Articles | Volume 18, issue 17
https://doi.org/10.5194/gmd-18-5451-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-18-5451-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
01 Sep 2025
Development and technical paper |
| 01 Sep 2025
| 01 Sep 2025
Implementation and validation of a supermodeling framework into Community Earth System Model version 2.1.5
National Center for Atmospheric Research, Boulder, CO, USA
Francine Schevenhoven
Geophysical Institute and Bjerknes Centre for Climate Research, University of Bergen, Bergen, Norway
Judith Berner
National Center for Atmospheric Research, Boulder, CO, USA
Noel Keenlyside
Geophysical Institute and Bjerknes Centre for Climate Research, University of Bergen, Bergen, Norway
Nansen Environmental and Remote Sensing Center, Bergen, Norway
Ingo Bethke
Geophysical Institute and Bjerknes Centre for Climate Research, University of Bergen, Bergen, Norway
Ping-Gin Chiu
Geophysical Institute and Bjerknes Centre for Climate Research, University of Bergen, Bergen, Norway
Alok Gupta
Nansen Environmental and Remote Sensing Center, Bergen, Norway
Jesse Nusbaumer
National Center for Atmospheric Research, Boulder, CO, USA
Related authors
Prabhat, Karthik Kashinath, Mayur Mudigonda, Sol Kim, Lukas Kapp-Schwoerer, Andre Graubner, Ege Karaismailoglu, Leo von Kleist, Thorsten Kurth, Annette Greiner, Ankur Mahesh, Kevin Yang, Colby Lewis, Jiayi Chen, Andrew Lou, Sathyavat Chandran, Ben Toms, Will Chapman, Katherine Dagon, Christine A. Shields, Travis O'Brien, Michael Wehner, and William Collins
Geosci. Model Dev., 14, 107–124, https://doi.org/10.5194/gmd-14-107-2021, https://doi.org/10.5194/gmd-14-107-2021, 2021
Short summary
Short summary
Detecting extreme weather events is a crucial step in understanding how they change due to climate change. Deep learning (DL) is remarkable at pattern recognition; however, it works best only when labeled datasets are available. We create
ClimateNet– an expert-labeled curated dataset – to train a DL model for detecting weather events and predicting changes in extreme precipitation. This work paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data.
Marianne Williams-Kerslake, Helene Reinertsen Langehaug, Ragnheid Skogseth, Frank Nilsen, Annette Samuelsen, Silvana Gonzalez, and Noel Keenlyside
EGUsphere, https://doi.org/10.5194/egusphere-2025-4269, https://doi.org/10.5194/egusphere-2025-4269, 2025
This preprint is open for discussion and under review for Ocean Science (OS).
Short summary
Short summary
Marine heatwaves—periods of extreme ocean temperatures—are increasing globally, posing a threat to marine ecosystems. One region where a high number of marine heatwave events per year has been observed is around Svalbard. This study characterises past marine heatwave events around Svalbard, including their extent in terms of both distance and depth. We identified eight events in western Svalbard that were largely driven by the movement of warmer water into the region by ocean currents.
Yiguo Wang, François Counillon, Lea Svendsen, Ping-Gin Chiu, Noel Keenlyside, Patrick Laloyaux, Mariko Koseki, and Eric de Boisseson
Earth Syst. Sci. Data, 17, 4185–4211, https://doi.org/10.5194/essd-17-4185-2025, https://doi.org/10.5194/essd-17-4185-2025, 2025
Short summary
Short summary
CoRea1860+ is a new climate dataset that reconstructs past climate conditions from 1860 to today. By using advanced modelling techniques and incorporating sea surface temperature observations, it provides a consistent picture of long-term climate variability. The dataset captures key ocean, sea ice, and atmosphere changes, helping scientists understand past climate changes and variability.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Shunya Koseki, Lander R. Crespo, Jerry Tjiputra, Filippa Fransner, Noel S. Keenlyside, and David Rivas
Biogeosciences, 21, 4149–4168, https://doi.org/10.5194/bg-21-4149-2024, https://doi.org/10.5194/bg-21-4149-2024, 2024
Short summary
Short summary
We investigated how the physical biases of an Earth system model influence the marine biogeochemical processes in the tropical Atlantic. With four different configurations of the model, we have shown that the versions with better SST reproduction tend to better represent the primary production and air–sea CO2 flux in terms of climatology, seasonal cycle, and response to climate variability.
Mario C. Acosta, Sergi Palomas, Stella V. Paronuzzi Ticco, Gladys Utrera, Joachim Biercamp, Pierre-Antoine Bretonniere, Reinhard Budich, Miguel Castrillo, Arnaud Caubel, Francisco Doblas-Reyes, Italo Epicoco, Uwe Fladrich, Sylvie Joussaume, Alok Kumar Gupta, Bryan Lawrence, Philippe Le Sager, Grenville Lister, Marie-Pierre Moine, Jean-Christophe Rioual, Sophie Valcke, Niki Zadeh, and Venkatramani Balaji
Geosci. Model Dev., 17, 3081–3098, https://doi.org/10.5194/gmd-17-3081-2024, https://doi.org/10.5194/gmd-17-3081-2024, 2024
Short summary
Short summary
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.
Akhilesh Sivaraman Nair, François Counillon, and Noel Keenlyside
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-217, https://doi.org/10.5194/gmd-2023-217, 2024
Publication in GMD not foreseen
Short summary
Short summary
This study demonstrates the importance of soil moisture (SM) in subseasonal-to-seasonal predictions. To addess this, we introduce the Norwegian Climate Prediction Model Land (NorCPM-Land), a land data assimilation system developed for the NorCPM. NorCPM-Land reduces error in SM by 10.5 % by assimilating satellite SM products. Enhanced land initialisation improves predictions up to a 3.5-month lead time for SM and a 1.5-month lead time for temperature and precipitation.
Lina Boljka, Nour-Eddine Omrani, and Noel S. Keenlyside
Weather Clim. Dynam., 4, 1087–1109, https://doi.org/10.5194/wcd-4-1087-2023, https://doi.org/10.5194/wcd-4-1087-2023, 2023
Short summary
Short summary
This study examines quasi-periodic variability in the tropical Pacific on interannual timescales and related physics using a recently developed time series analysis tool. We find that wind stress in the west Pacific and recharge–discharge of ocean heat content are likely related to each other on ~1.5–4.5-year timescales (but not on others) and dominate variability in sea surface temperatures on those timescales. This may have further implications for climate models and long-term prediction.
Shuang Gao, Jörg Schwinger, Jerry Tjiputra, Ingo Bethke, Jens Hartmann, Emilio Mayorga, and Christoph Heinze
Biogeosciences, 20, 93–119, https://doi.org/10.5194/bg-20-93-2023, https://doi.org/10.5194/bg-20-93-2023, 2023
Short summary
Short summary
We assess the impact of riverine nutrients and carbon (C) on projected marine primary production (PP) and C uptake using a fully coupled Earth system model. Riverine inputs alleviate nutrient limitation and thus lessen the projected PP decline by up to 0.7 Pg C yr−1 globally. The effect of increased riverine C may be larger than the effect of nutrient inputs in the future on the projected ocean C uptake, while in the historical period increased nutrient inputs are considered the largest driver.
Francine Schevenhoven and Alberto Carrassi
Geosci. Model Dev., 15, 3831–3844, https://doi.org/10.5194/gmd-15-3831-2022, https://doi.org/10.5194/gmd-15-3831-2022, 2022
Short summary
Short summary
In this study, we present a novel formulation to build a dynamical combination of models, the so-called supermodel, which needs to be trained based on data. Previously, we assumed complete and noise-free observations. Here, we move towards a realistic scenario and develop adaptations to the training methods in order to cope with sparse and noisy observations. The results are very promising and shed light on how to apply the method with state of the art general circulation models.
Ingo Bethke, Yiguo Wang, François Counillon, Noel Keenlyside, Madlen Kimmritz, Filippa Fransner, Annette Samuelsen, Helene Langehaug, Lea Svendsen, Ping-Gin Chiu, Leilane Passos, Mats Bentsen, Chuncheng Guo, Alok Gupta, Jerry Tjiputra, Alf Kirkevåg, Dirk Olivié, Øyvind Seland, Julie Solsvik Vågane, Yuanchao Fan, and Tor Eldevik
Geosci. Model Dev., 14, 7073–7116, https://doi.org/10.5194/gmd-14-7073-2021, https://doi.org/10.5194/gmd-14-7073-2021, 2021
Short summary
Short summary
The Norwegian Climate Prediction Model version 1 (NorCPM1) is a new research tool for performing climate reanalyses and seasonal-to-decadal climate predictions. It adds data assimilation capability to the Norwegian Earth System Model version 1 (NorESM1) and has contributed output to the Decadal Climate Prediction Project (DCPP) as part of the sixth Coupled Model Intercomparison Project (CMIP6). We describe the system and evaluate its baseline, reanalysis and prediction performance.
Tyler S. Harrington, Jesse Nusbaumer, and Christopher B. Skinner
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-284, https://doi.org/10.5194/hess-2021-284, 2021
Revised manuscript not accepted
Short summary
Short summary
The land surface supplies the atmosphere with water through the process of evaporation. Previous studies indicate land surface evaporation is important for precipitation, but the source origin of evaporation (plants, plant canopies, soils, or lakes) is largely unknown. We show that plants supply most of the land surface evaporation for precipitation across much of North America during the warm season. We also find links between evaporation in upwind land regions and precipitation downwind.
Anne L. Morée, Jörg Schwinger, Ulysses S. Ninnemann, Aurich Jeltsch-Thömmes, Ingo Bethke, and Christoph Heinze
Clim. Past, 17, 753–774, https://doi.org/10.5194/cp-17-753-2021, https://doi.org/10.5194/cp-17-753-2021, 2021
Short summary
Short summary
This modeling study of the Last Glacial Maximum (LGM, ~ 21 000 years ago) ocean explores the biological and physical changes in the ocean needed to satisfy marine proxy records, with a focus on the carbon isotope 13C. We estimate that the LGM ocean may have been up to twice as efficient at sequestering carbon and nutrients at depth as compared to preindustrial times. Our work shows that both circulation and biogeochemical changes must have occurred between the LGM and preindustrial times.
Prabhat, Karthik Kashinath, Mayur Mudigonda, Sol Kim, Lukas Kapp-Schwoerer, Andre Graubner, Ege Karaismailoglu, Leo von Kleist, Thorsten Kurth, Annette Greiner, Ankur Mahesh, Kevin Yang, Colby Lewis, Jiayi Chen, Andrew Lou, Sathyavat Chandran, Ben Toms, Will Chapman, Katherine Dagon, Christine A. Shields, Travis O'Brien, Michael Wehner, and William Collins
Geosci. Model Dev., 14, 107–124, https://doi.org/10.5194/gmd-14-107-2021, https://doi.org/10.5194/gmd-14-107-2021, 2021
Short summary
Short summary
Detecting extreme weather events is a crucial step in understanding how they change due to climate change. Deep learning (DL) is remarkable at pattern recognition; however, it works best only when labeled datasets are available. We create
ClimateNet– an expert-labeled curated dataset – to train a DL model for detecting weather events and predicting changes in extreme precipitation. This work paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data.
Øyvind Seland, Mats Bentsen, Dirk Olivié, Thomas Toniazzo, Ada Gjermundsen, Lise Seland Graff, Jens Boldingh Debernard, Alok Kumar Gupta, Yan-Chun He, Alf Kirkevåg, Jörg Schwinger, Jerry Tjiputra, Kjetil Schanke Aas, Ingo Bethke, Yuanchao Fan, Jan Griesfeller, Alf Grini, Chuncheng Guo, Mehmet Ilicak, Inger Helene Hafsahl Karset, Oskar Landgren, Johan Liakka, Kine Onsum Moseid, Aleksi Nummelin, Clemens Spensberger, Hui Tang, Zhongshi Zhang, Christoph Heinze, Trond Iversen, and Michael Schulz
Geosci. Model Dev., 13, 6165–6200, https://doi.org/10.5194/gmd-13-6165-2020, https://doi.org/10.5194/gmd-13-6165-2020, 2020
Short summary
Short summary
The second version of the coupled Norwegian Earth System Model (NorESM2) is presented and evaluated. The temperature and precipitation patterns has improved compared to NorESM1. The model reaches present-day warming levels to within 0.2 °C of observed temperature but with a delayed warming during the late 20th century. Under the four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5), the warming in the period of 2090–2099 compared to 1850–1879 reaches 1.3, 2.2, 3.1, and 3.9 K.
Cited articles
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., and Arkin, P.: The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979–Present), J. Hydrometeorol., 4, 1147–1167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2, 2003 (data available at: https://psl.noaa.gov/data/gridded/data.gpcp.html). a, b
AMWG: AMWG Diagnostics Package, NCAR CESM Atmosphere Model Working Group, GitHub [code], https://github.com/NCAR/ADF (last access: 15 Janury 2024), 2022. a
Anderson, J., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and Avellano, A.: The data assimilation research testbed: A community facility, B. Am. Meteorol. Soc., 90, 1283–1296, 2009. a
Beljaars, A. C., Brown, A. R., and Wood, N.: A new parametrization of turbulent orographic form drag, Q. J. Roy. Meteor. Soc., 130, 1327–1347, 2004. a
Berner, J., Doblas-Reyes, F. J., Palmer, T. N., Shutts, G., and Weisheimer, A.: Impact of a quasi-stochastic cellular automaton backscatter scheme on the systematic error and seasonal prediction skill of a global climate model, Philos. T. R. Soc. A, 366, 2559–2577, 2008. a
Berner, J., Jung, T., and Palmer, T. N.: Systematic model error: The impact of increased horizontal resolution versus improved stochastic and deterministic parameterizations, J. Climate, 25, 4946–4962, 2012. a
Berner, J., Achatz, U., Batté, L., Bengtsson, L., de la Cámara, A., Christensen, H. M., Colangeli, M., Coleman, D. R. B., Crommelin, D., Dolaptchiev, S. I., Franzke, C. L. E., Friederichs, P., Imkeller, P., Järvinen, H., Juricke, S., Kitsios, V., Lott, F., Lucarini, V., Mahajan, S., Palmer, T. N., Penland, C., Sakradzija, M., von Storch, J.-S., Weisheimer, A., Weniger, M., Williams, P. D., and Yano, J.-I.: Stochastic Parameterization: Toward a New View of Weather and Climate Models, B. Am. Meteorol. Soc., 98, 565–588, https://doi.org/10.1175/BAMS-D-15-00268.1, 2017. a
Bogenschutz, P. A., Gettelman, A., Morrison, H., Larson, V. E., Craig, C., and Schanen, D. P.: Higher-order turbulence closure and its impact on climate simulations in the Community Atmosphere Model, J. Climate, 26, 9655–9676, 2013. a
Bogenschutz, P. A., Gettelman, A., Hannay, C., Larson, V. E., Neale, R. B., Craig, C., and Chen, C.-C.: The path to CAM6: coupled simulations with CAM5.4 and CAM5.5, Geosci. Model Dev., 11, 235–255, https://doi.org/10.5194/gmd-11-235-2018, 2018. a
Branstator, G.: The relationship between zonal mean flow and quasi-stationary waves in the midtroposphere, J. Atmos. Sci., 41, 2163–2178, 1984. a
Branstator, G.: Low-frequency patterns induced by stationary waves, J. Atmos. Sci., 47, 629–649, 1990. a
Branstator, G.: The Maintenance of Low-Frequency Atmospheric Anomalies, J. Atmos. Sci., 49, 1924–1946, https://doi.org/10.1175/1520-0469(1992)049<1924:TMOLFA>2.0.CO;2, 1992. a, b, c
Brenowitz, N. D., Cohen, Y., Pathak, J., Mahesh, A., Bonev, B., Kurth, T., Durran, D. R., Harrington, P., and Pritchard, M. S.: A practical probabilistic benchmark for ai weather models, arXiv [preprint], https://doi.org/10.48550/arXiv.2401.15305, 12 November 2024. a
Bretherton, C. S., Henn, B., Kwa, A., Brenowitz, N. D., Watt-Meyer, O., McGibbon, J., Perkins, W. A., Clark, S. K., and Harris, L.: Correcting coarse-grid weather and climate models by machine learning from global storm-resolving simulations, J. Adv. Model. Earth Sy., 14, e2021MS002794, https://doi.org/10.1029/2021MS002794, 2022. a
Chapman, W.: WillyChap/Chapman_2025_GMD: Figure Release V1 (v1.1.0), Zenodo, https://doi.org/10.5281/zenodo.14983576, 2025. a
Chapman, W. and Berner, J.: Deterministic and Stochastic Tendency Adjustments Derived from Data Assimilation and Nudging, Q. J. Roy. Meteor. Soc., 150, 760, 1420–-1446, https://doi.org/10.1002/qj.4652, 2023. a
Chapman, W., Schevenhoven, F., Berner, J., Keenlyside, N., Nusbaumer, J., Bethke, I., Kumar Gupta, A., and Chiu, P.-G.: WillyChap/SuperModel_CAM: PauseResume_v1.1.0, Zenodo, https://doi.org/10.5281/zenodo.14983620, 2025a. a
Chapman, W., Schevenhoven, F., Berner, J., Keenlyside, N., Nusbaumer, J., Bethke, I., Kumar Gupta, A., and Chiu, P.-G.: SuperModel_CAM: PauseResume_v1.1.0, Zenodo [code], https://doi.org/10.5281/zenodo.12577788, 2025b.
Chapman, W., Schevenhoven, F., Berner, J., Keenlyside, N., Nusbaumer, J., Bethke, I., Kumar Gupta, A., and Chiu, P.-G.: Chapman_2024_GMD: Figure Release V1, Zenodo [code], https://doi.org/10.5281/zenodo.12578570, 2025c.
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R., Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System Model Version 2 (CESM2), J. Adv. Model. Earth Sy., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020. a
Davis, N. A., Callaghan, P., Simpson, I. R., and Tilmes, S.: Specified dynamics scheme impacts on wave-mean flow dynamics, convection, and tracer transport in CESM2 (WACCM6), Atmos. Chem. Phys., 22, 197–214, https://doi.org/10.5194/acp-22-197-2022, 2022. a, b, c
Deser, C., Simpson, I. R., McKinnon, K. A., and Phillips, A. S.: The Northern Hemisphere Extratropical Atmospheric Circulation Response to ENSO: How Well Do We Know It and How Do We Evaluate Models Accordingly?, J. Climate, 30, 5059–5082, https://doi.org/10.1175/JCLI-D-16-0844.1, 2017. a
Du, H. and Smith, L. A.: Multi-model cross-pollination in time, Physica D, 353–354, 31–38, https://doi.org/10.1016/j.physd.2017.06.001, 2017. a
Duane, G., Tribbia, J., and Kirtman, B.: Consensus on long-range prediction by adaptive synchronization of models, Geophys. Res. Abstr., EGU2009-13324-1, EGU General Assembly 2009, Vienna, Austria, 2009. a
Duane, G. S. and Tribbia, J. J.: Synchronized chaos in geophysical fluid dynamics, Phys. Rev. Lett., 86, 19, 4298–4301, https://doi.org/10.1103/PhysRevLett.86.4298, 2001. a
Duane, G. S., Tribbia, J. J., and Weiss, J. B.: Synchronicity in predictive modelling: a new view of data assimilation, Nonlin. Processes Geophys., 13, 601–612, https://doi.org/10.5194/npg-13-601-2006, 2006. a
Duane, G. S., Wiegerinck, W., Selten, F., Shen, M.-L., and Keenlyside, N.: Supermodeling: Synchronization of alternative dynamical models of a single objective process, Advances in Nonlinear Geosciences, 101–121, https://doi.org/10.1007/978-3-319-58895-7_5, 2018. a
Egger, J. and Schilling, H.-D.: On the theory of the long-term variability of the atmosphere, J. Atmos. Sci., 40, 1073–1085, 1983. a
European Centre for Medium-Range Weather Forecasts: ERA5 Reanalysis (0.25 Degree Latitude-Longitude Grid) (Updated monthly), Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/BH6N-5N20, 2019. a
Frederiksen, J. S.: A unified three-dimensional instability theory of the onset of blocking and cyclogenesis. II. Teleconnection patterns, J. Atmos. Sci., 40, 2593–2609, 1983. a
Gettelman, A. and Morrison, H.: Advanced two-moment bulk microphysics for global models. Part I: Off-line tests and comparison with other schemes, J. Climate, 28, 1268–1287, 2015. a
Gettelman, A., Liu, X., Ghan, S. J., Morrison, H., Park, S., Conley, A., Klein, S. A., Boyle, J., Mitchell, D., and Li, J.-L.: Global simulations of ice nucleation and ice supersaturation with an improved cloud scheme in the Community Atmosphere Model, J. Geophys. Res.-Atmos., 115, D18216, https://doi.org/10.1029/2009JD013797, 2010. a
Gettelman, A., Bresch, D. N., Chen, C. C., Truesdale, J. E., and Bacmeister, J. T.: Projections of future tropical cyclone damage with a high-resolution global climate model, Climatic Change, 146, 575–585, 2018. a
Golaz, J.-C., Larson, V. E., and Cotton, W. R.: A PDF-based model for boundary layer clouds. Part I: Method and model description, J. Atmos. Sci., 59, 3540–3551, 2002. a
Gregory, W., Bushuk, M., Adcroft, A., Zhang, Y., and Zanna, L.: Deep learning of systematic sea ice model errors from data assimilation increments, J. Adv. Model. Earth Sy., 15, e2023MS003757, https://doi.org/10.1029/2023MS003757, 2023. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hoskins, B. J. and Karoly, D. J.: The steady linear response of a spherical atmosphere to thermal and orographic forcing, J. Atmos. Sci., 38, 1179–1196, 1981. a
Judt, F.: Insights into atmospheric predictability through global convection-permitting model simulations, J. Atmos. Sci., 75, 1477–1497, 2018. a
Kang, I.-S.: Influence of zonal mean flow change on stationary wave fluctuations, J. Atmos. Sci., 47, 141–147, 1990. a
Kirtman, B. P. and Shukla, J.: Interactive coupled ensemble: A new coupling strategy for CGCMs, Geophys. Res. Lett., 29, https://doi.org/10.1029/2002GL014834, 2002. a
Lau, N.-C.: Variability of the observed midlatitude storm tracks in relation to low-frequency changes in the circulation pattern, J. Atmos. Sci., 45, 2718–2743, 1988. a
Morrison, H. and Gettelman, A.: A new two-moment bulk stratiform cloud microphysics scheme in the Community Atmosphere Model, version 3 (CAM3). Part I: Description and numerical tests, J. Climate, 21, 3642–3659, 2008. a
Palmer, T.: Climate forecasting: Build high-resolution global climate models, Nature, 515, 338–339, 2014. a
Palmer, T. and Stevens, B.: The scientific challenge of understanding and estimating climate change, P. Natl. Acad. Sci., 116, 24390–24395, 2019. a
Park, S. and Bretherton, C. S.: The University of Washington shallow convection and moist turbulence schemes and their impact on climate simulations with the Community Atmosphere Model, J. Climate, 22, 3449–3469, 2009. a
Pecora, L. M., Carroll, T. L., Johnson, G. A., Mar, D. J., and Heagy, J. F.: Fundamentals of synchronization in chaotic systems, concepts, and applications, Chaos, 7, 520–543, 1997. a
Phillips, A. S., Deser, C., and Fasullo, J.: Evaluating modes of variability in climate models, EOS T. Am. Geophys. Un., 95, 453–455, 2014. a
Raeder, K., Hoar, T. J., Gharamti, M. E., Johnson, B. K., Collins, N., Anderson, J. L., Steward, J., and Coady, M.: A new CAM6+ DART reanalysis with surface forcing from CAM6 to other CESM models, Sci. Rep., 11, 16384, https://doi.org/10.1038/s41598-021-92927-0, 2021. a
Sardeshmukh, P. D. and Hoskins, B. J.: The generation of global rotational flow by steady idealized tropical divergence, J. Atmos. Sci., 45, 1228–1251, 1988. a
Schevenhoven, F., Selten, F., Carrassi, A., and Keenlyside, N.: Improving weather and climate predictions by training of supermodels, Earth Syst. Dynam., 10, 789–807, https://doi.org/10.5194/esd-10-789-2019, 2019. a
Schevenhoven, F., Keenlyside, N., Counillon, F., Carrassi, A., Chapman, W. E., Devilliers, M., Gupta, A., Koseki, S., Selten, F., Shen, M.-L., Wang, S., Weiss, J. B., Wiegerinck, W., and Duane, G. S.: Supermodeling: Improving Predictions with an Ensemble of Interacting Models, B. Am. Meteorol. Soc., 104, E1670–E1686, https://doi.org/10.1175/BAMS-D-22-0070.1, 2023. a, b
Schevenhoven, F. J. and Selten, F. M.: An efficient training scheme for supermodels, Earth Syst. Dynam., 8, 429–438, https://doi.org/10.5194/esd-8-429-2017, 2017. a
Segura, H., Pedruzo-Bagazgoitia, X., Weiss, P., Müller, S. K., Rackow, T., Lee, J., Dolores-Tesillos, E., Benedict, I., Aengenheyster, M., Aguridan, R., Arduini, G., Baker, A. J., Bao, J., Bastin, S., Baulenas, E., Becker, T., Beyer, S., Bockelmann, H., Brüggemann, N., Brunner, L., Cheedela, S. K., Das, S., Denissen, J., Dragaud, I., Dziekan, P., Ekblom, M., Engels, J. F., Esch, M., Forbes, R., Frauen, C., Freischem, L., García-Maroto, D., Geier, P., Gierz, P., González-Cervera, Á., Grayson, K., Griffith, M., Gutjahr, O., Haak, H., Hadade, I., Haslehner, K., ul Hasson, S., Hegewald, J., Kluft, L., Koldunov, A., Koldunov, N., Kölling, T., Koseki, S., Kosukhin, S., Kousal, J., Kuma, P., Kumar, A. U., Li, R., Maury, N., Meindl, M., Milinski, S., Mogensen, K., Niraula, B., Nowak, J., Praturi, D. S., Proske, U., Putrasahan, D., Redler, R., Santuy, D., Sármány, D., Schnur, R., Scholz, P., Sidorenko, D., Spät, D., Sützl, B., Takasuka, D., Tompkins, A., Uribe, A., Valentini, M., Veerman, M., Voigt, A., Warnau, S., Wachsmann, F., Wacławczyk, M., Wedi, N., Wieners, K.-H., Wille, J., Winkler, M., Wu, Y., Ziemen, F., Zimmermann, J., Bender, F. A.-M., Bojovic, D., Bony, S., Bordoni, S., Brehmer, P., Dengler, M., Dutra, E., Faye, S., Fischer, E., van Heerwaarden, C., Hohenegger, C., Järvinen, H., Jochum, M., Jung, T., Jungclaus, J. H., Keenlyside, N. S., Klocke, D., Konow, H., Klose, M., Malinowski, S., Martius, O., Mauritsen, T., Mellado, J. P., Mieslinger, T., Mohino, E., Pawłowska, H., Peters-von Gehlen, K., Sarré, A., Sobhani, P., Stier, P., Tuppi, L., Vidale, P. L., Sandu, I., and Stevens, B.: nextGEMS: entering the era of kilometer-scale Earth system modeling, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-509, 2025. a
Severijns, C. A. and Hazeleger, W.: The efficient global primitive equation climate model SPEEDO V2.0, Geosci. Model Dev., 3, 105-–122, https://doi.org/10.5194/gmd-3-105-2010, 2010. a
Shen, M.-L., Keenlyside, N., Bhatt, B. C., and Duane, G. S.: Role of atmosphere-ocean interactions in supermodeling the tropical Pacific climate, Chaos, 27, 12, 126704, PMID 29289039, https://doi.org/10.1063/1.4990713, 2017. a
Tegegne, G., Kim, Y.-O., and Lee, J.-K.: Spatiotemporal reliability ensemble averaging of multimodel simulations, Geophys. Res. Lett., 46, 12321–12330, 2019. a
Ting, M. and Lau, N.-C.: A diagnostic and modeling study of the monthly mean wintertime anomalies appearing in a 100-year GCM experiment, J. Atmos. Sci., 50, 2845–2867, 1993. a
van den Berge, L. A., Selten, F. M., Wiegerinck, W., and Duane, G. S.: A multi-model ensemble method that combines imperfect models through learning, Earth Syst. Dynam., 2, 161–177, https://doi.org/10.5194/esd-2-161-2011, 2011. a, b
Watt-Meyer, O., Brenowitz, N. D., Clark, S. K., Henn, B., Kwa, A., McGibbon, J., Perkins, W. A., and Bretherton, C. S.: Correcting weather and climate models by machine learning nudged historical simulations, Geophys. Res. Lett., 48, e2021GL092555, https://doi.org/10.1029/2021GL092555, 2021. a
Watt-Meyer, O., Dresdner, G., McGibbon, J., Clark, S. K., Henn, B., Duncan, J., Brenowitz, N. D., Kashinath, K., Pritchard, M. S., Bonev, B., Peters, M. E., and Bretherton, C. S.: ACE: A fast, skillful learned global atmospheric model for climate prediction, arXiv, https://doi.org/10.48550/arXiv.2310.02074, 2023. a
Weigel, A. P., Knutti, R., Liniger, M. A., and Appenzeller, C.: Risks of model weighting in multimodel climate projections, J. Climate, 23, 4175–4191, 2010. a
Zhang, G. J. and McFarlane, N. A.: Sensitivity of climate simulations to the parameterization of cumulus convection in the Canadian Climate Centre general circulation model, Atmosphere-Ocean, 33, 407–446, 1995. a
Short summary
We introduce the first state-of-the-art atmosphere-connected supermodel, where two advanced atmospheric models share information in real time to form a new dynamical system. By synchronizing the models, particularly in storm track regions, we achieve better predictions without losing variability. This approach maintains key climate patterns and reduces bias in some variables compared to traditional models, demonstrating a useful technique for improving atmospheric simulations.
We introduce the first state-of-the-art atmosphere-connected supermodel, where two advanced...
