Articles | Volume 16, issue 6
https://doi.org/10.5194/essd-16-3001-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-3001-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
27 Jun 2024
Data description paper |
| 27 Jun 2024
| 27 Jun 2024
Characterizing clouds with the CCClim dataset, a machine learning cloud class climatology
Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Axel Lauer
Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Rémi Kazeroni
Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
now at: Laboratoire des Sciences du Climat et de l'Environnement (LSCE), LSCE/IPSL, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
Martin Stengel
Deutscher Wetterdienst (DWD), Offenbach, Germany
Veronika Eyring
Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany
Related authors
No articles found.
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
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
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.
Nikos Benas, Jan Fokke Meirink, Rob Roebeling, and Martin Stengel
Atmos. Chem. Phys., 25, 6957–6973, https://doi.org/10.5194/acp-25-6957-2025, https://doi.org/10.5194/acp-25-6957-2025, 2025
Short summary
Short summary
This study examines how ship emissions affect clouds over a shipping corridor in the southeastern Atlantic. Using satellite data from 2004 to 2023, we find that ship emissions increase the number of cloud droplets while reducing their size and slightly decrease cloud water content. Effects on seasonal and daily patterns vary based on regional factors. The impact of emissions weakened after stricter regulations were implemented in 2020.
Manuel Schlund, Bouwe Andela, Jörg Benke, Ruth Comer, Birgit Hassler, Emma Hogan, Peter Kalverla, Axel Lauer, Bill Little, Saskia Loosveldt Tomas, Francesco Nattino, Patrick Peglar, Valeriu Predoi, Stef Smeets, Stephen Worsley, Martin Yeo, and Klaus Zimmermann
Geosci. Model Dev., 18, 4009–4021, https://doi.org/10.5194/gmd-18-4009-2025, https://doi.org/10.5194/gmd-18-4009-2025, 2025
Short summary
Short summary
The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth system models. Here, we describe recent significant improvements of ESMValTool’s computational efficiency including parallel, out-of-core, and distributed computing. Evaluations with the enhanced version of ESMValTool are faster, use less computational resources, and can handle input data larger than the available memory.
Pauline Bonnet, Lorenzo Pastori, Mierk Schwabe, Marco Giorgetta, Fernando Iglesias-Suarez, and Veronika Eyring
Geosci. Model Dev., 18, 3681–3706, https://doi.org/10.5194/gmd-18-3681-2025, https://doi.org/10.5194/gmd-18-3681-2025, 2025
Short summary
Short summary
Tuning a climate model means adjusting uncertain parameters in the model to best match observations like the global radiation balance and cloud cover. This is usually done by running many simulations of the model with different settings, which can be time-consuming and relies heavily on expert knowledge. To make this process faster and more objective, we developed a machine learning emulator to create a large ensemble and apply a method called history matching to find the best settings.
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
Short summary
Short summary
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.
Konstantinos Rizos, Emmanouil Proestakis, Thanasis Georgiou, Antonis Gkikas, Eleni Marinou, Peristera Paschou, Kalliopi Artemis Voudouri, Athanasios Tsikerdekis, David Donovan, Gerd-Jan van Zadelhoff, Angela Benedetti, Holger Baars, Athena Augusta Floutsi, Nikos Benas, Martin Stengel, Christian Retscher, Edward Malina, and Vassilis Amiridis
EGUsphere, https://doi.org/10.5194/egusphere-2025-1175, https://doi.org/10.5194/egusphere-2025-1175, 2025
Short summary
Short summary
The Aeolus satellite's lidar system had limitations in detecting certain atmospheric layers and distinguishing between aerosol and cloud types. To improve accuracy, a new dust detection product was developed. By combining data from various sources and validating it with ground-based measurements, this enhanced product performs better than the original. It helps improve dust transport models and weather predictions, making it a valuable tool for atmospheric monitoring and forecasting.
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.
Lukas Lindenlaub, Katja Weigel, Birgit Hassler, Colin Jones, and Veronika Eyring
EGUsphere, https://doi.org/10.5194/egusphere-2025-1517, https://doi.org/10.5194/egusphere-2025-1517, 2025
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.
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.
Detlef van Vuuren, Brian O'Neill, Claudia Tebaldi, Louise Chini, Pierre Friedlingstein, Tomoko Hasegawa, Keywan Riahi, Benjamin Sanderson, Bala Govindasamy, Nico Bauer, Veronika Eyring, Cheikh Fall, Katja Frieler, Matthew Gidden, Laila Gohar, Andrew Jones, Andrew King, Reto Knutti, Elmar Kriegler, Peter Lawrence, Chris Lennard, Jason Lowe, Camila Mathison, Shahbaz Mehmood, Luciana Prado, Qiang Zhang, Steven Rose, Alexander Ruane, Carl-Friederich Schleussner, Roland Seferian, Jana Sillmann, Chris Smith, Anna Sörensson, Swapna Panickal, Kaoru Tachiiri, Naomi Vaughan, Saritha Vishwanathan, Tokuta Yokohata, and Tilo Ziehn
EGUsphere, https://doi.org/10.5194/egusphere-2024-3765, https://doi.org/10.5194/egusphere-2024-3765, 2025
Short summary
Short summary
We propose a set of six plausible 21st century emission scenarios, and their multi-century extensions, that will be used by the international community of climate modeling centers to produce the next generation of climate projections. These projections will support climate, impact and mitigation researchers, provide information to practitioners to address future risks from climate change, and contribute to policymakers’ considerations of the trade-offs among various levels of mitigation.
Paul J. Durack, Karl E. Taylor, Peter J. Gleckler, Gerald A. Meehl, Bryan N. Lawrence, Curt Covey, Ronald J. Stouffer, Guillaume Levavasseur, Atef Ben-Nasser, Sebastien Denvil, Martina Stockhause, Jonathan M. Gregory, Martin Juckes, Sasha K. Ames, Fabrizio Antonio, David C. Bader, John P. Dunne, Daniel Ellis, Veronika Eyring, Sandro L. Fiore, Sylvie Joussaume, Philip Kershaw, Jean-Francois Lamarque, Michael Lautenschlager, Jiwoo Lee, Chris F. Mauzey, Matthew Mizielinski, Paola Nassisi, Alessandra Nuzzo, Eleanor O’Rourke, Jeffrey Painter, Gerald L. Potter, Sven Rodriguez, and Dean N. Williams
EGUsphere, https://doi.org/10.5194/egusphere-2024-3729, https://doi.org/10.5194/egusphere-2024-3729, 2025
Short summary
Short summary
CMIP6 was the most expansive and ambitious Model Intercomparison Project (MIP), the latest in a history, extending four decades. CMIP engaged a growing community focused on improving climate understanding, and quantifying and attributing observed climate change being experienced today. The project's profound impact is due to the combining the latest climate science and technology, enabling the latest-generation climate simulations and increasing community attention in every successive phase.
Bettina K. Gier, Manuel Schlund, Pierre Friedlingstein, Chris D. Jones, Colin Jones, Sönke Zaehle, and Veronika Eyring
Biogeosciences, 21, 5321–5360, https://doi.org/10.5194/bg-21-5321-2024, https://doi.org/10.5194/bg-21-5321-2024, 2024
Short summary
Short summary
This study investigates present-day carbon cycle variables in CMIP5 and CMIP6 simulations. Overall, CMIP6 models perform better but also show many remaining biases. A significant improvement in the simulation of photosynthesis in models with a nitrogen cycle is found, with only small differences between emission- and concentration-based simulations. Thus, we recommend using emission-driven simulations in CMIP7 by default and including the nitrogen cycle in all future carbon cycle models.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
Short summary
Short summary
We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
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.
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.
William K. Jones, Martin Stengel, and Philip Stier
Atmos. Chem. Phys., 24, 5165–5180, https://doi.org/10.5194/acp-24-5165-2024, https://doi.org/10.5194/acp-24-5165-2024, 2024
Short summary
Short summary
Storm clouds cover large areas of the tropics. These clouds both reflect incoming sunlight and trap heat from the atmosphere below, regulating the temperature of the tropics. Over land, storm clouds occur in the late afternoon and evening and so exist both during the daytime and at night. Changes in this timing could upset the balance of the respective cooling and heating effects of these clouds. We find that isolated storms have a larger effect on this balance than their small size suggests.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data, 16, 2113–2122, https://doi.org/10.5194/essd-16-2113-2024, https://doi.org/10.5194/essd-16-2113-2024, 2024
Short summary
Short summary
To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
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.
Nikos Benas, Irina Solodovnik, Martin Stengel, Imke Hüser, Karl-Göran Karlsson, Nina Håkansson, Erik Johansson, Salomon Eliasson, Marc Schröder, Rainer Hollmann, and Jan Fokke Meirink
Earth Syst. Sci. Data, 15, 5153–5170, https://doi.org/10.5194/essd-15-5153-2023, https://doi.org/10.5194/essd-15-5153-2023, 2023
Short summary
Short summary
This paper describes CLAAS-3, the third edition of the Cloud property dAtAset using SEVIRI, which was created based on observations from geostationary Meteosat satellites. CLAAS-3 cloud properties are evaluated using a variety of reference datasets, with very good overall results. The demonstrated quality of CLAAS-3 ensures its usefulness in a wide range of applications, including studies of local- to continental-scale cloud processes and evaluation of climate models.
Cunbo Han, Corinna Hoose, Martin Stengel, Quentin Coopman, and Andrew Barrett
Atmos. Chem. Phys., 23, 14077–14095, https://doi.org/10.5194/acp-23-14077-2023, https://doi.org/10.5194/acp-23-14077-2023, 2023
Short summary
Short summary
Cloud phase has been found to significantly impact cloud thermodynamics and Earth’s radiation budget, and various factors influence it. This study investigates the sensitivity of the cloud-phase distribution to the ice-nucleating particle concentration and thermodynamics. Multiple simulation experiments were performed using the ICON model at the convection-permitting resolution of 1.2 km. Simulation results were compared to two different retrieval products based on SEVIRI measurements.
Karl-Göran Karlsson, Martin Stengel, Jan Fokke Meirink, Aku Riihelä, Jörg Trentmann, Tom Akkermans, Diana Stein, Abhay Devasthale, Salomon Eliasson, Erik Johansson, Nina Håkansson, Irina Solodovnik, Nikos Benas, Nicolas Clerbaux, Nathalie Selbach, Marc Schröder, and Rainer Hollmann
Earth Syst. Sci. Data, 15, 4901–4926, https://doi.org/10.5194/essd-15-4901-2023, https://doi.org/10.5194/essd-15-4901-2023, 2023
Short summary
Short summary
This paper presents a global climate data record on cloud parameters, radiation at the surface and at the top of atmosphere, and surface albedo. The temporal coverage is 1979–2020 (42 years) and the data record is also continuously updated until present time. Thus, more than four decades of climate parameters are provided. Based on CLARA-A3, studies on distribution of clouds and radiation parameters can be made and, especially, investigations of climate trends and evaluation of climate models.
Kameswara S. Vinjamuri, Marco Vountas, Luca Lelli, Martin Stengel, Matthew D. Shupe, Kerstin Ebell, and John P. Burrows
Atmos. Meas. Tech., 16, 2903–2918, https://doi.org/10.5194/amt-16-2903-2023, https://doi.org/10.5194/amt-16-2903-2023, 2023
Short summary
Short summary
Clouds play an important role in Arctic amplification. Cloud data from ground-based sites are valuable but cannot represent the whole Arctic. Therefore the use of satellite products is a measure to cover the entire Arctic. However, the quality of such cloud measurements from space is not well known. The paper discusses the differences and commonalities between satellite and ground-based measurements. We conclude that the satellite dataset, with a few exceptions, can be used in the Arctic.
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.
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.
Katja Weigel, Lisa Bock, Bettina K. Gier, Axel Lauer, Mattia Righi, Manuel Schlund, Kemisola Adeniyi, Bouwe Andela, Enrico Arnone, Peter Berg, Louis-Philippe Caron, Irene Cionni, Susanna Corti, Niels Drost, Alasdair Hunter, Llorenç Lledó, Christian Wilhelm Mohr, Aytaç Paçal, Núria Pérez-Zanón, Valeriu Predoi, Marit Sandstad, Jana Sillmann, Andreas Sterl, Javier Vegas-Regidor, Jost von Hardenberg, and Veronika Eyring
Geosci. Model Dev., 14, 3159–3184, https://doi.org/10.5194/gmd-14-3159-2021, https://doi.org/10.5194/gmd-14-3159-2021, 2021
Short summary
Short summary
This work presents new diagnostics for the Earth System Model Evaluation Tool (ESMValTool) v2.0 on the hydrological cycle, extreme events, impact assessment, regional evaluations, and ensemble member selection. The ESMValTool v2.0 diagnostics are developed by a large community of scientists aiming to facilitate the evaluation and comparison of Earth system models (ESMs) with a focus on the ESMs participating in the Coupled Model Intercomparison Project (CMIP).
James Keeble, Birgit Hassler, Antara Banerjee, Ramiro Checa-Garcia, Gabriel Chiodo, Sean Davis, Veronika Eyring, Paul T. Griffiths, Olaf Morgenstern, Peer Nowack, Guang Zeng, Jiankai Zhang, Greg Bodeker, Susannah Burrows, Philip Cameron-Smith, David Cugnet, Christopher Danek, Makoto Deushi, Larry W. Horowitz, Anne Kubin, Lijuan Li, Gerrit Lohmann, Martine Michou, Michael J. Mills, Pierre Nabat, Dirk Olivié, Sungsu Park, Øyvind Seland, Jens Stoll, Karl-Hermann Wieners, and Tongwen Wu
Atmos. Chem. Phys., 21, 5015–5061, https://doi.org/10.5194/acp-21-5015-2021, https://doi.org/10.5194/acp-21-5015-2021, 2021
Short summary
Short summary
Stratospheric ozone and water vapour are key components of the Earth system; changes to both have important impacts on global and regional climate. We evaluate changes to these species from 1850 to 2100 in the new generation of CMIP6 models. There is good agreement between the multi-model mean and observations, although there is substantial variation between the individual models. The future evolution of both ozone and water vapour is strongly dependent on the assumed future emissions scenario.
Claudia Tebaldi, Kevin Debeire, Veronika Eyring, Erich Fischer, John Fyfe, Pierre Friedlingstein, Reto Knutti, Jason Lowe, Brian O'Neill, Benjamin Sanderson, Detlef van Vuuren, Keywan Riahi, Malte Meinshausen, Zebedee Nicholls, Katarzyna B. Tokarska, George Hurtt, Elmar Kriegler, Jean-Francois Lamarque, Gerald Meehl, Richard Moss, Susanne E. Bauer, Olivier Boucher, Victor Brovkin, Young-Hwa Byun, Martin Dix, Silvio Gualdi, Huan Guo, Jasmin G. John, Slava Kharin, YoungHo Kim, Tsuyoshi Koshiro, Libin Ma, Dirk Olivié, Swapna Panickal, Fangli Qiao, Xinyao Rong, Nan Rosenbloom, Martin Schupfner, Roland Séférian, Alistair Sellar, Tido Semmler, Xiaoying Shi, Zhenya Song, Christian Steger, Ronald Stouffer, Neil Swart, Kaoru Tachiiri, Qi Tang, Hiroaki Tatebe, Aurore Voldoire, Evgeny Volodin, Klaus Wyser, Xiaoge Xin, Shuting Yang, Yongqiang Yu, and Tilo Ziehn
Earth Syst. Dynam., 12, 253–293, https://doi.org/10.5194/esd-12-253-2021, https://doi.org/10.5194/esd-12-253-2021, 2021
Short summary
Short summary
We present an overview of CMIP6 ScenarioMIP outcomes from up to 38 participating ESMs according to the new SSP-based scenarios. Average temperature and precipitation projections according to a wide range of forcings, spanning a wider range than the CMIP5 projections, are documented as global averages and geographic patterns. Times of crossing various warming levels are computed, together with benefits of mitigation for selected pairs of scenarios. Comparisons with CMIP5 are also discussed.
Manuel Schlund, Axel Lauer, Pierre Gentine, Steven C. Sherwood, and Veronika Eyring
Earth Syst. Dynam., 11, 1233–1258, https://doi.org/10.5194/esd-11-1233-2020, https://doi.org/10.5194/esd-11-1233-2020, 2020
Short summary
Short summary
As an important measure of climate change, the Equilibrium Climate Sensitivity (ECS) describes the change in surface temperature after a doubling of the atmospheric CO2 concentration. Climate models from the Coupled Model Intercomparison Project (CMIP) show a wide range in ECS. Emergent constraints are a technique to reduce uncertainties in ECS with observational data. Emergent constraints developed with data from CMIP phase 5 show reduced skill and higher ECS ranges when applied to CMIP6 data.
Bettina K. Gier, Michael Buchwitz, Maximilian Reuter, Peter M. Cox, Pierre Friedlingstein, and Veronika Eyring
Biogeosciences, 17, 6115–6144, https://doi.org/10.5194/bg-17-6115-2020, https://doi.org/10.5194/bg-17-6115-2020, 2020
Short summary
Short summary
Models from Coupled Model Intercomparison Project (CMIP) phases 5 and 6 are compared to a satellite data product of column-averaged CO2 mole fractions (XCO2). The previously believed discrepancy of the negative trend in seasonal cycle amplitude in the satellite product, which is not seen in in situ data nor in the models, is attributed to a sampling characteristic. Furthermore, CMIP6 models are shown to have made progress in reproducing the observed XCO2 time series compared to CMIP5.
Axel Lauer, Veronika Eyring, Omar Bellprat, Lisa Bock, Bettina K. Gier, Alasdair Hunter, Ruth Lorenz, Núria Pérez-Zanón, Mattia Righi, Manuel Schlund, Daniel Senftleben, Katja Weigel, and Sabrina Zechlau
Geosci. Model Dev., 13, 4205–4228, https://doi.org/10.5194/gmd-13-4205-2020, https://doi.org/10.5194/gmd-13-4205-2020, 2020
Short summary
Short summary
The Earth System Model Evaluation Tool is a community software tool designed for evaluation and analysis of climate models. New features of version 2.0 include analysis scripts for important large-scale features in climate models, diagnostics for extreme events, regional model and impact evaluation. In this paper, newly implemented climate metrics, emergent constraints for climate-relevant feedbacks and diagnostics for future model projections are described and illustrated with examples.
Caroline A. Poulsen, Gregory R. McGarragh, Gareth E. Thomas, Martin Stengel, Matthew W. Christensen, Adam C. Povey, Simon R. Proud, Elisa Carboni, Rainer Hollmann, and Roy G. Grainger
Earth Syst. Sci. Data, 12, 2121–2135, https://doi.org/10.5194/essd-12-2121-2020, https://doi.org/10.5194/essd-12-2121-2020, 2020
Short summary
Short summary
We have created a satellite cloud and radiation climatology from the ATSR-2 and AATSR on board ERS-2 and Envisat, respectively, which spans the period 1995–2012. The data set was created using a combination of optimal estimation and neural net techniques. The data set was created as part of the ESA Climate Change Initiative program. The data set has been compared with active CALIOP lidar measurements and compared with MAC-LWP AND CERES-EBAF measurements and is shown to have good performance.
Cited articles
arndtka: EyringMLClimateGroup/kaps22tgrs_ml_cloud_eval: RF and processing first release, Zenodo [code], https://doi.org/10.5281/zenodo.7248773, 2022. a
arndtka: EyringMLClimateGroup/kaps23ESSD_CCClim: Characterizing clouds with the CCClim dataset, a machine learning cloud class climatology, Zenodo [code], https://doi.org/10.5281/zenodo.10279992, 2023. a
Behrmann, J., Grathwohl, W., Chen, R. T. Q., Duvenaud, D., and Jacobsen, J.-H.: Invertible residual networks, in: International Conference on Machine Learning, PMLR, 97, 573–582, 2019. a
Bennartz, R. and Rausch, J.: Global and regional estimates of warm cloud droplet number concentration based on 13 years of AQUA-MODIS observations, Atmos. Chem. Phys., 17, 9815–9836, https://doi.org/10.5194/acp-17-9815-2017, 2017. a, b
Bodas-Salcedo, A., Williams, K. D., Field, P. R., and Lock, A. P.: The Surface Downwelling Solar Radiation Surplus over the Southern Ocean in the Met Office Model: The Role of Midlatitude Cyclone Clouds, J. Climate, 25, 7467–7486, https://doi.org/10.1175/jcli-d-11-00702.1, 2012. a
Bony, S., Dufresne, J.-L., Treut, H. L., Morcrette, J.-J., and Senior, C.: On dynamic and thermodynamic components of cloud changes, Clim. Dynam., 22, 71–86, https://doi.org/10.1007/s00382-003-0369-6, 2004. a, b
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/a:1010933404324, 2001. a
Ceppi, P., McCoy, D. T., and Hartmann, D. L.: Observational evidence for a negative shortwave cloud feedback in middle to high latitudes, Geophys. Res. Lett., 43, 1331–1339, https://doi.org/10.1002/2015gl067499, 2016. a
Cho, N., Tan, J., and Oreopoulos, L.: Classifying planetary cloudiness with an updated set of MODIS Cloud Regimes, J. Appl. Meteorol. Clim., 60, 981–997, https://doi.org/10.1175/jamc-d-20-0247.1, 2021. a
Crueger, T., Giorgetta, M. A., Brokopf, R., Esch, M., Fiedler, S., Hohenegger, C., Kornblueh, L., Mauritsen, T., Nam, C., Naumann, A. K., Peters, K., Rast, S., Roeckner, E., Sakradzija, M., Schmidt, H., Vial, J., Vogel, R., and Stevens, B.: ICON-A, The Atmosphere Component of the ICON Earth System Model: II. Model Evaluation, J. Adv. Model. Earth Sy., 10, 1638–1662, https://doi.org/10.1029/2017ms001233, 2018. a, b
Dubovik, O., Schuster, G. L., Xu, F., Hu, Y., Bösch, H., Landgraf, J., and Li, Z.: Grand Challenges in Satellite Remote Sensing, Front. Remote Sens., 2, 619818, https://doi.org/10.3389/frsen.2021.619818, 2021. a
Eliasson, S., Karlsson, K. G., van Meijgaard, E., Meirink, J. F., Stengel, M., and Willén, U.: The Cloud_cci simulator v1.0 for the Cloud_cci climate data record and its application to a global and a regional climate model, Geosci. Model Dev., 12, 829–847, https://doi.org/10.5194/gmd-12-829-2019, 2019. a
Evan, A. T., Heidinger, A. K., and Vimont, D. J.: Arguments against a physical long-term trend in global ISCCP cloud amounts, Geophys. Res. Lett., 34, L04701, https://doi.org/10.1029/2006gl028083, 2007. a
Gettelman, A., Bardeen, C. G., McCluskey, C. S., Järvinen, E., Stith, J., Bretherton, C., McFarquhar, G., Twohy, C., D'Alessandro, J., and Wu, W.: Simulating Observations of Southern Ocean Clouds and Implications for Climate, J. Geophys. Res.-Atmos., 125, e2020JD03261, https://doi.org/10.1029/2020jd032619, 2020. a
Giorgetta, M. A., Brokopf, R., Crueger, T., Esch, M., Fiedler, S., Helmert, J., Hohenegger, C., Kornblueh, L., Köhler, M., Manzini, E., Mauritsen, T., Nam, C., Raddatz, T., Rast, S., Reinert, D., Sakradzija, M., Schmidt, H., Schneck, R., Schnur, R., Silvers, L., Wan, H., Zängl, G., and Stevens, B.: ICON-A, the Atmosphere Component of the ICON Earth System Model: I. Model Description, J. Adv. Model. Earth Sy., 10, 1613–1637, https://doi.org/10.1029/2017ms001242, 2018. a
Haynes, J. M., Jakob, C., Rossow, W. B., Tselioudis, G., and Brown, J.: Major Characteristics of Southern Ocean Cloud Regimes and Their Effects on the Energy Budget, J. Climate, 24, 5061–5080, https://doi.org/10.1175/2011jcli4052.1, 2011. a, b
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., 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., 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, b
Heymsfield, A. J., Matrosov, S., and Baum, B.: Ice Water Path–Optical Depth Relationships for Cirrus and Deep Stratiform Ice Cloud Layers, J. Appl. Meteorol., 42, 1369–1390, https://doi.org/10.1175/1520-0450(2003)042<1369:iwpdrf>2.0.co;2, 2003. a
Horvath, A., Seethala, C., and Deneke, H.: View angle dependence of MODIS liquid water path retrievals in warm oceanic clouds, J. Geophys. Res.-Atmos., 119, 8304–8328, https://doi.org/10.1002/2013jd021355, 2014. a
Jiang, J. H., Su, H., Zhai, C., Perun, V. S., Genio, A. D., Nazarenko, L. S., Donner, L. J., Horowitz, L., Seman, C., Cole, J., Gettelman, A., Ringer, M. A., Rotstayn, L., Jeffrey, S., Wu, T., Brient, F., Dufresne, J.-L., Kawai, H., Koshiro, T., Watanabe, M., LÉcuyer, T. S., Volodin, E. M., Iversen, T., Drange, H., Mesquita, M. D. S., Read, W. G., Waters, J. W., Tian, B., Teixeira, J., and Stephens, G. L.: Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA “A-Train” satellite observations, J. Geophys. Res.-Atmos., 117, D14105, https://doi.org/10.1029/2011jd017237, 2012. a
Kaps, A., Lauer, A., and Eyring, V.: CCClim – A machine-learning powered cloud class climatology, Zenodo [data set], https://doi.org/10.5281/ZENODO.8369202, 2023b. a, b, c
Kodama, C., Noda, A. T., and Satoh, M.: An assessment of the cloud signals simulated by NICAM using ISCCP, CALIPSO, and CloudSat satellite simulators, J. Geophys. Res.-Atmos., 117, D12210, https://doi.org/10.1029/2011jd017317, 2012. a
Kuma, P., Bender, F. A.-M., Schuddeboom, A., McDonald, A. J., and Seland, Ø.: Machine learning of cloud types in satellite observations and climate models, Atmos. Chem. Phys., 23, 523–549, https://doi.org/10.5194/acp-23-523-2023, 2023. a, b
Kurihana, T., Moyer, E., Willett, R., Gilton, D., and Foster, I.: Data-Driven Cloud Clustering via a Rotationally Invariant Autoencoder, IEEE T. Geosci. Remote, 60, 1–25, https://doi.org/10.1109/tgrs.2021.3098008, 2021. a
Kurihana, T., Moyer, E. J., and Foster, I. T.: AICCA: AI-Driven Cloud Classification Atlas, Remote Sens., 14, 5690, https://doi.org/10.3390/rs14225690, 2022. a, b, c
Lauer, A., Bock, L., Hassler, B., Schröder, M., and Stengel, M.: Cloud Climatologies from Global Climate Models – A Comparison of CMIP5 and CMIP6 Models with Satellite Data, J. Climate, 36, 281–311, https://doi.org/10.1175/jcli-d-22-0181.1, 2023. a
Li, J., Huang, J., Stamnes, K., Wang, T., Lv, Q., and Jin, H.: A global survey of cloud overlap based on CALIPSO and CloudSat measurements, Atmos. Chem. Phys., 15, 519–536, https://doi.org/10.5194/acp-15-519-2015, 2015. a, b, c
Loeb, N. G., Wielicki, B. A., Doelling, D. R., Smith, G. L., Keyes, D. F., Kato, S., Manalo-Smith, N., and Wong, T.: Toward Optimal Closure of the Earth's Top-of-Atmosphere Radiation Budget, J. Climate, 22, 748–766, https://doi.org/10.1175/2008jcli2637.1, 2009. a
L'Ecuyer, T. S. and Jiang, J. H.: Touring the atmosphere aboard the A-Train, Phys. Today, 63, 36–41, 2010. a
L'Ecuyer, T. S., Hang, Y., Matus, A. V., and Wang, Z.: Reassessing the Effect of Cloud Type on Earth's Energy Balance in the Age of Active Spaceborne Observations. Part I: Top of Atmosphere and Surface, J. Climate, 32, 6197–6217, https://doi.org/10.1175/jcli-d-18-0753.1, 2019. a, b
Maddux, B. C., Ackerman, S. A., and Platnick, S.: Viewing Geometry Dependencies in MODIS Cloud Products, J. Atmos. Ocean. Tech., 27, 1519–1528, https://doi.org/10.1175/2010jtecha1432.1, 2010. a, b, c
Marchand, R., Mace, G. G., Ackerman, T., and Stephens, G.: Hydrometeor Detection Using Cloudsat – An Earth-Orbiting 94-GHz Cloud Radar, J. Atmos. Ocean. Tech., 25, 519–533, https://doi.org/10.1175/2007jtecha1006.1, 2008. a
Marchant, B., Platnick, S., Meyer, K., and Wind, G.: Evaluation of the MODIS Collection 6 multilayer cloud detection algorithm through comparisons with CloudSat Cloud Profiling Radar and CALIPSO CALIOP products, Atmos. Meas. Tech., 13, 3263–3275, https://doi.org/10.5194/amt-13-3263-2020, 2020. a
McDonald, A. J. and Parsons, S.: A Comparison of Cloud Classification Methodologies: Differences Between Cloud and Dynamical Regimes, J. Geophys. Res.-Atmos., 123, 11173–11193, https://doi.org/10.1029/2018jd028595, 2018. a
Muñoz Sabater, J.: ERA5-Land monthly averaged data from 2001 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/CDS.68D2BB30, 2019. a
Oreopoulos, L., Cho, N., Lee, D., Kato, S., and Huffman, G. J.: An examination of the nature of global MODIS cloud regimes, J. Geophys. Res.-Atmos., 119, 8362–8383, https://doi.org/10.1002/2013jd021409, 2014. a
Oreopoulos, L., Cho, N., Lee, D., and Kato, S.: Radiative effects of global MODIS cloud regimes, J. Geophys. Res.-Atmos., 121, 2299–2317, https://doi.org/10.1002/2015jd024502, 2016. a, b
Painemal, D., Spangenberg, D., Smith Jr., W. L., Minnis, P., Cairns, B., Moore, R. H., Crosbie, E., Robinson, C., Thornhill, K. L., Winstead, E. L., and Ziemba, L.: Evaluation of satellite retrievals of liquid clouds from the GOES-13 imager and MODIS over the midlatitude North Atlantic during the NAAMES campaign, Atmos. Meas. Tech., 14, 6633–6646, https://doi.org/10.5194/amt-14-6633-2021, 2021. a
Pincus, R. and Stevens, B.: Paths to accuracy for radiation parameterizations in atmospheric models, J. Adv. Model. Earth Sy., 5, 225–233, https://doi.org/10.1002/jame.20027, 2013. a
Platnick, S., King, M. D., Ackerman, S. A., Menzel, W. P., Baum, B. A., Riedi, J. C., and Frey, R. A.: The MODIS cloud products: algorithms and examples from terra, IEEE T. Geosci. Remote, 41, 459–473, https://doi.org/10.1109/tgrs.2002.808301, 2003. a, b
Platnick, S., Meyer, K. G., King, M. D., Wind, G., Amarasinghe, N., Marchant, B., Arnold, G. T., Zhang, Z., Hubanks, P. A., Holz, R. E., Yang, P., Ridgway, W. L., and Riedi, J.: The MODIS Cloud Optical and Microphysical Products: Collection 6 Updates and Examples From Terra and Aqua, IEEE T. Geosci. Remote, 55, 502–525, https://doi.org/10.1109/tgrs.2016.2610522, 2017. a, b, c
Rasp, S., Schulz, H., Bony, S., and Stevens, B.: Combining Crowdsourcing and Deep Learning to Explore the Mesoscale Organization of Shallow Convection, B. Am. Meteorol. Soc., 101, E1980–E1995, https://doi.org/10.1175/bams-d-19-0324.1, 2020. a
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., and Prabhat: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195–204, https://doi.org/10.1038/s41586-019-0912-1, 2019. a
Rossow, W. B. and Schiffer, R. A.: Advances in Understanding Clouds from ISCCP, B. Am. Meteorol. Soc., 80, 2261–2287, https://doi.org/10.1175/1520-0477(1999)080<2261:aiucfi>2.0.co;2, 1999. a, b
Sassen, K. and Wang, Z.: The Clouds of the Middle Troposphere: Composition, Radiative Impact, and Global Distribution, Surv. Geophys., 33, 677–691, https://doi.org/10.1007/s10712-011-9163-x, 2011. a
Sassen, K., Wang, Z., and Liu, D.: Global distribution of cirrus clouds from CloudSat/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measurements, J. Geophys. Res., 113, D00A12, https://doi.org/10.1029/2008jd009972, 2008. a
Schiffer, R. A. and Rossow, W. B.: The International Satellite Cloud Climatology Project (ISCCP): The First Project of the World Climate Research Programme, B. Am. Meteorol. Soc., 64, 779–784, https://doi.org/10.1175/1520-0477-64.7.779, 1983. a
Spreitzer, E. J., Marschalik, M. P., and Spichtinger, P.: Subvisible cirrus clouds – a dynamical system approach, Nonlin. Processes Geophys., 24, 307–328, https://doi.org/10.5194/npg-24-307-2017, 2017. a
Stengel, M., Sus, O., Stapelberg, S., Finkensieper, S., Würzler, B., Philipp, D., Hollmann, R., and Poulsen, C.: ESA Cloud_cci cloud property datasets retrieved from passive satellite sensors: AVHRR-PM L3C/L3U cloud products – Version 3.0, https://doi.org/10.5676/DWD/ESA_CLOUD_CCI/AVHRR-PM/V003, 2019. a, b
Stengel, M., Stapelberg, S., Sus, O., Finkensieper, S., Würzler, B., Philipp, D., Hollmann, R., Poulsen, C., Christensen, M., and McGarragh, G.: Cloud_cci Advanced Very High Resolution Radiometer post meridiem (AVHRR-PM) dataset version 3: 35-year climatology of global cloud and radiation properties, Earth Syst. Sci. Data, 12, 41–60, https://doi.org/10.5194/essd-12-41-2020, 2020. a, b, c
Stephens, G., Winker, D., Pelon, J., Trepte, C., Vane, D., Yuhas, C., L'Ecuyer, T., and Lebsock, M.: CloudSat and CALIPSO within the A-Train: Ten Years of Actively Observing the Earth System, B. Am. Meteorol. Soc., 99, 569–581, https://doi.org/10.1175/bams-d-16-0324.1, 2018. a, b
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z., Illingworth, A. J., O'connor, E. J., Rossow, W. B., Durden, S. L., Miller, S. D., Austin, R. T., Benedetti, A., and Mitrescu, C.: THE CLOUDSAT MISSION AND THE A-TRAIN, B. Am. Meteorol. Soc., 83, 1771–1790, https://doi.org/10.1175/bams-83-12-1771, 2002. a, b
Stevens, B., Bony, S., Brogniez, H., Hentgen, L., Hohenegger, C., Kiemle, C., L'Ecuyer, T. S., Naumann, A. K., Schulz, H., Siebesma, P. A., Vial, J., Winker, D. M., and Zuidema, P.: Sugar, gravel, fish and flowers: Mesoscale cloud patterns in the trade winds, Q. J. Roy. Meteor. Soc., 146, 141–152, https://doi.org/10.1002/qj.3662, 2019. a
Stubenrauch, C. J., Chédin, A., Rädel, G., Scott, N. A., and Serrar, S.: Cloud Properties and Their Seasonal and Diurnal Variability from TOVS Path-B, J. Climate, 19, 5531–5553, https://doi.org/10.1175/jcli3929.1, 2006. a
Stubenrauch, C. J., Feofilov, A. G., Protopapadaki, S. E., and Armante, R.: Cloud climatologies from the infrared sounders AIRS and IASI: strengths and applications, Atmos. Chem. Phys., 17, 13625–13644, https://doi.org/10.5194/acp-17-13625-2017, 2017. a
Sus, O., Stengel, M., Stapelberg, S., McGarragh, G., Poulsen, C., Povey, A. C., Schlundt, C., Thomas, G., Christensen, M., Proud, S., Jerg, M., Grainger, R., and Hollmann, R.: The Community Cloud retrieval for CLimate (CC4CL) – Part 1: A framework applied to multiple satellite imaging sensors, Atmos. Meas. Tech., 11, 3373–3396, https://doi.org/10.5194/amt-11-3373-2018, 2018. a
Tselioudis, G., Rossow, W., Zhang, Y., and Konsta, D.: Global Weather States and Their Properties from Passive and Active Satellite Cloud Retrievals, J. Climate, 26, 7734–7746, https://doi.org/10.1175/jcli-d-13-00024.1, 2013. a, b
Tselioudis, G., Rossow, W. B., Jakob, C., Remillard, J., Tropf, D., and Zhang, Y.: Evaluation of clouds, radiation, and precipitation in CMIP6 models using global weather states derived from ISCCP-H cloud property data, J. Climate, 34, 1–42, https://doi.org/10.1175/JCLI-D-21-0076.1, 2021. a
Turbeville, S. M., Nugent, J. M., Ackerman, T. P., Bretherton, C. S., and Blossey, P. N.: Tropical Cirrus in Global Storm-Resolving Models: 2. Cirrus Life Cycle and Top-of-Atmosphere Radiative Fluxes, Earth Space Sci., 9, e2021EA001978, https://doi.org/10.1029/2021ea001978, 2022. a
Tzallas, V., Hünerbein, A., Stengel, M., Meirink, J. F., Benas, N., Trentmann, J., and Macke, A.: CRAAS: A European Cloud Regime dAtAset Based on the CLAAS-2.1 Climate Data Record, Remote Sens., 14, 5548, https://doi.org/10.3390/rs14215548, 2022. a
Vignesh, P. P., Jiang, J. H., Kishore, P., Su, H., Smay, T., Brighton, N., and Velicogna, I.: Assessment of CMIP6 Cloud Fraction and Comparison with Satellite Observations, Earth Space Sci., 7, e2019EA000975, https://doi.org/10.1029/2019ea000975, 2020. a
Wall, C. J., Storelvmo, T., Norris, J. R., and Tan, I.: Observational Constraints on Southern Ocean Cloud-Phase Feedback, J. Climate, 35, 5087–5102, https://doi.org/10.1175/jcli-d-21-0812.1, 2022. a
Wang, T., Fetzer, E. J., Wong, S., Kahn, B. H., and Yue, Q.: Validation of MODIS cloud mask and multilayer flag using CloudSat-CALIPSO cloud profiles and a cross-reference of their cloud classifications, J. Geophys. Res.-Atmos., 121, 11620–11635, https://doi.org/10.1002/2016jd025239, 2016. a, b
Winker, D. M., Pelon, J. R., and McCormick, M. P.: The CALIPSO mission: spaceborne lidar for observation of aerosols and clouds, in: SPIE Proceedings, edited by: Singh, U. N., Itabe, T., and Liu, Z., SPIE, https://doi.org/10.1117/12.466539, 2003. a
Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z., Hunt, W. H., and Young, S. A.: Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms, J. Atmos. Ocean. Tech., 26, 2310–2323, https://doi.org/10.1175/2009jtecha1281.1, 2009. a
WMO: International Cloud Atlas, https://cloudatlas.wmo.int/, last access: 20 April 2023. a
Young, A. H., Knapp, K. R., Inamdar, A., Hankins, W., and Rossow, W. B.: The International Satellite Cloud Climatology Project H-Series climate data record product, Earth Syst. Sci. Data, 10, 583–593, https://doi.org/10.5194/essd-10-583-2018, 2018. a, b, c
Yuval, J. and O'Gorman, P. A.: Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions, Nat. Commun., 11, 3295, https://doi.org/10.1038/s41467-020-17142-3, 2020. a
Zantedeschi, V., Falasca, F., Douglas, A., Strange, R., Kusner, M. J., and Watson-Parris, D.: Cumulo, Github [code], https://github.com/FrontierDevelopmentLab/CUMULO (last access: 20 June 2024), 2019. a
Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P., Klein, S. A., and Taylor, K. E.: Causes of Higher Climate Sensitivity in CMIP6 Models, Geophys. Res. Lett., 47, e2019GL085782, https://doi.org/10.1029/2019gl085782, 2020. a
Zhang, M. H.: Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements, J. Geophys. Res., 110, D15S02, https://doi.org/10.1029/2004jd005021, 2005. a
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.
CCClim displays observations of clouds in terms of cloud classes that have been in use for a...
Altmetrics
Final-revised paper
Preprint