Articles | Volume 17, issue 5
https://doi.org/10.5194/esd-17-1589-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-17-1589-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
On combining climate models into weighted ensembles
Britta Grusdt
CORRESPONDING AUTHOR
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, Germany
Mahé Perrette
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, Germany
Alexander Robinson
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, Germany
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Lucía Gutiérrez-González, Ilaria Tabone, Alexander Robinson, Jorge Álvarez-Solas, Jan Swierczek-Jereczek, Britta Grusdt, and Marisa Montoya
EGUsphere, https://doi.org/10.5194/egusphere-2026-3331, https://doi.org/10.5194/egusphere-2026-3331, 2026
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The last deglaciation offers insights into ice-climate interactions, as paleoclimatic records document the retreat of ice sheets through a period of major climate changes. However, current ice-sheet models have difficulties reproducing key aspects of the Greenland Ice Sheet evolution. We present a new reconstruction based on an ensemble of 3000 simulations evaluated against observational constraints, improving upon previous studies, and we identify the parameters controlling model performance.
Antonio Juarez-Martinez, Alexander Robinson, Jan Swierczek-Jereczek, Javier Blasco, Jorge Alvarez-Solas, and Marisa Montoya
The Cryosphere, 20, 5345–5363, https://doi.org/10.5194/tc-20-5345-2026, https://doi.org/10.5194/tc-20-5345-2026, 2026
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Tidal-water intrusions can cause warm ocean water to extend beneath grounded Antarctic ice, increasing submarine melting beyond the grounding line. Here, a new parameterization representing this effect is introduced and tested in the Yelmo ice-sheet model, revealing that tides could amplify ice loss and sea-level rise. This new parameterization shows good agreement with inferred grounding zones.
Niklas Schwind, Mahé Perrette, Edward Byers, Annika Högner, Quentin Lejeune, Tessa Möller, Zebedee Nicholls, Peter Pfleiderer, Sarah Schöngart, Michaela Werning, and Carl-Friedrich Schleussner
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We study how regional climate and climate impact indicators may respond to different emissions scenarios. Their possible outcomes are shaped by uncertainties in future emissions, global warming, regional effects of global warming, and the chaotic climate system. We introduce RIME-X (Rapid Impact Model Emulator Extended), an emulator that combines multiple tools and datasets to estimate probabilistically how any emissions path may influence regional outcomes.
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EGUsphere, https://doi.org/10.5194/egusphere-2026-3331, https://doi.org/10.5194/egusphere-2026-3331, 2026
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The last deglaciation offers insights into ice-climate interactions, as paleoclimatic records document the retreat of ice sheets through a period of major climate changes. However, current ice-sheet models have difficulties reproducing key aspects of the Greenland Ice Sheet evolution. We present a new reconstruction based on an ensemble of 3000 simulations evaluated against observational constraints, improving upon previous studies, and we identify the parameters controlling model performance.
Kolja Kypke, Marisa Montoya, Alexander Robinson, Jorge Alvarez-Solas, Jan Swierczek-Jereczek, and Peter Ditlevsen
Earth Syst. Dynam., 17, 769–794, https://doi.org/10.5194/esd-17-769-2026, https://doi.org/10.5194/esd-17-769-2026, 2026
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James R. Jordan, Frank Pattyn, Daniel Abele, Torsten Albrecht, Jorge Alvarez-Solas, Jowan M. Barnes, Tijn Berends, Jorge A. Bernales, Javier Blasco, Gong Cheng, Youngmin Choi, Stephen L. Cornford, Cruz Garcia-Molina, Fabien Gillet-Chaulet, G. Hilmar Gudmundsson, Angelika Humbert, Gunter R. Leguy, William H. Lipscomb, Marisa Montoya, Daniel Moreno-Parada, Mathieu Morlighem, Tyler Pelle, Alexander Robinson, Martin Rückamp, Hélène Seroussi, Yanmei Tian, Luisa Wagner, Roderick S. W. van de Wal, Liyun Zhao, and Thomas Zwinger
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Ice calving is a recent inclusion in ice sheet models and there has not been a systematic test of how well it has been implemented. We show that models can accurately represent a given rate of ice calving, properties at the ice front evolve smoothly during calving, and that there are no consistent differences in ice behaviour between various approaches to representing calving in numerical ice models. This gives us confidence in their ability for use in sea level rise prediction simulations.
Nils Bochow, Philipp Hess, and Alexander Robinson
The Cryosphere, 20, 1841–1866, https://doi.org/10.5194/tc-20-1841-2026, https://doi.org/10.5194/tc-20-1841-2026, 2026
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This study presents a fast, physics-guided machine-learning method that downscales coarse climate fields to fine resolution while enforcing conservation of large-scale totals. Trained on regional climate simulations and driven by Earth system model output, it handles extremes and outperforms linear interpolation, providing realistic, high-resolution forcing for ice-sheet models and improving projections of Greenland’s sea-level contribution.
Sergio Pérez-Montero, Jorge Alvarez-Solas, Jan Swierczek-Jereczek, Daniel Moreno-Parada, Alexander Robinson, and Marisa Montoya
Clim. Past, 22, 625–646, https://doi.org/10.5194/cp-22-625-2026, https://doi.org/10.5194/cp-22-625-2026, 2026
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Almost 3 million years ago, the planet began to experience a succession of cold and warm periods every 40,000 years. However, about 1 million years ago, they began to occur every 100,000 years. In this paper we explore how the change in the basal velocity of the ice sheets could have produced this change in behavior. On the other hand, we also see that in our model, decreasing in time the sensitivity of snowfall to temperature is also an effective mechanism with which to reproduce the records.
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The Cryosphere, 20, 1139–1162, https://doi.org/10.5194/tc-20-1139-2026, https://doi.org/10.5194/tc-20-1139-2026, 2026
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The Greenland ice sheet is considered a tipping element: if temperatures exceed its threshold, it would transition to a virtually ice-free state and the ice losses could be irreversible on very long timescales. We study its stability across the full range of glacial-interglacial temperatures, as well as those expected in coming centuries. We find a future critical threshold between 1.5-2ºC of global warming, another under colder climates, and persistent hysteresis across the full range of study.
Jan Swierczek-Jereczek, Jorge Alvarez-Solas, Alexander Robinson, Lucía Gutiérrez-González, and Marisa Montoya
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The Antarctic Ice Sheet is subject to hysteresis, i.e. if ice is lost due to warming, a comparatively larger cooling is needed to recover the original state. Here, we show that this effect might be larger than previously assumed, with volume differences of as much as 35 metres of sea-level equivalent between retreat and regrowth. Due to the large population density along the coasts, this stresses the importance of mitigating future sea-level rise, since adapting to it will likely be much harder.
Johanna Beckmann, Ronja Reese, Felicity S. McCormack, Sue Cook, Lawrence Bird, Dawid Gwyther, Daniel Richards, Matthias Scheiter, Yu Wang, Hélène Seroussi, Ayako Abe‐Ouchi, Torsten Albrecht, Jorge Alvarez‐Solas, Xylar S. Asay‐Davis, Jean‐Baptiste Barre, Constantijn J. Berends, Jorge Bernales, Javier Blasco, Justine Caillet, David M. Chandler, Violaine Coulon, Richard Cullather, Christophe Dumas, Benjamin K. Galton‐Fenzi, Julius Garbe, Fabien Gillet‐Chaulet, Rupert Gladstone, Heiko Goelzer, Nicholas R. Golledge, Ralf Greve, G. Hilmar Gudmundsson, Holly Kyeore Han, Trevor R. Hillebrand, Matthew J. Hoffman, Philippe Huybrechts, Nicolas C. Jourdain, Ann Kristin Klose, Petra M. Langebroek, Gunter R. Leguy, William H. Lipscomb, Daniel P. Lowry, Pierre Mathiot, Marisa Montoya, Mathieu Morlighem, Sophie Nowicki, Frank Pattyn, Antony J. Payne, Tyler Pelle, Aurélien Quiquet, Alexander Robinson, Leopekka Saraste, Erika G. Simon, Sainan Sun, Jake P. Twarog, Luke D. Trusel, Benoit Urruty, Jonas Van Breedam, Roderik S. W. van de Wal, Chen Zhao, and Thomas Zwinger
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Antarctica holds enough ice to raise sea levels by many meters, but its future is uncertain. Warm ocean water melts ice shelves from below, letting inland ice flow faster into the sea. By 2300, Antarctica could add 0.6–4.4 m to sea levels. Our study identifies two key factors—how strongly shelves melt and how the ice responds. These explain much of the range, and refining them in models may improve future predictions.
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We introduce Nix, an ice-sheet model designed for understanding how large masses of ice behave. Nix is a computer programme that simulates the movement and temperature evolution in ice sheets. It helps us study how ice sheets respond to changes in the atmosphere and ocean. We found that ice temperatures play an essential role in determining the motion and stability of ice sheets. Nix is a useful tool for learning how climate change affects polar ice sheets.
Sergio Pérez-Montero, Jorge Alvarez-Solas, Jan Swierczek-Jereczek, Daniel Moreno-Parada, Alexander Robinson, and Marisa Montoya
Earth Syst. Dynam., 16, 915–937, https://doi.org/10.5194/esd-16-915-2025, https://doi.org/10.5194/esd-16-915-2025, 2025
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The climate of the last 3 Myr has varied between cold and warm periods. Numerous independent mechanisms have been proposed to explain this; however, no effort has been made to study their competing effects. Here we present a simple but physically motivated model that includes these mechanisms in a modular way. We identify ice-sheet dynamics and lithosphere displacement as main triggers, but reproducing the climate records additionally requires the natural darkening of ice.
Ricarda Winkelmann, Donovan P. Dennis, Jonathan F. Donges, Sina Loriani, Ann Kristin Klose, Jesse F. Abrams, Jorge Alvarez-Solas, Torsten Albrecht, David Armstrong McKay, Sebastian Bathiany, Javier Blasco Navarro, Victor Brovkin, Eleanor Burke, Gokhan Danabasoglu, Reik V. Donner, Markus Drüke, Goran Georgievski, Heiko Goelzer, Anna B. Harper, Gabriele Hegerl, Marina Hirota, Aixue Hu, Laura C. Jackson, Colin Jones, Hyungjun Kim, Torben Koenigk, Peter Lawrence, Timothy M. Lenton, Hannah Liddy, José Licón-Saláiz, Maxence Menthon, Marisa Montoya, Jan Nitzbon, Sophie Nowicki, Bette Otto-Bliesner, Francesco Pausata, Stefan Rahmstorf, Karoline Ramin, Alexander Robinson, Johan Rockström, Anastasia Romanou, Boris Sakschewski, Christina Schädel, Steven Sherwood, Robin S. Smith, Norman J. Steinert, Didier Swingedouw, Matteo Willeit, Wilbert Weijer, Richard Wood, Klaus Wyser, and Shuting Yang
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The Tipping Points Modelling Intercomparison Project (TIPMIP) is an international collaborative effort to systematically assess tipping point risks in the Earth system using state-of-the-art coupled and stand-alone domain models. TIPMIP will provide a first global atlas of potential tipping dynamics, respective critical thresholds and key uncertainties, generating an important building block towards a comprehensive scientific basis for policy- and decision-making.
Therese Rieckh, Andreas Born, Alexander Robinson, Robert Law, and Gerrit Gülle
Geosci. Model Dev., 17, 6987–7000, https://doi.org/10.5194/gmd-17-6987-2024, https://doi.org/10.5194/gmd-17-6987-2024, 2024
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We present the open-source model ELSA, which simulates the internal age structure of large ice sheets. It creates layers of snow accumulation at fixed times during the simulation, which are used to model the internal stratification of the ice sheet. Together with reconstructed isochrones from radiostratigraphy data, ELSA can be used to assess ice sheet models and to improve their parameterization. ELSA can be used coupled to an ice sheet model or forced with its output.
Antonio Juarez-Martinez, Javier Blasco, Alexander Robinson, Marisa Montoya, and Jorge Alvarez-Solas
The Cryosphere, 18, 4257–4283, https://doi.org/10.5194/tc-18-4257-2024, https://doi.org/10.5194/tc-18-4257-2024, 2024
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We present sea level projections for Antarctica in the context of ISMIP6-2300 with several forcings but extend the simulations to 2500, showing that more than 3 m of sea level contribution could be reached. We also test the sensitivity on a basal melting parameter and determine the timing of the loss of ice in the west region. All the simulations were carried out with the ice sheet model Yelmo.
Daniel Moreno-Parada, Alexander Robinson, Marisa Montoya, and Jorge Alvarez-Solas
The Cryosphere, 18, 4215–4232, https://doi.org/10.5194/tc-18-4215-2024, https://doi.org/10.5194/tc-18-4215-2024, 2024
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Our study tries to understand how the ice temperature evolves in a large mass as in the case of Antarctica. We found a relation that tells us the ice temperature at any point. These results are important because they also determine how the ice moves. In general, ice moves due to slow deformation (as if pouring honey from a jar). Nevertheless, in some regions the ice base warms enough and melts. The liquid water then serves as lubricant and the ice slides and its velocity increases rapidly.
Javier Blasco, Ilaria Tabone, Daniel Moreno-Parada, Alexander Robinson, Jorge Alvarez-Solas, Frank Pattyn, and Marisa Montoya
Clim. Past, 20, 1919–1938, https://doi.org/10.5194/cp-20-1919-2024, https://doi.org/10.5194/cp-20-1919-2024, 2024
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In this study, we assess Antarctic tipping points which may had been crossed during the mid-Pliocene Warm Period. For this, we use data from the PlioMIP2 ensemble. Additionally, we investigate various sources of uncertainty, like ice dynamics and bedrock configuration. Our research significantly enhances our comprehension of Antarctica's response to a warming climate, shedding light on potential future tipping points that may be surpassed.
Jan Swierczek-Jereczek, Marisa Montoya, Konstantin Latychev, Alexander Robinson, Jorge Alvarez-Solas, and Jerry Mitrovica
Geosci. Model Dev., 17, 5263–5290, https://doi.org/10.5194/gmd-17-5263-2024, https://doi.org/10.5194/gmd-17-5263-2024, 2024
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Ice sheets present a thickness of a few kilometres, leading to a vertical deformation of the crust of up to a kilometre. This process depends on properties of the solid Earth, which can be regionally very different. We propose a model that accounts for this often-ignored heterogeneity and run 100 000 simulation years in minutes. Thus, the evolution of ice sheets is modeled with better accuracy, which is critical for a good mitigation of climate change and, in particular, sea-level rise.
Daniel Moreno-Parada, Jorge Alvarez-Solas, Javier Blasco, Marisa Montoya, and Alexander Robinson
The Cryosphere, 17, 2139–2156, https://doi.org/10.5194/tc-17-2139-2023, https://doi.org/10.5194/tc-17-2139-2023, 2023
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We have reconstructed the Laurentide Ice Sheet, located in North America during the Last Glacial Maximum (21 000 years ago). The absence of direct measurements raises a number of uncertainties. Here we study the impact of different physical laws that describe the friction as the ice slides over its base. We found that the Laurentide Ice Sheet is closest to prior reconstructions when the basal friction takes into account whether the base is frozen or thawed during its motion.
Matteo Willeit, Andrey Ganopolski, Alexander Robinson, and Neil R. Edwards
Geosci. Model Dev., 15, 5905–5948, https://doi.org/10.5194/gmd-15-5905-2022, https://doi.org/10.5194/gmd-15-5905-2022, 2022
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In this paper we present the climate component of the newly developed fast Earth system model CLIMBER-X. It has a horizontal resolution of 5°x5° and is designed to simulate the evolution of the Earth system on temporal scales ranging from decades to >100 000 years. CLIMBER-X is available as open-source code and is expected to be a useful tool for studying past climate changes and for the investigation of the long-term future evolution of the climate.
Alexander Robinson, Daniel Goldberg, and William H. Lipscomb
The Cryosphere, 16, 689–709, https://doi.org/10.5194/tc-16-689-2022, https://doi.org/10.5194/tc-16-689-2022, 2022
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Here we investigate the numerical stability of several commonly used methods in order to determine which of them are capable of resolving the complex physics of the ice flow and are also computationally efficient. We find that the so-called DIVA solver outperforms the others. Its representation of the physics is consistent with more complex methods, while it remains computationally efficient at high resolution.
Cited articles
Abramowitz, G., Herger, N., Gutmann, E., Hammerling, D., Knutti, R., Leduc, M., Lorenz, R., Pincus, R., and Schmidt, G. A.: ESD Reviews: Model Dependence in Multi-Model Climate Ensembles: Weighting, Sub-Selection and out-of-Sample Testing, Earth Syst. Dynam., 10, 91–105, https://doi.org/10.5194/esd-10-91-2019, 2019. a, b
Bissiri, P. G., Holmes, C. C., and Walker, S. G.: A General Framework for Updating Belief Distributions, J. Roy. Stat. Soc. B, 78, 1103–1130, https://doi.org/10.1111/rssb.12158, 2016. a
Boé, J.: Interdependency in Multimodel Climate Projections: Component Replication and Result Similarity, Geophys. Res. Lett., 45, 2771–2779, https://doi.org/10.1002/2017GL076829, 2018. a
brittaGrusdt: awi-esc/WeightedEnsembles: code and data for final paper, Version v1.0.0, Zenodo [computer software], https://doi.org/10.5281/zenodo.23334598, 2026. a
Brunner, L., Pendergrass, A. G., Lehner, F., Merrifield, A. L., Lorenz, R., and Knutti, R.: Reduced Global Warming from CMIP6 Projections When Weighting Models by Performance and Independence, Earth Syst. Dynam., 11, 995–1012, https://doi.org/10.5194/esd-11-995-2020, 2020. a, b, c, d, e, f, g, h, i, j, k, l
Das Bhowmik, R. and Sankarasubramanian, A.: A Performance-Based Multi-Model Combination Approach to Reduce Uncertainty in Seasonal Temperature Change Projections, Int. J. Climatol., 41, E2615–E2632, https://doi.org/10.1002/joc.6870, 2021. a
Feng, J., Lee, D.-K., Fu, C., Tang, J., Sato, Y., Kato, H., Mcgregor, J. L., and Mabuchi, K.: Comparison of Four Ensemble Methods Combining Regional Climate Simulations over Asia, Meteorol. Atmos. Phys., 111, 41–53, https://doi.org/10.1007/s00703-010-0115-7, 2011. a
Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J.-L., Frame, D., Lunt, D., Mauritsen, T., Palmer, M., Watanabe, M., Wild, M., and Zhang, H.: The Earth's Energy Budget, Climate Feedbacks, and Climate Sensitivity, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 923–1054, https://doi.org/10.1017/9781009157896.009, 2021. a
Giorgi, F. and Mearns, L. O.: Calculation of Average, Uncertainty Range, and Reliability of Regional Climate Changes from AOGCM Simulations via the “Reliability Ensemble Averaging” (REA) Method, J. Climate, 15, 1141–1158, https://doi.org/10.1175/1520-0442(2002)015<1141:COAURA>2.0.CO;2, 2002. a
Gleckler, P. J., Taylor, K. E., and Doutriaux, C.: Performance Metrics for Climate Models, J. Geophys. Res.-Atmos., 113, https://doi.org/10.1029/2007JD008972, 2008. a
Gneiting, T. and Raftery, A. E.: Strictly Proper Scoring Rules, Prediction, and Estimation, J. Am. Stat. Assoc., 102, 359–378, https://doi.org/10.1198/016214506000001437, 2007. a
Hausfather, Z., Marvel, K., Schmidt, G. A., Nielsen-Gammon, J. W., and Zelinka, M.: Climate Simulations: Recognize the `Hot Model' Problem, Nature, 605, 26–29, https://doi.org/10.1038/d41586-022-01192-2, 2022. 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. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Jun, M., Knutti, R., and Nychka, D. W.: Spatial Analysis to Quantify Numerical Model Bias and Dependence: How Many Climate Models Are There?, J. Am. Stat. Assoc., 103, 934–947, https://doi.org/10.1198/016214507000001265, 2008. a
Katzenberger, A., Pérez-Carrasquilla, J. S., Gemmell, K., Galytska, E., Leclerc, C., P, P., Roy, I., Varuolo-Clarke, A., Tošić, M., and Črnivec, N.: Developing Guidelines for working with Multi-Model Ensembles in CMIP, Earth Syst. Dynam., 17, 495–532, https://doi.org/10.5194/esd-17-495-2026, 2026. a
Knutti, R., Sedláček, J., Sanderson, B. M., Lorenz, R., Fischer, E. M., and Eyring, V.: A Climate Model Projection Weighting Scheme Accounting for Performance and Interdependence, Geophys. Res. Lett., 44, 1909–1918, https://doi.org/10.1002/2016GL072012, 2017. a, b, c, d
Kuma, P., Bender, F. A.-M., and Jönsson, A. R.: Climate Model Code Genealogy and Its Relation to Climate Feedbacks and Sensitivity, J. Adv. Model. Earth Syst., 15, e2022MS003588, https://doi.org/10.1029/2022MS003588, 2023. a
Lorenz, R., Herger, N., Sedláček, J., Eyring, V., Fischer, E. M., and Knutti, R.: Prospects and Caveats of Weighting Climate Models for Summer Maximum Temperature Projections Over North America, J. Geophys. Res.-Atmos., 123, 4509–4526, https://doi.org/10.1029/2017JD027992, 2018. a
Masson, D. and Knutti, R.: Climate Model Genealogy, Geophys. Res. Lett., 38, https://doi.org/10.1029/2011GL046864, 2011. a
Massoud, E., Espinoza, V., Guan, B., and Waliser, D.: Global Climate Model Ensemble Approaches for Future Projections of Atmospheric Rivers, Earth's Future, 7, 1136–1151, https://doi.org/10.1029/2019EF001249, 2019. a
Massoud, E. C., Lee, H., Gibson, P. B., Loikith, P., and Waliser, D. E.: Bayesian Model Averaging of Climate Model Projections Constrained by Precipitation Observations over the Contiguous United States, J. Hydrometeorol., 21, 2401–2418, https://doi.org/10.1175/JHM-D-19-0258.1, 2020. a, b, c, d, e, f, g
Massoud, E. C., Lee, H. K., Terando, A., and Wehner, M.: Bayesian Weighting of Climate Models Based on Climate Sensitivity, Commun. Earth Environ., 4, 1–8, https://doi.org/10.1038/s43247-023-01009-8, 2023. a, b, c
Pennell, C. and Reichler, T.: On the Effective Number of Climate Models, J. Climate, 24, 2358–2367, https://doi.org/10.1175/2010JCLI3814.1, 2011. a
Potempski, S. and Galmarini, S.: Est Modus in Rebus: Analytical Properties of Multi-Model Ensembles, Atmos. Chem. Phys., 9, 9471–9489, https://doi.org/10.5194/acp-9-9471-2009, 2009. a
Raftery, A. E., Gneiting, T., Balabdaoui, F., and Polakowski, M.: Using Bayesian Model Averaging to Calibrate Forecast Ensembles, Mon. Weather Rev., 133, 1155–1174, https://doi.org/10.1175/MWR2906.1, 2005. a, b
Riccio, A., Giunta, G., and Galmarini, S.: Seeking for the Rational Basis of the Median Model: The Optimal Combination of Multi-Model Ensemble Results, Atmos. Chem. Phys., 7, 6085–6098, https://doi.org/10.5194/acp-7-6085-2007, 2007. a
Sanderson, B. M., Knutti, R., and Caldwell, P.: Addressing Interdependency in a Multimodel Ensemble by Interpolation of Model Properties, J. Climate, 28, 5150–5170, 2015a. a
Sanderson, B. M., Knutti, R., and Caldwell, P.: A Representative Democracy to Reduce Interdependency in a Multimodel Ensemble, J. Climate, 28, 5171–5194, https://doi.org/10.1175/JCLI-D-14-00362.1, 2015b. a
Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M., Hargreaves, J. C., Hegerl, G., Klein, S. A., Marvel, K. D., Rohling, E. J., Watanabe, M., Andrews, T., Braconnot, P., Bretherton, C. S., Foster, G. L., Hausfather, Z., von der Heydt, A. S., Knutti, R., Mauritsen, T., Norris, J. R., Proistosescu, C., Rugenstein, M., Schmidt, G. A., Tokarska, K. B., and Zelinka, M. D.: An Assessment of Earth's Climate Sensitivity Using Multiple Lines of Evidence, Rev. Geophys., 58, e2019RG000678, https://doi.org/10.1029/2019RG000678, 2020. a, b, c
Sierra, C. A. and Muñoz, E.: An Information-Theoretic Approach to Obtain Ensemble Averages from Earth System Models, Geosci. Model Dev., 18, 6701–6716, https://doi.org/10.5194/gmd-18-6701-2025, 2025. a, b, c
Solazzo, E., Riccio, A., Kioutsioukis, I., and Galmarini, S.: Pauci Ex Tanto Numero: Reduce Redundancy in Multi-Model Ensembles, Atmos. Chem. Phys., 13, 8315–8333, https://doi.org/10.5194/acp-13-8315-2013, 2013. a
Thao, S., Garvik, M., Mariethoz, G., and Vrac, M.: Combining Global Climate Models Using Graph Cuts, Clim. Dynam., 59, 2345–2361, https://doi.org/10.1007/s00382-022-06213-4, 2022. a
Tokarska, K. B., Stolpe, M. B., Sippel, S., Fischer, E. M., Smith, C. J., Lehner, F., and Knutti, R.: Past Warming Trend Constrains Future Warming in CMIP6 Models, Sci. Adv., 6, eaaz9549, https://doi.org/10.1126/sciadv.aaz9549, 2020. a
Wootten, A. M., Massoud, E. C., Sengupta, A., Waliser, D. E., and Lee, H.: The Effect of Statistical Downscaling on the Weighting of Multi-Model Ensembles of Precipitation, Climate, 8, 138, https://doi.org/10.3390/cli8120138, 2020. a
Wootten, A. M., Massoud, E. C., Waliser, D. E., and Lee, H.: Assessing Sensitivities of Climate Model Weighting to Multiple Methods, Variables, and Domains in the South-Central United States, Earth Syst. Dynam., 14, 121–145, https://doi.org/10.5194/esd-14-121-2023, 2023. a
Yao, Y., Vehtari, A., Simpson, D., and Gelman, A.: Using Stacking to Average Bayesian Predictive Distributions (with Discussion), Bayesian Anal., 13, 917–1007, https://doi.org/10.1214/17-BA1091, 2018. a
Short summary
We provide practical guidelines for producing weighted ensembles of climate models. We distinguish between two approaches, the individual- and the ensemble performance weighting approach, and formulate both in probabilistic terms, making the underlying assumptions transparent. Using constructed examples, we show the advantage of the ensemble weighting approach to account for model co-dependencies and examine how methodological choices affect the climate projections of the weighted ensemble.
We provide practical guidelines for producing weighted ensembles of climate models. We...
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