Articles | Volume 16, issue 23
https://doi.org/10.5194/gmd-16-7059-2023
© Author(s) 2023. 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-16-7059-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
05 Dec 2023
Methods for assessment of models |
| 05 Dec 2023
| 05 Dec 2023
An emulation-based approach for interrogating reactive transport models
Department of Earth Sciences, University of Cambridge, Cambridge, UK
Harold J. Bradbury
Department of Earth Sciences, University of Cambridge, Cambridge, UK
Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Vancouver, Canada
Jennifer L. Druhan
Department of Geology, University of Illinois at Urbana Champaign, Urbana, IL, United States of America
Alexandra V. Turchyn
Department of Earth Sciences, University of Cambridge, Cambridge, UK
Related authors
No articles found.
Babette A.A. Hoogakker, Catherine Davis, Yi Wang, Stephanie Kusch, Katrina Nilsson-Kerr, Dalton S. Hardisty, Allison Jacobel, Dharma Reyes Macaya, Nicolaas Glock, Sha Ni, Julio Sepúlveda, Abby Ren, Alexandra Auderset, Anya V. Hess, Katrin J. Meissner, Jorge Cardich, Robert Anderson, Christine Barras, Chandranath Basak, Harold J. Bradbury, Inda Brinkmann, Alexis Castillo, Madelyn Cook, Kassandra Costa, Constance Choquel, Paula Diz, Jonas Donnenfield, Felix J. Elling, Zeynep Erdem, Helena L. Filipsson, Sebastián Garrido, Julia Gottschalk, Anjaly Govindankutty Menon, Jeroen Groeneveld, Christian Hallmann, Ingrid Hendy, Rick Hennekam, Wanyi Lu, Jean Lynch-Stieglitz, Lélia Matos, Alfredo Martínez-García, Giulia Molina, Práxedes Muñoz, Simone Moretti, Jennifer Morford, Sophie Nuber, Svetlana Radionovskaya, Morgan Reed Raven, Christopher J. Somes, Anja S. Studer, Kazuyo Tachikawa, Raúl Tapia, Martin Tetard, Tyler Vollmer, Xingchen Wang, Shuzhuang Wu, Yan Zhang, Xin-Yuan Zheng, and Yuxin Zhou
Biogeosciences, 22, 863–957, https://doi.org/10.5194/bg-22-863-2025, https://doi.org/10.5194/bg-22-863-2025, 2025
Short summary
Short summary
Paleo-oxygen proxies can extend current records, constrain pre-anthropogenic baselines, provide datasets necessary to test climate models under different boundary conditions, and ultimately understand how ocean oxygenation responds on longer timescales. Here we summarize current proxies used for the reconstruction of Cenozoic seawater oxygen levels. This includes an overview of the proxy's history, how it works, resources required, limitations, and future recommendations.
Gabrielle E. Kleber, Leonard Magerl, Alexandra V. Turchyn, Stefan Schloemer, Mark Trimmer, Yizhu Zhu, and Andrew Hodson
Biogeosciences, 22, 659–674, https://doi.org/10.5194/bg-22-659-2025, https://doi.org/10.5194/bg-22-659-2025, 2025
Short summary
Short summary
Our research on Svalbard shows that glacier melt rivers can transport large amounts of methane, a potent greenhouse gas. By studying a glacier over one summer, we found that its river was highly concentrated in methane, suggesting that rivers could provide a significant source of methane emissions as the Arctic warms and glaciers melt. This is the first time such emissions have been measured on Svalbard, indicating a wider environmental concern as such processes are occurring across the Arctic.
Miguel Bartolomé, Gérard Cazenave, Marc Luetscher, Christoph Spötl, Fernando Gázquez, Ánchel Belmonte, Alexandra V. Turchyn, Juan Ignacio López-Moreno, and Ana Moreno
The Cryosphere, 17, 477–497, https://doi.org/10.5194/tc-17-477-2023, https://doi.org/10.5194/tc-17-477-2023, 2023
Short summary
Short summary
In this work we study the microclimate and the geomorphological features of Devaux ice cave in the Central Pyrenees. The research is based on cave monitoring, geomorphology, and geochemical analyses. We infer two different thermal regimes. The cave is impacted by flooding in late winter/early spring when the main outlets freeze, damming the water inside. Rock temperatures below 0°C and the absence of drip water indicate frozen rock, while relict ice formations record past damming events.
Andrew J. Hodson, Aga Nowak, Mikkel T. Hornum, Kim Senger, Kelly Redeker, Hanne H. Christiansen, Søren Jessen, Peter Betlem, Steve F. Thornton, Alexandra V. Turchyn, Snorre Olaussen, and Alina Marca
The Cryosphere, 14, 3829–3842, https://doi.org/10.5194/tc-14-3829-2020, https://doi.org/10.5194/tc-14-3829-2020, 2020
Short summary
Short summary
Methane stored below permafrost is an unknown quantity in the Arctic greenhouse gas budget. In coastal areas with rising sea levels, much of the methane seeps into the sea and is removed before it reaches the atmosphere. However, where land uplift outpaces rising sea levels, the former seabed freezes, pressurising methane-rich groundwater beneath, which then escapes via permafrost seepages called pingos. We describe this mechanism and the origins of the methane discharging from Svalbard pingos.
Yuchen Liu, Matthew J. Winnick, Hsiao-Tieh Hsu, Corey R. Lawrence, Kate Maher, and Jennifer L. Druhan
Biogeosciences Discuss., https://doi.org/10.5194/bg-2018-10, https://doi.org/10.5194/bg-2018-10, 2018
Preprint withdrawn
Short summary
Short summary
Microbes naturally occur in soils and respire CO2, thus constituting a significant source of atmospheric greenhouse gases. We seek to improve predictions for the amount of CO2 emitted from soil by contrasting two models compared against lab measured respiration rates using natural soil samples at a range of soil moistures. Results show that a simplified model is more suitable for interpreting soil respiration rates below 100 cm, while a more complex approach is necessary for shallower depths.
Related subject area
Climate and Earth system modeling
The Earth Science Box Modeling Toolkit (ESBMTK 0.14.0.11): a Python library for research and teaching
CropSuite v1.0 – a comprehensive open-source crop suitability model considering climate variability for climate impact assessment
ICON ComIn – the ICON Community Interface (ComIn version 0.1.0, with ICON version 2024.01-01)
Using feature importance as an exploratory data analysis tool on Earth system models
A new metrics framework for quantifying and intercomparing atmospheric rivers in observations, reanalyses, and climate models
The real challenges for climate and weather modelling on its way to sustained exascale performance: a case study using ICON (v2.6.6)
Improving the representation of major Indian crops in the Community Land Model version 5.0 (CLM5) using site-scale crop data
Evaluation of CORDEX ERA5-forced NARCliM2.0 regional climate models over Australia using the Weather Research and Forecasting (WRF) model version 4.1.2
Design, evaluation, and future projections of the NARCliM2.0 CORDEX-CMIP6 Australasia regional climate ensemble
Amending the algorithm of aerosol–radiation interactions in WRF-Chem (v4.4)
The very-high-resolution configuration of the EC-Earth global model for HighResMIP
GOSI9: UK Global Ocean and Sea Ice configurations
Decomposition of skill scores for conditional verification: impact of Atlantic Multidecadal Oscillation phases on the predictability of decadal temperature forecasts
Virtual Integration of Satellite and In-situ Observation Networks (VISION) v1.0: In-Situ Observations Simulator (ISO_simulator)
Climate model downscaling in central Asia: a dynamical and a neural network approach
Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5 and NEMOv3.4
Subsurface hydrological controls on the short-term effects of hurricanes on nitrate–nitrogen runoff loading: a case study of Hurricane Ida using the Energy Exascale Earth System Model (E3SM) Land Model (v2.1)
CARIB12: a regional Community Earth System Model/Modular Ocean Model 6 configuration of the Caribbean Sea
Architectural insights into and training methodology optimization of Pangu-Weather
Evaluation of global fire simulations in CMIP6 Earth system models
Evaluating downscaled products with expected hydroclimatic co-variances
Software sustainability of global impact models
fair-calibrate v1.4.1: calibration, constraining, and validation of the FaIR simple climate model for reliable future climate projections
ISOM 1.0: a fully mesoscale-resolving idealized Southern Ocean model and the diversity of multiscale eddy interactions
A computationally lightweight model for ensemble forecasting of environmental hazards: General TAMSAT-ALERT v1.2.1
Introducing the MESMER-M-TPv0.1.0 module: spatially explicit Earth system model emulation for monthly precipitation and temperature
Investigating Carbon and Nitrogen Conservation in Reported CMIP6 Earth System Model Data
The need for carbon-emissions-driven climate projections in CMIP7
Robust handling of extremes in quantile mapping – “Murder your darlings”
A protocol for model intercomparison of impacts of marine cloud brightening climate intervention
An extensible perturbed parameter ensemble for the Community Atmosphere Model version 6
Coupling the regional climate model ICON-CLM v2.6.6 to the Earth system model GCOAST-AHOI v2.0 using OASIS3-MCT v4.0
A fully coupled solid-particle microphysics scheme for stratospheric aerosol injections within the aerosol–chemistry–climate model SOCOL-AERv2
The Tropical Basin Interaction Model Intercomparison Project (TBIMIP)
An improved representation of aerosol in the ECMWF IFS-COMPO 49R1 through the integration of EQSAM4Climv12 – a first attempt at simulating aerosol acidity
At-scale Model Output Statistics in mountain environments (AtsMOS v1.0)
Impact of ocean vertical-mixing parameterization on Arctic sea ice and upper-ocean properties using the NEMO-SI3 model
Bridging the gap: a new module for human water use in the Community Earth System Model version 2.2.1
From Weather Data to River Runoff: Leveraging Spatiotemporal Convolutional Networks for Comprehensive Discharge Forecasting
Historical Trends and Controlling Factors of Isoprene Emissions in CMIP6 Earth System Models
Modeling Commercial-Scale CO2 Storage in the Gas Hydrate Stability Zone with PFLOTRAN v6.0
A new lightning scheme in the Canadian Atmospheric Model (CanAM5.1): implementation, evaluation, and projections of lightning and fire in future climates
Methane dynamics in the Baltic Sea: investigating concentration, flux, and isotopic composition patterns using the coupled physical–biogeochemical model BALTSEM-CH4 v1.0
Split-explicit external mode solver in the finite volume sea ice–ocean model FESOM2
Applying double cropping and interactive irrigation in the North China Plain using WRF4.5
Coupled Carbon-Nitrogen Cycle in MAGICC v1.0.0: Model Description and Calibration
The sea ice component of GC5: coupling SI3 to HadGEM3 using conductive fluxes
CICE on a C-grid: new momentum, stress, and transport schemes for CICEv6.5
SURFER v3.0: a fast model with ice sheet tipping points and carbon cycle feedbacks for short and long-term climate scenarios
HyPhAICC v1.0: a hybrid physics–AI approach for probability fields advection shown through an application to cloud cover nowcasting
Ulrich G. Wortmann, Tina Tsan, Mahrukh Niazi, Irene A. Ma, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
Geosci. Model Dev., 18, 1155–1167, https://doi.org/10.5194/gmd-18-1155-2025, https://doi.org/10.5194/gmd-18-1155-2025, 2025
Short summary
Short summary
The Earth Science Box Modeling Toolkit (ESBMTK) is a user-friendly Python library that simplifies the creation of models to study earth system processes, such as the carbon cycle and ocean chemistry. It enhances learning by emphasizing concepts over programming and is accessible to students and researchers alike. By automating complex calculations and promoting code clarity, ESBMTK accelerates model development while improving reproducibility and the usability of scientific research.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
Geosci. Model Dev., 18, 1067–1087, https://doi.org/10.5194/gmd-18-1067-2025, https://doi.org/10.5194/gmd-18-1067-2025, 2025
Short summary
Short summary
CropSuite is a new open-source crop suitability model. It provides a GUI and a wide range of options, including a spatial downscaling of climate data. We apply CropSuite to 48 staple and opportunity crops at a 1 km spatial resolution in Africa. We find that climate variability significantly impacts suitable areas but also affects optimal sowing dates and multiple cropping potential. The results provide valuable information for climate impact assessments, adaptation, and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev., 18, 1001–1015, https://doi.org/10.5194/gmd-18-1001-2025, https://doi.org/10.5194/gmd-18-1001-2025, 2025
Short summary
Short summary
The ICOsahedral Non-hydrostatic (ICON) model system Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++, and Python), and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev., 18, 1041–1065, https://doi.org/10.5194/gmd-18-1041-2025, https://doi.org/10.5194/gmd-18-1041-2025, 2025
Short summary
Short summary
Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis A. O'Brien
Geosci. Model Dev., 18, 961–976, https://doi.org/10.5194/gmd-18-961-2025, https://doi.org/10.5194/gmd-18-961-2025, 2025
Short summary
Short summary
A metrics package designed for easy analysis of atmospheric river (AR) characteristics and statistics is presented. The tool is efficient for diagnosing systematic AR bias in climate models and useful for evaluating new AR characteristics in model simulations. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the North and South Atlantic (South Pacific and Indian Ocean).
Panagiotis Adamidis, Erik Pfister, Hendryk Bockelmann, Dominik Zobel, Jens-Olaf Beismann, and Marek Jacob
Geosci. Model Dev., 18, 905–919, https://doi.org/10.5194/gmd-18-905-2025, https://doi.org/10.5194/gmd-18-905-2025, 2025
Short summary
Short summary
In this paper, we investigated performance indicators of the climate model ICON (ICOsahedral Nonhydrostatic) on different compute architectures to answer the question of how to generate high-resolution climate simulations. Evidently, it is not enough to use more computing units of the conventionally used architectures; higher memory throughput is the most promising approach. More potential can be gained from single-node optimization rather than simply increasing the number of compute nodes.
Kangari Narender Reddy, Somnath Baidya Roy, Sam S. Rabin, Danica L. Lombardozzi, Gudimetla Venkateswara Varma, Ruchira Biswas, and Devavat Chiru Naik
Geosci. Model Dev., 18, 763–785, https://doi.org/10.5194/gmd-18-763-2025, https://doi.org/10.5194/gmd-18-763-2025, 2025
Short summary
Short summary
The study aimed to improve the representation of wheat and rice in a land model for the Indian region. The modified model performed significantly better than the default model in simulating crop phenology, yield, and carbon, water, and energy fluxes compared to observations. The study highlights the need for global land models to use region-specific crop parameters for accurately simulating vegetation processes and land surface processes.
Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason P. Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew L. Riley
Geosci. Model Dev., 18, 703–724, https://doi.org/10.5194/gmd-18-703-2025, https://doi.org/10.5194/gmd-18-703-2025, 2025
Short summary
Short summary
We evaluate the skill in simulating the Australian climate of some of the latest generation of regional climate models. We show when and where the models simulate this climate with high skill versus model limitations. We show how new models perform relative to the previous-generation models, assessing how model design features may underlie key performance improvements. This work is of national and international relevance as it can help guide the use and interpretation of climate projections.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-162, https://doi.org/10.5194/gmd-2024-162, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most dangerous effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a sub-sea CO2 injection.
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
Short summary
Short summary
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.
Erik Gustafsson, Bo G. Gustafsson, Martijn Hermans, Christoph Humborg, and Christian Stranne
Geosci. Model Dev., 17, 7157–7179, https://doi.org/10.5194/gmd-17-7157-2024, https://doi.org/10.5194/gmd-17-7157-2024, 2024
Short summary
Short summary
Methane (CH4) cycling in the Baltic Proper is studied through model simulations, enabling a first estimate of key CH4 fluxes. A preliminary budget identifies benthic CH4 release as the dominant source and two main sinks: CH4 oxidation in the water (92 % of sinks) and outgassing to the atmosphere (8 % of sinks). This study addresses CH4 emissions from coastal seas and is a first step toward understanding the relative importance of open-water outgassing compared with local coastal hotspots.
Tridib Banerjee, Patrick Scholz, Sergey Danilov, Knut Klingbeil, and Dmitry Sidorenko
Geosci. Model Dev., 17, 7051–7065, https://doi.org/10.5194/gmd-17-7051-2024, https://doi.org/10.5194/gmd-17-7051-2024, 2024
Short summary
Short summary
In this paper we propose a new alternative to one of the functionalities of the sea ice model FESOM2. The alternative we propose allows the model to capture and simulate fast changes in quantities like sea surface elevation more accurately. We also demonstrate that the new alternative is faster and more adept at taking advantages of highly parallelized computing infrastructure. We therefore show that this new alternative is a great addition to the sea ice model FESOM2.
Yuwen Fan, Zhao Yang, Min-Hui Lo, Jina Hur, and Eun-Soon Im
Geosci. Model Dev., 17, 6929–6947, https://doi.org/10.5194/gmd-17-6929-2024, https://doi.org/10.5194/gmd-17-6929-2024, 2024
Short summary
Short summary
Irrigated agriculture in the North China Plain (NCP) has a significant impact on the local climate. To better understand this impact, we developed a specialized model specifically for the NCP region. This model allows us to simulate the double-cropping vegetation and the dynamic irrigation practices that are commonly employed in the NCP. This model shows improved performance in capturing the general crop growth, such as crop stages, biomass, crop yield, and vegetation greenness.
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
EGUsphere, https://doi.org/10.5194/egusphere-2024-1941, https://doi.org/10.5194/egusphere-2024-1941, 2024
Short summary
Short summary
We studied the coupled carbon-nitrogen cycle effect in Earth System Models by developing a carbon-nitrogen coupling in a reduced complexity model, MAGICC. Our model successfully emulated the global carbon-nitrogen cycle dynamics seen in CMIP6 complex models. Results indicate consistent nitrogen limitations on plant growth (net primary production) from 1850 to 2100. Our findings suggest that nitrogen deficiency could reduce future land carbon sequestration.
Ed Blockley, Emma Fiedler, Jeff Ridley, Luke Roberts, Alex West, Dan Copsey, Daniel Feltham, Tim Graham, David Livings, Clement Rousset, David Schroeder, and Martin Vancoppenolle
Geosci. Model Dev., 17, 6799–6817, https://doi.org/10.5194/gmd-17-6799-2024, https://doi.org/10.5194/gmd-17-6799-2024, 2024
Short summary
Short summary
This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used as the physical basis for UK contributions to CMIP7. Documentation of science options used in the configuration are given along with a brief model evaluation. This is the first UK configuration to use NEMO’s new SI3 sea ice model. We provide details on how SI3 was adapted to work with Met Office coupling methodology and documentation of coupling processes in the model.
Jean-François Lemieux, William H. Lipscomb, Anthony Craig, David A. Bailey, Elizabeth C. Hunke, Philippe Blain, Till A. S. Rasmussen, Mats Bentsen, Frédéric Dupont, David Hebert, and Richard Allard
Geosci. Model Dev., 17, 6703–6724, https://doi.org/10.5194/gmd-17-6703-2024, https://doi.org/10.5194/gmd-17-6703-2024, 2024
Short summary
Short summary
We present the latest version of the CICE model. It solves equations that describe the dynamics and the growth and melt of sea ice. To do so, the domain is divided into grid cells and variables are positioned at specific locations in the cells. A new implementation (C-grid) is presented, with the velocity located on cell edges. Compared to the previous B-grid, the C-grid allows for a natural coupling with some oceanic and atmospheric models. It also allows for ice transport in narrow channels.
Victor Couplet, Marina Martínez Montero, and Michel Crucifix
EGUsphere, https://doi.org/10.5194/egusphere-2024-2279, https://doi.org/10.5194/egusphere-2024-2279, 2024
Short summary
Short summary
We present SURFER v3.0, a simple climate model designed to estimate the impact of CO2 and CH4 emissions on global temperatures, sea levels, and ocean pH. We added new carbon cycle processes and calibrated the model to observations and results from more complex models, enabling use over time scales ranging from decades to millions of years. SURFER v3.0 is fast, transparent, and easy to use, making it an ideal tool for policy assessments and suitable for educational purposes.
Rachid El Montassir, Olivier Pannekoucke, and Corentin Lapeyre
Geosci. Model Dev., 17, 6657–6681, https://doi.org/10.5194/gmd-17-6657-2024, https://doi.org/10.5194/gmd-17-6657-2024, 2024
Short summary
Short summary
This study introduces a novel approach that combines physics and artificial intelligence (AI) for improved cloud cover forecasting. This approach outperforms traditional deep learning (DL) methods in producing realistic and physically consistent results while requiring less training data. This architecture provides a promising solution to overcome the limitations of classical AI methods and contributes to open up new possibilities for combining physical knowledge with deep learning models.
Cited articles
Abd, A. S. and Abushaikha, A. S.: Reactive transport in porous media: a review of recent mathematical efforts in modeling geochemical reactions in petroleum subsurface reservoirs, SN Appl. Sci., 3, 401, https://doi.org/10.1007/s42452-021-04396-9, 2021.
Ahmmed, B., Mudunuru, M. K., Karra, S., James, S. C., and Vesselinov, V. V.: A comparative study of machine learning models for predicting the state of reactive mixing, J. Comput. Phys., 432, 110147, https://doi.org/10.1016/j.jcp.2021.110147, 2021.
Anderson, R. T., Vrionis, H. A., Ortiz-Bernad, I., Resch, C. T., Long, P. E., Dayvault, R., Karp, K., Marutzky, S., Metzler, D. R., Peacock, A., White, D. C., Lowe, M., and Lovley, D. R.: Stimulating the In Situ Activity of Geobacter Species To Remove Uranium from the Groundwater of a Uranium-Contaminated Aquifer, Applied and Environmental Microbiology, 69, 5884–5891, https://doi.org/10.1128/AEM.69.10.5884-5891.2003, 2003.
Arora, B., Dwivedi, D., Faybishenko, B., Wainwright, H. M., and Jana, R. B.: 10. Understanding and Predicting Vadose Zone Processes, in: Reviews in Mineralogy & Geochemistry, vol. 85, edited by: Druhan, J. L. and Tournassat, C., De Gruyter, 303–328, https://doi.org/10.1515/9781501512001-011, 2020.
Bain, J. G., Blowes, D. W., Robertson, W. D., and Frind, E. O.: Modelling of sulfide oxidation with reactive transport at a mine drainage site, J. Contam. Hydrol., 41, 23–47, https://doi.org/10.1016/S0169-7722(99)00069-8, 2000.
Bargar, J. R., Williams, K. H., Campbell, K. M., Long, P. E., Stubbs, J. E., Suvorova, E. I., Lezama-Pacheco, J. S., Alessi, D. S., Stylo, M., Webb, S. M., Davis, J. A., Giammar, D. E., Blue, L. Y., and Bernier-Latmani, R.: Uranium redox transition pathways in acetate-amended sediments, P. Natl. Acad. Sci. USA, 110, 4506–4511, https://doi.org/10.1073/pnas.1219198110, 2013.
Bethke, C. M., Sanford, R. A., Kirk, M. F., Jin, Q., and Flynn, T. M.: The Thermodynamic Ladder in Geomicrobiology, Am. J. Sci., 311, 183–210, https://doi.org/10.2475/03.2011.01, 2011.
Beucler, T., Rasp, S., Pritchard, M., and Gentine, P.: Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling, arXiv [preprint], https://doi.org/10.48550/ARXIV.1906.06622, 2019.
Bianchi, M., Zheng, L., and Birkholzer, J. T.: Combining multiple lower-fidelity models for emulating complex model responses for CCS environmental risk assessment, Int. J.. Green. Gas Con., 46, 248–258, https://doi.org/10.1016/j.ijggc.2016.01.009, 2016.
Bone, S. E., Dynes, J. J., Cliff, J., and Bargar, J. R.: Uranium(IV) adsorption by natural organic matter in anoxic sediments, P. Natl. Acad. Sci. USA, 114, 711–716, https://doi.org/10.1073/pnas.1611918114, 2017.
Cama, J., Soler, J. M., and Ayora, C.: 15. Acid Water–Rock–Cement Interaction and Multicomponent Reactive Transport Modeling, in: Reviews in Mineralogy & Geochemistry, vol. 85, edited by: Druhan, J. L. and Tournassat, C., De Gruyter, 459–498, https://doi.org/10.1515/9781501512001-016, 2020.
Castruccio, S., McInerney, D. J., Stein, M. L., Liu Crouch, F., Jacob, R. L., and Moyer, E. J.: Statistical Emulation of Climate Model Projections Based on Precomputed GCM Runs, J. Climate, 27, 1829–1844, https://doi.org/10.1175/JCLI-D-13-00099.1, 2014.
Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., and Zhou, T.: XGBoost: A Scalable Tree Boosting System, in: KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2016, https://doi.org/10.1145/2939672.2939785, 2016.
Claesen, M. and De Moor, B.: Hyperparameter Search in Machine Learning, arXiv [preprint], https://doi.org/10.48550/arxiv.1502.02127, 2015.
Doherty, J.: PEST model-independent parameter estimation user manual, Watermark Numerical Computing, Brisbane, Australia, 3338, 3349, 2004.
Dolgaleva, I. V., Gorichev, I. G., Izotov, A. D., and Stepanov, V. M.: Modeling of the Effect of pH on the Calcite Dissolution Kinetics, Theor. Found. Chem. Eng., 39, 614–621, https://doi.org/10.1007/s11236-005-0125-1, 2005.
Druhan, J. L. and Tournassat, C.: Preface, Rev. Mineral. Geochem., 85, IV–V, https://doi.org/10.2138/rmg.2019.85.0, 2019.
Druhan, J. L., Steefel, C. I., Williams, K. H., and DePaolo, D. J.: Calcium isotope fractionation in groundwater: Molecular scale processes influencing field scale behavior, Geochim. Cosmochim. Ac., 119, 93–116, https://doi.org/10.1016/j.gca.2013.05.022, 2013.
Druhan, J. L., Steefel, C. I., Conrad, M. E., and DePaolo, D. J.: A large column analog experiment of stable isotope variations during reactive transport: I. A comprehensive model of sulfur cycling and δ34S fractionation, Geochim. Cosmochim. Ac., 124, 366–393, https://doi.org/10.1016/j.gca.2013.08.037, 2014.
Druhan, J. L., Winnick, M. J., and Thullner, M.: 8. Stable Isotope Fractionation by Transport and Transformation, in: Reviews in Mineralogy & Geochemistry, vol. 85, edited by: Druhan, J. L. and Tournassat, C., De Gruyter, 239–264, https://doi.org/10.1515/9781501512001-009, 2020.
Dullies, F., Lutze, W., Gong, W., and Nuttall, H. E.: Biological reduction of uranium—From the laboratory to the field, Sci. Total Environ., 408, 6260–6271, https://doi.org/10.1016/j.scitotenv.2010.08.018, 2010.
Dwivedi, D., Steefel, I. C., Arora, B., and Bisht, G.: Impact of Intra-meander Hyporheic Flow on Nitrogen Cycling, Proced. Earth Plan. Sc., 17, 404–407, https://doi.org/10.1016/j.proeps.2016.12.102, 2017.
Dwivedi, D., Steefel, C. I., Arora, B., Newcomer, M., Moulton, J. D., Dafflon, B., Faybishenko, B., Fox, P., Nico, P., Spycher, N., Carroll, R., and Williams, K. H.: Geochemical Exports to River From the Intrameander Hyporheic Zone Under Transient Hydrologic Conditions: East River Mountainous Watershed, Colorado, Water Resour. Res., 54, 8456–8477, https://doi.org/10.1029/2018WR023377, 2018.
Finsterle, S., Commer, M., Edmiston, J. K., Jung, Y., Kowalsky, M. B., Pau, G. S. H., Wainwright, H. M., and Zhang, Y.: iTOUGH2: A multiphysics simulation-optimization framework for analyzing subsurface systems, Comput. Geosci., 108, 8–20, https://doi.org/10.1016/j.cageo.2016.09.005, 2017.
Fotherby, A.: a-fotherby/dissertation_xgboost: Initial release (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.7113324, 2022a.
Fotherby, A.: a-fotherby/GMD_2022: Archival release, (v1.0.1), Zenodo [data set], https://doi.org/10.5281/zenodo.7113380, 2022b.
Fotherby, A. and Bradbury, H.: a-fotherby/Omphalos: Initial release (v0.9.0), Zenodo [code], https://doi.org/10.5281/zenodo.7113299, 2022.
Frazier, P. I.: A Tutorial on Bayesian Optimization, arXiv [cs, math, stat], arXiv:1807.02811, 2018.
Gatel, L., Lauvernet, C., Carluer, N., Weill, S., Tournebize, J., and Paniconi, C.: Global evaluation and sensitivity analysis of a physically based flow and reactive transport model on a laboratory experiment, Environ. Model. Softw., 113, 73–83, https://doi.org/10.1016/j.envsoft.2018.12.006, 2019.
Gaus, I., Azaroual, M., and Czernichowski-Lauriol, I.: Reactive transport modelling of the impact of CO2 injection on the clayey cap rock at Sleipner (North Sea), Chem. Geol., 217, 319–337, https://doi.org/10.1016/j.chemgeo.2004.12.016, 2005.
Gharasoo, M., Elsner, M., Van Cappellen, P., and Thullner, M.: Pore-Scale Heterogeneities Improve the Degradation of a Self-Inhibiting Substrate: Insights from Reactive Transport Modeling, Environ. Sci. Technol., 56, 13008–13018, https://doi.org/10.1021/acs.est.2c01433, 2022.
Grzeszczuk, R., Terzopoulos, D., and Hinton, G.: NeuroAnimator: fast neural network emulation and control of physics-based models, in: Proceedings of the 25th annual conference on Computer graphics and interactive techniques – SIGGRAPH '98, Orlando, Florida, United States of America 19–24 July 1998, 9–20, https://doi.org/10.1145/280814.280816, 1998.
Gu, X., Rempe, D. M., Dietrich, W. E., West, A. J., Lin, T.-C., Jin, L., and Brantley, S. L.: Chemical reactions, porosity, and microfracturing in shale during weathering: The effect of erosion rate, Geochim. Cosmochim. Ac., 269, 63–100, https://doi.org/10.1016/j.gca.2019.09.044, 2020.
Hubbard, S. S., Williams, K. H., Agarwal, D., Banfield, J., Beller, H., Bouskill, N., Brodie, E., Carroll, R., Dafflon, B., Dwivedi, D., Falco, N., Faybishenko, B., Maxwell, R., Nico, P., Steefel, C., Steltzer, H., Tokunaga, T., Tran, P. A., Wainwright, H., and Varadharajan, C.: The East River, Colorado, Watershed: A Mountainous Community Testbed for Improving Predictive Understanding of Multiscale Hydrological–Biogeochemical Dynamics, Vadose Zone J., 17, 180061, https://doi.org/10.2136/vzj2018.03.0061, 2018.
Hubbard, S. S., Agarwal, D., Arora, B., Banfield, J. F., Bouskill, N., Brodie, E., Carroll, R. W. H., Dwivedi, D., Gilbert, B., Maavara, T., Maxwell, R. M., Newcomer, M. E., Nico, P. S., Sorensen, P., Steefel, C. I., Steltzer, H., Tokunaga, T. K., Varadharajan, C., Wainwright, H. M., Wan, J., and Williams, K. H.: Key Controls on Water and Nitrogen Exports occurring across Lifezones, Compartments and Interfaces of the Mountainous East River Watershed, in: AGU Fall Meeting Abstracts, CO, H23B-01, 2019.
Jin, Q. and Bethke, C. M.: A New Rate Law Describing Microbial Respiration, Appl. Environ. Microb., 69, 2340–2348, https://doi.org/10.1128/AEM.69.4.2340-2348.2003, 2003.
Jin, Q. and Bethke, C. M.: Predicting the rate of microbial respiration in geochemical environments, Geochim. Cosmochim. Ac., 69, 1133–1143, 2005.
Jin, Q. and Bethke, C. M.: The thermodynamics and kinetics of microbial metabolism, Am. J. Sci., 307, 643–677, https://doi.org/10.2475/04.2007.01, 2007.
Jin, Q. and Kirk, M. F.: Thermodynamic and Kinetic Response of Microbial Reactions to High CO2, Front. Microbiol., 7, 1696, https://doi.org/10.3389/fmicb.2016.01696, 2016.
Jin, Q. and Kirk, M. F.: pH as a Primary Control in Environmental Microbiology: 1. Thermodynamic Perspective, Frontiers in Environmental Science, 6, 21, https://doi.org/10.3389/fenvs.2018.00021, 2018.
Johnson, J. W., Nitao, J. J., and Knauss, K. G.: Reactive transport modelling of CO2 storage in saline aquifers to elucidate fundamental processes, trapping mechanisms and sequestration partitioning, Geological Society, London, Special Publications, 233, 107–128, https://doi.org/10.1144/GSL.SP.2004.233.01.08, 2004.
Jung, H. and Navarre-Sitchler, A.: Scale effect on the time dependence of mineral dissolution rates in physically heterogeneous porous media, Geochim. Cosmochim. Ac., 234, 70–83, https://doi.org/10.1016/j.gca.2018.05.009, 2018.
Kashinath, K., Mustafa, M., Albert, A., Wu, J.-L., Jiang, C., Esmaeilzadeh, S., Azizzadenesheli, K., Wang, R., Chattopadhyay, A., Singh, A., Manepalli, A., Chirila, D., Yu, R., Walters, R., White, B., Xiao, H., Tchelepi, H. A., Marcus, P., Anandkumar, A., Hassanzadeh, P., and Prabhat: Physics-informed machine learning: case studies for weather and climate modelling, Philos. T. R. Soc. A,, 379, 20200093, https://doi.org/10.1098/rsta.2020.0093, 2021.
Komlos, J., Peacock, A., Kukkadapu, R. K., and Jaffé, P. R.: Long-term dynamics of uranium reduction/reoxidation under low sulfate conditions, Geochim. Cosmochim. Ac., 72, 3603–3615, https://doi.org/10.1016/j.gca.2008.05.040, 2008.
Krasnopolsky, V. M., Fox-Rabinovitz, M. S., and Chalikov, D. V.: New Approach to Calculation of Atmospheric Model Physics: Accurate and Fast Neural Network Emulation of Longwave Radiation in a Climate Model, Mon. Weather Rev., 133, 1370–1383, https://doi.org/10.1175/MWR2923.1, 2005.
Kyas, S., Volpatto, D., Saar, M. O., and Leal, A. M. M.: Accelerated reactive transport simulations in heterogeneous porous media using Reaktoro and Firedrake, Comput. Geosci., 26, 295–327, https://doi.org/10.1007/s10596-021-10126-2, 2022.
Laloy, E. and Jacques, D.: Speeding up reactive transport simulations in cement systems by surrogate geochemical modeling: deep neural networks and k-nearest neighbors, arXiv, arXiv:2107.07598, 30 November 2021.
Li, L., Maher, K., Navarre-Sitchler, A., Druhan, J., Meile, C., Lawrence, C., Moore, J., Perdrial, J., Sullivan, P., Thompson, A., Jin, L., Bolton, E. W., Brantley, S. L., Dietrich, W. E., Mayer, K. U., Steefel, C. I., Valocchi, A., Zachara, J., Kocar, B., Mcintosh, J., Tutolo, B. M., Kumar, M., Sonnenthal, E., Bao, C., and Beisman, J.: Expanding the role of reactive transport models in critical zone processes, Earth-Sci. Rev., 165, 280–301, https://doi.org/10.1016/j.earscirev.2016.09.001, 2017.
Li, Y., Lu, P., and Zhang, G.: An artificial-neural-network-based surrogate modeling workflow for reactive transport modeling, Petroleum Research, 7, 13–20, https://doi.org/10.1016/j.ptlrs.2021.06.002, 2022.
Long, P. E., Williams, K. H., Davis, J. A., Fox, P. M., Wilkins, M. J., Yabusaki, S. B., Fang, Y., Waichler, S. R., Berman, E. S. F., Gupta, M., Chandler, D. P., Murray, C., Peacock, A. D., Giloteaux, L., Handley, K. M., Lovley, D. R., and Banfield, J. F.: Bicarbonate impact on U(VI) bioreduction in a shallow alluvial aquifer, Geochim. Cosmochim. Ac., 150, 106–124, https://doi.org/10.1016/j.gca.2014.11.013, 2015.
Lu, H., Ermakova, D., Wainwright, H. M., Zheng, L., and Tartakovsky, D. M.: DATA-INFORMED EMULATORS FOR MULTI-PHYSICS SIMULATIONS, J. Mach. Learn Model Comput., 2, 33–54, https://doi.org/10.1615/JMachLearnModelComput.2021038577, 2021.
Maavara, T., Siirila-Woodburn, E. R., Maina, F., Maxwell, R. M., Sample, J. E., Chadwick, K. D., Carroll, R., Newcomer, M. E., Dong, W., Williams, K. H., Steefel, C. I., and Bouskill, N. J.: Modeling geogenic and atmospheric nitrogen through the East River Watershed, Colorado Rocky Mountains, PLOS ONE, 16, e0247907, https://doi.org/10.1371/journal.pone.0247907, 2021a.
Maavara, T., Siirila-Woodburn, E. R., Maina, F., Maxwell, R. M., Sample, J. E., Chadwick, K. D., Carroll, R., Newcomer, M. E., Dong, W., Williams, K. H., Steefel, C. I., and Bouskill, N. J.: Nitrate, ammonium, and DON mass time series output for East River stream, vadose zone and groundwater subwatersheds from HAN-SoMo model, Environmental System Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE), United States; Watershed Function SFA, https://doi.org/10.15485/1766811, 2021b.
Maher, K. and Mayer, K. U.: The art of reactive transport model building, Elements, 15, 117–118, 2019.
Malaguerra, F., Albrechtsen, H.-J., and Binning, P. J.: Assessment of the contamination of drinking water supply wells by pesticides from surface water resources using a finite element reactive transport model and global sensitivity analysis techniques, J. Hydrol., 476, 321–331, https://doi.org/10.1016/j.jhydrol.2012.11.010, 2013.
Martinez, B. C., DeJong, J. T., and Ginn, T. R.: Bio-geochemical reactive transport modeling of microbial induced calcite precipitation to predict the treatment of sand in one-dimensional flow, Comput. Geotech., 58, 1–13, https://doi.org/10.1016/j.compgeo.2014.01.013, 2014.
Molins, S. and Knabner, P.: 2. Multiscale Approaches in Reactive Transport Modeling, in: Reviews in Mineralogy & Geochemistry, vol. 85, edited by: Druhan, J. L. and Tournassat, C., De Gruyter, 27–48, https://doi.org/10.1515/9781501512001-003, 2020.
Moon, H. S., McGuinness, L., Kukkadapu, R. K., Peacock, A. D., Komlos, J., Kerkhof, L. J., Long, P. E., and Jaffé, P. R.: Microbial reduction of uranium under iron- and sulfate-reducing conditions: Effect of amended goethite on microbial community composition and dynamics, Water Res., 44, 4015–4028, https://doi.org/10.1016/j.watres.2010.05.003, 2010.
Paper, J. M., Flynn, T. M., Boyanov, M. I., Kemner, K. M., Haller, B. R., Crank, K., Lower, A., Jin, Q., and Kirk, M. F.: Influences of pH and substrate supply on the ratio of iron to sulfate reduction, Geobiology, 19, 405–420, https://doi.org/10.1111/gbi.12444, 2021.
Richter, F. M. and DePaolo, D. J.: Numerical models for diagenesis and the Neogene Sr isotopic evolution of seawater from DSDP Site 590B, Earth Planet. Sc. Lett., 83, 27–38, https://doi.org/10.1016/0012-821X(87)90048-3, 1987.
Rolle, M. and Borgne, T. L.: 5. Mixing and Reactive Fronts in the Subsurface, in: Reviews in Mineralogy & Geochemistry. vol. 85, edited by: Druhan, J. L. and Tournassat, C., De Gruyter, 111–142, https://doi.org/10.1515/9781501512001-006, 2020.
Seigneur, N., Vriens, B., Beckie, R. D., and Mayer, K. U.: Reactive transport modelling to investigate multi-scale waste rock weathering processes, J. Contam. Hydrol., 236, 103752, https://doi.org/10.1016/j.jconhyd.2020.103752, 2021.
Steefel, C. I., DePaolo, D. J., and Lichtner, P. C.: Reactive transport modeling: An essential tool and a new research approach for the Earth sciences, Earth Planet. Sc. Lett., 240, 539–558, https://doi.org/10.1016/j.epsl.2005.09.017, 2005a.
Steefel, C. I., DePaolo, D. J., and Lichtner, P. C.: Reactive transport modeling: An essential tool and a new research approach for the Earth sciences, Earth Planet. Sc. Lett., 240, 539–558, https://doi.org/10.1016/j.epsl.2005.09.017, 2005b.
Steefel, C. I., Appelo, C. A. J., Arora, B., Jacques, D., Kalbacher, T., Kolditz, O., Lagneau, V., Lichtner, P. C., Mayer, K. U., Meeussen, J. C. L., Molins, S., Moulton, D., Shao, H., Šimůnek, J., Spycher, N., Yabusaki, S. B., and Yeh, G. T.: Reactive transport codes for subsurface environmental simulation, Comput. Geosci., 19, 445–478, https://doi.org/10.1007/s10596-014-9443-x, 2015.
Torres, E., Couture, R. M., Shafei, B., Nardi, A., Ayora, C., and Van Cappellen, P.: Reactive transport modeling of early diagenesis in a reservoir lake affected by acid mine drainage: Trace metals, lake overturn, benthic fluxes and remediation, Chem. Geol., 419, 75–91, https://doi.org/10.1016/j.chemgeo.2015.10.023, 2015.
van Breukelen, B. M., Griffioen, J., Röling, W. F. M., and van Verseveld, H. W.: Reactive transport modelling of biogeochemical processes and carbon isotope geochemistry inside a landfill leachate plume, J. Contam. Hydrol., 70, 249–269, https://doi.org/10.1016/j.jconhyd.2003.09.003, 2004.
Williams, K. H., Long, P. E., Davis, J. A., Wilkins, M. J., N'Guessan, A. L., Steefel, C. I., Yang, L., Newcomer, D., Spane, F. A., Kerkhof, L. J., McGuinness, L., Dayvault, R., and Lovley, D. R.: Acetate Availability and its Influence on Sustainable Bioremediation of Uranium-Contaminated Groundwater, Geomicrobiol. J., 28, 519–539, https://doi.org/10.1080/01490451.2010.520074, 2011.
Wu, W.-M., Carley, J., Gentry, T., Ginder-Vogel, M. A., Fienen, M., Mehlhorn, T., Yan, H., Caroll, S., Pace, M. N., Nyman, J., Luo, J., Gentile, M. E., Fields, M. W., Hickey, R. F., Gu, B., Watson, D., Cirpka, O. A., Zhou, J., Fendorf, S., Kitanidis, P. K., Jardine, P. M., and Criddle, C. S.: Pilot-Scale in Situ Bioremedation of Uranium in a Highly Contaminated Aquifer. 2. Reduction of U(VI) and Geochemical Control of U(VI) Bioavailability, Environ. Sci. Technol., 40, 3986–3995, https://doi.org/10.1021/es051960u, 2006.
Yoo, A. B., Jette, M. A., and Grondona, M.: SLURM: Simple Linux Utility for Resource Management, in: Job Scheduling Strategies for Parallel Processing, vol. 2862, edited by: Feitelson, D., Rudolph, L., and Schwiegelshohn, U., Springer Berlin Heidelberg, Berlin, Heidelberg, 44–60, https://doi.org/10.1007/10968987_3, 2003.
Short summary
We demonstrate how, given a simulation of fluid and rock interacting, we can emulate the system using machine learning. This means that, for a given initial condition, we can predict the final state, avoiding the simulation step once the model has been trained. We present a workflow for applying this approach to any fluid–rock simulation and showcase two applications to different fluid–rock simulations. This approach has applications for improving model development and sensitivity analyses.
We demonstrate how, given a simulation of fluid and rock interacting, we can emulate the system...
