Articles | Volume 19, issue 19
https://doi.org/10.5194/gmd-19-9263-2026
© 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/gmd-19-9263-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
30 Sep 2026
Model description paper |
| 30 Sep 2026
| 30 Sep 2026
METEORv1.6: Spatial climate variability and integrated impact emulation
Benjamin M. Sanderson
CORRESPONDING AUTHOR
CICERO Center for International Climate Research, Oslo 0349, Norway
Marit Sandstad
CICERO Center for International Climate Research, Oslo 0349, Norway
Maura Dewey
CICERO Center for International Climate Research, Oslo 0349, Norway
Norman Julius Steinert
CICERO Center for International Climate Research, Oslo 0349, Norway
Johannes L. Fjeldså
Section for Meteorology and Oceanography, University of Oslo, Oslo 0349, Norway
Related authors
Abigail L. S. Swann, Charles D. Koven, Cristian Proistosecu, Rosie A. Fisher, Benjamin M. Sanderson, Victor Brovkin, Tomohiro Hajima, Chris D. Jones, Nancy Y. Kiang, David M. Lawrence, Spencer Liddicoat, Hannah Liddy, Anastasia Romanou, Roland Séférian, Lori T. Sentman, Norman J. Steinert, Jerry Tjiputra, and Tilo Ziehn
Earth Syst. Dynam., 17, 1237–1275, https://doi.org/10.5194/esd-17-1237-2026, https://doi.org/10.5194/esd-17-1237-2026, 2026
Short summary
Short summary
We analyzed simulations from Earth system models (ESM) with a constant rate of emissions, zero emissions, and negative emissions of CO2 to quantify the response of land carbon sinks. We found that under positive emissions vegetation in the tropics gained carbon. Under zero emissions and negative emissions most ESMs lost carbon from vegetation in the tropics but gained carbon in mid- and high-latitudes, mostly in soils. Our findings imply that tropical carbon is vulnerable under zero emissions.
Alejandro Romero-Prieto, Marit Sandstad, Benjamin M. Sanderson, Zebedee R. J. Nicholls, Norman J. Steinert, Thomas Gasser, Camilla Mathison, Jarmo Kikstra, Thomas J. Aubry, Katsumasa Tanaka, Konstantin Weber, and Chris Smith
Geosci. Model Dev., 19, 7111–7133, https://doi.org/10.5194/gmd-19-7111-2026, https://doi.org/10.5194/gmd-19-7111-2026, 2026
Short summary
Short summary
Reduced-complexity models are an important tool in climate science, helping us understand and estimate future climate change. We present the experimental protocol for the next phase of the reduced-complexity model intercomparison project, which aims to compare results from many such models to better understand their behaviour. This knowledge will guide how these models are developed and used in the future, including in the upcoming Intergovernmental Panel on Climate Change (IPCC) assessment report (AR7).
Colin Jones, Isaline Bossert, Donovan P. Dennis, Hazel A. Jeffery, Chris D. Jones, Torben Koenigk, Sina Loriani, Benjamin Sanderson, Roland Séférian, Klaus Wyser, Shuting Yang, Manabu Abe, Sebastian Bathiany, Pascale Braconnot, Victor Brovkin, Friedrich A. Burger, Patrica Cadule, Frederic S. Castruccio, Gokhan Danabasoglu, Andrea Dittus, Jonathan F. Donges, Friederike Fröb, Thomas L. Frölicher, Goran Georgievski, Chuncheng Guo, Aixue Hu, Peter Lawrence, Paul Lerner, José Licón-Saláiz, Bette Otto-Bliesner, Anastasia Romanou, Elena Shevliakova, Yona Silvy, Didier Swingedouw, Jerry Tjiputra, Jeremy Walton, Andy Wiltshire, Ricarda Winkelmann, Richard Wood, Tokuta Yokohata, and Tilo Ziehn
Geosci. Model Dev., 19, 6941–6966, https://doi.org/10.5194/gmd-19-6941-2026, https://doi.org/10.5194/gmd-19-6941-2026, 2026
Short summary
Short summary
We introduce a new Earth system model experiment protocol to help researchers understand how Earth might respond to positive, zero, and negative carbon emissions. This protocol enables different models to be compared following similar warming and cooling rates. Researchers use the models to explore how the Earth reacts to different climate futures, including the risk of tipping points being exceeded and whether changes can be reversed. The results will support improved long-term climate policy.
Benjamin M. Sanderson, Marit Sandstad, Alejandro Romero-Prieto, Stuart Jenkins, Charles Koven, Glen Peters, Roland Séfèrian, Andrew H. MacDougall, Ashwin Seshadri, Victor Brovkin, John Dunne, Abigail L. S. Swann, Torben Koenigk, Rosie A. Fisher, David Hohn, Tatiana Ilyina, Chris D. Jones, Hongmei Li, Peter Lawrence, Spencer Liddicoat, Nadine Mengis, Anastasia Romanou, Lori T. Sentman, Chris Smith, Norman J. Steinert, Jerry Tjiputra, and Tilo Ziehn
EGUsphere, https://doi.org/10.5194/egusphere-2026-1875, https://doi.org/10.5194/egusphere-2026-1875, 2026
Short summary
Short summary
When humanity stops emitting carbon dioxide, will temperatures keep rising, stabilize, or fall? Our study reveals in most models, temperatures remain nearly stable, but this hides a balance between the warming in response to radiation imbalance, and the cooling due to uptake of carbon in the land and ocean. Understanding this balance helps refine how much more CO2 we can safely emit.
John W. Rostron, Alejandro Bodas-Salcedo, David M. H. Sexton, Colin G. Jones, Edward W. Blockley, Till Kuhlbrodt, Jane P. Mulcahy, Tamzin E. Palmer, Saloua Peatier, Mark A. Ringer, Steven T. Rumbold, Benjamin M. Sanderson, Yongming Tang, and Martin R. Willet
EGUsphere, https://doi.org/10.5194/egusphere-2026-1676, https://doi.org/10.5194/egusphere-2026-1676, 2026
Short summary
Short summary
The Met Office’s latest weather and climate model warms very strongly in response to increases in carbon dioxide. We created a modified version of the model with a more moderate warming response by adjusting key model parameters, using both automated methods and expert judgement. The new model matches historical temperatures more closely and is better suited for studies of long‑term climate, but has reduced overall accuracy when simulating the baseline climate.
Ken S. Carslaw, Leighton A. Regayre, Ulrike Proske, Andrew Gettelman, David M. H. Sexton, Yun Qian, Lauren R. Marshall, Oliver Wild, Marcus van Lier-Walqui, Annika Oertel, Saloua Peatier, Ben Yang, Jill S. Johnson, Sihan Li, Daniel T. McCoy, Benjamin M. Sanderson, Christina J. Williamson, Gregory S. Elsaesser, Kuniko Yamazaki, and Ben B. B. Booth
Atmos. Chem. Phys., 26, 4651–4667, https://doi.org/10.5194/acp-26-4651-2026, https://doi.org/10.5194/acp-26-4651-2026, 2026
Short summary
Short summary
A major challenge in climate science is reducing projection uncertainty despite advances in models and observational constraints. Perturbed parameter ensembles (PPEs) offer a powerful tool to explore and reduce uncertainty by revealing model weaknesses and guiding development. PPEs are now widely applied across climate systems and scales. We argue they should be prioritized alongside complexity and resolution in model resource planning.
Detlef P. Van Vuuren, Brian C. O'Neill, Claudia Tebaldi, Benjamin M. Sanderson, Louise P. Chini, Pierre Friedlingstein, Tomoko Hasegawa, Keywan Riahi, Bala Govindasamy, Nico Bauer, Veronika Eyring, Cheikh M. N. Fall, Katja Frieler, Matthew J. Gidden, Laila K. Gohar, Annika Högner, Andrew D. Jones, Jarmo Kikstra, Andrew King, Reto Knutti, Elmar Kriegler, Peter Lawrence, Chris Lennard, Jason Lowe, Camilla Mathison, Shahbaz Mehmood, Zebedee Nicholls, Luciana F. Prado, Qiang Zhang, Steven K. Rose, Alex C. Ruane, Marit Sandstad, Carl-Friedrich Schleussner, Roland Seferian, Jana Sillmann, Chris Smith, Anna A. Sörensson, Swapna Panickal, Kaoru Tachiiri, Naomi Vaughan, Saritha S. Vishwanathan, Tokuta Yokohata, Marco Zecchetto, and Tilo Ziehn
Geosci. Model Dev., 19, 2627–2656, https://doi.org/10.5194/gmd-19-2627-2026, https://doi.org/10.5194/gmd-19-2627-2026, 2026
Short summary
Short summary
We propose a set of seven plausible 21st century emission scenarios, and their multi-century extensions, that will be used by the international community of climate modeling centers to produce the next generation of climate projections. These projections will support climate, impact and mitigation researchers, provide information to practitioners to address future risks from climate change, and contribute to policymakers’ considerations of the trade-offs among various levels of mitigation.
Benjamin M. Sanderson, Susanne Baur, Carl-Freidrich Schleussner, Glen P. Peters, Shivika Mittal, Marit Sandstad, Steffen Kallbekken, Chris Smith, Sabine Fuss, Bas van Ruijven, Rosie A. Fisher, Joeri Rogelj, Roland Séférian, Bjørn Samset, Norman J. Steinert, Laurent Terray, and Jan Fuglestvedt
EGUsphere, https://doi.org/10.5194/egusphere-2026-28, https://doi.org/10.5194/egusphere-2026-28, 2026
Preprint archived
Short summary
Short summary
Solar Radiation Modification by adding aerosols to the stratosphere could rapidly and temporarily cool the Earth, but this speed creates unprecedented risks. Fast climate responses coupled with political instability create risks of failure to decarbonise, super-rapid climate change, and conflict. Idealized scenarios or conventional modeling tools could lead to systematic ignorance of these risks. We thus introduce a framework outlining what must be represented in future modeling and assessment.
Marit Sandstad, Norman Julius Steinert, Susanne Baur, and Benjamin Mark Sanderson
Geosci. Model Dev., 18, 8269–8312, https://doi.org/10.5194/gmd-18-8269-2025, https://doi.org/10.5194/gmd-18-8269-2025, 2025
Short summary
Short summary
We present METEORv1.0.1, a climate model emulator, that can be trained on full spatially resolved and widely available climate model data to reproduce climate variables and make predictions from unseen emission trajectories. The methodology identifies patterns with timescales of impact for one or more forcers using idealised experiments and anomaly calculations. Results for precipitation and temperature show good model performance and can reproduce hysteresis for overshoot scenarios.
Norman J. Steinert and Benjamin M. Sanderson
Earth Syst. Dynam., 16, 1711–1721, https://doi.org/10.5194/esd-16-1711-2025, https://doi.org/10.5194/esd-16-1711-2025, 2025
Short summary
Short summary
In this study, we explore how carbon emissions from thawing permafrost, known as the permafrost carbon feedback, affect two important climate metrics: how much the Earth warms per amount of carbon we emit and how much warming continues after we stop emitting carbon. Our study tackles a major gap in how we estimate future climate change. Using simplified climate models, we find a generalizable relationship between the permafrost carbon feedback and its additional warming impact on climate.
John P. Dunne, Helene T. Hewitt, Julie M. Arblaster, Frédéric Bonou, Olivier Boucher, Tereza Cavazos, Beth Dingley, Paul J. Durack, Birgit Hassler, Martin Juckes, Tomoki Miyakawa, Matt Mizielinski, Vaishali Naik, Zebedee Nicholls, Eleanor O'Rourke, Robert Pincus, Benjamin M. Sanderson, Isla R. Simpson, and Karl E. Taylor
Geosci. Model Dev., 18, 6671–6700, https://doi.org/10.5194/gmd-18-6671-2025, https://doi.org/10.5194/gmd-18-6671-2025, 2025
Short summary
Short summary
The seventh phase of the Coupled Model Intercomparison Project (CMIP7) coordinates efforts to answer key and timely climate science questions and facilitate delivery of relevant multi-model simulations for prediction and projection; characterization, attribution, and process understanding; and vulnerability, impact, and adaptation analysis. Key to the CMIP7 design are the mandatory Diagnostic, Evaluation and Characterization of Klima and optional Assessment Fast Track experiments.
Benjamin M. Sanderson, Victor Brovkin, Rosie A. Fisher, David Hohn, Tatiana Ilyina, Chris D. Jones, Torben Koenigk, Charles Koven, Hongmei Li, David M. Lawrence, Peter Lawrence, Spencer Liddicoat, Andrew H. MacDougall, Nadine Mengis, Zebedee Nicholls, Eleanor O'Rourke, Anastasia Romanou, Marit Sandstad, Jörg Schwinger, Roland Séférian, Lori T. Sentman, Isla R. Simpson, Chris Smith, Norman J. Steinert, Abigail L. S. Swann, Jerry Tjiputra, and Tilo Ziehn
Geosci. Model Dev., 18, 5699–5724, https://doi.org/10.5194/gmd-18-5699-2025, https://doi.org/10.5194/gmd-18-5699-2025, 2025
Short summary
Short summary
This study investigates how climate models warm in response to simplified carbon emissions trajectories, refining the understanding of climate reversibility and commitment. Metrics are defined for warming response to cumulative emissions and for the cessation of emissions or ramp-down to net-zero and net-negative levels. Results indicate that previous concentration-driven experiments may have overstated the Zero Emissions Commitment due to emissions rates exceeding historical levels.
Susanne Baur, Benjamin M. Sanderson, Roland Séférian, and Laurent Terray
Earth Syst. Dynam., 16, 667–681, https://doi.org/10.5194/esd-16-667-2025, https://doi.org/10.5194/esd-16-667-2025, 2025
Short summary
Short summary
Stratospheric aerosol injection (SAI) could be used alongside mitigation to reduce global warming. Previous studies suggest that more atmospheric CO2 is taken up when SAI is deployed. Here, we look at the entire SAI deployment from start to after termination. We show how the initial CO2 uptake benefit, and hence lower mitigation burden, is reduced in later stages of SAI, where the reduction in natural CO2 uptake turns into an additional mitigation burden.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
Short summary
Short summary
We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Colin G. Jones, Fanny Adloff, Ben B. B. Booth, Peter M. Cox, Veronika Eyring, Pierre Friedlingstein, Katja Frieler, Helene T. Hewitt, Hazel A. Jeffery, Sylvie Joussaume, Torben Koenigk, Bryan N. Lawrence, Eleanor O'Rourke, Malcolm J. Roberts, Benjamin M. Sanderson, Roland Séférian, Samuel Somot, Pier Luigi Vidale, Detlef van Vuuren, Mario Acosta, Mats Bentsen, Raffaele Bernardello, Richard Betts, Ed Blockley, Julien Boé, Tom Bracegirdle, Pascale Braconnot, Victor Brovkin, Carlo Buontempo, Francisco Doblas-Reyes, Markus Donat, Italo Epicoco, Pete Falloon, Sandro Fiore, Thomas Frölicher, Neven S. Fučkar, Matthew J. Gidden, Helge F. Goessling, Rune Grand Graversen, Silvio Gualdi, José M. Gutiérrez, Tatiana Ilyina, Daniela Jacob, Chris D. Jones, Martin Juckes, Elizabeth Kendon, Erik Kjellström, Reto Knutti, Jason Lowe, Matthew Mizielinski, Paola Nassisi, Michael Obersteiner, Pierre Regnier, Romain Roehrig, David Salas y Mélia, Carl-Friedrich Schleussner, Michael Schulz, Enrico Scoccimarro, Laurent Terray, Hannes Thiemann, Richard A. Wood, Shuting Yang, and Sönke Zaehle
Earth Syst. Dynam., 15, 1319–1351, https://doi.org/10.5194/esd-15-1319-2024, https://doi.org/10.5194/esd-15-1319-2024, 2024
Short summary
Short summary
We propose a number of priority areas for the international climate research community to address over the coming decade. Advances in these areas will both increase our understanding of past and future Earth system change, including the societal and environmental impacts of this change, and deliver significantly improved scientific support to international climate policy, such as future IPCC assessments and the UNFCCC Global Stocktake.
Marit Sandstad, Borgar Aamaas, Ane Nordlie Johansen, Marianne Tronstad Lund, Glen Philip Peters, Bjørn Hallvard Samset, Benjamin Mark Sanderson, and Ragnhild Bieltvedt Skeie
Geosci. Model Dev., 17, 6589–6625, https://doi.org/10.5194/gmd-17-6589-2024, https://doi.org/10.5194/gmd-17-6589-2024, 2024
Short summary
Short summary
The CICERO-SCM has existed as a Fortran model since 1999 that calculates the radiative forcing and concentrations from emissions and is an upwelling diffusion energy balance model of the ocean that calculates temperature change. In this paper, we describe an updated version ported to Python and publicly available at https://github.com/ciceroOslo/ciceroscm (https://doi.org/10.5281/zenodo.10548720). This version contains functionality for parallel runs and automatic calibration.
Saloua Peatier, Benjamin M. Sanderson, and Laurent Terray
Earth Syst. Dynam., 15, 987–1014, https://doi.org/10.5194/esd-15-987-2024, https://doi.org/10.5194/esd-15-987-2024, 2024
Short summary
Short summary
The calibration of Earth system model parameters is a high-dimensionality problem subject to data, time, and computational constraints. In this study, we propose a practical solution for finding diverse near-optimal solutions. We argue that the effective degrees of freedom in the model performance response to parameter input is relatively small. Comparably performing parameter configurations exist and showcase different trade-offs in model errors, providing insights for model development.
Malte Meinshausen, Carl-Friedrich Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, Josep G. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatima Driouech, Erich Fischer, Piers Forster, Michael Grose, Gerrit Hansen, Zeke Hausfather, Tatiana Ilyina, Jarmo S. Kikstra, Joyce Kimutai, Andrew D. King, June-Yi Lee, Chris Lennard, Tabea Lissner, Alexander Nauels, Glen P. Peters, Anna Pirani, Gian-Kasper Plattner, Hans Pörtner, Joeri Rogelj, Maisa Rojas, Joyashree Roy, Bjørn H. Samset, Benjamin M. Sanderson, Roland Séférian, Sonia Seneviratne, Christopher J. Smith, Sophie Szopa, Adelle Thomas, Diana Urge-Vorsatz, Guus J. M. Velders, Tokuta Yokohata, Tilo Ziehn, and Zebedee Nicholls
Geosci. Model Dev., 17, 4533–4559, https://doi.org/10.5194/gmd-17-4533-2024, https://doi.org/10.5194/gmd-17-4533-2024, 2024
Short summary
Short summary
The scientific community is considering new scenarios to succeed RCPs and SSPs for the next generation of Earth system model runs to project future climate change. To contribute to that effort, we reflect on relevant policy and scientific research questions and suggest categories for representative emission pathways. These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.
Susanne Baur, Benjamin M. Sanderson, Roland Séférian, and Laurent Terray
Earth Syst. Dynam., 15, 307–322, https://doi.org/10.5194/esd-15-307-2024, https://doi.org/10.5194/esd-15-307-2024, 2024
Short summary
Short summary
Most solar radiation modification (SRM) simulations assume no physical coupling between mitigation and SRM. We analyze the impact of SRM on photovoltaic (PV) and concentrated solar power (CSP) and find that almost all regions have reduced PV and CSP potential compared to a mitigated or unmitigated scenario, especially in the middle and high latitudes. This suggests that SRM could pose challenges for meeting energy demands with solar renewable resources.
Susanne Baur, Alexander Nauels, Zebedee Nicholls, Benjamin M. Sanderson, and Carl-Friedrich Schleussner
Earth Syst. Dynam., 14, 367–381, https://doi.org/10.5194/esd-14-367-2023, https://doi.org/10.5194/esd-14-367-2023, 2023
Short summary
Short summary
Solar radiation modification (SRM) artificially cools global temperature without acting on the cause of climate change. This study looks at how long SRM would have to be deployed to limit warming to 1.5 °C and how this timeframe is affected by different levels of mitigation, negative emissions and climate uncertainty. None of the three factors alone can guarantee short SRM deployment. Due to their uncertainty at the time of SRM initialization, any deployment risks may be several centuries long.
Benjamin M. Sanderson and Maria Rugenstein
Earth Syst. Dynam., 13, 1715–1736, https://doi.org/10.5194/esd-13-1715-2022, https://doi.org/10.5194/esd-13-1715-2022, 2022
Short summary
Short summary
Equilibrium climate sensitivity (ECS) is a measure of how much long-term warming should be expected in response to a change in greenhouse gas concentrations. It is generally calculated in climate models by extrapolating global average temperatures to a point of where the planet is no longer a net absorber of energy. Here we show that some climate models experience energy leaks which change as the planet warms, undermining the standard approach and biasing some existing model estimates of ECS.
Jerry F. Tjiputra, Dirk Olivié, Jörg Schwinger, Nadine Goris, Norman J. Steinert, Rosie Fisher, and Huiji Lee
EGUsphere, https://doi.org/10.5194/egusphere-2026-5188, https://doi.org/10.5194/egusphere-2026-5188, 2026
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
Without radical changes in anthropogenic CO2 emissions, global warming will likely exceed 2 degrees. This study uses an Earth system model to investigate the physical and biogeochemical responses to stratospheric aerosol injection (SAI) to limit global warming. Depending on the deployment location, SAI influences the evolution of the Atlantic Meridional Overturning Circulation, which induces long-term spatially heterogeneous projected temperature and other climatic changes.
Abigail L. S. Swann, Charles D. Koven, Cristian Proistosecu, Rosie A. Fisher, Benjamin M. Sanderson, Victor Brovkin, Tomohiro Hajima, Chris D. Jones, Nancy Y. Kiang, David M. Lawrence, Spencer Liddicoat, Hannah Liddy, Anastasia Romanou, Roland Séférian, Lori T. Sentman, Norman J. Steinert, Jerry Tjiputra, and Tilo Ziehn
Earth Syst. Dynam., 17, 1237–1275, https://doi.org/10.5194/esd-17-1237-2026, https://doi.org/10.5194/esd-17-1237-2026, 2026
Short summary
Short summary
We analyzed simulations from Earth system models (ESM) with a constant rate of emissions, zero emissions, and negative emissions of CO2 to quantify the response of land carbon sinks. We found that under positive emissions vegetation in the tropics gained carbon. Under zero emissions and negative emissions most ESMs lost carbon from vegetation in the tropics but gained carbon in mid- and high-latitudes, mostly in soils. Our findings imply that tropical carbon is vulnerable under zero emissions.
Alejandro Romero-Prieto, Marit Sandstad, Benjamin M. Sanderson, Zebedee R. J. Nicholls, Norman J. Steinert, Thomas Gasser, Camilla Mathison, Jarmo Kikstra, Thomas J. Aubry, Katsumasa Tanaka, Konstantin Weber, and Chris Smith
Geosci. Model Dev., 19, 7111–7133, https://doi.org/10.5194/gmd-19-7111-2026, https://doi.org/10.5194/gmd-19-7111-2026, 2026
Short summary
Short summary
Reduced-complexity models are an important tool in climate science, helping us understand and estimate future climate change. We present the experimental protocol for the next phase of the reduced-complexity model intercomparison project, which aims to compare results from many such models to better understand their behaviour. This knowledge will guide how these models are developed and used in the future, including in the upcoming Intergovernmental Panel on Climate Change (IPCC) assessment report (AR7).
Colin Jones, Isaline Bossert, Donovan P. Dennis, Hazel A. Jeffery, Chris D. Jones, Torben Koenigk, Sina Loriani, Benjamin Sanderson, Roland Séférian, Klaus Wyser, Shuting Yang, Manabu Abe, Sebastian Bathiany, Pascale Braconnot, Victor Brovkin, Friedrich A. Burger, Patrica Cadule, Frederic S. Castruccio, Gokhan Danabasoglu, Andrea Dittus, Jonathan F. Donges, Friederike Fröb, Thomas L. Frölicher, Goran Georgievski, Chuncheng Guo, Aixue Hu, Peter Lawrence, Paul Lerner, José Licón-Saláiz, Bette Otto-Bliesner, Anastasia Romanou, Elena Shevliakova, Yona Silvy, Didier Swingedouw, Jerry Tjiputra, Jeremy Walton, Andy Wiltshire, Ricarda Winkelmann, Richard Wood, Tokuta Yokohata, and Tilo Ziehn
Geosci. Model Dev., 19, 6941–6966, https://doi.org/10.5194/gmd-19-6941-2026, https://doi.org/10.5194/gmd-19-6941-2026, 2026
Short summary
Short summary
We introduce a new Earth system model experiment protocol to help researchers understand how Earth might respond to positive, zero, and negative carbon emissions. This protocol enables different models to be compared following similar warming and cooling rates. Researchers use the models to explore how the Earth reacts to different climate futures, including the risk of tipping points being exceeded and whether changes can be reversed. The results will support improved long-term climate policy.
Srinath Krishnan, Ragnhild Bieltvedt Skeie, Øivind Hodnebrog, Gunnar Myhre, Maria Sand, Marit Sandstad, Hannah Bryant, Didier A. Hauglustaine, Fabien Paulot, Michael Prather, and David Stevenson
Atmos. Chem. Phys., 26, 9509–9539, https://doi.org/10.5194/acp-26-9509-2026, https://doi.org/10.5194/acp-26-9509-2026, 2026
Short summary
Short summary
Hydrogen (H2) is an indirect greenhouse gas that can affect climate through chemical reactions in the atmosphere. To better understand this impact, it is important to constrain the sources and sinks of hydrogen. Using a suite of three-dimensional and one-dimensional models, we find that atmospheric production of hydrogen is 37–60 Tg/yr and that the geological source of H2 is much smaller than suggested. More field and isotopic measurements are needed to improve these estimates.
Rebecca M. Varney, Daniel Hooke, Norman J. Steinert, T. Luke Smallman, Camilla Mathison, and Eleanor J. Burke
Earth Syst. Dynam., 17, 913–928, https://doi.org/10.5194/esd-17-913-2026, https://doi.org/10.5194/esd-17-913-2026, 2026
Short summary
Short summary
The northern high latitudes mitigate climate change by land ecosystems absorbing more carbon from the atmosphere than they release. This carbon sink is sensitive to long-term CO2 emissions. Here, we use a probabilistic framework to quantify the timing and magnitude of a transition to a carbon source in different future emission scenarios, including overshoot. The fate of this region is dependent on the balance between carbon loss from permafrost and gain from increased vegetation productivity.
Benjamin M. Sanderson, Marit Sandstad, Alejandro Romero-Prieto, Stuart Jenkins, Charles Koven, Glen Peters, Roland Séfèrian, Andrew H. MacDougall, Ashwin Seshadri, Victor Brovkin, John Dunne, Abigail L. S. Swann, Torben Koenigk, Rosie A. Fisher, David Hohn, Tatiana Ilyina, Chris D. Jones, Hongmei Li, Peter Lawrence, Spencer Liddicoat, Nadine Mengis, Anastasia Romanou, Lori T. Sentman, Chris Smith, Norman J. Steinert, Jerry Tjiputra, and Tilo Ziehn
EGUsphere, https://doi.org/10.5194/egusphere-2026-1875, https://doi.org/10.5194/egusphere-2026-1875, 2026
Short summary
Short summary
When humanity stops emitting carbon dioxide, will temperatures keep rising, stabilize, or fall? Our study reveals in most models, temperatures remain nearly stable, but this hides a balance between the warming in response to radiation imbalance, and the cooling due to uptake of carbon in the land and ocean. Understanding this balance helps refine how much more CO2 we can safely emit.
John W. Rostron, Alejandro Bodas-Salcedo, David M. H. Sexton, Colin G. Jones, Edward W. Blockley, Till Kuhlbrodt, Jane P. Mulcahy, Tamzin E. Palmer, Saloua Peatier, Mark A. Ringer, Steven T. Rumbold, Benjamin M. Sanderson, Yongming Tang, and Martin R. Willet
EGUsphere, https://doi.org/10.5194/egusphere-2026-1676, https://doi.org/10.5194/egusphere-2026-1676, 2026
Short summary
Short summary
The Met Office’s latest weather and climate model warms very strongly in response to increases in carbon dioxide. We created a modified version of the model with a more moderate warming response by adjusting key model parameters, using both automated methods and expert judgement. The new model matches historical temperatures more closely and is better suited for studies of long‑term climate, but has reduced overall accuracy when simulating the baseline climate.
Ken S. Carslaw, Leighton A. Regayre, Ulrike Proske, Andrew Gettelman, David M. H. Sexton, Yun Qian, Lauren R. Marshall, Oliver Wild, Marcus van Lier-Walqui, Annika Oertel, Saloua Peatier, Ben Yang, Jill S. Johnson, Sihan Li, Daniel T. McCoy, Benjamin M. Sanderson, Christina J. Williamson, Gregory S. Elsaesser, Kuniko Yamazaki, and Ben B. B. Booth
Atmos. Chem. Phys., 26, 4651–4667, https://doi.org/10.5194/acp-26-4651-2026, https://doi.org/10.5194/acp-26-4651-2026, 2026
Short summary
Short summary
A major challenge in climate science is reducing projection uncertainty despite advances in models and observational constraints. Perturbed parameter ensembles (PPEs) offer a powerful tool to explore and reduce uncertainty by revealing model weaknesses and guiding development. PPEs are now widely applied across climate systems and scales. We argue they should be prioritized alongside complexity and resolution in model resource planning.
Detlef P. Van Vuuren, Brian C. O'Neill, Claudia Tebaldi, Benjamin M. Sanderson, Louise P. Chini, Pierre Friedlingstein, Tomoko Hasegawa, Keywan Riahi, Bala Govindasamy, Nico Bauer, Veronika Eyring, Cheikh M. N. Fall, Katja Frieler, Matthew J. Gidden, Laila K. Gohar, Annika Högner, Andrew D. Jones, Jarmo Kikstra, Andrew King, Reto Knutti, Elmar Kriegler, Peter Lawrence, Chris Lennard, Jason Lowe, Camilla Mathison, Shahbaz Mehmood, Zebedee Nicholls, Luciana F. Prado, Qiang Zhang, Steven K. Rose, Alex C. Ruane, Marit Sandstad, Carl-Friedrich Schleussner, Roland Seferian, Jana Sillmann, Chris Smith, Anna A. Sörensson, Swapna Panickal, Kaoru Tachiiri, Naomi Vaughan, Saritha S. Vishwanathan, Tokuta Yokohata, Marco Zecchetto, and Tilo Ziehn
Geosci. Model Dev., 19, 2627–2656, https://doi.org/10.5194/gmd-19-2627-2026, https://doi.org/10.5194/gmd-19-2627-2026, 2026
Short summary
Short summary
We propose a set of seven plausible 21st century emission scenarios, and their multi-century extensions, that will be used by the international community of climate modeling centers to produce the next generation of climate projections. These projections will support climate, impact and mitigation researchers, provide information to practitioners to address future risks from climate change, and contribute to policymakers’ considerations of the trade-offs among various levels of mitigation.
Gunnar Myhre, Øivind Hodnebrog, Srinath Krishnan, Maria Sand, Marit Sandstad, Ragnhild B. Skeie, Lieven Clarisse, Bruno Franco, Dylan B. Millet, Kelley C. Wells, Alexander Archibald, Hannah N. Bryant, Alex T. Chaudhri, David S. Stevenson, Didier Hauglustaine, Michael Prather, J. Christopher Kaiser, Dirk J. L. Olivie, Michael Schulz, Oliver Wild, Ye Wang, Thérèse Salameh, Jason E. Williams, Philippe Le Sager, Fabien Paulot, Kostas Tsigaridis, and Haley E. Plaas
Geosci. Model Dev., 19, 2577–2591, https://doi.org/10.5194/gmd-19-2577-2026, https://doi.org/10.5194/gmd-19-2577-2026, 2026
Short summary
Short summary
Volatile organic compounds (VOCs) affect air quality and climate, but their behavior in the atmosphere is still uncertain. We launched a global research effort to compare how different models represent these compounds and to improve their accuracy. By analyzing model results alongside observations and satellite data, we aim to better understand the atmospheric composition of these compounds.
Nina Schuhen, Carley E. Iles, Marit Sandstad, Viktor Ananiev, and Jana Sillmann
Nat. Hazards Earth Syst. Sci., 26, 753–773, https://doi.org/10.5194/nhess-26-753-2026, https://doi.org/10.5194/nhess-26-753-2026, 2026
Short summary
Short summary
As climate changes, extremes are becoming increasingly frequent. We investigate the time of emergence for a large range of different extremes, meaning the earliest time when a significant change in these extremes can be detected beyond natural variability, whether in the past or in the future. The results are based on 21 global climate models and show considerable differences between regions, types of indices and emissions scenarios, as well as between temperature and precipitation extremes.
Benjamin M. Sanderson, Susanne Baur, Carl-Freidrich Schleussner, Glen P. Peters, Shivika Mittal, Marit Sandstad, Steffen Kallbekken, Chris Smith, Sabine Fuss, Bas van Ruijven, Rosie A. Fisher, Joeri Rogelj, Roland Séférian, Bjørn Samset, Norman J. Steinert, Laurent Terray, and Jan Fuglestvedt
EGUsphere, https://doi.org/10.5194/egusphere-2026-28, https://doi.org/10.5194/egusphere-2026-28, 2026
Preprint archived
Short summary
Short summary
Solar Radiation Modification by adding aerosols to the stratosphere could rapidly and temporarily cool the Earth, but this speed creates unprecedented risks. Fast climate responses coupled with political instability create risks of failure to decarbonise, super-rapid climate change, and conflict. Idealized scenarios or conventional modeling tools could lead to systematic ignorance of these risks. We thus introduce a framework outlining what must be represented in future modeling and assessment.
Félix García-Pereira, Jesús Fidel González-Rouco, Nagore Meabe-Yanguas, Philipp de Vrese, Norman Julius Steinert, Johann Jungclaus, and Stephan Lorenz
The Cryosphere, 19, 5959–5981, https://doi.org/10.5194/tc-19-5959-2025, https://doi.org/10.5194/tc-19-5959-2025, 2025
Short summary
Short summary
This work shows that changing the hydrological state of permafrost produces differences of up to 3 °C in the annual ground temperature, 1–2 m in the active layer thickness, and 5 million km2 in the permafrost extent. Including a deeper vertical thermal scheme reduces the extent decline by more than 2 million km2 in the highest radiative emission scenario. This is shown for the first time in fully-coupled experiments with an Earth System Model.
Marit Sandstad, Norman Julius Steinert, Susanne Baur, and Benjamin Mark Sanderson
Geosci. Model Dev., 18, 8269–8312, https://doi.org/10.5194/gmd-18-8269-2025, https://doi.org/10.5194/gmd-18-8269-2025, 2025
Short summary
Short summary
We present METEORv1.0.1, a climate model emulator, that can be trained on full spatially resolved and widely available climate model data to reproduce climate variables and make predictions from unseen emission trajectories. The methodology identifies patterns with timescales of impact for one or more forcers using idealised experiments and anomaly calculations. Results for precipitation and temperature show good model performance and can reproduce hysteresis for overshoot scenarios.
Norman J. Steinert and Benjamin M. Sanderson
Earth Syst. Dynam., 16, 1711–1721, https://doi.org/10.5194/esd-16-1711-2025, https://doi.org/10.5194/esd-16-1711-2025, 2025
Short summary
Short summary
In this study, we explore how carbon emissions from thawing permafrost, known as the permafrost carbon feedback, affect two important climate metrics: how much the Earth warms per amount of carbon we emit and how much warming continues after we stop emitting carbon. Our study tackles a major gap in how we estimate future climate change. Using simplified climate models, we find a generalizable relationship between the permafrost carbon feedback and its additional warming impact on climate.
John P. Dunne, Helene T. Hewitt, Julie M. Arblaster, Frédéric Bonou, Olivier Boucher, Tereza Cavazos, Beth Dingley, Paul J. Durack, Birgit Hassler, Martin Juckes, Tomoki Miyakawa, Matt Mizielinski, Vaishali Naik, Zebedee Nicholls, Eleanor O'Rourke, Robert Pincus, Benjamin M. Sanderson, Isla R. Simpson, and Karl E. Taylor
Geosci. Model Dev., 18, 6671–6700, https://doi.org/10.5194/gmd-18-6671-2025, https://doi.org/10.5194/gmd-18-6671-2025, 2025
Short summary
Short summary
The seventh phase of the Coupled Model Intercomparison Project (CMIP7) coordinates efforts to answer key and timely climate science questions and facilitate delivery of relevant multi-model simulations for prediction and projection; characterization, attribution, and process understanding; and vulnerability, impact, and adaptation analysis. Key to the CMIP7 design are the mandatory Diagnostic, Evaluation and Characterization of Klima and optional Assessment Fast Track experiments.
Benjamin M. Sanderson, Victor Brovkin, Rosie A. Fisher, David Hohn, Tatiana Ilyina, Chris D. Jones, Torben Koenigk, Charles Koven, Hongmei Li, David M. Lawrence, Peter Lawrence, Spencer Liddicoat, Andrew H. MacDougall, Nadine Mengis, Zebedee Nicholls, Eleanor O'Rourke, Anastasia Romanou, Marit Sandstad, Jörg Schwinger, Roland Séférian, Lori T. Sentman, Isla R. Simpson, Chris Smith, Norman J. Steinert, Abigail L. S. Swann, Jerry Tjiputra, and Tilo Ziehn
Geosci. Model Dev., 18, 5699–5724, https://doi.org/10.5194/gmd-18-5699-2025, https://doi.org/10.5194/gmd-18-5699-2025, 2025
Short summary
Short summary
This study investigates how climate models warm in response to simplified carbon emissions trajectories, refining the understanding of climate reversibility and commitment. Metrics are defined for warming response to cumulative emissions and for the cessation of emissions or ramp-down to net-zero and net-negative levels. Results indicate that previous concentration-driven experiments may have overstated the Zero Emissions Commitment due to emissions rates exceeding historical levels.
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-1899, https://doi.org/10.5194/egusphere-2025-1899, 2025
Short summary
Short summary
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.
Ragnhild Bieltvedt Skeie, Marit Sandstad, Srinath Krishnan, Gunnar Myhre, and Maria Sand
Atmos. Chem. Phys., 25, 4929–4942, https://doi.org/10.5194/acp-25-4929-2025, https://doi.org/10.5194/acp-25-4929-2025, 2025
Short summary
Short summary
Hydrogen leakages can alter the amount of climate gases in the atmosphere and hence have a climate impact. In this study we investigate, using an atmospheric chemistry model, how this indirect climate effect differs with different amounts of leakages and with where the hydrogen leaks and if this effect changes in the future. The effect is largest for emissions far from areas where hydrogen is removed from the atmosphere by the soil, but these are not relevant locations for a future hydrogen economy.
Susanne Baur, Benjamin M. Sanderson, Roland Séférian, and Laurent Terray
Earth Syst. Dynam., 16, 667–681, https://doi.org/10.5194/esd-16-667-2025, https://doi.org/10.5194/esd-16-667-2025, 2025
Short summary
Short summary
Stratospheric aerosol injection (SAI) could be used alongside mitigation to reduce global warming. Previous studies suggest that more atmospheric CO2 is taken up when SAI is deployed. Here, we look at the entire SAI deployment from start to after termination. We show how the initial CO2 uptake benefit, and hence lower mitigation burden, is reduced in later stages of SAI, where the reduction in natural CO2 uptake turns into an additional mitigation burden.
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
Short summary
Short summary
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.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
Short summary
Short summary
We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Colin G. Jones, Fanny Adloff, Ben B. B. Booth, Peter M. Cox, Veronika Eyring, Pierre Friedlingstein, Katja Frieler, Helene T. Hewitt, Hazel A. Jeffery, Sylvie Joussaume, Torben Koenigk, Bryan N. Lawrence, Eleanor O'Rourke, Malcolm J. Roberts, Benjamin M. Sanderson, Roland Séférian, Samuel Somot, Pier Luigi Vidale, Detlef van Vuuren, Mario Acosta, Mats Bentsen, Raffaele Bernardello, Richard Betts, Ed Blockley, Julien Boé, Tom Bracegirdle, Pascale Braconnot, Victor Brovkin, Carlo Buontempo, Francisco Doblas-Reyes, Markus Donat, Italo Epicoco, Pete Falloon, Sandro Fiore, Thomas Frölicher, Neven S. Fučkar, Matthew J. Gidden, Helge F. Goessling, Rune Grand Graversen, Silvio Gualdi, José M. Gutiérrez, Tatiana Ilyina, Daniela Jacob, Chris D. Jones, Martin Juckes, Elizabeth Kendon, Erik Kjellström, Reto Knutti, Jason Lowe, Matthew Mizielinski, Paola Nassisi, Michael Obersteiner, Pierre Regnier, Romain Roehrig, David Salas y Mélia, Carl-Friedrich Schleussner, Michael Schulz, Enrico Scoccimarro, Laurent Terray, Hannes Thiemann, Richard A. Wood, Shuting Yang, and Sönke Zaehle
Earth Syst. Dynam., 15, 1319–1351, https://doi.org/10.5194/esd-15-1319-2024, https://doi.org/10.5194/esd-15-1319-2024, 2024
Short summary
Short summary
We propose a number of priority areas for the international climate research community to address over the coming decade. Advances in these areas will both increase our understanding of past and future Earth system change, including the societal and environmental impacts of this change, and deliver significantly improved scientific support to international climate policy, such as future IPCC assessments and the UNFCCC Global Stocktake.
Marit Sandstad, Borgar Aamaas, Ane Nordlie Johansen, Marianne Tronstad Lund, Glen Philip Peters, Bjørn Hallvard Samset, Benjamin Mark Sanderson, and Ragnhild Bieltvedt Skeie
Geosci. Model Dev., 17, 6589–6625, https://doi.org/10.5194/gmd-17-6589-2024, https://doi.org/10.5194/gmd-17-6589-2024, 2024
Short summary
Short summary
The CICERO-SCM has existed as a Fortran model since 1999 that calculates the radiative forcing and concentrations from emissions and is an upwelling diffusion energy balance model of the ocean that calculates temperature change. In this paper, we describe an updated version ported to Python and publicly available at https://github.com/ciceroOslo/ciceroscm (https://doi.org/10.5281/zenodo.10548720). This version contains functionality for parallel runs and automatic calibration.
Saloua Peatier, Benjamin M. Sanderson, and Laurent Terray
Earth Syst. Dynam., 15, 987–1014, https://doi.org/10.5194/esd-15-987-2024, https://doi.org/10.5194/esd-15-987-2024, 2024
Short summary
Short summary
The calibration of Earth system model parameters is a high-dimensionality problem subject to data, time, and computational constraints. In this study, we propose a practical solution for finding diverse near-optimal solutions. We argue that the effective degrees of freedom in the model performance response to parameter input is relatively small. Comparably performing parameter configurations exist and showcase different trade-offs in model errors, providing insights for model development.
Malte Meinshausen, Carl-Friedrich Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, Josep G. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatima Driouech, Erich Fischer, Piers Forster, Michael Grose, Gerrit Hansen, Zeke Hausfather, Tatiana Ilyina, Jarmo S. Kikstra, Joyce Kimutai, Andrew D. King, June-Yi Lee, Chris Lennard, Tabea Lissner, Alexander Nauels, Glen P. Peters, Anna Pirani, Gian-Kasper Plattner, Hans Pörtner, Joeri Rogelj, Maisa Rojas, Joyashree Roy, Bjørn H. Samset, Benjamin M. Sanderson, Roland Séférian, Sonia Seneviratne, Christopher J. Smith, Sophie Szopa, Adelle Thomas, Diana Urge-Vorsatz, Guus J. M. Velders, Tokuta Yokohata, Tilo Ziehn, and Zebedee Nicholls
Geosci. Model Dev., 17, 4533–4559, https://doi.org/10.5194/gmd-17-4533-2024, https://doi.org/10.5194/gmd-17-4533-2024, 2024
Short summary
Short summary
The scientific community is considering new scenarios to succeed RCPs and SSPs for the next generation of Earth system model runs to project future climate change. To contribute to that effort, we reflect on relevant policy and scientific research questions and suggest categories for representative emission pathways. These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.
David Gampe, Clemens Schwingshackl, Andrea Böhnisch, Magdalena Mittermeier, Marit Sandstad, and Raul R. Wood
Earth Syst. Dynam., 15, 589–605, https://doi.org/10.5194/esd-15-589-2024, https://doi.org/10.5194/esd-15-589-2024, 2024
Short summary
Short summary
Using a special suite of climate simulations, we determine if and when climate change is detectable and translate this to the global warming prevalent in the corresponding year. Our results show that, at 1.5°C warming, >85 % of the global population (>95 % at 2.0° warming) is already exposed to nighttime temperatures altered by climate change beyond natural variability. Furthermore, even incremental changes in global warming levels result in increased human exposure to emerged climate signals.
Félix García-Pereira, Jesús Fidel González-Rouco, Camilo Melo-Aguilar, Norman Julius Steinert, Elena García-Bustamante, Philip de Vrese, Johann Jungclaus, Stephan Lorenz, Stefan Hagemann, Francisco José Cuesta-Valero, Almudena García-García, and Hugo Beltrami
Earth Syst. Dynam., 15, 547–564, https://doi.org/10.5194/esd-15-547-2024, https://doi.org/10.5194/esd-15-547-2024, 2024
Short summary
Short summary
According to climate model estimates, the land stored 2 % of the system's heat excess in the last decades, while observational studies show it was around 6 %. This difference stems from these models using land components that are too shallow to constrain land heat uptake. Deepening the land component does not affect the surface temperature. This result can be used to derive land heat uptake estimates from different sources, which are much closer to previous observational reports.
Susanne Baur, Benjamin M. Sanderson, Roland Séférian, and Laurent Terray
Earth Syst. Dynam., 15, 307–322, https://doi.org/10.5194/esd-15-307-2024, https://doi.org/10.5194/esd-15-307-2024, 2024
Short summary
Short summary
Most solar radiation modification (SRM) simulations assume no physical coupling between mitigation and SRM. We analyze the impact of SRM on photovoltaic (PV) and concentrated solar power (CSP) and find that almost all regions have reduced PV and CSP potential compared to a mitigated or unmitigated scenario, especially in the middle and high latitudes. This suggests that SRM could pose challenges for meeting energy demands with solar renewable resources.
Nico Wunderling, Anna S. von der Heydt, Yevgeny Aksenov, Stephen Barker, Robbin Bastiaansen, Victor Brovkin, Maura Brunetti, Victor Couplet, Thomas Kleinen, Caroline H. Lear, Johannes Lohmann, Rosa Maria Roman-Cuesta, Sacha Sinet, Didier Swingedouw, Ricarda Winkelmann, Pallavi Anand, Jonathan Barichivich, Sebastian Bathiany, Mara Baudena, John T. Bruun, Cristiano M. Chiessi, Helen K. Coxall, David Docquier, Jonathan F. Donges, Swinda K. J. Falkena, Ann Kristin Klose, David Obura, Juan Rocha, Stefanie Rynders, Norman Julius Steinert, and Matteo Willeit
Earth Syst. Dynam., 15, 41–74, https://doi.org/10.5194/esd-15-41-2024, https://doi.org/10.5194/esd-15-41-2024, 2024
Short summary
Short summary
This paper maps out the state-of-the-art literature on interactions between tipping elements relevant for current global warming pathways. We find indications that many of the interactions between tipping elements are destabilizing. This means that tipping cascades cannot be ruled out on centennial to millennial timescales at global warming levels between 1.5 and 2.0 °C or on shorter timescales if global warming surpasses 2.0 °C.
Félix García-Pereira, Jesús Fidel González-Rouco, Thomas Schmid, Camilo Melo-Aguilar, Cristina Vegas-Cañas, Norman Julius Steinert, Pedro José Roldán-Gómez, Francisco José Cuesta-Valero, Almudena García-García, Hugo Beltrami, and Philipp de Vrese
SOIL, 10, 1–21, https://doi.org/10.5194/soil-10-1-2024, https://doi.org/10.5194/soil-10-1-2024, 2024
Short summary
Short summary
This work addresses air–ground temperature coupling and propagation into the subsurface in a mountainous area in central Spain using surface and subsurface data from six meteorological stations. Heat transfer of temperature changes at the ground surface occurs mainly by conduction controlled by thermal diffusivity of the subsurface, which varies with depth and time. A new methodology shows that near-surface diffusivity and soil moisture content changes with time are closely related.
Philipp de Vrese, Goran Georgievski, Jesus Fidel Gonzalez Rouco, Dirk Notz, Tobias Stacke, Norman Julius Steinert, Stiig Wilkenskjeld, and Victor Brovkin
The Cryosphere, 17, 2095–2118, https://doi.org/10.5194/tc-17-2095-2023, https://doi.org/10.5194/tc-17-2095-2023, 2023
Short summary
Short summary
The current generation of Earth system models exhibits large inter-model differences in the simulated climate of the Arctic and subarctic zone. We used an adapted version of the Max Planck Institute (MPI) Earth System Model to show that differences in the representation of the soil hydrology in permafrost-affected regions could help explain a large part of this inter-model spread and have pronounced impacts on important elements of Earth systems as far to the south as the tropics.
Susanne Baur, Alexander Nauels, Zebedee Nicholls, Benjamin M. Sanderson, and Carl-Friedrich Schleussner
Earth Syst. Dynam., 14, 367–381, https://doi.org/10.5194/esd-14-367-2023, https://doi.org/10.5194/esd-14-367-2023, 2023
Short summary
Short summary
Solar radiation modification (SRM) artificially cools global temperature without acting on the cause of climate change. This study looks at how long SRM would have to be deployed to limit warming to 1.5 °C and how this timeframe is affected by different levels of mitigation, negative emissions and climate uncertainty. None of the three factors alone can guarantee short SRM deployment. Due to their uncertainty at the time of SRM initialization, any deployment risks may be several centuries long.
Jarmo S. Kikstra, Zebedee R. J. Nicholls, Christopher J. Smith, Jared Lewis, Robin D. Lamboll, Edward Byers, Marit Sandstad, Malte Meinshausen, Matthew J. Gidden, Joeri Rogelj, Elmar Kriegler, Glen P. Peters, Jan S. Fuglestvedt, Ragnhild B. Skeie, Bjørn H. Samset, Laura Wienpahl, Detlef P. van Vuuren, Kaj-Ivar van der Wijst, Alaa Al Khourdajie, Piers M. Forster, Andy Reisinger, Roberto Schaeffer, and Keywan Riahi
Geosci. Model Dev., 15, 9075–9109, https://doi.org/10.5194/gmd-15-9075-2022, https://doi.org/10.5194/gmd-15-9075-2022, 2022
Short summary
Short summary
Assessing hundreds or thousands of emission scenarios in terms of their global mean temperature implications requires standardised procedures of infilling, harmonisation, and probabilistic temperature assessments. We here present the open-source
climate-assessmentworkflow that was used in the IPCC AR6 Working Group III report. The paper provides key insight for anyone wishing to understand the assessment of climate outcomes of mitigation pathways in the context of the Paris Agreement.
Benjamin M. Sanderson and Maria Rugenstein
Earth Syst. Dynam., 13, 1715–1736, https://doi.org/10.5194/esd-13-1715-2022, https://doi.org/10.5194/esd-13-1715-2022, 2022
Short summary
Short summary
Equilibrium climate sensitivity (ECS) is a measure of how much long-term warming should be expected in response to a change in greenhouse gas concentrations. It is generally calculated in climate models by extrapolating global average temperatures to a point of where the planet is no longer a net absorber of energy. Here we show that some climate models experience energy leaks which change as the planet warms, undermining the standard approach and biasing some existing model estimates of ECS.
Jörg Schwinger, Ali Asaadi, Norman Julius Steinert, and Hanna Lee
Earth Syst. Dynam., 13, 1641–1665, https://doi.org/10.5194/esd-13-1641-2022, https://doi.org/10.5194/esd-13-1641-2022, 2022
Short summary
Short summary
We test whether climate change can be partially reversed if CO2 is removed from the atmosphere to compensate for too large past and near-term emissions by using idealized model simulations of overshoot pathways. On a timescale of 100 years, we find a high degree of reversibility if the overshoot size remains small, and we do not find tipping points even for intense overshoots. We caution that current Earth system models are most likely not able to skilfully model tipping points in ecosystems.
Cited articles
Bassetti, S., Hutchinson, B., Tebaldi, C., and Kravitz, B.: DiffESM: Conditional Emulation of Temperature and Precipitation in Earth System Models With 3D Diffusion Models, J. Adv. Model. Earth Sy., 16, https://doi.org/10.1029/2023MS004194, 2024. a
Bentsen, M., Oliviè, D. J. L., Seland, Ø., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O. A., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: NCC NorESM2-MM model output prepared for CMIP6 CMIP, WDC Climate [data set], https://doi.org/10.22033/ESGF/CMIP6.506, 2019a. a
Bentsen, M., Oliviè, D. J. L., Seland, Ø., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O. A., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP, WDC Climate [data set], https://doi.org/10.22033/ESGF/CMIP6.608, 2019b. a
Bentsen, M., Oliviè, D. J. L., Seland, Ø., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O. A., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: NCC NorESM2-MM model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.8040, 2019c. a
Bentsen, M., Oliviè, D. J. L., Seland, Ø., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O. A., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: NCC NorESM2-MM model output prepared for CMIP6 CMIP abrupt-4xCO2, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.7840, 2019d. a
Bentsen, M., Oliviè, D. J. L., Seland, Ø., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O. A., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: NCC NorESM2-MM model output prepared for CMIP6 CMIP piControl, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.8221, 2019e. a
Bentsen, M., Oliviè, D. J. L., Seland, Ø., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O. A., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: NCC NorESM2-MM model output prepared for CMIP6 ScenarioMIP ssp245, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.8255, 2019f. a
Beusch, L., Gudmundsson, L., and Seneviratne, S. I.: Emulating Earth system model temperatures with MESMER: from global mean temperature trajectories to grid-point-level realizations on land, Earth Syst. Dynam., 11, 139–159, https://doi.org/10.5194/esd-11-139-2020, 2020. a, b
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Ghattas, J., Lebas, N., Lurton, T., Mellul, L., Musat, I., Mignot, J., and Cheruy, F.: IPSL IPSL-CM6A-LR model output prepared for CMIP6 CMIP, WDC Climate [data set], https://doi.org/10.22033/ESGF/CMIP6.1534, 2018a. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Ghattas, J., Lebas, N., Lurton, T., Mellul, L., Musat, I., Mignot, J., and Cheruy, F.: IPSL IPSL-CM6A-LR model output prepared for CMIP6 CMIP abrupt-4xCO2, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.5109, 2018b. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Ghattas, J., Lebas, N., Lurton, T., Mellul, L., Musat, I., Mignot, J., and Cheruy, F.: IPSL IPSL-CM6A-LR model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.5195, 2018c. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Ghattas, J., Lebas, N., Lurton, T., Mellul, L., Musat, I., Mignot, J., and Cheruy, F.: IPSL IPSL-CM6A-LR model output prepared for CMIP6 CMIP piControl, https://doi.org/10.22033/ESGF/CMIP6.5251, 2018d. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Dupont, E., and Lurton, T.: IPSL IPSL-CM6A-LR model output prepared for CMIP6 ScenarioMIP, WDC Climate [data set], https://doi.org/10.22033/ESGF/CMIP6.1532, 2019a. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Dupont, E., and Lurton, T.: IPSL IPSL-CM6A-LR model output prepared for CMIP6 ScenarioMIP ssp126, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.5262, 2019b. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Dupont, E., and Lurton, T.: IPSL IPSL-CM6A-LR model output prepared for CMIP6 ScenarioMIP ssp245, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.5264, 2019c. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Dupont, E., and Lurton, T.: IPSL IPSL-CM6A-LR model output prepared for CMIP6 ScenarioMIP ssp534-over, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.5269, 2019d. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Dupont, E., and Lurton, T.: IPSL IPSL-CM6A-LR model output prepared for CMIP6 ScenarioMIP ssp585, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.5271, 2019e. a
Danabasoglu, G.: NCAR CESM2-WACCM model output prepared for CMIP6 CMIP, WDC Climate [data set], https://doi.org/10.22033/ESGF/CMIP6.10024, 2019a. a
Danabasoglu, G.: NCAR CESM2-WACCM model output prepared for CMIP6 ScenarioMIP, WDC Climate [data set], https://doi.org/10.22033/ESGF/CMIP6.10026, 2019b. a
Danabasoglu, G.:. NCAR CESM2-WACCM model output prepared for CMIP6 CMIP abrupt-4xCO2, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.10039, 2019c. a
Danabasoglu, G.:. NCAR CESM2-WACCM model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.10071, 2019d. a
Danabasoglu, G.:. NCAR CESM2-WACCM model output prepared for CMIP6 CMIP piControl, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.10094, 2019e. a
Danabasoglu, G.:. NCAR CESM2-WACCM model output prepared for CMIP6 ScenarioMIP ssp126, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.10100, 2019f. a
Danabasoglu, G.:. NCAR CESM2-WACCM model output prepared for CMIP6 ScenarioMIP ssp245, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.10101, 2019g. a
Danabasoglu, G.:. NCAR CESM2-WACCM model output prepared for CMIP6 ScenarioMIP ssp534-over, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.10114, 2019h. a
Danabasoglu, G.:. NCAR CESM2-WACCM model output prepared for CMIP6 ScenarioMIP ssp585, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.10115, 2019i. a
Dasgupta, S., Van Maanen, N., Gosling, S. N., Piontek, F., Otto, C., and Schleussner, C.-F.: Effects of climate change on combined labour productivity and supply: an empirical, multi-model study, Lancet Planet. Health, 5, e455–e465, 2021. a
Dewey, M., Hansson, H.-C., Watson-Parris, D., Samset, B. H., Wilcox, L. J., Lewinschal, A., Sand, M., Seland, Ø., Krishnan, S., and Ekman, A. M.: AeroGP: machine learning how aerosols impact regional climate, J. Geophys. Res.-Mach. Learn. Comput., 2, e2025JH000741, https://doi.org/10.1029/2025JH000741, 2025. a
Erbs, D., Klein, S., and Duffie, J.: Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation, Sol. Energy, 28, 293–302, 1982. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Farley, J., MacMartin, D. G., Visioni, D., Kravitz, B., Bednarz, E., Duffey, A., and Henry, M.: A Climate Intervention Dynamical Emulator (CIDER) for Scenario Space Exploration, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-1830, 2025. a
Freese, L. M., Giani, P., Fiore, A. M., and Selin, N. E.: Spatially Resolved Temperature Response Functions to CO2 Emissions, Geophys. Res. Lett., 51, e2024GL108788, https://doi.org/10.1029/2024GL108788, 2024. a
Giani, P., Fiore, A. M., Flierl, G., Ferrari, R., and Selin, N. E.: Origin and Limits of Invariant Warming Patterns in Climate Models, J. Climate, 39, 1681–1701, https://doi.org/10.1175/JCLI-D-24-0683.1, 2025. a, b
Good, P., Lowe, J. A., Andrews, T., Wiltshire, A., Chadwick, R., Ridley, J. K., Menary, M. B., Bouttes, N., Dufresne, J. L., Gregory, J. M., Schaller, N., and Shiogama, H.: Nonlinear regional warming with increasing CO2 concentrations, Nat. Clim. Change, 5, https://doi.org/10.1038/nclimate2498, 2015. a, b
Good, P., Sellar, A., Tang, Y., Rumbold, S., Ellis, R., Kelley, D., Kuhlbrodt, T., and Walton, J.: MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP, WDC Climate [data set], https://doi.org/10.22033/ESGF/CMIP6.1567, 2019a. a
Good, P., Sellar, A., Tang, Y., Rumbold, S., Ellis, R., Kelley, D., and Kuhlbrodt, T.: MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP ssp126, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6333, 2019b. a
Good, P., Sellar, A., Tang, Y., Rumbold, S., Ellis, R., Kelley, D., and Kuhlbrodt, T.: MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP ssp245, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6339, 2019c. a
Good, P., Sellar, A., Tang, Y., Rumbold, S., Ellis, R., Kelley, D., and Kuhlbrodt, T.: MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP ssp534-over, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6397 2019d. a
Good, P., Sellar, A., Tang, Y., Rumbold, S., Ellis, R., Kelley, D., and Kuhlbrodt, T.: MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP ssp585, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6405 2019e. a
Hamilton, J. D.: Time series analysis, Princeton University Press, https://doi.org/10.1515/9780691218632, 2020. a, b
Harder, P., Hernandez-Garcia, A., Ramesh, V., Yang, Q., Sattegeri, P., Szwarcman, D., Watson, C., and Rolnick, D.: Generating physically-consistent high-resolution climate data with hard-constrained neural networks, arXiv [preprint], https://doi.org/10.48550/arXiv.2208.05424, 2024. a
Hasselmann, K., Hasselmann, S., Giering, R., Ocana, V., and Storch, H.: Sensitivity study of optimal CO2 emission paths using a simplified structural integrated assessment model (SIAM), Climatic Change, 37, 345–386, 1997. a
Herger, N., Sanderson, B. M., and Knutti, R.: Improved pattern scaling approaches for the use in climate impact studies, Geophys. Res. Lett., 42, 3486–3494, https://doi.org/10.1002/2015GL063569, 2015. a, b
IPCC: Climate Change 2021: The Physical Science Basis, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, https://doi.org/10.1017/9781009157896, 2021. a, b
IPCC: Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK and New York, NY, USA, https://doi.org/10.1017/9781009325844, 2022. a
Kajbaf, A. A., Bensi, M., and Brubaker, K. L.: Temporal downscaling of precipitation from climate model projections using machine learning, Stoch. Env. Res. Risk. A, 36, 2173–2194, 2022. a
Kendon, E. J., Addison, H., Doury, A., Somot, S., Watson, P. A. G., Booth, B. B. B., Coppola, E., Gutiérrez, J. M., Murphy, J., and Scullion, C.: Potential for Machine Learning Emulators to Augment Regional Climate Simulations in Provision of Local Climate Change Information, B. Am. Meteorol. Soc., 106, E1175–E1203, https://doi.org/10.1175/BAMS-D-24-0114.1, 2025. a
Kheir, A. M., Elnashar, A., Mosad, A., and Govind, A.: An improved deep learning procedure for statistical downscaling of climate data, Heliyon, 9, https://doi.org/10.1016/j.heliyon.2023.e18200, 2023. a
Kitsios, V., O’Kane, T. J., and Newth, D.: A machine learning approach to rapidly project climate responses under a multitude of net-zero emission pathways, Commun. Earth Environ., 4, 355, https://doi.org/10.1038/s43247-023-01011-0, 2023. a
Lam, T. N., Wan, K. K., Wong, S., and Lam, J. C.: Impact of climate change on commercial sector air conditioning energy consumption in subtropical Hong Kong, Appl. Energ., 87, 2321–2327, 2010. a
Mann, M. E. and Park, J.: Global-scale modes of surface temperature variability on interannual to century timescales, J. Geophys. Res.-Atmos., 99, 25819–25833, 1994. a
Mathison, C., Burke, E. J., Munday, G., Jones, C. D., Smith, C. J., Steinert, N. J., Wiltshire, A. J., Huntingford, C., Kovacs, E., Gohar, L. K., Varney, R. M., and McNeall, D.: A rapid-application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME), Geosci. Model Dev., 18, 1785–1808, https://doi.org/10.5194/gmd-18-1785-2025, 2025. a
Mitchell, T. D.: Pattern Scaling: An Examination of the Accuracy of the Technique for Describing Future Climates, Climatic Change, 60, 217–242, https://doi.org/10.1023/A:1026035305597, 2003. a
Nath, S., Lejeune, Q., Beusch, L., Seneviratne, S. I., and Schleussner, C.-F.: MESMER-M: an Earth system model emulator for spatially resolved monthly temperature, Earth Syst. Dynam., 13, 851–877, https://doi.org/10.5194/esd-13-851-2022, 2022. a, b, c
Nicholls, Z. R. J., Meinshausen, M., Lewis, J., Gieseke, R., Dommenget, D., Dorheim, K., Fan, C.-S., Fuglestvedt, J. S., Gasser, T., Golüke, U., Goodwin, P., Hartin, C., Hope, A. P., Kriegler, E., Leach, N. J., Marchegiani, D., McBride, L. A., Quilcaille, Y., Rogelj, J., Salawitch, R. J., Samset, B. H., Sandstad, M., Shiklomanov, A. N., Skeie, R. B., Smith, C. J., Smith, S., Tanaka, K., Tsutsui, J., and Xie, Z.: Reduced Complexity Model Intercomparison Project Phase 1: introduction and evaluation of global-mean temperature response, Geosci. Model Dev., 13, 5175–5190, https://doi.org/10.5194/gmd-13-5175-2020, 2020. a
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016. a, b
Pfleiderer, P., Schleussner, C.-F., and Sillmann, J.: Limited reversal of regional climate signals in overshoot scenarios, Environ. Res.-Climate, 3, 015005, https://doi.org/10.1088/2752-5295/ad1c45, 2024. a
Quilcaille, Y., Gudmundsson, L., Beusch, L., Hauser, M., and Seneviratne, S. I.: Showcasing MESMER-X: Spatially Resolved Emulation of Annual Maximum Temperatures of Earth System Models, Geophys. Res. Lett., 49, e2022GL099012, https://doi.org/10.1029/2022GL099012, 2022. a, b
Sanderson, B., Sandstad, M., Steinert, N. J., Dewey, M., and Fjeldså, J. L.: benmsanderson/METEOR: v1.6.0, Zenodo [code], https://doi.org/10.5281/zenodo.22650303, 2026. a
Santer, B. D., Wigley, T. M., Schlesinger, M. E., and Mitchell, J. F.: Developing Climate Scenarios from Equilibrium GCM Results, Max-Planck-Institut für Meteorologie report, Max-Planck-Institut für Meteorologie, https://api.semanticscholar.org/CorpusID:127017512 (last access: 26 September 2026), 1990. a
Schleussner, C.-F., Ganti, G., Lejeune, Q., Zhu, B., Pfleiderer, P., Prütz, R., Ciais, P., Frölicher, T. L., Fuss, S., Gasser, T., Gidden, M. J., Kropf, C. M., Lacroix, F., Lamboll, R., Martyr, R., Maussion, F., McCaughey, J. W., Meinshausen, M., Mengel, M., Nicholls, Z., Quilcaille, Y., Sanderson, B., Seneviratne, S. I., Sillmann, J., Smith, C. J., Steinert, N. J., Theokritoff, E., Warren, R., Price, J., and Rogelj, J.: Overconfidence in climate overshoot, Nature, 634, 366–373, 2024. a
Schöngart, S., Gudmundsson, L., Hauser, M., Pfleiderer, P., Lejeune, Q., Nath, S., Seneviratne, S. I., and Schleussner, C.-F.: Introducing the MESMER-M-TPv0.1.0 module: spatially explicit Earth system model emulation for monthly precipitation and temperature, Geosci. Model Dev., 17, 8283–8320, https://doi.org/10.5194/gmd-17-8283-2024, 2024. a, b, c
Schwind, N., Perrette, M., Byers, E., Högner, A., Lejeune, Q., Möller, T., Nicholls, Z., Pfleiderer, P., Schöngart, S., Werning, M., and Schleussner, C.-F.: RIME-X v1.0: combining simple climate models, Earth system models, and climate impact models into a unified statistical emulator for regional climate indicators, Geosci. Model Dev., 19, 6797–6828, https://doi.org/10.5194/gmd-19-6797-2026, 2026. a
Seferian, R.: CNRM-CERFACS CNRM-ESM2-1 model output prepared for CMIP6 CMIP, WDC Climate [data set], https://doi.org/10.22033/ESGF/CMIP6.1391, 2018a. a
Seferian, R.: CNRM-CERFACS CNRM-ESM2-1 model output prepared for CMIP6 CMIP for experiment abrupt-4xCO2, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.3918, 2018b. a
Seferian, R.: CNRM-CERFACS CNRM-ESM2-1 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4068, 2018c. a
Seferian, R.: CNRM-CERFACS CNRM-ESM2-1 model output prepared for CMIP6 CMIP piControl, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4165, 2018d. a
Seferian, R.: CNRM-CERFACS CNRM-ESM2-1 model output prepared for CMIP6 ScenarioMIP, WDC Climate [data set], https://doi.org/10.22033/ESGF/CMIP6.1395, 2019. a
Sejas, S. A. and Taylor, P. C.: The role of sea ice in establishing the seasonal Arctic warming pattern, Environ. Res.-Climate, 2, 035008, https://doi.org/10.1088/2752-5295/ace20f, 2023. a
Seneviratne, S., Quilcaille, Y., Windisch, M., Gudmundsson, L., Biess, B., Jaeger, F., Hauser, M., and Hirschi, M.: Using regional ESM emulators to assess climate feedbacks to IAMs: The “FASTMIP” experimental protocol, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18731, https://doi.org/10.5194/egusphere-egu24-18731, 2024. a
Sims, C. A.: Macroeconomics and reality, Econometrica, 1–48, https://doi.org/10.2307/1912017, 1980. a, b
Swart, N. C., Cole, J. N., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L., von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5 model output prepared for CMIP6 CMIP, WDC Climate [data set], https://doi.org/10.22033/ESGF/CMIP6.1303, 2019a. a
Swart, N. C., Cole, J. N., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L., von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP, WDC Climate [data set], https://doi.org/10.22033/ESGF/CMIP6.1317, 2019b. a
Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L., von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5 model output prepared for CMIP6 CMIP abrupt-4xCO2, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.3532, 2019c. a
Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L., von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.3610, 2019d. a
Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L., von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5 model output prepared for CMIP6 CMIP piControl, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.3673, 2019e. a
Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L., von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP ssp126, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.3683, 2019f. a
Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L., von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP ssp245, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.3685, 2019g. a
Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L., von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP ssp534-over, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.3694, 2019h. a
Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L., von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5 model output prepared for CMIP6 ScenarioMIP ssp585, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.3696, 2019i. a
Tang, Y., Rumbold, S., Ellis, R., Kelley, D., Mulcahy, J., Sellar, A., Walton, J., and Jones, C.: MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP, WDC Climate [data set], https://doi.org/10.22033/ESGF/CMIP6.1569, 2019a. a
Tang, Y., Rumbold, S., Ellis, R., Kelley, D., Mulcahy, J., Sellar, A., Walton, J., and Jones, C.: MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP abrupt-4xCO2, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.5843, 2019b. a
Tang, Y., Rumbold, S., Ellis, R., Kelley, D., Mulcahy, J., Sellar, A., Walton, J., and Jones, C.: MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6113, 2019c. a
Tang, Y., Rumbold, S., Ellis, R., Kelley, D., Mulcahy, J., Sellar, A., Walton, J., and Jones, C.: MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP piControl, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6298, 2019d. a
Tebaldi, C., Debeire, K., Eyring, V., Fischer, E., Fyfe, J., Friedlingstein, P., Knutti, R., Lowe, J., O'Neill, B., Sanderson, B., van Vuuren, D., Riahi, K., Meinshausen, M., Nicholls, Z., Tokarska, K. B., Hurtt, G., Kriegler, E., Lamarque, J.-F., Meehl, G., Moss, R., Bauer, S. E., Boucher, O., Brovkin, V., Byun, Y.-H., Dix, M., Gualdi, S., Guo, H., John, J. G., Kharin, S., Kim, Y., Koshiro, T., Ma, L., Olivié, D., Panickal, S., Qiao, F., Rong, X., Rosenbloom, N., Schupfner, M., Séférian, R., Sellar, A., Semmler, T., Shi, X., Song, Z., Steger, C., Stouffer, R., Swart, N., Tachiiri, K., Tang, Q., Tatebe, H., Voldoire, A., Volodin, E., Wyser, K., Xin, X., Yang, S., Yu, Y., and Ziehn, T.: Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6, Earth Syst. Dynam., 12, 253–293, https://doi.org/10.5194/esd-12-253-2021, 2021. a, b
Tebaldi, C., Snyder, A., and Dorheim, K.: STITCHES: creating new scenarios of climate model output by stitching together pieces of existing simulations, Earth Syst. Dynam., 13, 1557–1609, https://doi.org/10.5194/esd-13-1557-2022, 2022. a
Wang, F. and Tian, D.: On deep learning-based bias correction and downscaling of multiple climate models simulations, Clim. Dynam., 59, 3451–3468, 2022. a
Voldoire, A.: CNRM-CERFACS CNRM-ESM2-1 model output prepared for CMIP6 ScenarioMIP ssp126, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4186, 2019a. a
Voldoire, A.: CNRM-CERFACS CNRM-ESM2-1 model output prepared for CMIP6 ScenarioMIP ssp245, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4191, 2019b. a
Voldoire, A.: CNRM-CERFACS CNRM-ESM2-1 model output prepared for CMIP6 ScenarioMIP ssp534-over, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4221, 2019c. a
Voldoire, A.: CNRM-CERFACS CNRM-ESM2-1 model output prepared for CMIP6 ScenarioMIP ssp585, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4226, 2019d. a
Watson-Parris, D., Williams, A., Deaconu, L., and Stier, P.: Model calibration using ESEm v1.1.0 – an open, scalable Earth system emulator, Geosci. Model Dev., 14, 7659–7672, https://doi.org/10.5194/gmd-14-7659-2021, 2021. a
Womack, C. B., Giani, P., Eastham, S. D., and Selin, N. E.: Rapid Emulation of Spatially Resolved Temperature Response to Effective Radiative Forcing, J. Adv. Model. Earth Sy., 17, e2024MS004523, https://doi.org/10.1029/2024MS004523, 2025. a
Zeng, Z., Kim, J.-H. J., Tan, H., Hu, Y., Cameron-Rastogi, P., Villa, D., New, J., Wang, J., and Muehleisen, R. T.: A review of future weather data for assessing climate change impacts on buildings and energy systems, Renew. Sust. Energ. Rev., 212, 115213, https://doi.org/10.1016/j.rser.2024.115213, 2025. a
Zhao, B., Reichler, T., Strong, C., and Penland, C.: Simultaneous Evolution of Gyre and Atlantic Meridional Overturning Circulation Anomalies as an Eigenmode of the North Atlantic System, J. Climate, 30, 6737–6755, https://doi.org/10.1175/JCLI-D-16-0751.1, 2017. a
Short summary
Planning for climate change requires understanding how climate and weather interact to drive possible impacts. We developed a tool that simulates realistic monthly climate patterns and translates them into useful measures like energy needs. It captures seasonal cycles and year-to-year variations to bridges the gap between global climate predictions and local planning, making climate risk assessment faster and more accessible.
Planning for climate change requires understanding how climate and weather interact to drive...
