Articles | Volume 17, issue 4
https://doi.org/10.5194/esd-17-955-2026
© 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-17-955-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Jul 2026
Research article |
| 13 Jul 2026
| 13 Jul 2026
A multivariate analysis of atmospheric drivers for Western European heatwaves
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany
Birgit Hassler
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Katja Weigel
University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Miguel-Ángel Fernández-Torres
Department of Signal Theory and Communications, Universidad Carlos III de Madrid (UC3M), Leganés, Madrid, Spain
Gustau Camps-Valls
Image Processing Laboratory (IPL), Universitat de València (UV), València, Spain
Veronika Eyring
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany
Related authors
No articles found.
Vittorio Del Tatto, Andrei Gavrilov, and Gustau Camps-Valls
EGUsphere, https://doi.org/10.5194/egusphere-2026-5668, https://doi.org/10.5194/egusphere-2026-5668, 2026
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
Climate projections differ widely between models, so they are often combined by giving more weight to models that better reproduce the observed climate. We show that the usual way of calibrating this weighting, which asks only whether the truth falls inside a predicted range, can be improved by one that scores the whole distribution. Applied to rainfall over land, it constrains the projections more than the standard approach; using synthetic data, we show that it does so without overconfidence.
Feini Huang, Ziqian Li, Lijun Wang, Gustau Camps-Valls, Wei Su, Maria Piles, Ruqing Zhang, Wei Shangguan, and Yongjiu Dai
EGUsphere, https://doi.org/10.5194/egusphere-2026-5166, https://doi.org/10.5194/egusphere-2026-5166, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Observation-driven 1–14-day soil moisture forecasts remain reliable in some places but lose accuracy rapidly in others. We show that this contrast is governed by how soils filter rainfall. Under intense rainfall, low-silt soils provide little buffering, accelerate downward water movement, erode moisture memory, and constrain forecast skill. This finding shifts attention from better algorithms to physical forecast limits and guides the design of reliable models and monitoring networks.
Sean M. Davis, William T. Ball, Yue Jia, Gabriel Chiodo, Justin A. Alsing, James Keeble, Hideharu Akiyoshi, Carlo Arosio, Ewa M. Bednarz, Andreas Chrysanthou, Melanie Coldewey-Egbers, Robert Damadeo, Sandip Dhomse, Mohamadou A. Diallo, Simone Dietmuller, Roland Eichinger, Stacey Frith, Birgit Hassler, Michaela I. Hegglin, Daan Hubert, Patrick Jöckel, Béatrice Josse, Natalya Kramarova, Diego Loyola, Eliane Maillard Barras, Marion Marchand, Olaf Morgenstern, David Plummer, Robert W. Portmann, Karen H. Rosenlof, Alexei Rozanov, Viktoria F. Sofieva, Johannes Staehelin, Timofei Sukhodolov, Kleareti Tourpali, Ronald J. Van der A, H. J. Ray Wang, Krzysztof Wargan, Shingo Watanabe, Mark Weber, Jeannette D. Wild, Yousuke Yamashita, and Jerry Ziemke
Atmos. Chem. Phys., 26, 13817–13843, https://doi.org/10.5194/acp-26-13817-2026, https://doi.org/10.5194/acp-26-13817-2026, 2026
Short summary
Short summary
This study investigates tropical ozone changes in models and satellite observations to determine how well they agree and whether observations can constrain model projections. At some levels, satellite records disagree significantly on the magnitude of ozone changes. While early and late 21st century trends are correlated across models, suggesting a potential emergent constraint, the spread in observations means they are unable to provide guidance on the credibility of model projections.
Axel Lauer, Manuel Schlund, Lisa Bock, Birgit Hassler, Gunnar Behrens, Bettina Gier, Lukas Lindenlaub, Stephan Lorenz, Jan-Hendrik Malles, Wolfgang A. Müller, Trang van Pham, Katja Weigel, Guang Zeng, and Veronika Eyring
Geosci. Model Dev., 19, 8731–8754, https://doi.org/10.5194/gmd-19-8731-2026, https://doi.org/10.5194/gmd-19-8731-2026, 2026
Short summary
Short summary
ICONEval is a new framework to facilitate researchers to continuously monitor and check their climate models during the development phase. It builds on the ESMValTool software package to run tests verifying that the results are plausible, follow physical laws, and that the model has sufficient skill in reproducing the observed climate. An important aim is to make it easier to spot model errors early, for example when implementing new model components that use machine learning.
Diajeng W. Atmojo, Katja Weigel, Arthur Grundner, Marika M. Holland, Dmitry Sidorenko, and Veronika Eyring
The Cryosphere, 20, 4437–4464, https://doi.org/10.5194/tc-20-4437-2026, https://doi.org/10.5194/tc-20-4437-2026, 2026
Short summary
Short summary
This study presents an observation-driven sea ice albedo parametrisation by discovering an equation using symbolic regression, an interpretable machine learning method. Leveraging satellite and reanalyses data, our discovered equation identifies high sensitivity to thin snow and the weighted temperature difference between sea ice surface and 2 m air as critical to determine sea ice albedo. Our findings contribute to improving Arctic climate projections and understanding.
Forrest M. Hoffman, Birgit Hassler, Ranjini Swaminathan, Jared Lewis, Bouwe Andela, Nathan Collier, Dóra Hegedűs, Jiwoo Lee, Charlotte Pascoe, Mika Pflüger, Martina Stockhause, Paul Ullrich, Min Xu, Lisa Bock, Felicity Chun, Bettina K. Gier, Douglas I. Kelley, Axel Lauer, Julien Lenhardt, Manuel Schlund, Mohanan G. Sreeush, Katja Weigel, Ed Blockley, Rebecca Beadling, Romain Beucher, Demiso D. Dugassa, Valerio Lembo, Jianhua Lu, Swen Brands, Jerry Tjiputra, Elizaveta Malinina, Brian Medeiros, Enrico Scoccimarro, Jeremy Walton, Phil Kershaw, André Lanfer Marquez, Malcolm J. Roberts, Eleanor O'Rourke, Beth Dingley, Briony Turner, Helene Hewitt, and John P. Dunne
Geosci. Model Dev., 19, 7415–7455, https://doi.org/10.5194/gmd-19-7415-2026, https://doi.org/10.5194/gmd-19-7415-2026, 2026
Short summary
Short summary
The Rapid Evaluation Framework (REF) is a community-driven platform for benchmarking and performance assessment of Earth system models. Built upon four disparate community evaluation tools, the REF is designed to provide model-data comparisons for the Assessment Fast Track for the Seventh Phase of the Coupled Model Intercomparison Project. The REF will be run at the Earth System Grid Federation to enable model devleopers and scientists to quickly identify model biases and performance issues.
Franziska Müller, Laura Eifler, Felix Cremer, Pieter Beck, Gustau Camps-Valls, and Ana Bastos
Nat. Hazards Earth Syst. Sci., 26, 2785–2815, https://doi.org/10.5194/nhess-26-2785-2026, https://doi.org/10.5194/nhess-26-2785-2026, 2026
Short summary
Short summary
Forest health is increasingly threatened, but disturbances like wind damage and insect outbreaks are hard to track. Our Sentinel-1 Disturbance Mapping (S1DM) approach combines satellite radar with survey data, improving detection for wind and bark beetle impacts and often spotting them earlier. Defoliators remain difficult to capture, but this method strengthens monitoring and supports better forest management.
Evgenia Galytska, Birgit Hassler, Carlo Arosio, Martyn P. Chipperfield, Sandip S. Dhomse, Kimberlee Dubé, Wuhu Feng, Fernando Iglesias-Suarez, and Jakob Runge
Atmos. Chem. Phys., 26, 8185–8209, https://doi.org/10.5194/acp-26-8185-2026, https://doi.org/10.5194/acp-26-8185-2026, 2026
Short summary
Short summary
We explore how chemical and dynamical processes shape ozone in the tropical middle stratosphere using satellite data and a chemistry-transport model. We apply a framework to identify cause–effect relationships and estimate their strength. For 2004-2021 monthly data, we detect an indirect pathway controlling ozone variability, with effects increasing after two–three months. We further assess how different wind regimes influence this pathway.
Katharina Hafner, Sara Shamekh, Guillaume Bertoli, Axel Lauer, Robert Pincus, Julien Savre, and Veronika Eyring
Geosci. Model Dev., 19, 3875–3891, https://doi.org/10.5194/gmd-19-3875-2026, https://doi.org/10.5194/gmd-19-3875-2026, 2026
Short summary
Short summary
Most climate models cannot resolve clouds and cloud-radiation interactions at coarse horizontal resolutions of about 100 km, which introduces uncertainties. High-resolution models resolve clouds better but are expensive to run. We use short high-resolution simulations and artificial intelligence to learn the cloud-radiation interactions without making any assumptions about the small scales. We propose a new method that significantly reduces cloud related errors.
Yue Li, Gang Tang, Eleanor O'Rourke, Samar Minallah, Martim Mas e Braga, Sophie Nowicki, Robin S. Smith, David M. Lawrence, George C. Hurtt, Daniele Peano, Gesa Meyer, Birgit Hassler, Jiafu Mao, Yongkang Xue, and Martin Juckes
Geosci. Model Dev., 19, 3129–3155, https://doi.org/10.5194/gmd-19-3129-2026, https://doi.org/10.5194/gmd-19-3129-2026, 2026
Short summary
Short summary
Land and Land Ice Theme Opportunities describe a list that contains 25 variable groups with 716 variables, which are potentially available to the broad scientific audience for performing analysis in land–atmosphere coupling, hydrological processes and freshwater systems, glacier and ice sheet mass balance and their influence on the sea levels, land use, and plant phenology.
Beth Dingley, James A. Anstey, Marta Abalos, Carsten Abraham, Tommi Bergman, Lisa Bock, Sonya Fiddes, Birgit Hassler, Ryan J. Kramer, Fei Luo, Fiona M. O'Connor, Petr Šácha, Isla R. Simpson, Laura J. Wilcox, and Mark D. Zelinka
Geosci. Model Dev., 19, 2945–2984, https://doi.org/10.5194/gmd-19-2945-2026, https://doi.org/10.5194/gmd-19-2945-2026, 2026
Short summary
Short summary
This manuscript defines as a list of variables and scientific opportunities which are requested from the Coupled Model Intercomparison Project Phase 7 (CMIP7) Assessment Fast Track to address open atmospheric science questions. The list reflects the output of a large public community engagement effort, coordinated across autumn 2025 through to summer 2025.
Mara Y. McPartland, Tomas Lovato, Charles Koven, Jamie D. Wilson, Briony Turner, Colleen M. Petrik, José Licón-Saláiz, Fang Li, Fanny Lhardy, Jaclyn Clement Kinney, Michio Kawamiya, Birgit Hassler, Nathan P. Gillett, Cheikh Modou Noreyni Fall, Christopher Danek, Chris M. Brierley, Ana Bastos, and Oliver Andrews
Geosci. Model Dev., 19, 2849–2880, https://doi.org/10.5194/gmd-19-2849-2026, https://doi.org/10.5194/gmd-19-2849-2026, 2026
Short summary
Short summary
The Coupled Model Intercomparison Project (CMIP) is an international consortium of climate modeling groups that produce coordinated experiments in order to evaluate human influence on the climate and test knowledge of Earth systems. This paper describes the data requested for Earth systems research in CMIP7. We detail the request for model output of the carbon cycle, the flows of energy among the atmosphere, land and the oceans, and interactions between these and the global climate.
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.
Peter Pfleiderer, Anna Merrifield, István Dunkl, Homer Durand, Enora Cariou, Julien Cattiaux, Gustau Camps-Valls, and Sebastian Sippel
Weather Clim. Dynam., 7, 89–108, https://doi.org/10.5194/wcd-7-89-2026, https://doi.org/10.5194/wcd-7-89-2026, 2026
Short summary
Short summary
Due to changes in atmospheric circulation some regions are warming quicker than others. Statistical methods are used to estimate how much of the local summer temperature changes are due to circulation changes. We evaluate these methods by comparing their estimates to special simulations representing only temperature changes related to circulation changes. By applying the methods to observations of 1979–2023 we find that half of the warming over parts of Europe is related to circulation changes.
Lukas Lindenlaub, Katja Weigel, Birgit Hassler, Colin Jones, and Veronika Eyring
Earth Syst. Dynam., 17, 81–105, https://doi.org/10.5194/esd-17-81-2026, https://doi.org/10.5194/esd-17-81-2026, 2026
Short summary
Short summary
This study explores changes in drought characteristic based on projections by 18 different Earth system models. Their performance is evaluated by comparing historical simulations to observation based reanalysis. The analysis of a standardized drought index under different future scenarios revealed that the harvest area that is projected to experience extreme drought conditions towards the end of this century ranges from 10 % to 40 % depending on the emission scenario.
Jingyu Wang, Gabriel Chiodo, Blanca Ayarzagüena, William T. Ball, Mohamadou Diallo, Birgit Hassler, James Keeble, Peer Nowack, Clara Orbe, Sandro Vattioni, and Timofei Sukhodolov
Atmos. Chem. Phys., 25, 17819–17844, https://doi.org/10.5194/acp-25-17819-2025, https://doi.org/10.5194/acp-25-17819-2025, 2025
Short summary
Short summary
We analyzed the ozone response under elevated CO2 using the data from CMIP6 DECK experiments. We then examined the relations between ozone response and changes in temperature and circulation to identify the drivers of ozone change. The climate feedback of ozone is investigated through offline calculations and by comparing models with and without interactive chemistry. We find that ozone–climate interactions are important for Earth system models and thus should be considered in future model development.
Alex C. Ruane, Charlotte L. Pascoe, Claas Teichmann, David J. Brayshaw, Carlo Buontempo, Ibrahima Diouf, Jesus Fernandez, Paula L. M. Gonzalez, Birgit Hassler, Vanessa Hernaman, Ulas Im, Doroteaciro Iovino, Martin Juckes, Iréne L. Lake, Timothy Lam, Xiaomao Lin, Jiafu Mao, Negin Nazarian, Sylvie Parey, Indrani Roy, Wan-Ling Tseng, Briony Turner, Andrew Wiebe, Lei Zhao, and Damaris Zurell
Geosci. Model Dev., 18, 9497–9540, https://doi.org/10.5194/gmd-18-9497-2025, https://doi.org/10.5194/gmd-18-9497-2025, 2025
Short summary
Short summary
This paper describes how the Coupled Model Intercomparison Project organized its 7th phase (CMIP7) to encourage the production of Earth system model outputs relevant for impacts and adaptation. Community engagement identified 13 opportunities for application across human and natural systems, 60 variable groups and 539 unique variables. We also show how simulations can more efficiently meet applications needs by targeting appropriate resolution, time slices, experiments and variable groups.
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.
Manuel Schlund, Bouwe Andela, Jörg Benke, Ruth Comer, Birgit Hassler, Emma Hogan, Peter Kalverla, Axel Lauer, Bill Little, Saskia Loosveldt Tomas, Francesco Nattino, Patrick Peglar, Valeriu Predoi, Stef Smeets, Stephen Worsley, Martin Yeo, and Klaus Zimmermann
Geosci. Model Dev., 18, 4009–4021, https://doi.org/10.5194/gmd-18-4009-2025, https://doi.org/10.5194/gmd-18-4009-2025, 2025
Short summary
Short summary
The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth system models. Here, we describe recent significant improvements of ESMValTool’s computational efficiency including parallel, out-of-core, and distributed computing. Evaluations with the enhanced version of ESMValTool are faster, use less computational resources, and can handle input data larger than the available memory.
Pauline Bonnet, Lorenzo Pastori, Mierk Schwabe, Marco Giorgetta, Fernando Iglesias-Suarez, and Veronika Eyring
Geosci. Model Dev., 18, 3681–3706, https://doi.org/10.5194/gmd-18-3681-2025, https://doi.org/10.5194/gmd-18-3681-2025, 2025
Short summary
Short summary
Tuning a climate model means adjusting uncertain parameters in the model to best match observations like the global radiation balance and cloud cover. This is usually done by running many simulations of the model with different settings, which can be time-consuming and relies heavily on expert knowledge. To make this process faster and more objective, we developed a machine learning emulator to create a large ensemble and apply a method called history matching to find the best settings.
Kevin Debeire, Lisa Bock, Peer Nowack, Jakob Runge, and Veronika Eyring
Earth Syst. Dynam., 16, 607–630, https://doi.org/10.5194/esd-16-607-2025, https://doi.org/10.5194/esd-16-607-2025, 2025
Short summary
Short summary
Projecting future precipitation is essential for preparing for climate change, but current climate models still have large uncertainties, especially over land. This study presents a new method to improve precipitation projections by identifying which models best capture key climate patterns. By giving more weight to models that better represent these patterns, our approach leads to more reliable future precipitation projections over land.
Axel Lauer, Lisa Bock, Birgit Hassler, Patrick Jöckel, Lukas Ruhe, and Manuel Schlund
Geosci. Model Dev., 18, 1169–1188, https://doi.org/10.5194/gmd-18-1169-2025, https://doi.org/10.5194/gmd-18-1169-2025, 2025
Short summary
Short summary
Earth system models are important tools to improve our understanding of current climate and to project climate change. Thus, it is crucial to understand possible shortcomings in the models. New features of the ESMValTool software package allow one to compare and visualize a model's performance with respect to reproducing observations in the context of other climate models in an easy and user-friendly way. We aim to help model developers assess and monitor climate simulations more efficiently.
Paul J. Durack, Karl E. Taylor, Peter J. Gleckler, Gerald A. Meehl, Bryan N. Lawrence, Curt Covey, Ronald J. Stouffer, Guillaume Levavasseur, Atef Ben-Nasser, Sebastien Denvil, Martina Stockhause, Jonathan M. Gregory, Martin Juckes, Sasha K. Ames, Fabrizio Antonio, David C. Bader, John P. Dunne, Daniel Ellis, Veronika Eyring, Sandro L. Fiore, Sylvie Joussaume, Philip Kershaw, Jean-Francois Lamarque, Michael Lautenschlager, Jiwoo Lee, Chris F. Mauzey, Matthew Mizielinski, Paola Nassisi, Alessandra Nuzzo, Eleanor O’Rourke, Jeffrey Painter, Gerald L. Potter, Sven Rodriguez, and Dean N. Williams
EGUsphere, https://doi.org/10.5194/egusphere-2024-3729, https://doi.org/10.5194/egusphere-2024-3729, 2025
Short summary
Short summary
CMIP6 was the most expansive and ambitious Model Intercomparison Project (MIP), the latest in a history, extending four decades. CMIP engaged a growing community focused on improving climate understanding, and quantifying and attributing observed climate change being experienced today. The project's profound impact is due to the combining the latest climate science and technology, enabling the latest-generation climate simulations and increasing community attention in every successive phase.
Bettina K. Gier, Manuel Schlund, Pierre Friedlingstein, Chris D. Jones, Colin Jones, Sönke Zaehle, and Veronika Eyring
Biogeosciences, 21, 5321–5360, https://doi.org/10.5194/bg-21-5321-2024, https://doi.org/10.5194/bg-21-5321-2024, 2024
Short summary
Short summary
This study investigates present-day carbon cycle variables in CMIP5 and CMIP6 simulations. Overall, CMIP6 models perform better but also show many remaining biases. A significant improvement in the simulation of photosynthesis in models with a nitrogen cycle is found, with only small differences between emission- and concentration-based simulations. Thus, we recommend using emission-driven simulations in CMIP7 by default and including the nitrogen cycle in all future carbon cycle models.
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.
Jacob A. Nelson, Sophia Walther, Fabian Gans, Basil Kraft, Ulrich Weber, Kimberly Novick, Nina Buchmann, Mirco Migliavacca, Georg Wohlfahrt, Ladislav Šigut, Andreas Ibrom, Dario Papale, Mathias Göckede, Gregory Duveiller, Alexander Knohl, Lukas Hörtnagl, Russell L. Scott, Jiří Dušek, Weijie Zhang, Zayd Mahmoud Hamdi, Markus Reichstein, Sergio Aranda-Barranco, Jonas Ardö, Maarten Op de Beeck, Dave Billesbach, David Bowling, Rosvel Bracho, Christian Brümmer, Gustau Camps-Valls, Shiping Chen, Jamie Rose Cleverly, Ankur Desai, Gang Dong, Tarek S. El-Madany, Eugenie Susanne Euskirchen, Iris Feigenwinter, Marta Galvagno, Giacomo A. Gerosa, Bert Gielen, Ignacio Goded, Sarah Goslee, Christopher Michael Gough, Bernard Heinesch, Kazuhito Ichii, Marcin Antoni Jackowicz-Korczynski, Anne Klosterhalfen, Sara Knox, Hideki Kobayashi, Kukka-Maaria Kohonen, Mika Korkiakoski, Ivan Mammarella, Mana Gharun, Riccardo Marzuoli, Roser Matamala, Stefan Metzger, Leonardo Montagnani, Giacomo Nicolini, Thomas O'Halloran, Jean-Marc Ourcival, Matthias Peichl, Elise Pendall, Borja Ruiz Reverter, Marilyn Roland, Simone Sabbatini, Torsten Sachs, Marius Schmidt, Christopher R. Schwalm, Ankit Shekhar, Richard Silberstein, Maria Lucia Silveira, Donatella Spano, Torbern Tagesson, Gianluca Tramontana, Carlo Trotta, Fabio Turco, Timo Vesala, Caroline Vincke, Domenico Vitale, Enrique R. Vivoni, Yi Wang, William Woodgate, Enrico A. Yepez, Junhui Zhang, Donatella Zona, and Martin Jung
Biogeosciences, 21, 5079–5115, https://doi.org/10.5194/bg-21-5079-2024, https://doi.org/10.5194/bg-21-5079-2024, 2024
Short summary
Short summary
The movement of water, carbon, and energy from the Earth's surface to the atmosphere, or flux, is an important process to understand because it impacts our lives. Here, we outline a method called FLUXCOM-X to estimate global water and CO2 fluxes based on direct measurements from sites around the world. We go on to demonstrate how these new estimates of net CO2 uptake/loss, gross CO2 uptake, total water evaporation, and transpiration from plants compare to previous and independent estimates.
Francesco Martinuzzi, Miguel D. Mahecha, Gustau Camps-Valls, David Montero, Tristan Williams, and Karin Mora
Nonlin. Processes Geophys., 31, 535–557, https://doi.org/10.5194/npg-31-535-2024, https://doi.org/10.5194/npg-31-535-2024, 2024
Short summary
Short summary
We investigated how machine learning can forecast extreme vegetation responses to weather. Examining four models, no single one stood out as the best, though "echo state networks" showed minor advantages. Our results indicate that while these tools are able to generally model vegetation states, they face challenges under extreme conditions. This underlines the potential of artificial intelligence in ecosystem modeling, also pinpointing areas that need further research.
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.
Arndt Kaps, Axel Lauer, Rémi Kazeroni, Martin Stengel, and Veronika Eyring
Earth Syst. Sci. Data, 16, 3001–3016, https://doi.org/10.5194/essd-16-3001-2024, https://doi.org/10.5194/essd-16-3001-2024, 2024
Short summary
Short summary
CCClim displays observations of clouds in terms of cloud classes that have been in use for a long time. CCClim is a machine-learning-powered product based on multiple existing observational products from different satellites. We show that the cloud classes in CCClim are physically meaningful and can be used to study cloud characteristics in more detail. The goal of this is to make real-world clouds more easily understandable to eventually improve the simulation of clouds in climate models.
Soufiane Karmouche, Evgenia Galytska, Gerald A. Meehl, Jakob Runge, Katja Weigel, and Veronika Eyring
Earth Syst. Dynam., 15, 689–715, https://doi.org/10.5194/esd-15-689-2024, https://doi.org/10.5194/esd-15-689-2024, 2024
Short summary
Short summary
This study explores Atlantic–Pacific interactions and their response to external factors. Causal analysis of 1950–2014 data reveals a shift from a Pacific- to an Atlantic-driven regime. Contrasting impacts between El Niño and tropical Atlantic temperatures are highlighted, along with different pathways connecting the two oceans. The findings also suggest increasing remote contributions of forced Atlantic responses in modulating local Pacific responses during the most recent analyzed decades.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data, 16, 2113–2122, https://doi.org/10.5194/essd-16-2113-2024, https://doi.org/10.5194/essd-16-2113-2024, 2024
Short summary
Short summary
To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Michael Kiefer, Dale F. Hurst, Gabriele P. Stiller, Stefan Lossow, Holger Vömel, John Anderson, Faiza Azam, Jean-Loup Bertaux, Laurent Blanot, Klaus Bramstedt, John P. Burrows, Robert Damadeo, Bianca Maria Dinelli, Patrick Eriksson, Maya García-Comas, John C. Gille, Mark Hervig, Yasuko Kasai, Farahnaz Khosrawi, Donal Murtagh, Gerald E. Nedoluha, Stefan Noël, Piera Raspollini, William G. Read, Karen H. Rosenlof, Alexei Rozanov, Christopher E. Sioris, Takafumi Sugita, Thomas von Clarmann, Kaley A. Walker, and Katja Weigel
Atmos. Meas. Tech., 16, 4589–4642, https://doi.org/10.5194/amt-16-4589-2023, https://doi.org/10.5194/amt-16-4589-2023, 2023
Short summary
Short summary
We quantify biases and drifts (and their uncertainties) between the stratospheric water vapor measurement records of 15 satellite-based instruments (SATs, with 31 different retrievals) and balloon-borne frost point hygrometers (FPs) launched at 27 globally distributed stations. These comparisons of measurements during the period 2000–2016 are made using robust, consistent statistical methods. With some exceptions, the biases and drifts determined for most SAT–FP pairs are < 10 % and < 1 % yr−1.
Soufiane Karmouche, Evgenia Galytska, Jakob Runge, Gerald A. Meehl, Adam S. Phillips, Katja Weigel, and Veronika Eyring
Earth Syst. Dynam., 14, 309–344, https://doi.org/10.5194/esd-14-309-2023, https://doi.org/10.5194/esd-14-309-2023, 2023
Short summary
Short summary
This study uses a causal discovery method to evaluate the ability of climate models to represent the interactions between the Atlantic multidecadal variability (AMV) and the Pacific decadal variability (PDV). The approach and findings in this study present a powerful methodology that can be applied to a number of environment-related topics, offering tremendous insights to improve the understanding of the complex Earth system and the state of the art of climate modeling.
Bryan J. Johnson, Patrick Cullis, John Booth, Irina Petropavlovskikh, Glen McConville, Birgit Hassler, Gary A. Morris, Chance Sterling, and Samuel Oltmans
Atmos. Chem. Phys., 23, 3133–3146, https://doi.org/10.5194/acp-23-3133-2023, https://doi.org/10.5194/acp-23-3133-2023, 2023
Short summary
Short summary
In 1986, soon after the discovery of the Antarctic ozone hole, NOAA began year-round ozonesonde observations at South Pole Station to measure vertical profiles of ozone and temperature from the surface to 35 km. Balloon-borne ozonesondes launched at this unique site allow for tracking all phases of the yearly springtime ozone hole beginning in late winter and after sunrise, when rapid ozone depletion begins over the South Pole throughout the month of September.
Manuel Schlund, Birgit Hassler, Axel Lauer, Bouwe Andela, Patrick Jöckel, Rémi Kazeroni, Saskia Loosveldt Tomas, Brian Medeiros, Valeriu Predoi, Stéphane Sénési, Jérôme Servonnat, Tobias Stacke, Javier Vegas-Regidor, Klaus Zimmermann, and Veronika Eyring
Geosci. Model Dev., 16, 315–333, https://doi.org/10.5194/gmd-16-315-2023, https://doi.org/10.5194/gmd-16-315-2023, 2023
Short summary
Short summary
The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for routine evaluation of Earth system models. Originally, ESMValTool was designed to process reformatted output provided by large model intercomparison projects like the Coupled Model Intercomparison Project (CMIP). Here, we describe a new extension of ESMValTool that allows for reading and processing native climate model output, i.e., data that have not been reformatted before.
William G. Read, Gabriele Stiller, Stefan Lossow, Michael Kiefer, Farahnaz Khosrawi, Dale Hurst, Holger Vömel, Karen Rosenlof, Bianca M. Dinelli, Piera Raspollini, Gerald E. Nedoluha, John C. Gille, Yasuko Kasai, Patrick Eriksson, Christopher E. Sioris, Kaley A. Walker, Katja Weigel, John P. Burrows, and Alexei Rozanov
Atmos. Meas. Tech., 15, 3377–3400, https://doi.org/10.5194/amt-15-3377-2022, https://doi.org/10.5194/amt-15-3377-2022, 2022
Short summary
Short summary
This paper attempts to provide an assessment of the accuracy of 21 satellite-based instruments that remotely measure atmospheric humidity in the upper troposphere of the Earth's atmosphere. The instruments made their measurements from 1984 to the present time; however, most of these instruments began operations after 2000, and only a few are still operational. The objective of this study is to quantify the accuracy of each satellite humidity data set.
Cited articles
Adams, D.: The Hitch Hiker's Guide to the Galaxy, Pan Original, Pan Books, London, ISBN 978-3320258641, 1979. a
Alaya, M. A. B., Zwiers, F., and Zhang, X.: An evaluation of block-maximum-based estimation of very long return period precipitation extremes with a large ensemble climate simulation, J. Climate, 33, 6957–6970, https://doi.org/10.1175/JCLI-D-19-0011.1, 2020. a
Albuquerque Filho, J. E. D., Brandão, L. C. P., Fernandes, B. J. T., and Maciel, A. M. A.: A review of neural networks for anomaly detection, IEEE Access, 10, 112342–112367, https://doi.org/10.1109/ACCESS.2022.3216007, 2022. a
Behrens, G., Beucler, T., Gentine, P., Iglesias-Suarez, F., Pritchard, M., and Eyring, V.: Non-linear dimensionality reduction with a variational encoder decoder to understand convective processes in climate models, J. Adv. Model. Earth Sy., 14, e2022MS003130, https://doi.org/10.1029/2022MS003130, 2022. a, b, c
Beobide-Arsuaga, G., Suarez-Gutierrez, L., Barkhordarian, A., Olonscheck, D., and Baehr, J.: Increasing central and northern European summer heatwave intensity due to forced changes in internal variability, Nat. Commun., 16, 9485, https://doi.org/10.1038/s41467-025-65392-w, 2025. a, b, c, d
Bischof, S., Pilch Kedzierski, R., Hänsch, M., Wahl, S., and Matthes, K.: The role of the North Atlantic for heat wave characteristics in Europe, an ECHAM6 study, Geophys. Res. Lett., 50, e2023GL105280, https://doi.org/10.1029/2023GL105280, 2023. a, b, c
Boboc, L., Dima, M., Vaideanu, P., and Ionita, M.: Trends and variability of heat waves in Europe and the association with large-scale circulation patterns, Weather and Climate Extremes, 49, 100794, https://doi.org/10.1016/j.wace.2025.100794, 2025. a, b
Boni, Z., Bieńkowska, Z., Chwałczyk, F., Jancewicz, B., Marginean, I., and Serrano, P. Y.: What is a heat(wave)? An interdisciplinary perspective, Climatic Change, 176, 129, https://doi.org/10.1007/s10584-023-03592-3, 2023. a, b
Brunner, L. and Voigt, A.: Pitfalls in diagnosing temperature extremes, Nat. Commun., 15, 2087, https://doi.org/10.1038/s41467-024-46349-x, 2024. a, b
Burke, M., Hsiang, S. M., and Miguel, E.: Global non-linear effect of temperature on economic production, Nature, 527, 235–239, https://doi.org/10.1038/nature15725, 2015. a
Camps-Valls, G., Fernandez-Torres, M. Á., Cohrs, K.-H., Höhl, A., Castelletti, A., Pacal, A., Robin, C., Martinuzzi, F., Papoutsis, I., Prapas, I., Pérez-Aracil, J., Weigel, K., Gonzalez-Calabuig, M., Reichstein, M., Rabel, M., Giuliani, M., Mahecha, M. D., Popescu, O.-I., Pellicer-Valero, O. J., Ouala, S., Salcedo-Sanz, S., Sippel, S., Kondylatos, S., Happé, T., and Williams, T.: Artificial intelligence for modeling and understanding extreme weather and climate events, Nat. Commun., 16, 1919, https://doi.org/10.1038/s41467-025-56573-8, 2025. a, b, c, d, e
Carril, A. F., Gualdi, S., Cherchi, A., and Navarra, A.: Heatwaves in Europe: areas of homogeneous variability and links with the regional to large-scale atmospheric and SSTs anomalies, Clim. Dynam., 30, 77–98, https://doi.org/10.1007/s00382-007-0274-5, 2008. a, b
Chen, S., Ji, P., Yuan, S., Xu, Q., Lu, C., and Zhang, J.: Contrary effects of soil moisture-atmosphere feedback on dry and humid heatwaves, Nat. Commun., 17, 2626, https://doi.org/10.1038/s41467-026-70210-y, 2026. a, b
Chitsaz, F., Gohari, A., Najafi, M. R., Zareian, M. J., and Haghighi, A. T.: Heatwave duration and heating rate in a non-stationary climate: spatiotemporal pattern and key drivers, Earths Future, 11, e2023EF003995, https://doi.org/10.1029/2023EF003995, 2023. a
Coumou, D. and Rahmstorf, S.: A decade of weather extremes, Nat. Clim. Change, 2, 491–496, https://doi.org/10.1038/nclimate1452, 2012. a
De Maesschalck, R., Jouan-Rimbaud, D., and Massart, D. L.: The Mahalanobis distance, Chemometr. Intell. Lab., 50, 1–18, https://doi.org/10.1016/S0169-7439(99)00047-7, 2000. a
Debeire, K., Bock, L., Nowack, P., Runge, J., and Eyring, V.: Constraining uncertainty in projected precipitation over land with causal discovery, Earth Syst. Dynam., 16, 607–630, https://doi.org/10.5194/esd-16-607-2025, 2025. a
Domeisen, D. I. V., Eltahir, E. A. B., Fischer, E. M., Knutti, R., Perkins-Kirkpatrick, S. E., Schär, C., Seneviratne, S. I., Weisheimer, A., and Wernli, H.: Prediction and projection of heatwaves, Nature Reviews Earth and Environment, https://doi.org/10.1038/s43017-022-00371-z, 2022. a, b, c
Dong, J., Brönnimann, S., Hu, T., Cheng, X., Liu, Y., and Peng, J.: Trends of the intra-annual onset and end of humid heatwaves in the Northern Hemisphere, Earths Future, 12, e2024EF005163, https://doi.org/10.1029/2024EF005163, 2024. a
Dosio, A., Mentaschi, L., Fischer, E. M., and Wyser, K.: Extreme heat waves under 1.5 °C and 2 °C global warming, Environ. Res. Lett., 13, 054006, https://doi.org/10.1088/1748-9326/aab827, 2018. a
Elguindi, N., Rauscher, S. A., and Giorgi, F.: Historical and future changes in maximum and minimum temperature records over Europe, Climatic Change, 117, 415–431, https://doi.org/10.1007/s10584-012-0528-z, 2013. a, b, c
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
Fan, Y., Wen, G., Li, D., Qiu, S., Levine, M. D., and Xiao, F.: Video anomaly detection and localization via Gaussian mixture fully convolutional variational autoencoder, Comput. Vis. Image Und., 195, 102920, https://doi.org/10.1016/j.cviu.2020.102920, 2020. a
Fischer, E. M. and Knutti, R.: Detection of spatially aggregated changes in temperature and precipitation extremes, Geophys. Res. Lett., 41, https://doi.org/10.1002/2013GL058499, 2014. a
Fischer, E. M., Sippel, S., and Knutti, R.: Increasing probability of record-shattering climate extremes, Nat. Clim. Change, 11, 689–695, https://doi.org/10.1038/s41558-021-01092-9, 2021. a, b, c
García-León, D., Casanueva, A., Standardi, G., Burgstall, A., Flouris, A. D., and Nybo, L.: Current and projected regional economic impacts of heatwaves in Europe, Nat. Commun., 12, 5807, https://doi.org/10.1038/s41467-021-26050-z, 2021. a
Happé, T., Wijnands, J. S., Fernández-Torres, M. Á., Scussolini, P., Muntjewerf, L., and Coumou, D.: Detecting spatiotemporal dynamics of Western European heatwaves using deep learning, Artificial Intelligence for the Earth Systems, 3, e230107, https://doi.org/10.1175/AIES-D-23-0107.1, 2024. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w
Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A. K., and Davis, L. S.: Learning Temporal Regularity in Video Sequences, arXiv [preprint], https://doi.org/10.48550/arXiv.1604.04574, 2016. 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., 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.: Complete ERA5 from 1940: Fifth generation of ECMWF atmospheric reanalyses of the global climate, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), Data distribution by the German Climate Computing Center (DKRZ) [data set], https://doi.org/10.24381/cds.143582cf, 2017. a, b, c, d
Heuer, H., Schwabe, M., Gentine, P., Giorgetta, M. A., and Eyring, V.: Interpretable multiscale machine learning-based parameterizations of convection for ICON, J. Adv. Model. Earth Sy., 16, e2024MS004398, https://doi.org/10.1029/2024MS004398, 2024. a
Horton, D. E., Johnson, N. C., Singh, D., Swain, D. L., Rajaratnam, B., and Diffenbaugh, N. S.: Contribution of changes in atmospheric circulation patterns to extreme temperature trends, Nature, 522, 465–469, https://doi.org/10.1038/nature14550, 2015. a
Huang, W. K., Stein, M. L., McInerney, D. J., Sun, S., and Moyer, E. J.: Estimating changes in temperature extremes from millennial-scale climate simulations using generalized extreme value (GEV) distributions, Adv. Stat. Clim. Meteorol. Oceanogr., 2, 79–103, https://doi.org/10.5194/ascmo-2-79-2016, 2016. a
Huntingford, C., Cox, P. M., Ritchie, P. D. L., Clarke, J. J., Parry, I. M., and Williamson, M. S.: Acceleration of daily land temperature extremes and correlations with surface energy fluxes, npj Climate and Atmospheric Science, 7, 1–10, https://doi.org/10.1038/s41612-024-00626-0, 2024. a
IPCC: Climate Change 2021: The Physical Science Basis, Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, https://doi.org/10.1017/9781009157896, 2021. a, b, c, d, e, f, g, h, i
Jézéquel, A., Yiou, P., and Radanovics, S.: Role of circulation in European heatwaves using flow analogues, Clim. Dynam., 50, 1145–1159, https://doi.org/10.1007/s00382-017-3667-0, 2018. a, b
Kautz, L.-A., Martius, O., Pfahl, S., Pinto, J. G., Ramos, A. M., Sousa, P. M., and Woollings, T.: Atmospheric blocking and weather extremes over the Euro-Atlantic sector – a review, Weather Clim. Dynam., 3, 305–336, https://doi.org/10.5194/wcd-3-305-2022, 2022. a
Kharin, V. V., Flato, G. M., Zhang, X., Gillett, N. P., Zwiers, F., and Anderson, K. J.: Risks from climate extremes change differently from 1.5 °C to 2.0°C depending on rarity, Earths Future, 6, 704–715, https://doi.org/10.1002/2018EF000813, 2018. a
Kim, J.-Y. and Seo, K.-H.: Physical mechanisms for the dominant summertime high-latitude atmospheric teleconnection pattern and the related Northern Eurasian climates, Environ. Res. Lett., 18, 104022, https://doi.org/10.1088/1748-9326/acfa13, 2023. a
King, A. D. and Harrington, L. J.: The inequality of climate change from 1.5 to 2 °C of global warming, Geophys. Res. Lett., 45, 5030–5033, https://doi.org/10.1029/2018GL078430, 2018. a
Kornhuber, K., Petoukhov, V., Karoly, D., Petri, S., Rahmstorf, S., and Coumou, D.: Summertime planetary wave resonance in the Northern and Southern Hemispheres, J. Climate, https://doi.org/10.1175/JCLI-D-16-0703.1, 2017. a
Kornhuber, K., Bartusek, S., Seager, R., Schellnhuber, H. J., and Ting, M.: Global emergence of regional heatwave hotspots outpaces climate model simulations, P. Natl. Acad. Sci. USA, 121, e2411258121, https://doi.org/10.1073/pnas.2411258121, 2024. a, b
Kotz, M., Levermann, A., and Wenz, L.: The economic commitment of climate change, Nature, 628, 551–557, https://doi.org/10.1038/s41586-024-07219-0, 2024. a
Krüger, J., Kjellsson, J., Kedzierski, R. P., and Claus, M.: Connecting North Atlantic SST variability to European heat events over the past decades, Tellus A, 75, https://doi.org/10.16993/tellusa.3235, 2023. a, b, c
Lacombe, R., Grossman, H., Hendren, L., and Lüdeke, D.: Improving extreme weather events detection with light-weight neural networks, arXiv [preprint], https://doi.org/10.48550/arXiv.2304.00176, 2023. a
Lai, C.-Y., Hassanzadeh, P., Sheshadri, A., Sonnewald, M., Ferrari, R., and Balaji, V.: Machine learning for climate physics and simulations, Annu. Rev. Conden. Ma. P., 16, 343–365, https://doi.org/10.1146/annurev-conmatphys-043024-114758, 2025. a
Leach, N. J., Weisheimer, A., Allen, M. R., and Palmer, T.: Forecast-based attribution of a winter heatwave within the limit of predictability, P. Natl. Acad. Sci. USA, 118, e2112087118, https://doi.org/10.1073/pnas.2112087118, 2021. a
Lee, D. Y., Yeh, S.-W., Lee, Y.-H., Cai, W., Wang, G., and Yang, Y.-M.: The emergence of a dipole-like mode in Arctic atmospheric circulation conducive to European heat waves, Communications Earth and Environment, 6, 1–9, https://doi.org/10.1038/s43247-025-02020-x, 2025. a
Li, C., Zwiers, F., Zhang, X., Li, G., Sun, Y., and Wehner, M.: Changes in annual extremes of daily temperature and precipitation in CMIP6 models, J. Climate, 34, 3441–3460, https://doi.org/10.1175/JCLI-D-19-1013.1, 2021. a, b
Lindenlaub, L., Weigel, K., Hassler, B., Jones, C., and Eyring, V.: Characteristics of agricultural droughts in CMIP6 historical simulations and future projections, Earth Syst. Dynam., 17, 81–105, https://doi.org/10.5194/esd-17-81-2026, 2026. a
Lindhe, A., Ringqvist, C., and Hult, H.: Variational Auto Encoder Gradient Clustering, arXiv [preprint], https://doi.org/10.48550/arXiv.2105.06246, 2021. a, b
Lipfert, L., Hand, R., and Brönnimann, S.: A global assessment of heatwaves since 1850 in different observational and model data sets, Geophys. Res. Lett., 51, e2023GL106212, https://doi.org/10.1029/2023GL106212, 2024. a
Lynas, M., Houlton, B. Z., and Perry, S.: Greater than 99 % consensus on human caused climate change in the peer-reviewed scientific literature, Environ. Res. Lett., 16, 114005, https://doi.org/10.1088/1748-9326/ac2966, 2021. a
López-Bueno, J. A., Navas-Martín, M. A., Linares, C., Mirón, I. J., Luna, M. Y., Sánchez-Martínez, G., Culqui, D., and Díaz, J.: Analysis of the impact of heat waves on daily mortality in urban and rural areas in Madrid, Environ. Res., 195, 110892, https://doi.org/10.1016/j.envres.2021.110892, 2021. a
Materia, S., García, L. P., van Straaten, C., O, S., Mamalakis, A., Cavicchia, L., Coumou, D., de Luca, P., Kretschmer, M., and Donat, M.: Artificial intelligence for climate prediction of extremes: state of the art, challenges, and future perspectives, WIREs Clim. Change, 15, e914, https://doi.org/10.1002/wcc.914, 2024. a
Mazdiyasni, O., Sadegh, M., Chiang, F., and AghaKouchak, A.: Heat wave intensity duration frequency curve: a multivariate approach for hazard and attribution analysis, Sci. Rep., 9, 14117, https://doi.org/10.1038/s41598-019-50643-w, 2019. a
McKinnon, K. A., Simpson, I. R., and Williams, A. P.: The pace of change of summertime temperature extremes, P. Natl. Acad. Sci. USA, 121, e2406143121, https://doi.org/10.1073/pnas.2406143121, 2024. a
McPhillips, L. E., Chang, H., Chester, M. V., Depietri, Y., Friedman, E., Grimm, N. B., Kominoski, J. S., McPhearson, T., Méndez-Lázaro, P., Rosi, E. J., and Shafiei Shiva, J.: Defining extreme events: a cross-disciplinary review, Earths Future, 6, 441–455, https://doi.org/10.1002/2017EF000686, 2018. a, b
Mooers, G., Pritchard, M., Beucler, T., Srivastava, P., Mangipudi, H., Peng, L., Gentine, P., and Mandt, S.: Comparing storm resolving models and climates via unsupervised machine learning, Sci. Rep., 13, 22365, https://doi.org/10.1038/s41598-023-49455-w, 2023. a, b
Mora, C., Counsell, C. W., Bielecki, C. R., and Louis, L. V.: Twenty-seven ways a heat wave can kill you, Circ.-Cardiovasc. Qual., 10, e004233, https://doi.org/10.1161/CIRCOUTCOMES.117.004233, 2017a. a, b
Mora, C., Dousset, B., Caldwell, I. R., Powell, F. E., Geronimo, R. C., Bielecki, C. R., Counsell, C. W. W., Dietrich, B. S., Johnston, E. T., Louis, L. V., Lucas, M. P., McKenzie, M. M., Shea, A. G., Tseng, H., Giambelluca, T. W., Leon, L. R., Hawkins, E., and Trauernicht, C.: Global risk of deadly heat, Nat. Clim. Change, 7, 501–506, https://doi.org/10.1038/nclimate3322, 2017b. a, b
Nassif, A. B., Talib, M. A., Nasir, Q., and Dakalbab, F. M.: Machine learning for anomaly detection: a systematic review, IEEE Access, 9, 78658–78700, https://doi.org/10.1109/ACCESS.2021.3083060, 2021. a
Newell, R. G., Prest, B. C., and Sexton, S. E.: The GDP-temperature relationship: implications for climate change damages, J. Environ. Econ. Manage., 108, 102445, https://doi.org/10.1016/j.jeem.2021.102445, 2021. a
Newman, R. and Noy, I.: The global costs of extreme weather that are attributable to climate change, Nat. Commun., 14, 6103, https://doi.org/10.1038/s41467-023-41888-1, 2023. a
Oliveira, D. A. B., Diaz, J. G., Zadrozny, B., Watson, C. D., and Zhu, X. X.: Controlling weather field synthesis using variational autoencoders, in: IGARSS 2022–2022 IEEE International Geoscience and Remote Sensing Symposium, https://doi.org/10.1109/IGARSS46834.2022.9884668, 5027–5030, 2022. a
Pang, G., Shen, C., Cao, L., and Hengel, A. V. D.: Deep learning for anomaly detection: a review, ACM Comput. Surv., 54, 1–38, https://doi.org/10.1145/3439950, 2021. a, b
Patterson, M.: North-West Europe hottest days are warming twice as fast as mean summer days, Geophys. Res. Lett., 50, e2023GL102757, https://doi.org/10.1029/2023GL102757, 2023. a
Paçal, A.: EyringMLClimateGroup/pacal25esd_UnderstandingHeatwaves_VAE: Understanding European Heatwaves with Variational Autoencoders (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.15828268, 2025. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.: Scikit-learn: machine learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011. a
Perkins, S. E.: A review on the scientific understanding of heatwaves – Their measurement, driving mechanisms, and changes at the global scale, Atmos. Res., 164–165, 242–267, https://doi.org/10.1016/j.atmosres.2015.05.014, 2015. a
Perkins-Kirkpatrick, S. E. and Lewis, S. C.: Increasing trends in regional heatwaves, Nat. Commun., 11, 3357, https://doi.org/10.1038/s41467-020-16970-7, 2020. a, b, c, d
Perkins-Kirkpatrick, S. E., Fischer, E. M., Angélil, O., and Gibson, P. B.: The influence of internal climate variability on heatwave frequency trends, Environ. Res. Lett., 12, 044005, https://doi.org/10.1088/1748-9326/aa63fe, 2017. a
Prabhat, Kashinath, K., Mudigonda, M., Kim, S., Kapp-Schwoerer, L., Graubner, A., Karaismailoglu, E., von Kleist, L., Kurth, T., Greiner, A., Mahesh, A., Yang, K., Lewis, C., Chen, J., Lou, A., Chandran, S., Toms, B., Chapman, W., Dagon, K., Shields, C. A., O'Brien, T., Wehner, M., and Collins, W.: ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather, Geosci. Model Dev., 14, 107–124, https://doi.org/10.5194/gmd-14-107-2021, 2021. a
Rahmstorf, S. and Coumou, D.: Increase of extreme events in a warming world, P. Natl. Acad. Sci. USA, 108, 17905–17909, https://doi.org/10.1073/pnas.1101766108, 2011. a
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195–204, https://doi.org/10.1038/s41586-019-0912-1, 2019. a
Reid, P. C., Hari, R. E., Beaugrand, G., Livingstone, D. M., Marty, C., Straile, D., Barichivich, J., Goberville, E., Adrian, R., Aono, Y., Brown, R., Foster, J., Groisman, P., Hélaouët, P., Hsu, H.-H., Kirby, R., Knight, J., Kraberg, A., Li, J., Lo, T.-T., Myneni, R. B., North, R. P., Pounds, J. A., Sparks, T., Stübi, R., Tian, Y., Wiltshire, K. H., Xiao, D., and Zhu, Z.: Global impacts of the 1980s regime shift, Glob. Change Biol., 22, 682–703, https://doi.org/10.1111/gcb.13106, 2016. a, b
Ronco, M., Tárraga, J. M., Muñoz, J., Piles, M., Marco, E. S., Wang, Q., Espinosa, M. T. M., Ponserre, S., and Camps-Valls, G.: Exploring interactions between socioeconomic context and natural hazards on human population displacement, Nat. Commun., 14, 8004, https://doi.org/10.1038/s41467-023-43809-8, 2023. a
Rousi, E., Kornhuber, K., Beobide-Arsuaga, G., Luo, F., and Coumou, D.: Accelerated western European heatwave trends linked to more-persistent double jets over Eurasia, Nat. Commun., 13, 3851, https://doi.org/10.1038/s41467-022-31432-y, 2022. a
Ruff, L., Kauffmann, J. R., Vandermeulen, R. A., Montavon, G., Samek, W., Kloft, M., Dietterich, T. G., and Müller, K.-R.: A unifying review of deep and shallow anomaly detection, P. IEEE, 109, 756–795, https://doi.org/10.1109/JPROC.2021.3052449, 2021. a
Russo, E. and Domeisen, D. I. V.: Increasing intensity of extreme heatwaves: the crucial role of metrics, Geophys. Res. Lett., 50, e2023GL103540, https://doi.org/10.1029/2023GL103540, 2023. a, b
Russo, S., Sillmann, J., and Fischer, E. M.: Top ten European heatwaves since 1950 and their occurrence in the coming decades, Environ. Res. Lett., 10, 124003, https://doi.org/10.1088/1748-9326/10/12/124003, 2015. a, b
Russo, S., Sillmann, J., and Sterl, A.: Humid heat waves at different warming levels, Sci. Rep., 7, 7477, https://doi.org/10.1038/s41598-017-07536-7, 2017. a
Salcedo-Sanz, S., Pérez-Aracil, J., Ascenso, G., Del Ser, J., Casillas-Pérez, D., Kadow, C., Fister, D., Barriopedro, D., García-Herrera, R., Giuliani, M., and Castelletti, A.: Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review, Theor. Appl. Climatol., https://doi.org/10.1007/s00704-023-04571-5, 2023. a
Sander, J., Ester, M., Kriegel, H.-P., and Xu, X.: Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications, Data Min. Knowl. Disc., 2, 169–194, https://doi.org/10.1023/A:1009745219419, 1998. a, b
Sardeshmukh, P. D., Compo, G. P., and Penland, C.: Need for caution in interpreting extreme weather statistics, J. Climate, 28, 9166–9187, https://doi.org/10.1175/JCLI-D-15-0020.1, 2015. a
Schielicke, L. and Pfahl, S.: European heatwaves in present and future climate simulations: a Lagrangian analysis, Weather Clim. Dynam., 3, 1439–1459, https://doi.org/10.5194/wcd-3-1439-2022, 2022. a
Schumacher, D. L., Singh, J., Hauser, M., Fischer, E. M., Wild, M., and Seneviratne, S. I.: Exacerbated summer European warming not captured by climate models neglecting long-term aerosol changes, Communications Earth and Environment, 5, 182, https://doi.org/10.1038/s43247-024-01332-8, 2024. a
Shamekh, S., Lamb, K. D., Huang, Y., and Gentine, P.: Implicit learning of convective organization explains precipitation stochasticity, P. Natl. Acad. Sci. USA, 120, e2216158120, https://doi.org/10.1073/pnas.2216158120, 2023. a, b
Shaw, T. A., Arblaster, J. M., Birner, T., Butler, A. H., Domeisen, D. I. V., Garfinkel, C. I., Garny, H., Grise, K. M., and Karpechko, A. Y.: Emerging climate change signals in atmospheric circulation, AGU Advances, 5, e2024AV001297, https://doi.org/10.1029/2024AV001297, 2024. a, b, c
Singh, J., Sippel, S., and Fischer, E. M.: Circulation dampened heat extremes intensification over the Midwest USA and amplified over Western Europe, Communications Earth and Environment, 4, 432, https://doi.org/10.1038/s43247-023-01096-7, 2023. a
Skinner, C. B., Touma, D., Barlow, M., Singh, D., and King, T.: The spatial extent of heat waves has changed over the past four decades, Communications Earth and Environment, 6, 662, https://doi.org/10.1038/s43247-025-02661-y, 2025. a
Soci, C., Hersbach, H., Simmons, A., Poli, P., Bell, B., Berrisford, P., Horányi, A., Muñoz Sabater, J., Nicolas, J., Radu, R., Schepers, D., Villaume, S., Haimberger, L., Woollen, J., Buontempo, C., and Thépaut, J.: The ERA5 global reanalysis from 1940 to 2022, Q. J. Roy. Meteor. Soc., https://doi.org/10.1002/qj.4803, 2024. a, b
Spanjers, B., Beutner, E., Coumou, D., and Schaumburg, J.: Increased persistence of warm and wet winter weather in recent decades in north-western Europe, Communications Earth and Environment, 6, 760, https://doi.org/10.1038/s43247-025-02588-4, 2025. a, b
Spuler, F. R., Kretschmer, M., Kovalchuk, Y., Balmaseda, M. A., and Shepherd, T. G.: Identifying probabilistic weather regimes targeted to a local-scale impact variable, Environmental Data Science, 3, e25, https://doi.org/10.1017/eds.2024.29, 2024. a
Suarez-Gutierrez, L., Müller, W. A., Li, C., and Marotzke, J.: Dynamical and thermodynamical drivers of variability in European summer heat extremes, Clim. Dynam., 54, 4351–4366, https://doi.org/10.1007/s00382-020-05233-2, 2020. a
Sulikowska, A. and Wypych, A.: Summer temperature extremes in Europe: how does the definition affect the results?, Theor. Appl. Climatol., 141, 19–30, https://doi.org/10.1007/s00704-020-03166-8, 2020. a
Szwarcman, D., Guevara, J., Macedo, M. M. G., Zadrozny, B., Watson, C., Rosa, L., and Oliveira, D. A. B.: Quantizing reconstruction losses for improving weather data synthesis, Sci. Rep., 14, 3396, https://doi.org/10.1038/s41598-024-52773-2, 2024. a
Tian, Y., Kleidon, A., Lesk, C., Zhou, S., Luo, X., Ghausi, S. A., Wang, G., Zhong, D., and Zscheischler, J.: Characterizing heatwaves based on land surface energy budget, Communications Earth and Environment, 5, 1–9, https://doi.org/10.1038/s43247-024-01784-y, 2024. a
Tomczyk, A. M., Sulikowska, A., Bednorz, E., and Półrolniczak, M.: Atmospheric circulation conditions during winter warm spells in Central Europe, Nat. Hazards, 96, 1413–1428, https://doi.org/10.1007/s11069-019-03621-4, 2019. a, b, c
Träger-Chatterjee, C., Müller, R. W., and Bendix, J.: Analysis of extreme summers and prior late winter/spring conditions in central Europe, Nat. Hazards Earth Syst. Sci., 13, 1243–1257, https://doi.org/10.5194/nhess-13-1243-2013, 2013. a, b
Van Oldenborgh, G. J., Wehner, M. F., Vautard, R., Otto, F. E. L., Seneviratne, S. I., Stott, P. A., Hegerl, G. C., Philip, S. Y., and Kew, S. F.: Attributing and projecting heatwaves is hard: we can do better, Earths Future, 10, e2021EF002271, https://doi.org/10.1029/2021EF002271, 2022. a
Vautard, R., Cattiaux, J., Happé, T., Singh, J., Bonnet, R., Cassou, C., Coumou, D., D'Andrea, F., Faranda, D., Fischer, E., Ribes, A., Sippel, S., and Yiou, P.: Heat extremes in Western Europe increasing faster than simulated due to atmospheric circulation trends, Nat. Commun., 14, 6803, https://doi.org/10.1038/s41467-023-42143-3, 2023. a, b
Wang, C., Li, Z., Chen, Y., Li, Y., Ouyang, L., Zhu, J., Sun, F., Song, S., and Li, H.: Changes in global heatwave risk and its drivers over one century, Earths Future, 12, e2024EF004430, https://doi.org/10.1029/2024EF004430, 2024. a, b
Wang, J., Guan, Y., Wu, L., Guan, X., Cai, W., Huang, J., Dong, W., and Zhang, B.: Changing lengths of the four seasons by global warming, Geophys. Res. Lett., 48, e2020GL091753, https://doi.org/10.1029/2020GL091753, 2021. a, b
World Economic Forum: The Cost of Inaction: A CEO Guide to Navigating Climate Risk, in collaboration with Boston Consulting Group (BCG), https://www.weforum.org/publications/the-cost-of-inaction-a-ceo-guide-to-navigating-climate-risk/ (last access: 22 May 2025), 2024. a
Yuan, X., Chen, X., Ochege, F. U., Hamdi, R., Tabari, H., Li, B., He, B., Zhang, C., De Maeyer, P., and Luo, G.: Weakening of global terrestrial carbon sequestration capacity under increasing intensity of warm extremes, Nature Ecology and Evolution, 1–10, https://doi.org/10.1038/s41559-024-02576-5, 2024. a
Zhang, R., Sun, C., Zhu, J., Zhang, R., and Li, W.: Increased European heat waves in recent decades in response to shrinking Arctic sea ice and Eurasian snow cover, npj Climate and Atmospheric Science, 3, 1–9, https://doi.org/10.1038/s41612-020-0110-8, 2020. a
Zhang, X., Alexander, L., Hegerl, G. C., Jones, P., Tank, A. K., Peterson, T. C., Trewin, B., and Zwiers, F. W.: Indices for monitoring changes in extremes based on daily temperature and precipitation data, WIREs Clim. Change, 2, 851–870, https://doi.org/10.1002/wcc.147, 2011. a
Zhu, J.-J., Yang, M., and Ren, Z. J.: Machine learning in environmental research: common pitfalls and best practices, Environ. Sci. Technol., 57, 17671–17689, https://doi.org/10.1021/acs.est.3c00026, 2023. a
Zschenderlein, P., Pfahl, S., Wernli, H., and Fink, A. H.: A Lagrangian analysis of upper-tropospheric anticyclones associated with heat waves in Europe, Weather Clim. Dynam., 1, 191–206, https://doi.org/10.5194/wcd-1-191-2020, 2020. a
Short summary
Heatwaves are among the deadliest natural hazards, yet their evolving atmospheric drivers remain unclear. We apply an unsupervised machine learning model to reanalysis data to identify multivariate heatwave patterns over Europe. The model captures seasonally distinct regimes and reveals significant shifts between historical and recent events across all seasons, highlighting changes in heatwave dynamics over time.
Heatwaves are among the deadliest natural hazards, yet their evolving atmospheric drivers remain...
Altmetrics
Final-revised paper
Preprint