Articles | Volume 18, issue 1
https://doi.org/10.5194/gmd-18-161-2025
© Author(s) 2025. 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-18-161-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
15 Jan 2025
Model experiment description paper |
| 15 Jan 2025
| 15 Jan 2025
Climate model downscaling in central Asia: a dynamical and a neural network approach
Bijan Fallah
CORRESPONDING AUTHOR
German Climate Computing Center (DKRZ), Hamburg, Germany
Potsdam Institute for Climate Impact Research (PIK), P.O. Box 601203, 14412 Potsdam, Germany
Masoud Rostami
Potsdam Institute for Climate Impact Research (PIK), P.O. Box 601203, 14412 Potsdam, Germany
Laboratoire de Météorologie Dynamique (LMD), Sorbonne University (SU), Ecole Normale Supérieure (ENS), Paris, France
Emmanuele Russo
ETH Zurich, Department of Environmental Systems Science, Universitätstrasse 16, 8092 Zurich, Switzerland
Paula Harder
Mila Quebec AI Institute, Montreal, Canada
Christoph Menz
Potsdam Institute for Climate Impact Research (PIK), P.O. Box 601203, 14412 Potsdam, Germany
Peter Hoffmann
Potsdam Institute for Climate Impact Research (PIK), P.O. Box 601203, 14412 Potsdam, Germany
Iulii Didovets
Potsdam Institute for Climate Impact Research (PIK), P.O. Box 601203, 14412 Potsdam, Germany
Fred F. Hattermann
Potsdam Institute for Climate Impact Research (PIK), P.O. Box 601203, 14412 Potsdam, Germany
Faculty of Forest and Environment, Eberswalde University for Sustainable Development (HNEE), Eberswalde, Germany
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In this study a set of simulations are performed with the regional climate model COSMO-CLM for Europe, for the mid-Holocene and pre-industrial periods. The main aim is to better understand the drivers of differences between models and pollen-based summer temperatures. Results show that a fundamental role is played by spring soil moisture availability. Additionally, results suggest that model bias is not stationary, and an optimal configuration could not be the best under different forcing.
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This paper describes the experiments and data sets necessary to run historic and future impact projections, and the underlying assumptions of future climate change as defined by the 3rd round of the ISIMIP Project (Inter-sectoral Impactmodel Intercomparison Project, isimip.org). ISIMIP provides a framework for cross-sectorally consistent climate impact simulations to contribute to a comprehensive and consistent picture of the world under different climate-change scenarios.
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Clim. Past, 20, 1939–1988, https://doi.org/10.5194/cp-20-1939-2024, https://doi.org/10.5194/cp-20-1939-2024, 2024
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During the last ice age (21 000 yr BP) in Europe, the composition and extent of forest and its associated climate remain unclear, with models indicating more forest north of the Alps and a warmer and somewhat wetter climate than suggested by the data. A new compilation of pollen records with improved dating suggests greater agreement with model climates but still suggests models overestimate forest cover, especially in the west.
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Clim. Past, 20, 449–465, https://doi.org/10.5194/cp-20-449-2024, https://doi.org/10.5194/cp-20-449-2024, 2024
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We present a series of experiments conducted for the Last Glacial Maximum (~21 ka) over Europe using the regional climate Weather Research and Forecasting model (WRF) at convection-permitting resolutions. The model, with new developments better suited to paleo-studies, agrees well with pollen-based climate reconstructions. This agreement is improved when considering different sources of uncertainty. The effect of convection-permitting resolutions is also assessed.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
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Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Jonathan Robert Buzan, Emmanuele Russo, Woon Mi Kim, and Christoph C. Raible
EGUsphere, https://doi.org/10.5194/egusphere-2023-324, https://doi.org/10.5194/egusphere-2023-324, 2023
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Paleoclimate is used to test climate models to verify that simulations accurately project both future and past climate states. We present fully coupled climate sensitivity simulations of Preindustrial, Last Glacial Maximum, and the Quaternary climate periods. We show distinct climate states derived from non-linear responses to ice sheet heights and orbits. The implication is that as paleo proxy data become more reliable, they may constrain the specific climate states produced by climate models.
Emmanuele Russo, Bijan Fallah, Patrick Ludwig, Melanie Karremann, and Christoph C. Raible
Clim. Past, 18, 895–909, https://doi.org/10.5194/cp-18-895-2022, https://doi.org/10.5194/cp-18-895-2022, 2022
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Silje Lund Sørland, Roman Brogli, Praveen Kumar Pothapakula, Emmanuele Russo, Jonas Van de Walle, Bodo Ahrens, Ivonne Anders, Edoardo Bucchignani, Edouard L. Davin, Marie-Estelle Demory, Alessandro Dosio, Hendrik Feldmann, Barbara Früh, Beate Geyer, Klaus Keuler, Donghyun Lee, Delei Li, Nicole P. M. van Lipzig, Seung-Ki Min, Hans-Jürgen Panitz, Burkhardt Rockel, Christoph Schär, Christian Steger, and Wim Thiery
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Basit Khan, Sabine Banzhaf, Edward C. Chan, Renate Forkel, Farah Kanani-Sühring, Klaus Ketelsen, Mona Kurppa, Björn Maronga, Matthias Mauder, Siegfried Raasch, Emmanuele Russo, Martijn Schaap, and Matthias Sühring
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An atmospheric chemistry model has been implemented in the microscale PALM model system 6.0. This article provides a detailed description of the model, its structure, input requirements, various features and limitations. Several pre-compiled ready-to-use chemical mechanisms are included in the chemistry model code; however, users can also easily implement other mechanisms. A case study is presented to demonstrate the application of the new chemistry model in the urban environment.
Emmanuele Russo, Silje Lund Sørland, Ingo Kirchner, Martijn Schaap, Christoph C. Raible, and Ulrich Cubasch
Geosci. Model Dev., 13, 5779–5797, https://doi.org/10.5194/gmd-13-5779-2020, https://doi.org/10.5194/gmd-13-5779-2020, 2020
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The parameter space of the COSMO-CLM RCM is investigated for the Central Asia CORDEX domain using a perturbed physics ensemble (PPE) with different parameter values. Results show that only a subset of model parameters presents relevant changes in model performance and these changes depend on the considered region and variable: objective calibration methods are highly necessary in this case. Additionally, the results suggest the need for calibrating an RCM when targeting different domains.
Björn Maronga, Sabine Banzhaf, Cornelia Burmeister, Thomas Esch, Renate Forkel, Dominik Fröhlich, Vladimir Fuka, Katrin Frieda Gehrke, Jan Geletič, Sebastian Giersch, Tobias Gronemeier, Günter Groß, Wieke Heldens, Antti Hellsten, Fabian Hoffmann, Atsushi Inagaki, Eckhard Kadasch, Farah Kanani-Sühring, Klaus Ketelsen, Basit Ali Khan, Christoph Knigge, Helge Knoop, Pavel Krč, Mona Kurppa, Halim Maamari, Andreas Matzarakis, Matthias Mauder, Matthias Pallasch, Dirk Pavlik, Jens Pfafferott, Jaroslav Resler, Sascha Rissmann, Emmanuele Russo, Mohamed Salim, Michael Schrempf, Johannes Schwenkel, Gunther Seckmeyer, Sebastian Schubert, Matthias Sühring, Robert von Tils, Lukas Vollmer, Simon Ward, Björn Witha, Hauke Wurps, Julian Zeidler, and Siegfried Raasch
Geosci. Model Dev., 13, 1335–1372, https://doi.org/10.5194/gmd-13-1335-2020, https://doi.org/10.5194/gmd-13-1335-2020, 2020
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In this paper, we describe the PALM model system 6.0. PALM is a Fortran-based turbulence-resolving code and has been applied for studying a variety of atmospheric and oceanic boundary layers for about 20 years. The model is optimized for use on massively parallel computer architectures. During the last years, PALM has been significantly improved and now offers a variety of new components that are especially designed to simulate the urban atmosphere at building-resolving resolution.
Emmanuele Russo, Ingo Kirchner, Stephan Pfahl, Martijn Schaap, and Ulrich Cubasch
Geosci. Model Dev., 12, 5229–5249, https://doi.org/10.5194/gmd-12-5229-2019, https://doi.org/10.5194/gmd-12-5229-2019, 2019
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This is an investigation of COSMO-CLM 5.0 sensitivity for the CORDEX Central Asia domain, with the main goal of evaluating general model performances for the area, proposing a model optimal configuration to be used in projection studies.
Results show that the model seems to be particularly sensitive to those parameterizations that deal with soil and surface features and that could positively affect the repartition of incoming radiation.
Bijan Fallah, Emmanuele Russo, Walter Acevedo, Achille Mauri, Nico Becker, and Ulrich Cubasch
Clim. Past, 14, 1345–1360, https://doi.org/10.5194/cp-14-1345-2018, https://doi.org/10.5194/cp-14-1345-2018, 2018
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We try to test and evaluate an approach for using two main sources of information on the climate of the past: climate model simulations and proxies. This is done via data assimilation (DA), a method that blends these two sources of information in an intelligent way. However, DA and climate models are computationally very expensive. Here, we tested the ability of a computationally affordable DA to reconstruct high-resolution climate fields.
Stefan Liersch, Julia Tecklenburg, Henning Rust, Andreas Dobler, Madlen Fischer, Tim Kruschke, Hagen Koch, and Fred Fokko Hattermann
Hydrol. Earth Syst. Sci., 22, 2163–2185, https://doi.org/10.5194/hess-22-2163-2018, https://doi.org/10.5194/hess-22-2163-2018, 2018
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Application-oriented regional impact studies require accurate simulations of future climate variables and water availability. We analyse the quality of global and regional climate projections and discuss potentials of correction methods that partly overcome this quality issue. The model ensemble used in this study projects increasing average annual discharges and a shift in seasonal patterns, with decreasing discharges in June and July and increasing discharges from August to November.
Katja Frieler, Stefan Lange, Franziska Piontek, Christopher P. O. Reyer, Jacob Schewe, Lila Warszawski, Fang Zhao, Louise Chini, Sebastien Denvil, Kerry Emanuel, Tobias Geiger, Kate Halladay, George Hurtt, Matthias Mengel, Daisuke Murakami, Sebastian Ostberg, Alexander Popp, Riccardo Riva, Miodrag Stevanovic, Tatsuo Suzuki, Jan Volkholz, Eleanor Burke, Philippe Ciais, Kristie Ebi, Tyler D. Eddy, Joshua Elliott, Eric Galbraith, Simon N. Gosling, Fred Hattermann, Thomas Hickler, Jochen Hinkel, Christian Hof, Veronika Huber, Jonas Jägermeyr, Valentina Krysanova, Rafael Marcé, Hannes Müller Schmied, Ioanna Mouratiadou, Don Pierson, Derek P. Tittensor, Robert Vautard, Michelle van Vliet, Matthias F. Biber, Richard A. Betts, Benjamin Leon Bodirsky, Delphine Deryng, Steve Frolking, Chris D. Jones, Heike K. Lotze, Hermann Lotze-Campen, Ritvik Sahajpal, Kirsten Thonicke, Hanqin Tian, and Yoshiki Yamagata
Geosci. Model Dev., 10, 4321–4345, https://doi.org/10.5194/gmd-10-4321-2017, https://doi.org/10.5194/gmd-10-4321-2017, 2017
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This paper describes the simulation scenario design for the next phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), which is designed to facilitate a contribution to the scientific basis for the IPCC Special Report on the impacts of 1.5 °C global warming. ISIMIP brings together over 80 climate-impact models, covering impacts on hydrology, biomes, forests, heat-related mortality, permafrost, tropical cyclones, fisheries, agiculture, energy, and coastal infrastructure.
Emmanuele Russo and Ulrich Cubasch
Clim. Past, 12, 1645–1662, https://doi.org/10.5194/cp-12-1645-2016, https://doi.org/10.5194/cp-12-1645-2016, 2016
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In this study we use a RCM for three different goals.
Proposing a model configuration suitable for paleoclimate studies; evaluating the added value of a regional climate model for paleoclimate studies; investigating temperature evolution of the European continent during mid-to-late Holocene.
Results suggest that the RCM seems to produce results in better agreement with reconstructions than its driving GCM. Simulated temperature evolution seems to be too sensitive to changes in insolation.
Fred Fokko Hattermann, Shaochun Huang, Olaf Burghoff, Peter Hoffmann, and Zbigniew W. Kundzewicz
Nat. Hazards Earth Syst. Sci., 16, 1617–1622, https://doi.org/10.5194/nhess-16-1617-2016, https://doi.org/10.5194/nhess-16-1617-2016, 2016
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We report that a considerable increase in flood-related losses can be expected in Germany in a future warmer climate. The general significance of the study is supported by the fact that the outcome of an ensemble of global climate models (GCMs) and regional climate models (RCMs) was used as a climate driver for a hydrological model considering more than 3000 river basins in Germany.
T. Vetter, S. Huang, V. Aich, T. Yang, X. Wang, V. Krysanova, and F. Hattermann
Earth Syst. Dynam., 6, 17–43, https://doi.org/10.5194/esd-6-17-2015, https://doi.org/10.5194/esd-6-17-2015, 2015
V. Aich, B. Koné, F. F. Hattermann, and E. N. Müller
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhessd-2-5171-2014, https://doi.org/10.5194/nhessd-2-5171-2014, 2014
Revised manuscript not accepted
V. Aich, S. Liersch, T. Vetter, S. Huang, J. Tecklenburg, P. Hoffmann, H. Koch, S. Fournet, V. Krysanova, E. N. Müller, and F. F. Hattermann
Hydrol. Earth Syst. Sci., 18, 1305–1321, https://doi.org/10.5194/hess-18-1305-2014, https://doi.org/10.5194/hess-18-1305-2014, 2014
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This study assesses volcanic aerosol representation in E3SM (Energy Exascale Earth System Model), showing that an emission-based approach moderately improves temperature variability and cloud responses compared to a prescribed forcing approach, yet significant bias persists.
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., 18, 4009–4021, https://doi.org/10.5194/gmd-18-4009-2025, https://doi.org/10.5194/gmd-18-4009-2025, 2025
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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.
Philipp Weiss, Ross Herbert, and Philip Stier
Geosci. Model Dev., 18, 3877–3894, https://doi.org/10.5194/gmd-18-3877-2025, https://doi.org/10.5194/gmd-18-3877-2025, 2025
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Aerosols strongly influence Earth's climate as they interact with radiation and clouds. New Earth system models run at resolutions of a few kilometers. To simulate the Earth system with interactive aerosols, we developed a new aerosol module. It represents aerosols as an ensemble of lognormal modes with given sizes and compositions. We present a year-long simulation with four modes at a resolution of 5 km. It captures key processes like the formation of dust storms in the Sahara.
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev., 18, 3799–3817, https://doi.org/10.5194/gmd-18-3799-2025, https://doi.org/10.5194/gmd-18-3799-2025, 2025
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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 vital 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 multi-objective optimization of rice irrigation schemes at a large scale.
Carolina A. Bieri, Francina Dominguez, Gonzalo Miguez-Macho, and Ying Fan
Geosci. Model Dev., 18, 3755–3779, https://doi.org/10.5194/gmd-18-3755-2025, https://doi.org/10.5194/gmd-18-3755-2025, 2025
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Access to deep moisture below the Earth's surface is important for vegetation in areas of the Amazon where there is little precipitation for part of the year. Most existing numerical models of the Earth system do not adequately capture where and when deep root water uptake occurs. We address this by adding deep soil layers and a root water uptake feature to an existing model. Out modifications lead to increased dry-month transpiration and improved simulation of the annual transpiration cycle.
Sergi Palomas, Mario C. Acosta, Gladys Utrera, and Etienne Tourigny
Geosci. Model Dev., 18, 3661–3679, https://doi.org/10.5194/gmd-18-3661-2025, https://doi.org/10.5194/gmd-18-3661-2025, 2025
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We present an automatic tool that optimizes resource distribution in coupled climate models, enhancing speed and reducing computational costs without requiring expert knowledge. Users can set energy/time criteria or limit resource usage. Tested on various European Community Earth System Model (EC-Earth) configurations and high-performance computing (HPC) platforms, it achieved up to 34 % faster simulations with fewer resources.
Maria Lucia Ferreira Barbosa, Douglas I. Kelley, Chantelle A. Burton, Igor J. M. Ferreira, Renata Moura da Veiga, Anna Bradley, Paulo Guilherme Molin, and Liana O. Anderson
Geosci. Model Dev., 18, 3533–3557, https://doi.org/10.5194/gmd-18-3533-2025, https://doi.org/10.5194/gmd-18-3533-2025, 2025
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As fire seasons in Brazil become increasingly severe, confidently understanding the factors driving fires is more critical than ever. To address this challenge, we developed FLAME (Fire Landscape Analysis using Maximum Entropy), a new model designed to predict fires and to analyse the spatial influence of both environmental and human factors while accounting for uncertainties. By adapting the model to different regions, we can enhance fire management strategies, making FLAME a powerful tool for protecting landscapes in Brazil and beyond.
Victor Couplet, Marina Martínez Montero, and Michel Crucifix
Geosci. Model Dev., 18, 3081–3129, https://doi.org/10.5194/gmd-18-3081-2025, https://doi.org/10.5194/gmd-18-3081-2025, 2025
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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 timescales 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.
Yong-He Liu and Zong-Liang Yang
Geosci. Model Dev., 18, 3157–3174, https://doi.org/10.5194/gmd-18-3157-2025, https://doi.org/10.5194/gmd-18-3157-2025, 2025
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NMH-CS 3.0 is a C#-based ecohydrological model reconstructed from the WRF-Hydro/Noah-MP model by translating the Fortran code of WRF-Hydro 3.0 and integrating a parallel river routing module. It enables efficient execution on multi-core personal computers. Simulations in the Yellow River basin demonstrate its consistency with WRF-Hydro outputs, providing a reliable alternative to the original Noah-MP model.
Conor T. Doherty, Weile Wang, Hirofumi Hashimoto, and Ian G. Brosnan
Geosci. Model Dev., 18, 3003–3016, https://doi.org/10.5194/gmd-18-3003-2025, https://doi.org/10.5194/gmd-18-3003-2025, 2025
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We present, analyze, and validate a methodology for quantifying uncertainty in gridded meteorological data products produced by spatial interpolation. In a validation case study using daily maximum near-surface air temperature (Tmax), the method works well and produces predictive distributions with closely matching theoretical versus actual coverage levels. Application of the method reveals that the magnitude of uncertainty in interpolated Tmax varies significantly in both space and time.
Martin Juckes, Karl E. Taylor, Fabrizio Antonio, David Brayshaw, Carlo Buontempo, Jian Cao, Paul J. Durack, Michio Kawamiya, Hyungjun Kim, Tomas Lovato, Chloe Mackallah, Matthew Mizielinski, Alessandra Nuzzo, Martina Stockhause, Daniele Visioni, Jeremy Walton, Briony Turner, Eleanor O'Rourke, and Beth Dingley
Geosci. Model Dev., 18, 2639–2663, https://doi.org/10.5194/gmd-18-2639-2025, https://doi.org/10.5194/gmd-18-2639-2025, 2025
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The Baseline Climate Variables for Earth System Modelling (ESM-BCVs) are defined as a list of 135 variables which have high utility for the evaluation and exploitation of climate simulations. The list reflects the most frequently used variables from Earth system models based on an assessment of data publication and download records from the largest archive of global climate projects.
Yucheng Lin, Robert E. Kopp, Alexander Reedy, Matteo Turilli, Shantenu Jha, and Erica L. Ashe
Geosci. Model Dev., 18, 2609–2637, https://doi.org/10.5194/gmd-18-2609-2025, https://doi.org/10.5194/gmd-18-2609-2025, 2025
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PaleoSTeHM v1.0 is a state-of-the-art framework designed to reconstruct past environmental conditions using geological data. Built on modern machine learning techniques, it efficiently handles the sparse and noisy nature of paleo-records, allowing scientists to make accurate and scalable inferences about past environmental change. By using flexible statistical models, PaleoSTeHM separates different sources of uncertainty, improving the precision of historical climate reconstructions.
Ingo Richter, Ping Chang, Ping-Gin Chiu, Gokhan Danabasoglu, Takeshi Doi, Dietmar Dommenget, Guillaume Gastineau, Zoe E. Gillett, Aixue Hu, Takahito Kataoka, Noel S. Keenlyside, Fred Kucharski, Yuko M. Okumura, Wonsun Park, Malte F. Stuecker, Andréa S. Taschetto, Chunzai Wang, Stephen G. Yeager, and Sang-Wook Yeh
Geosci. Model Dev., 18, 2587–2608, https://doi.org/10.5194/gmd-18-2587-2025, https://doi.org/10.5194/gmd-18-2587-2025, 2025
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Tropical ocean basins influence each other through multiple pathways and mechanisms, referred to here 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. The Tropical Basin Interaction Model Intercomparison Project (TBIMIP) aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Daniel F. J. Gunning, Kerim H. Nisancioglu, Emilie Capron, and Roderik S. W. van de Wal
Geosci. Model Dev., 18, 2479–2508, https://doi.org/10.5194/gmd-18-2479-2025, https://doi.org/10.5194/gmd-18-2479-2025, 2025
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This work documents the first results from ZEMBA: an energy balance model of the climate system. The model is a computationally efficient tool designed to study the response of climate to changes in the Earth's orbit. We demonstrate that ZEMBA reproduces many features of the Earth's climate for both the pre-industrial period and the Earth's most recent cold extreme – the Last Glacial Maximum. We intend to develop ZEMBA further and investigate the glacial cycles of the last 2.5 million years.
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev., 18, 2443–2460, https://doi.org/10.5194/gmd-18-2443-2025, https://doi.org/10.5194/gmd-18-2443-2025, 2025
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Improving climate predictions has significant socio-economic impacts. In this study, we develop and apply a new weakly coupled ocean data assimilation (WCODA) system to a coupled climate model. The WCODA system improves simulations of ocean temperature and salinity across many global regions. This system is meant to advance our understanding of the ocean's role in climate predictability.
Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You
Geosci. Model Dev., 18, 2427–2442, https://doi.org/10.5194/gmd-18-2427-2025, https://doi.org/10.5194/gmd-18-2427-2025, 2025
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Our research presents a novel deep learning approach called "TemDeep" for downscaling atmospheric variables at arbitrary time resolutions based on temporal coherence. Results show that our method can accurately recover evolution details superior to other methods, reaching 53.7 % in the restoration rate. Our findings are important for advancing weather forecasting models and enabling more precise and reliable predictions to support disaster preparedness, agriculture, and sustainable development.
Teo Price-Broncucia, Allison Baker, Dorit Hammerling, Michael Duda, and Rebecca Morrison
Geosci. Model Dev., 18, 2349–2372, https://doi.org/10.5194/gmd-18-2349-2025, https://doi.org/10.5194/gmd-18-2349-2025, 2025
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The ensemble consistency test (ECT) and its ultrafast variant (UF-ECT) have become powerful tools in the development community for the identification of unwanted changes in the Community Earth System Model (CESM). We develop a generalized setup framework to enable easy adoption of the ECT approach for other model developers and communities. This framework specifies test parameters to accurately characterize model variability and balance test sensitivity and computational cost.
Esteban Fernández Villanueva and Gary Shaffer
Geosci. Model Dev., 18, 2161–2192, https://doi.org/10.5194/gmd-18-2161-2025, https://doi.org/10.5194/gmd-18-2161-2025, 2025
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We describe, calibrate and test the Danish Center for Earth System Science (DCESS) II model, a new, broad, adaptable and fast Earth system model. DCESS II is designed for global simulations over timescales of years to millions of years using limited computer resources like a personal computer. With its flexibility and comprehensive treatment of the global carbon cycle, DCESS II is a useful, computationally friendly tool for simulations of past climates as well as for future Earth system projections.
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
Geosci. Model Dev., 18, 2193–2230, https://doi.org/10.5194/gmd-18-2193-2025, https://doi.org/10.5194/gmd-18-2193-2025, 2025
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We studied carbon–nitrogen coupling in Earth system models by developing a global carbon–nitrogen cycle model (CNit v1.0) within the widely used emulator MAGICC. CNit effectively reproduced the global carbon–nitrogen cycle dynamics observed in complex models. Our results show persistent nitrogen limitations on plant growth (net primary production) from 1850 to 2100, suggesting that nitrogen deficiency may constrain future land carbon sequestration.
Ngoc Thi Nhu Do, Kengo Sudo, Akihiko Ito, Louisa K. Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
Geosci. Model Dev., 18, 2079–2109, https://doi.org/10.5194/gmd-18-2079-2025, https://doi.org/10.5194/gmd-18-2079-2025, 2025
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Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth system models mainly due to partially incorporating CO2 effects and land cover changes rather than to climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant–climate interactions.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
Geosci. Model Dev., 18, 2111–2136, https://doi.org/10.5194/gmd-18-2111-2025, https://doi.org/10.5194/gmd-18-2111-2025, 2025
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We analyzed carbon and nitrogen mass conservation in data from various Earth system models. Our findings reveal significant discrepancies between flux and pool size data, where cumulative imbalances can reach hundreds of gigatons of carbon or nitrogen. 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.
Zhongwang Wei, Qingchen Xu, Fan Bai, Xionghui Xu, Zixin Wei, Wenzong Dong, Hongbin Liang, Nan Wei, Xingjie Lu, Lu Li, Shupeng Zhang, Hua Yuan, Laibo Liu, and Yongjiu Dai
EGUsphere, https://doi.org/10.5194/egusphere-2025-1380, https://doi.org/10.5194/egusphere-2025-1380, 2025
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Land surface models are used for simulating earth's surface interacts with the atmosphere. As models grow more complex and detailed, researchers need better tools to evaluate their performance. OpenBench, a new software system that makes evaluation process more comprehensive and efficient. It stands out by incorporating various factors and working with data at any scale which enabling scientists to incorporate new types of models and measurements as our understanding of Earth’s systems evolves.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
Geosci. Model Dev., 18, 2005–2019, https://doi.org/10.5194/gmd-18-2005-2025, https://doi.org/10.5194/gmd-18-2005-2025, 2025
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Forecasting river runoff, which is 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.
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025, https://doi.org/10.5194/gmd-18-1917-2025, 2025
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Earth system models (ESMs) struggle with the uncertainties associated with parameterizing subgrid physics. Machine learning (ML) algorithms offer a solution by learning the important relationships and features from high-resolution models. To incorporate ML parameterizations into ESMs, we develop a Fortran–Python interface that allows for calling Python functions within Fortran-based ESMs. Through two case studies, this interface demonstrates its feasibility, modularity, and effectiveness.
Kostas Tsigaridis, Andrew S. Ackerman, Igor Aleinov, Mark A. Chandler, Thomas L. Clune, Christopher M. Colose, Anthony D. Del Genio, Maxwell Kelley, Nancy Y. Kiang, Anthony Leboissetier, Jan P. Perlwitz, Reto A. Ruedy, Gary L. Russell, Linda E. Sohl, Michael J. Way, and Eric T. Wolf
EGUsphere, https://doi.org/10.5194/egusphere-2025-925, https://doi.org/10.5194/egusphere-2025-925, 2025
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We present the second generation of ROCKE-3D, a generalized 3-dimensional model for use in Solar System and exoplanetary simulations of rocky planet climates. We quantify how the different component choices affect model results, and discuss strengths and limitations of using each component, together with how one can select which component to use. ROCKE-3D is publicly available and tutorial sessions are available for the community, greatly facilitating its use by any interested group.
Camilla Mathison, Eleanor J. Burke, Gregory Munday, Chris D. Jones, Chris J. Smith, Norman J. Steinert, Andy J. Wiltshire, Chris Huntingford, Eszter Kovacs, Laila K. Gohar, Rebecca M. Varney, and Douglas McNeall
Geosci. Model Dev., 18, 1785–1808, https://doi.org/10.5194/gmd-18-1785-2025, https://doi.org/10.5194/gmd-18-1785-2025, 2025
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We present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), which is designed to take new emissions scenarios and rapidly provide regional impact information. PRIME allows large ensembles to be run on multi-centennial timescales, including the analysis of many important variables for impact assessments. Our evaluation shows that PRIME reproduces the climate response for known scenarios, providing confidence in using PRIME for novel scenarios.
Beiyao Xu, Steven Dobbie, Huiyi Yang, Lianxin Yang, Yu Jiang, Andrew Challinor, Karina Williams, Yunxia Wang, and Tijian Wang
EGUsphere, https://doi.org/10.5194/egusphere-2024-4077, https://doi.org/10.5194/egusphere-2024-4077, 2025
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Ozone (O3) pollution harms rice production and threatens food security. To understand these impacts, we calibrated a crop model using unique data from experiments where rice was grown in open fields under controlled O3 exposure (free air). This is the first time such data has been used to improve a model’s ability to predict how rice responds to O3 pollution. Our work provides a more accurate tool to study O3’s effects and guide strategies to protect agriculture.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
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Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
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We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
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).
Hans Segura, Xabier Pedruzo-Bagazgoitia, Philipp Weiss, Sebastian K. Müller, Thomas Rackow, Junhong Lee, Edgar Dolores-Tesillos, Imme Benedict, Matthias Aengenheyster, Razvan Aguridan, Gabriele Arduini, Alexander J. Baker, Jiawei Bao, Swantje Bastin, Eulàlia Baulenas, Tobias Becker, Sebastian Beyer, Hendryk Bockelmann, Nils Brüggemann, Lukas Brunner, Suvarchal K. Cheedela, Sushant Das, Jasper Denissen, Ian Dragaud, Piotr Dziekan, Madeleine Ekblom, Jan Frederik Engels, Monika Esch, Richard Forbes, Claudia Frauen, Lilli Freischem, Diego García-Maroto, Philipp Geier, Paul Gierz, Álvaro González-Cervera, Katherine Grayson, Matthew Griffith, Oliver Gutjahr, Helmuth Haak, Ioan Hadade, Kerstin Haslehner, Shabeh ul Hasson, Jan Hegewald, Lukas Kluft, Aleksei Koldunov, Nikolay Koldunov, Tobias Kölling, Shunya Koseki, Sergey Kosukhin, Josh Kousal, Peter Kuma, Arjun U. Kumar, Rumeng Li, Nicolas Maury, Maximilian Meindl, Sebastian Milinski, Kristian Mogensen, Bimochan Niraula, Jakub Nowak, Divya Sri Praturi, Ulrike Proske, Dian Putrasahan, René Redler, David Santuy, Domokos Sármány, Reiner Schnur, Patrick Scholz, Dmitry Sidorenko, Dorian Spät, Birgit Sützl, Daisuke Takasuka, Adrian Tompkins, Alejandro Uribe, Mirco Valentini, Menno Veerman, Aiko Voigt, Sarah Warnau, Fabian Wachsmann, Marta Wacławczyk, Nils Wedi, Karl-Hermann Wieners, Jonathan Wille, Marius Winkler, Yuting Wu, Florian Ziemen, Janos Zimmermann, Frida A.-M. Bender, Dragana Bojovic, Sandrine Bony, Simona Bordoni, Patrice Brehmer, Marcus Dengler, Emanuel Dutra, Saliou Faye, Erich Fischer, Chiel van Heerwaarden, Cathy Hohenegger, Heikki Järvinen, Markus Jochum, Thomas Jung, Johann H. Jungclaus, Noel S. Keenlyside, Daniel Klocke, Heike Konow, Martina Klose, Szymon Malinowski, Olivia Martius, Thorsten Mauritsen, Juan Pedro Mellado, Theresa Mieslinger, Elsa Mohino, Hanna Pawłowska, Karsten Peters-von Gehlen, Abdoulaye Sarré, Pajam Sobhani, Philip Stier, Lauri Tuppi, Pier Luigi Vidale, Irina Sandu, and Bjorn Stevens
EGUsphere, https://doi.org/10.5194/egusphere-2025-509, https://doi.org/10.5194/egusphere-2025-509, 2025
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The nextGEMS project developed two Earth system models that resolve processes of the order of 10 km, giving more fidelity to the representation of local phenomena, globally. In its fourth cycle, nextGEMS performed simulations with coupled ocean, land, and atmosphere over the 2020–2049 period under the SSP3-7.0 scenario. Here, we provide an overview of nextGEMS, insights into the model development, and the realism of multi-decadal, kilometer-scale simulations.
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.
Jonah K. Shaw, Dustin J. Swales, Sergio DeSouza-Machado, David D. Turner, Jennifer E. Kay, and David P. Schneider
EGUsphere, https://doi.org/10.5194/egusphere-2025-169, https://doi.org/10.5194/egusphere-2025-169, 2025
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Satellites have observed earth's emission of infrared radiation since the 1970s. Because infrared wavelengths interact with the atmosphere in distinct ways, these observations contain information about the earth and atmosphere. We present a tool that runs alongside global climate models and produces output that can be directly compared with satellite measurements of infrared radiation. We then use this tool for climate model evaluation, climate change detection, and satellite mission design.
Noribeth Mariscal, Louisa K. Emmons, Duseong S. Jo, Ying Xiong, Laura M. Judd, Scott J. Janz, Jiajue Chai, and Yaoxian Huang
EGUsphere, https://doi.org/10.5194/egusphere-2025-228, https://doi.org/10.5194/egusphere-2025-228, 2025
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The distribution of ozone (O3) and its precursors (NOx, VOCs) is explored using the chemistry-climate model, MUSICAv0, and evaluated using measurements from the Michigan-Ontario Ozone Source Experiment. A custom grid of ~7 km was created over Michigan. A sector-based diurnal cycle for anthropogenic nitric oxide was included in the model. This work shows that grid resolution played a more important role for O3 precursors, and the diurnal cycle significantly impacted nighttime O3 formation.
Maria Vittoria Struglia, Alessandro Anav, Marta Antonelli, Sandro Calmanti, Franco Catalano, Alessandro Dell'Aquila, Emanuela Pichelli, and Giovanna Pisacane
EGUsphere, https://doi.org/10.5194/egusphere-2025-387, https://doi.org/10.5194/egusphere-2025-387, 2025
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We present the results of downscaling global climate projections for the Mediterranean and Italian regions aiming to produce high-resolution climate information for the assessment of climate change signals, focusing on extreme events. A general warming is foreseen by the end of century with a mean precipitation reduction accompanied, over Italian Peninsula, by a strong increase in the intensity of extreme precipitation events, particularly relevant for the high emissions scenario during autumn
Cited articles
Ban, N., Schmidli, J., and Schär, C.: Heavy precipitation in a changing climate: Does short-term summer precipitation increase faster?, Geophys. Res. Lett., 42, 1165–1172, 2015. a
Chokkavarapu, N. and Mandla, V. R.: Comparative study of GCMs, RCMs, downscaling and hydrological models: a review toward future climate change impact estimation, SN Applied Sciences, 1, 1698, https://doi.org/10.1007/s42452-019-1764-x, 2019. a
Ciarlo`, J. M., Coppola, E., Fantini, A., Giorgi, F., Gao, X., Tong, Y., Glazer, R. H., Torres Alavez, J. A., Sines, T., Pichelli, E., Raffaele, F., Das, S., Bukovsky, M., Ashfaq, M., Im, E.-S., Nguyen-Xuan, T., Teichmann, C., Remedio, A., Remke, T., Bülow, K., Weber, T., Buntemeyer, L., Sieck, K., Rechid, D., and Jacob, D.: A new spatially distributed added value index for regional climate models: the EURO-CORDEX and the CORDEX-CORE highest resolution ensembles, Clim. Dynam., 57, 1403–1424, 2021. a, b, c
Cui, T., Li, C., and Tian, F.: Evaluation of temperature and precipitation simulations in CMIP6 models over the Tibetan Plateau, Earth Space Sci., 8, e2020EA001620, https://doi.org/10.1029/2020EA001620, 2021. a
Demory, M.-E., Berthou, S., Fernández, J., Sørland, S. L., Brogli, R., Roberts, M. J., Beyerle, U., Seddon, J., Haarsma, R., Schär, C., Buonomo, E., Christensen, O. B., Ciarlo ̀, J. M., Fealy, R., Nikulin, G., Peano, D., Putrasahan, D., Roberts, C. D., Senan, R., Steger, C., Teichmann, C., and Vautard, R.: European daily precipitation according to EURO-CORDEX regional climate models (RCMs) and high-resolution global climate models (GCMs) from the High-Resolution Model Intercomparison Project (HighResMIP), Geosci. Model Dev., 13, 5485–5506, https://doi.org/10.5194/gmd-13-5485-2020, 2020. a
Didovets, I., Krysanova, V., Nurbatsina, A., Fallah, B., Krylova, V., Saparova, A., Niyazov, J., Kalashnikova, O., and Hattermann, F. F.: Attribution of current trends in streamflow to climate change for 12 Central Asian catchments, Clim. Change, 177, 16, https://doi.org/10.1007/s10584-023-03673-3, 2024. a
Döscher, R., Acosta, M., Alessandri, A., Anthoni, P., Arsouze, T., Bergman, T., Bernardello, R., Boussetta, S., Caron, L.-P., Carver, G., Castrillo, M., Catalano, F., Cvijanovic, I., Davini, P., Dekker, E., Doblas-Reyes, F. J., Docquier, D., Echevarria, P., Fladrich, U., Fuentes-Franco, R., Gröger, M., v. Hardenberg, J., Hieronymus, J., Karami, M. P., Keskinen, J.-P., Koenigk, T., Makkonen, R., Massonnet, F., Ménégoz, M., Miller, P. A., Moreno-Chamarro, E., Nieradzik, L., van Noije, T., Nolan, P., O'Donnell, D., Ollinaho, P., van den Oord, G., Ortega, P., Prims, O. T., Ramos, A., Reerink, T., Rousset, C., Ruprich-Robert, Y., Le Sager, P., Schmith, T., Schrödner, R., Serva, F., Sicardi, V., Sloth Madsen, M., Smith, B., Tian, T., Tourigny, E., Uotila, P., Vancoppenolle, M., Wang, S., Wårlind, D., Willén, U., Wyser, K., Yang, S., Yepes-Arbós, X., and Zhang, Q.: The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6, Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, 2022. a
Dosio, A. and Panitz, H.-J.: Climate change projections for CORDEX-Africa with COSMO-CLM regional climate model and differences with the driving global climate models, Clim. Dynam., 46, 1599–1625, 2016. a
Fallah, B.: Climate Model Downscaling in Central Asia: A Dynamical and a Neural Network Approach (1.0.0), Zenodo [software], https://doi.org/10.5281/zenodo.10417111, 2023. a, b
Fallah, B. and Rostami, M.: Exploring the impact of the recent global warming on extreme weather events in Central Asia using the counterfactual climate data ATTRICI v1.1, Clim. Change, 177, 80, https://doi.org/10.1007/s10584-024-03743-0, 2024. a
Fallah, B., Saberi, A. A., and Sodoudi, S.: Emergence of global scaling behaviour in the coupled Earth-atmosphere interaction, Sci. Rep., 6, 34005, https://doi.org/10.1038/srep34005, 2016a. a
Fallah, B., Sodoudi, S., and Cubasch, U.: Westerly jet stream and past millennium climate change in Arid Central Asia simulated by COSMO-CLM model, Theor. Appl. Climatol., 124, 1079–1088, 2016b. a
Fallah, B., Russo, E., Menz, C., Hoffmann, P., Didovets, I., and Hattermann, F. F.: Anthropogenic influence on extreme temperature and precipitation in Central Asia, Sci. Rep., 13, 6854, https://doi.org/10.1038/s41598-023-33921-6, 2023. a, b
Fick, S. E. and Hijmans, R. J.: WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas, Int. J. Climatol., 37, 4302–4315, 2017. a
Fotso-Nguemo, T. C., Vondou, D. A., Pokam, W. M., Djomou, Z. Y., Diallo, I., Haensler, A., Tchotchou, L. A. D., Kamsu-Tamo, P. H., Gaye, A. T., and Tchawoua, C.: On the added value of the regional climate model REMO in the assessment of climate change signal over Central Africa, Clim. Dynam., 49, 3813–3838, 2017. a
Fowler, H. J., Blenkinsop, S., and Tebaldi, C.: Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling, Int. J. Climatol., 27, 1547–1578, 2007. a
Frei, C., Christensen, J. H., Déqué, M., Jacob, D., Jones, R. G., and Vidale, P. L.: Daily precipitation statistics in regional climate models: Evaluation and intercomparison for the European Alps, J. Geophys. Res.-Atmos., 108, 4124, https://doi.org/10.1029/2002JD002287, 2003. a
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: The climate hazards infrared precipitation with stations – a new environmental record for monitoring extremes, Sci. Data, 2, 1–21, 2015. a
Galea, D., Ma, H.-Y., Wu, W.-Y., and Kobayashi, D.: Deep Learning Image Segmentation for Atmospheric Rivers, Artificial Intelligence for the Earth Systems, 3, 230048, https://doi.org/10.1175/AIES-D-23-0048.1, 2024. a
Gao, Y., Xu, Y., Shi, Y., Giorgi, F., and Zhai, P.: Evaluating the Performance of RegCM4 over CORDEX East Asia against the Reanalysis Data, J. Climate, 28, 660–676, 2015. a
Gardoll, S. and Boucher, O.: Classification of tropical cyclone containing images using a convolutional neural network: performance and sensitivity to the learning dataset, Geosci. Model Dev., 15, 7051–7073, https://doi.org/10.5194/gmd-15-7051-2022, 2022. a
Giot, O., Termonia, P., Degrauwe, D., De Troch, R., Caluwaerts, S., Smet, G., Berckmans, J., Deckmyn, A., De Cruz, L., De Meutter, P., Duerinckx, A., Gerard, L., Hamdi, R., Van den Bergh, J., Van Ginderachter, M., and Van Schaeybroeck, B.: Validation of the ALARO-0 model within the EURO-CORDEX framework, Geosci. Model Dev., 9, 1143–1152, https://doi.org/10.5194/gmd-9-1143-2016, 2016. a
Harder, P.: Hard-Constrained Deep Learning for Climate Downscaling (v1.0.0), Zenodo [software], https://doi.org/10.5281/zenodo.8150694, 2023. a
Harder, P., Yang, Q., Ramesh, V., Sattigeri, P., Hernandez-Garcia, A., Watson, C., Szwarcman, D., and Rolnick, D.: Generating physically-consistent high-resolution climate data with hard-constrained neural networks, arXiv [preprint], arXiv:2208.05424, 18, 109–122, 2022.
Hodson, T. O.: Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not, Geosci. Model Dev., 15, 5481–5487, https://doi.org/10.5194/gmd-15-5481-2022, 2022. a
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, https://doi.org/10.1017/9781009157896, 2021. a, b, c
Jacob, D. and Podzun, R.: Sensitivity studies with the regional climate model REMO, Meteorol. Atmos. Phys., 63, 119–129, 1997. a
Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer, L. M., Braun, A., Colette, A., Déqué, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A., Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kröner, N., Kotlarski, S., Kriegsmann, A., Martin, E., van Meijgaard, E., Moseley, C., Pfeifer, S., Preuschmann, S., Radermacher, C., Radtke, K., Rechid, D., Rounsevell, M., Samuelsson, P., Somot, S., Soussana, J.-F., Teichmann, C., Valentini, R., Vautard, R., Weber, B., and Yiou, P.: EURO-CORDEX: New high-resolution climate change projections for European impact research, Reg. Environ. Change, 14, 563–578, 2014. a
Kendon, E., Roberts, N., Fowler, H., Roberts, M., Chan, S., and Senior, C.: Heavier summer downpours with climate change revealed by weather forecast resolution model. Nat. Clim. Change, 4, 570–576, 2014. a
Kikstra, J. S., Nicholls, Z. R. J., Smith, C. J., Lewis, J., Lamboll, R. D., Byers, E., Sandstad, M., Meinshausen, M., Gidden, M. J., Rogelj, J., Kriegler, E., Peters, G. P., Fuglestvedt, J. S., Skeie, R. B., Samset, B. H., Wienpahl, L., van Vuuren, D. P., van der Wijst, K.-I., Al Khourdajie, A., Forster, P. M., Reisinger, A., Schaeffer, R., and Riahi, K.: The IPCC Sixth Assessment Report WGIII climate assessment of mitigation pathways: from emissions to global temperatures, Geosci. Model Dev., 15, 9075–9109, https://doi.org/10.5194/gmd-15-9075-2022, 2022. a
Kirschbaum, D. B., Adler, R., Hong, Y., Hill, S., and Lerner-Lam, A.: A global landslide catalog for hazard applications: method, results, and limitations, Natural Hazards, 52, 561–575, 2010. a
Kjellström, E., Nikulin, G., Hansson, U., Strandberg, G., Ullerstig, A., Willén, U., and Wyser, K.: 21st century changes in the European climate: uncertainties derived from an ensemble of regional climate model simulations, Tellus A, 63, 24–40, 2011. a
Klok, E. and Klein Tank, A.: Updated and extended European dataset of daily weather observations, Int. J. Climatol., 28, 2081–2095, 2008. a
Kotlarski, S., Keuler, K., Christensen, O. B., Colette, A., Déqué, M., Gobiet, A., Goergen, K., Jacob, D., Lüthi, D., van Meijgaard, E., Nikulin, G., Schär, C., Teichmann, C., Vautard, R., Warrach-Sagi, K., and Wulfmeyer, V.: Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble, Geosci. Model Dev., 7, 1297–1333, https://doi.org/10.5194/gmd-7-1297-2014, 2014. a
Kurth, T., Treichler, S., Romero, J., Mudigonda, M., Luehr, N., Phillips, E., Mahesh, A., Matheson, M., Deslippe, J., Fatica, M., Prabhat, and Houston, M.: Exascale deep learning for climate analytics, in: SC18: International conference for high performance computing, networking, storage and analysis, 11–16 November 2018, Dallas,Texas, USA, 649–660, IEEE, 2018. a
Laflamme, E. M., Linder, E., and Pan, Y.: Statistical downscaling of regional climate model output to achieve projections of precipitation extremes, Weather and Climate Extremes, 12, 15–23, 2016. a
Li, L., Bisht, G., and Leung, L. R.: Spatial heterogeneity effects on land surface modeling of water and energy partitioning, Geosci. Model Dev., 15, 5489–5510, https://doi.org/10.5194/gmd-15-5489-2022, 2022. a
Maraun, D. and Widmann, M.: Statistical downscaling and bias correction for climate research, Cambridge University Press, ISBN 9781107066052, https://doi.org/10.1017/9781107588783, 2018. a
Maraun, D., Widmann, M., Gutiérrez, J. M., Kotlarski, S., Chandler, R. E., Hertig, E., Wibig, J., Huth, R., and Wilcke, R. A.: VALUE: A framework to validate downscaling approaches for climate change studies, Earth's Future, 3, 1–14, 2015. a
Meredith, E. P., Rust, H. W., and Ulbrich, U.: A classification algorithm for selective dynamical downscaling of precipitation extremes, Hydrol. Earth Syst. Sci., 22, 4183–4200, https://doi.org/10.5194/hess-22-4183-2018, 2018. a
Muttaqien, F. H., Rahadianti, L., and Latifah, A. L.: Downscaling for Climate Data in Indonesia Using Image-to-Image Translation Approach, in: 2021 International Conference on Advanced Computer Science and Information Systems (ICACSIS), 23–25 October 2021, Depok, Indonesia, 1–8, IEEE, 2021. a
Naddaf, M.: Climate change is costing trillions-and low-income countries are paying the price, Nature, https://doi.org/10.1038/d41586-022-03573-z, 2022. a
Panitz, H.-J., Dosio, A., Büchner, M., Lüthi, D., and Keuler, K.: COSMO-CLM (CCLM) climate simulations over CORDEX-Africa domain: analysis of the ERA-Interim driven simulations at 0.44 and 0.22 resolution, Clim. Dynam., 42, 3015–3038, 2014. a
Qiu, Y., Feng, J., Yan, Z., and Wang, J.: HCPD-CA: high-resolution climate projection dataset in central Asia, Earth Syst. Sci. Data, 14, 2195–2208, https://doi.org/10.5194/essd-14-2195-2022, 2022. a
Racah, E., Beckham, C., Maharaj, T., Ebrahimi Kahou, S., Prabhat, M., and Pal, C.: Extremeweather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events, Adv. Neur. In., 30, 3403–3414, https://doi.org/10.5555/3295222.3295380, 2017. a
Rai, P., Bangelesa, F., Abel, D., Ziegler, K., Huang, J., Schaffhauser, T., Pollinger, F., Disse, M., and Paeth, H.: Extreme precipitation and temperature indices under future climate change in central Asia based on CORDEX-CORE, Theor. Appl. Climatol., 155, 6015–6039, https://doi.org/10.1007/s00704-024-04976-w, 2024. a
Rajczak, J., Pall, P., and Schär, C.: Projections of extreme precipitation events in regional climate simulations for Europe and the Alpine Region, J. Geophys. Res.-Atmos., 118, 3610–3626, 2013. a
Rampal, N., Hobeichi, S., Gibson, P. B., Baño-Medina, J., Abramowitz, G., Beucler, T., González-Abad, J., Chapman, W., Harder, P., and Gutiérrez, J. M.: Enhancing Regional Climate Downscaling through Advances in Machine Learning, Artificial Intelligence for the Earth Systems, 3, 230066, https://doi.org/10.1175/AIES-D-23-0066.1, 2024. a
Randall, D. A., Wood, R. A., Bony, S., Colman, R., Fichefet, T., Fyfe, J., Kattsov, V., Pitman, A., Shukla, J., Srinivasan, J., Stouffer, R. J., Sumi, A., Taylor, K. E., AchutaRao, K., Allan, R., Berger, A., Blatter, H., Bonfils, C., Boone, A., Bretherton, C., Broccoli, A., Brovkin, V., Cai, W., Claussen, M., Dirmeyer, P., Doutriaux, C., Drange, H., Dufresne, J.-L., Emori, S., Forster, P., Frei, A., Ganopolski, A., Gent, P., Gleckler, P., Goosse, H., Graham, R., Gregory, J., Gudgel, R., Hall, A., Hallegatte, S., Hasumi, H., Henderson-Sellers, A., Hendon, H., Hodges, K., Holland, M., Holtslag, A., Hunke, E., Huybrechts, P., Ingram, W., Joos, F., Kirtman, B., Klein, S., Koster, R., Kushner, P., Lanzante, J., Latif, M., Lau, N.-C., Meinshausen, M., Monahan, A., Murphy, J., Osborn, T., Pavlova, T., Petoukhov, V., Phillips, T., Power, S., Rahmstorf, S., Raper, S., Renssen, H., Rind, D., Roberts, M., Rosati, A., Schär, C., Schmittner, A., Scinocca, J., Seidov, D., Slater, A., Slingo, J., Smith, D., Soden, B., Stern, W., Stone, D., Sudo, K., Takemura, T., Tselioudis, G., Webb, M., and Wild, M.: Climate models and their evaluation, in: Climate change 2007: The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the IPCC (FAR), edited by: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M., and Miller, H. L., 589–662, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, https://www.ipcc.ch/site/assets/uploads/2018/02/ar4-wg1-chapter8-1.pdf (last access: 14 January 2025), 2007. a
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat, f.: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195–204, 2019. a
Reyer, C. P. O., Otto, I. M., Adams, S., Albrecht, T., Baarsch, F., Cartsburg, M., Coumou, D., Eden, A., Ludi, E., Marcus, R., Mengel, M., Mosello, B., Robinson, A., Schleussner, C.-F., Serdeczny, O., and Stagl, J.: Climate change impacts in Central Asia and their implications for development, Reg. Environ. Change, 17, 1639–1650, 2017. a, b
Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp, A., Cuaresma, J. C., KC, S., Leimbach, M., Jiang, L., Kram, T., Rao, S., Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenöder, F., Da Silva, L. A., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D., Masui, T., Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G., Harmsen, M., Takahashi, K., Baumstark, L., Doelman, J. C., Kainuma, M., Klimont, Z., Marangoni, G., Lotze-Campen, H., Obersteiner, M., Tabeau, A., and Tavoni, M.: The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview, Global Environ. Change, 42, 153–168, 2017. a
Rockel, B. and Geyer, B.: The performance of the regional climate model CLM in different climate regions, as simulated in a transient climate change experiment, Clim. Dynam., 31, 713–728, 2008. a
Rummukainen, M.: State-of-the-art with regional climate models, Wiley Interdisciplinary Reviews: Climate Change, 1, 82–96, 2010. a
Russo, E., Fallah, B., Ludwig, P., Karremann, M., and Raible, C. C.: The long-standing dilemma of European summer temperatures at the mid-Holocene and other considerations on learning from the past for the future using a regional climate model, Clim. Past, 18, 895–909, https://doi.org/10.5194/cp-18-895-2022, 2022. a
Sørland, S. L., Brogli, R., Pothapakula, P. K., Russo, E., Van de Walle, J., Ahrens, B., Anders, I., Bucchignani, E., Davin, E. L., Demory, M.-E., Dosio, A., Feldmann, H., Früh, B., Geyer, B., Keuler, K., Lee, D., Li, D., van Lipzig, N. P. M., Min, S.-K., Panitz, H.-J., Rockel, B., Schär, C., Steger, C., and Thiery, W.: COSMO-CLM regional climate simulations in the Coordinated Regional Climate Downscaling Experiment (CORDEX) framework: a review, Geosci. Model Dev., 14, 5125–5154, https://doi.org/10.5194/gmd-14-5125-2021, 2021. a
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and the experiment design, B. Am. Meteorol. Soc., 93, 485–498, 2012. a
Volosciuk, C., Maraun, D., Vrac, M., and Widmann, M.: A combined statistical bias correction and stochastic downscaling method for precipitation, Hydrol. Earth Syst. Sci., 21, 1693–1719, https://doi.org/10.5194/hess-21-1693-2017, 2017. a
Wang, X., Otto, M., and Scherer, D.: Atmospheric triggering conditions and climatic disposition of landslides in Kyrgyzstan and Tajikistan at the beginning of the 21st century, Nat. Hazards Earth Syst. Sci., 21, 2125–2144, https://doi.org/10.5194/nhess-21-2125-2021, 2021. a
Watson-Parris, D., Rao, Y., Olivié, D., Seland, O., Nowack, P., Camps-Valls, G., Stier, P., Bouabid, S., Dewey, M., Fons, E., Gonzalez, J., Harder, P., Jeggle, K., Lenhardt, J., Manshausen, P., Novitasari, M., Ricard, L., and Roesch, C.: ClimateBench v1. 0: A benchmark for data-driven climate projections, J. Adv. Model. Earth Sy., 14, e2021MS002954, https://doi.org/10.1029/2021MS002954, 2022. a
Xu, P., Wang, L., and Ming, J.: Central Asian precipitation extremes affected by an intraseasonal planetary wave pattern, J. Climate, 35, 2603–2616, 2022. a
Xu, Z., Han, Y., Tam, C.-Y., Yang, Z.-L., and Fu, C.: Bias-corrected CMIP6 global dataset for dynamical downscaling of the historical and future climate (1979–2100), Sci. Data, 8, 293, https://doi.org/10.1038/s41597-021-01079-3, 2021. a
Yan, Y., You, Q., Wu, F., Pepin, N., and Kang, S.: Surface mean temperature from the observational stations and multiple reanalyses over the Tibetan Plateau, Clim. Dynam., 55, 2405–2419, 2020. a
Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., and Fraundorfer, F.: Deep learning in remote sensing: A comprehensive review and list of resources, IEEE Geoscience and Remote Sensing Magazine, 5, 8–36, 2017. a
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.
We tried to contribute to a local climate change impact study in central Asia, a region that is...
