Articles | Volume 18, issue 23
https://doi.org/10.5194/gmd-18-9497-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-18-9497-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
04 Dec 2025
Development and technical paper |
| 04 Dec 2025
| 04 Dec 2025
CMIP7 data request: impacts and adaptation priorities and opportunities
NASA Goddard Institute for Space Studies, New York, 10025 USA
Charlotte L. Pascoe
UKRI Science and Technology Facilities Council, Harwell, Didcot, OX11 0QX, UK
Claas Teichmann
Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, 20095 Hamburg, Germany
David J. Brayshaw
Department of Meteorology, University of Reading, Reading, RG6 6ET, United Kingdom
Carlo Buontempo
European Centre for Medium-Range Weather Forecast (ECMWF), 53175 Bonn, Germany
Ibrahima Diouf
University Cheikh Anta Diop (UCAD), Dakar, Senegal
The University of Labé (UL), Labé, Guinea
Jesus Fernandez
Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, 39005 Santander, Spain
Paula L. M. Gonzalez
Met Office, Exeter, EX1 3PB, United Kingdom
Birgit Hassler
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Vanessa Hernaman
Climate Intelligence, Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia
Department of Environmental Science/iClimate, Aarhus University, Roskilde, 4000, Denmark
Doroteaciro Iovino
Foundation Euro-Mediterranean Center on Climate Change, CMCC, Bologna, 40131, Italy
Martin Juckes
Kellogg College, University of Oxford, Oxford, OX2 6PN, UK
UKRI STFC, Harwell Campus, Didcot, OX11 0QX, UK
National Centre for Atmospheric Science, Leeds, UK
Iréne L. Lake
Swedish Meteorological and Hydrological Institution, SMHI 601 76 Norrköping, Sweden
Timothy Lam
Potsdam Institute for Climate Impact Research (PIK), 14473 Potsdam, Germany
Xiaomao Lin
Kansas Climate Center, Kansas State University, Manhattan, 66506, Kansas, USA
Jiafu Mao
Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
Negin Nazarian
School of Built Environment, University of New South Wales, Sydney, NSW 2052, Australia
Australian Research Council Centre of Excellence for Climate Extremes, Kensington, NSW 2052, Australia
Australian Research Council Centre of Excellence for the 21st Century Weather, Kensington, NSW 2052, Australia
Sylvie Parey
Electricité de France, EDF Lab Saclay, 91120 Palaiseau, France
Indrani Roy
Department of Earth Sciences, University College London, London, WC1E 6BT, UK
Wan-Ling Tseng
Ocean Center, National Taiwan University, 10617 Taipei, Taiwan
Briony Turner
CMIP International Project Office, European Space Agency, Harwell, Didcot OX11 OFD, UK
Andrew Wiebe
Riskthinking.AI, Toronto M6G 2K9, Canada
Department of Civil and Environmental Engineering, The Grainger College of Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, USA
Damaris Zurell
Institute of Biochemistry and Biology, University of Potsdam, 14469 Potsdam, Germany
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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
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Emmanuel Gobet, David Métivier, and Sylvie Parey
Adv. Stat. Clim. Meteorol. Oceanogr., 11, 159–201, https://doi.org/10.5194/ascmo-11-159-2025, https://doi.org/10.5194/ascmo-11-159-2025, 2025
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William J. Collins, Fiona M. O'Connor, Rachael E. Byrom, Øivind Hodnebrog, Patrick Jöckel, Mariano Mertens, Gunnar Myhre, Matthias Nützel, Dirk Olivié, Ragnhild Bieltvedt Skeie, Laura Stecher, Larry W. Horowitz, Vaishali Naik, Gregory Faluvegi, Ulas Im, Lee T. Murray, Drew Shindell, Kostas Tsigaridis, Nathan Luke Abraham, and James Keeble
Atmos. Chem. Phys., 25, 9031–9060, https://doi.org/10.5194/acp-25-9031-2025, https://doi.org/10.5194/acp-25-9031-2025, 2025
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We used 7 climate models that include atmospheric chemistry and find that in a scenario with weak controls on air quality, the warming effects (over 2015 to 2050) of decreases in ozone-depleting substances and increases in air quality pollutants are approximately equal and would make ozone the second highest contributor to warming over this period. We find that for stratospheric ozone recovery, the standard measure of climate effects underestimates a more comprehensive measure.
Wanmin Gong, Stephen R. Beagley, Kenjiro Toyota, Henrik Skov, Jesper Heile Christensen, Alex Lupu, Diane Pendlebury, Junhua Zhang, Ulas Im, Yugo Kanaya, Alfonso Saiz-Lopez, Roberto Sommariva, Peter Effertz, John W. Halfacre, Nis Jepsen, Rigel Kivi, Theodore K. Koenig, Katrin Müller, Claus Nordstrøm, Irina Petropavlovskikh, Paul B. Shepson, William R. Simpson, Sverre Solberg, Ralf M. Staebler, David W. Tarasick, Roeland Van Malderen, and Mika Vestenius
Atmos. Chem. Phys., 25, 8355–8405, https://doi.org/10.5194/acp-25-8355-2025, https://doi.org/10.5194/acp-25-8355-2025, 2025
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This study showed that the springtime O3 depletion plays a critical role in driving the surface O3 seasonal cycle in the central Arctic. The O3 depletion events, while occurring most notably within the lowest few hundred metres above the Arctic Ocean, can induce a 5–7 % loss in the pan-Arctic tropospheric O3 burden during springtime. The study also found enhancements in O3 and NOy (mostly peroxyacetyl nitrate) concentrations in the Arctic due to northern boreal wildfires, particularly at higher altitudes.
Forrest M. Hoffman, Birgit Hassler, Ranjini Swaminathan, Jared Lewis, Bouwe Andela, Nathaniel Collier, Dóra Hegedűs, Jiwoo Lee, Charlotte Pascoe, Mika Pflüger, Martina Stockhause, Paul Ullrich, Min Xu, Lisa Bock, Felicity Chun, Bettina K. Gier, Douglas I. Kelley, Axel Lauer, Julien Lenhardt, Manuel Schlund, Mohanan G. Sreeush, Katja Weigel, Ed Blockley, Rebecca Beadling, Romain Beucher, Demiso D. Dugassa, Valerio Lembo, Jianhua Lu, Swen Brands, Jerry Tjiputra, Elizaveta Malinina, Brian Mederios, Enrico Scoccimarro, Jeremy Walton, Philip Kershaw, André L. Marquez, Malcolm J. Roberts, Eleanor O’Rourke, Elisabeth Dingley, Briony Turner, Helene Hewitt, and John P. Dunne
EGUsphere, https://doi.org/10.5194/egusphere-2025-2685, https://doi.org/10.5194/egusphere-2025-2685, 2025
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As Earth system models become more complex, rapid and comprehensive evaluation through comparison with observational data is necessary. The upcoming Assessment Fast Track for the Seventh Phase of the Coupled Model Intercomparison Project (CMIP7) will require fast analysis. This paper describes a new Rapid Evaluation Framework (REF) that was developed for the Assessment Fast Track that will be run at the Earth System Grid Federation (ESGF) to inform the community about the performance of models.
Baylor Fox-Kemper, Patricia DeRepentigny, Anne Marie Treguier, Christian Stepanek, Eleanor O’Rourke, Chloe Mackallah, Alberto Meucci, Yevgeny Aksenov, Paul J. Durack, Nicole Feldl, 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
EGUsphere, https://doi.org/10.5194/egusphere-2025-3083, https://doi.org/10.5194/egusphere-2025-3083, 2025
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The earth system model variables needed for studies of the ocean and sea ice are prioritized and requested.
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
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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.
Jakob Boyd Pernov, William H. Aeberhard, Michele Volpi, Eliza Harris, Benjamin Hohermuth, Sakiko Ishino, Ragnhild B. Skeie, Stephan Henne, Ulas Im, Patricia K. Quinn, Lucia M. Upchurch, and Julia Schmale
Atmos. Chem. Phys., 25, 6497–6537, https://doi.org/10.5194/acp-25-6497-2025, https://doi.org/10.5194/acp-25-6497-2025, 2025
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Particulate methanesulfonic acid (MSAp) is vital for the Arctic climate system. Numerical models struggle to reproduce the MSAp seasonal cycle. We evaluate three numerical models and one reanalysis product’s ability to simulate MSAp. We develop data-driven models for MSAp at four Arctic stations. The data-driven models outperform the numerical models and reanalysis product and identified precursor source-, chemical-processing-, and removal-related features as being important for modeling MSAp.
Katherine Shu-Min Li, Nadun Sinhabahu, Ben-Jei Tsuang, Fang-Chi Wu, Wan-Ling Tseng, Pei-Hsuan Kuo, Sying-Jyan Wang, Pang-Yen Liu, Jen-Her Chen, Bin-Ming Wang, Yung-Yao Lan, and Sun-Yuan Kung
EGUsphere, https://doi.org/10.5194/egusphere-2025-142, https://doi.org/10.5194/egusphere-2025-142, 2025
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This study underscores the transformative potential of machine learning algorithms in environmental forecasting. The superior performance of Bi-LSTM in reducing SST bias, coupled with its broader applicability in time-series analysis, makes it a valuable tool for improving the accuracy and reliability of numerical weather prediction models.
Aytaç Paçal, Birgit Hassler, Katja Weigel, Miguel-Ángel Fernández-Torres, Gustau Camps-Valls, and Veronika Eyring
EGUsphere, https://doi.org/10.5194/egusphere-2025-2460, https://doi.org/10.5194/egusphere-2025-2460, 2025
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Heatwaves are among the deadliest natural hazards, yet their causes and changes over time are not fully understood. We analyzed European heatwaves using a machine learning method that detects atmospheric patterns from these data. Our findings show that recent summer heatwaves differ from historical ones, indicating a shift in atmospheric dynamics consistent with climate change. This approach improves our understanding of the temporal evolution of heatwaves.
Yifan Cheng, Lei Zhao, TC Chakraborty, Keith Oleson, Matthias Demuzere, Xiaoping Liu, Yangzi Che, Weilin Liao, Yuyu Zhou, and Xinchang “Cathy” Li
Earth Syst. Sci. Data, 17, 2147–2174, https://doi.org/10.5194/essd-17-2147-2025, https://doi.org/10.5194/essd-17-2147-2025, 2025
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The absence of globally consistent and spatially continuous urban surface input has long hindered large-scale high-resolution urban climate modeling. Using remote sensing, cloud computing, and machine learning, we developed U-Surf, a 1 km dataset providing key urban surface properties worldwide. U-Surf enhances urban representation across scales and supports kilometer-scale urban-resolving Earth system modeling unprecedentedly, with broader applications in urban studies and beyond.
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
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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.
Dilli Paudel, Michiel Kallenberg, Stella Ofori-Ampofo, Hilmy Baja, Ron van Bree, Aike Potze, Pratishtha Poudel, Abdelrahman Saleh, Weston Anderson, Malte von Bloh, Andres Castellano, Oumnia Ennaji, Raed Hamed, Rahel Laudien, Donghoon Lee, Inti Luna, Michele Meroni, Janet Mumo Mutuku, Siyabusa Mkuhlani, Jonathan Richetti, Alex C. Ruane, Ritvik Sahajpal, Guanyuan Shai, Vasileios Sitokonstantinou, Rogério de Souza Nóia Júnior, Amit Kumar Srivastava, Robert Strong, Lily-belle Sweet, Petar Vojnovic, and Ioannis N. Athanasiadis
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-83, https://doi.org/10.5194/essd-2025-83, 2025
Revised manuscript under review for ESSD
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Improving crop yield predictions is crucial for food security. Prior research relied on case studies, making it hard to compare methods & track progress. We introduce CY-Bench, a global dataset for forecasting maize and wheat yields across diverse farming systems in over 25 countries. It includes standardized weather, soil, and satellite data, curated by a diverse set of experts. CY-Bench supports the development of better forecasting tools to help decision-makers plan for global food security.
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
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Earth system models are important tools to improve our understanding of current climate and to project climate change. Thus, it is crucial to understand possible shortcomings in the models. New features of the ESMValTool software package allow one to compare and visualize a model's performance with respect to reproducing observations in the context of other climate models in an easy and user-friendly way. We aim to help model developers assess and monitor climate simulations more efficiently.
Paul J. Durack, Karl E. Taylor, Peter J. Gleckler, Gerald A. Meehl, Bryan N. Lawrence, Curt Covey, Ronald J. Stouffer, Guillaume Levavasseur, Atef Ben-Nasser, Sebastien Denvil, Martina Stockhause, Jonathan M. Gregory, Martin Juckes, Sasha K. Ames, Fabrizio Antonio, David C. Bader, John P. Dunne, Daniel Ellis, Veronika Eyring, Sandro L. Fiore, Sylvie Joussaume, Philip Kershaw, Jean-Francois Lamarque, Michael Lautenschlager, Jiwoo Lee, Chris F. Mauzey, Matthew Mizielinski, Paola Nassisi, Alessandra Nuzzo, Eleanor O’Rourke, Jeffrey Painter, Gerald L. Potter, Sven Rodriguez, and Dean N. Williams
EGUsphere, https://doi.org/10.5194/egusphere-2024-3729, https://doi.org/10.5194/egusphere-2024-3729, 2025
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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.
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
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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.
Francesco Cocetta, Lorenzo Zampieri, Julia Selivanova, and Doroteaciro Iovino
The Cryosphere, 18, 4687–4702, https://doi.org/10.5194/tc-18-4687-2024, https://doi.org/10.5194/tc-18-4687-2024, 2024
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Arctic sea ice is thinning and retreating because of global warming. Thus, the region is transitioning to a new state featuring an expansion of the marginal ice zone, a region where mobile ice interacts with waves from the open ocean. By analyzing 30 years of sea ice reconstructions that combine numerical models and observations, this paper proves that an ensemble of global ocean and sea ice reanalyses is an adequate tool for investigating the changing Arctic sea ice cover.
Karina von Schuckmann, Lorena Moreira, Mathilde Cancet, Flora Gues, Emmanuelle Autret, Jonathan Baker, Clément Bricaud, Romain Bourdalle-Badie, Lluis Castrillo, Lijing Cheng, Frederic Chevallier, Daniele Ciani, Alvaro de Pascual-Collar, Vincenzo De Toma, Marie Drevillon, Claudia Fanelli, Gilles Garric, Marion Gehlen, Rianne Giesen, Kevin Hodges, Doroteaciro Iovino, Simon Jandt-Scheelke, Eric Jansen, Melanie Juza, Ioanna Karagali, Thomas Lavergne, Simona Masina, Ronan McAdam, Audrey Minière, Helen Morrison, Tabea Rebekka Panteleit, Andrea Pisano, Marie-Isabelle Pujol, Ad Stoffelen, Sulian Thual, Simon Van Gennip, Pierre Veillard, Chunxue Yang, and Hao Zuo
State Planet, 4-osr8, 1, https://doi.org/10.5194/sp-4-osr8-1-2024, https://doi.org/10.5194/sp-4-osr8-1-2024, 2024
Jiemei Liu, Jesper H. Christensen, Zhuyun Ye, Shikui Dong, Camilla Geels, Jørgen Brandt, Athanasios Nenes, Yuan Yuan, and Ulas Im
Atmos. Chem. Phys., 24, 10849–10867, https://doi.org/10.5194/acp-24-10849-2024, https://doi.org/10.5194/acp-24-10849-2024, 2024
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China was chosen as an example to conduct a quantitative analysis using the Danish Eulerian Hemispheric Model (DEHM) system with meteorological input from the Weather Research and Forecasting (WRF) model. Meteorological conditions and emission inventories contributed 46 % (65 %) and 54 % (35 %) to the variations in PM2.5 concentrations (oxidative potential – OP), respectively, highlighting secondary aerosol formation and biomass burning as the primary contributors to PM2.5 and OP levels.
Paula L. M. Gonzalez, Lesley J. Gray, Stergios Misios, Scott Osprey, and Hedi Ma
EGUsphere, https://doi.org/10.5194/egusphere-2024-2487, https://doi.org/10.5194/egusphere-2024-2487, 2024
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This study has examined a set of reanalyses, both modern and 20th Century, to evaluate the robustness of the signatures of the 11-yr solar cycle in the North Atlantic climate. We find a robust response to the 11-yr solar cycle over the North Atlantic sector with a positive SLP anomaly north of the Azores region at lags of +2–3 years following solar maximum. An ocean reanalysis dataset shows that thermal inertia of the ocean could explain the lag in the SC response.
Alberto Martilli, Negin Nazarian, E. Scott Krayenhoff, Jacob Lachapelle, Jiachen Lu, Esther Rivas, Alejandro Rodriguez-Sanchez, Beatriz Sanchez, and José Luis Santiago
Geosci. Model Dev., 17, 5023–5039, https://doi.org/10.5194/gmd-17-5023-2024, https://doi.org/10.5194/gmd-17-5023-2024, 2024
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Here, we present a model that quantifies the thermal stress and its microscale variability at a city scale with a mesoscale model. This tool can have multiple applications, from early warnings of extreme heat to the vulnerable population to the evaluation of the effectiveness of heat mitigation strategies. It is the first model that includes information on microscale variability in a mesoscale model, something that is essential for fully evaluating heat stress.
Julia Selivanova, Doroteaciro Iovino, and Francesco Cocetta
The Cryosphere, 18, 2739–2763, https://doi.org/10.5194/tc-18-2739-2024, https://doi.org/10.5194/tc-18-2739-2024, 2024
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Climate models show differences in sea ice representation in comparison to observations. Increasing the model resolution is a recognized way to improve model realism and obtain more reliable future projections. We find no strong impact of resolution on sea ice representation; it rather depends on the analysed variable and the model used. By 2050, the marginal ice zone (MIZ) becomes a dominant feature of the Arctic ice cover, suggesting a shift to a new regime similar to that in Antarctica.
Piers M. Forster, Chris Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Bradley Hall, Mathias Hauser, Aurélien Ribes, Debbie Rosen, Nathan P. Gillett, Matthew D. Palmer, Joeri Rogelj, Karina von Schuckmann, Blair Trewin, Myles Allen, Robbie Andrew, Richard A. Betts, Alex Borger, Tim Boyer, Jiddu A. Broersma, Carlo Buontempo, Samantha Burgess, Chiara Cagnazzo, Lijing Cheng, Pierre Friedlingstein, Andrew Gettelman, Johannes Gütschow, Masayoshi Ishii, Stuart Jenkins, Xin Lan, Colin Morice, Jens Mühle, Christopher Kadow, John Kennedy, Rachel E. Killick, Paul B. Krummel, Jan C. Minx, Gunnar Myhre, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Carl-Friedrich Schleussner, Sonia I. Seneviratne, Sophie Szopa, Peter Thorne, Mahesh V. M. Kovilakam, Elisa Majamäki, Jukka-Pekka Jalkanen, Margreet van Marle, Rachel M. Hoesly, Robert Rohde, Dominik Schumacher, Guido van der Werf, Russell Vose, Kirsten Zickfeld, Xuebin Zhang, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 16, 2625–2658, https://doi.org/10.5194/essd-16-2625-2024, https://doi.org/10.5194/essd-16-2625-2024, 2024
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This paper tracks some key indicators of global warming through time, from 1850 through to the end of 2023. It is designed to give an authoritative estimate of global warming to date and its causes. We find that in 2023, global warming reached 1.3 °C and is increasing at over 0.2 °C per decade. This is caused by all-time-high greenhouse gas emissions.
Yung-Yao Lan, Huang-Hsiung Hsu, and Wan-Ling Tseng
Geosci. Model Dev., 17, 3897–3918, https://doi.org/10.5194/gmd-17-3897-2024, https://doi.org/10.5194/gmd-17-3897-2024, 2024
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This study uses the CAM5–SIT coupled model to investigate the effects of SST feedback frequency on the MJO simulations with intervals at 30 min, 1, 3, 6, 12, 18, 24, and 30 d. The simulations become increasingly unrealistic as the frequency of the SST feedback decreases. Our results suggest that more spontaneous air--sea interaction (e.g., ocean response within 3 d in this study) with high vertical resolution in the ocean model is key to the realistic simulation of the MJO.
Nenghan Wan, Xiaomao Lin, Roger A. Pielke Sr., Xubin Zeng, and Amanda M. Nelson
Hydrol. Earth Syst. Sci., 28, 2123–2137, https://doi.org/10.5194/hess-28-2123-2024, https://doi.org/10.5194/hess-28-2123-2024, 2024
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Global warming occurs at a rate of 0.21 K per decade, resulting in about 9.5 % K−1 of water vapor response to temperature from 1993 to 2021. Terrestrial areas experienced greater warming than the ocean, with a ratio of 2 : 1. The total precipitable water change in response to surface temperature changes showed a variation around 6 % K−1–8 % K−1 in the 15–55° N latitude band. Further studies are needed to identify the mechanisms leading to different water vapor responses.
Elena Bianco, Doroteaciro Iovino, Simona Masina, Stefano Materia, and Paolo Ruggieri
The Cryosphere, 18, 2357–2379, https://doi.org/10.5194/tc-18-2357-2024, https://doi.org/10.5194/tc-18-2357-2024, 2024
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Changes in ocean heat transport and surface heat fluxes in recent decades have altered the Arctic Ocean heat budget and caused warming of the upper ocean. Using two eddy-permitting ocean reanalyses, we show that this has important implications for sea ice variability. In the Arctic regional seas, upper-ocean heat content acts as an important precursor for sea ice anomalies on sub-seasonal timescales, and this link has strengthened since the 2000s.
Yi-Chi Wang, Chia-Hao Chiang, Chiung-Jui Su, Ko-Chih Wang, Wan-Ling Tseng, Cheng-Ta Chen, and Hsin-Chien Liang
EGUsphere, https://doi.org/10.5194/egusphere-2024-1022, https://doi.org/10.5194/egusphere-2024-1022, 2024
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Our study introduces a deep learning model, the EDA, to refine rainfall data in Taiwan. This model significantly improves bias correction by integrating surface wind and topography data, crucial in areas like Taiwan where traditional methods fall short. The EDA excels in adjusting low-intensity and misplaced rainfall, enhancing water management, agriculture, and disaster prevention. This work showcases deep learning's potential to improve climate downscaling in complex terrains.
Jose Rafael Guarin, Jonas Jägermeyr, Elizabeth A. Ainsworth, Fabio A. A. Oliveira, Senthold Asseng, Kenneth Boote, Joshua Elliott, Lisa Emberson, Ian Foster, Gerrit Hoogenboom, David Kelly, Alex C. Ruane, and Katrina Sharps
Geosci. Model Dev., 17, 2547–2567, https://doi.org/10.5194/gmd-17-2547-2024, https://doi.org/10.5194/gmd-17-2547-2024, 2024
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The effects of ozone (O3) stress on crop photosynthesis and leaf senescence were added to maize, rice, soybean, and wheat crop models. The modified models reproduced growth and yields under different O3 levels measured in field experiments and reported in the literature. The combined interactions between O3 and additional stresses were reproduced with the new models. These updated crop models can be used to simulate impacts of O3 stress under future climate change and air pollution scenarios.
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
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Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
Rongyun Tang, Mingzhou Jin, Jiafu Mao, Daniel M. Ricciuto, Anping Chen, and Yulong Zhang
Geosci. Model Dev., 17, 1525–1542, https://doi.org/10.5194/gmd-17-1525-2024, https://doi.org/10.5194/gmd-17-1525-2024, 2024
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Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
Kelsey T. Foster, Wu Sun, Yoichi P. Shiga, Jiafu Mao, and Anna M. Michalak
Biogeosciences, 21, 869–891, https://doi.org/10.5194/bg-21-869-2024, https://doi.org/10.5194/bg-21-869-2024, 2024
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Assessing agreement between bottom-up and top-down methods across spatial scales can provide insights into the relationship between ensemble spread (difference across models) and model accuracy (difference between model estimates and reality). We find that ensemble spread is unlikely to be a good indicator of actual uncertainty in the North American carbon balance. However, models that are consistent with atmospheric constraints show stronger agreement between top-down and bottom-up estimates.
Chao Wang, Stephen Leisz, Li Li, Xiaoying Shi, Jiafu Mao, Yi Zheng, and Anping Chen
Earth Syst. Dynam., 15, 75–90, https://doi.org/10.5194/esd-15-75-2024, https://doi.org/10.5194/esd-15-75-2024, 2024
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Climate change can significantly impact river runoff; however, predicting future runoff is challenging. Using historical runoff gauge data to evaluate model performances in runoff simulations for the Mekong River, we quantify future runoff changes in the Mekong River with the best simulation combination. Results suggest a significant increase in the annual runoff, along with varied seasonal distributions, thus heightening the need for adapted water resource management measures.
Qiang Wang, Qi Shu, Alexandra Bozec, Eric P. Chassignet, Pier Giuseppe Fogli, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Nikolay Koldunov, Julien Le Sommer, Yiwen Li, Pengfei Lin, Hailong Liu, Igor Polyakov, Patrick Scholz, Dmitry Sidorenko, Shizhu Wang, and Xiaobiao Xu
Geosci. Model Dev., 17, 347–379, https://doi.org/10.5194/gmd-17-347-2024, https://doi.org/10.5194/gmd-17-347-2024, 2024
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Increasing resolution improves model skills in simulating the Arctic Ocean, but other factors such as parameterizations and numerics are at least of the same importance for obtaining reliable simulations.
Doroteaciro Iovino, Pier Giuseppe Fogli, and Simona Masina
Geosci. Model Dev., 16, 6127–6159, https://doi.org/10.5194/gmd-16-6127-2023, https://doi.org/10.5194/gmd-16-6127-2023, 2023
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This paper describes the model performance of three global ocean–sea ice configurations, from non-eddying (1°) to eddy-rich (1/16°) resolutions. Model simulations are obtained following the Ocean Model Intercomparison Project phase 2 (OMIP2) protocol. We compare key global climate variables across the three models and against observations, emphasizing the relative advantages and disadvantages of running forced ocean–sea ice models at higher resolution.
Jonathan Andrew Baker, Richard Renshaw, Laura Claire Jackson, Clotilde Dubois, Doroteaciro Iovino, Hao Zuo, Renellys C. Perez, Shenfu Dong, Marion Kersalé, Michael Mayer, Johannes Mayer, Sabrina Speich, and Tarron Lamont
State Planet, 1-osr7, 4, https://doi.org/10.5194/sp-1-osr7-4-2023, https://doi.org/10.5194/sp-1-osr7-4-2023, 2023
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We use ocean reanalyses, in which ocean models are combined with observations, to infer past changes in ocean circulation and heat transport in the South Atlantic. Comparing these estimates with other observation-based estimates, we find differences in their trends, variability, and mean heat transport but closer agreement in their mean overturning strength. Ocean reanalyses can help us understand the cause of these differences, which could improve estimates of ocean transports in this region.
Michael Mayer, Takamasa Tsubouchi, Susanna Winkelbauer, Karin Margretha H. Larsen, Barbara Berx, Andreas Macrander, Doroteaciro Iovino, Steingrímur Jónsson, and Richard Renshaw
State Planet, 1-osr7, 14, https://doi.org/10.5194/sp-1-osr7-14-2023, https://doi.org/10.5194/sp-1-osr7-14-2023, 2023
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This paper compares oceanic fluxes across the Greenland–Scotland Ridge (GSR) from ocean reanalyses to largely independent observational data. Reanalyses tend to underestimate the inflow of warm waters of subtropical Atlantic origin and hence oceanic heat transport across the GSR. Investigation of a strong negative heat transport anomaly around 2018 highlights the interplay of variability on different timescales and the need for long-term monitoring of the GSR to detect forced climate signals.
Andrea Cipollone, Deep Sankar Banerjee, Doroteaciro Iovino, Ali Aydogdu, and Simona Masina
Ocean Sci., 19, 1375–1392, https://doi.org/10.5194/os-19-1375-2023, https://doi.org/10.5194/os-19-1375-2023, 2023
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Sea-ice volume is characterized by low predictability compared to the sea ice area or the extent. A joint initialization of the thickness and concentration using satellite data could improve the predictive power, although it is still absent in the present global analysis–reanalysis systems. This study shows a scheme to correct the two features together that can be easily extended to include ocean variables. The impact of such a joint initialization is shown and compared among different set-ups.
Yi-Chi Wang, Wan-Ling Tseng, Yu-Luen Chen, Shih-Yu Lee, Huang-Hsiung Hsu, and Hsin-Chien Liang
Geosci. Model Dev., 16, 4599–4616, https://doi.org/10.5194/gmd-16-4599-2023, https://doi.org/10.5194/gmd-16-4599-2023, 2023
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This study focuses on evaluating the performance of the Taiwan Earth System Model version 1 (TaiESM1) in simulating the El Niño–Southern Oscillation (ENSO), a significant tropical climate pattern with global impacts. Our findings reveal that TaiESM1 effectively captures several characteristics of ENSO, such as its seasonal variation and remote teleconnections. Its pronounced ENSO strength bias is also thoroughly investigated, aiming to gain insights to improve climate model performance.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
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The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
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
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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.
Fa Li, Qing Zhu, William J. Riley, Lei Zhao, Li Xu, Kunxiaojia Yuan, Min Chen, Huayi Wu, Zhipeng Gui, Jianya Gong, and James T. Randerson
Geosci. Model Dev., 16, 869–884, https://doi.org/10.5194/gmd-16-869-2023, https://doi.org/10.5194/gmd-16-869-2023, 2023
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We developed an interpretable machine learning model to predict sub-seasonal and near-future wildfire-burned area over African and South American regions. We found strong time-lagged controls (up to 6–8 months) of local climate wetness on burned areas. A skillful use of such time-lagged controls in machine learning models results in highly accurate predictions of wildfire-burned areas; this will also help develop relevant early-warning and management systems for tropical wildfires.
Nenghan Wan, Xiaozhen Xiong, Gerard J. Kluitenberg, J. M. Shawn Hutchinson, Robert Aiken, Haidong Zhao, and Xiaomao Lin
Atmos. Chem. Phys., 23, 711–724, https://doi.org/10.5194/acp-23-711-2023, https://doi.org/10.5194/acp-23-711-2023, 2023
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This study used new TROPOMI measurements of NO2 and CO to characterize regional biomass burning characteristics and efficiency. We found that the NO2 / CO emission ratio was consistent with recent studies over temperate forest fires but slightly lower in savanna vegetation fires. Our results can help identify the relative contribution of smoldering and flaming activities as well as their impacts on the regional atmospheric composition and air quality.
Cynthia H. Whaley, Kathy S. Law, Jens Liengaard Hjorth, Henrik Skov, Stephen R. Arnold, Joakim Langner, Jakob Boyd Pernov, Garance Bergeron, Ilann Bourgeois, Jesper H. Christensen, Rong-You Chien, Makoto Deushi, Xinyi Dong, Peter Effertz, Gregory Faluvegi, Mark Flanner, Joshua S. Fu, Michael Gauss, Greg Huey, Ulas Im, Rigel Kivi, Louis Marelle, Tatsuo Onishi, Naga Oshima, Irina Petropavlovskikh, Jeff Peischl, David A. Plummer, Luca Pozzoli, Jean-Christophe Raut, Tom Ryerson, Ragnhild Skeie, Sverre Solberg, Manu A. Thomas, Chelsea Thompson, Kostas Tsigaridis, Svetlana Tsyro, Steven T. Turnock, Knut von Salzen, and David W. Tarasick
Atmos. Chem. Phys., 23, 637–661, https://doi.org/10.5194/acp-23-637-2023, https://doi.org/10.5194/acp-23-637-2023, 2023
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This study summarizes recent research on ozone in the Arctic, a sensitive and rapidly warming region. We find that the seasonal cycles of near-surface atmospheric ozone are variable depending on whether they are near the coast, inland, or at high altitude. Several global model simulations were evaluated, and we found that because models lack some of the ozone chemistry that is important for the coastal Arctic locations, they do not accurately simulate ozone there.
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
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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.
Giulia Bonino, Doroteaciro Iovino, Laurent Brodeau, and Simona Masina
Geosci. Model Dev., 15, 6873–6889, https://doi.org/10.5194/gmd-15-6873-2022, https://doi.org/10.5194/gmd-15-6873-2022, 2022
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The sea surface temperature (SST) is highly influenced by the transfer of energy driven by turbulent air–sea fluxes (TASFs). In the NEMO ocean general circulation model, TASFs are computed by means of bulk formulas. Bulk formulas require the choice of a given bulk parameterization, which influences the magnitudes of the TASFs. Our results show that parameterization-related SST differences are primarily sensitive to the wind stress differences across parameterizations.
Jorge Baño-Medina, Rodrigo Manzanas, Ezequiel Cimadevilla, Jesús Fernández, Jose González-Abad, Antonio S. Cofiño, and José Manuel Gutiérrez
Geosci. Model Dev., 15, 6747–6758, https://doi.org/10.5194/gmd-15-6747-2022, https://doi.org/10.5194/gmd-15-6747-2022, 2022
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Deep neural networks are used to produce downscaled regional climate change projections over Europe for temperature and precipitation for the first time. The resulting dataset, DeepESD, is analyzed against state-of-the-art downscaling methodologies, reproducing more accurately the observed climate in the historical period and showing plausible future climate change signals with low computational requirements.
Yung-Yao Lan, Huang-Hsiung Hsu, Wan-Ling Tseng, and Li-Chiang Jiang
Geosci. Model Dev., 15, 5689–5712, https://doi.org/10.5194/gmd-15-5689-2022, https://doi.org/10.5194/gmd-15-5689-2022, 2022
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This study has shown that coupling a high-resolution 1-D ocean model (SIT 1.06) with the Community Atmosphere Model 5.3 (CAM5.3) significantly improves the simulation of the Madden–Julian Oscillation (MJO) over the standalone CAM5.3. Systematic sensitivity experiments resulted in more realistic simulations of the tropical MJO because they had better upper-ocean resolution, adequate upper-ocean thickness, coupling regions including the eastern Pacific and southern tropics, and a diurnal cycle.
Wan-Ling Tseng, Huang-Hsiung Hsu, Yung-Yao Lan, Wei-Liang Lee, Chia-Ying Tu, Pei-Hsuan Kuo, Ben-Jei Tsuang, and Hsin-Chien Liang
Geosci. Model Dev., 15, 5529–5546, https://doi.org/10.5194/gmd-15-5529-2022, https://doi.org/10.5194/gmd-15-5529-2022, 2022
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We show that coupling a high-resolution one-column ocean model to three atmospheric general circulation models dramatically improves Madden–Julian oscillation (MJO) simulations. It suggests two major improvements to the coupling process in the preconditioning phase and strongest convection phase over the Maritime Continent. Our results demonstrate a simple but effective way to significantly improve MJO simulations and potentially seasonal to subseasonal prediction.
Hannah C. Bloomfield, David J. Brayshaw, Matthew Deakin, and David Greenwood
Earth Syst. Sci. Data, 14, 2749–2766, https://doi.org/10.5194/essd-14-2749-2022, https://doi.org/10.5194/essd-14-2749-2022, 2022
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There is a global increase in renewable generation to meet carbon targets and reduce the impacts of climate change. Renewable generation and electricity demand depend on the weather. This means there is a need for high-quality weather data for energy system modelling. We present a new European-level, 70-year dataset which has been specifically designed to support the energy sector. We provide hourly, sub-national climate outputs and include the impacts of near-term climate change.
Cynthia H. Whaley, Rashed Mahmood, Knut von Salzen, Barbara Winter, Sabine Eckhardt, Stephen Arnold, Stephen Beagley, Silvia Becagli, Rong-You Chien, Jesper Christensen, Sujay Manish Damani, Xinyi Dong, Konstantinos Eleftheriadis, Nikolaos Evangeliou, Gregory Faluvegi, Mark Flanner, Joshua S. Fu, Michael Gauss, Fabio Giardi, Wanmin Gong, Jens Liengaard Hjorth, Lin Huang, Ulas Im, Yugo Kanaya, Srinath Krishnan, Zbigniew Klimont, Thomas Kühn, Joakim Langner, Kathy S. Law, Louis Marelle, Andreas Massling, Dirk Olivié, Tatsuo Onishi, Naga Oshima, Yiran Peng, David A. Plummer, Olga Popovicheva, Luca Pozzoli, Jean-Christophe Raut, Maria Sand, Laura N. Saunders, Julia Schmale, Sangeeta Sharma, Ragnhild Bieltvedt Skeie, Henrik Skov, Fumikazu Taketani, Manu A. Thomas, Rita Traversi, Kostas Tsigaridis, Svetlana Tsyro, Steven Turnock, Vito Vitale, Kaley A. Walker, Minqi Wang, Duncan Watson-Parris, and Tahya Weiss-Gibbons
Atmos. Chem. Phys., 22, 5775–5828, https://doi.org/10.5194/acp-22-5775-2022, https://doi.org/10.5194/acp-22-5775-2022, 2022
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Air pollutants, like ozone and soot, play a role in both global warming and air quality. Atmospheric models are often used to provide information to policy makers about current and future conditions under different emissions scenarios. In order to have confidence in those simulations, in this study we compare simulated air pollution from 18 state-of-the-art atmospheric models to measured air pollution in order to assess how well the models perform.
Patricia Tarín-Carrasco, Ulas Im, Camilla Geels, Laura Palacios-Peña, and Pedro Jiménez-Guerrero
Atmos. Chem. Phys., 22, 3945–3965, https://doi.org/10.5194/acp-22-3945-2022, https://doi.org/10.5194/acp-22-3945-2022, 2022
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The evidence of the effects of atmospheric pollution (and particularly fine particulate matter, PM2.5) on human mortality is now unquestionable. Here, 895 000 annual premature deaths (PD) are estimated for the present (1991–2010), which increases to 1 540 000 in the year 2050 due to the ageing of the European population. The implementation of a mitigation scenario (80 % of the energy production in Europe from renewable sources) could lead to a decrease of over 60 000 annual PD for the year 2050.
Qing Zhu, Fa Li, William J. Riley, Li Xu, Lei Zhao, Kunxiaojia Yuan, Huayi Wu, Jianya Gong, and James Randerson
Geosci. Model Dev., 15, 1899–1911, https://doi.org/10.5194/gmd-15-1899-2022, https://doi.org/10.5194/gmd-15-1899-2022, 2022
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Wildfire is a devastating Earth system process that burns about 500 million hectares of land each year. It wipes out vegetation including trees, shrubs, and grasses and causes large losses of economic assets. However, modeling the spatial distribution and temporal changes of wildfire activities at a global scale is challenging. This study built a machine-learning-based wildfire surrogate model within an existing Earth system model and achieved high accuracy.
Zhonghua Zheng, Matthew West, Lei Zhao, Po-Lun Ma, Xiaohong Liu, and Nicole Riemer
Atmos. Chem. Phys., 21, 17727–17741, https://doi.org/10.5194/acp-21-17727-2021, https://doi.org/10.5194/acp-21-17727-2021, 2021
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Aerosol mixing state is an important emergent property that affects aerosol radiative forcing and aerosol–cloud interactions, but it has not been easy to constrain this property globally. We present a framework for evaluating the error in aerosol mixing state induced by aerosol representation assumptions, which is one of the important contributors to structural uncertainty in aerosol models. Our study provides insights into potential improvements to model process representation for aerosols.
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
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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.
Joaquín Muñoz-Sabater, Emanuel Dutra, Anna Agustí-Panareda, Clément Albergel, Gabriele Arduini, Gianpaolo Balsamo, Souhail Boussetta, Margarita Choulga, Shaun Harrigan, Hans Hersbach, Brecht Martens, Diego G. Miralles, María Piles, Nemesio J. Rodríguez-Fernández, Ervin Zsoter, Carlo Buontempo, and Jean-Noël Thépaut
Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, https://doi.org/10.5194/essd-13-4349-2021, 2021
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The creation of ERA5-Land responds to a growing number of applications requiring global land datasets at a resolution higher than traditionally reached. ERA5-Land provides operational, global, and hourly key variables of the water and energy cycles over land surfaces, at 9 km resolution, from 1981 until the present. This work provides evidence of an overall improvement of the water cycle compared to previous reanalyses, whereas the energy cycle variables perform as well as those of ERA5.
Camilla Geels, Morten Winther, Camilla Andersson, Jukka-Pekka Jalkanen, Jørgen Brandt, Lise M. Frohn, Ulas Im, Wing Leung, and Jesper H. Christensen
Atmos. Chem. Phys., 21, 12495–12519, https://doi.org/10.5194/acp-21-12495-2021, https://doi.org/10.5194/acp-21-12495-2021, 2021
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In this study, we set up new shipping emissions scenarios and use two chemistry transport models and a health assessment model to assess the development of air quality and related health impacts in the Nordic region. Shipping alone is associated with about 850 premature deaths during present-day conditions, decreasing to approximately 550–600 cases in the 2050 scenarios.
Amy Solomon, Céline Heuzé, Benjamin Rabe, Sheldon Bacon, Laurent Bertino, Patrick Heimbach, Jun Inoue, Doroteaciro Iovino, Ruth Mottram, Xiangdong Zhang, Yevgeny Aksenov, Ronan McAdam, An Nguyen, Roshin P. Raj, and Han Tang
Ocean Sci., 17, 1081–1102, https://doi.org/10.5194/os-17-1081-2021, https://doi.org/10.5194/os-17-1081-2021, 2021
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Freshwater in the Arctic Ocean plays a critical role in the global climate system by impacting ocean circulations, stratification, mixing, and emergent regimes. In this review paper we assess how Arctic Ocean freshwater changed in the 2010s relative to the 2000s. Estimates from observations and reanalyses show a qualitative stabilization in the 2010s due to a compensation between a freshening of the Beaufort Gyre and a reduction in freshwater in the Amerasian and Eurasian basins.
Haidong Zhao, Gretchen F. Sassenrath, Mary Beth Kirkham, Nenghan Wan, and Xiaomao Lin
Hydrol. Earth Syst. Sci., 25, 4357–4372, https://doi.org/10.5194/hess-25-4357-2021, https://doi.org/10.5194/hess-25-4357-2021, 2021
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This study was done to develop an improved soil temperature model for the USA Great Plains by using common weather station variables as inputs. After incorporating knowledge of estimated soil moisture and observed daily snow depth, the improved model showed a near 50 % gain in performance compared to the original model. We conclude that our improved model can better estimate soil temperature at the surface soil layer where most hydrological and biological processes occur.
Ulas Im, Kostas Tsigaridis, Gregory Faluvegi, Peter L. Langen, Joshua P. French, Rashed Mahmood, Manu A. Thomas, Knut von Salzen, Daniel C. Thomas, Cynthia H. Whaley, Zbigniew Klimont, Henrik Skov, and Jørgen Brandt
Atmos. Chem. Phys., 21, 10413–10438, https://doi.org/10.5194/acp-21-10413-2021, https://doi.org/10.5194/acp-21-10413-2021, 2021
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Future (2015–2050) simulations of the aerosol burdens and their radiative forcing and climate impacts over the Arctic under various emission projections show that although the Arctic aerosol burdens are projected to decrease significantly by 10 to 60 %, regardless of the magnitude of aerosol reductions, surface air temperatures will continue to increase by 1.9–2.6 ℃, while sea-ice extent will continue to decrease, implying reductions of greenhouse gases are necessary to mitigate climate change.
Hannah C. Bloomfield, David J. Brayshaw, Paula L. M. Gonzalez, and Andrew Charlton-Perez
Earth Syst. Sci. Data, 13, 2259–2274, https://doi.org/10.5194/essd-13-2259-2021, https://doi.org/10.5194/essd-13-2259-2021, 2021
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Energy systems are becoming more exposed to weather as more renewable generation is built. This means access to high-quality weather forecasts is becoming more important. This paper showcases past forecasts of electricity demand and wind power and solar power generation across 28 European countries. The timescale of interest is from 5 d out to 1 month ahead. This paper highlights the recent improvements in forecast skill and hopes to promote collaboration in the energy–meteorology community.
Yasser Hamdi, Ivan D. Haigh, Sylvie Parey, and Thomas Wahl
Nat. Hazards Earth Syst. Sci., 21, 1461–1465, https://doi.org/10.5194/nhess-21-1461-2021, https://doi.org/10.5194/nhess-21-1461-2021, 2021
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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.
This paper describes how the Coupled Model Intercomparison Project organized its 7th phase...
Special issue