Articles | Volume 9, issue 10
https://doi.org/10.5194/gmd-9-3605-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/gmd-9-3605-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
11 Oct 2016
Development and technical paper |
| 11 Oct 2016
Coarse-grained component concurrency in Earth system modeling: parallelizing atmospheric radiative transfer in the GFDL AM3 model using the Flexible Modeling System coupling framework
Princeton University, Cooperative Institute of Climate Science, Princeton NJ, USA
Rusty Benson
National Oceanic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory (NOAA/GFDL), Princeton NJ, USA
Bruce Wyman
National Oceanic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory (NOAA/GFDL), Princeton NJ, USA
Isaac Held
National Oceanic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory (NOAA/GFDL), Princeton NJ, USA
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We present a collection of performance metrics gathered during the Coupled Model Intercomparison Project Phase 6 (CMIP6), a worldwide initiative to study climate change. We analyse the metrics that resulted from collaboration efforts among many partners and models and describe our findings to demonstrate the utility of our study for the scientific community. The research contributes to understanding climate modelling performance on the current high-performance computing (HPC) architectures.
Venkatramani Balaji, Karl E. Taylor, Martin Juckes, Bryan N. Lawrence, Paul J. Durack, Michael Lautenschlager, Chris Blanton, Luca Cinquini, Sébastien Denvil, Mark Elkington, Francesca Guglielmo, Eric Guilyardi, David Hassell, Slava Kharin, Stefan Kindermann, Sergey Nikonov, Aparna Radhakrishnan, Martina Stockhause, Tobias Weigel, and Dean Williams
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We present recommendations for the global data infrastructure needed to support CMIP scientific design and its future growth and evolution. We follow a dataset-centric design less prone to systemic failure. Scientific publication in the digital age is evolving to make data a primary scientific output, alongside articles. We design toward that future scientific data ecosystem, informed by the need for reproducibility, data provenance, future data technologies, and measures of costs and benefits.
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Climate models are among the most computationally expensive scientific applications in the world. We present a set of measures of computational performance that can be used to compare models that are independent of underlying hardware and the model formulation. They are easy to collect and reflect performance actually achieved in practice. We are preparing a systematic effort to collect these metrics for the world's climate models during CMIP6, the next Climate Model Intercomparison Project.
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This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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We introduce a new high-resolution model that couple the atmosphere and ocean to better simulate extreme weather events. It combines GFDL’s advanced atmospheric and ocean models with a powerful coupling system that allows robust and efficient two-way interactions. Simulations show the model accurately captures hurricane behavior and its impact on the ocean. It also runs efficiently on supercomputers. This model is a key step toward improving extreme weather forecast.
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The single-nest capability in GFDL's dynamical core, FV3, is upgraded to support multiple same-level and telescoping nests. Grid nesting adds a refined grid over an area of interest to better resolve small-scale flow features necessary to accurately predict special weather events such as severe storms and hurricanes. This work allows concurrent execution of multiple same-level and telescoping multi-level nested grids in both global and regional setups.
Venkatramani Balaji, Karl E. Taylor, Martin Juckes, Bryan N. Lawrence, Paul J. Durack, Michael Lautenschlager, Chris Blanton, Luca Cinquini, Sébastien Denvil, Mark Elkington, Francesca Guglielmo, Eric Guilyardi, David Hassell, Slava Kharin, Stefan Kindermann, Sergey Nikonov, Aparna Radhakrishnan, Martina Stockhause, Tobias Weigel, and Dean Williams
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Venkatramani Balaji, Eric Maisonnave, Niki Zadeh, Bryan N. Lawrence, Joachim Biercamp, Uwe Fladrich, Giovanni Aloisio, Rusty Benson, Arnaud Caubel, Jeffrey Durachta, Marie-Alice Foujols, Grenville Lister, Silvia Mocavero, Seth Underwood, and Garrett Wright
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Climate models are among the most computationally expensive scientific applications in the world. We present a set of measures of computational performance that can be used to compare models that are independent of underlying hardware and the model formulation. They are easy to collect and reflect performance actually achieved in practice. We are preparing a systematic effort to collect these metrics for the world's climate models during CMIP6, the next Climate Model Intercomparison Project.
Veronika Eyring, Peter J. Gleckler, Christoph Heinze, Ronald J. Stouffer, Karl E. Taylor, V. Balaji, Eric Guilyardi, Sylvie Joussaume, Stephan Kindermann, Bryan N. Lawrence, Gerald A. Meehl, Mattia Righi, and Dean N. Williams
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Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev., 18, 1413–1425, https://doi.org/10.5194/gmd-18-1413-2025, https://doi.org/10.5194/gmd-18-1413-2025, 2025
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Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most severe 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 subsea CO2 injection.
Reyk Börner, Jan O. Haerter, and Romain Fiévet
Geosci. Model Dev., 18, 1333–1356, https://doi.org/10.5194/gmd-18-1333-2025, https://doi.org/10.5194/gmd-18-1333-2025, 2025
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The daily cycle of sea surface temperature (SST) impacts clouds above the ocean and could influence the clustering of thunderstorms linked to extreme rainfall and hurricanes. However, daily SST variability is often poorly represented in modeling studies of how clouds cluster. We present a simple, wind-responsive model of upper-ocean temperature for use in atmospheric simulations. Evaluating the model against observations, we show that it performs significantly better than common slab models.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
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HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
Geosci. Model Dev., 18, 1287–1305, https://doi.org/10.5194/gmd-18-1287-2025, https://doi.org/10.5194/gmd-18-1287-2025, 2025
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We present and validate enhancements to the process-based T&C model aimed at improving its representation of crop growth and management practices. The updated model, T&C-CROP, enables applications such as analysing the hydrological and carbon storage impacts of land use transitions (e.g. conversions between crops, forests, and pastures) and optimizing irrigation and fertilization strategies in response to climate change.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev., 18, 1241–1263, https://doi.org/10.5194/gmd-18-1241-2025, https://doi.org/10.5194/gmd-18-1241-2025, 2025
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This article details a new feature we implemented in the popular regional atmospheric model WRF. This feature allows for data exchange between WRF and any other model (e.g. an ocean model) using the coupling library Ocean–Atmosphere–Sea–Ice–Soil Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Axel Lauer, Lisa Bock, Birgit Hassler, Patrick Jöckel, Lukas Ruhe, and Manuel Schlund
Geosci. Model Dev., 18, 1169–1188, https://doi.org/10.5194/gmd-18-1169-2025, https://doi.org/10.5194/gmd-18-1169-2025, 2025
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Earth system models are important tools to improve our understanding of current climate and to project climate change. Thus, it is crucial to understand possible shortcomings in the models. New features of the ESMValTool software package allow one to compare and visualize a model's performance with respect to reproducing observations in the context of other climate models in an easy and user-friendly way. We aim to help model developers assess and monitor climate simulations more efficiently.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
EGUsphere, https://doi.org/10.5194/egusphere-2024-4086, https://doi.org/10.5194/egusphere-2024-4086, 2025
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Climate model simulations of the response to human and natural influences together, natural climate influences alone, and greenhouse gases alone, among others, are key to quantifying human influence on the climate. The last set of such coordinated simulations underpinned key findings in the last Intergovernmental Panel on Climate Change (IPCC) report. Here we propose a new set of such simulations to be used in the next generation of attribution studies, and to underpin the next IPCC report.
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.
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
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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.
Manuel Schlund, Bouwe Andela, Jörg Benke, Ruth Comer, Birgit Hassler, Emma Hogan, Peter Kalverla, Axel Lauer, Bill Little, Saskia Loosveldt Tomas, Francesco Nattino, Patrick Peglar, Valeriu Predoi, Stef Smeets, Stephen Worsley, Martin Yeo, and Klaus Zimmermann
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-236, https://doi.org/10.5194/gmd-2024-236, 2025
Revised manuscript accepted for GMD
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The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth system models. Here, we describe recent significant improvements of ESMValTool’s computational efficiency including parallel, out-of-core, and distributed computing. Evaluations with the enhanced version of ESMValTool are faster, use less computational resources, and can handle input data larger than the available memory.
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.
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-212, https://doi.org/10.5194/gmd-2024-212, 2024
Revised manuscript accepted for GMD
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This study proposed an advancing framework for modeling regional rice production, water use, and greenhouse gas emissions. The framework integrated a process-based soil-crop model with key physiological effects, a novel model upscaling method, and the NSGA-II multi-objective optimization algorithm at a parallel computing platform. The framework provides a valuable tool for irrigation optimization to deliver co-benefits of ensuring food production, reducing water use and greenhouse gas emissions.
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.
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.
Cited articles
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P.-P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., and Nelkin, E.: The Version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present), J. Hydrometeorol., 4, 1147–1167, 2003.
Alexander, K. and Easterbrook, S. M.: The software architecture of climate models: a graphical comparison of CMIP5 and EMICAR5 configurations, Geosci. Model Dev., 8, 1221–1232, https://doi.org/10.5194/gmd-8-1221-2015, 2015.
Balaji, V.: The Flexible Modeling System, in: Earth System Modelling – Volume 3, edited by: Valcke, S., Redler, R., and Budich, R., SpringerBriefs in Earth System Sciences, Springer Berlin Heidelberg, 33–41, 2012.
Balaji, V.: Climate Computing: The State of Play, Comput. Sci. Eng., 17, 9–13, 2015.
Balaji, V., Anderson, J., Held, I., Winton, M., Durachta, J., Malyshev, S., and Stouffer, R. J.: The Exchange Grid: a mechanism for data exchange between Earth System components on independent grids, in: Parallel Computational Fluid Dynamics: Theory and Applications, Proceedings of the 2005 International Conference on Parallel Computational Fluid Dynamics, 24–27 May, College Park, MD, USA, edited by: Deane, A., Brenner, G., Ecer, A., Emerson, D., McDonough, J., Periaux, J., Satofuka, N., and Tromeur-Dervout, D., Elsevier, 2006.
Balaji, V., Maisonnave, E., Zadeh, N., Lawrence, B. N., Biercamp, J., Fladrich, U., Aloisio, G., Benson, R., Caubel, A., Durachta, J., Foujols, M.-A., Lister, G., Mocavero, S., Underwood, S., and Wright, G.: CPMIP: Measurements of Real Computational Performance of Earth System Models, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2016-197, in review, 2016.
Bozzo, A., Pincus, R., Sandu, I., and Morcrette, J.-J.: Impact of a spectral sampling technique for radiation on ECMWF weather forecasts, J. Adv. Model. Earth Syst., 6, 1288–1300, 2014.
Chandra, R., Menon, R., Dagum, L., Kohr, D., Maydan, D., and McDonald, J.: Parallel Programming in OpenMP, Morgan-Kaufmann, Inc., 2001.
Charles, J., Sawyer, W., Dolz, M. F., and Catalán, S.: Evaluating the performance and energy efficiency of the COSMO-ART model system, Computer Science-Research and Development, 30, 177–186, 2015.
Chien, A. A. and Karamcheti, V.: Moore's Law: The First Ending and A New Beginning, Computer, 12, 48–53, 2013.
Cumming, B., Fourestey, G., Fuhrer, O., Gysi, T., Fatica, M., and Schulthess, T. C.: Application centric energy-efficiency study of distributed multi-core and hybrid CPU-GPU systems, in: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, IEEE Press, 819–829, 2014.
Dahan-Dalmedico, A.: History and Epistemology of Models: Meteorology (1946–1963) as a Case Study, Arch. Hist. Exact Sci., 55, 395–422, https://doi.org/10.1007/s004070000032, 2001.
Dennis, J. M., Vertenstein, M., Worley, P. H., Mirin, A. A., Craig, A. P., Jacob, R., and Mickelson, S.: Computational performance of ultra-high-resolution capability in the Community Earth System Model, Int. J. High Perform. C., 26, 5–16, 2012.
Donner, L. J., Wyman, B. L., Hemler, R. S., Horowitz, L. W., Ming, Y., Zhao, M., Golaz, J.-C., Ginoux, P., Lin, S.-J., Schwarzkopf, M. D., Austin, J., Alaka, G., Cooke, W. F., Delworth, T. L., Freidenreich, S. M., Gordon, C. T., Griffies, S. M., Held, I. M., Hurlin, W. J., Klein, S. A., Knutson, T. R., Langenhorst, A. R., Lee, H.-C., Lin, Y., Magi, B. I., Malyshev, S. L., Milly, P. C. D., Naik, V., Nath, M. J., Pincus, R., Ploshay, J. J., Ramaswamy, V., Seman, C. J., Shevliakova, E., Sirutis, J. J., Stern, W. F., Stouffer, R. J., Wilson, R. J., Winton, M., Wittenberg, A. T., and Zeng, F.: The Dynamical Core, Physical Parameterizations, and Basic Simulation Characteristics of the Atmospheric Component AM3 of the GFDL Global Coupled Model CM3, J. Climate, 24, 3484–3519, 2011.
Düben, P. D., Joven, J., Lingamneni, A., McNamara, H., De Micheli, G., Palem, K. V., and Palmer, T.: On the use of inexact, pruned hardware in atmospheric modelling, Philos. T. Roy. Soc. A., 372, https://doi.org/10.1098/rsta.2013.0276, 2014.
Durran, D. R.: Numerical Methods for Wave Equations in Geophysical Flud Dynamics, Springer-Verlag, 1999.
Ford, R., Glover, M., Ham, D., Hobson, M., Maynard, C., Mitchell, L., Mullerworth, S., Pickles, S., Rezny, M., Riley, G., Wood, N., and Ashworth, M.: Towards Performance Portability with GungHo, in: EGU General Assembly Conference Abstracts, EGU General Assembly, Vienna, p. 13243, 2014.
Freidenreich, S. and Ramaswamy, V.: A New Multiple-Band Solar Radiative Parameterization for General Circulation Models, J. Geophys. Res.-Atmos., 104, 31389–31409, 1999.
Fuhrer, O., Osuna, C., Lapillonne, X., Gysi, T., Cumming, B., Bianco, M., Arteaga, A., and Schulthess, T. C.: Towards a performance portable, architecture agnostic implementation strategy for weather and climate models, Supercomputing Frontiers and Innovations, 1, 45–62, 2014.
Gates, W. L.: AMIP: The Atmospheric Model Intercomparison Project, B. Am. Meteorol. Soc., 73, 1962–1970, 1992.
Govett, M., Middlecoff, J., and Henderson, T.: Directive-based parallelization of the NIM weather model for GPUs, in: Proceedings of the First Workshop on Accelerator Programming using Directive, IEEE Press, 55–61, 2014.
Gropp, W., Huss-Lederman, S., Lumsdaine, A., Lusk, E., Nitzberg, B., Saphir, W., and Snir, M.: MPI: The Complete Reference. The MPI-2 Extensions, vol. 2, MIT Press, 1998.
Harris, L. M. and Lin, S.-J.: A two-way nested global-regional dynamical core on the cubed-sphere grid, Mon. Weather Rev., 141, 283–306, 2013.
Hourdin, F., Mauriten, T., Getellman, A., Golaz, J.-C., Balaji, V., Duan, Q., Folini, D., Ji, D., Klocke, D., Qian, Y., Rauser, F., Rio, C., Tomassini, L., Watanabe, M., and Williamson, D.: The art and science of climate model tuning, B. Am. Meteorol. Soc., 97, online first, https://doi.org/10.1175/BAMS-D-15-00135.1, 2016.
Iacono, M. J., Berthiaume, D., and Michalakes, J.: Enhancing Efficiency Of The RRTMG Radiation Code With GPU And MIC Approaches For Numerical Weather Prediction Models, in: 14th Conf. on Atmospheric Radiation, Boston, MA, Amer. Meteor. Soc., p. 156, 2014.
Kogge, P., Bergman, K., Borkar, S., Campbell, D., Carson, W., Dally, W., Denneau, M., Franzon, P., Harrod, W., Hill, K., Hiller, J., Karp, S., Keckler, S., Klein, D., Lucas, R., Richards, M., Scarpelli, A., Scott, S., Snavely, A., Sterling, T., Williams, R. S., and Yelick, K.: Exascale computing study: Technology challenges in achieving exascale systems, Tech. Rep. 15, DARPA Information Processing Techniques Office, 2008.
Korkmaz, P., Akgul, B., Chakrapani, L., and Palem, K.: Advocating noise as an agent for ultra low-energy computing: Probabilistic CMOS devices and their characteristics, Jpn. J. Appl. Phys., SSDM Special Issue Part, 1, 3307–3316, 2006.
Loeb, N. G., Wielicki, B. A., Doelling, D. R., Smith, G. L., Keyes, D. F., Kato, S., Manalo-Smith, N., and Wong, T.: Toward optimal closure of the Earth's top-of-atmosphere radiation budget, J. Climate, 22, 748–766, 2009.
Manners, J., Thelen, J., Petch, J., Hill, P., and Edwards, J.: Two fast radiative transfer methods to improve the temporal sampling of clouds in numerical weather prediction and climate models, Q. J. Roy. Meteor. Soc., 135, 457–468, 2009.
Mittal, S. and Vetter, J. S.: A Survey of CPU-GPU Heterogeneous Computing Techniques, ACM Comput. Surv., 2015.
Morcrette, J.-J.: On the effects of the temporal and spatial sampling of radiation fields on the ECMWF forecasts and analyses, Mon. Weather Rev., 128, 876–887, 2000.
Morcrette, J.-J., Mozdzynski, G., and Leutbecher, M.: A reduced radiation grid for the ECMWF Integrated Forecasting System, Mon. Weather Rev., 136, 4760–4772, 2008.
Pauluis, O. and Emanuel, K.: Numerical instability resulting from infrequent calculation of radiative heating, Mon. Weather Rev., 132, 673–686, 2004.
Pincus, R. and Stevens, B.: Monte Carlo spectral integration: A consistent approximation for radiative transfer in large eddy simulations, J. Adv. Model. Earth Syst., 1, online only, https://doi.org/10.3894/JAMES.2009.1.1, 2009.
Platzman, G. W.: The ENIAC Computations of 1950 – Gateway to Numerical Weather Prediction, B. Am. Meteorol. Soc., 60, 302–312, 1979.
Ridge, D., Becker, D., Merkey, P., and Sterling, T.: Beowulf: harnessing the power of parallelism in a pile-of-PCs, in: Aerospace Conference, 1997 Proceedings, vol. 2, 79–91, 1997.
Schwarzkopf, M. D. and Ramaswamy, V.: Radiative effects of CH4, N2O, halocarbons and the foreign-broadened H2O continuum: A GCM experiment, J. Geophys. Res.-Atmos., 104, 9467–9488, 1999.
Solomon, S.: Climate change 2007-the physical science basis: Working group I contribution to the fourth assessment report of the IPCC, vol. 4, Cambridge University Press, 2007.
Sterling, T. L.: Beowulf cluster computing with Linux, MIT press, 2002.
Xu, K.-M. and Randall, D. A.: Impact of interactive radiative transfer on the macroscopic behavior of cumulus ensembles. Part I: Radiation parameterization and sensitivity tests, J. Atmos. Sci., 52, 785–799, 1995.
Xue, W., Yang, C., Fu, H., Wang, X., Xu, Y., Gan, L., Lu, Y., and Zhu, X.: Enabling and Scaling a Global Shallow-Water Atmospheric Model on Tianhe-2, in: Parallel and Distributed Processing Symposium, 2014 IEEE 28th International, 745–754, https://doi.org/10.1109/IPDPS.2014.82, 2014.
Zhao, M., Golaz, J.-C., Held, I., Ramaswamy, V., Lin, S.-J., Ming, Y., Ginoux, P., Wyman, B., Donner, L., Paynter, D., and Guo, H.: Uncertainty in model climate sensitivity traced to representations of cumulus precipitation microphysics, J. Climate, 29, 543–560, 2016.
Short summary
In nature, the many processes that make up the Earth system take place
simultaneously, for instance the condensation of water vapour into
clouds, and the blocking of sunlight by those clouds. In computer
simulations, these often take place in sequence. We demonstrate how to
make these processes also execute in parallel in computer simulations.
This should prove a large benefit in the new era of computing, where
arithmetic does not get faster, but we can perform more of it in parallel.
In nature, the many processes that make up the Earth system take place
simultaneously, for...
