G. Sangiorgi, A. M. Toppetti, G. P. Gobbi, and E. Bolzacchini
Geosci. Model Dev. Discuss., 7, 6133–6171, https://doi.org/10.5194/gmdd-7-6133-2014, https://doi.org/10.5194/gmdd-7-6133-2014, 2014
Revised manuscript has not been submitted (discussion: closed, 4 comments)
11 Sep 2014
Integration of Geographic Information System frameworks into domain discretisation and meshing processes for geophysical models
A. S. Candy, A. Avdis, J. Hill, G. J. Gorman, and M. D. Piggott
Geosci. Model Dev. Discuss., 7, 5993–6060, https://doi.org/10.5194/gmdd-7-5993-2014, https://doi.org/10.5194/gmdd-7-5993-2014, 2014
Revised manuscript has not been submitted (discussion: closed, 3 comments)
17 Jul 2014
Enhancing reproducibility of numerical simulation result on the C-Coupler platform
L. Liu, R. Li, C. Zhang, G. Yang, and B. Wang
Geosci. Model Dev. Discuss., 7, 4429–4461, https://doi.org/10.5194/gmdd-7-4429-2014, https://doi.org/10.5194/gmdd-7-4429-2014, 2014
Revised manuscript not accepted (discussion: closed, 7 comments)
26 Jun 2014
Development and evaluation of a hydrostatic dynamical core using the spectral element/discontinuous Galerkin methods
S.-J. Choi and F. X. Giraldo
Geosci. Model Dev. Discuss., 7, 4119–4151, https://doi.org/10.5194/gmdd-7-4119-2014, https://doi.org/10.5194/gmdd-7-4119-2014, 2014
Preprint withdrawn (discussion: closed, 4 comments)
11 Jun 2014
Parameters sensitivity analysis for a~crop growth model applied to winter wheat in the Huanghuaihai Plain in China
M. Liu, B. He, A. Lü, L. Zhou, and J. Wu
Geosci. Model Dev. Discuss., 7, 3867–3888, https://doi.org/10.5194/gmdd-7-3867-2014, https://doi.org/10.5194/gmdd-7-3867-2014, 2014
Revised manuscript has not been submitted (discussion: closed, 3 comments)
19 May 2014
Homogeneized modeling of mineral dust emissions over Europe and Africa using the CHIMERE model
R. Briant, L. Menut, G. Siour, and C. Prigent
Geosci. Model Dev. Discuss., 7, 3441–3480, https://doi.org/10.5194/gmdd-7-3441-2014, https://doi.org/10.5194/gmdd-7-3441-2014, 2014
Revised manuscript not accepted (discussion: closed, 3 comments)
28 Apr 2014
Towards a representation of halogen chemistry within volcanic plumes in a chemistry transport model
L. Grellier, V. Marécal, B. Josse, P. D. Hamer, T. J. Roberts, A. Aiuppa, and M. Pirre
Geosci. Model Dev. Discuss., 7, 2581–2650, https://doi.org/10.5194/gmdd-7-2581-2014, https://doi.org/10.5194/gmdd-7-2581-2014, 2014
Revised manuscript not accepted (discussion: closed, 4 comments)
11 Apr 2014
An improved coupling model for water flow, sediment transport and bed evolution (CASFE v.1)
S. He, W. Liu, X. Li, and C. Ouyang
Geosci. Model Dev. Discuss., 7, 2429–2454, https://doi.org/10.5194/gmdd-7-2429-2014, https://doi.org/10.5194/gmdd-7-2429-2014, 2014
Revised manuscript not accepted (discussion: closed, 6 comments)
18 Mar 2014
A linear algorithm for solving non-linear isothermal ice-shelf equations
A. Sargent and J. L. Fastook
Geosci. Model Dev. Discuss., 7, 1829–1864, https://doi.org/10.5194/gmdd-7-1829-2014, https://doi.org/10.5194/gmdd-7-1829-2014, 2014
Revised manuscript has not been submitted (discussion: closed, 5 comments)
14 Mar 2014
Explicit planktic calcifiers in the University of Victoria Earth System Climate Model
K. F. Kvale, K. J. Meissner, D. P. Keller, M. Eby, and A. Schmittner
Geosci. Model Dev. Discuss., 7, 1709–1758, https://doi.org/10.5194/gmdd-7-1709-2014, https://doi.org/10.5194/gmdd-7-1709-2014, 2014
Revised manuscript not accepted (discussion: closed, 3 comments)
24 Feb 2014
A cusp catastrophe model for alluvial channel regime and classification of channel patterns
Y. Xiao, X. J. Shao, and Y. Yang
Geosci. Model Dev. Discuss., 7, 1477–1497, https://doi.org/10.5194/gmdd-7-1477-2014, https://doi.org/10.5194/gmdd-7-1477-2014, 2014
Revised manuscript not accepted (discussion: closed, 5 comments)
17 Jan 2014
Three-dimensional phase-field study of crack-seal microstructures – insights from innovative post-processing techniques
K. Ankit, M. Selzer, and B. Nestler
Geosci. Model Dev. Discuss., 7, 631–658, https://doi.org/10.5194/gmdd-7-631-2014, https://doi.org/10.5194/gmdd-7-631-2014, 2014
Revised manuscript not accepted (discussion: closed, 4 comments)
16 Jan 2014
Simulation of trace gases and aerosols over the Indian domain: evaluation of the WRF-Chem model
M. Michael, A. Yadav, S. N. Tripathi, V. P. Kanawade, A. Gaur, P. Sadavarte, and C. Venkataraman
Geosci. Model Dev. Discuss., 7, 431–482, https://doi.org/10.5194/gmdd-7-431-2014, https://doi.org/10.5194/gmdd-7-431-2014, 2014
Revised manuscript not accepted (discussion: closed, 4 comments)
19 Dec 2013
Modelling economic and biophysical drivers of agricultural land-use change. Calibration and evaluation of the Nexus Land-Use model over 1961–2006
F. Souty, B. Dorin, T. Brunelle, P. Dumas, and P. Ciais
Geosci. Model Dev. Discuss., 6, 6975–7046, https://doi.org/10.5194/gmdd-6-6975-2013, https://doi.org/10.5194/gmdd-6-6975-2013, 2013
Revised manuscript has not been submitted (discussion: closed, 2 comments)
17 Dec 2013
Influences of calibration data length and data period on model parameterization and quantification of terrestrial ecosystem carbon dynamics
Q. Zhu and Q. Zhuang
Geosci. Model Dev. Discuss., 6, 6835–6865, https://doi.org/10.5194/gmdd-6-6835-2013, https://doi.org/10.5194/gmdd-6-6835-2013, 2013
Revised manuscript not accepted (discussion: closed, 8 comments)
04 Nov 2013
Are vegetation-specific model parameters required for estimating gross primary production?
W. Yuan, S. Liu, W. Cai, W. Dong, J. Chen, A. Arain, P. D. Blanken, A. Cescatti, G. Wohlfahrt, T. Georgiadis, L. Genesio, D. Gianelle, A. Grelle, G. Kiely, A. Knohl, D. Liu, M. Marek, L. Merbold, L. Montagnani, O. Panferov, M. Peltoniemi, S. Rambal, A. Raschi, A. Varlagin, and J. Xia
Geosci. Model Dev. Discuss., 6, 5475–5488, https://doi.org/10.5194/gmdd-6-5475-2013, https://doi.org/10.5194/gmdd-6-5475-2013, 2013
Revised manuscript not accepted (discussion: closed, 6 comments)
09 Oct 2013
ADISM v.1.0: an adjoint of a thermomechanical ice-sheet model obtained using an algorithmic differentiation tool
J. McGovern, I. Rutt, J. Utke, and T. Murray
Geosci. Model Dev. Discuss., 6, 5251–5288, https://doi.org/10.5194/gmdd-6-5251-2013, https://doi.org/10.5194/gmdd-6-5251-2013, 2013
Revised manuscript has not been submitted (discussion: closed, 5 comments)
13 Jul 2013
CUDA-C implementation of the ADER-DG method for linear hyperbolic PDEs
C. E. Castro, J. Behrens, and C. Pelties
Geosci. Model Dev. Discuss., 6, 3743–3786, https://doi.org/10.5194/gmdd-6-3743-2013, https://doi.org/10.5194/gmdd-6-3743-2013, 2013
Revised manuscript not accepted (discussion: closed, 5 comments)
28 Jun 2013
A coupled two-dimensional hydrodynamic and terrestrial input model to simulate CO2 diffusive emissions from lake systems
H. Wu, C. Peng, M. Lucotte, N. Soumis, Y. Gélinas, É. Duchemin, J.-B. Plouhinec, A. Ouellet, and Z. Guo
Geosci. Model Dev. Discuss., 6, 3509–3556, https://doi.org/10.5194/gmdd-6-3509-2013, https://doi.org/10.5194/gmdd-6-3509-2013, 2013
Revised manuscript not accepted (discussion: closed, 4 comments)
28 May 2013
Test of validity of a dynamic soil carbon model using data from leaf litter decomposition in a West African tropical forest
G. H. S. Guendehou, J. Liski, M. Tuomi, M. Moudachirou, B. Sinsin, and R. Mäkipää
Geosci. Model Dev. Discuss., 6, 3003–3032, https://doi.org/10.5194/gmdd-6-3003-2013, https://doi.org/10.5194/gmdd-6-3003-2013, 2013
Revised manuscript has not been submitted (discussion: closed, 7 comments)
04 Apr 2013
The Simulator of the Timing and Magnitude of Pollen Season (STaMPS) model: a pollen production model for regional emission and transport modeling
T. R. Duhl, R. Zhang, A. Guenther, S. H. Chung, M. T. Salam, J. M. House, R. C. Flagan, E. L. Avol, F. D. Gilliland, B. K. Lamb, T. M. VanReken, Y. Zhang, and E. Salathé
Geosci. Model Dev. Discuss., 6, 2325–2368, https://doi.org/10.5194/gmdd-6-2325-2013, https://doi.org/10.5194/gmdd-6-2325-2013, 2013
Revised manuscript not accepted (discussion: closed, 7 comments)
06 Feb 2013
One-dimensional simulation of fire injection heights in contrasted meteorological scenarios with PRM and Meso-NH models
S. Strada, S. R. Freitas, C. Mari, K. M. Longo, and R. Paugam
Geosci. Model Dev. Discuss., 6, 721–790, https://doi.org/10.5194/gmdd-6-721-2013, https://doi.org/10.5194/gmdd-6-721-2013, 2013
Preprint withdrawn (discussion: closed, 3 comments)
22 Jan 2013
Calibration of the Crop model in the Community Land Model
X. Zeng, B. A. Drewniak, and E. M. Constantinescu
Geosci. Model Dev. Discuss., 6, 379–398, https://doi.org/10.5194/gmdd-6-379-2013, https://doi.org/10.5194/gmdd-6-379-2013, 2013
Revised manuscript not accepted (discussion: closed, 5 comments)
14 Nov 2012
The hybrid Eulerian Lagrangian numerical scheme tested with Chemistry
A. B. Hansen, B. Sørensen, P. Tarning-Andersen, J. H. Christensen, J. Brandt, and E. Kaas
Geosci. Model Dev. Discuss., 5, 3695–3732, https://doi.org/10.5194/gmdd-5-3695-2012, https://doi.org/10.5194/gmdd-5-3695-2012, 2012
Revised manuscript not accepted (discussion: closed, 4 comments)
17 Oct 2012
COSTRICE – three model online coupling using OASIS: problems and solutions
H. T. M. Ho, B. Rockel, H. Kapitza, B. Geyer, and E. Meyer
Geosci. Model Dev. Discuss., 5, 3261–3310, https://doi.org/10.5194/gmdd-5-3261-2012, https://doi.org/10.5194/gmdd-5-3261-2012, 2012
Revised manuscript not accepted (discussion: closed, 4 comments)
12 Sep 2012
A methodology for estimating seasonal cycles of atmospheric CO2 resulting from terrestrial net ecosystem exchange (NEE) fluxes using the Transcom T3L2 pulse-response functions
C. D. Nevison, D. F. Baker, and K. R. Gurney
Geosci. Model Dev. Discuss., 5, 2789–2809, https://doi.org/10.5194/gmdd-5-2789-2012, https://doi.org/10.5194/gmdd-5-2789-2012, 2012
Revised manuscript not accepted (discussion: closed, 4 comments)
23 Jul 2012
A simulation study of the ensemble-based data assimilation of satellite-borne lidar aerosol observations
T. T. Sekiyama, T. Y. Tanaka, and T. Miyoshi
Geosci. Model Dev. Discuss., 5, 1877–1947, https://doi.org/10.5194/gmdd-5-1877-2012, https://doi.org/10.5194/gmdd-5-1877-2012, 2012
Revised manuscript has not been submitted (discussion: closed, 3 comments)
16 Jul 2012
Activation of the operational ecohydrodynamic model (3-D CEMBS) – the hydrodynamic part
L. Dzierzbicka-Głowacka, J. Jakacki, M. Janecki, and A. Nowicki
Geosci. Model Dev. Discuss., 5, 1851–1875, https://doi.org/10.5194/gmdd-5-1851-2012, https://doi.org/10.5194/gmdd-5-1851-2012, 2012
Revised manuscript not accepted (discussion: closed, 6 comments)
24 Oct 2011
Description of EQSAM4: gas-liquid-solid partitioning model for global simulations
S. Metzger, B. Steil, L. Xu, J. E. Penner, and J. Lelieveld
Geosci. Model Dev. Discuss., 4, 2791–2847, https://doi.org/10.5194/gmdd-4-2791-2011, https://doi.org/10.5194/gmdd-4-2791-2011, 2011
Revised manuscript has not been submitted (discussion: closed, 3 comments)
28 Sep 2011
Modelling oxygen isotopes in the University of Victoria Earth System Climate Model
C. E. Brennan, A. J. Weaver, M. Eby, and K. J. Meissner
Geosci. Model Dev. Discuss., 4, 2545–2576, https://doi.org/10.5194/gmdd-4-2545-2011, https://doi.org/10.5194/gmdd-4-2545-2011, 2011
Preprint withdrawn (discussion: closed, 3 comments)
01 Aug 2011
Application of CMAQ at a hemispheric scale for atmospheric mercury simulations
P. Pongprueksa, C. J. Lin, P. Singhasuk, L. Pan, T. C. Ho, and H. W. Chu
Geosci. Model Dev. Discuss., 4, 1723–1754, https://doi.org/10.5194/gmdd-4-1723-2011, https://doi.org/10.5194/gmdd-4-1723-2011, 2011
Revised manuscript not accepted (discussion: closed, 4 comments)
14 Jun 2011
Carbon monoxide as a tracer for tropical troposphere to stratosphere transport in the Chemical Lagrangian Model of the Stratosphere (CLaMS)
R. Pommrich, R. Müller, J.-U. Grooß, P. Konopka, G. Günther, H.-C. Pumphrey, S. Viciani, F. D'Amato, and M. Riese
Geosci. Model Dev. Discuss., 4, 1185–1211, https://doi.org/10.5194/gmdd-4-1185-2011, https://doi.org/10.5194/gmdd-4-1185-2011, 2011
Revised manuscript not accepted (discussion: closed, 4 comments)
15 Feb 2011
An aerosol dynamics model for simulating particle formation and growth in a mixed flow chamber
M. Vesterinen, H. Korhonen, J. Joutsensaari, P. Yli-Pirilä, A. Laaksonen, and K. E. J. Lehtinen
Geosci. Model Dev. Discuss., 4, 385–417, https://doi.org/10.5194/gmdd-4-385-2011, https://doi.org/10.5194/gmdd-4-385-2011, 2011
Revised manuscript has not been submitted (discussion: closed, 2 comments)
15 Feb 2011
Ground-level ozone concentration over Spain: an application of Kalman Filter post-processing to reduce model uncertainties
V. Sicardi, J. Ortiz, A. Rincón, O. Jorba, M. T. Pay, S. Gassó, and J. M. Baldasano
Geosci. Model Dev. Discuss., 4, 343–384, https://doi.org/10.5194/gmdd-4-343-2011, https://doi.org/10.5194/gmdd-4-343-2011, 2011
Revised manuscript not accepted (discussion: closed, 5 comments)
14 Jan 2011
A two-layer flow model to represent ice-ocean interactions beneath Antarctic ice shelves
V. Lee, A. J. Payne, and J. M. Gregory
Geosci. Model Dev. Discuss., 4, 65–136, https://doi.org/10.5194/gmdd-4-65-2011, https://doi.org/10.5194/gmdd-4-65-2011, 2011
Revised manuscript not accepted (discussion: closed, 4 comments)
13 Sep 2010
Linkage between an advanced air quality model and a mechanistic watershed model
K. Vijayaraghavan, J. Herr, S.-Y. Chen, and E. Knipping
Geosci. Model Dev. Discuss., 3, 1503–1548, https://doi.org/10.5194/gmdd-3-1503-2010, https://doi.org/10.5194/gmdd-3-1503-2010, 2010
Revised manuscript has not been submitted (discussion: closed, 3 comments)
01 Jul 2009
Implementation of a new aerosol HAM model within the Weather Research and Forecasting (WRF) modeling system
R. Mashayekhi, P. Irannejad, J. Feichter, and A. A. Bidokhti
Geosci. Model Dev. Discuss., 2, 681–707, https://doi.org/10.5194/gmdd-2-681-2009, https://doi.org/10.5194/gmdd-2-681-2009, 2009
Revised manuscript has not been submitted (discussion: closed, 4 comments)
17 Mar 2009
Next generation framework for aquatic modeling of the Earth System
B. M. Fekete, W. M. Wollheim, D. Wisser, and C. J. Vörösmarty
Geosci. Model Dev. Discuss., 2, 279–307, https://doi.org/10.5194/gmdd-2-279-2009, https://doi.org/10.5194/gmdd-2-279-2009, 2009
Revised manuscript has not been submitted (discussion: closed, 4 comments)
11 Mar 2009
Evaluation of the parametrized transport of lead-210 in high-altitude European sites
I. Dombrowski-Etchevers, V.-H. Peuch, B. Josse, and M. Legrand
Geosci. Model Dev. Discuss., 2, 247–278, https://doi.org/10.5194/gmdd-2-247-2009, https://doi.org/10.5194/gmdd-2-247-2009, 2009
Revised manuscript has not been submitted (discussion: closed, 2 comments)
06 Feb 2009
Derivation of a numerical solution of the 3D coupled velocity field for an ice sheet – ice shelf system, incorporating both full and approximate stress solutions
T. J. Reerink, R. S. W. van de Wal, and P.-P. Borsboom
Geosci. Model Dev. Discuss., 2, 81–158, https://doi.org/10.5194/gmdd-2-81-2009, https://doi.org/10.5194/gmdd-2-81-2009, 2009
Revised manuscript has not been submitted (discussion: closed, 5 comments)
15 Sep 2008
Historical reconstruction of the Aral Sea shrinking by a full 3-D wetting and drying model ECOSMO
I. Alekseeva and C. Schrum
Geosci. Model Dev. Discuss., 1, 243–283, https://doi.org/10.5194/gmdd-1-243-2008, https://doi.org/10.5194/gmdd-1-243-2008, 2008
Revised manuscript has not been submitted (discussion: closed, 5 comments)