Articles | Volume 19, issue 15
https://doi.org/10.5194/gmd-19-7415-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-19-7415-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| Highlight paper
| 
13 Aug 2026
Methods for assessment of models | Highlight paper |
| 13 Aug 2026
| 13 Aug 2026
Rapid Evaluation Framework for the CMIP7 Assessment Fast Track
Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6301, United States of America
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Ranjini Swaminathan
CORRESPONDING AUTHOR
Department of Meteorology and National Centre for Earth Observation, University of Reading, Reading RG6 6UR, United Kingdom
Jared Lewis
Climate Resource, Fitzroy, Victoria, Australia
Bouwe Andela
Netherlands eScience Center, Amsterdam, the Netherlands
Nathan Collier
Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6301, United States of America
Dóra Hegedűs
RAL Space, Science and Technology Facilities Council, Rutherford Appleton Laboratory, Harwell Campus, Didcot OX11 OFD, United Kingdom
CMIP International Project Office, European Space Agency, Harwell Campus, Didcot OX11 OFD, United Kingdom
Jiwoo Lee
Lawrence Livermore National Laboratory, Livermore, California 94550, United States of America
Charlotte Pascoe
RAL Space, Science and Technology Facilities Council, Rutherford Appleton Laboratory, Harwell Campus, Didcot OX11 OFD, United Kingdom
Mika Pflüger
Climate Resource S GmbH, Berlin, Germany
Martina Stockhause
Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain
Paul Ullrich
Lawrence Livermore National Laboratory, Livermore, California 94550, United States of America
Min Xu
Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6301, United States of America
Lisa Bock
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Felicity Chun
Australia's Climate Simulator (ACCESS-NRI), Australian National University, Canberra, Australia
Bettina K. Gier
University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Douglas I. Kelley
United Kingdom Centre for Ecology & Hydrology, Wallingford, Oxfordshire OX10 8BB, United Kingdom
Axel Lauer
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Julien Lenhardt
Swedish Meteorological and Hydrological Institute, Norrköping, Sweden
Manuel Schlund
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Mohanan G. Sreeush
Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven, 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
Ed Blockley
Met Office, FitzRoy Road, Exeter EX1 3PB, United Kingdom
Rebecca Beadling
Department of Earth and Environmental Science, Temple University, Philadelphia, Pennsylvania 19122, United States of America
Romain Beucher
Australia's Climate Simulator (ACCESS-NRI), Australian National University, Canberra, Australia
Demiso D. Dugassa
Water Technology Institute, Faculty of Hydraulic and Water Resources Engineering, Arba Minch University, Arba Minch, Ethiopia
Valerio Lembo
Institute of Atmospheric Sciences and Climate, National Research Council of Italy (CNR-ISAC), 00133 Rome, Italy
Jianhua Lu
School of Atmospheric Sciences, Sun Yat-sen University & Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
Swen Brands
Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain
Jerry Tjiputra
NORCE Norwegian Research Centre, Bjerknes Centre for Climate Research, Bergen, Norway
Elizaveta Malinina
Climate Modelling Research Division, Environment and Climate Change Canada, Victoria, British Columbia, Canada
Brian Medeiros
NSF National Center for Atmospheric Research, Boulder, Colorado 80307, United States of America
Enrico Scoccimarro
CMCC Foundation – Euro-Mediterranean Center on Climate Change, 73100 Lecce LE, Italy
Jeremy Walton
Met Office, FitzRoy Road, Exeter EX1 3PB, United Kingdom
Phil Kershaw
RAL Space, Science and Technology Facilities Council, Rutherford Appleton Laboratory, Harwell Campus, Didcot OX11 OFD, United Kingdom
André Lanfer Marquez
National Institute for Space Research (INPE), São José dos Campos, Brazil
Malcolm J. Roberts
Met Office, FitzRoy Road, Exeter EX1 3PB, United Kingdom
Eleanor O'Rourke
CMIP International Project Office, European Space Agency, Harwell Campus, Didcot OX11 OFD, United Kingdom
Beth Dingley
CMIP International Project Office, European Space Agency, Harwell Campus, Didcot OX11 OFD, United Kingdom
Briony Turner
CMIP International Project Office, European Space Agency, Harwell Campus, Didcot OX11 OFD, United Kingdom
Helene Hewitt
Met Office, FitzRoy Road, Exeter EX1 3PB, United Kingdom
John P. Dunne
NOAA Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey 08540, United States of America
Related authors
Bin Wang, M. Luke McCormack, Daniel M. Ricciuto, Xiaojuan Yang, Min Xu, and Forrest M. Hoffman
EGUsphere, https://doi.org/10.5194/egusphere-2026-2759, https://doi.org/10.5194/egusphere-2026-2759, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Plants send about a third of their captured carbon below ground, to roots and soil fungi that take up water and nutrients. Most climate models still treat the entire root system as one uniform mass. We built a new land-model version that separates roots into three types: transport roots, absorptive roots, and fungal partners. Tested at twenty sites worldwide, it cut errors in simulated ecosystem carbon exchange by more than half, sharpening projections of how land will respond to climate change.
Elias C. Massoud, Nathan Collier, Yaoping Wang, Jiafu Mao, Adrian Harpold, Steven A. Kannenberg, Gerbrand Koren, Mukesh Kumar, Pushpendra Raghav, Pallav Ray, Mingjie Shi, Jing Tao, Sreedevi P. Vasu, Huiqi Wang, Qing Zhu, and Forrest M. Hoffman
Geosci. Model Dev., 19, 3427–3453, https://doi.org/10.5194/gmd-19-3427-2026, https://doi.org/10.5194/gmd-19-3427-2026, 2026
Short summary
Short summary
Soil moisture helps control how plants grow and how land exchanges water and energy with the atmosphere. We evaluated how well 16 Earth System Models represent soil moisture and its links to plant activity using a benchmarking system and observational data. Many models simulate surface soil moisture well but show larger errors deeper in the soil and in colder regions. Models that represent plant–soil water interactions more directly better capture how ecosystems respond to water availability.
Kamal Nyaupane, Umakant Mishra, Feng Tao, Kyongmin Yeo, William J. Riley, Forrest M. Hoffman, and Sagar Gautam
Biogeosciences, 21, 5173–5183, https://doi.org/10.5194/bg-21-5173-2024, https://doi.org/10.5194/bg-21-5173-2024, 2024
Short summary
Short summary
Representing soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon–climate feedbacks. Using machine learning, we develop and compare predictive relationships in observations (Obs) and ESMs. We find different relationships between environmental factors and SOC stocks in Obs and ESMs. SOC prediction in ESMs may be improved by representing the functional relationships of environmental controllers in a way consistent with observations.
Xiaojuan Yang, Peter Thornton, Daniel Ricciuto, Yilong Wang, and Forrest Hoffman
Biogeosciences, 20, 2813–2836, https://doi.org/10.5194/bg-20-2813-2023, https://doi.org/10.5194/bg-20-2813-2023, 2023
Short summary
Short summary
We evaluated the performance of a land surface model (ELMv1-CNP) that includes both nitrogen (N) and phosphorus (P) limitation on carbon cycle processes. We show that ELMv1-CNP produces realistic estimates of present-day carbon pools and fluxes. We show that global C sources and sinks are significantly affected by P limitation. Our study suggests that introduction of P limitation in land surface models is likely to have substantial consequences for projections of future carbon uptake.
Doaa Aboelyazeed, Chonggang Xu, Forrest M. Hoffman, Jiangtao Liu, Alex W. Jones, Chris Rackauckas, Kathryn Lawson, and Chaopeng Shen
Biogeosciences, 20, 2671–2692, https://doi.org/10.5194/bg-20-2671-2023, https://doi.org/10.5194/bg-20-2671-2023, 2023
Short summary
Short summary
Photosynthesis is critical for life and has been affected by the changing climate. Many parameters come into play while modeling, but traditional calibration approaches face many issues. Our framework trains coupled neural networks to provide parameters to a photosynthesis model. Using big data, we independently found parameter values that were correlated with those in the literature while giving higher correlation and reduced biases in photosynthesis rates.
Bharat Sharma, Jitendra Kumar, Auroop R. Ganguly, and Forrest M. Hoffman
Biogeosciences, 20, 1829–1841, https://doi.org/10.5194/bg-20-1829-2023, https://doi.org/10.5194/bg-20-1829-2023, 2023
Short summary
Short summary
Rising atmospheric carbon dioxide increases vegetation growth and causes more heatwaves and droughts. The impact of such climate extremes is detrimental to terrestrial carbon uptake capacity. We found that due to overall climate warming, about 88 % of the world's regions towards the end of 2100 will show anomalous losses in net biospheric productivity (NBP) rather than gains. More than 50 % of all negative NBP extremes were driven by the compound effect of dry, hot, and fire conditions.
Martijn M. T. A. Pallandt, Jitendra Kumar, Marguerite Mauritz, Edward A. G. Schuur, Anna-Maria Virkkala, Gerardo Celis, Forrest M. Hoffman, and Mathias Göckede
Biogeosciences, 19, 559–583, https://doi.org/10.5194/bg-19-559-2022, https://doi.org/10.5194/bg-19-559-2022, 2022
Short summary
Short summary
Thawing of Arctic permafrost soils could trigger the release of vast amounts of carbon to the atmosphere, thus enhancing climate change. Our study investigated how well the current network of eddy covariance sites to monitor greenhouse gas exchange at local scales captures pan-Arctic flux patterns. We identified large coverage gaps, e.g., in Siberia, but also demonstrated that a targeted addition of relatively few sites can significantly improve network performance.
Yaoping Wang, Jiafu Mao, Mingzhou Jin, Forrest M. Hoffman, Xiaoying Shi, Stan D. Wullschleger, and Yongjiu Dai
Earth Syst. Sci. Data, 13, 4385–4405, https://doi.org/10.5194/essd-13-4385-2021, https://doi.org/10.5194/essd-13-4385-2021, 2021
Short summary
Short summary
We developed seven global soil moisture datasets (1970–2016, monthly, half-degree, and multilayer) by merging a wide range of data sources, including in situ and satellite observations, reanalysis, offline land surface model simulations, and Earth system model simulations. Given the great value of long-term, multilayer, gap-free soil moisture products to climate research and applications, we believe this paper and the presented datasets would be of interest to many different communities.
Chantelle Burton, Francesca Di Giuseppe, Matthew W. Jones, Douglas I. Kelley, Maria L. F. Barbosa, Joe McNorton, Robert Potts, Carlota Segura-Garcia, John T. Abatzoglou, Iulian Alin Rosu, Niels Andela, Liana O. Anderson, Sally Archibald, Esther Brambleby, Emilio Chuvieco, Andy Ciavarella, Hamish Clarke, Francisco de la Barrera, Carlos M. Di Bella, Enza Di Tomaso, Kebonye Dintwe, Tim S. Doherty, Victoria M. Donovan, Jonathan M. Eden, Paulo M. Fernandes, Lukas Fiedler, Louis Giglio, Olivia Haas, Douglas Hamilton, Stijn Hantson, Sarah Harris, Andrew J. Hartley, Theodore R. Keeping, Crystal A. Kolden, Jean-Luc Kouassi, Elena Kukavskaya, Seppe Lampe, Zhongwei Liu, Guilherme Mataveli, Sarah Meier, Duarte Oom, Mark Parrington, Yuquan Qu, Victor Resco de Dios, Julien Ruffault, Mojtaba Sadegh, Cristina Santín, Fernando Sedano, Jacquelyn K. Shuman, Patrícia S. Silva, Fiona R. Spuler, Carmen B. Steinmann, Veerachai Tanpipat, Miguel Ángel Torres-Vazquez, Marco Turco, Guido R. van der Werf, Dave van Wees, Sander Veraverbeke, Jakob B. Wessel, and Bo Zheng
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-587, https://doi.org/10.5194/essd-2026-587, 2026
Preprint under review for ESSD
Short summary
Short summary
The third State of Wildfires report examines extreme wildfire events from 2025 to early 2026. It analyses key regional events in Midwestern Canadian Shield Forests, Northwest Iberia, and Chilean Temperate Forests and Matorral, assessing their impacts, drivers, and attributing them to climate change. Climate change greatly increased the likelihood of these fires, and without strong mitigation, such events will become more frequent.
Lara Leko, Gunnar Behrens, Nils Müller, Adrià Amell, Axel Lauer, and Patrick Eriksson
EGUsphere, https://doi.org/10.5194/egusphere-2026-3869, https://doi.org/10.5194/egusphere-2026-3869, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We look at daily cycles of tropical ice clouds and their representation in models. New satellite data of ice clouds captures daily cycles over land and oceans, which was not possible yet. These are often shifted in time in reanalysis, and biased depending on the variable. High-resolution models show improvements but still have regional biases. We show that combining different satellite observations helps to understand daily cycles and improve the simulation of tropical ice clouds and rainfall.
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.
Bin Wang, M. Luke McCormack, Daniel M. Ricciuto, Xiaojuan Yang, Min Xu, and Forrest M. Hoffman
EGUsphere, https://doi.org/10.5194/egusphere-2026-2759, https://doi.org/10.5194/egusphere-2026-2759, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Plants send about a third of their captured carbon below ground, to roots and soil fungi that take up water and nutrients. Most climate models still treat the entire root system as one uniform mass. We built a new land-model version that separates roots into three types: transport roots, absorptive roots, and fungal partners. Tested at twenty sites worldwide, it cut errors in simulated ecosystem carbon exchange by more than half, sharpening projections of how land will respond to climate change.
Prince Xavier, Martin Willett, Tim Graham, Paul Earnshaw, Dan Copsey, Nikesh Narayan, Charline Marzin, Alistair Sellar, Duncan Ackerley, Adam Blaker, Ed Blockley, Alejandro Bodas-Salcedo, Andrew Bushell, Nakbin Choi, Xin Rong Chua, Catherine Guiavarc'h, Muhammad Hassim, Julian Heming, Debra Hudson, Sarah Ineson, Anthony Jones, Colin Jones, Richard Keane, Kiwook Kim, Jiyeong Kim, Till Kuhlbrodt, Myong-In Lee, Richard Levine, Chen Li, Gill Martin, Alex Megann, Anne Mccabe, Aurel Moise, Leighton Regayre, Jeff Ridley, Luke Roberts, Sandeep Sahany, Reinhard K. H. Schiemann, David Storkey, Warren Tennant, Lorenzo Tomassini, Yoko Tsushima, Graham P. Weedon, Alex West, Matthew C. Wheeler, Keith Williams, Xiaobing Zhou, and Hongyan Zhu
EGUsphere, https://doi.org/10.5194/egusphere-2026-4281, https://doi.org/10.5194/egusphere-2026-4281, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
GC5 is the Met Office's newest climate and weather model, combining an updated atmosphere/land component with an improved ocean and sea-ice component. Compared to its predecessor GC4, it better simulates global temperatures, winds, monsoons over India and SE Asia, and African weather, and forecasts tropical cyclones more accurately. Southern Ocean errors shrank too. Some tropical patterns and regional biases worsened slightly. A lower-resolution version will underpin the UK's Earth System Model.
Xiao Liu, John P. Dunne, Liwei Jia, Charles A. Stock, Xiaosong Yang, Matthew J. Harrison, Liping Zhang, and Anthony Rosati
EGUsphere, https://doi.org/10.5194/egusphere-2026-4194, https://doi.org/10.5194/egusphere-2026-4194, 2026
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
This study uses NOAA GFDL’s SPEAR model to improve predictions of how the ocean absorbs human emitted CO2. While standard models capture long-term trends, they struggle with yearly fluctuations. Researchers found that "initializing" the model with observed data significantly boosts their prediction accuracy for up to two years. This framework helps separate the predictions of natural variability from human-driven carbon changes, a vital step for verifying future marine carbon removal efforts.
Hongyu Chen, Paul A. Ullrich, Julian Panetta, David Marsico, Moritz Hanke, Rajeev Jain, Chengzhu Zhang, and Robert L. Jacob
Geosci. Model Dev., 19, 6545–6570, https://doi.org/10.5194/gmd-19-6545-2026, https://doi.org/10.5194/gmd-19-6545-2026, 2026
Short summary
Short summary
Accurate data transfer between different grids is essential in climate and weather modeling. This study analyzes the geometric operations that underpin such transfers on the spherical Earth, identifies gaps and limitations in current methods, and introduces improved algorithms to ensure accuracy, reliability, and performance for next-generation modeling and data analysis systems.
Flossie Brown, Sabine Bischof, Brodie Pearson, Aditya Sengupta, Pauline Bonnet, Swen Brands, Jayasankar C.B., James Carruthers, Marion Devilliers, F. Feba, Mehmet Sedat Gözlet, Saurabh Kelkar, Sifan A. Koriche, Michael Lai, Andre Lanfer Marquez, Xiaohan Sally Li, Yiwen Li, Abhnil Prasad, Angelica R. Rodriguez, Serena Schroeter, Gopika Suresh, and Rebecca Varney
EGUsphere, https://doi.org/10.5194/egusphere-2026-3510, https://doi.org/10.5194/egusphere-2026-3510, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
The Coupled Model Intercomparison Project (CMIP) creates simulations of the Earth and climate, but its representation of many features are imperfect. In this review, we show how these biases are connected across the atmosphere, land and ocean and discuss current understanding of their causes and effects. In addition, we provide a perspective on the modelling advances expected in CMIP7 aimed at end users, those new to CMIP data and those looking to connect different aspects of the Earth system.
Aytaç Paçal, Birgit Hassler, Katja Weigel, Miguel-Ángel Fernández-Torres, Gustau Camps-Valls, and Veronika Eyring
Earth Syst. Dynam., 17, 955–986, https://doi.org/10.5194/esd-17-955-2026, https://doi.org/10.5194/esd-17-955-2026, 2026
Short summary
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.
Jung Choi, Jiwoo Lee, Kristin Chang, Paul A. Ullrich, Peter J. Gleckler, and Sang-Yoon Jun
Geosci. Model Dev., 19, 6189–6206, https://doi.org/10.5194/gmd-19-6189-2026, https://doi.org/10.5194/gmd-19-6189-2026, 2026
Short summary
Short summary
As climate risks grow, society needs reliable predictions for the coming years and decades. We developed a framework to collectively compare climate prediction systems and examine their performances on global temperature, rainfall, and sea ice. As a complementary to traditional analyses, our new framework offers tracking evolution of model performance in simulation time, helping scientists and stakeholders better understand strengths and limits of decadal climate prediction.
Baylor Fox-Kemper, Patricia DeRepentigny, Anne Marie Treguier, Christian Stepanek, Eleanor O'Rourke, Chloe Mackallah, Alberto Meucci, Yevgeny Aksenov, Paul J. Durack, Nicole Feldl, Oluwayemi Garuba, Vanessa Hernaman, Céline Heuzé, Doroteaciro Iovino, Gaurav Madan, André L. Marquez, François Massonnet, Jenny Mecking, Dhrubajyoti Samanta, Patrick C. Taylor, Wan-Ling Tseng, and Martin Vancoppenolle
Geosci. Model Dev., 19, 6043–6078, https://doi.org/10.5194/gmd-19-6043-2026, https://doi.org/10.5194/gmd-19-6043-2026, 2026
Short summary
Short summary
The ocean and sea ice are central to Earth's climate system, influencing global heat and carbon cycles, weather patterns, and sea level rise. Here the earth system model variables needed for studies of the ocean and sea ice are prioritized and requested. These requested variables were developed through an international, community-based process.
Louise C. Sime, Rachel Diamond, Christian Stepanek, Chris Brierley, David Schroeder, Masa Kageyama, Matthew Pollock, Irene Malmierca-Vallet, Ed Blockley, Alex West, Danny Feltham, Jeff Ridley, Pascale Braconnot, Charles J. R. Williams, Xiaoxu Shi, Bette L. Otto-Bliesner, Sophia I. Macarewich, Silvana Ramos Buarque, Qiong Zhang, Allegra LeGrande, Weipeng Zheng, Dabang Jiang, Polina Morozova, Chuncheng Guo, Zhongshi Zhang, Nicholas Yeung, Laurie Menviel, Sandeep Narayanasetti, Masakazu Yoshimori, Olivia Reeves, and Anni Zhao
Geosci. Model Dev., 19, 5881–5905, https://doi.org/10.5194/gmd-19-5881-2026, https://doi.org/10.5194/gmd-19-5881-2026, 2026
Short summary
Short summary
The Arctic may have lost its summer sea ice 127,000 years ago during a naturally warm period in Earth’s past. Climate models can be tested by recreating those conditions, with similar sunlight and greenhouse gas levels. Analysing the large sea ice changes in these simulations helps us understand how the Arctic might respond in the near future and improves how we test and trust our climate models.
João C. M. Teixeira, Chantelle Burton, Douglas I. Kelley, Gerd A. Folberth, Fiona M. O'Connor, Richard A. Betts, and Apostolos Voulgarakis
Earth Syst. Dynam., 17, 739–767, https://doi.org/10.5194/esd-17-739-2026, https://doi.org/10.5194/esd-17-739-2026, 2026
Short summary
Short summary
Burned area has declined globally since the late 1990s, especially in tropical savannas, yet many climate models miss this trend because they poorly represent human fire use and suppression. We tested whether adding a simple link to human development improves a fire model. This reduced large regional errors and better captured downward trends in several areas, though it increased global underestimation and reduced variability. Human influence is key, but simple assumptions have trade-offs.
Giuliana Pallotta, Shiheng Duan, Celine Bonfils, Jiwoo Lee, Seth Goodnight, and Paul Ullrich
EGUsphere, https://doi.org/10.48550/arXiv.2604.06567, https://doi.org/10.48550/arXiv.2604.06567, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Deep-learning Earth system models (DL-ESMs) offer a computationally efficient alternative to traditional Earth system models, but their suitability for Earth system applications requires rigorous evaluation. We apply the PCMDI Metrics Package to assess ACE2 and NeuralGCM using climatology, variability, monsoon, and precipitation diagnostics, providing one of the first systematic Earth system model evaluations of DL-ESMs.
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.
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
EGUsphere, https://doi.org/10.5194/egusphere-2026-2288, https://doi.org/10.5194/egusphere-2026-2288, 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.
Francesco De Rovere, Giulia Bonino, Ronan McAdam, Enrico Scoccimarro, Samuel Somot, Iván Manuel Parras-Berrocal, Bodo Ahrens, Vladimir Djurdjevic, Laurent Li, and Simona Masina
EGUsphere, https://doi.org/10.5194/egusphere-2026-2752, https://doi.org/10.5194/egusphere-2026-2752, 2026
Short summary
Short summary
Marine heatwaves in the Mediterranean Sea are intensifying, posing growing threats to marine life and coastal communities. We evaluated whether high-resolution regional climate models better capture these extremes compared to the global models driving them. Regional models reliably improve sea surface temperature patterns and marine heatwave duration, but struggle more with heatwave intensity. Higher resolution alone is not enough, advances in other aspects of model design are equally needed.
Jishi Zhang, Jean-Christophe Golaz, Matthew Vincent Signorotti, Hsiang-He Lee, Peter Bogenschutz, Minda Monteagudo, Paul Aaron Ullrich, Robert S. Arthur, Stephen Po-Chedley, Philip Cameron-Smith, and Jean-Paul Watson
Geosci. Model Dev., 19, 4513–4545, https://doi.org/10.5194/gmd-19-4513-2026, https://doi.org/10.5194/gmd-19-4513-2026, 2026
Short summary
Short summary
We ran a convection-permitting model with regional mesh refinement (3.25 km and 800 m) to simulate present-day wind and solar capacity factors over California, coupling it to an energy generation model. The high-resolution models captured realistic seasonal and diurnal cycles, with wind markedly better than a 25 km model and solar outperforming a 3 km operational forecast. We highlight the critical role of resolution, modeling assumptions, and data reliability in renewable energy assessment.
Damien Couespel, Xabier Davila, Nadine Goris, Emil Jeansson, Siv K. Lauvset, and Jerry Tjiputra
Biogeosciences, 23, 3433–3466, https://doi.org/10.5194/bg-23-3433-2026, https://doi.org/10.5194/bg-23-3433-2026, 2026
Short summary
Short summary
Remineralised carbon storage is projected to increase along the 21st century, but the magnitude of increase varies depending on the Earth system models. To constrain projections, we explore the relationship between remineralised carbon and deep ocean circulation. We show that the sensitivity of remineralised carbon to circulation slowdown varies considerably between models. More observational data and a larger model ensemble is needed to constrain projections of remineralised carbon storage.
Moetasim Ashfaq, Erika Coppola, Chris Lennard, Claas Teichmann, Deeksha Rastogi, Elias Massoud, Erasmo Buonomo, Eun-Soon Im, George Zittis, Jason P. Evans, Jesus Fernandez, Katherine J. Evans, Maria Leidinice da Silva, Marianna Adinolfi, Melissa Bukovsky, Rosmeri Porfirio da Rocha, Shabeh ul Hasson, Silvina A. Solman, Stefan Sobolowski, Sushant Das, Swen Brands, Tereza Cavazos, Thanh Ngo-Duc, and Xuejie Gao
EGUsphere, https://doi.org/10.5194/egusphere-2026-2649, https://doi.org/10.5194/egusphere-2026-2649, 2026
Short summary
Short summary
Producing reliable regional climate projections requires carefully selecting which global climate models drive them. We developed a transparent and reproducible framework to identify a balanced, representative subset of models for the Coordinated Regional Climate Downscaling Experiment, a major international effort to generate high-resolution climate projections worldwide. While designed for this initiative, the framework is broadly applicable to other models sub-selection efforts.
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.
Debashis Paul, Eun-Jin Park, Eun Young Kwon, Sharif Jahfer, Sahil Sharma, and Mohanan Geethalekshmi Sreeush
EGUsphere, https://doi.org/10.5194/egusphere-2026-1920, https://doi.org/10.5194/egusphere-2026-1920, 2026
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
Climate shapes ecosystems on land and in the ocean. Using a climate model, we project biome changes to 2300 under continued emissions. Land biomes shift poleward and eastward, with deserts expanding from ~15% to ~20% in step with warming. In contrast, ocean oligotrophic regions remain stable this century, then expand rapidly after 2100 to ~20% by 2300. This delay reflects temporary biological buffering. Ocean projections remain more uncertain than land.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Kjetil Aas, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Nicolas Bellouin, Alice Benoit-Cattin, Carla F. Berghoff, Raffaele Bernardello, Laurent Bopp, Ida Bagus Mandhara Brasika, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Nathan O. Collier, Thomas H. Colligan, Margot Cronin, Laique M. Djeutchouang, Xinyu Dou, Matt P. Enright, Kazutaka Enyo, Michael Erb, Wiley Evans, Richard A. Feely, Liang Feng, Daniel J. Ford, Adrianna Foster, Filippa Fransner, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Jefferson Goncalves De Souza, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Bertrand Guenet, Özgür Gürses, Kirsty Harrington, Ian Harris, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Akihiko Ito, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Steve D. Jones, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Yawen Kong, Jan Ivar Korsbakken, Charles Koven, Taro Kunimitsu, Xin Lan, Junjie Liu, Zhiqiang Liu, Zhu Liu, Claire Lo Monaco, Lei Ma, Gregg Marland, Patrick C. McGuire, Galen A. McKinley, Joe R. Melton, Natalie Monacci, Erwan Monier, Eric J. Morgan, David R. Munro, Jens D. Müller, Shin-Ichiro Nakaoka, Lorna R. Nayagam, Yosuke Niwa, Tobias Nutzel, Are Olsen, Abdirahman M. Omar, Naiqing Pan, Sudhanshu Pandey, Denis Pierrot, Zhangcai Qin, Pierre Regnier, Gregor Rehder, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, Ingunn Skjelvan, T. Luke Smallman, Victoria Spada, Mohanan G. Sreeush, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Didier Swingedouw, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Xiangjun Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Erik van Ooijen, Guido R. van der Werf, Sebastiaan J. van de Velde, Anthony P. Walker, Rik Wanninkhof, Xiaojuan Yang, Wenping Yuan, Xu Yue, and Jiye Zeng
Earth Syst. Sci. Data, 18, 3211–3288, https://doi.org/10.5194/essd-18-3211-2026, https://doi.org/10.5194/essd-18-3211-2026, 2026
Short summary
Short summary
The Global Carbon Budget 2025 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2025). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
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.
Jozef Skákala, Shubha Sathyendranath, Yuri Artioli, Deep S. Banerjee, Heather Bouman, Robert J. W. Brewin, Momme Butenschön, Stefano Ciavatta, Stephanie Dutkiewicz, Yanna Fidai, David Ford, Grinson George, Karen Guihou, Bror Jönsson, Marija Bačeković Koloper, Žarko Kovač, Lekshmi Krishnakumary, Gemma Kulk, Charlotte Laufkötter, Gennadi Lessin, Jann Paul Mattern, Angélique Melet, Alexandre Mignot, David Moffat, Fanny Monteiro, Mayra Rodriguez Bennadji, Cécile S. Rousseaux, Ranjini Swaminathan, Osvaldo Ulloa, and Jerry Tjiputra
Ocean Sci., 22, 1457–1481, https://doi.org/10.5194/os-22-1457-2026, https://doi.org/10.5194/os-22-1457-2026, 2026
Short summary
Short summary
Marine primary production (PP) is a key component of the Earth's climate system, but its current estimates and future projections are highly uncertain. We review the PP uncertainties and discuss their sources both across the ecosystem and satellite models. We propose to reduce the PP uncertainties by better addressing the PP model structures and parametrizations. We also argue that for many models it is desirable to consider spatial and temporal variability in the model parameter values.
Robin S. Smith, Tarkan A. Bilge, Thomas J. Bracegirdle, Paul R. Holland, Till Kuhlbrodt, Charlotte Lang, Spencer Liddicoat, Tom Mitcham, Jane Mulcahy, Kaitlin A. Naughten, Andrew Orr, Julien Palmieri, Antony J. Payne, Steven Rumbold, Marc Stringer, Ranjini Swaminathan, Sarah Taylor, Jeremy Walton, and Colin Jones
Earth Syst. Dynam., 17, 475–493, https://doi.org/10.5194/esd-17-475-2026, https://doi.org/10.5194/esd-17-475-2026, 2026
Short summary
Short summary
There is a dangerous amount of uncertainty in our predictions of climate change in polar regions because some of feedbacks that might lead to changes that are too rapid for us to adapt to, or that cannot be reversed. We have run a set of simulations with a state-of-the-art Earth System Model that helps improve our understanding of how climate in these regions might change. Some of the aspects we investigate are reversible but many are not, especially those affecting ice sheets and sea level.
Elias C. Massoud, Nathan Collier, Yaoping Wang, Jiafu Mao, Adrian Harpold, Steven A. Kannenberg, Gerbrand Koren, Mukesh Kumar, Pushpendra Raghav, Pallav Ray, Mingjie Shi, Jing Tao, Sreedevi P. Vasu, Huiqi Wang, Qing Zhu, and Forrest M. Hoffman
Geosci. Model Dev., 19, 3427–3453, https://doi.org/10.5194/gmd-19-3427-2026, https://doi.org/10.5194/gmd-19-3427-2026, 2026
Short summary
Short summary
Soil moisture helps control how plants grow and how land exchanges water and energy with the atmosphere. We evaluated how well 16 Earth System Models represent soil moisture and its links to plant activity using a benchmarking system and observational data. Many models simulate surface soil moisture well but show larger errors deeper in the soil and in colder regions. Models that represent plant–soil water interactions more directly better capture how ecosystems respond to water availability.
Julien Lenhardt, Johannes Quaas, Dino Sejdinovic, and Daniel Klocke
Atmos. Chem. Phys., 26, 5447–5475, https://doi.org/10.5194/acp-26-5447-2026, https://doi.org/10.5194/acp-26-5447-2026, 2026
Short summary
Short summary
Clouds come in various shapes and sizes and constitute a fundamental element of the Earth's climate system. Different cloud types show variable impacts on climate change. We present a new cloud type classification method called CloudViT (Cloud Vision Transformer) relying on spatial patterns of cloud properties obtained from satellite data using machine learning. We can thus help understanding the effects of different cloud types on climate change.
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.
Chloe Mackallah, Martin Juckes, James Anstey, Beth Dingley, Charlotte Pascoe, Gaëlle Rigoudy, Marie-Pierre Moine, Tomas Lovato, Alison Pamment, Martin Schupfner, Michio Kawamiya, Tommi Bergman, Charles Koven, Eleanor O'Rourke, Briony Turner, Daniel Ellis, and Matthew Mizielinski
EGUsphere, https://doi.org/10.5194/egusphere-2026-1641, https://doi.org/10.5194/egusphere-2026-1641, 2026
Short summary
Short summary
This paper describes the creation of a new set of output data requirements for upcoming global climate model experiments performed for CMIP7, an international climate modelling activity. Experts from the community helped to co-create a database that describes which data should be produced, and the scientific justifications behind these choices. It supports growing climate research and policy needs by linking experiments and variables to scientific objectives and real‑world applications.
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.
Andrew G. Pauling, Inga J. Smith, Torge Martin, Jeff K. Ridley, David P. Stevens, Max Thomas, Rebecca L. Beadling, Christopher Danek, Tore Hattermann, Qian Li, John Marshall, Morven Muilwijk, Ariaan Purich, and Neil C. Swart
EGUsphere, https://doi.org/10.5194/egusphere-2026-658, https://doi.org/10.5194/egusphere-2026-658, 2026
Short summary
Short summary
Climate models typically do not include meltwater entering the Southern Ocean due to Antarctic ice sheet mass loss. Previous work shows this meltwater drives sea ice growth, but the varying responses have been difficult to compare across models. We ran 11 climate models using the same meltwater input and found a wide range of sea ice responses depending on the background state in each model. Understanding this uncertainty in response is important for future projections of Antarctic sea ice.
Didier Swingedouw, Laura Jackson, Aixue Hu, Anastasia Romanou, Nicole C. Laureanti, Wilbert Weijer, Sina Loriani, Bette Otto-Bliesner, Ayako Abe-Ouchi, Lucas Almeida, Alessio Bellucci, Reyk Börner, Gokhan Danabasoglu, Donovan P. Dennis, Marion Devilliers, Sybren Drijfhout, Jonathan Donges, Friederike Fröb, Thomas L. Frölicher, Guillaume Gastineau, Heiko Goelzer, Chuncheng Guo, Urs Hofmann, Anna Höse, Colin Jones, Torben Koenigk, Ann Kristin Klose, Valerio Lembo, Jose Licon-Salaiz, Ken Mankoff, Virna Meccia, Irina Melnikova, Oliver Mehling, Laurie Menviel, Juliette Mignot, Jon I. Robson, Gavin A. Schmidt, Robin Smith, Yuchen Sun, Irene Trombini, Matteo Willeit, Richard Wood, Fanghua Wu, Lin Zhaohui, and Ricarda Winkelmann
EGUsphere, https://doi.org/10.5194/egusphere-2026-1698, https://doi.org/10.5194/egusphere-2026-1698, 2026
Short summary
Short summary
This study presents a plan for climate model experiments to better understand how changes in freshwater in the North Atlantic affect major ocean currents. We designed coordinated simulations to test their response to warming, added freshwater, and possible recovery after weakening. Comparing results across models and past climate evidence helps improve confidence in projections and assess risks of large ocean circulation 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
EGUsphere, https://doi.org/10.5194/egusphere-2026-1673, https://doi.org/10.5194/egusphere-2026-1673, 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.
Zhixiang Li, Jianhua Lu, and Yimin Liu
EGUsphere, https://doi.org/10.5194/egusphere-2026-1156, https://doi.org/10.5194/egusphere-2026-1156, 2026
Short summary
Short summary
This work studies the dynamical linkages, wave parameters, and subseasonal predictability of three successive atmospheric blocking events that are associated with continual extreme events during May–June 2023. We suggest that these interconnected blocking events are dominated by the regime of slowly propagating, large-amplitude planetary-scale waves. The skillful subseasonal predictions depend on capturing upstream quasi-stationary troughs, highlighting sources of subseasonal predictability.
Valerio Lembo, Gabriele Messori, Davide Faranda, Vera Melinda Galfi, Rune Grand Graversen, and Flavio Emanuele Pons
Weather Clim. Dynam., 7, 453–473, https://doi.org/10.5194/wcd-7-453-2026, https://doi.org/10.5194/wcd-7-453-2026, 2026
Short summary
Short summary
Hemispheric heatwaves have fundamental implications for ecosystems and societies. They are studied together with large-scale atmospheric dynamics, through the lens of poleward heat transports by planetary-scale waves. Extremely weak transports of heat towards the North Pole are found to be associated with hemispheric heatwaves in the Northern Hemisphere mid-latitudes. Therefore, we conclude that heat transports are a possible indicator and a precursor of concurrent hemispheric heatwaves.
Sean Davis, William Ball, Yue Jia, Gabriel Chiodo, Justin Alsing, James Keeble, Hideharu Akiyoshi, Carlo Arosio, Ewa Bednarz, Andreas Chrysanthou, Melanie Coldewey-Egbers, Robert Damadeo, Sandip Dhomse, Mohamadou Diallo, Simone Dietmuller, Roland Eichinger, Stacey Frith, Birgit Hassler, Michaela Hegglin, Daan Hubert, Patrick Jöckel, Béatrice Josse, Natalya Kramarova, Diego Loyola, Eliane Maillard Barras, Marion Marchand, Olaf Morgenstern, David Plummer, Robert Portmann, Karen Rosenlof, Alexei Rozanov, Viktoria Sofieva, Johannes Staehelin, Timofei Sukhodolov, Kleareti Tourpali, Ronald Van der A, H. J. Ray Wang, Krzysztof Wargan, Shingo Watanabe, Mark Weber, Jeannette Wild, Yousuke Yamashita, and Jerry Ziemke
EGUsphere, https://doi.org/10.5194/egusphere-2026-532, https://doi.org/10.5194/egusphere-2026-532, 2026
Short summary
Short summary
This study investigates how tropical ozone levels have changed since 2000 in chemistry climate models and satellite observations to determine how well they agree with one another, and to see if current trends can help predict future levels. At some, satellite records disagree significantly on the magnitude of ozone changes. The study shows a connection between recent ozone trends and future ozone levels, suggesting that satellite measurements could help constrain future ozone changes.
Lisa Bernini, Leone Cavicchia, Fabien Desbiolles, Enrico Scoccimarro, and Claudia Pasquero
Weather Clim. Dynam., 7, 367–391, https://doi.org/10.5194/wcd-7-367-2026, https://doi.org/10.5194/wcd-7-367-2026, 2026
Short summary
Short summary
We studied why some Mediterranean storms intensify and begin to resemble tropical cyclones, becoming especially damaging. By comparing many storms, we found that strong cyclones form when powerful disturbances high in the atmosphere are present. If this happens over warm seas, storms are more likely to develop tropical-like features, while the cooling of the sea by the storm itself helps limit its lifetime. These findings can help improve early warnings and reduce risks in the region.
Morven Muilwijk, Tore Hattermann, Rebecca L. Beadling, Neil C. Swart, Aleksi Nummelin, Chuncheng Guo, David M. Chandler, Petra M. Langebroek, Shenjie Zhou, Pierre Dutrieux, Jia-Jia Chen, Christopher Danek, Matthew H. England, Stephen M. Griffies, F. Alexander Haumann, André Jüling, Ombeline Jouet, Qian Li, Torge Martin, John Marshall, Andrew G. Pauling, Ariaan Purich, Zihan Song, Inga J. Smith, Max Thomas, Irene Trombini, Eveline C. van der Linden, and Xiaoqi Xu
The Cryosphere, 20, 1087–1117, https://doi.org/10.5194/tc-20-1087-2026, https://doi.org/10.5194/tc-20-1087-2026, 2026
Short summary
Short summary
Antarctic meltwater affects ocean stratification and temperature, which in turn influences the rate of ice shelf melting – a coupling missing in most climate models. We analyze a suite of climate models with added meltwater to explore this feedback in different regions. While meltwater generally enhances ocean warming and melt, in West Antarctica most models simulate coastal cooling, suggesting a negative feedback that could slow future ice loss there.
Bianca Mezzina, Christopher David Roberts, Matthias Aengenheyster, Rohit Ghosh, Malcolm John Roberts, and Marc Batlle Martin
EGUsphere, https://doi.org/10.5194/egusphere-2026-547, https://doi.org/10.5194/egusphere-2026-547, 2026
Short summary
Short summary
El Nino-Southern Oscllation (ENSO) is a climate phenomenon that can influence climate worldwide, but current climate models still struggle to properly simulate its impacts. We investigate if a new set of models with unprecedented fine detail in the ocean and the atmosphere can simulate this better. By comparing several models and experiments, we find that higher detail does not always improve results and that natural climate variability strongly limits clear gains.
Seppe Lampe, Lukas Gudmundsson, Basil Kraft, Stijn Hantson, Douglas Kelley, Vincent Humphrey, Bertrand Le Saux, Emilio Chuvieco, and Wim Thiery
Geosci. Model Dev., 19, 955–988, https://doi.org/10.5194/gmd-19-955-2026, https://doi.org/10.5194/gmd-19-955-2026, 2026
Short summary
Short summary
We introduce BuRNN (BUrned area modelling by Recurrent Neural Networks), a model which estimates monthly burned area based on satellite observations and climate, vegetation, and socio-economic data using machine learning. BuRNN outperforms existing process-based fire models, which improves our capabilities of modelling past and future burned areas.
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.
Philip J. Wallhead, Jörg Schwinger, Jerry Tjiputra, Trond Kristiansen, and Richard G. J. Bellerby
Earth Syst. Sci. Data, 17, 6763–6805, https://doi.org/10.5194/essd-17-6763-2025, https://doi.org/10.5194/essd-17-6763-2025, 2025
Short summary
Short summary
We developed a novel method to combine ocean data from observations and models, and applied it to produce gridded estimates of nutrients, oxygen, dissolved inorganic carbon and total alkalinity concentrations at latitudes >40° N and years 1980–2020. The new estimates showed improved accuracy and coverage relative to previous estimates, but highlighted remaining uncertainty in some poorly sampled regions. The work was largely motivated by a need for accurate input data for regional ocean models.
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.
Wolfgang A. Müller, Stephan Lorenz, Trang V. Pham, Andrea Schneidereit, Renate Brokopf, Victor Brovkin, Nils Brüggemann, Fatemeh Chegini, Dietmar Dommenget, Kristina Fröhlich, Barbara Früh, Veronika Gayler, Helmuth Haak, Stefan Hagemann, Moritz Hanke, Tatiana Ilyina, Johann Jungclaus, Martin Köhler, Peter Korn, Luis Kornblueh, Clarissa A. Kroll, Julian Krüger, Karel Castro-Morales, Ulrike Niemeier, Holger Pohlmann, Iuliia Polkova, Roland Potthast, Thomas Riddick, Manuel Schlund, Tobias Stacke, Roland Wirth, Dakuan Yu, and Jochem Marotzke
Geosci. Model Dev., 18, 9385–9415, https://doi.org/10.5194/gmd-18-9385-2025, https://doi.org/10.5194/gmd-18-9385-2025, 2025
Short summary
Short summary
We provide a new Earth System model configuration framed into the ICON architecture, which provides the baseline for the next generation of climate predictions and projections (hereafter ICON XPP). Two resolutions of ICON XPP are presented that show high runtime performances making it suitable to run long integrations and large-ensemble experiments. ICON XPP similarly perform to CMIP6-class of climate models making it a good basis for climate forecasts and projections, and climate research.
Victor Brovkin, Benjamin M. Sanderson, Noel G. Brizuela, Tomohiro Hajima, Tatiana Ilyina, Chris D. Jones, Charles Koven, David Lawrence, Peter Lawrence, Hongmei Li, Spencer Liddcoat, Anastasia Romanou, Roland Séférian, Lori T. Sentman, Abigail L. S. Swann, Jerry Tjiputra, Tilo Ziehn, and Alexander J. Winkler
Earth Syst. Dynam., 16, 2021–2034, https://doi.org/10.5194/esd-16-2021-2025, https://doi.org/10.5194/esd-16-2021-2025, 2025
Short summary
Short summary
Idealized experiments with Earth system models provide a basis for understanding the response of the carbon cycle to emissions. We show that most models exhibit a quasi-linear relationship between cumulative carbon uptake on land and in the ocean and hypothesize that this relationship does not depend on emission pathways. We reduce the coupled system to only one differential equation, which represents a powerful simplification of the Earth system dynamics as a function of fossil fuel emissions.
Ting-Chen Chen, Hugues Goosse, Cécile Davrinche, Stephy Libera, Christopher Roberts, Matthias Aengenheyster, Kristian Strommen, Malcolm Roberts, Rohit Ghosh, and Jin-Song von Storch
Weather Clim. Dynam., 6, 1179–1193, https://doi.org/10.5194/wcd-6-1179-2025, https://doi.org/10.5194/wcd-6-1179-2025, 2025
Short summary
Short summary
Southern Annular Mode (SAM) is a leading mode of Southern Hemisphere climate variability, but global models often overestimate its persistence in summer. Bias in SAM persistence is reduced and jet latitude improved using high-resolution sea surface temperature (SST)-prescribed models, pointing to the central role of SSTs. Control experiments explore the influence of model resolution and mesoscale ocean features on SAM persistence, highlighting the complex and subtle nature of air-sea coupling.
Douglas I. Kelley, Chantelle Burton, Francesca Di Giuseppe, Matthew W. Jones, Maria L. F. Barbosa, Esther Brambleby, Joe R. McNorton, Zhongwei Liu, Anna S. I. Bradley, Katie Blackford, Eleanor Burke, Andrew Ciavarella, Enza Di Tomaso, Jonathan Eden, Igor José M. Ferreira, Lukas Fiedler, Andrew J. Hartley, Theodore R. Keeping, Seppe Lampe, Anna Lombardi, Guilherme Mataveli, Yuquan Qu, Patrícia S. Silva, Fiona R. Spuler, Carmen B. Steinmann, Miguel Ángel Torres-Vázquez, Renata Veiga, Dave van Wees, Jakob B. Wessel, Emily Wright, Bibiana Bilbao, Mathieu Bourbonnais, Cong Gao, Carlos M. Di Bella, Kebonye Dintwe, Victoria M. Donovan, Sarah Harris, Elena A. Kukavskaya, Aya Brigitte N'Dri, Cristina Santín, Galia Selaya, Johan Sjöström, John T. Abatzoglou, Niels Andela, Rachel Carmenta, Emilio Chuvieco, Louis Giglio, Douglas S. Hamilton, Stijn Hantson, Sarah Meier, Mark Parrington, Mojtaba Sadegh, Jesus San-Miguel-Ayanz, Fernando Sedano, Marco Turco, Guido R. van der Werf, Sander Veraverbeke, Liana O. Anderson, Hamish Clarke, Paulo M. Fernandes, and Crystal A. Kolden
Earth Syst. Sci. Data, 17, 5377–5488, https://doi.org/10.5194/essd-17-5377-2025, https://doi.org/10.5194/essd-17-5377-2025, 2025
Short summary
Short summary
The second State of Wildfires report examines extreme wildfire events from 2024 to early 2025. It analyses key regional events in Southern California, Northeast Amazonia, Pantanal–Chiquitano, and the Congo Basin, assessing their drivers and predictability and attributing them to climate change and land use. Seasonal outlooks and decadal projections are provided. Climate change greatly increased the likelihood of these fires, and without strong mitigation, such events will become more frequent.
Lise Seland Graff, Jerry Tjiputra, Ada Gjermundsen, Andreas Born, Jens Boldingh Debernard, Heiko Goelzer, Yan-Chun He, Petra Margaretha Langebroek, Aleksi Nummelin, Dirk Olivié, Øyvind Seland, Trude Storelvmo, Mats Bentsen, Chuncheng Guo, Andrea Rosendahl, Dandan Tao, Thomas Toniazzo, Camille Li, Stephen Outten, and Michael Schulz
Earth Syst. Dynam., 16, 1671–1698, https://doi.org/10.5194/esd-16-1671-2025, https://doi.org/10.5194/esd-16-1671-2025, 2025
Short summary
Short summary
The magnitude of future Arctic amplification is highly uncertain. Using the Norwegian Earth System Model, we explore the effect of improving the representation of clouds, ocean eddies, the Greenland ice sheet, sea ice, and ozone on the projected Arctic winter warming in a coordinated experiment set. These improvements all lead to enhanced projected Arctic warming, with the largest changes found in the sea ice retreat regions and the largest uncertainty found on the Atlantic side.
Diajeng W. Atmojo, Katja Weigel, Arthur Grundner, Marika M. Holland, Dmitry Sidorenko, and Veronika Eyring
EGUsphere, https://doi.org/10.5194/egusphere-2025-3556, https://doi.org/10.5194/egusphere-2025-3556, 2025
Short summary
Short summary
This study improves the sea ice albedo parametrisation in the Finite-Element Sea Ice Model 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.
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.
Pratapaditya Ghosh, Ian Boutle, Paul Field, Adrian Hill, Marie Mazoyer, Katherine J. Evans, Salil Mahajan, Hyun-Gyu Kang, Min Xu, Wei Zhang, and Hamish Gordon
Atmos. Chem. Phys., 25, 11157–11182, https://doi.org/10.5194/acp-25-11157-2025, https://doi.org/10.5194/acp-25-11157-2025, 2025
Short summary
Short summary
We study the life cycle of fog events in Europe using a weather and climate model. By incorporating droplet formation and growth driven by radiative cooling, our model better simulates the total liquid water in foggy atmospheric columns. We show that both adiabatic and radiative cooling play significant, often equally important, roles in driving droplet formation and growth. We discuss strategies to address droplet number overpredictions by improving model physics and addressing model artifacts.
Pratapaditya Ghosh, Ian Boutle, Paul Field, Adrian Hill, Anthony Jones, Marie Mazoyer, Katherine J. Evans, Salil Mahajan, Hyun-Gyu Kang, Min Xu, Wei Zhang, Noah Asch, and Hamish Gordon
Atmos. Chem. Phys., 25, 11129–11156, https://doi.org/10.5194/acp-25-11129-2025, https://doi.org/10.5194/acp-25-11129-2025, 2025
Short summary
Short summary
We study aerosol–fog interactions near Paris using a weather and climate model with high spatial resolution. We show that our model can simulate the fog life cycle effectively. We find that the fog droplet number concentrations, the amount of liquid water in the fog, and the vertical structure of the fog are highly sensitive to the parameterization that simulates droplet formation and growth. The changes we propose could improve fog forecasts significantly without increasing computational costs.
Renata Moura da Veiga, Celso von Randow, Chantelle Burton, Douglas I. Kelley, Manoel Cardoso, and Fabiano Morelli
Nat. Hazards Earth Syst. Sci., 25, 3581–3601, https://doi.org/10.5194/nhess-25-3581-2025, https://doi.org/10.5194/nhess-25-3581-2025, 2025
Short summary
Short summary
We systematically reviewed 77 papers to understand the Cerrado’s fire emissions within the global carbon budget by evaluating how fire parameters can inform emission estimates and mitigation strategies. Estimating fire emissions in the Cerrado requires a holistic approach, combining fire carbon emission estimates, fire dynamic parameters, and fire management and policy. We highlight key research gaps that could provide more comprehensive insights into accounting for fire emissions in the Cerrado.
Sun-Seon Lee, Sahil Sharma, Nan Rosenbloom, Keith B. Rodgers, Ji-Eun Kim, Eun Young Kwon, Christian L. E. Franzke, In-Won Kim, Mohanan Geethalekshmi Sreeush, and Karl Stein
Earth Syst. Dynam., 16, 1427–1451, https://doi.org/10.5194/esd-16-1427-2025, https://doi.org/10.5194/esd-16-1427-2025, 2025
Short summary
Short summary
A new 10-member ensemble simulation with the state-of-the-art Earth system model was employed to study the long-term climate response to sustained greenhouse warming through to the year 2500. The findings show that the projected changes in the forced mean state and internal variability during 2101–2500 differ substantially from the 21st-century projections, emphasizing the importance of multi-century perspectives for understanding future climate change and informing effective mitigation strategies.
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.
Pratapaditya Ghosh, Katherine J. Evans, Daniel P. Grosvenor, Hyun-Gyu Kang, Salil Mahajan, Min Xu, Wei Zhang, and Hamish Gordon
Geosci. Model Dev., 18, 4899–4913, https://doi.org/10.5194/gmd-18-4899-2025, https://doi.org/10.5194/gmd-18-4899-2025, 2025
Short summary
Short summary
The most popular algorithm for calculating cloud droplet number concentrations in climate models is sensitive to parameters that control simulated aerosol particle number concentrations at different sizes. We recommend small modifications to functions in the algorithm to improve its performance. Implementing the changes in the UK Met Office climate model reduced average bias in simulated global droplet number concentrations, leading to more reflected solar radiation and a net cooling effect.
Ross J. Herbert, Andrew I. L. Williams, Philipp Weiss, Duncan Watson-Parris, Elisabeth Dingley, Daniel Klocke, and Philip Stier
Atmos. Chem. Phys., 25, 7789–7814, https://doi.org/10.5194/acp-25-7789-2025, https://doi.org/10.5194/acp-25-7789-2025, 2025
Short summary
Short summary
Clouds exist at scales that climate models struggle to represent, limiting our knowledge of how climate change may impact clouds. Here we use a new kilometer-scale global model representing an important step towards the necessary scale. We focus on how aerosol particles modify clouds, radiation, and precipitation. We find the magnitude and manner of responses tend to vary from region to region, highlighting the potential of global kilometer-scale simulations and a need to represent aerosols in climate models.
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.
Maria Lucia Ferreira Barbosa, Douglas I. Kelley, Chantelle A. Burton, Igor J. M. Ferreira, Renata Moura da Veiga, Anna Bradley, Paulo Guilherme Molin, and Liana O. Anderson
Geosci. Model Dev., 18, 3533–3557, https://doi.org/10.5194/gmd-18-3533-2025, https://doi.org/10.5194/gmd-18-3533-2025, 2025
Short summary
Short summary
As fire seasons in Brazil become increasingly severe, confidently understanding the factors driving fires is more critical than ever. To address this challenge, we developed FLAME (Fire Landscape Analysis using Maximum Entropy), a new model designed to predict fires and to analyse the spatial influence of both environmental and human factors while accounting for uncertainties. By adapting the model to different regions, we can enhance fire management strategies, making FLAME a powerful tool for protecting landscapes in Brazil and beyond.
Davi Mignac, Jennifer Waters, Daniel J. Lea, Matthew J. Martin, James While, Anthony T. Weaver, Arthur Vidard, Catherine Guiavarc'h, Dave Storkey, David Ford, Edward W. Blockley, Jonathan Baker, Keith Haines, Martin R. Price, Michael J. Bell, and Richard Renshaw
Geosci. Model Dev., 18, 3405–3425, https://doi.org/10.5194/gmd-18-3405-2025, https://doi.org/10.5194/gmd-18-3405-2025, 2025
Short summary
Short summary
We describe major improvements of the Met Office's global ocean–sea ice forecasting system. The models and the way observations are used to improve the forecasts were changed, which led to a significant error reduction of 1 d forecasts. The new system performance in past conditions, where subsurface observations are scarce, was improved with more consistent ocean heat content estimates. The new system will be of better use for climate studies and will provide improved forecasts for end users.
Laurent Bertino, Patrick Heimbach, Ed Blockley, and Einar Ólason
State Planet, 5-opsr, 14, https://doi.org/10.5194/sp-5-opsr-14-2025, https://doi.org/10.5194/sp-5-opsr-14-2025, 2025
Short summary
Short summary
Forecasts of sea ice are in high demand in the polar regions, and they are also quickly improving and becoming more easily accessible to non-experts. We provide here a brief status of the short-term forecasting services – typically 10 d ahead – and an outlook of their upcoming developments.
Alex E. West and Edward W. Blockley
Geosci. Model Dev., 18, 3041–3064, https://doi.org/10.5194/gmd-18-3041-2025, https://doi.org/10.5194/gmd-18-3041-2025, 2025
Short summary
Short summary
This study uses ice mass balance buoys – temperature- and height-measuring devices frozen into sea ice – to find how well climate models simulate (1) melt and growth of Arctic sea ice and (2) conduction of heat through Arctic sea ice. This may help understand why models produce varying amounts of sea ice in the present day. We find that models tend to show more melt, growth or conduction for a given ice thickness than the buoys, although the difference is smaller for models with more physically realistic thermodynamics.
David Storkey, Pierre Mathiot, Michael J. Bell, Dan Copsey, Catherine Guiavarc'h, Helene T. Hewitt, Jeff Ridley, and Malcolm J. Roberts
Geosci. Model Dev., 18, 2725–2745, https://doi.org/10.5194/gmd-18-2725-2025, https://doi.org/10.5194/gmd-18-2725-2025, 2025
Short summary
Short summary
The Southern Ocean is a key region of the world ocean in the context of climate change studies. We show that the Met Office Hadley Centre coupled model with intermediate ocean resolution struggles to accurately simulate the Southern Ocean. Increasing the frictional drag that the seafloor exerts on ocean currents and introducing a representation of unresolved ocean eddies both appear to reduce the large-scale biases in this model.
Martin Juckes, Karl E. Taylor, Fabrizio Antonio, David Brayshaw, Carlo Buontempo, Jian Cao, Paul J. Durack, Michio Kawamiya, Hyungjun Kim, Tomas Lovato, Chloe Mackallah, Matthew Mizielinski, Alessandra Nuzzo, Martina Stockhause, Daniele Visioni, Jeremy Walton, Briony Turner, Eleanor O'Rourke, and Beth Dingley
Geosci. Model Dev., 18, 2639–2663, https://doi.org/10.5194/gmd-18-2639-2025, https://doi.org/10.5194/gmd-18-2639-2025, 2025
Short summary
Short summary
The Baseline Climate Variables for Earth System Modelling (ESM-BCVs) are defined as a list of 135 variables which have high utility for the evaluation and exploitation of climate simulations. The list reflects the most frequently used variables from Earth system models based on an assessment of data publication and download records from the largest archive of global climate projects.
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.
Ngoc Thi Nhu Do, Kengo Sudo, Akihiko Ito, Louisa K. Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
Geosci. Model Dev., 18, 2079–2109, https://doi.org/10.5194/gmd-18-2079-2025, https://doi.org/10.5194/gmd-18-2079-2025, 2025
Short summary
Short summary
Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth system models mainly due to partially incorporating CO2 effects and land cover changes rather than to climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant–climate interactions.
Inika Taylor, Douglas I. Kelley, Camilla Mathison, Karina E. Williams, Andrew J. Hartley, Richard A. Betts, and Chantelle Burton
EGUsphere, https://doi.org/10.5194/egusphere-2025-720, https://doi.org/10.5194/egusphere-2025-720, 2025
Short summary
Short summary
Climate change is reshaping fire seasons worldwide and, in many places, increasing fire weather risk. We use climate model simulations to project future changes in fire danger at different levels of global warming, focusing on Australia, Brazil, and the USA. Keeping warming below 2 °C significantly limits the increase in fire risk, but even at 1.5 °C, fire seasons lengthen, with more extreme conditions. However, low-fire weather periods remain, offering critical windows for fire management.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
Short summary
Short summary
Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
Short summary
Short summary
HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
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.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis A. O'Brien
Geosci. Model Dev., 18, 961–976, https://doi.org/10.5194/gmd-18-961-2025, https://doi.org/10.5194/gmd-18-961-2025, 2025
Short summary
Short summary
A metrics package designed for easy analysis of atmospheric river (AR) characteristics and statistics is presented. The tool is efficient for diagnosing systematic AR bias in climate models and useful for evaluating new AR characteristics in model simulations. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the North and South Atlantic (South Pacific and Indian Ocean).
Brandon M. Duran, Casey J. Wall, Nicholas J. Lutsko, Takuro Michibata, Po-Lun Ma, Yi Qin, Margaret L. Duffy, Brian Medeiros, and Matvey Debolskiy
Atmos. Chem. Phys., 25, 2123–2146, https://doi.org/10.5194/acp-25-2123-2025, https://doi.org/10.5194/acp-25-2123-2025, 2025
Short summary
Short summary
We use satellite simulator data generated by global climate models to investigate how aerosol particles impact the radiative properties of liquid clouds. Specifically, we quantify the radiative perturbations arising from aerosol-driven changes in the number density of cloud droplets, the vertically integrated cloud water mass, and the cloud amount. Our results show that, in models, aerosol effects on the number density of cloud droplets contribute the most to anthropogenic climate forcing.
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
Short summary
Short summary
The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
Lorenzo Sangelantoni, Stefano Tibaldi, Leone Cavicchia, Enrico Scoccimarro, Pier Luigi Vidale, Kevin Hodges, Vivien Mavel, Mattia Almansi, Chiara Cagnazzo, and Samuel Almond
EGUsphere, https://doi.org/10.5194/egusphere-2024-4157, https://doi.org/10.5194/egusphere-2024-4157, 2025
Preprint archived
Short summary
Short summary
We introduce a new dataset of European windstorms linked to extratropical cyclones, spanning whole ERA5 reanalysis period (1940–present). Developed under Copernicus Climate Change Service, the dataset provides standardized, high-quality information on windstorm tracks and footprints for industries like insurance and risk management. Preliminary findings show an increase in cold-season windstorms and their impacts in parts of Europe. Tracking methods contribute to uncertainties in key statistics.
Alex T. Archibald, Bablu Sinha, Maria R. Russo, Emily Matthews, Freya A. Squires, N. Luke Abraham, Stephane J.-B. Bauguitte, Thomas J. Bannan, Thomas G. Bell, David Berry, Lucy J. Carpenter, Hugh Coe, Andrew Coward, Peter Edwards, Daniel Feltham, Dwayne Heard, Jim Hopkins, James Keeble, Elizabeth C. Kent, Brian A. King, Isobel R. Lawrence, James Lee, Claire R. Macintosh, Alex Megann, Bengamin I. Moat, Katie Read, Chris Reed, Malcolm J. Roberts, Reinhard Schiemann, David Schroeder, Timothy J. Smyth, Loren Temple, Navaneeth Thamban, Lisa Whalley, Simon Williams, Huihui Wu, and Mingxi Yang
Earth Syst. Sci. Data, 17, 135–164, https://doi.org/10.5194/essd-17-135-2025, https://doi.org/10.5194/essd-17-135-2025, 2025
Short summary
Short summary
Here, we present an overview of the data generated as part of the North Atlantic Climate System Integrated Study (ACSIS) programme that are available through dedicated repositories at the Centre for Environmental Data Analysis (CEDA; www.ceda.ac.uk) and the British Oceanographic Data Centre (BODC; bodc.ac.uk). The datasets described here cover the North Atlantic Ocean, the atmosphere above (it including its composition), and Arctic sea ice.
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.
Yona Silvy, Thomas L. Frölicher, Jens Terhaar, Fortunat Joos, Friedrich A. Burger, Fabrice Lacroix, Myles Allen, Raffaele Bernardello, Laurent Bopp, Victor Brovkin, Jonathan R. Buzan, Patricia Cadule, Martin Dix, John Dunne, Pierre Friedlingstein, Goran Georgievski, Tomohiro Hajima, Stuart Jenkins, Michio Kawamiya, Nancy Y. Kiang, Vladimir Lapin, Donghyun Lee, Paul Lerner, Nadine Mengis, Estela A. Monteiro, David Paynter, Glen P. Peters, Anastasia Romanou, Jörg Schwinger, Sarah Sparrow, Eric Stofferahn, Jerry Tjiputra, Etienne Tourigny, and Tilo Ziehn
Earth Syst. Dynam., 15, 1591–1628, https://doi.org/10.5194/esd-15-1591-2024, https://doi.org/10.5194/esd-15-1591-2024, 2024
Short summary
Short summary
The adaptive emission reduction approach is applied with Earth system models to generate temperature stabilization simulations. These simulations provide compatible emission pathways and budgets for a given warming level, uncovering uncertainty ranges previously missing in the Coupled Model Intercomparison Project scenarios. These target-based emission-driven simulations offer a more coherent assessment across models for studying both the carbon cycle and its impacts under climate stabilization.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
Geosci. Model Dev., 17, 8665–8681, https://doi.org/10.5194/gmd-17-8665-2024, https://doi.org/10.5194/gmd-17-8665-2024, 2024
Short summary
Short summary
We evaluate downscaled products by examining locally relevant co-variances during precipitation events. Common statistical downscaling techniques preserve expected co-variances during convective precipitation (a stationary phenomenon). However, they dampen future intensification of frontal precipitation (a non-stationary phenomenon) captured in global climate models and dynamical downscaling. Our study quantifies a ramification of the stationarity assumption underlying statistical downscaling.
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.
Alexei Rozanov, Christine Pohl, Carlo Arosio, Adam Bourassa, Klaus Bramstedt, Elizaveta Malinina, Landon Rieger, and John P. Burrows
Atmos. Meas. Tech., 17, 6677–6695, https://doi.org/10.5194/amt-17-6677-2024, https://doi.org/10.5194/amt-17-6677-2024, 2024
Short summary
Short summary
We developed a new algorithm to retrieve vertical distributions of aerosol extinction coefficients in the stratosphere. The algorithm is applied to measurements of scattered solar light from the spaceborne OMPS-LP (Ozone Mapper and Profiler Suite Limb Profiler) instrument. The retrieval results are compared to data from other spaceborne instruments and used to investigate the evolution of the aerosol plume following the eruption of the Hunga Tonga–Hunga Ha'apai volcano in January 2022.
Kamal Nyaupane, Umakant Mishra, Feng Tao, Kyongmin Yeo, William J. Riley, Forrest M. Hoffman, and Sagar Gautam
Biogeosciences, 21, 5173–5183, https://doi.org/10.5194/bg-21-5173-2024, https://doi.org/10.5194/bg-21-5173-2024, 2024
Short summary
Short summary
Representing soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon–climate feedbacks. Using machine learning, we develop and compare predictive relationships in observations (Obs) and ESMs. We find different relationships between environmental factors and SOC stocks in Obs and ESMs. SOC prediction in ESMs may be improved by representing the functional relationships of environmental controllers in a way consistent with observations.
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.
Shunya Koseki, Lander R. Crespo, Jerry Tjiputra, Filippa Fransner, Noel S. Keenlyside, and David Rivas
Biogeosciences, 21, 4149–4168, https://doi.org/10.5194/bg-21-4149-2024, https://doi.org/10.5194/bg-21-4149-2024, 2024
Short summary
Short summary
We investigated how the physical biases of an Earth system model influence the marine biogeochemical processes in the tropical Atlantic. With four different configurations of the model, we have shown that the versions with better SST reproduction tend to better represent the primary production and air–sea CO2 flux in terms of climatology, seasonal cycle, and response to climate variability.
Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic
Atmos. Meas. Tech., 17, 5655–5677, https://doi.org/10.5194/amt-17-5655-2024, https://doi.org/10.5194/amt-17-5655-2024, 2024
Short summary
Short summary
Clouds play a key role in the regulation of the Earth's climate. Aspects like the height of their base are of essential interest to quantify their radiative effects but remain difficult to derive from satellite data. In this study, we combine observations from the surface and satellite retrievals of cloud properties to build a robust and accurate method to retrieve the cloud base height, based on a computer vision model and ordinal regression.
Ed Blockley, Emma Fiedler, Jeff Ridley, Luke Roberts, Alex West, Dan Copsey, Daniel Feltham, Tim Graham, David Livings, Clement Rousset, David Schroeder, and Martin Vancoppenolle
Geosci. Model Dev., 17, 6799–6817, https://doi.org/10.5194/gmd-17-6799-2024, https://doi.org/10.5194/gmd-17-6799-2024, 2024
Short summary
Short summary
This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used as the physical basis for UK contributions to CMIP7. Documentation of science options used in the configuration are given along with a brief model evaluation. This is the first UK configuration to use NEMO’s new SI3 sea ice model. We provide details on how SI3 was adapted to work with Met Office coupling methodology and documentation of coupling processes in the model.
Matthew W. Jones, Douglas I. Kelley, Chantelle A. Burton, Francesca Di Giuseppe, Maria Lucia F. Barbosa, Esther Brambleby, Andrew J. Hartley, Anna Lombardi, Guilherme Mataveli, Joe R. McNorton, Fiona R. Spuler, Jakob B. Wessel, John T. Abatzoglou, Liana O. Anderson, Niels Andela, Sally Archibald, Dolors Armenteras, Eleanor Burke, Rachel Carmenta, Emilio Chuvieco, Hamish Clarke, Stefan H. Doerr, Paulo M. Fernandes, Louis Giglio, Douglas S. Hamilton, Stijn Hantson, Sarah Harris, Piyush Jain, Crystal A. Kolden, Tiina Kurvits, Seppe Lampe, Sarah Meier, Stacey New, Mark Parrington, Morgane M. G. Perron, Yuquan Qu, Natasha S. Ribeiro, Bambang H. Saharjo, Jesus San-Miguel-Ayanz, Jacquelyn K. Shuman, Veerachai Tanpipat, Guido R. van der Werf, Sander Veraverbeke, and Gavriil Xanthopoulos
Earth Syst. Sci. Data, 16, 3601–3685, https://doi.org/10.5194/essd-16-3601-2024, https://doi.org/10.5194/essd-16-3601-2024, 2024
Short summary
Short summary
This inaugural State of Wildfires report catalogues extreme fires of the 2023–2024 fire season. For key events, we analyse their predictability and drivers and attribute them to climate change and land use. We provide a seasonal outlook and decadal projections. Key anomalies occurred in Canada, Greece, and western Amazonia, with other high-impact events catalogued worldwide. Climate change significantly increased the likelihood of extreme fires, and mitigation is required to lessen future risk.
Christine Pohl, Felix Wrana, Alexei Rozanov, Terry Deshler, Elizaveta Malinina, Christian von Savigny, Landon A. Rieger, Adam E. Bourassa, and John P. Burrows
Atmos. Meas. Tech., 17, 4153–4181, https://doi.org/10.5194/amt-17-4153-2024, https://doi.org/10.5194/amt-17-4153-2024, 2024
Short summary
Short summary
Knowledge of stratospheric aerosol characteristics is important for understanding chemical and climate aerosol feedbacks. Two particle size distribution parameters, the aerosol extinction coefficient and the effective radius, are obtained from SCIAMACHY limb observations. The aerosol characteristics show good agreement with independent data sets from balloon-borne and satellite observations. This data set expands the limited knowledge of stratospheric aerosol characteristics.
Minjin Lee, Charles A. Stock, John P. Dunne, and Elena Shevliakova
Geosci. Model Dev., 17, 5191–5224, https://doi.org/10.5194/gmd-17-5191-2024, https://doi.org/10.5194/gmd-17-5191-2024, 2024
Short summary
Short summary
Modeling global freshwater solid and nutrient loads, in both magnitude and form, is imperative for understanding emerging eutrophication problems. Such efforts, however, have been challenged by the difficulty of balancing details of freshwater biogeochemical processes with limited knowledge, input, and validation datasets. Here we develop a global freshwater model that resolves intertwined algae, solid, and nutrient dynamics and provide performance assessment against measurement-based estimates.
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.
Dennis Booge, Jerry F. Tjiputra, Dirk J. L. Olivié, Birgit Quack, and Kirstin Krüger
Earth Syst. Dynam., 15, 801–816, https://doi.org/10.5194/esd-15-801-2024, https://doi.org/10.5194/esd-15-801-2024, 2024
Short summary
Short summary
Oceanic bromoform, produced by algae, is an important precursor of atmospheric bromine, highlighting the importance of implementing these emissions in climate models. The simulated mean oceanic concentrations align well with observations, while the mean atmospheric values are lower than the observed ones. Modelled annual mean emissions mostly occur from the sea to the air and are driven by oceanic concentrations, sea surface temperature, and wind speed, which depend on season and location.
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.
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
Geosci. Model Dev., 17, 3919–3948, https://doi.org/10.5194/gmd-17-3919-2024, https://doi.org/10.5194/gmd-17-3919-2024, 2024
Short summary
Short summary
We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a comprehensive comparison of Earth system models (ESMs) with real-world observations. Using diverse metrics evaluating climatology, variability, and extremes simulated in thousands of simulations from the Coupled Model Intercomparison Project (CMIP), PMP aids in benchmarking model improvements across generations. PMP also enables efficient tracking of performance evolutions during ESM developments.
Federico Fabiano, Paolo Davini, Virna L. Meccia, Giuseppe Zappa, Alessio Bellucci, Valerio Lembo, Katinka Bellomo, and Susanna Corti
Earth Syst. Dynam., 15, 527–546, https://doi.org/10.5194/esd-15-527-2024, https://doi.org/10.5194/esd-15-527-2024, 2024
Short summary
Short summary
Even after the concentration of greenhouse gases is stabilized, the climate will continue to adapt, seeking a new equilibrium. We study this long-term stabilization through a set of 1000-year simulations, obtained by suddenly "freezing" the atmospheric composition at different levels. If frozen at the current state, global warming surpasses 3° in the long term with our model. We then study how climate impacts will change after various centuries and how the deep ocean will warm.
Bryce E. Harrop, Jian Lu, L. Ruby Leung, William K. M. Lau, Kyu-Myong Kim, Brian Medeiros, Brian J. Soden, Gabriel A. Vecchi, Bosong Zhang, and Balwinder Singh
Geosci. Model Dev., 17, 3111–3135, https://doi.org/10.5194/gmd-17-3111-2024, https://doi.org/10.5194/gmd-17-3111-2024, 2024
Short summary
Short summary
Seven new experimental setups designed to interfere with cloud radiative heating have been added to the Energy Exascale Earth System Model (E3SM). These experiments include both those that test the mean impact of cloud radiative heating and those examining its covariance with circulations. This paper documents the code changes and steps needed to run these experiments. Results corroborate prior findings for how cloud radiative heating impacts circulations and rainfall patterns.
Justin L. Willson, Kevin A. Reed, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Mark A. Taylor, Paul A. Ullrich, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Hiroaki Miura, Tomoki Ohno, and Ryuji Yoshida
Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, https://doi.org/10.5194/gmd-17-2493-2024, 2024
Short summary
Short summary
Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.
Vera Melinda Galfi, Tommaso Alberti, Lesley De Cruz, Christian L. E. Franzke, and Valerio Lembo
Nonlin. Processes Geophys., 31, 185–193, https://doi.org/10.5194/npg-31-185-2024, https://doi.org/10.5194/npg-31-185-2024, 2024
Short summary
Short summary
In the online seminar series "Perspectives on climate sciences: from historical developments to future frontiers" (2020–2021), well-known and established scientists from several fields – including mathematics, physics, climate science and ecology – presented their perspectives on the evolution of climate science and on relevant scientific concepts. In this paper, we first give an overview of the content of the seminar series, and then we introduce the written contributions to this special issue.
Lisa Bock and Axel Lauer
Atmos. Chem. Phys., 24, 1587–1605, https://doi.org/10.5194/acp-24-1587-2024, https://doi.org/10.5194/acp-24-1587-2024, 2024
Short summary
Short summary
Climate model simulations still show a large range of effective climate sensitivity (ECS) with high uncertainties. An important contribution to ECS is cloud climate feedback. We investigate the representation of cloud physical and radiative properties from Coupled Model Intercomparison Project models grouped by ECS. We compare the simulated cloud properties of today’s climate from three ECS groups and quantify how the projected changes in cloud properties and cloud radiative effects differ.
Ali Asaadi, Jörg Schwinger, Hanna Lee, Jerry Tjiputra, Vivek Arora, Roland Séférian, Spencer Liddicoat, Tomohiro Hajima, Yeray Santana-Falcón, and Chris D. Jones
Biogeosciences, 21, 411–435, https://doi.org/10.5194/bg-21-411-2024, https://doi.org/10.5194/bg-21-411-2024, 2024
Short summary
Short summary
Carbon cycle feedback metrics are employed to assess phases of positive and negative CO2 emissions. When emissions become negative, we find that the model disagreement in feedback metrics increases more strongly than expected from the assumption that the uncertainties accumulate linearly with time. The geographical patterns of such metrics over land highlight that differences in response between tropical/subtropical and temperate/boreal ecosystems are a major source of model disagreement.
Lele Shu, Paul Ullrich, Xianhong Meng, Christopher Duffy, Hao Chen, and Zhaoguo Li
Geosci. Model Dev., 17, 497–527, https://doi.org/10.5194/gmd-17-497-2024, https://doi.org/10.5194/gmd-17-497-2024, 2024
Short summary
Short summary
Our team developed rSHUD v2.0, a toolkit that simplifies the use of the SHUD, a model simulating water movement in the environment. We demonstrated its effectiveness in two watersheds, one in the USA and one in China. The toolkit also facilitated the creation of the Global Hydrological Data Cloud, a platform for automatic data processing and model deployment, marking a significant advancement in hydrological research.
Neil C. Swart, Torge Martin, Rebecca Beadling, Jia-Jia Chen, Christopher Danek, Matthew H. England, Riccardo Farneti, Stephen M. Griffies, Tore Hattermann, Judith Hauck, F. Alexander Haumann, André Jüling, Qian Li, John Marshall, Morven Muilwijk, Andrew G. Pauling, Ariaan Purich, Inga J. Smith, and Max Thomas
Geosci. Model Dev., 16, 7289–7309, https://doi.org/10.5194/gmd-16-7289-2023, https://doi.org/10.5194/gmd-16-7289-2023, 2023
Short summary
Short summary
Current climate models typically do not include full representation of ice sheets. As the climate warms and the ice sheets melt, they add freshwater to the ocean. This freshwater can influence climate change, for example by causing more sea ice to form. In this paper we propose a set of experiments to test the influence of this missing meltwater from Antarctica using multiple different climate models.
Lee de Mora, Ranjini Swaminathan, Richard P. Allan, Jerry C. Blackford, Douglas I. Kelley, Phil Harris, Chris D. Jones, Colin G. Jones, Spencer Liddicoat, Robert J. Parker, Tristan Quaife, Jeremy Walton, and Andrew Yool
Earth Syst. Dynam., 14, 1295–1315, https://doi.org/10.5194/esd-14-1295-2023, https://doi.org/10.5194/esd-14-1295-2023, 2023
Short summary
Short summary
We investigate the flux of carbon from the atmosphere into the land surface and ocean for multiple models and over a range of future scenarios. We did this by comparing simulations after the same change in the global-mean near-surface temperature. Using this method, we show that the choice of scenario can impact the carbon allocation to the land, ocean, and atmosphere. Scenarios with higher emissions reach the same warming levels sooner, but also with relatively more carbon in the atmosphere.
Veli Çağlar Yumruktepe, Erik Askov Mousing, Jerry Tjiputra, and Annette Samuelsen
Geosci. Model Dev., 16, 6875–6897, https://doi.org/10.5194/gmd-16-6875-2023, https://doi.org/10.5194/gmd-16-6875-2023, 2023
Short summary
Short summary
We present an along BGC-Argo track 1D modelling framework. The model physics is constrained by the BGC-Argo temperature and salinity profiles to reduce the uncertainties related to mixed layer dynamics, allowing the evaluation of the biogeochemical formulation and parameterization. We objectively analyse the model with BGC-Argo and satellite data and improve the model biogeochemical dynamics. We present the framework, example cases and routines for model improvement and implementations.
Jon Seddon, Ag Stephens, Matthew S. Mizielinski, Pier Luigi Vidale, and Malcolm J. Roberts
Geosci. Model Dev., 16, 6689–6700, https://doi.org/10.5194/gmd-16-6689-2023, https://doi.org/10.5194/gmd-16-6689-2023, 2023
Short summary
Short summary
The PRIMAVERA project aimed to develop a new generation of advanced global climate models. The large volume of data generated was uploaded to a central analysis facility (CAF) and was analysed by 100 PRIMAVERA scientists there. We describe how the PRIMAVERA project used the CAF's facilities to enable users to analyse this large dataset. We believe that similar, multi-institute, big-data projects could also use a CAF to efficiently share, organise and analyse large volumes of data.
Michael Sigmond, James Anstey, Vivek Arora, Ruth Digby, Nathan Gillett, Viatcheslav Kharin, William Merryfield, Catherine Reader, John Scinocca, Neil Swart, John Virgin, Carsten Abraham, Jason Cole, Nicolas Lambert, Woo-Sung Lee, Yongxiao Liang, Elizaveta Malinina, Landon Rieger, Knut von Salzen, Christian Seiler, Clint Seinen, Andrew Shao, Reinel Sospedra-Alfonso, Libo Wang, and Duo Yang
Geosci. Model Dev., 16, 6553–6591, https://doi.org/10.5194/gmd-16-6553-2023, https://doi.org/10.5194/gmd-16-6553-2023, 2023
Short summary
Short summary
We present a new activity which aims to organize the analysis of biases in the Canadian Earth System model (CanESM) in a systematic manner. Results of this “Analysis for Development” (A4D) activity includes a new CanESM version, CanESM5.1, which features substantial improvements regarding the simulation of dust and stratospheric temperatures, a second CanESM5.1 variant with reduced climate sensitivity, and insights into potential avenues to reduce various other model biases.
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.
Christoph Heinze, Thorsten Blenckner, Peter Brown, Friederike Fröb, Anne Morée, Adrian L. New, Cara Nissen, Stefanie Rynders, Isabel Seguro, Yevgeny Aksenov, Yuri Artioli, Timothée Bourgeois, Friedrich Burger, Jonathan Buzan, B. B. Cael, Veli Çağlar Yumruktepe, Melissa Chierici, Christopher Danek, Ulf Dieckmann, Agneta Fransson, Thomas Frölicher, Giovanni Galli, Marion Gehlen, Aridane G. González, Melchor Gonzalez-Davila, Nicolas Gruber, Örjan Gustafsson, Judith Hauck, Mikko Heino, Stephanie Henson, Jenny Hieronymus, I. Emma Huertas, Fatma Jebri, Aurich Jeltsch-Thömmes, Fortunat Joos, Jaideep Joshi, Stephen Kelly, Nandini Menon, Precious Mongwe, Laurent Oziel, Sólveig Ólafsdottir, Julien Palmieri, Fiz F. Pérez, Rajamohanan Pillai Ranith, Juliano Ramanantsoa, Tilla Roy, Dagmara Rusiecka, J. Magdalena Santana Casiano, Yeray Santana-Falcón, Jörg Schwinger, Roland Séférian, Miriam Seifert, Anna Shchiptsova, Bablu Sinha, Christopher Somes, Reiner Steinfeldt, Dandan Tao, Jerry Tjiputra, Adam Ulfsbo, Christoph Völker, Tsuyoshi Wakamatsu, and Ying Ye
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-182, https://doi.org/10.5194/bg-2023-182, 2023
Revised manuscript not accepted
Short summary
Short summary
For assessing the consequences of human-induced climate change for the marine realm, it is necessary to not only look at gradual changes but also at abrupt changes of environmental conditions. We summarise abrupt changes in ocean warming, acidification, and oxygen concentration as the key environmental factors for ecosystems. Taking these abrupt changes into account requires greenhouse gas emissions to be reduced to a larger extent than previously thought to limit respective damage.
Jonathan D. Sharp, Andrea J. Fassbender, Brendan R. Carter, Gregory C. Johnson, Cristina Schultz, and John P. Dunne
Earth Syst. Sci. Data, 15, 4481–4518, https://doi.org/10.5194/essd-15-4481-2023, https://doi.org/10.5194/essd-15-4481-2023, 2023
Short summary
Short summary
Dissolved oxygen content is a critical metric of ocean health. Recently, expanding fleets of autonomous platforms that measure oxygen in the ocean have produced a wealth of new data. We leverage machine learning to take advantage of this growing global dataset, producing a gridded data product of ocean interior dissolved oxygen at monthly resolution over nearly 2 decades. This work provides novel information for investigations of spatial, seasonal, and interannual variability in ocean oxygen.
Joao Carlos Martins Teixeira, Chantelle Burton, Douglas I. Kelly, Gerd A. Folberth, Fiona M. O'Connor, Richard A. Betts, and Apostolos Voulgarakis
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-136, https://doi.org/10.5194/bg-2023-136, 2023
Revised manuscript not accepted
Short summary
Short summary
Representing socio-economic impacts on fires is crucial to underpin the confidence in global fire models. Introducing these into INFERNO, reduces biases and improves the modelled burnt area (BA) trends when compared to observations. Including socio-economic factors in the representation of fires in Earth System Models is important for realistically simulating BA, quantifying trends in the recent past, and for understanding the main drivers of those at regional scales.
Camilla Mathison, Eleanor Burke, Andrew J. Hartley, Douglas I. Kelley, Chantelle Burton, Eddy Robertson, Nicola Gedney, Karina Williams, Andy Wiltshire, Richard J. Ellis, Alistair A. Sellar, and Chris D. Jones
Geosci. Model Dev., 16, 4249–4264, https://doi.org/10.5194/gmd-16-4249-2023, https://doi.org/10.5194/gmd-16-4249-2023, 2023
Short summary
Short summary
This paper describes and evaluates a new modelling methodology to quantify the impacts of climate change on water, biomes and the carbon cycle. We have created a new configuration and set-up for the JULES-ES land surface model, driven by bias-corrected historical and future climate model output provided by the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). This allows us to compare projections of the impacts of climate change across multiple impact models and multiple sectors.
Emmanouil Flaounas, Leonardo Aragão, Lisa Bernini, Stavros Dafis, Benjamin Doiteau, Helena Flocas, Suzanne L. Gray, Alexia Karwat, John Kouroutzoglou, Piero Lionello, Mario Marcello Miglietta, Florian Pantillon, Claudia Pasquero, Platon Patlakas, María Ángeles Picornell, Federico Porcù, Matthew D. K. Priestley, Marco Reale, Malcolm J. Roberts, Hadas Saaroni, Dor Sandler, Enrico Scoccimarro, Michael Sprenger, and Baruch Ziv
Weather Clim. Dynam., 4, 639–661, https://doi.org/10.5194/wcd-4-639-2023, https://doi.org/10.5194/wcd-4-639-2023, 2023
Short summary
Short summary
Cyclone detection and tracking methods (CDTMs) have different approaches in defining and tracking cyclone centers. This leads to disagreements on extratropical cyclone climatologies. We present a new approach that combines tracks from individual CDTMs to produce new composite tracks. These new tracks are shown to correspond to physically meaningful systems with distinctive life stages.
Xiaojuan Yang, Peter Thornton, Daniel Ricciuto, Yilong Wang, and Forrest Hoffman
Biogeosciences, 20, 2813–2836, https://doi.org/10.5194/bg-20-2813-2023, https://doi.org/10.5194/bg-20-2813-2023, 2023
Short summary
Short summary
We evaluated the performance of a land surface model (ELMv1-CNP) that includes both nitrogen (N) and phosphorus (P) limitation on carbon cycle processes. We show that ELMv1-CNP produces realistic estimates of present-day carbon pools and fluxes. We show that global C sources and sinks are significantly affected by P limitation. Our study suggests that introduction of P limitation in land surface models is likely to have substantial consequences for projections of future carbon uptake.
Min-Seop Ahn, Paul A. Ullrich, Peter J. Gleckler, Jiwoo Lee, Ana C. Ordonez, and Angeline G. Pendergrass
Geosci. Model Dev., 16, 3927–3951, https://doi.org/10.5194/gmd-16-3927-2023, https://doi.org/10.5194/gmd-16-3927-2023, 2023
Short summary
Short summary
We introduce a framework for regional-scale evaluation of simulated precipitation distributions with 62 climate reference regions and 10 metrics and apply it to evaluate CMIP5 and CMIP6 models against multiple satellite-based precipitation products. The common model biases identified in this study are mainly associated with the overestimated light precipitation and underestimated heavy precipitation. These biases persist from earlier-generation models and have been slightly improved in CMIP6.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
Short summary
Short summary
High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
Leroy J. Bird, Matthew G. W. Walker, Greg E. Bodeker, Isaac H. Campbell, Guangzhong Liu, Swapna Josmi Sam, Jared Lewis, and Suzanne M. Rosier
Geosci. Model Dev., 16, 3785–3808, https://doi.org/10.5194/gmd-16-3785-2023, https://doi.org/10.5194/gmd-16-3785-2023, 2023
Short summary
Short summary
Deriving the statistics of expected future changes in extreme precipitation is challenging due to these events being rare. Regional climate models (RCMs) are computationally prohibitive for generating ensembles capable of capturing large numbers of extreme precipitation events with statistical robustness. Stochastic precipitation generators (SPGs) provide an alternative to RCMs. We describe a novel single-site SPG that learns the statistics of precipitation using a machine-learning approach.
Doaa Aboelyazeed, Chonggang Xu, Forrest M. Hoffman, Jiangtao Liu, Alex W. Jones, Chris Rackauckas, Kathryn Lawson, and Chaopeng Shen
Biogeosciences, 20, 2671–2692, https://doi.org/10.5194/bg-20-2671-2023, https://doi.org/10.5194/bg-20-2671-2023, 2023
Short summary
Short summary
Photosynthesis is critical for life and has been affected by the changing climate. Many parameters come into play while modeling, but traditional calibration approaches face many issues. Our framework trains coupled neural networks to provide parameters to a photosynthesis model. Using big data, we independently found parameter values that were correlated with those in the literature while giving higher correlation and reduced biases in photosynthesis rates.
Abhishekh Kumar Srivastava, Paul Aaron Ullrich, Deeksha Rastogi, Pouya Vahmani, Andrew Jones, and Richard Grotjahn
Geosci. Model Dev., 16, 3699–3722, https://doi.org/10.5194/gmd-16-3699-2023, https://doi.org/10.5194/gmd-16-3699-2023, 2023
Short summary
Short summary
Stakeholders need high-resolution regional climate data for applications such as assessing water availability and mountain snowpack. This study examines 3 h and 24 h historical precipitation over the contiguous United States in the 12 km WRF version 4.2.1-based dynamical downscaling of the ERA5 reanalysis. WRF improves precipitation characteristics such as the annual cycle and distribution of the precipitation maxima, but it also displays regionally and seasonally varying precipitation biases.
Bharat Sharma, Jitendra Kumar, Auroop R. Ganguly, and Forrest M. Hoffman
Biogeosciences, 20, 1829–1841, https://doi.org/10.5194/bg-20-1829-2023, https://doi.org/10.5194/bg-20-1829-2023, 2023
Short summary
Short summary
Rising atmospheric carbon dioxide increases vegetation growth and causes more heatwaves and droughts. The impact of such climate extremes is detrimental to terrestrial carbon uptake capacity. We found that due to overall climate warming, about 88 % of the world's regions towards the end of 2100 will show anomalous losses in net biospheric productivity (NBP) rather than gains. More than 50 % of all negative NBP extremes were driven by the compound effect of dry, hot, and fire conditions.
Zeyu Xue, Paul Ullrich, and Lai-Yung Ruby Leung
Hydrol. Earth Syst. Sci., 27, 1909–1927, https://doi.org/10.5194/hess-27-1909-2023, https://doi.org/10.5194/hess-27-1909-2023, 2023
Short summary
Short summary
We examine the sensitivity and robustness of conclusions drawn from the PGW method over the NEUS by conducting multiple PGW experiments and varying the perturbation spatial scales and choice of perturbed meteorological variables to provide a guideline for this increasingly popular regional modeling method. Overall, we recommend PGW experiments be performed with perturbations to temperature or the combination of temperature and wind at the gridpoint scale, depending on the research question.
Claire Waelbroeck, Jerry Tjiputra, Chuncheng Guo, Kerim H. Nisancioglu, Eystein Jansen, Natalia Vázquez Riveiros, Samuel Toucanne, Frédérique Eynaud, Linda Rossignol, Fabien Dewilde, Elodie Marchès, Susana Lebreiro, and Silvia Nave
Clim. Past, 19, 901–913, https://doi.org/10.5194/cp-19-901-2023, https://doi.org/10.5194/cp-19-901-2023, 2023
Short summary
Short summary
The precise geometry and extent of Atlantic circulation changes that accompanied rapid climate changes of the last glacial period are still unknown. Here, we combine carbon isotopic records from 18 Atlantic sediment cores with numerical simulations and decompose the carbon isotopic change across a cold-to-warm transition into remineralization and circulation components. Our results show that the replacement of southern-sourced by northern-sourced water plays a dominant role below ~ 3000 m depth.
Nadine Goris, Klaus Johannsen, and Jerry Tjiputra
Geosci. Model Dev., 16, 2095–2117, https://doi.org/10.5194/gmd-16-2095-2023, https://doi.org/10.5194/gmd-16-2095-2023, 2023
Short summary
Short summary
Climate projections of a high-CO2 future are highly uncertain. A new study provides a novel approach to identifying key regions that dynamically explain the model uncertainty. To yield an accurate estimate of the future North Atlantic carbon uptake, we find that a correct simulation of the upper- and interior-ocean volume transport at 25–30° N is key. However, results indicate that models rarely perform well for both indicators and point towards inconsistencies within the model ensemble.
Alban Planchat, Lester Kwiatkowski, Laurent Bopp, Olivier Torres, James R. Christian, Momme Butenschön, Tomas Lovato, Roland Séférian, Matthew A. Chamberlain, Olivier Aumont, Michio Watanabe, Akitomo Yamamoto, Andrew Yool, Tatiana Ilyina, Hiroyuki Tsujino, Kristen M. Krumhardt, Jörg Schwinger, Jerry Tjiputra, John P. Dunne, and Charles Stock
Biogeosciences, 20, 1195–1257, https://doi.org/10.5194/bg-20-1195-2023, https://doi.org/10.5194/bg-20-1195-2023, 2023
Short summary
Short summary
Ocean alkalinity is critical to the uptake of atmospheric carbon and acidification in surface waters. We review the representation of alkalinity and the associated calcium carbonate cycle in Earth system models. While many parameterizations remain present in the latest generation of models, there is a general improvement in the simulated alkalinity distribution. This improvement is related to an increase in the export of biotic calcium carbonate, which closer resembles observations.
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.
Jane P. Mulcahy, Colin G. Jones, Steven T. Rumbold, Till Kuhlbrodt, Andrea J. Dittus, Edward W. Blockley, Andrew Yool, Jeremy Walton, Catherine Hardacre, Timothy Andrews, Alejandro Bodas-Salcedo, Marc Stringer, Lee de Mora, Phil Harris, Richard Hill, Doug Kelley, Eddy Robertson, and Yongming Tang
Geosci. Model Dev., 16, 1569–1600, https://doi.org/10.5194/gmd-16-1569-2023, https://doi.org/10.5194/gmd-16-1569-2023, 2023
Short summary
Short summary
Recent global climate models simulate historical global mean surface temperatures which are too cold, possibly to due to excessive aerosol cooling. This raises questions about the models' ability to simulate important climate processes and reduces confidence in future climate predictions. We present a new version of the UK Earth System Model, which has an improved aerosols simulation and a historical temperature record. Interestingly, the long-term response to CO2 remains largely unchanged.
David H. Marsico and Paul A. Ullrich
Geosci. Model Dev., 16, 1537–1551, https://doi.org/10.5194/gmd-16-1537-2023, https://doi.org/10.5194/gmd-16-1537-2023, 2023
Short summary
Short summary
Climate models involve several different components, such as the atmosphere, ocean, and land models. Information needs to be exchanged, or remapped, between these models, and devising algorithms for performing this exchange is important for ensuring the accuracy of climate simulations. In this paper, we examine the efficacy of several traditional and novel approaches to remapping on the sphere and demonstrate where our approaches offer improvement.
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.
Shuang Gao, Jörg Schwinger, Jerry Tjiputra, Ingo Bethke, Jens Hartmann, Emilio Mayorga, and Christoph Heinze
Biogeosciences, 20, 93–119, https://doi.org/10.5194/bg-20-93-2023, https://doi.org/10.5194/bg-20-93-2023, 2023
Short summary
Short summary
We assess the impact of riverine nutrients and carbon (C) on projected marine primary production (PP) and C uptake using a fully coupled Earth system model. Riverine inputs alleviate nutrient limitation and thus lessen the projected PP decline by up to 0.7 Pg C yr−1 globally. The effect of increased riverine C may be larger than the effect of nutrient inputs in the future on the projected ocean C uptake, while in the historical period increased nutrient inputs are considered the largest driver.
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.
Chengzhu Zhang, Jean-Christophe Golaz, Ryan Forsyth, Tom Vo, Shaocheng Xie, Zeshawn Shaheen, Gerald L. Potter, Xylar S. Asay-Davis, Charles S. Zender, Wuyin Lin, Chih-Chieh Chen, Chris R. Terai, Salil Mahajan, Tian Zhou, Karthik Balaguru, Qi Tang, Cheng Tao, Yuying Zhang, Todd Emmenegger, Susannah Burrows, and Paul A. Ullrich
Geosci. Model Dev., 15, 9031–9056, https://doi.org/10.5194/gmd-15-9031-2022, https://doi.org/10.5194/gmd-15-9031-2022, 2022
Short summary
Short summary
Earth system model (ESM) developers run automated analysis tools on data from candidate models to inform model development. This paper introduces a new Python package, E3SM Diags, that has been developed to support ESM development and use routinely in the development of DOE's Energy Exascale Earth System Model. This tool covers a set of essential diagnostics to evaluate the mean physical climate from simulations, as well as several process-oriented and phenomenon-based evaluation diagnostics.
Diego Bruciaferri, Marina Tonani, Isabella Ascione, Fahad Al Senafi, Enda O'Dea, Helene T. Hewitt, and Andrew Saulter
Geosci. Model Dev., 15, 8705–8730, https://doi.org/10.5194/gmd-15-8705-2022, https://doi.org/10.5194/gmd-15-8705-2022, 2022
Short summary
Short summary
More accurate predictions of the Gulf's ocean dynamics are needed. We investigate the impact on the predictive skills of a numerical shelf sea model of the Gulf after changing a few key aspects. Increasing the lateral and vertical resolution and optimising the vertical coordinate system to best represent the leading physical processes at stake significantly improve the accuracy of the simulated dynamics. Additional work may be needed to get real benefit from using a more realistic bathymetry.
Julia F. Lockwood, Galina S. Guentchev, Alexander Alabaster, Simon J. Brown, Erika J. Palin, Malcolm J. Roberts, and Hazel E. Thornton
Nat. Hazards Earth Syst. Sci., 22, 3585–3606, https://doi.org/10.5194/nhess-22-3585-2022, https://doi.org/10.5194/nhess-22-3585-2022, 2022
Short summary
Short summary
We describe how we developed a set of 1300 years' worth of European winter windstorm footprints, using a multi-model ensemble of high-resolution global climate models, for use by the insurance industry to analyse windstorm risk. The large amount of data greatly reduces uncertainty on risk estimates compared to using shorter observational data sets and also allows the relationship between windstorm risk and predictable large-scale climate indices to be quantified.
Alex West, Edward Blockley, and Matthew Collins
The Cryosphere, 16, 4013–4032, https://doi.org/10.5194/tc-16-4013-2022, https://doi.org/10.5194/tc-16-4013-2022, 2022
Short summary
Short summary
In this study we explore a method of examining model differences in ice volume by looking at the seasonal ice growth and melt. We use simple physical relationships to judge how model differences in key variables affect ice growth and melt and apply these to three case study models with ice volume ranging from very thin to very thick. Results suggest that differences in snow and melt pond cover in early summer are most important in causing the sea ice differences for these models.
Valerio Lembo, Federico Fabiano, Vera Melinda Galfi, Rune Grand Graversen, Valerio Lucarini, and Gabriele Messori
Weather Clim. Dynam., 3, 1037–1062, https://doi.org/10.5194/wcd-3-1037-2022, https://doi.org/10.5194/wcd-3-1037-2022, 2022
Short summary
Short summary
Eddies in mid-latitudes characterize the exchange of heat between the tropics and the poles. This exchange is largely uneven, with a few extreme events bearing most of the heat transported across latitudes in a season. It is thus important to understand what the dynamical mechanisms are behind these events. Here, we identify recurrent weather regime patterns associated with extreme transports, and we identify scales of mid-latitudinal eddies that are mostly responsible for the transport.
Vijay S. Mahadevan, Jorge E. Guerra, Xiangmin Jiao, Paul Kuberry, Yipeng Li, Paul Ullrich, David Marsico, Robert Jacob, Pavel Bochev, and Philip Jones
Geosci. Model Dev., 15, 6601–6635, https://doi.org/10.5194/gmd-15-6601-2022, https://doi.org/10.5194/gmd-15-6601-2022, 2022
Short summary
Short summary
Coupled Earth system models require transfer of field data between multiple components with varying spatial resolutions to determine the correct climate behavior. We present the Metrics for Intercomparison of Remapping Algorithms (MIRA) protocol to evaluate the accuracy, conservation properties, monotonicity, and local feature preservation of four different remapper algorithms for various unstructured mesh problems of interest. Future extensions to more practical use cases are also discussed.
Martina Stockhause and Michael Lautenschlager
Geosci. Model Dev., 15, 6047–6058, https://doi.org/10.5194/gmd-15-6047-2022, https://doi.org/10.5194/gmd-15-6047-2022, 2022
Short summary
Short summary
The Data Distribution Centre (DDC) of the Intergovernmental Panel on Climate Change (IPCC) celebrates its 25th anniversary in 2022. DDC Partner DKRZ has supported the IPCC Assessments and preserved the quality-assured, citable climate model data underpinning the Assessment Reports over these years over the long term. With the introduction of the IPCC FAIR Guidelines into the current AR6, the value of DDC services has been recognized. However, DDC sustainability remains unresolved.
Pradeebane Vaittinada Ayar, Laurent Bopp, Jim R. Christian, Tatiana Ilyina, John P. Krasting, Roland Séférian, Hiroyuki Tsujino, Michio Watanabe, Andrew Yool, and Jerry Tjiputra
Earth Syst. Dynam., 13, 1097–1118, https://doi.org/10.5194/esd-13-1097-2022, https://doi.org/10.5194/esd-13-1097-2022, 2022
Short summary
Short summary
The El Niño–Southern Oscillation is the main driver for the natural variability of global atmospheric CO2. It modulates the CO2 fluxes in the tropical Pacific with anomalous CO2 influx during El Niño and outflux during La Niña. This relationship is projected to reverse by half of Earth system models studied here under the business-as-usual scenario. This study shows models that simulate a positive bias in surface carbonate concentrations simulate a shift in the ENSO–CO2 flux relationship.
Rolf Hut, Niels Drost, Nick van de Giesen, Ben van Werkhoven, Banafsheh Abdollahi, Jerom Aerts, Thomas Albers, Fakhereh Alidoost, Bouwe Andela, Jaro Camphuijsen, Yifat Dzigan, Ronald van Haren, Eric Hutton, Peter Kalverla, Maarten van Meersbergen, Gijs van den Oord, Inti Pelupessy, Stef Smeets, Stefan Verhoeven, Martine de Vos, and Berend Weel
Geosci. Model Dev., 15, 5371–5390, https://doi.org/10.5194/gmd-15-5371-2022, https://doi.org/10.5194/gmd-15-5371-2022, 2022
Short summary
Short summary
With the eWaterCycle platform, we are providing the hydrological community with a platform to conduct their research that is fully compatible with the principles of both open science and FAIR science. The eWatercyle platform gives easy access to well-known hydrological models, big datasets and example experiments. Using eWaterCycle hydrologists can easily compare the results from different models, couple models and do more complex hydrological computational research.
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.
Rahayu Adzhar, Douglas I. Kelley, Ning Dong, Charles George, Mireia Torello Raventos, Elmar Veenendaal, Ted R. Feldpausch, Oliver L. Phillips, Simon L. Lewis, Bonaventure Sonké, Herman Taedoumg, Beatriz Schwantes Marimon, Tomas Domingues, Luzmila Arroyo, Gloria Djagbletey, Gustavo Saiz, and France Gerard
Biogeosciences, 19, 1377–1394, https://doi.org/10.5194/bg-19-1377-2022, https://doi.org/10.5194/bg-19-1377-2022, 2022
Short summary
Short summary
The MODIS Vegetation Continuous Fields (VCF) product underestimates tree cover compared to field data and could be underestimating tree cover significantly across the tropics. VCF is used to represent land cover or validate model performance in many land surface and global vegetation models and to train finer-scaled Earth observation products. Because underestimation in VCF may render it unsuitable for training data and bias model predictions, it should be calibrated before use in the tropics.
Enrico Scoccimarro, Daniele Peano, Silvio Gualdi, Alessio Bellucci, Tomas Lovato, Pier Giuseppe Fogli, and Antonio Navarra
Geosci. Model Dev., 15, 1841–1854, https://doi.org/10.5194/gmd-15-1841-2022, https://doi.org/10.5194/gmd-15-1841-2022, 2022
Short summary
Short summary
This study evaluated the ability of the CMCC-CM2 climate model participating to the last CMIP6 effort, in representing extreme events of precipitation and temperature at the daily and 6-hourly frequencies. The 1/4° resolution version of the atmospheric model provides better results than the version at 1° resolution for temperature extremes, at both time frequencies. For precipitation extremes, especially at the daily time frequency, the higher resolution does not improve model results.
Swen Brands
Geosci. Model Dev., 15, 1375–1411, https://doi.org/10.5194/gmd-15-1375-2022, https://doi.org/10.5194/gmd-15-1375-2022, 2022
Short summary
Short summary
The present study evaluates the last two global climate model generations in terms of their capability to reproduce recurrent regional atmospheric circulation patterns in the Northern Hemisphere mid-to-high latitudes under present climate conditions. These patterns are linked with many environmental variables on the local scale and thus provide an overarching concept for model verification. The results are expected to be of interest for model developers and regional climate scientists.
Filippa Fransner, Friederike Fröb, Jerry Tjiputra, Nadine Goris, Siv K. Lauvset, Ingunn Skjelvan, Emil Jeansson, Abdirahman Omar, Melissa Chierici, Elizabeth Jones, Agneta Fransson, Sólveig R. Ólafsdóttir, Truls Johannessen, and Are Olsen
Biogeosciences, 19, 979–1012, https://doi.org/10.5194/bg-19-979-2022, https://doi.org/10.5194/bg-19-979-2022, 2022
Short summary
Short summary
Ocean acidification, a direct consequence of the CO2 release by human activities, is a serious threat to marine ecosystems. In this study, we conduct a detailed investigation of the acidification of the Nordic Seas, from 1850 to 2100, by using a large set of samples taken during research cruises together with numerical model simulations. We estimate the effects of changes in different environmental factors on the rate of acidification and its potential effects on cold-water corals.
Martijn M. T. A. Pallandt, Jitendra Kumar, Marguerite Mauritz, Edward A. G. Schuur, Anna-Maria Virkkala, Gerardo Celis, Forrest M. Hoffman, and Mathias Göckede
Biogeosciences, 19, 559–583, https://doi.org/10.5194/bg-19-559-2022, https://doi.org/10.5194/bg-19-559-2022, 2022
Short summary
Short summary
Thawing of Arctic permafrost soils could trigger the release of vast amounts of carbon to the atmosphere, thus enhancing climate change. Our study investigated how well the current network of eddy covariance sites to monitor greenhouse gas exchange at local scales captures pan-Arctic flux patterns. We identified large coverage gaps, e.g., in Siberia, but also demonstrated that a targeted addition of relatively few sites can significantly improve network performance.
Eduardo Moreno-Chamarro, Louis-Philippe Caron, Saskia Loosveldt Tomas, Javier Vegas-Regidor, Oliver Gutjahr, Marie-Pierre Moine, Dian Putrasahan, Christopher D. Roberts, Malcolm J. Roberts, Retish Senan, Laurent Terray, Etienne Tourigny, and Pier Luigi Vidale
Geosci. Model Dev., 15, 269–289, https://doi.org/10.5194/gmd-15-269-2022, https://doi.org/10.5194/gmd-15-269-2022, 2022
Short summary
Short summary
Climate models do not fully reproduce observations: they show differences (biases) in regional temperature, precipitation, or cloud cover. Reducing model biases is important to increase our confidence in their ability to reproduce present and future climate changes. Model realism is set by its resolution: the finer it is, the more physical processes and interactions it can resolve. We here show that increasing resolution of up to ~ 25 km can help reduce model biases but not remove them entirely.
Emma K. Fiedler, Matthew J. Martin, Ed Blockley, Davi Mignac, Nicolas Fournier, Andy Ridout, Andrew Shepherd, and Rachel Tilling
The Cryosphere, 16, 61–85, https://doi.org/10.5194/tc-16-61-2022, https://doi.org/10.5194/tc-16-61-2022, 2022
Short summary
Short summary
Sea ice thickness (SIT) observations derived from CryoSat-2 satellite measurements have been successfully used to initialise an ocean and sea ice forecasting model (FOAM). Other centres have previously used gridded and averaged SIT observations for this purpose, but we demonstrate here for the first time that SIT measurements along the satellite orbit track can be used. Validation of the resulting modelled SIT demonstrates improvements in the model performance compared to a control.
Mark R. Muetzelfeldt, Reinhard Schiemann, Andrew G. Turner, Nicholas P. Klingaman, Pier Luigi Vidale, and Malcolm J. Roberts
Hydrol. Earth Syst. Sci., 25, 6381–6405, https://doi.org/10.5194/hess-25-6381-2021, https://doi.org/10.5194/hess-25-6381-2021, 2021
Short summary
Short summary
Simulating East Asian Summer Monsoon (EASM) rainfall poses many challenges because of its multi-scale nature. We evaluate three setups of a 14 km global climate model against observations to see if they improve simulated rainfall. We do this over catchment basins of different sizes to estimate how model performance depends on spatial scale. Using explicit convection improves rainfall diurnal cycle, yet more model tuning is needed to improve mean and intensity biases in simulated summer rainfall.
Ingo Bethke, Yiguo Wang, François Counillon, Noel Keenlyside, Madlen Kimmritz, Filippa Fransner, Annette Samuelsen, Helene Langehaug, Lea Svendsen, Ping-Gin Chiu, Leilane Passos, Mats Bentsen, Chuncheng Guo, Alok Gupta, Jerry Tjiputra, Alf Kirkevåg, Dirk Olivié, Øyvind Seland, Julie Solsvik Vågane, Yuanchao Fan, and Tor Eldevik
Geosci. Model Dev., 14, 7073–7116, https://doi.org/10.5194/gmd-14-7073-2021, https://doi.org/10.5194/gmd-14-7073-2021, 2021
Short summary
Short summary
The Norwegian Climate Prediction Model version 1 (NorCPM1) is a new research tool for performing climate reanalyses and seasonal-to-decadal climate predictions. It adds data assimilation capability to the Norwegian Earth System Model version 1 (NorESM1) and has contributed output to the Decadal Climate Prediction Project (DCPP) as part of the sixth Coupled Model Intercomparison Project (CMIP6). We describe the system and evaluate its baseline, reanalysis and prediction performance.
Elizaveta Malinina, Alexei Rozanov, Ulrike Niemeier, Sandra Wallis, Carlo Arosio, Felix Wrana, Claudia Timmreck, Christian von Savigny, and John P. Burrows
Atmos. Chem. Phys., 21, 14871–14891, https://doi.org/10.5194/acp-21-14871-2021, https://doi.org/10.5194/acp-21-14871-2021, 2021
Short summary
Short summary
In the paper, changes in the stratospheric aerosol loading after the 2018 Ambae eruption were analyzed using OMPS-LP observations. The eruption was also simulated with the MAECHAM5-HAM global climate model. Generally, the model and observations agree very well. We attribute the good consistency of the results to a precisely determined altitude and mass of the volcanic injection, as well as nudging of the meteorological data. The radiative forcing from the eruption was estimated to be −0.13 W m−2.
Yaoping Wang, Jiafu Mao, Mingzhou Jin, Forrest M. Hoffman, Xiaoying Shi, Stan D. Wullschleger, and Yongjiu Dai
Earth Syst. Sci. Data, 13, 4385–4405, https://doi.org/10.5194/essd-13-4385-2021, https://doi.org/10.5194/essd-13-4385-2021, 2021
Short summary
Short summary
We developed seven global soil moisture datasets (1970–2016, monthly, half-degree, and multilayer) by merging a wide range of data sources, including in situ and satellite observations, reanalysis, offline land surface model simulations, and Earth system model simulations. Given the great value of long-term, multilayer, gap-free soil moisture products to climate research and applications, we believe this paper and the presented datasets would be of interest to many different communities.
Paul A. Ullrich, Colin M. Zarzycki, Elizabeth E. McClenny, Marielle C. Pinheiro, Alyssa M. Stansfield, and Kevin A. Reed
Geosci. Model Dev., 14, 5023–5048, https://doi.org/10.5194/gmd-14-5023-2021, https://doi.org/10.5194/gmd-14-5023-2021, 2021
Short summary
Short summary
TempestExtremes (TE) is a multifaceted framework for feature detection, tracking, and scientific analysis of regional or global Earth system datasets. Version 2.1 of TE now provides extensive support for nodal and areal features. This paper describes the algorithms that have been added to the TE framework since version 1.0 and gives several examples of how these can be combined to produce composite algorithms for evaluating and understanding atmospheric features.
Cited articles
Adler, R., Wang, J.-J., Sapiano, M., Huffman, G., Chiu, L., Xie, P.-P., Ferraro, R., Schneider, U., Becker, A., Bolvin, D., Nelkin, E., Gu, G., and NOAA CDR Program: Global Precipitation Climatology Project (GPCP) Climate Data Record (CDR), Version 2.3 (Monthly), NOAA National Centers for Environmental Information, https://doi.org/10.7289/V56971M6, 2017. a, b
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P.-P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., and Nelkin, E.: The Version-2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979–Present), J. Hydrometeorol., 4, 1147–1167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2, 2003. a, b
Adler, R. F., Sapiano, M. R. P., Huffman, G. J., Wang, J.-J., Gu, G., Bolvin, D., Chiu, L., Schneider, U., Becker, A., Nelkin, E., Xie, P., Ferraro, R., and Shin, D.-B.: The Global Precipitation Climatology Project (GPCP) Monthly Analysis (New Version 2.3) and a Review of 2017 Global Precipitation, Atmosphere, 9, 138, https://doi.org/10.3390/atmos9040138, 2018. a, b
Agarwal, D., Ayliffe, J., Buck, J. J. H., Damerow, J., Parton, G., Stall, S., Stockhause, M., and Wyborn, L.: Complex Citation Working Group Recommendation, Research Data Alliance (RDA), https://doi.org/10.15497/RDA00130, 2025. a
Alemohammad, S. H., Fang, B., Konings, A. G., Aires, F., Green, J. K., Kolassa, J., Miralles, D., Prigent, C., and Gentine, P.: Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence, Biogeosciences, 14, 4101–4124, https://doi.org/10.5194/bg-14-4101-2017, 2017. a
Anav, A., Friedlingstein, P., Beer, C., Ciais, P., Harper, A., Jones, C., Murray-Tortarolo, G., Papale, D., Parazoo, N. C., Peylin, P., Piao, S., Sitch, S., Viovy, N., Wiltshire, A., and Zhao, M.: Spatiotemporal Patterns of Terrestrial Gross Primary Production: A Review, Rev. Geophys., 53, 785–818, https://doi.org/10.1002/2015RG000483, 2015. a
Andela, B., Broetz, B., de Mora, L., Drost, N., Eyring, V., Koldunov, N., Lauer, A., Mueller, B., Predoi, V., Righi, M., Schlund, M., Vegas-Regidor, J., Zimmermann, K., Adeniyi, K., Castellani, G., Arnone, E., Bellprat, O., Berg, P., Billows, C., Blockley, E., Bock, L., Bodas-Salcedo, A., Caron, L.-P., Carvalhais, N., Cionni, I., Cortesi, N., Corti, S., Crezee, B., Davin, E. L., Davini, P., Deser, C., Diblen, F., Docquier, D., Dreyer, L., Ehbrecht, C., Earnshaw, P., Geddes, T., Gier, B., Gillett, E., Gonzalez-Reviriego, N., Goodman, P., Hagemann, S., Hall, S., Hardacre, C., von Hardenberg, J., Hassler, B., Heuer, H., Hogan, E., Hunter, A., Kadow, C., Kindermann, S., Koirala, S., Kuehbacher, B., Lledó, L., Lejeune, Q., Lembo, V., Little, B., Loosveldt-Tomas, S., Lorenz, R., Lovato, T., Lucarini, V., Malinina, E., Massonnet, F., Mohr, C. W., Amarjiit, P., Parsons, N., Pérez-Zanón, N., Phillips, A., Proft, M., Russell, J., Sandstad, M., Sellar, A., Senftleben, D., Serva, F., Sillmann, J., Stacke, T., Storkey, D., Swaminathan, R., Tomkins, K., Torralba, V., Weigel, K., Sarauer, E., Schulze, K., Roberts, C., Kalverla, P., Alidoost, S., Verhoeven, S., Vreede, B., Smeets, S., Soares Siqueira, A., Kazeroni, R., Potter, J., Winterstein, F., Beucher, R., Kraft, J., Ruhe, L., Bonnet, P., Munday, G., Chun, F., and Ellis, H.: ESMValTool, Zenodo, https://doi.org/10.5281/zenodo.18998400, 2025. a
Arora, V. K., Katavouta, A., Williams, R. G., Jones, C. D., Brovkin, V., Friedlingstein, P., Schwinger, J., Bopp, L., Boucher, O., Cadule, P., Chamberlain, M. A., Christian, J. R., Delire, C., Fisher, R. A., Hajima, T., Ilyina, T., Joetzjer, E., Kawamiya, M., Koven, C. D., Krasting, J. P., Law, R. M., Lawrence, D. M., Lenton, A., Lindsay, K., Pongratz, J., Raddatz, T., Séférian, R., Tachiiri, K., Tjiputra, J. F., Wiltshire, A., Wu, T., and Ziehn, T.: Carbon–concentration and carbon–climate feedbacks in CMIP6 models and their comparison to CMIP5 models, Biogeosciences, 17, 4173–4222, https://doi.org/10.5194/bg-17-4173-2020, 2020. a
Bacer, S., Jomaa, F., Beaumet, J., Gallée, H., Le Bouëdec, E., Ménégoz, M., and Staquet, C.: Impact of climate change on wintertime European atmospheric blocking, Weather Clim. Dynam., 3, 377–389, https://doi.org/10.5194/wcd-3-377-2022, 2022. a
Baker, A. H., Hammerling, D. M., Levy, M. N., Xu, H., Dennis, J. M., Eaton, B. E., Edwards, J., Hannay, C., Mickelson, S. A., Neale, R. B., Nychka, D., Shollenberger, J., Tribbia, J., Vertenstein, M., and Williamson, D.: A new ensemble-based consistency test for the Community Earth System Model (pyCECT v1.0), Geosci. Model Dev., 8, 2829–2840, https://doi.org/10.5194/gmd-8-2829-2015, 2015. a
Barnett, T. P., Dümenil, L., Schlese, U., Roeckner, E., and Latif, M.: The Effect of Eurasian Snow Cover on Regional and Global Climate Variations, J. Atmos. Sci., 46, 661–686, https://doi.org/10.1175/1520-0469(1989)046<0661:TEOESC>2.0.CO;2, 1989. a
Beadling, R. and Swaminathan, R.: AMOC example of REF interactive dashboard plots, Zenodo, https://doi.org/10.5281/zenodo.20055564, 2026. a
Beadling, R. L., Swaminathan, R., Beucher, R., Blockley, E., Brands, S., Hassler, B., Hegedűs, D., Hoffman, F. M., Lee, J., Lewis, J., Lu, J., Malinina, E., Medeiros, B., Scoccimarro, E., Tjiputra, J., Turner, B., and Watson-Parris, D.: Observational Data for Next-Generation Climate Model Evaluation: Requirements, Considerations, and Best Practices, B. Am. Meteorol. Soc., 107, E813–E835, https://doi.org/10.1175/BAMS-D-25-0079.1, 2026. a
Bokhorst, S., Pedersen, S. H., Brucker, L., Anisimov, O., Bjerke, J. W., Brown, R. D., Ehrich, D., Essery, R. L. H., Heilig, A., Ingvander, S., Johansson, C., Johansson, M., Jónsdóttir, I. S., Inga, N., Luojus, K., Macelloni, G., Mariash, H., McLennan, D., Rosqvist, G. N., Sato, A., Savela, H., Schneebeli, M., Sokolov, A., Sokratov, S. A., Terzago, S., Vikhamar-Schuler, D., Williamson, S., Qiu, Y., and Callaghan, T. V.: Changing Arctic Snow Cover: A Review of Recent Developments and Assessment of Future Needs for Observations, Modelling, and Impacts, Ambio, 45, 516–537, https://doi.org/10.1007/s13280-016-0770-0, 2016. a
Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y., Bastrikov, V., Bekki, S., Bonnet, R., Bony, S., Bopp, L., Braconnot, P., Brockmann, P., Cadule, P., Caubel, A., Cheruy, F., Codron, F., Cozic, A., Cugnet, D., D'Andrea, F., Davini, P., de Lavergne, C., Denvil, S., Deshayes, J., Devilliers, M., Ducharne, A., Dufresne, J.-L., Dupont, E., Éthé, C., Fairhead, L., Falletti, L., Flavoni, S., Foujols, M.-A., Gardoll, S., Gastineau, G., Ghattas, J., Grandpeix, J.-Y., Guenet, B., Guez, Lionel, E., Guilyardi, E., Guimberteau, M., Hauglustaine, D., Hourdin, F., Idelkadi, A., Joussaume, S., Kageyama, M., Khodri, M., Krinner, G., Lebas, N., Levavasseur, G., Lévy, C., Li, L., Lott, F., Lurton, T., Luyssaert, S., Madec, G., Madeleine, J.-B., Maignan, F., Marchand, M., Marti, O., Mellul, L., Meurdesoif, Y., Mignot, J., Musat, I., Ottlé, C., Peylin, P., Planton, Y., Polcher, J., Rio, C., Rochetin, N., Rousset, C., Sepulchre, P., Sima, A., Swingedouw, D., Thiéblemont, R., Traore, A. K., Vancoppenolle, M., Vial, J., Vialard, J., Viovy, N., and Vuichard, N.: Presentation and Evaluation of the IPSL-CM6A-LR Climate Model, J. Adv. Model. Earth Sy., 12, e2019MS002010, https://doi.org/10.1029/2019MS002010, 2020. a
Brands, S.: A circulation-based performance atlas of the CMIP5 and 6 models for regional climate studies in the Northern Hemisphere mid-to-high latitudes, Geosci. Model Dev., 15, 1375–1411, https://doi.org/10.5194/gmd-15-1375-2022, 2022. a
Brands, S.: pyLamb – A Climate Model Verification Tool Based on Lamb Weather Types, Zenodo, https://doi.org/10.5281/zenodo.15346363, 2025. a
Brands, S., Fernández-Granja, J. A., Bedia, J., Casanueva, A., and Fernández, J.: A Global Climate Model Performance Atlas for the Southern Hemisphere Extratropics Based on Regional Atmospheric Circulation Patterns, Geophys. Res. Lett., 50, e2023GL103531, https://doi.org/10.1029/2023GL103531, 2023. a
Burrows, S. M., Maltrud, M., Yang, X., Zhu, Q., Jeffery, N., Shi, X., Ricciuto, D. M., Wang, S., Bisht, G., Tang, J., Wolfe, J., Harrop, B. E., Singh, B., Brent, L., Baldwin, S., Zhou, T., Cameron-Smith, P., Keen, N., Collier, N., Xu, M., Hunke, E. C., Elliott, S. M., Turner, A. K., Li, H.-Y., Wang, H., Golaz, J.-C., Bond-Lamberty, B., Hoffman, F. M., Riley, W. J., Thornton, P. E., Calvin, K., and Leung, L. R.: The DOE E3SM v1.1 Biogeochemistry Configuration: Description and Simulated Ecosystem-Climate Responses to Historical Changes in Forcing, J. Adv. Model. Earth Sy., 12, e2019MS001766, https://doi.org/10.1029/2019MS001766, 2020. a
Chen, X. and Wallace, J. M.: ENSO-Like Variability: 1900–2013, J. Climate, 28, 9623–9641, https://doi.org/10.1175/JCLI-D-15-0322.1, 2015. a
Chen, Y., Hall, J., Van Wees, D., Andela, N., Hantson, S., Giglio, L., Van Der Werf, G. R., Morton, D. C., and Randerson, J. T.: Global Fire Emissions Database (GFED5) Burned Area, Zenodo [data set], https://doi.org/10.5281/zenodo.7668424, 2023a. a
Chen, Y., Hall, J., van Wees, D., Andela, N., Hantson, S., Giglio, L., van der Werf, G. R., Morton, D. C., and Randerson, J. T.: Multi-decadal trends and variability in burned area from the fifth version of the Global Fire Emissions Database (GFED5), Earth Syst. Sci. Data, 15, 5227–5259, https://doi.org/10.5194/essd-15-5227-2023, 2023b. a
Cheng, L., Pan, Y., Tan, Z., Zheng, H., Zhu, Y., Wei, W., Du, J., Yuan, H., Li, G., Ye, H., Gouretski, V., Li, Y., Trenberth, K. E., Abraham, J., Jin, Y., Reseghetti, F., Lin, X., Zhang, B., Chen, G., Mann, M. E., and Zhu, J.: IAPv4 ocean temperature and ocean heat content gridded dataset, Earth Syst. Sci. Data, 16, 3517–3546, https://doi.org/10.5194/essd-16-3517-2024, 2024. a
Cinquini, L., Crichton, D., Mattmann, C., Harney, J., Shipman, G., Wang, F., Ananthakrishnan, R., Miller, N., Denvil, S., Morgan, M., Pobre, Z., Bell, G. M., Doutriaux, C., Drach, R., Williams, D., Kershaw, P., Pascoe, S., Gonzalez, E., Fiore, S., and Schweitzer, R.: The Earth System Grid Federation: An Open Infrastructure for Access to Distributed Geospatial Data, Future Gener. Comp. Sy., 36, 400–417, https://doi.org/10.1016/j.future.2013.07.002, 2014. a
Claverie, M., Vermote, E., and NOAA CDR Program: NOAA Climate Data Record (CDR) of Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Version 4 (Version Superseded), NOAA National Centers for Environmental Information, https://doi.org/10.7289/V5M043BX, 2014. a
Claverie, M., Vermote, E., Justice, C., Csiszar, I., Myneni, R., Baret, F., Masuoka, E., Wolfe, R., Ray, J. P., and NOAA CDR Program: NOAA Climate Data Record (CDR) of VIIRS Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Version 1, NOAA National Centers for Environmental Information, https://doi.org/10.25921/9X3M-0E02, 2024. a
CMIP Model Benchmarking Task Team: CMIP7 Assessment Fast Track Diagnostics list for the Rapid Evaluation Framework, Version 2, Zenodo, https://doi.org/10.5281/zenodo.20175394, 2026a. a, b
CMIP Model Benchmarking Task Team: CMIP7 Assessment Fast Track Diagnostics list for the Rapid Evaluation Framework, All versions, Zenodo, https://doi.org/10.5281/zenodo.14284374, 2026b. a, b, c
Cohen, J. and Entekhabi, D.: Eurasian Snow Cover Variability and Northern Hemisphere Climate Predictability, Geophys. Res. Lett., 26, 345–348, https://doi.org/10.1029/1998GL900321, 1999. a
Cohen, J., Screen, J. A., Furtado, J. C., Barlow, M., Whittleston, D., Coumou, D., Francis, J., Dethloff, K., Entekhabi, D., Overland, J., and Jones, J.: Recent Arctic Amplification and Extreme Mid-latitude Weather, Nat. Geosci., 7, 627–637, https://doi.org/10.1038/ngeo2234, 2014. a
Coldewey-Egbers, M., Loyola, D. G., Koukouli, M., Balis, D., Lambert, J.-C., Verhoelst, T., Granville, J., van Roozendael, M., Lerot, C., Spurr, R., Frith, S. M., and Zehner, C.: The GOME-type Total Ozone Essential Climate Variable (GTO-ECV) data record from the ESA Climate Change Initiative, Atmos. Meas. Tech., 8, 3923–3940, https://doi.org/10.5194/amt-8-3923-2015, 2015. a
Coldewey-Egbers, M., Loyola R., D. G., Latter, B., Siddans, R., Kerridge, B., Hubert, D., van Roozendael, M., and Eisinger, M.: The novel GOME-type Ozone Profile Essential Climate Variable (GOP-ECV) data record covering the past 26 years, Atmos. Meas. Tech., 18, 5485–5505, https://doi.org/10.5194/amt-18-5485-2025, 2025. a, b
Collier, N., Hoffman, F. M., Lawrence, D. M., Keppel-Aleks, G., Koven, C. D., Riley, W. J., Mu, M., and Randerson, J. T.: The International Land Model Benchmarking (ILAMB) System: Design, Theory, and Implementation, J. Adv. Model. Earth Sy., 10, 2731–2754, https://doi.org/10.1029/2018MS001354, 2018. a
Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J., Yin, X., Gleason, B. E., Vose, R. S., Rutledge, G., Bessemoulin, P., Brönnimann, S., Brunet, M., Crouthamel, R. I., Grant, A. N., Groisman, P. Y., Jones, P. D., Kruk, M. C., Kruger, A. C., Marshall, G. J., Maugeri, M., Mok, H. Y., Nordli, Ø., Ross, T. F., Trigo, R. M., Wang, X. L., Woodruff, S. D., and Worley, S. J.: The Twentieth Century Reanalysis Project, Q. J. Roy. Meteor. Soc., 137, 1–28, https://doi.org/10.1002/qj.776, 2011. a
Copernicus Climate Data Store: Ozone Monthly Gridded Data from 1970 to Present Derived from Satellite Observations, European Center for Medium-range Weather Forecast, https://doi.org/10.24381/CDS.4EBFE4EB, 2020. a
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R., Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System Model Version 2 (CESM2), J. Adv. Model. Earth Sy., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020. a
Davini, P.: MiLES – Mid Latitude Evaluation System, Zenodo, https://doi.org/10.5281/zenodo.2578139, 2019. a
Davini, P. and D'Andrea, F.: Northern Hemisphere Atmospheric Blocking Representation in Global Climate Models: Twenty Years of Improvements?, J. Climate, 29, 8823–8840, https://doi.org/10.1175/JCLI-D-16-0242.1, 2016. a
Davis, E., Taylor, K. E., Adloff, F., Gregory, J., Lawrence, B., and Lee, D.: Supporting Open Science with the CF Metadata Conventions for NetCDF, slides for the presentation given on 11 December 2024 at the AGU Annual Meeting to the session for the AGU Open Science Recognition Prize, Zenodo, https://doi.org/10.5281/zenodo.15015065, 2024. a
Deser, C., Phillips, A., Bourdette, V., and Teng, H.: Uncertainty in Climate Change Projections: The Role of Internal Variability, Clim. Dynam., 38, 527–546, https://doi.org/10.1007/s00382-010-0977-x, 2012. a, b
Deser, C., Lehner, F., Rodgers, K. B., Ault, T., Delworth, T. L., DiNezio, P. N., Fiore, A., Frankignoul, C., Fyfe, J. C., Horton, D. E., Kay, J. E., Knutti, R., Lovenduski, N. S., Marotzke, J., McKinnon, K. A., Minobe, S., Randerson, J., Screen, J. A., Simpson, I. R., and Ting, M.: Insights from Earth System Model Initial-condition Large Ensembles and Future Prospects, Natu. Clim. Change, 10, 277–286, https://doi.org/10.1038/s41558-020-0731-2, 2020. a
Diez-Sierra, J., Iturbide, M., Gutiérrez, J. M., Fernández, J., Milovac, J., Cofiño, A. S., Cimadevilla, E., Nikulin, G., Levavasseur, G., Kjellström, E., Bülow, K., Horányi, A., Brookshaw, A., García-Díez, M., Pérez, A., Baño-Medina, J., Ahrens, B., Alias, A., Ashfaq, M., Bukovsky, M., Buonomo, E., Caluwaerts, S., Chou, S. C., Christensen, O. B., Ciarlò, J. M., Coppola, E., Corre, L., Demory, M.-E., Djurdjevic, V., Evans, J. P., Fealy, R., Feldmann, H., Jacob, D., Jayanarayanan, S., Katzfey, J., Keuler, K., Kittel, C., Kurnaz, M. L., Laprise, R., Lionello, P., McGinnis, S., Mercogliano, P., Nabat, P., Önol, B., Ozturk, T., Panitz, H.-J., Paquin, D., Pieczka, I., Raffaele, F., Remedio, A. R., Scinocca, J., Sevault, F., Somot, S., Steger, C., Tangang, F., Teichmann, C., Termonia, P., Thatcher, M., Torma, C., Meijgaard, E. v., Vautard, R., Warrach-Sagi, K., Winger, K., and Zittis, G.: The Worldwide C3S CORDEX Grand Ensemble: A Major Contribution to Assess Regional Climate Change in the IPCC AR6 Atlas, B. Am. Meteorol. Soc., 103, E2804–E2826, https://doi.org/10.1175/BAMS-D-22-0111.1, 2022. a
Dingley, B., Anstey, J. A., Abalos, M., Abraham, C., Bergman, T., Bock, L., Fiddes, S., Hassler, B., Kramer, R. J., Luo, F., O'Connor, F. M., Šácha, P., Simpson, I. R., Wilcox, L. J., and Zelinka, M. D.: CMIP7 Data Request: atmosphere priorities and opportunities, Geosci. Model Dev., 19, 2945–2984, https://doi.org/10.5194/gmd-19-2945-2026, 2026. a
Dolores-Tesillos, E., Martius, O., and Quinting, J.: On the role of moist and dry processes in atmospheric blocking biases in the Euro-Atlantic region in CMIP6, Weather Clim. Dynam., 6, 471–487, https://doi.org/10.5194/wcd-6-471-2025, 2025. a
Dorrington, J., Strommen, K., and Fabiano, F.: Quantifying climate model representation of the wintertime Euro-Atlantic circulation using geopotential-jet regimes, Weather Clim. Dynam., 3, 505–533, https://doi.org/10.5194/wcd-3-505-2022, 2022. a
Dunne, J. P., Hewitt, H. T., Arblaster, J. M., Bonou, F., Boucher, O., Cavazos, T., Dingley, B., Durack, P. J., Hassler, B., Juckes, M., Miyakawa, T., Mizielinski, M., Naik, V., Nicholls, Z., O'Rourke, E., Pincus, R., Sanderson, B. M., Simpson, I. R., and Taylor, K. E.: An evolving Coupled Model Intercomparison Project phase 7 (CMIP7) and Fast Track in support of future climate assessment, Geosci. Model Dev., 18, 6671–6700, https://doi.org/10.5194/gmd-18-6671-2025, 2025. a, b
Döscher, R., Acosta, M., Alessandri, A., Anthoni, P., Arsouze, T., Bergman, T., Bernardello, R., Boussetta, S., Caron, L.-P., Carver, G., Castrillo, M., Catalano, F., Cvijanovic, I., Davini, P., Dekker, E., Doblas-Reyes, F. J., Docquier, D., Echevarria, P., Fladrich, U., Fuentes-Franco, R., Gröger, M., v. Hardenberg, J., Hieronymus, J., Karami, M. P., Keskinen, J.-P., Koenigk, T., Makkonen, R., Massonnet, F., Ménégoz, M., Miller, P. A., Moreno-Chamarro, E., Nieradzik, L., van Noije, T., Nolan, P., O'Donnell, D., Ollinaho, P., van den Oord, G., Ortega, P., Prims, O. T., Ramos, A., Reerink, T., Rousset, C., Ruprich-Robert, Y., Le Sager, P., Schmith, T., Schrödner, R., Serva, F., Sicardi, V., Sloth Madsen, M., Smith, B., Tian, T., Tourigny, E., Uotila, P., Vancoppenolle, M., Wang, S., Wårlind, D., Willén, U., Wyser, K., Yang, S., Yepes-Arbós, X., and Zhang, Q.: The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6, Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, 2022. a
Eaton, B., Gregory, J., Drach, B., Taylor, K., Hankin, S., Caron, J., Signell, R., Bentley, P., Rappa, G., Höck, H., Pamment, A., Juckes, M., Raspaud, M., Blower, J., Horne, R., Whiteaker, T., Blodgett, D., Zender, C., Lee, D., Hassell, D., Snow, A. D., Kölling, T., Allured, D., Jelenak, A., Soerensen, A. M., Gaultier, L., Herlédan, S., Manzano, F., Bärring, L., Barker, C., and Bartholomew, S. L.: NetCDF Climate and Forecast (CF) Metadata Conventions, CF Community, Zenodo, https://doi.org/10.5281/zenodo.14275599, 2024. 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
Eyring, V., Cox, P. M., Flato, G. M., Gleckler, P. J., Abramowitz, G., Caldwell, P., Collins, W. D., Gier, B. K., Hall, A. D., Hoffman, F. M., Hurtt, G. C., Jahn, A., Jones, C. D., Klein, S. A., Krasting, J. P., Kwiatkowski, L., Lorenz, R., Maloney, E., Meehl, G. A., Pendergrass, A. G., Pincus, R., Ruane, A. C., Russell, J. L., Sanderson, B. M., Santer, B. D., Sherwood, S. C., Simpson, I. R., Stouffer, R. J., and Williamson, M. S.: Taking Climate Model Evaluation to the Next Level, Nat. Clim. Change, 9, 102–110, https://doi.org/10.1038/s41558-018-0355-y, 2019. a
Eyring, V., Bock, L., Lauer, A., Righi, M., Schlund, M., Andela, B., Arnone, E., Bellprat, O., Brötz, B., Caron, L.-P., Carvalhais, N., Cionni, I., Cortesi, N., Crezee, B., Davin, E. L., Davini, P., Debeire, K., de Mora, L., Deser, C., Docquier, D., Earnshaw, P., Ehbrecht, C., Gier, B. K., Gonzalez-Reviriego, N., Goodman, P., Hagemann, S., Hardiman, S., Hassler, B., Hunter, A., Kadow, C., Kindermann, S., Koirala, S., Koldunov, N., Lejeune, Q., Lembo, V., Lovato, T., Lucarini, V., Massonnet, F., Müller, B., Pandde, A., Pérez-Zanón, N., Phillips, A., Predoi, V., Russell, J., Sellar, A., Serva, F., Stacke, T., Swaminathan, R., Torralba, V., Vegas-Regidor, J., von Hardenberg, J., Weigel, K., and Zimmermann, K.: Earth System Model Evaluation Tool (ESMValTool) v2.0 – an extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth system models in CMIP, Geosci. Model Dev., 13, 3383–3438, https://doi.org/10.5194/gmd-13-3383-2020, 2020. a
Fang, H., Baret, F., Plummer, S., and Schaepman-Strub, G.: An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications, Rev. Geophys., 57, 739–799, https://doi.org/10.1029/2018RG000608, 2019. a
FAO and IIASA: Harmonized World Soil Database Version 2.0, Food and Agriculture Organization (FAO) and International Institute for Applied Systems Analysis (IIASA), ISBN 978-92-5-137499-3, https://doi.org/10.4060/cc3823en, 2023. a
Fasullo, J. T., Phillips, A. S., and Deser, C.: Evaluation of Leading Modes of Climate Variability in the CMIP Archives, J. Climate, 33, 5527–5545, https://doi.org/10.1175/JCLI-D-19-1024.1, 2020. a
Fernández-Granja, J. A., Bedia, J., Casanueva, A., Brands, S., and Fernández, J.: The Signature of the Main Modes of Climatic Variability as Revealed by the Jenkinson-Collison Classification over Europe, Int. J. Climatol., 44, 4076–4088, https://doi.org/10.1002/joc.8569, 2024. a
Ferraro, R., Waliser, D. E., Gleckler, P., Taylor, K. E., and Eyring, V.: Evolving Obs4MIPs to Support Phase 6 of the Coupled Model Intercomparison Project (CMIP6), B. Am. Meteorol. Soc., 96, ES131–ES133, https://doi.org/10.1175/BAMS-D-14-00216.1, 2015. a
Fu, W., Moore, J. K., Primeau, F., Collier, N., Ogunro, O. O., Hoffman, F. M., and Randerson, J. T.: Evaluation of Ocean Biogeochemistry and Carbon Cycling in CMIP Earth System Models With the International Ocean Model Benchmarking (IOMB) Software System, J. Geophys. Res.-Oceans, 127, e2022JC018965, https://doi.org/10.1029/2022JC018965, 2022. a
Giorgi, F. and Francisco, R.: Uncertainties in Regional Climate Change Prediction: A Regional Analysis of Ensemble Simulations with the HADCM2 Coupled AOGCM, Climate Dynamics, 16, 169–182, https://doi.org/10.1007/PL00013733, 2000. a
Gleckler, P., Ferraro, R., and Waliser, D.: Improving Use of Satellite Data in Evaluating Climate Models, EOS T. Am. Geophys. Un., 92, 172–172, https://doi.org/10.1029/2011EO200005, 2011. a
Gleckler, P., Taylor, K. E., Durack, P. J., Nadeau, D., Biard, J. C., Elsaesser, G., Ferraro, R., Finkensieper, S., Hassler, B., Manaster, A., Mears, C., Pinnock, S., Stevens, S., Tuma, M., Turner, B., Waterfall, A., and Willet, K. M.: Obs4MIPs Data Specifications Version 2.5 (ODS2.5), Earth System Model and Observations (ESMO), Zenodo, https://doi.org/10.5281/zenodo.11500474, 2024. a
Gleckler, P. J., Taylor, K. E., and Doutriaux, C.: Performance Metrics for Climate Models, J. Geophys. Res.-Atmos., 113, https://doi.org/10.1029/2007JD008972, 2008. a
Gong, D. and Wang, S.: Definition of Antarctic Oscillation index, Geophys. Res. Lett., 26, 459–462, https://doi.org/10.1029/1999GL900003, 1999. a
Grams, C. M., Beerli, R., Pfenninger, S., Staffell, I., and Wernli, H.: Balancing Europe's Wind-power Output Through Spatial Deployment Informed by Weather Regimes, Nat. Clim. Change, 7, 557–562, https://doi.org/10.1038/nclimate3338, 2017. a
Gregory, J. M., Ingram, W. J., Palmer, M. A., Jones, G. S., Stott, P. A., Thorpe, R. B., Lowe, J. A., Johns, T. C., and Williams, K. D.: A New Method for Diagnosing Radiative Forcing and Climate Sensitivity, Geophys. Res. Lett., 31, https://doi.org/10.1029/2003GL018747, 2004. a
Gregory, J. M., Jones, C. D., Cadule, P., and Friedlingstein, P.: Quantifying Carbon Cycle Feedbacks, J. Climate, 22, 5232–5250, https://doi.org/10.1175/2009JCLI2949.1, 2009. a, b
Hannachi, A., Finke, K., and Trendafilov, N.: Common EOFs: A Tool for Multi-model Comparison and Evaluation, Clim. Dynam., 60, 1689–1703, https://doi.org/10.1007/s00382-022-06409-8, 2023. a
Harris, I., Osborn, T. J., Jones, P., and Lister, D.: Version 4 of the CRU TS Monthly High-resolution Gridded Multivariate Climate Dataset, Sci. Data, 7, 109, https://doi.org/10.1038/s41597-020-0453-3, 2020. a
Hassler, B., Hoffman, F. M., Beadling, R., Blockley, E., Huang, B., Lee, J., Lembo, V., Lewis, J., Lu, J., Madaus, L., Malinina, E., Medeiros, B., Pokam, W., Scoccimarro, E., and Swaminathan, R.: Systematic Benchmarking of Climate Models: Methodologies, Applications, and New Directions, Rev. Geophys., 64, e2025RG000891, https://doi.org/10.1029/2025RG000891, 2026a. a, b, c
Hassler, B., Lewis, J., Lee, J., Andela, B., Swaminathan, R., Hoffman, F. M., Turner, B., and O'Rourke, E.: Climate – Rapid Evaluation Framework CMIP7 Quality Checklist, Zenodo, https://doi.org/10.5281/zenodo.18915234, 2026b. a
Hawkins, E. and Sutton, R.: Connecting Climate Model Projections of Global Temperature Change with the Real World, B. Am. Meteorol. Soc., 97, 963–980, https://doi.org/10.1175/BAMS-D-14-00154.1, 2016. a
Hegedűs, D., Swaminathan, R., and Woolliams, E.: Climate – Rapid Evaluation Framework Uncertainty Checklist, Zenodo, https://doi.org/10.5281/zenodo.17312883, 2025. a
Hellinger, E.: Neue Begründung der Theorie quadratischer Formen von unendlichvielen Veränderlichen, J. Reine Angew. Math., 1909, 210–271, https://doi.org/10.1515/crll.1909.136.210, 1909. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 Global Reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hobeichi, S., Abramowitz, G., Evans, J., and Beck, H. E.: Linear Optimal Runoff Aggregate (LORA): a global gridded synthesis runoff product, Hydrol. Earth Syst. Sci., 23, 851–870, https://doi.org/10.5194/hess-23-851-2019, 2019. a
Hoffman, F.: Gross Primary Production (GPP) example of REF interactive dashboard plots, Zenodo, https://doi.org/10.5281/zenodo.20162790, 2026. a
Hoffman, F., Hassler, B., Model Benchmarking Task Team, and Dingley, B.: Rapid Evaluation Framework Overview, Zenodo, https://doi.org/10.5281/zenodo.15594501, 2026. a
Hoffman, F. M., Hargrove, W. W., Erickson, D. J., and Oglesby, R. J.: Using Clustered Climate Regimes to Analyze and Compare Predictions from Fully Coupled General Circulation Models, Earth Interact., 9, 1–27, https://doi.org/10.1175/EI110.1, 2005. a
Hoffman, F. M., Randerson, J. T., Arora, V. K., Bao, Q., Cadule, P., Ji, D., Jones, C. D., Kawamiya, M., Khatiwala, S., Lindsay, K., Obata, A., Shevliakova, E., Six, K. D., Tjiputra, J. F., Volodin, E. M., and Wu, T.: Causes and Implications of Persistent Atmospheric Carbon Dioxide Biases in Earth System Models, J. Geophys. Res.-Biogeo., 119, 141–162, https://doi.org/10.1002/2013JG002381, 2014. a, b, c
Hoffman, F. M., Koven, C. D., Keppel-Aleks, G., Lawrence, D. M., Riley, W. J., Randerson, J. T., Ahlström, A., Abramowitz, G., Baldocchi, D. D., Best, M. J., Bond-Lamberty, B., De Kauwe, M. G., Denning, A. S., Desai, A. R., Eyring, V., Fisher, J. B., Fisher, R. A., Gleckler, P. J., Huang, M., Hugelius, G., Jain, A. K., Kiang, N. Y., Kim, H., Koster, R. D., Kumar, S. V., Li, H., Luo, Y., Mao, J., McDowell, N. G., Mishra, U., Moorcroft, P. R., Pau, G. S. H., Ricciuto, D. M., Schaefer, K., Schwalm, C. R., Serbin, S. P., Shevliakova, E., Slater, A. G., Tang, J., Williams, M., Xia, J., Xu, C., Joseph, R., and Koch, D.: International Land Model Benchmarking (ILAMB) 2016 Workshop Report, Tech. Rep. DOE/SC-0186, U.S. Department of Energy, Office of Science, Germantown, Maryland, USA, https://doi.org/10.2172/1330803, 2017. a
Hurrell, J. W., Kushnir, Y., and Visbeck, M.: The North Atlantic Oscillation, Science, 291, 603–605, https://doi.org/10.1126/science.1058761, 2001. a
IPCC: Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, 1st edn., ISBN 978-1-009-15789-6, https://doi.org/10.1017/9781009157896, 2023. a, b, c
Irving, D. B., Wijffels, S., and Church, J. A.: Anthropogenic Aerosols, Greenhouse Gases, and the Uptake, Transport, and Storage of Excess Heat in the Climate System, Geophys. Res. Lett., 46, 4894–4903, https://doi.org/10.1029/2019GL082015, 2019. a
Iturbide, M., Gutiérrez, J. M., Alves, L. M., Bedia, J., Cerezo-Mota, R., Cimadevilla, E., Cofiño, A. S., Di Luca, A., Faria, S. H., Gorodetskaya, I. V., Hauser, M., Herrera, S., Hennessy, K., Hewitt, H. T., Jones, R. G., Krakovska, S., Manzanas, R., Martínez-Castro, D., Narisma, G. T., Nurhati, I. S., Pinto, I., Seneviratne, S. I., van den Hurk, B., and Vera, C. S.: An update of IPCC climate reference regions for subcontinental analysis of climate model data: definition and aggregated datasets, Earth Syst. Sci. Data, 12, 2959–2970, https://doi.org/10.5194/essd-12-2959-2020, 2020. a
Juckes, M., Taylor, K. E., Antonio, F., Brayshaw, D., Buontempo, C., Cao, J., Durack, P. J., Kawamiya, M., Kim, H., Lovato, T., Mackallah, C., Mizielinski, M., Nuzzo, A., Stockhause, M., Visioni, D., Walton, J., Turner, B., O'Rourke, E., and Dingley, B.: Baseline Climate Variables for Earth System Modelling, Geosci. Model Dev., 18, 2639–2663, https://doi.org/10.5194/gmd-18-2639-2025, 2025. a
Karlsson, K.-G., Stengel, M., Meirink, J. F., Riihelä, A., Trentmann, J., Akkermans, T., Stein, D., Devasthale, A., Eliasson, S., Johansson, E., Håkansson, N., Solodovnik, I., Benas, N., Clerbaux, N., Selbach, N., Schröder, M., and Hollmann, R.: CLARA-A3: The third edition of the AVHRR-based CM SAF climate data record on clouds, radiation and surface albedo covering the period 1979 to 2023, Earth Syst. Sci. Data, 15, 4901–4926, https://doi.org/10.5194/essd-15-4901-2023, 2023. a
Keenan, T. F. and Williams, C. A.: The Terrestrial Carbon Sink, Annu. Rev. Env. Resour., 43, 219–243, https://doi.org/10.1146/annurev-environ-102017-030204, 2018. a
Kelley, D., Swaminathan, R., and Lenhardt, J.: Data for CMIP AR7 REF fire controls assessment, Zenodo, https://doi.org/10.5281/zenodo.14917245, 2025. a
Keppel-Aleks, G., Randerson, J. T., Lindsay, K., Stephens, B. B., Moore, J. K., Doney, S. C., Thornton, P. E., Mahowald, N. M., Hoffman, F. M., Sweeney, C., Tans, P. P., Wennberg, P. O., and Wofsy, S. C.: Atmospheric Carbon Dioxide Variability in the Community Earth System Model: Evaluation and Transient Dynamics during the Twentieth and Twenty-First Centuries, J. Climate, 26, 4447–4475, https://doi.org/10.1175/JCLI-D-12-00589.1, 2013. a
Kumar, P. B., Vialard, J., Lengaigne, M., Murty, V. S. N., and McPhaden, M. J.: TropFlux: Air-sea Fluxes for the Global Tropical Ocean – Description and Evaluation, Clim. Dynam., 38, 1521–1543, https://doi.org/10.1007/s00382-011-1115-0, 2012. a
Lauer, A., Eyring, V., Bellprat, O., Bock, L., Gier, B. K., Hunter, A., Lorenz, R., Pérez-Zanón, N., Righi, M., Schlund, M., Senftleben, D., Weigel, K., and Zechlau, S.: Earth System Model Evaluation Tool (ESMValTool) v2.0 – diagnostics for emergent constraints and future projections from Earth system models in CMIP, Geosci. Model Dev., 13, 4205–4228, https://doi.org/10.5194/gmd-13-4205-2020, 2020. a
Lauer, A., Bock, L., Hassler, B., Jöckel, P., Ruhe, L., and Schlund, M.: Monitoring and benchmarking Earth system model simulations with ESMValTool v2.12.0, Geosci. Model Dev., 18, 1169–1188, https://doi.org/10.5194/gmd-18-1169-2025, 2025. a
Lauritzen, P. H., Kevlahan, N. K.-R., Toniazzo, T., Eldred, C., Dubos, T., Gassmann, A., Larson, V. E., Jablonowski, C., Guba, O., Shipway, B., Harrop, B. E., Lemarié, F., Tailleux, R., Herrington, A. R., Large, W., Rasch, P. J., Donahue, A. S., Wan, H., Conley, A., and Bacmeister, J. T.: Reconciling and Improving Formulations for Thermodynamics and Conservation Principles in Earth System Models (ESMs), J. Adv. Model. Earth Sy., 14, e2022MS003117, https://doi.org/10.1029/2022MS003117, 2022. a
Lavergne, T., Sørensen, A. M., Kern, S., Tonboe, R., Notz, D., Aaboe, S., Bell, L., Dybkjær, G., Eastwood, S., Gabarro, C., Heygster, G., Killie, M. A., Brandt Kreiner, M., Lavelle, J., Saldo, R., Sandven, S., and Pedersen, L. T.: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records, The Cryosphere, 13, 49–78, https://doi.org/10.5194/tc-13-49-2019, 2019. a, b
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G., Collier, N., Ghimire, B., van Kampenhout, L., Kennedy, D., Kluzek, E., Lawrence, P. J., Li, F., Li, H., Lombardozzi, D., Riley, W. J., Sacks, W. J., Shi, M., Vertenstein, M., Wieder, W. R., Xu, C., Ali, A. A., Badger, A. M., Bisht, G., van den Broeke, M., Brunke, M. A., Burns, S. P., Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J. B., Flanner, M., Fox, A. M., Gentine, P., Hoffman, F. M., Keppel-Aleks, G., Knox, R., Kumar, S., Lenaerts, J., Leung, L. R., Lipscomb, W. H., Lu, Y., Pandey, A., Pelletier, J. D., Perket, J., Randerson, J. T., Ricciuto, D. M., Sanderson, B. M., Slater, A., Subin, Z. M., Tang, J., Thomas, R. Q., Val Martin, M., and Zeng, X.: The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty, J. Adv. Model. Earth Sy., 11, 4245–4287, https://doi.org/10.1029/2018MS001583, 2019. a
Le Bras, I. A.-A., Willis, J., and Fenty, I.: The Atlantic Meridional Overturning Circulation at 35° N From Deep Moorings, Floats, and Satellite Altimeter, Geophys. Res. Lett., 50, e2022GL101931, https://doi.org/10.1029/2022GL101931, 2023. a
Lee, J.: CMIP6 Community Survey: Model Benchmarking and Evaluation Results, Zenodo, https://doi.org/10.5281/zenodo.15478482, 2024. a
Lee, J.: Northern Annular Mode (NAM) example of REF interactive dashboard plots, Zenodo, https://doi.org/10.5281/zenodo.20162569, 2026. a
Lee, J., Sperber, K. R., Gleckler, P. J., Bonfils, C. J. W., and Taylor, K. E.: Quantifying the Agreement Between Observed and Simulated Extratropical Modes of Interannual Variability, Clim. Dynam., 52, 4057–4089, https://doi.org/10.1007/s00382-018-4355-4, 2019. a, b
Lee, J., Gleckler, P. J., Ahn, M.-S., Ordonez, A., Ullrich, P. A., Sperber, K. R., Taylor, K. E., Planton, Y. Y., Guilyardi, E., Durack, P., Bonfils, C., Zelinka, M. D., Chao, L.-W., Dong, B., Doutriaux, C., Zhang, C., Vo, T., Boutte, J., Wehner, M. F., Pendergrass, A. G., Kim, D., Xue, Z., Wittenberg, A. T., and Krasting, J.: Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3, Geosci. Model Dev., 17, 3919–3948, https://doi.org/10.5194/gmd-17-3919-2024, 2024. a, b
Lembo, V., Folini, D., Wild, M., and Lionello, P.: Inter-hemispheric Differences in Energy Budgets and Cross-equatorial Transport Anomalies During the 20th Century, Clim. Dynam., 53, 115–135, https://doi.org/10.1007/s00382-018-4572-x, 2019. a
Le Quéré, C., Andrew, R. M., Friedlingstein, P., Sitch, S., Hauck, J., Pongratz, J., Pickers, P. A., Korsbakken, J. I., Peters, G. P., Canadell, J. G., Arneth, A., Arora, V. K., Barbero, L., Bastos, A., Bopp, L., Chevallier, F., Chini, L. P., Ciais, P., Doney, S. C., Gkritzalis, T., Goll, D. S., Harris, I., Haverd, V., Hoffman, F. M., Hoppema, M., Houghton, R. A., Hurtt, G., Ilyina, T., Jain, A. K., Johannessen, T., Jones, C. D., Kato, E., Keeling, R. F., Goldewijk, K. K., Landschützer, P., Lefèvre, N., Lienert, S., Liu, Z., Lombardozzi, D., Metzl, N., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S., Neill, C., Olsen, A., Ono, T., Patra, P., Peregon, A., Peters, W., Peylin, P., Pfeil, B., Pierrot, D., Poulter, B., Rehder, G., Resplandy, L., Robertson, E., Rocher, M., Rödenbeck, C., Schuster, U., Schwinger, J., Séférian, R., Skjelvan, I., Steinhoff, T., Sutton, A., Tans, P. P., Tian, H., Tilbrook, B., Tubiello, F. N., van der Laan-Luijkx, I. T., van der Werf, G. R., Viovy, N., Walker, A. P., Wiltshire, A. J., Wright, R., Zaehle, S., and Zheng, B.: Global Carbon Budget 2018, Earth Syst. Sci. Data, 10, 2141–2194, https://doi.org/10.5194/essd-10-2141-2018, 2018. a
Lewis, J., Andela, B., Collier, N., Lee, J., Hegedűs, D., Pflüger, M., and Xu, M.: Rapid Evaluation Framework, All versions, Zenodo [code], https://doi.org/10.5281/zenodo.15103441, 2026a. a, b, c
Lewis, J., Hoffman, F., REF Delivery Team, and Dingley, B.: REF technical workflow, Zenodo [figure], https://doi.org/10.5281/zenodo.15595005, 2026b. a
Loeb, N. G., Doelling, D. R., Wang, H., Su, W., Nguyen, C., Corbett, J. G., Liang, L., Mitrescu, C., Rose, F. G., and Kato, S.: Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) Edition-4.0 Data Product, J. Climate, 31, 895–918, https://doi.org/10.1175/JCLI-D-17-0208.1, 2018. a, b
Loeb, N. G., Wang, H., Allan, R. P., Andrews, T., Armour, K., Cole, J. N. S., Dufresne, J.-L., Forster, P., Gettelman, A., Guo, H., Mauritsen, T., Ming, Y., Paynter, D., Proistosescu, C., Stuecker, M. F., Willén, U., and Wyser, K.: New Generation of Climate Models Track Recent Unprecedented Changes in Earth's Radiation Budget Observed by CERES, Geophys. Res. Lett., 47, e2019GL086705, https://doi.org/10.1029/2019GL086705, 2020. a
Lucarini, V., Ragone, F., and Lunkeit, F.: Predicting Climate Change Using Response Theory: Global Averages and Spatial Patterns, J. Stat. Phys., 166, 1036–1064, https://doi.org/10.1007/s10955-016-1506-z, 2017. a
Luo, Y. and Hoffman, F. M.: Benchmark Analysis, in: Land Carbon Cycle Modeling: Matrix Approach, Data Assimilation, & Ecological Forecasting, edited by Luo, Y. and Smith, B., 157–162, CRC Press, Boca Raton, FL, USA, ISBN 978-1-4987-3701-2, https://doi.org/10.1201/9780429155659-24, 2022. a
Luo, Y. Q., Randerson, J. T., Abramowitz, G., Bacour, C., Blyth, E., Carvalhais, N., Ciais, P., Dalmonech, D., Fisher, J. B., Fisher, R., Friedlingstein, P., Hibbard, K., Hoffman, F., Huntzinger, D., Jones, C. D., Koven, C., Lawrence, D., Li, D. J., Mahecha, M., Niu, S. L., Norby, R., Piao, S. L., Qi, X., Peylin, P., Prentice, I. C., Riley, W., Reichstein, M., Schwalm, C., Wang, Y. P., Xia, J. Y., Zaehle, S., and Zhou, X. H.: A framework for benchmarking land models, Biogeosciences, 9, 3857–3874, https://doi.org/10.5194/bg-9-3857-2012, 2012. a
MacDougall, A. H., Frölicher, T. L., Jones, C. D., Rogelj, J., Matthews, H. D., Zickfeld, K., Arora, V. K., Barrett, N. J., Brovkin, V., Burger, F. A., Eby, M., Eliseev, A. V., Hajima, T., Holden, P. B., Jeltsch-Thömmes, A., Koven, C., Mengis, N., Menviel, L., Michou, M., Mokhov, I. I., Oka, A., Schwinger, J., Séférian, R., Shaffer, G., Sokolov, A., Tachiiri, K., Tjiputra , J., Wiltshire, A., and Ziehn, T.: Is there warming in the pipeline? A multi-model analysis of the Zero Emissions Commitment from CO2, Biogeosciences, 17, 2987–3016, https://doi.org/10.5194/bg-17-2987-2020, 2020. a
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017. a
Mauritsen, T., Stevens, B., Roeckner, E., Crueger, T., Esch, M., Giorgetta, M., Haak, H., Jungclaus, J., Klocke, D., Matei, D., Mikolajewicz, U., Notz, D., Pincus, R., Schmidt, H., and Tomassini, L.: Tuning the Climate of a Global Model, J. Adv. Model. Earth Sy., 4, https://doi.org/10.1029/2012MS000154, 2012. a
Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R., Brovkin, V., Claussen, M., Crueger, T., Esch, M., Fast, I., Fiedler, S., Fläschner, D., Gayler, V., Giorgetta, M., Goll, D. S., Haak, H., Hagemann, S., Hedemann, C., Hohenegger, C., Ilyina, T., Jahns, T., Jimenéz-de-la Cuesta, D., Jungclaus, J., Kleinen, T., Kloster, S., Kracher, D., Kinne, S., Kleberg, D., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Möbis, B., Müller, W. A., Nabel, J. E. M. S., Nam, C. C. W., Notz, D., Nyawira, S.-S., Paulsen, H., Peters, K., Pincus, R., Pohlmann, H., Pongratz, J., Popp, M., Raddatz, T. J., Rast, S., Redler, R., Reick, C. H., Rohrschneider, T., Schemann, V., Schmidt, H., Schnur, R., Schulzweida, U., Six, K. D., Stein, L., Stemmler, I., Stevens, B., von Storch, J.-S., Tian, F., Voigt, A., Vrese, P., Wieners, K.-H., Wilkenskjeld, S., Winkler, A., and Roeckner, E.: Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and Its Response to Increasing CO2, J. Adv. Model. Earth Sy., 11, 998–1038, https://doi.org/10.1029/2018MS001400, 2019. a
Mayer, M., Kato, S., Bosilovich, M., Bechtold, P., Mayer, J., Schröder, M., Behrangi, A., Wild, M., Kobayashi, S., Li, Z., and L'Ecuyer, T.: Assessment of Atmospheric and Surface Energy Budgets Using Observation-Based Data Products, Surv. Geophys., 45, 1827–1854, https://doi.org/10.1007/s10712-024-09827-x, 2024. a
Meehl, G. A., Senior, C. A., Eyring, V., Flato, G., Lamarque, J.-F., Stouffer, R. J., Taylor, K. E., and Schlund, M.: Context for Interpreting Equilibrium Climate Sensitivity and Transient Climate Response from the CMIP6 Earth System Models, Sci. Adv., 6, eaba1981, https://doi.org/10.1126/sciadv.aba1981, 2020. a
Miller, S. M., Hayek, M. N., Andrews, A. E., Fung, I., and Liu, J.: Biases in atmospheric CO2 estimates from correlated meteorology modeling errors, Atmos. Chem. Phys., 15, 2903–2914, https://doi.org/10.5194/acp-15-2903-2015, 2015. a
Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011. a
Moat, B. I., Smeed, D., Rayner, D., Johns, W. E., Smith, R. H., Volkov, D. L., Elipot, S., Petit, T., Kajtar, J. B., Baringer, M. O., and Collins, J.: Atlantic Meridional Overturning Circulation Observed by the RAPID-MOCHA-WBTS Array at 26N from 2004 to 2023 (v2023.1a), Archive Location: North Atlantic Ocean, Straits of Florida, Atlantic Ocean, Northeast Atlantic Ocean (40° W), Northwest Atlantic Ocean (40° W), NERC EDS British Oceanographic Data Centre NOC, https://doi.org/10.5285/33826d6e-801c-b0a7-e063-7086abc0b9db, 2025. a, b
Morice, C. P., Kennedy, J. J., Rayner, N. A., Winn, J. P., Hogan, E., Killick, R. E., Dunn, R. J. H., Osborn, T. J., Jones, P. D., and Simpson, I. R.: An Updated Assessment of Near-Surface Temperature Change From 1850: The HadCRUT5 Data Set, J. Geophys. Res.-Atmos., 126, e2019JD032361, https://doi.org/10.1029/2019JD032361, 2021. a
Notz, D. and SIMIP Community: Arctic Sea Ice in CMIP6, Geophys. Res. Lett., 47, e2019GL086749, https://doi.org/10.1029/2019GL086749, 2020. a
Nowicki, S. M. J., Payne, A., Larour, E., Seroussi, H., Goelzer, H., Lipscomb, W., Gregory, J., Abe-Ouchi, A., and Shepherd, A.: Ice Sheet Model Intercomparison Project (ISMIP6) contribution to CMIP6, Geosci. Model Dev., 9, 4521–4545, https://doi.org/10.5194/gmd-9-4521-2016, 2016. a
Ordonez, A., Ullrich, P., and Lee, J.: cmecmetrics/cmec-driver: v1.1.9-release, Zenodo, https://doi.org/10.5281/zenodo.15548748, 2025. a
O'Rourke, E.: CMIP AR7 Fast Track modelling centres survey: Spin up metrics, ocean regridding, BECCS and Rapid Evaluation Tool, Zenodo, https://doi.org/10.5281/zenodo.12731068, 2024. a
O'Rourke, E.: CMIP6 Community Survey Results, Zenodo, https://doi.org/10.5281/zenodo.11654909, 2025. a
Pastorello, G., Trotta, C., Canfora, E., Chu, H., Christianson, D., Cheah, Y.-W., Poindexter, C., Chen, J., Elbashandy, A., Humphrey, M., Isaac, P., Polidori, D., Reichstein, M., Ribeca, A., van Ingen, C., Vuichard, N., Zhang, L., Amiro, B., Ammann, C., Arain, M. A., Ardö, J., Arkebauer, T., Arndt, S. K., Arriga, N., Aubinet, M., Aurela, M., Baldocchi, D., Barr, A., Beamesderfer, E., Marchesini, L. B., Bergeron, O., Beringer, J., Bernhofer, C., Berveiller, D., Billesbach, D., Black, T. A., Blanken, P. D., Bohrer, G., Boike, J., Bolstad, P. V., Bonal, D., Bonnefond, J.-M., Bowling, D. R., Bracho, R., Brodeur, J., Brümmer, C., Buchmann, N., Burban, B., Burns, S. P., Buysse, P., Cale, P., Cavagna, M., Cellier, P., Chen, S., Chini, I., Christensen, T. R., Cleverly, J., Collalti, A., Consalvo, C., Cook, B. D., Cook, D., Coursolle, C., Cremonese, E., Curtis, P. S., D’Andrea, E., da Rocha, H., Dai, X., Davis, K. J., Cinti, B. D., Grandcourt, A. d., Ligne, A. D., De Oliveira, R. C., Delpierre, N., Desai, A. R., Di Bella, C. M., Tommasi, P. d., Dolman, H., Domingo, F., Dong, G., Dore, S., Duce, P., Dufrêne, E., Dunn, A., Dušek, J., Eamus, D., Eichelmann, U., ElKhidir, H. A. M., Eugster, W., Ewenz, C. M., Ewers, B., Famulari, D., Fares, S., Feigenwinter, I., Feitz, A., Fensholt, R., Filippa, G., Fischer, M., Frank, J., Galvagno, M., Gharun, M., Gianelle, D., Gielen, B., Gioli, B., Gitelson, A., Goded, I., Goeckede, M., Goldstein, A. H., Gough, C. M., Goulden, M. L., Graf, A., Griebel, A., Gruening, C., Grünwald, T., Hammerle, A., Han, S., Han, X., Hansen, B. U., Hanson, C., Hatakka, J., He, Y., Hehn, M., Heinesch, B., Hinko-Najera, N., Hörtnagl, L., Hutley, L., Ibrom, A., Ikawa, H., Jackowicz-Korczynski, M., Janouš, D., Jans, W., Jassal, R., Jiang, S., Kato, T., Khomik, M., Klatt, J., Knohl, A., Knox, S., Kobayashi, H., Koerber, G., Kolle, O., Kosugi, Y., Kotani, A., Kowalski, A., Kruijt, B., Kurbatova, J., Kutsch, W. L., Kwon, H., Launiainen, S., Laurila, T., Law, B., Leuning, R., Li, Y., Liddell, M., Limousin, J.-M., Lion, M., Liska, A. J., Lohila, A., López-Ballesteros, A., López-Blanco, E., Loubet, B., Loustau, D., Lucas-Moffat, A., Lüers, J., Ma, S., Macfarlane, C., Magliulo, V., Maier, R., Mammarella, I., Manca, G., Marcolla, B., Margolis, H. A., Marras, S., Massman, W., Mastepanov, M., Matamala, R., Matthes, J. H., Mazzenga, F., McCaughey, H., McHugh, I., McMillan, A. M. S., Merbold, L., Meyer, W., Meyers, T., Miller, S. D., Minerbi, S., Moderow, U., Monson, R. K., Montagnani, L., Moore, C. E., Moors, E., Moreaux, V., Moureaux, C., Munger, J. W., Nakai, T., Neirynck, J., Nesic, Z., Nicolini, G., Noormets, A., Northwood, M., Nosetto, M., Nouvellon, Y., Novick, K., Oechel, W., Olesen, J. E., Ourcival, J.-M., Papuga, S. A., Parmentier, F.-J., Paul-Limoges, E., Pavelka, M., Peichl, M., Pendall, E., Phillips, R. P., Pilegaard, K., Pirk, N., Posse, G., Powell, T., Prasse, H., Prober, S. M., Rambal, S., Rannik, U., Raz-Yaseef, N., Rebmann, C., Reed, D., Dios, V. R. d., Restrepo-Coupe, N., Reverter, B. R., Roland, M., Sabbatini, S., Sachs, T., Saleska, S. R., Sánchez-Cañete, E. P., Sanchez-Mejia, Z. M., Schmid, H. P., Schmidt, M., Schneider, K., Schrader, F., Schroder, I., Scott, R. L., Sedlák, P., Serrano-Ortíz, P., Shao, C., Shi, P., Shironya, I., Siebicke, L., Šigut, L., Silberstein, R., Sirca, C., Spano, D., Steinbrecher, R., Stevens, R. M., Sturtevant, C., Suyker, A., Tagesson, T., Takanashi, S., Tang, Y., Tapper, N., Thom, J., Tomassucci, M., Tuovinen, J.-P., Urbanski, S., Valentini, R., van der Molen, M., van Gorsel, E., van Huissteden, K., Varlagin, A., Verfaillie, J., Vesala, T., Vincke, C., Vitale, D., Vygodskaya, N., Walker, J. P., Walter-Shea, E., Wang, H., Weber, R., Westermann, S., Wille, C., Wofsy, S., Wohlfahrt, G., Wolf, S., Woodgate, W., Li, Y., Zampedri, R., Zhang, J., Zhou, G., Zona, D., Agarwal, D., Biraud, S., Torn, M., and Papale, D.: The FLUXNET2015 Dataset and the ONEFlux Processing Pipeline for Eddy Covariance Data, Sci. Data, 7, 225, https://doi.org/10.1038/s41597-020-0534-3, 2020. a
Planton, Y. Y., Guilyardi, E., Wittenberg, A. T., Lee, J., Gleckler, P. J., Bayr, T., McGregor, S., McPhaden, M. J., Power, S., Roehrig, R., Vialard, J., and Voldoire, A.: Evaluating Climate Models with the CLIVAR 2020 ENSO Metrics Package, B. Am. Meteorol. Soc., 102, E193–E217, https://doi.org/10.1175/BAMS-D-19-0337.1, 2021. a
Priestley, M. D. K., Ackerley, D., Catto, J. L., Hodges, K. I., McDonald, R. E., and Lee, R. W.: An Overview of the Extratropical Storm Tracks in CMIP6 Historical Simulations, J. Climate, 33, 6315–6343, https://doi.org/10.1175/JCLI-D-19-0928.1, 2020. a
Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L. V., Rowell, D. P., Kent, E. C., and Kaplan, A.: Global Analyses of Sea Surface Temperature, Sea Ice, and Night Marine Air Temperature Since the Late Nineteenth Century, J. Geophys. Res.-Atmos., 108, https://doi.org/10.1029/2002JD002670, 2003. a
Reagan, J. R., Boyer, T. P., García, H. E., Locarnini, R. A., Baranova, O. K., Bouchard, C., Cross, S. L., Mishonov, A. V., Paver, C. R., Seidov, D., Wang, Z., and Dukhovskoy, D.: World Ocean Atlas 2023, NOAA National Centers for Environmental Information, https://doi.org/10.25921/VA26-HV25, 2023. a
Righi, M., Andela, B., Eyring, V., Lauer, A., Predoi, V., Schlund, M., Vegas-Regidor, J., Bock, L., Brötz, B., de Mora, L., Diblen, F., Dreyer, L., Drost, N., Earnshaw, P., Hassler, B., Koldunov, N., Little, B., Loosveldt Tomas, S., and Zimmermann, K.: Earth System Model Evaluation Tool (ESMValTool) v2.0 – technical overview, Geosci. Model Dev., 13, 1179–1199, https://doi.org/10.5194/gmd-13-1179-2020, 2020. a
Roach, L. A., Dörr, J., Holmes, C. R., Massonnet, F., Blockley, E. W., Notz, D., Rackow, T., Raphael, M. N., O'Farrell, S. P., Bailey, D. A., and Bitz, C. M.: Antarctic Sea Ice Area in CMIP6, Geophys. Res. Lett., 47, e2019GL086729, https://doi.org/10.1029/2019GL086729, 2020. a
Roberts, M. J., Camp, J., Seddon, J., Vidale, P. L., Hodges, K., Vannière, B., Mecking, J., Haarsma, R., Bellucci, A., Scoccimarro, E., Caron, L.-P., Chauvin, F., Terray, L., Valcke, S., Moine, M.-P., Putrasahan, D., Roberts, C. D., Senan, R., Zarzycki, C., Ullrich, P., Yamada, Y., Mizuta, R., Kodama, C., Fu, D., Zhang, Q., Danabasoglu, G., Rosenbloom, N., Wang, H., and Wu, L.: Projected Future Changes in Tropical Cyclones Using the CMIP6 HighResMIP Multimodel Ensemble, Geophys. Res. Lett., 47, e2020GL088662, https://doi.org/10.1029/2020GL088662, 2020. a
Rutz, J. J., Shields, C. A., Lora, J. M., Payne, A. E., Guan, B., Ullrich, P., O'Brien, T., Leung, L. R., Ralph, F. M., Wehner, M., Brands, S., Collow, A., Goldenson, N., Gorodetskaya, I., Griffith, H., Kashinath, K., Kawzenuk, B., Krishnan, H., Kurlin, V., Lavers, D., Magnusdottir, G., Mahoney, K., McClenny, E., Muszynski, G., Nguyen, P. D., Prabhat, M., Qian, Y., Ramos, A. M., Sarangi, C., Sellars, S., Shulgina, T., Tome, R., Waliser, D., Walton, D., Wick, G., Wilson, A. M., and Viale, M.: The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying Uncertainties in Atmospheric River Climatology, J. Geophys. Res.-Atmos., 124, 13777–13802, https://doi.org/10.1029/2019JD030936, 2019. a
Sanderson, B. M., Brovkin, V., Fisher, R. A., Hohn, D., Ilyina, T., Jones, C. D., Koenigk, T., Koven, C., Li, H., Lawrence, D. M., Lawrence, P., Liddicoat, S., MacDougall, A. H., Mengis, N., Nicholls, Z., O'Rourke, E., Romanou, A., Sandstad, M., Schwinger, J., Séférian, R., Sentman, L. T., Simpson, I. R., Smith, C., Steinert, N. J., Swann, A. L. S., Tjiputra, J., and Ziehn, T.: flat10MIP: an emissions-driven experiment to diagnose the climate response to positive, zero and negative CO2 emissions, Geosci. Model Dev., 18, 5699–5724, https://doi.org/10.5194/gmd-18-5699-2025, 2025. a, b
Scaife, A. A., Woollings, T., Knight, J., Martin, G., and Hinton, T.: Atmospheric Blocking and Mean Biases in Climate Models, J. Climate, 23, 6143–6152, https://doi.org/10.1175/2010JCLI3728.1, 2010. a
Schlund, M., Lauer, A., Gentine, P., Sherwood, S. C., and Eyring, V.: Emergent constraints on equilibrium climate sensitivity in CMIP5: do they hold for CMIP6?, Earth Syst. Dynam., 11, 1233–1258, https://doi.org/10.5194/esd-11-1233-2020, 2020. a
Schlund, M., Hassler, B., Lauer, A., Andela, B., Jöckel, P., Kazeroni, R., Loosveldt Tomas, S., Medeiros, B., Predoi, V., Sénési, S., Servonnat, J., Stacke, T., Vegas-Regidor, J., Zimmermann, K., and Eyring, V.: Evaluation of native Earth system model output with ESMValTool v2.6.0, Geosci. Model Dev., 16, 315–333, https://doi.org/10.5194/gmd-16-315-2023, 2023. a
Schlund, M., Andela, B., Benke, J., Comer, R., Hassler, B., Hogan, E., Kalverla, P., Lauer, A., Little, B., Loosveldt Tomas, S., Nattino, F., Peglar, P., Predoi, V., Smeets, S., Worsley, S., Yeo, M., and Zimmermann, K.: Advanced climate model evaluation with ESMValTool v2.11.0 using parallel, out-of-core, and distributed computing, Geosci. Model Dev., 18, 4009–4021, https://doi.org/10.5194/gmd-18-4009-2025, 2025. a
Séférian, R., Nabat, P., Michou, M., Saint-Martin, D., Voldoire, A., Colin, J., Decharme, B., Delire, C., Berthet, S., Chevallier, M., Sénési, S., Franchisteguy, L., Vial, J., Mallet, M., Joetzjer, E., Geoffroy, O., Guérémy, J.-F., Moine, M.-P., Msadek, R., Ribes, A., Rocher, M., Roehrig, R., Salas-y Mélia, D., Sanchez, E., Terray, L., Valcke, S., Waldman, R., Aumont, O., Bopp, L., Deshayes, J., Éthé, C., and Madec, G.: Evaluation of CNRM Earth System Model, CNRM-ESM2-1: Role of Earth System Processes in Present-Day and Future Climate, J. Adv. Model. Earth Sy., 11, 4182–4227, https://doi.org/10.1029/2019MS001791, 2019. a
Séférian, R., Berthet, S., Yool, A., Palmiéri, J., Bopp, L., Tagliabue, A., Kwiatkowski, L., Aumont, O., Christian, J., Dunne, J., Gehlen, M., Ilyina, T., John, J. G., Li, H., Long, M. C., Luo, J. Y., Nakano, H., Romanou, A., Schwinger, J., Stock, C., Santana-Falcón, Y., Takano, Y., Tjiputra, J., Tsujino, H., Watanabe, M., Wu, T., Wu, F., and Yamamoto, A.: Tracking Improvement in Simulated Marine Biogeochemistry Between CMIP5 and CMIP6, Current Climate Change Reports, 6, 95–119, https://doi.org/10.1007/s40641-020-00160-0, 2020. a
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., Orlowsky, B., and Teuling, A. J.: Investigating Soil Moisture–Climate Interactions in a Changing Climate: A Review, Earth-Sci. Rev., 99, 125–161, https://doi.org/10.1016/j.earscirev.2010.02.004, 2010. a
Senior, C. A., Jones, C. G., Wood, R. A., Sellar, A., Belcher, S., Klein-Tank, A., Sutton, R., Walton, J., Lawrence, B., Andrews, T., and Mulcahy, J. P.: U.K. Community Earth System Modeling for CMIP6, J. Adv. Model. Earth Sy., 12, e2019MS002004, https://doi.org/10.1029/2019MS002004, 2020. a
Slivinski, L. C., Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Giese, B. S., McColl, C., Allan, R., Yin, X., Vose, R., Titchner, H., Kennedy, J., Spencer, L. J., Ashcroft, L., Brönnimann, S., Brunet, M., Camuffo, D., Cornes, R., Cram, T. A., Crouthamel, R., Domínguez-Castro, F., Freeman, J. E., Gergis, J., Hawkins, E., Jones, P. D., Jourdain, S., Kaplan, A., Kubota, H., Blancq, F. L., Lee, T.-C., Lorrey, A., Luterbacher, J., Maugeri, M., Mock, C. J., Moore, G. K., Przybylak, R., Pudmenzky, C., Reason, C., Slonosky, V. C., Smith, C. A., Tinz, B., Trewin, B., Valente, M. A., Wang, X. L., Wilkinson, C., Wood, K., and Wyszyński, P.: Towards a More Reliable Historical Reanalysis: Improvements for Version 3 of the Twentieth Century Reanalysis System, Q. J. Roy. Meteor. Soc., 145, 2876–2908, https://doi.org/10.1002/qj.3598, 2019. a
Sobolowski, S., Somot, S., Fernandez, J., Evin, G., Brands, S., Maraun, D., Kotlarski, S., Jury, M., Benestad, R. E., Teichmann, C., Christensen, O. B., Bülow, K., Buonomo, E., Katragkou, E., Steger, C., Sørland, S., Nikulin, G., McSweeney, C., Dobler, A., Palmer, T., Wilcke, R., Boé, J., Brunner, L., Ribes, A., Qasmi, S., Nabat, P., Sevault, F., and Oudar, T.: GCM Selection and Ensemble Design: Best Practices and Recommendations from the EURO-CORDEX Community, B. Am. Meteorol. Soc., E1834–E1850, https://doi.org/10.1175/BAMS-D-23-0189.1, 2025. a, b
Sofieva, V. F., Szelag, M., Tamminen, J., Arosio, C., Rozanov, A., Weber, M., Degenstein, D., Bourassa, A., Zawada, D., Kiefer, M., Laeng, A., Walker, K. A., Sheese, P., Hubert, D., van Roozendael, M., Retscher, C., Damadeo, R., and Lumpe, J. D.: Updated merged SAGE-CCI-OMPS+ dataset for the evaluation of ozone trends in the stratosphere, Atmos. Meas. Tech., 16, 1881–1899, https://doi.org/10.5194/amt-16-1881-2023, 2023. a
Solberg, R., Rudjord, Ø., Salberg, A.-B., Killie, M. A., Eastwood, S., Sørensen, A., Marin, C., Premier, V., Schwaizer, G., and Nagler, T.: ESA Snow Climate Change Initiative (Snow_cci): Fractional Snow Cover in CryoClim, v1.0, NERC EDS Centre for Environmental Data Analysis, https://doi.org/10.5285/F4654030223445B0BAC63A23AAA60620, 2023. a
Stainforth, D. A., Allen, M. R., Tredger, E. R., and Smith, L. A.: Confidence, Uncertainty and Decision-support Relevance in Climate Predictions, Philos. T. Roy. Soc. A, https://doi.org/10.1098/rsta.2007.2074, 2007. a
Stockhause, M., Huard, D., Al Khourdajie, A., Gutiérrez, J. M., Kawamiya, M., Klutse, N. A. B., Krey, V., Milward, D., Okem, A. E., Pirani, A., Sitz, L. E., Solman, S. A., Spinuso, A., and Xing, X.: Implementing FAIR Data Principles in the IPCC Seventh Assessment Cycle: Lessons Learned and Future Prospects, PLOS Climate, 3, e0000533, https://doi.org/10.1371/journal.pclm.0000533, 2024. a
Swaminathan, R., Parker, R. J., Jones, C. G., Allan, R. P., Quaife, T., Kelley, D. I., Mora, L. D., and Walton, J.: The Physical Climate at Global Warming Thresholds as Seen in the U.K. Earth System Model, J. Climate, 35, 29–48, https://doi.org/10.1175/JCLI-D-21-0234.1, 2022. a
Taylor, K., Doutriaux, C., and Peterschmitt, J.: Climate Model Output Rewriter (CMOR), Tech. Rep. UCRL-TR-204637, 15014202, Lawrence Livermore National Laboratory, https://doi.org/10.2172/15014202, 2004. a, b
Taylor, K. E.: Summarizing Multiple Aspects of Model Performance in a Single Diagram, J. Geophys. Res.-Atmos., 106, 7183–7192, https://doi.org/10.1029/2000JD900719, 2001. a
Taylor, K. E., Troussellier, L., Ames, S., Hassell, D., Molina, M., Nicholls, Z., Schupfner, M., Anstey, J., Ellis, D., Dingley, B., Durack, P. J., LEVAVASSEUR, G., Mizielinski, M., and Moine, M.-P.: CMIP7 Global Attributes, DRS, Filenames, Directory Structure, and CVs, Zenodo, https://doi.org/10.5281/zenodo.17250296, 2026. a
Teixeira, J., Waliser, D., Ferraro, R., Gleckler, P., Lee, T., and Potter, G.: Satellite Observations for CMIP5: The Genesis of Obs4MIPs, B. Am. Meteorol. Soc., 95, 1329–1334, https://doi.org/10.1175/BAMS-D-12-00204.1, 2025. a
Thompson, D. W. J. and Wallace, J. M.: Annular Modes in the Extratropical Circulation. Part I: Month-to-Month Variability, J. Climate, 13, 1000–1016, https://doi.org/10.1175/1520-0442(2000)013<1000:AMITEC>2.0.CO;2, 2000. a
Tjiputra, J. F., Negrel, J., and Olsen, A.: Early Detection of Anthropogenic Climate Change Signals in the Ocean Interior, Sci. Rep., 13, 3006, https://doi.org/10.1038/s41598-023-30159-0, 2023. a
Trenberth, K. E. and Caron, J. M.: Estimates of Meridional Atmosphere and Ocean Heat Transports, J. Climate, 14, 3433–3443, https://doi.org/10.1175/1520-0442(2001)014<3433:EOMAAO>2.0.CO;2, 2001. a, b
Trenberth, K. E., Branstator, G. W., Karoly, D., Kumar, A., Lau, N.-C., and Ropelewski, C.: Progress During TOGA in Understanding and Modeling Global Teleconnections Associated with Tropical Sea Surface Temperatures, J. Geophys. Res.-Oceans, 103, 14291–14324, https://doi.org/10.1029/97JC01444, 1998. a
Trenberth, K. E., Smith, L., Qian, T., Dai, A., and Fasullo, J.: Estimates of the Global Water Budget and Its Annual Cycle Using Observational and Model Data, J. Hydrometeorol., 8, 758–769, https://doi.org/10.1175/JHM600.1, 2007. a
Trumbore, S. E. and Czimczik, C. I.: An Uncertain Future for Soil Carbon, Science, 321, 1455–1456, https://doi.org/10.1126/science.1160232, 2008. a
Vaittinada Ayar, P., Battisti, D. S., Li, C., King, M., Vrac, M., and Tjiputra, J.: A Regime View of ENSO Flavors Through Clustering in CMIP6 Models, Earth's Future, 11, e2022EF003460, https://doi.org/10.1029/2022EF003460, 2023. a
Waliser, D., Gleckler, P. J., Ferraro, R., Taylor, K. E., Ames, S., Biard, J., Bosilovich, M. G., Brown, O., Chepfer, H., Cinquini, L., Durack, P. J., Eyring, V., Mathieu, P.-P., Lee, T., Pinnock, S., Potter, G. L., Rixen, M., Saunders, R., Schulz, J., Thépaut, J.-N., and Tuma, M.: Observations for Model Intercomparison Project (Obs4MIPs): status for CMIP6, Geosci. Model Dev., 13, 2945–2958, https://doi.org/10.5194/gmd-13-2945-2020, 2020. a, b, c
Wallace, J. M. and Gutzler, D. S.: Teleconnections in the Geopotential Height Field during the Northern Hemisphere Winter, Mon. Weather Rev., 109, 784–812, https://doi.org/10.1175/1520-0493(1981)109<0784:TITGHF>2.0.CO;2, 1981. a
Wan, H., Zhang, K., Rasch, P. J., Singh, B., Chen, X., and Edwards, J.: A new and inexpensive non-bit-for-bit solution reproducibility test based on time step convergence (TSC1.0), Geosci. Model Dev., 10, 537–552, https://doi.org/10.5194/gmd-10-537-2017, 2017. a
Wang, B., Kim, H.-J., Kikuchi, K., and Kitoh, A.: Diagnostic Metrics for Evaluation of Annual and Diurnal Cycles, Clim. Dynam., 37, 941–955, https://doi.org/10.1007/s00382-010-0877-0, 2011. a
Wang, H., Pearson, B., Hou, A., Lanfer Marquez, A., and Bonnet, P.: Model Evaluation and Benchmarking: Community Survey Summary Report, Zenodo, https://doi.org/10.5281/zenodo.15212597, 2025. a
Wang, Y. and Mao, J.: Global Multi-layer Soil Moisture Products, figshare [data set], https://doi.org/10.6084/M9.FIGSHARE.13661312.V1, 2021. a
Wang, Y., Mao, J., Jin, M., Hoffman, F. M., Shi, X., Wullschleger, S. D., and Dai, Y.: Development of observation-based global multilayer soil moisture products for 1970 to 2016, Earth Syst. Sci. Data, 13, 4385–4405, https://doi.org/10.5194/essd-13-4385-2021, 2021. a
Warszawski, L., Frieler, K., Huber, V., Piontek, F., Serdeczny, O., and Schewe, J.: The Inter-Sectoral Impact Model Intercomparison Project (ISI–MIP): Project Framework, P. Natl. Acad. Sci., 111, 3228–3232, https://doi.org/10.1073/pnas.1312330110, 2014. a
Weigel, K., Bock, L., Gier, B. K., Lauer, A., Righi, M., Schlund, M., Adeniyi, K., Andela, B., Arnone, E., Berg, P., Caron, L.-P., Cionni, I., Corti, S., Drost, N., Hunter, A., Lledó, L., Mohr, C. W., Paçal, A., Pérez-Zanón, N., Predoi, V., Sandstad, M., Sillmann, J., Sterl, A., Vegas-Regidor, J., von Hardenberg, J., and Eyring, V.: Earth System Model Evaluation Tool (ESMValTool) v2.0 – diagnostics for extreme events, regional and impact evaluation, and analysis of Earth system models in CMIP, Geosci. Model Dev., 14, 3159–3184, https://doi.org/10.5194/gmd-14-3159-2021, 2021. a
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., Da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., Gonzalez-Beltran, A., Gray, A. J., Groth, P., Goble, C., Grethe, J. S., Heringa, J., ’T Hoen, P. A., Hooft, R., Kuhn, T., Kok, R., Kok, J., Lusher, S. J., Martone, M. E., Mons, A., Packer, A. L., Persson, B., Rocca-Serra, P., Roos, M., Van Schaik, R., Sansone, S.-A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz, M. A., Thompson, M., Van Der Lei, J., Van Mulligen, E., Velterop, J., Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J., and Mons, B.: The FAIR Guiding Principles for Scientific Data Management and Stewardship, Sci. Data, 3, 160018, https://doi.org/10.1038/sdata.2016.18, 2016. a
Winker, D.: CALIPSO Lidar Level 3 Ice Cloud Data, Standard V1-00, NASA Langley Atmospheric Science Data Center Distributed Active Archive Center, https://doi.org/10.5067/CALIOP/CALIPSO/L3_ICE_CLOUD-STANDARD-V1-00, 2024. a
Winker, D., Cai, X., Vaughan, M., Garnier, A., Magill, B., Avery, M., and Getzewich, B.: A Level 3 monthly gridded ice cloud dataset derived from 12 years of CALIOP measurements, Earth Syst. Sci. Data, 16, 2831–2855, https://doi.org/10.5194/essd-16-2831-2024, 2024. a
Yang, X., Ricciuto, D. M., Thornton, P. E., Shi, X., Xu, M., Hoffman, F. M., and Norby, R. J.: The Effects of Phosphorus Cycle Dynamics on Carbon Sources and Sinks in the Amazon Region: A Modeling Study Using ELM v1, J. Geophys. Res.-Biogeo., 124, 3686–3698, https://doi.org/10.1029/2019JG005082, 2019. a
Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S., Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yabu, S., Yoshimura, H., Shindo, E., Mizuta, R., Obata, A., Adachi, Y., and Ishii, M.: The Meteorological Research Institute Earth System Model Version 2.0, MRI-ESM2.0: Description and Basic Evaluation of the Physical Component, J. Meteorol. Soc. Jpn. Ser. II, 97, 931–965, https://doi.org/10.2151/jmsj.2019-051, 2019. a
Zhu, Q., Riley, W. J., Tang, J., Collier, N., Hoffman, F. M., Yang, X., and Bisht, G.: Representing Nitrogen, Phosphorus, and Carbon Interactions in the E3SM Land Model: Development and Global Benchmarking, J. Adv. Model. Earth Sy., 11, 2238–2258, https://doi.org/10.1029/2018MS001571, 2019. a
Editorial statement
The Rapid Evaluation Framework plays a key role in the evaluation of models used in CMIP. The process is a significant step forward for the pace, consistency and transparency of the model evaluation process. This paper will be key reading for all those who need to understand the CMIP process in depth.
The Rapid Evaluation Framework plays a key role in the evaluation of models used in CMIP. The...
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.
The Rapid Evaluation Framework (REF) is a community-driven platform for benchmarking and...
Special issue