Articles | Volume 12, issue 1
https://doi.org/10.5194/gmd-12-261-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-261-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
16 Jan 2019
Development and technical paper |
| 16 Jan 2019
| 16 Jan 2019
Independent perturbations for physics parametrization tendencies in a convection-permitting ensemble (pSPPT)
Clemens Wastl
Department of Forecasting Models, Zentralanstalt für Meteorologie und
Geodynamik, Vienna, Austria
Department of Forecasting Models, Zentralanstalt für Meteorologie und
Geodynamik, Vienna, Austria
Aitor Atencia
Department of Forecasting Models, Zentralanstalt für Meteorologie und
Geodynamik, Vienna, Austria
Christoph Wittmann
Department of Forecasting Models, Zentralanstalt für Meteorologie und
Geodynamik, Vienna, Austria
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Giovanni Di Virgilio, Jason P. Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew L. Riley, and Jyothi Lingala
Geosci. Model Dev., 18, 671–702, https://doi.org/10.5194/gmd-18-671-2025, https://doi.org/10.5194/gmd-18-671-2025, 2025
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We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.
Jiawang Feng, Chun Zhao, Qiuyan Du, Zining Yang, and Chen Jin
Geosci. Model Dev., 18, 585–603, https://doi.org/10.5194/gmd-18-585-2025, https://doi.org/10.5194/gmd-18-585-2025, 2025
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In this study, we improved the calculation of how aerosols in the air interact with radiation in WRF-Chem. The original model used a simplified method, but we developed a more accurate approach. We found that this method significantly changes the properties of the estimated aerosols and their effects on radiation, especially for dust aerosols. It also impacts the simulated weather conditions. Our work highlights the importance of correctly representing aerosol–radiation interactions in models.
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Geosci. Model Dev., 18, 461–482, https://doi.org/10.5194/gmd-18-461-2025, https://doi.org/10.5194/gmd-18-461-2025, 2025
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We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10–15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100 km and a 25 km grid. The three models are compared with observations to study the improvements thanks to the increased resolution.
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
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The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
Andy Richling, Jens Grieger, and Henning W. Rust
Geosci. Model Dev., 18, 361–375, https://doi.org/10.5194/gmd-18-361-2025, https://doi.org/10.5194/gmd-18-361-2025, 2025
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The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score (a measure of forecast performance) as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
Maria R. Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev., 18, 181–191, https://doi.org/10.5194/gmd-18-181-2025, https://doi.org/10.5194/gmd-18-181-2025, 2025
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Observational data and modelling capabilities have expanded in recent years, but there are still barriers preventing these two data sources from being used in synergy. Proper comparison requires generating, storing, and handling a large amount of data. This work describes the first step in the development of a new set of software tools, the VISION toolkit, which can enable the easy and efficient integration of observational and model data required for model evaluation.
Bijan Fallah, Masoud Rostami, Emmanuele Russo, Paula Harder, Christoph Menz, Peter Hoffmann, Iulii Didovets, and Fred F. Hattermann
Geosci. Model Dev., 18, 161–180, https://doi.org/10.5194/gmd-18-161-2025, https://doi.org/10.5194/gmd-18-161-2025, 2025
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We tried to contribute to a local climate change impact study in central Asia, a region that is water-scarce and vulnerable to global climate change. We use regional models and machine learning to produce reliable local data from global climate models. We find that regional models show more realistic and detailed changes in heavy precipitation than global climate models. Our work can help assess the future risks of extreme events and plan adaptation strategies in central Asia.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
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Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
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Hurricanes may worsen water quality in the lower Mississippi River basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate–nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Giovanni Seijo-Ellis, Donata Giglio, Gustavo Marques, and Frank Bryan
Geosci. Model Dev., 17, 8989–9021, https://doi.org/10.5194/gmd-17-8989-2024, https://doi.org/10.5194/gmd-17-8989-2024, 2024
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A CESM–MOM6 regional configuration of the Caribbean Sea was developed in response to the rising need for high-resolution models for climate impact studies. The configuration is validated for the period 2000–2020 and improves significant errors in a low-resolution model. Oceanic properties are well represented. Patterns of freshwater associated with the Amazon River are well captured, and the mean flows of ocean waters across multiple passages in the Caribbean Sea agree with observations.
Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
Geosci. Model Dev., 17, 8873–8884, https://doi.org/10.5194/gmd-17-8873-2024, https://doi.org/10.5194/gmd-17-8873-2024, 2024
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Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
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This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
Geosci. Model Dev., 17, 8665–8681, https://doi.org/10.5194/gmd-17-8665-2024, https://doi.org/10.5194/gmd-17-8665-2024, 2024
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We evaluate downscaled products by examining locally relevant co-variances during precipitation events. Common statistical downscaling techniques preserve expected co-variances during convective precipitation (a stationary phenomenon). However, they dampen future intensification of frontal precipitation (a non-stationary phenomenon) captured in global climate models and dynamical downscaling. Our study quantifies a ramification of the stationarity assumption underlying statistical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev., 17, 8593–8611, https://doi.org/10.5194/gmd-17-8593-2024, https://doi.org/10.5194/gmd-17-8593-2024, 2024
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Research software is vital for scientific progress but is often developed by scientists with limited skills, time, and funding, leading to challenges in usability and maintenance. Our study across 10 sectors shows strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. We recommend workshops; code quality metrics; funding; and following the findable, accessible, interoperable, and reusable (FAIR) standards.
Chris Smith, Donald P. Cummins, Hege-Beate Fredriksen, Zebedee Nicholls, Malte Meinshausen, Myles Allen, Stuart Jenkins, Nicholas Leach, Camilla Mathison, and Antti-Ilari Partanen
Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, https://doi.org/10.5194/gmd-17-8569-2024, 2024
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Climate projections are only useful if the underlying models that produce them are well calibrated and can reproduce observed climate change. We formalise a software package that calibrates the open-source FaIR simple climate model to full-complexity Earth system models. Observations, including historical warming, and assessments of key climate variables such as that of climate sensitivity are used to constrain the model output.
Jingwei Xie, Xi Wang, Hailong Liu, Pengfei Lin, Jiangfeng Yu, Zipeng Yu, Junlin Wei, and Xiang Han
Geosci. Model Dev., 17, 8469–8493, https://doi.org/10.5194/gmd-17-8469-2024, https://doi.org/10.5194/gmd-17-8469-2024, 2024
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We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should resolve the first baroclinic deformation radius and ensure the numerical dissipative effects do not directly contaminate the mesoscale motions. It can be a benchmark for testing mesoscale ocean large eddy simulation (MOLES) methods in ocean models. We build an idealized Southern Ocean model using MITgcm to generate a type of MODNS. We also illustrate the diversity of multiscale eddy interactions.
Emily Black, John Ellis, and Ross I. Maidment
Geosci. Model Dev., 17, 8353–8372, https://doi.org/10.5194/gmd-17-8353-2024, https://doi.org/10.5194/gmd-17-8353-2024, 2024
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We present General TAMSAT-ALERT, a computationally lightweight and versatile tool for generating ensemble forecasts from time series data. General TAMSAT-ALERT is capable of combining multiple streams of monitoring and meteorological forecasting data into probabilistic hazard assessments. In this way, it complements existing systems and enhances their utility for actionable hazard assessment.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleussner
Geosci. Model Dev., 17, 8283–8320, https://doi.org/10.5194/gmd-17-8283-2024, https://doi.org/10.5194/gmd-17-8283-2024, 2024
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Precipitation and temperature are two of the most impact-relevant climatic variables. Yet, projecting future precipitation and temperature data under different emission scenarios relies on complex models that are computationally expensive. In this study, we propose a method that allows us to generate monthly means of local precipitation and temperature at low computational costs. Our modelling framework is particularly useful for all downstream applications of climate model data.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3522, https://doi.org/10.5194/egusphere-2024-3522, 2024
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We analyzed carbon and nitrogen mass conservation in data from CMIP6 Earth System Models. Our findings reveal significant discrepancies between flux and pool size data, particularly in nitrogen, where cumulative imbalances can reach hundreds of gigatons. These imbalances appear primarily due to missing or inconsistently reported fluxes – especially for land use and fire emissions. To enhance data quality, we recommend that future climate data protocols address this issue at the reporting stage.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
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We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Peter Berg, Thomas Bosshard, Denica Bozhinova, Lars Bärring, Joakim Löw, Carolina Nilsson, Gustav Strandberg, Johan Södling, Johan Thuresson, Renate Wilcke, and Wei Yang
Geosci. Model Dev., 17, 8173–8179, https://doi.org/10.5194/gmd-17-8173-2024, https://doi.org/10.5194/gmd-17-8173-2024, 2024
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When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do at the tails of the distribution, where a larger data range is likely encountered outside of the calibration period. The end result is highly dependent on the method used. We show that, to avoid discontinuities in the time series, one needs to exclude data in the calibration range to also activate the extrapolation functionality in that time period.
Philip J. Rasch, Haruki Hirasawa, Mingxuan Wu, Sarah J. Doherty, Robert Wood, Hailong Wang, Andy Jones, James Haywood, and Hansi Singh
Geosci. Model Dev., 17, 7963–7994, https://doi.org/10.5194/gmd-17-7963-2024, https://doi.org/10.5194/gmd-17-7963-2024, 2024
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We introduce a protocol to compare computer climate simulations to better understand a proposed strategy intended to counter warming and climate impacts from greenhouse gas increases. This slightly changes clouds in six ocean regions to reflect more sunlight and cool the Earth. Example changes in clouds and climate are shown for three climate models. Cloud changes differ between the models, but precipitation and surface temperature changes are similar when their cooling effects are made similar.
Trude Eidhammer, Andrew Gettelman, Katherine Thayer-Calder, Duncan Watson-Parris, Gregory Elsaesser, Hugh Morrison, Marcus van Lier-Walqui, Ci Song, and Daniel McCoy
Geosci. Model Dev., 17, 7835–7853, https://doi.org/10.5194/gmd-17-7835-2024, https://doi.org/10.5194/gmd-17-7835-2024, 2024
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We describe a dataset where 45 parameters related to cloud processes in the Community Earth System Model version 2 (CESM2) Community Atmosphere Model version 6 (CAM6) are perturbed. Three sets of perturbed parameter ensembles (263 members) were created: current climate, preindustrial aerosol loading and future climate with sea surface temperature increased by 4 K.
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
Geosci. Model Dev., 17, 7815–7834, https://doi.org/10.5194/gmd-17-7815-2024, https://doi.org/10.5194/gmd-17-7815-2024, 2024
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The regional Earth system model GCOAST-AHOI v2.0 that includes the regional climate model ICON-CLM coupled to the ocean model NEMO and the hydrological discharge model HD via the OASIS3-MCT coupler can be a useful tool for conducting long-term regional climate simulations over the EURO-CORDEX domain. The new OASIS3-MCT coupling interface implemented in ICON-CLM makes it more flexible for coupling to an external ocean model and an external hydrological discharge model.
Sandro Vattioni, Rahel Weber, Aryeh Feinberg, Andrea Stenke, John A. Dykema, Beiping Luo, Georgios A. Kelesidis, Christian A. Bruun, Timofei Sukhodolov, Frank N. Keutsch, Thomas Peter, and Gabriel Chiodo
Geosci. Model Dev., 17, 7767–7793, https://doi.org/10.5194/gmd-17-7767-2024, https://doi.org/10.5194/gmd-17-7767-2024, 2024
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We quantified impacts and efficiency of stratospheric solar climate intervention via solid particle injection. Microphysical interactions of solid particles with the sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code of an aerosol–chemistry–climate model. Compared to injection of SO2 we only find a stronger cooling efficiency for solid particles when normalizing to the aerosol load but not when normalizing to the injection rate.
Ingo Richter, Ping Chang, Gokhan Danabasoglu, Dietmar Dommenget, Guillaume Gastineau, Aixue Hu, Takahito Kataoka, Noel Keenlyside, Fred Kucharski, Yuko Okumura, Wonsun Park, Malte Stuecker, Andrea Taschetto, Chunzai Wang, Stephen Yeager, and Sang-Wook Yeh
EGUsphere, https://doi.org/10.5194/egusphere-2024-3110, https://doi.org/10.5194/egusphere-2024-3110, 2024
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The tropical ocean basins influence each other through multiple pathways and mechanisms, here referred to as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models, but have obtained conflicting results. This may be partly due to differences in experiment protocols, and partly due to systematic model errors. TBIMIP aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Samuel Rémy, Swen Metzger, Vincent Huijnen, Jason E. Williams, and Johannes Flemming
Geosci. Model Dev., 17, 7539–7567, https://doi.org/10.5194/gmd-17-7539-2024, https://doi.org/10.5194/gmd-17-7539-2024, 2024
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In this paper we describe the development of the future operational cycle 49R1 of the IFS-COMPO system, used for operational forecasts of atmospheric composition in the CAMS project, and focus on the implementation of the thermodynamical model EQSAM4Clim version 12. The implementation of EQSAM4Clim significantly improves the simulated secondary inorganic aerosol surface concentration. The new aerosol and precipitation acidity diagnostics showed good agreement against observational datasets.
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev., 17, 7629–7643, https://doi.org/10.5194/gmd-17-7629-2024, https://doi.org/10.5194/gmd-17-7629-2024, 2024
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This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev., 17, 7445–7466, https://doi.org/10.5194/gmd-17-7445-2024, https://doi.org/10.5194/gmd-17-7445-2024, 2024
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We study the parameters of the turbulent-kinetic-energy mixed-layer-penetration scheme in the NEMO model with regard to sea-ice-covered regions of the Arctic Ocean. This evaluation reveals the impact of these parameters on mixed-layer depth, sea surface temperature and salinity, and ocean stratification. Our findings demonstrate significant impacts on sea ice thickness and sea ice concentration, emphasizing the need for accurately representing ocean mixing to understand Arctic climate dynamics.
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-183, https://doi.org/10.5194/gmd-2024-183, 2024
Revised manuscript accepted for GMD
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Improving climate predictions has significant socio-economic impacts. In this study, we developed and applied a weakly coupled ocean data assimilation (WCODA) system to a coupled climate model. The WCODA system improves simulations of ocean temperature and salinity across many global regions. It also enhances the simulation of interannual precipitation and temperature variability over the southern US. This system is to support future predictability studies.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
Geosci. Model Dev., 17, 7365–7399, https://doi.org/10.5194/gmd-17-7365-2024, https://doi.org/10.5194/gmd-17-7365-2024, 2024
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In this study, we improved a climate model by adding the representation of water use sectors such as domestic, industry, and agriculture. This new feature helps us understand how water is used and supplied in various areas. We tested our model from 1971 to 2010 and found that it accurately identifies areas with water scarcity. By modelling the competition between sectors when water availability is limited, the model helps estimate the intensity and extent of individual sectors' water shortages.
Yucheng Lin, Robert E. Kopp, Alexander Reedy, Matteo Turilli, Shantenu Jha, and Erica L. Ashe
EGUsphere, https://doi.org/10.5194/egusphere-2024-2183, https://doi.org/10.5194/egusphere-2024-2183, 2024
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PaleoSTeHM v1.0-rc is a state-of-the-art framework designed to reconstruct past environmental conditions using geological data. Built on modern machine learning techniques, it efficiently handles the sparse and noisy nature of paleo records, allowing scientists to make accurate and scalable inferences about past environmental change. By using flexible statistical models, PaleoSTeHM separates different sources of uncertainty, improving the precision of historical climate reconstructions.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
EGUsphere, https://doi.org/10.5194/egusphere-2024-2685, https://doi.org/10.5194/egusphere-2024-2685, 2024
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Forecasting river runoff, crucial for managing water resources and understanding climate impacts, can be challenging. This study introduces a new method using Convolutional Long Short-Term Memory (ConvLSTM) networks, a machine learning model that processes spatial and temporal data. Focusing on the Baltic Sea region, our model uses weather data as input to predict daily river runoff for 97 rivers.
Thi Nhu Ngoc Do, Kengo Sudo, Akihiko Ito, Louisa Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
EGUsphere, https://doi.org/10.5194/egusphere-2024-2313, https://doi.org/10.5194/egusphere-2024-2313, 2024
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Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth System Models mainly due to partially incorporating CO2 effects and land cover changes rather than climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant-climate interactions.
Cynthia Whaley, Montana Etten-Bohm, Courtney Schumacher, Ayodeji Akingunola, Vivek Arora, Jason Cole, Michael Lazare, David Plummer, Knut von Salzen, and Barbara Winter
Geosci. Model Dev., 17, 7141–7155, https://doi.org/10.5194/gmd-17-7141-2024, https://doi.org/10.5194/gmd-17-7141-2024, 2024
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This paper describes how lightning was added as a process in the Canadian Earth System Model in order to interactively respond to climate changes. As lightning is an important cause of global wildfires, this new model development allows for more realistic projections of how wildfires may change in the future, responding to a changing climate.
Cited articles
Bénard, P., Vivoda, J., Mašek, J., Smolıková, P., Yessad, K.,
Smith, C., Brožková, R., and Geleyn, J. F.: Dynamical kernel of the
Aladin-NH spectral limited-area model: revised formulation and sensitivity
experiments, Q. J. Roy. Meteor. Soc., 139, 155–169, https://doi.org/10.1002/qj.522,
2010.
Bengtsson, L., Steinheimer, M., Bechtold, P., and Geleyn, J. F.: A
stochastic parametrization for deep convection using cellular automata,
Q. J. Roy. Meteor. Soc., 139, 1533–1543, https://doi.org/10.1002/qj.2108,
2013.
Bengtsson, L., Andrae, U., Aspelien, T., Batrak, Y., Calvo, J., de Rooy, W.,
Gleeson, E., Hansen-Sass, B., Homleid, M., Hortal, M., Ivarsson, K.,
Lenderink, G., Niemelä, S., Nielsen, K. P., Onvlee, J., Rontu, L.,
Samuelsson, P., Muñoz, D., S., Subias, A., Tijm, S., Toll, V., Yang, X.,
and Køltzow, M. Ø.: The HARMONIE – AROME Model Configuration in the
ALADIN – HIRLAM NWP System, Mon. Weather Rev., 145, 1919–1935,
https://doi.org/10.1175/MWR-D-16-0417.1, 2017.
Berner, J., Shutts, G. J., Leutbecher, M., and Palmer, T. N.: A spectral
stochastic kinetic energy backscatter scheme and its impact on flow
dependent predictability in the ECMWF ensemble prediction system, J. Atmos.
Sci., 66, 603–626, https://doi.org/10.1175/2008JAS2677.1, 2009.
Berner, J., Fossell, K. R., Ha, S. Y., Hacker, J. P., and Snyder, C.:
Increasing the skill of probabilistic forecasts: Understanding performance
improvements from model-error representations, Mon. Weather Rev., 143,
1295–1320, https://doi.org/10.1175/MWR-D-14-00091.1, 2015.
Berre, L.: Estimation of synoptic and mesoscale forecast error covariances
in a limited area model, Mon. Weather Rev., 128, 644–667,
https://doi.org/10.1175/1520-0493(2000)128<0644:EOSAMF>2.0.CO;2, 2000.
Boisserie, M., Arbogast, P., Descamps, L., Pannekoucke, O., and Raynaud, L.:
Estimating and diagnosing model error variances in the Meteo-France global
NWP model, Q. J. Roy. Meteor. Soc., 140, 846–854, https://doi.org/10.1002/qj.2173, 2013.
Bouteloup, Y., Seity, Y., and Bazile, E.: Description of the sedimentation
scheme used operationally in all Météo-France NWP models, Tellus A,
63, 300–311, https://doi.org/10.1111/j.1600-0870.2010.00484.x, 2011.
Bouttier, F., Vié, B., Nuissier, O., and Raynaud, L.: Impact of
Stochastic Physics in a Convection-Permitting Ensemble, Mon. Weather Rev., 140,
3706–3721, https://doi.org/10.1175/MWR-D-12-00031.1, 2012.
Bowler, N. E., Arribas, A., Mylne, K. R., Robertson, K. B., and Beare, S.
E.: The MOGREPS short-range ensemble prediction system, Q. J. Roy.
Meteor. Soc., 134, 703–722, https://doi.org/10.1002/qj.234, 2008.
Bubnova, R., Hello, G., Bénard, P., and Geleyn, J. F.: Integration of
the fully elastic equations cast in the hydrostatic pressure
terrain-following coordinate in the framework of the ARPEGE/ALADIN NWP
system, Mon. Weather Rev., 123, 515–535, https://doi.org/10.1175/1520-0493(1995)123<0515:IOTFEE>2.0.CO;2, 1995.
Buizza, R., Miller, M., and Palmer, T. N.: Stochastic representation of
model uncertainties in the ECMWF ensemble prediction system, Q. J. Roy.
Meteor. Soc., 125, 2887–2908, 1999.
Christensen, H. M., Lock, S.-J., Moroz, I. M., and Palmer, T. N.:
Introducing independent patterns into the Stochastically Perturbed
Parametrization Tendencies (SPPT) scheme, Q. J. Roy. Meteor. Soc., 143,
2168–2181, https://doi.org/10.1002/qj.3075, 2017.
Cuxart, J., Bougeault, P., and Redelsperger, J.-L.: A turbulence scheme
allowing for mesoscale and large-eddy simulations, Q. J. Roy. Meteor.
Soc., 126, 1–30, https://doi.org/10.1002/qj.49712656202, 2000.
Davies, H.: A lateral boundary formulation for multi-level prediction
models, Q. J. Roy. Meteor. Soc., 102, 405–418, https://doi.org/10.1002/qj.49710243210, 1976.
Davini, P., von Hardenberg, J., Corti, S., Christensen, H. M., Juricke, S.,
Subramanian, A., Watson, P. A. G., Weisheimer, A., and Palmer, T. N.:
Climate SPHINX: evaluating the impact of resolution and stochastic physics
parametrisations in the EC-Earth global climate model, Geosci. Model Dev.,
10, 1383–1402, https://doi.org/10.5194/gmd-10-1383-2017, 2017.
Fouquart, Y. and Bonnel, B.: Computations of solar heating of the earth's
atmosphere: A new parameterization, Beitr. Phys. Atmos., 53, 35–62,
https://doi.org/10.1029/JD093iD09p11063, 1980.
Haiden, T., Kann, A., Wittmann, C., Pistotnik, G., Bica, B., and Gruber, C.:
The Integrated Nowcasting through Comprehensive Analysis (INCA) System and
Its Validation over the Eastern Alpine Region, Weather Forecast., 26,
166–183, https://doi.org/10.1175/2010WAF2222451.1, 2011.
Hamill, T. and Colucci, S. J.: Verification of Eta–RSM short-range ensemble
forecasts, Mon. Weather Rev., 125, 1312–1327,
https://doi.org/10.1175/1520-0493(1997)125<1312:VOERSR>2.0.CO;2, 1997.
Isaksen, L., Bonavita, M., Buizza, R., Fisher, M., Haseler, J., Leutbecher,
M., and Raynaud, L.: Ensemble of data assimilations at ECMWF, Tech. Mem.
ECMWF, 636, 1–48, 2010.
Kober, K. and Craig, G. C.: Physically based stochastic perturbations (PSP)
in the boundary layer to represent uncertainty in convective initiation, J.
Atmos. Sci., 73, 2893–2911, https://doi.org/10.1175/JAS-D-15-0144.1, 2016.
Leutbecher, M. and Lang, S. T. K.: On the reliability of ensemble variance
in subspaces defined by singular vectors, Q. J. Roy. Meteor. Soc., 140,
1453–1466, https://doi.org/10.1002/qj.2229, 2013.
Leutbecher, M., Lock, S., Ollinaho, P., Lang, S. T., Balsamo, G., Bechtold,
P. , Bonavita, M., Christensen, H. M., Diamantakis, M., Dutra, E., English,
S., Fisher, M., Forbes, R. M., Goddard, J., Haiden, T., Hogan, R. J.,
Juricke, S., Lawrence, H., MacLeod, D., Magnusson, L., Malardel, S.,
Massart, S., Sandu, I., Smolarkiewicz, P. K., Subramanian, A., Vitart, F.,
Wedi, N., and Weisheimer, A.: Stochastic representations of model
uncertainties at ECMWF: State of the art and future vision, Q. J. Roy.
Meteor. Soc., 143, 2315–2339, https://doi.org/10.1002/qj.3094, 2017.
Mascart, P. J. and Bougeault, P.: The Meso-NH atmospheric simulation
system: Scientific documentation, Tech. rep. Meteo France, 2011.
Masson, V., Le Moigne, P., Martin, E., Faroux, S., Alias, A., Alkama, R.,
Belamari, S., Barbu, A., Boone, A., Bouyssel, F., Brousseau, P., Brun, E.,
Calvet, J.-C., Carrer, D., Decharme, B., Delire, C., Donier, S., Essaouini,
K., Gibelin, A.-L., Giordani, H., Habets, F., Jidane, M., Kerdraon, G.,
Kourzeneva, E., Lafaysse, M., Lafont, S., Lebeaupin Brossier, C., Lemonsu,
A., Mahfouf, J.-F., Marguinaud, P., Mokhtari, M., Morin, S., Pigeon, G.,
Salgado, R., Seity, Y., Taillefer, F., Tanguy, G., Tulet, P., Vincendon, B.,
Vionnet, V., and Voldoire, A.: The SURFEXv7.2 land and ocean surface
platform for coupled or offline simulation of earth surface variables and
fluxes, Geosci. Model. Dev., 6, 929–960, https://doi.org/10.5194/gmd-6-929-2013, 2013.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S.
A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated
correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 663–682,
https://doi.org/10.1029/97JD00237, 1997.
Ollinaho, P., Lock, S.-J., Leutbecher, M., Bechtold, P., Beljaars, A.,
Bozzo, A., Forbes, R. M., Haiden, T., Hogan, R., and Sandu, I.: Towards
process-level representation of model uncertainties: Stochastically
perturbed parametrisations in the ECMWF ensemble, Q. J. Roy. Meteor.
Soc., 143, 408–422, https://doi.org/10.1002/qj.2931, 2017.
Palmer, T. N., Buizza, R., Doblas-Reyes, F., Jung, T., Leutbecher, M.,
Shutts, G. J., Steinheimer, M., and Weisheimer, A.: Stochastic
parametrization and model uncertainty, Tech. Mem. ECMWF, 598,
available at: https://www.ecmwf.int/en/elibrary/11577-stochastic-parametrization-and-model-uncertainty
(last access: 10 July 2018),
2009.
Pergaud, J., Masson, V., and Malardel, S.: A parameterization of dry
thermals and shallow cumuli for mesoscale numerical weather prediction,
Bound.-Layer Meteor., 132, 83–106, https://doi.org/10.1007/s10546-009-9388-0, 2009.
Pinty, J.-P. and Jabouille, P.: A mixed-phase cloud parameterization for
use in mesoscale non-hydrostatic model: simulations of a squall line and of
orographic precipitations, Proc. Conf. Cloud Phys., 1999, 217–220,
https://doi.org/10.1256/qj.02.50, 1998.
Plant, R. S. and Craig, G. C.: A stochastic parameterization for deep
convection based on equilibrium statistics, J. Atmos. Sci., 65, 87–104,
https://doi.org/10.1175/2007JAS2263.1, 2008.
Sanchez, C., Williams, K. D., and Collins, M.: Improved stochastic physics
schemes for global weather and climate models, Q. J. Roy. Meteor. Soc.,
142, 147–159, https://doi.org/10.1002/qj.2640, 2016.
Seity, Y., Brousseau, P., Malardel, S., Hello, G., Bénard, P., Bouttier,
F., Lac, C., and Masson, V.: The AROME-France Convective-Scale Operational
Model, Mon. Weather Rev., 139, 976–991, https://doi.org/10.1175/2010MWR3425.1, 2011.
Shutts, G. J. and Pallares, A. C.: Assessing parametrization uncertainty
associated with horizontal resolution in numerical weather prediction
models, Philos. Trans. R. Soc. A, 372, 1–14, 2014.
Szűcs, M.: SPPT in AROME and ALARO, Presentation at HIRLAM WW on EPS and
Predictability, available at:
http://www.rclace.eu/File/Predictability/2014/LACE_report_Mihaly_Szucs_2014.pdf
(last access: 15 July 2018),
2016.
Termonia, P., Fischer, C., Bazile, E., Bouyssel, F., Brožková, R.,
Bénard, P., Bochenek, B., Degrauwe, D., Derková, M., El Khatib, R.,
Hamdi, R., Mašek, J., Pottier, P., Pristov, N., Seity, Y.,
Smolíková, P., Španiel, O., Tudor, M., Wang, Y., Wittmann, C.,
and Joly, A.: The ALADIN System and its Canonical Model Configurations AROME
CY41T1 and ALARO CY40T1, Geosci. Model. Dev., 11, 257–281,
https://doi.org/10.5194/gmd-11-257-2018, 2018.
Wang, Y., Meirold-Mautner, I., Kann, A., Ŝajn Slak, A., Simon, A.,
Vivoda, J., Bica, B., Böcskör, E., Brezková, L., Dantinger, J.,
Giszterowicz, M., Heizler, G., Iwanski, R., Jachs, S., Bernard, T.,
Kršmanc, R., Merše, J., Micheletti, S., Schmid, F., Steininger, M.,
Haiden, T., Regec, A., Buzzi, M., Derková, M., Kozarić, T., Qiu, X.,
Reyniers, M., Yang, J., Huang, Y., and Vadislavsky, E.: Integrating
nowcasting with crisis management and risk prevention in a transnational and
interdisciplinary framework, Meteor. Z., 26, 459–473, https://doi.org/10.1127/metz/2017/0843, 2017.
Wang, Y., Belluš, M., Ehrlich, A., Mile, M., Pristov, N., Smoliková,
P., Španiel, O., Trojáková, A., Brožkov, R., Cedelnik, J.,
Klarić, D., Kovačić, T., Mašek, J., Meier, F., Szintai, B.,
Tascu, S., Vivoda, Wastl, C., and Wittmann, C.: 27 years of Regional
Cooperation for Limited Area Modelling in Central Europe (RC LACE), B.
Am. Meteorol. Soc., 99, 1415–1432, https://doi.org/10.1175/BAMS-D-16-0321.1, 2018.
Weidle, F., Wang, Y., Tian, W., and Wang, T.: Validation of strategies using
Clustering analysis of ECMWF-EPS for initial perturbations in a Limited Area
Model Ensemble Prediction System, Atmos.-Ocean, 51, 248–295, https://doi.org/10.1080/07055900.2013.802217, 2013.
Weisheimer, A., Corti, S., Palmer, T. N., and Vitart, F.: Addressing model
error through atmospheric stochastic physical parametrizations: impact on
the coupled ECMWF seasonal forecasting system, Philos. Trans. R. Soc. A,
372, 1–21, https://doi.org/10.1098/rsta.2013.0284, 2014.
Wilks, D.: Statistical Methods in the Atmospheric Sciences, Volume 100, 3rd
Edition, Academic Press, 704 pp., 2011.
Short summary
Ensemble forecasting at the convection-permitting scale (< 3 km) requires new methodologies in representing model uncertainties. In this paper a new stochastic scheme is proposed and tested in the complex terrain of the Alps. In this scheme the tendencies of the physical parametrizations are perturbed separately, which sustains a physically consistent relationship between the processes. This scheme increases the stability of the model and leads to improvements in the probabilistic performance.
Ensemble forecasting at the convection-permitting scale ( 3 km) requires new methodologies in...
