Articles | Volume 16, issue 4
https://doi.org/10.5194/esd-16-1103-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-16-1103-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Jul 2025
Research article |
| 17 Jul 2025
| 17 Jul 2025
Earth's future climate and its variability simulated at 9 km global resolution
Ja-Yeon Moon
Center for Climate Physics, Institute for Basic Science, Busan, 46241, Republic of Korea
Pusan National University, Busan, 46241, Republic of Korea
Jan Streffing
Climate Dynamics Department, Climate Sciences Division, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, 27570, Germany
Paleoclimate Dynamics Department, Climate Sciences Division, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, 27570, Germany
Department of Mathematics & Logistics, Jacobs University Bremen, Campus Ring 1, 28759, Bremen, Germany
Sun-Seon Lee
Center for Climate Physics, Institute for Basic Science, Busan, 46241, Republic of Korea
Pusan National University, Busan, 46241, Republic of Korea
Tido Semmler
Climate Dynamics Department, Climate Sciences Division, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, 27570, Germany
Research and Applications Division, Met Éireann, 65-67 Glasnevin Hill, D09 Y921, Dublin, Ireland
Miguel Andrés-Martínez
Climate Dynamics Department, Climate Sciences Division, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, 27570, Germany
High-Performance Computing and Data Processing Group, Scientific Computing Department, Computing and Data Centre, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, 27570, Germany
Jiao Chen
Climate Dynamics Department, Climate Sciences Division, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, 27570, Germany
Eun-Byeoul Cho
Center for Climate Physics, Institute for Basic Science, Busan, 46241, Republic of Korea
Pusan National University, Busan, 46241, Republic of Korea
Jung-Eun Chu
School of Energy and Environment, City University of Hong Kong, Hong Kong, China
Christian L. E. Franzke
Center for Climate Physics, Institute for Basic Science, Busan, 46241, Republic of Korea
Department of Integrated Climate System Science, Pusan National University, Busan, 46241, Republic of Korea
Jan P. Gärtner
Climate Dynamics Department, Climate Sciences Division, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, 27570, Germany
Rohit Ghosh
Climate Dynamics Department, Climate Sciences Division, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, 27570, Germany
Jan Hegewald
Climate Dynamics Department, Climate Sciences Division, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, 27570, Germany
Development Department, Gauß-IT-Zentrum, Braunschweig University of Technology (GITZ), Braunschweig, Germany
Songyee Hong
SSG International ISG Services, Lenovo Global Technology Korea LLC, Seoul, 06141, Republic of Korea
Dae-Won Kim
Center for Climate Physics, Institute for Basic Science, Busan, 46241, Republic of Korea
Pusan National University, Busan, 46241, Republic of Korea
Nikolay Koldunov
Climate Dynamics Department, Climate Sciences Division, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, 27570, Germany
June-Yi Lee
Center for Climate Physics, Institute for Basic Science, Busan, 46241, Republic of Korea
Department of Integrated Climate System Science, Pusan National University, Busan, 46241, Republic of Korea
Zihao Lin
School of Energy and Environment, City University of Hong Kong, Hong Kong, China
School of Earth and Environmental Sciences, Seoul National University, Seoul, 08826, Republic of Korea
Svetlana N. Loza
Climate Dynamics Department, Climate Sciences Division, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, 27570, Germany
Wonsun Park
Center for Climate Physics, Institute for Basic Science, Busan, 46241, Republic of Korea
Department of Integrated Climate System Science, Pusan National University, Busan, 46241, Republic of Korea
Woncheol Roh
Center for Climate Physics, Institute for Basic Science, Busan, 46241, Republic of Korea
Pusan National University, Busan, 46241, Republic of Korea
Dmitry V. Sein
Climate Dynamics Department, Climate Sciences Division, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, 27570, Germany
Paleoclimate Dynamics Department, Climate Sciences Division, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, 27570, Germany
Moscow Institute of Physics and Technology, Moscow, Russia
Sahil Sharma
Center for Climate Physics, Institute for Basic Science, Busan, 46241, Republic of Korea
Pusan National University, Busan, 46241, Republic of Korea
Dmitry Sidorenko
Climate Dynamics Department, Climate Sciences Division, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, 27570, Germany
Jun-Hyeok Son
Center for Climate Physics, Institute for Basic Science, Busan, 46241, Republic of Korea
Pusan National University, Busan, 46241, Republic of Korea
Climate Prediction Division, Korea Meteorological Administration, Daejeon, 35208, Republic of Korea
Malte F. Stuecker
Department of Oceanography and International Pacific Research Center, University of Hawai`i at Mānoa, Honolulu, 96822, USA
Qiang Wang
Climate Dynamics Department, Climate Sciences Division, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, 27570, Germany
Gyuseok Yi
Center for Climate Physics, Institute for Basic Science, Busan, 46241, Republic of Korea
Department of Climate System, Pusan National University, Busan, 46241, Republic of Korea
Martina Zapponini
Climate Dynamics Department, Climate Sciences Division, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, 27570, Germany
Climate Dynamics Department, Climate Sciences Division, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, 27570, Germany
Department of Physics and Electrical Engineering, University of Bremen, 28359, Bremen, Germany
Center for Climate Physics, Institute for Basic Science, Busan, 46241, Republic of Korea
Pusan National University, Busan, 46241, Republic of Korea
Related authors
No articles found.
Hans Segura, Xabier Pedruzo-Bagazgoitia, Philipp Weiss, Sebastian K. Müller, Thomas Rackow, Junhong Lee, Edgar Dolores-Tesillos, Imme Benedict, Matthias Aengenheyster, Razvan Aguridan, Gabriele Arduini, Alexander J. Baker, Jiawei Bao, Swantje Bastin, Eulàlia Baulenas, Tobias Becker, Sebastian Beyer, Hendryk Bockelmann, Nils Brüggemann, Lukas Brunner, Suvarchal K. Cheedela, Sushant Das, Jasper Denissen, Ian Dragaud, Piotr Dziekan, Madeleine Ekblom, Jan Frederik Engels, Monika Esch, Richard Forbes, Claudia Frauen, Lilli Freischem, Diego García-Maroto, Philipp Geier, Paul Gierz, Álvaro González-Cervera, Katherine Grayson, Matthew Griffith, Oliver Gutjahr, Helmuth Haak, Ioan Hadade, Kerstin Haslehner, Shabeh ul Hasson, Jan Hegewald, Lukas Kluft, Aleksei Koldunov, Nikolay Koldunov, Tobias Kölling, Shunya Koseki, Sergey Kosukhin, Josh Kousal, Peter Kuma, Arjun U. Kumar, Rumeng Li, Nicolas Maury, Maximilian Meindl, Sebastian Milinski, Kristian Mogensen, Bimochan Niraula, Jakub Nowak, Divya Sri Praturi, Ulrike Proske, Dian Putrasahan, René Redler, David Santuy, Domokos Sármány, Reiner Schnur, Patrick Scholz, Dmitry Sidorenko, Dorian Spät, Birgit Sützl, Daisuke Takasuka, Adrian Tompkins, Alejandro Uribe, Mirco Valentini, Menno Veerman, Aiko Voigt, Sarah Warnau, Fabian Wachsmann, Marta Wacławczyk, Nils Wedi, Karl-Hermann Wieners, Jonathan Wille, Marius Winkler, Yuting Wu, Florian Ziemen, Janos Zimmermann, Frida A.-M. Bender, Dragana Bojovic, Sandrine Bony, Simona Bordoni, Patrice Brehmer, Marcus Dengler, Emanuel Dutra, Saliou Faye, Erich Fischer, Chiel van Heerwaarden, Cathy Hohenegger, Heikki Järvinen, Markus Jochum, Thomas Jung, Johann H. Jungclaus, Noel S. Keenlyside, Daniel Klocke, Heike Konow, Martina Klose, Szymon Malinowski, Olivia Martius, Thorsten Mauritsen, Juan Pedro Mellado, Theresa Mieslinger, Elsa Mohino, Hanna Pawłowska, Karsten Peters-von Gehlen, Abdoulaye Sarré, Pajam Sobhani, Philip Stier, Lauri Tuppi, Pier Luigi Vidale, Irina Sandu, and Bjorn Stevens
Geosci. Model Dev., 18, 7735–7761, https://doi.org/10.5194/gmd-18-7735-2025, https://doi.org/10.5194/gmd-18-7735-2025, 2025
Short summary
Short summary
The Next Generation of Earth Modeling Systems project (nextGEMS) developed two Earth system models that use horizontal grid spacing of 10 km and finer, giving more fidelity to the representation of local phenomena, globally. In its fourth cycle, nextGEMS simulated the Earth System climate over the 2020–2049 period under the SSP3-7.0 scenario. Here, we provide an overview of nextGEMS, insights into the model development, and the realism of multi-decadal, kilometer-scale simulations.
Ting-Chen Chen, Hugues Goosse, Cécile Davrinche, Stephy Libera, Christopher Roberts, Matthias Aengenheyster, Kristian Strommen, Malcolm Roberts, Rohit Ghosh, and Jin-Song von Storch
Weather Clim. Dynam., 6, 1179–1193, https://doi.org/10.5194/wcd-6-1179-2025, https://doi.org/10.5194/wcd-6-1179-2025, 2025
Short summary
Short summary
Southern Annular Mode (SAM) is a leading mode of Southern Hemisphere climate variability, but global models often overestimate its persistence in summer. Bias in SAM persistence is reduced and jet latitude improved using high-resolution sea surface temperature (SST)-prescribed models, pointing to the central role of SSTs. Control experiments explore the influence of model resolution and mesoscale ocean features on SAM persistence, highlighting the complex and subtle nature of air-sea coupling.
Zhi-Bo Li, Chao Liu, Cesar Azorin-Molina, Soon-Il An, Yang Zhao, Yang Xu, Jongsoo Shin, Deliang Chen, and Cheng Shen
Weather Clim. Dynam., 6, 1107–1117, https://doi.org/10.5194/wcd-6-1107-2025, https://doi.org/10.5194/wcd-6-1107-2025, 2025
Short summary
Short summary
Our research explores how Northern Hemisphere near-surface wind speeds respond to the removal of CO2 from the atmosphere. Using advanced CESM (Community Earth System Model) simulations, we discovered that wind speeds react differently during periods of increased and decreased carbon dioxide levels. Different responses are attributed to changes in global surface temperature and AMOC (Atlantic Meridional Overturning Circulation). This study not only advances our understanding of climate dynamics but also aids in optimizing strategies for wind energy.
Luc Hallali, Eirik Myrvoll-Nilsen, and Christian L. E. Franzke
Nonlin. Processes Geophys., 32, 383–395, https://doi.org/10.5194/npg-32-383-2025, https://doi.org/10.5194/npg-32-383-2025, 2025
Short summary
Short summary
We present an alternative statistical methodology to detect whether the Atlantic Ocean’s circulation system is approaching a tipping point. Our approach separates natural variability from real early warning signals of tipping, reducing false alarms. When applied to a proxy of the Atlantic Ocean’s circulation strength, we find significant signs that the system is undergoing destabilization, suggesting that it may be approaching a tipping point, which could have major impacts on global climate patterns.
Diajeng W. Atmojo, Katja Weigel, Arthur Grundner, Marika M. Holland, Dmitry Sidorenko, and Veronika Eyring
EGUsphere, https://doi.org/10.5194/egusphere-2025-3556, https://doi.org/10.5194/egusphere-2025-3556, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
This study improves the sea ice albedo parametrisation in the Finite-Element Sea Ice Model by discovering an equation using symbolic regression, an interpretable machine learning method. Leveraging satellite and reanalyses data, our discovered equation identifies high sensitivity to thin snow and the weighted temperature difference between sea ice surface and 2 m air as critical to determine sea ice albedo. Our findings contribute to improving Arctic climate projections and understanding.
Moritz Zeising, Laurent Oziel, Silke Thoms, Özgür Gürses, Judith Hauck, Bernd Heinold, Svetlana N. Losa, Manuela van Pinxteren, Christoph Völker, Sebastian Zeppenfeld, and Astrid Bracher
EGUsphere, https://doi.org/10.5194/egusphere-2025-4190, https://doi.org/10.5194/egusphere-2025-4190, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
We assess the implementation of additional organic carbon pathways into a global setup of a numerical model, which simulates the ocean circulation, sea ice, and biogeochemical processes. With a focus on the Arctic Ocean, this model tracks the temporal and spatial dynamics of phytoplankton, exudation of organic carbon, and its aggregation to so-called transparent exopolymer particles. We evaluate the simulation using measurements from ship-based and remote-sensing campaigns in the Arctic Ocean.
Sun-Seon Lee, Sahil Sharma, Nan Rosenbloom, Keith B. Rodgers, Ji-Eun Kim, Eun Young Kwon, Christian L. E. Franzke, In-Won Kim, Mohanan Geethalekshmi Sreeush, and Karl Stein
Earth Syst. Dynam., 16, 1427–1451, https://doi.org/10.5194/esd-16-1427-2025, https://doi.org/10.5194/esd-16-1427-2025, 2025
Short summary
Short summary
A new 10-member ensemble simulation with the state-of-the-art Earth system model was employed to study the long-term climate response to sustained greenhouse warming through to the year 2500. The findings show that the projected changes in the forced mean state and internal variability during 2101–2500 differ substantially from the 21st-century projections, emphasizing the importance of multi-century perspectives for understanding future climate change and informing effective mitigation strategies.
Yingxue Liu, Joakim Kjellsson, Abhishek Savita, and Wonsun Park
Geosci. Model Dev., 18, 5435–5449, https://doi.org/10.5194/gmd-18-5435-2025, https://doi.org/10.5194/gmd-18-5435-2025, 2025
Short summary
Short summary
The impact of horizontal resolution and model time step on extreme precipitation over Europe is examined in OpenIFS. We find that the biases are reduced with higher horizontal resolution but not with a shorter time step. The large-scale precipitation is sensitive to the horizontal resolution and time step; however, the convective precipitation is sensitive to the model time step. Higher horizontal resolution is more important for extreme precipitation simulation than a shorter time step.
Francisco J. Doblas-Reyes, Jenni Kontkanen, Irina Sandu, Mario Acosta, Mohammed Hussam Al Turjmam, Ivan Alsina-Ferrer, Miguel Andrés-Martínez, Leo Arriola, Marvin Axness, Marc Batlle Martín, Peter Bauer, Tobias Becker, Daniel Beltrán, Sebastian Beyer, Hendryk Bockelmann, Pierre-Antoine Bretonnière, Sebastien Cabaniols, Silvia Caprioli, Miguel Castrillo, Aparna Chandrasekar, Suvarchal Cheedela, Victor Correal, Emanuele Danovaro, Paolo Davini, Jussi Enkovaara, Claudia Frauen, Barbara Früh, Aina Gaya Àvila, Paolo Ghinassi, Rohit Ghosh, Supriyo Ghosh, Iker González, Katherine Grayson, Matthew Griffith, Ioan Hadade, Christopher Haine, Carl Hartick, Utz-Uwe Haus, Shane Hearne, Heikki Järvinen, Bernat Jiménez, Amal John, Marlin Juchem, Thomas Jung, Jessica Kegel, Matthias Kelbling, Kai Keller, Bruno Kinoshita, Theresa Kiszler, Daniel Klocke, Lukas Kluft, Nikolay Koldunov, Tobias Kölling, Joonas Kolstela, Luis Kornblueh, Sergey Kosukhin, Aleksander Lacima-Nadolnik, Jeisson Javier Leal Rojas, Jonni Lehtiranta, Tuomas Lunttila, Anna Luoma, Pekka Manninen, Alexey Medvedev, Sebastian Milinski, Ali Omar Abdelazim Mohammed, Sebastian Müller, Devaraju Naryanappa, Natalia Nazarova, Sami Niemelä, Bimochan Niraula, Henrik Nortamo, Aleksi Nummelin, Matteo Nurisso, Pablo Ortega, Stella Paronuzzi, Xabier Pedruzo Bagazgoitia, Charles Pelletier, Carlos Peña, Suraj Polade, Himansu Pradhan, Rommel Quintanilla, Tiago Quintino, Thomas Rackow, Jouni Räisänen, Maqsood Mubarak Rajput, René Redler, Balthasar Reuter, Nuno Rocha Monteiro, Francesc Roura-Adserias, Silva Ruppert, Susan Sayed, Reiner Schnur, Tanvi Sharma, Dmitry Sidorenko, Outi Sievi-Korte, Albert Soret, Christian Steger, Bjorn Stevens, Jan Streffing, Jaleena Sunny, Luiggi Tenorio, Stephan Thober, Ulf Tigerstedt, Oriol Tinto, Juha Tonttila, Heikki Tuomenvirta, Lauri Tuppi, Ginka Van Thielen, Emanuele Vitali, Jost von Hardenberg, Ingo Wagner, Nils Wedi, Jan Wehner, Sven Willner, Xavier Yepes-Arbós, Florian Ziemen, and Janos Zimmermann
EGUsphere, https://doi.org/10.5194/egusphere-2025-2198, https://doi.org/10.5194/egusphere-2025-2198, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
The Climate Change Adaptation Digital Twin (Climate DT) pioneers the operationalisation of climate projections. The system produces global simulations with local granularity for adaptation decision-making. Applications are embedded to generate tailored indicators. A unified workflow orchestrates all components in several supercomputers. Data management ensures consistency and streaming enables real-time use. It is a complementary innovation to initiatives like CMIP, CORDEX, and climate services.
Xue Feng, Matthew J. Widlansky, Tong Lee, Ou Wang, Magdalena A. Balmaseda, Hao Zuo, Gregory Dusek, William Sweet, and Malte F. Stuecker
Ocean Sci., 21, 1663–1676, https://doi.org/10.5194/os-21-1663-2025, https://doi.org/10.5194/os-21-1663-2025, 2025
Short summary
Short summary
Forecasting sea level changes months in advance along the Gulf Coast and East Coast of the United States is challenging. Here, we present a method that uses past ocean states to forecast future sea levels, while assuming no knowledge of how the atmosphere will evolve other than its typical annual cycle near the ocean's surface. Our findings indicate that this method improves sea level outlooks for many locations along the Gulf Coast and East Coast, especially south of Cape Hatteras.
Vanessa Teske, Ralph Timmermann, Cara Nissen, Rolf Zentek, Tido Semmler, and Günther Heinemann
Ocean Sci., 21, 1205–1221, https://doi.org/10.5194/os-21-1205-2025, https://doi.org/10.5194/os-21-1205-2025, 2025
Short summary
Short summary
We investigate the structural changes the Antarctic Slope Front in the southern Weddell Sea experiences in a warming climate by conducting two ocean simulations driven by atmospheric data of different horizontal resolutions. Cross-slope currents associated with a regime shift from a cold to a warm Filchner Trough on the continental shelf temporarily disturb the structure of the slope front and reduce its depth, but the primary reason for a regime shift is the cross-slope density gradient.
Piers M. Forster, Chris Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Christophe Cassou, Mathias Hauser, Zeke Hausfather, June-Yi Lee, Matthew D. Palmer, Karina von Schuckmann, Aimée B. A. Slangen, Sophie Szopa, Blair Trewin, Jeongeun Yun, Nathan P. Gillett, Stuart Jenkins, H. Damon Matthews, Krishnan Raghavan, Aurélien Ribes, Joeri Rogelj, Debbie Rosen, Xuebin Zhang, Myles Allen, Lara Aleluia Reis, Robbie M. Andrew, Richard A. Betts, Alex Borger, Jiddu A. Broersma, Samantha N. Burgess, Lijing Cheng, Pierre Friedlingstein, Catia M. Domingues, Marco Gambarini, Thomas Gasser, Johannes Gütschow, Masayoshi Ishii, Christopher Kadow, John Kennedy, Rachel E. Killick, Paul B. Krummel, Aurélien Liné, Didier P. Monselesan, Colin Morice, Jens Mühle, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Jan C. Minx, Matthew Rigby, Robert Rohde, Abhishek Savita, Sonia I. Seneviratne, Peter Thorne, Christopher Wells, Luke M. Western, Guido R. van der Werf, Susan E. Wijffels, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 17, 2641–2680, https://doi.org/10.5194/essd-17-2641-2025, https://doi.org/10.5194/essd-17-2641-2025, 2025
Short summary
Short summary
In a rapidly changing climate, evidence-based decision-making benefits from up-to-date and timely information. Here we compile monitoring datasets to track real-world changes over time. To make our work relevant to policymakers, we follow methods from the Intergovernmental Panel on Climate Change (IPCC). Human activities are increasing the Earth's energy imbalance and driving faster sea-level rise compared to the IPCC assessment.
Fernanda DI Alzira Oliveira Matos, Dmitry Sidorenko, Xiaoxu Shi, Lars Ackermann, Janini Pereira, Gerrit Lohmann, and Christian Stepanek
EGUsphere, https://doi.org/10.5194/egusphere-2025-2326, https://doi.org/10.5194/egusphere-2025-2326, 2025
Short summary
Short summary
The Atlantic Meridional Overturning Circulation (AMOC) is responsible for about 25 % of the poleward ocean heat transport. Currently, the AMOC strength is mostly calculated in depth space (z-AMOC). However, we argue that, in warmer climates, the AMOC should be calculated in density space (ρ-AMOC). We performed simulations with CO2 forcing of 280 ppmv (PI) and 1120 ppmv of (4xCO2) and find that ρ-AMOC provides more physical and meaningful information about the AMOC in warmer climates.
Ingo Richter, Ping Chang, Ping-Gin Chiu, Gokhan Danabasoglu, Takeshi Doi, Dietmar Dommenget, Guillaume Gastineau, Zoe E. Gillett, Aixue Hu, Takahito Kataoka, Noel S. Keenlyside, Fred Kucharski, Yuko M. Okumura, Wonsun Park, Malte F. Stuecker, Andréa S. Taschetto, Chunzai Wang, Stephen G. Yeager, and Sang-Wook Yeh
Geosci. Model Dev., 18, 2587–2608, https://doi.org/10.5194/gmd-18-2587-2025, https://doi.org/10.5194/gmd-18-2587-2025, 2025
Short summary
Short summary
Tropical ocean basins influence each other through multiple pathways and mechanisms, referred to here as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models but have obtained conflicting results. This may be partly due to differences in experiment protocols and partly due to systematic model errors. The Tropical Basin Interaction Model Intercomparison Project (TBIMIP) aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Jan P. Gärtner, Martin Losch, Markus Jochum, and Roman Nuterman
EGUsphere, https://doi.org/10.22541/essoar.173940251.11733929/v1, https://doi.org/10.22541/essoar.173940251.11733929/v1, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Climate simulations help us understand the Earth systems and inform climate policies. These complex models require advanced programming and significant energy, as they run on large grids over long timescales. A key component of a climate model is its sea ice component. We present a sea ice model that simplifies development while maintaining high performance. By utilizing GPUs, our model can replace dozens to hundreds of CPUs, drastically reducing the energy usage of running climate simulations.
Swantje Bastin, Aleksei Koldunov, Florian Schütte, Oliver Gutjahr, Marta Agnieszka Mrozowska, Tim Fischer, Radomyra Shevchenko, Arjun Kumar, Nikolay Koldunov, Helmuth Haak, Nils Brüggemann, Rebecca Hummels, Mia Sophie Specht, Johann Jungclaus, Sergey Danilov, Marcus Dengler, and Markus Jochum
Geosci. Model Dev., 18, 1189–1220, https://doi.org/10.5194/gmd-18-1189-2025, https://doi.org/10.5194/gmd-18-1189-2025, 2025
Short summary
Short summary
Vertical mixing is an important process, for example, for tropical sea surface temperature, but cannot be resolved by ocean models. Comparisons of mixing schemes and settings have usually been done with a single model, sometimes yielding conflicting results. We systematically compare two widely used schemes with different parameter settings in two different ocean models and show that most effects from mixing scheme parameter changes are model-dependent.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
Short summary
Short summary
Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Ulrike Proske, Nils Brüggemann, Jan P. Gärtner, Oliver Gutjahr, Helmuth Haak, Dian Putrasahan, and Karl-Hermann Wieners
EGUsphere, https://doi.org/10.5194/egusphere-2024-3493, https://doi.org/10.5194/egusphere-2024-3493, 2024
Preprint archived
Short summary
Short summary
Climate models contain coding mistakes, which may look mundane, but can affect the results of interconnected and complex models in unforeseen ways. We describe a sea ice bug in the coupled atmosphere-ocean-sea ice model ICON, giving an example of visual and concise bug communication. This bug represents a novel species of resolution-dependent bugs. The case illustrates the value of open documentation of bugs in climate models and to encourage our community to adopt a similar approach.
Shunya Koseki, Rubén Vázquez, William Cabos, Claudia Gutiérrez, Dmitry V. Sein, and Marie-Lou Bachèlery
Earth Syst. Dynam., 15, 1401–1416, https://doi.org/10.5194/esd-15-1401-2024, https://doi.org/10.5194/esd-15-1401-2024, 2024
Short summary
Short summary
Using a high-resolution regionally coupled model, we suggest that Dakar Niño variability will be reinforced under the Representative Concentration Pathway (RCP) 8.5 scenario. This may be induced by intensified surface heat flux anomalies and, secondarily, by anomalies in horizontal and vertical advection. Increased sea surface temperature (SST) variability can be associated with stronger wind variability, attributed to amplified surface temperature anomalies between ocean and land.
Tridib Banerjee, Patrick Scholz, Sergey Danilov, Knut Klingbeil, and Dmitry Sidorenko
Geosci. Model Dev., 17, 7051–7065, https://doi.org/10.5194/gmd-17-7051-2024, https://doi.org/10.5194/gmd-17-7051-2024, 2024
Short summary
Short summary
In this paper we propose a new alternative to one of the functionalities of the sea ice model FESOM2. The alternative we propose allows the model to capture and simulate fast changes in quantities like sea surface elevation more accurately. We also demonstrate that the new alternative is faster and more adept at taking advantages of highly parallelized computing infrastructure. We therefore show that this new alternative is a great addition to the sea ice model FESOM2.
Shreya Trivedi, Imke Sievers, Marylou Athanase, Antonio Sánchez Benítez, and Tido Semmler
EGUsphere, https://doi.org/10.5194/egusphere-2024-2214, https://doi.org/10.5194/egusphere-2024-2214, 2024
Preprint archived
Short summary
Short summary
Our study introduces a new method to compare CMIP6 models' sea ice and snow simulations with in-situ (MOSAiC) measurements. We assessed models for their accuracy in replicating Arctic sea ice and snow thicknesses, using two sea-ice and atmosphere-based methods to select "proxy years." We show that the models often overestimate snow thickness and mistime sea ice cycles. Despite limitations, this approach provides a valuable tool for evaluating climate models in localized time and space.
Sebastian Steinig, Wolf Dummann, Peter Hofmann, Martin Frank, Wonsun Park, Thomas Wagner, and Sascha Flögel
Clim. Past, 20, 1537–1558, https://doi.org/10.5194/cp-20-1537-2024, https://doi.org/10.5194/cp-20-1537-2024, 2024
Short summary
Short summary
The opening of the South Atlantic Ocean, starting ~ 140 million years ago, had the potential to influence the global carbon cycle and climate trends. We use 36 climate model experiments to simulate the evolution of ocean circulation in this narrow basin. We test different combinations of palaeogeographic and atmospheric CO2 reconstructions with geochemical data to not only quantify the influence of individual processes on ocean circulation but also to find nonlinear interactions between them.
Jisun Shin, Dae-Won Kim, So-Hyun Kim, Gi Seop Lee, Boo-Keun Khim, and Young-Heon Jo
Earth Syst. Sci. Data, 16, 3193–3211, https://doi.org/10.5194/essd-16-3193-2024, https://doi.org/10.5194/essd-16-3193-2024, 2024
Short summary
Short summary
We overcame the limitations of satellite and reanalysis sea surface salinity (SSS) datasets and produced a gap-free gridded SSS product with reasonable accuracy and a spatial resolution of 1 km using a machine learning model. Our data enabled the recognition of SSS distribution and movement patterns of the Changjiang diluted water (CDW) front in the East China Sea (ECS) during summer. These results will further advance our understanding and monitoring of long-term SSS variations in the ECS.
Holly C. Ayres, David Ferreira, Wonsun Park, Joakim Kjellsson, and Malin Ödalen
Weather Clim. Dynam., 5, 805–820, https://doi.org/10.5194/wcd-5-805-2024, https://doi.org/10.5194/wcd-5-805-2024, 2024
Short summary
Short summary
The Weddell Sea Polynya (WSP) is a large, closed-off opening in winter sea ice that has opened only a couple of times since we started using satellites to observe sea ice. The aim of this study is to determine the impact of the WSP on the atmosphere. We use three numerical models of the atmosphere, and for each, we use two levels of detail. We find that the WSP causes warming but only locally, alongside an increase in precipitation, and shows some dependence on the large-scale background winds.
Malte Meinshausen, Carl-Friedrich Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, Josep G. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatima Driouech, Erich Fischer, Piers Forster, Michael Grose, Gerrit Hansen, Zeke Hausfather, Tatiana Ilyina, Jarmo S. Kikstra, Joyce Kimutai, Andrew D. King, June-Yi Lee, Chris Lennard, Tabea Lissner, Alexander Nauels, Glen P. Peters, Anna Pirani, Gian-Kasper Plattner, Hans Pörtner, Joeri Rogelj, Maisa Rojas, Joyashree Roy, Bjørn H. Samset, Benjamin M. Sanderson, Roland Séférian, Sonia Seneviratne, Christopher J. Smith, Sophie Szopa, Adelle Thomas, Diana Urge-Vorsatz, Guus J. M. Velders, Tokuta Yokohata, Tilo Ziehn, and Zebedee Nicholls
Geosci. Model Dev., 17, 4533–4559, https://doi.org/10.5194/gmd-17-4533-2024, https://doi.org/10.5194/gmd-17-4533-2024, 2024
Short summary
Short summary
The scientific community is considering new scenarios to succeed RCPs and SSPs for the next generation of Earth system model runs to project future climate change. To contribute to that effort, we reflect on relevant policy and scientific research questions and suggest categories for representative emission pathways. These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.
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.
Vera Melinda Galfi, Tommaso Alberti, Lesley De Cruz, Christian L. E. Franzke, and Valerio Lembo
Nonlin. Processes Geophys., 31, 185–193, https://doi.org/10.5194/npg-31-185-2024, https://doi.org/10.5194/npg-31-185-2024, 2024
Short summary
Short summary
In the online seminar series "Perspectives on climate sciences: from historical developments to future frontiers" (2020–2021), well-known and established scientists from several fields – including mathematics, physics, climate science and ecology – presented their perspectives on the evolution of climate science and on relevant scientific concepts. In this paper, we first give an overview of the content of the seminar series, and then we introduce the written contributions to this special issue.
Sergey Danilov, Carolin Mehlmann, Dmitry Sidorenko, and Qiang Wang
Geosci. Model Dev., 17, 2287–2297, https://doi.org/10.5194/gmd-17-2287-2024, https://doi.org/10.5194/gmd-17-2287-2024, 2024
Short summary
Short summary
Sea ice models are a necessary component of climate models. At very high resolution they are capable of simulating linear kinematic features, such as leads, which are important for better prediction of heat exchanges between the ocean and atmosphere. Two new discretizations are described which improve the sea ice component of the Finite volumE Sea ice–Ocean Model (FESOM version 2) by allowing simulations of finer scales.
Abhishek Savita, Joakim Kjellsson, Robin Pilch Kedzierski, Mojib Latif, Tabea Rahm, Sebastian Wahl, and Wonsun Park
Geosci. Model Dev., 17, 1813–1829, https://doi.org/10.5194/gmd-17-1813-2024, https://doi.org/10.5194/gmd-17-1813-2024, 2024
Short summary
Short summary
The OpenIFS model is used to examine the impact of horizontal resolutions (HR) and model time steps. We find that the surface wind biases over the oceans, in particular the Southern Ocean, are sensitive to the model time step and HR, with the HR having the smallest biases. When using a coarse-resolution model with a shorter time step, a similar improvement is also found. Climate biases can be reduced in the OpenIFS model at a cheaper cost by reducing the time step rather than increasing the HR.
Nathan Beech, Thomas Rackow, Tido Semmler, and Thomas Jung
Geosci. Model Dev., 17, 529–543, https://doi.org/10.5194/gmd-17-529-2024, https://doi.org/10.5194/gmd-17-529-2024, 2024
Short summary
Short summary
Cost-reducing modeling strategies are applied to high-resolution simulations of the Southern Ocean in a changing climate. They are evaluated with respect to observations and traditional, lower-resolution modeling methods. The simulations effectively reproduce small-scale ocean flows seen in satellite data and are largely consistent with traditional model simulations after 4 °C of warming. Small-scale flows are found to intensify near bathymetric features and to become more variable.
Arjun Babu Nellikkattil, Danielle Lemmon, Travis Allen O'Brien, June-Yi Lee, and Jung-Eun Chu
Geosci. Model Dev., 17, 301–320, https://doi.org/10.5194/gmd-17-301-2024, https://doi.org/10.5194/gmd-17-301-2024, 2024
Short summary
Short summary
This study introduces a new computational framework called Scalable Feature Extraction and Tracking (SCAFET), designed to extract and track features in climate data. SCAFET stands out by using innovative shape-based metrics to identify features without relying on preconceived assumptions about the climate model or mean state. This approach allows more accurate comparisons between different models and scenarios.
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
Short summary
Short summary
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.
Kyung-Sook Yun, Axel Timmermann, Sun-Seon Lee, Matteo Willeit, Andrey Ganopolski, and Jyoti Jadhav
Clim. Past, 19, 1951–1974, https://doi.org/10.5194/cp-19-1951-2023, https://doi.org/10.5194/cp-19-1951-2023, 2023
Short summary
Short summary
To quantify the sensitivity of the earth system to orbital-scale forcings, we conducted an unprecedented quasi-continuous coupled general climate model simulation with the Community Earth System Model, which covers the climatic history of the past 3 million years. This study could stimulate future transient paleo-climate model simulations and perspectives to further highlight and document the effect of anthropogenic CO2 emissions in the broader paleo-climatic context.
Hongyan Xi, Marine Bretagnon, Svetlana N. Losa, Vanda Brotas, Mara Gomes, Ilka Peeken, Leonardo M. A. Alvarado, Antoine Mangin, and Astrid Bracher
State Planet, 1-osr7, 5, https://doi.org/10.5194/sp-1-osr7-5-2023, https://doi.org/10.5194/sp-1-osr7-5-2023, 2023
Short summary
Short summary
Continuous monitoring of phytoplankton groups using satellite data is crucial for understanding global ocean phytoplankton variability on different scales in both space and time. This study focuses on four important phytoplankton groups in the Atlantic Ocean to investigate their trend, anomaly and phenological characteristics both over the whole region and at subscales. This study paves the way to promote potentially important ocean monitoring indicators to help sustain the ocean health.
Xiaoxu Shi, Alexandre Cauquoin, Gerrit Lohmann, Lukas Jonkers, Qiang Wang, Hu Yang, Yuchen Sun, and Martin Werner
Geosci. Model Dev., 16, 5153–5178, https://doi.org/10.5194/gmd-16-5153-2023, https://doi.org/10.5194/gmd-16-5153-2023, 2023
Short summary
Short summary
We developed a new climate model with isotopic capabilities and simulated the pre-industrial and mid-Holocene periods. Despite certain regional model biases, the modeled isotope composition is in good agreement with observations and reconstructions. Based on our analyses, the observed isotope–temperature relationship in polar regions may have a summertime bias. Using daily model outputs, we developed a novel isotope-based approach to determine the onset date of the West African summer monsoon.
Özgür Gürses, Laurent Oziel, Onur Karakuş, Dmitry Sidorenko, Christoph Völker, Ying Ye, Moritz Zeising, Martin Butzin, and Judith Hauck
Geosci. Model Dev., 16, 4883–4936, https://doi.org/10.5194/gmd-16-4883-2023, https://doi.org/10.5194/gmd-16-4883-2023, 2023
Short summary
Short summary
This paper assesses the biogeochemical model REcoM3 coupled to the ocean–sea ice model FESOM2.1. The model can be used to simulate the carbon uptake or release of the ocean on timescales of several hundred years. A detailed analysis of the nutrients, ocean productivity, and ecosystem is followed by the carbon cycle. The main conclusion is that the model performs well when simulating the observed mean biogeochemical state and variability and is comparable to other ocean–biogeochemical models.
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
Short summary
Short summary
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.
Iván M. Parras-Berrocal, Rubén Vázquez, William Cabos, Dimitry V. Sein, Oscar Álvarez, Miguel Bruno, and Alfredo Izquierdo
Ocean Sci., 19, 941–952, https://doi.org/10.5194/os-19-941-2023, https://doi.org/10.5194/os-19-941-2023, 2023
Short summary
Short summary
Global warming may strongly affect dense water formation in the eastern Mediterranean, potentially impacting basin circulation and water properties. We find that at the end of the century dense water formation is reduced by 75 % for the Adriatic, 84 % for the Aegean, and 83 % for the Levantine Sea. This reduction is caused by changes in the temperature and salinity of surface and intermediate waters, which strengthen the vertical stratification, hampering deep convection.
Daniel Gliksman, Paul Averbeck, Nico Becker, Barry Gardiner, Valeri Goldberg, Jens Grieger, Dörthe Handorf, Karsten Haustein, Alexia Karwat, Florian Knutzen, Hilke S. Lentink, Rike Lorenz, Deborah Niermann, Joaquim G. Pinto, Ronald Queck, Astrid Ziemann, and Christian L. E. Franzke
Nat. Hazards Earth Syst. Sci., 23, 2171–2201, https://doi.org/10.5194/nhess-23-2171-2023, https://doi.org/10.5194/nhess-23-2171-2023, 2023
Short summary
Short summary
Wind and storms are a major natural hazard and can cause severe economic damage and cost human lives. Hence, it is important to gauge the potential impact of using indices, which potentially enable us to estimate likely impacts of storms or other wind events. Here, we review basic aspects of wind and storm generation and provide an extensive overview of wind impacts and available indices. This is also important to better prepare for future climate change and corresponding changes to winds.
Piers M. Forster, Christopher J. Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Mathias Hauser, Aurélien Ribes, Debbie Rosen, Nathan Gillett, Matthew D. Palmer, Joeri Rogelj, Karina von Schuckmann, Sonia I. Seneviratne, Blair Trewin, Xuebin Zhang, Myles Allen, Robbie Andrew, Arlene Birt, Alex Borger, Tim Boyer, Jiddu A. Broersma, Lijing Cheng, Frank Dentener, Pierre Friedlingstein, José M. Gutiérrez, Johannes Gütschow, Bradley Hall, Masayoshi Ishii, Stuart Jenkins, Xin Lan, June-Yi Lee, Colin Morice, Christopher Kadow, John Kennedy, Rachel Killick, Jan C. Minx, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Carl-Friedrich Schleussner, Sophie Szopa, Peter Thorne, Robert Rohde, Maisa Rojas Corradi, Dominik Schumacher, Russell Vose, Kirsten Zickfeld, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 15, 2295–2327, https://doi.org/10.5194/essd-15-2295-2023, https://doi.org/10.5194/essd-15-2295-2023, 2023
Short summary
Short summary
This is a critical decade for climate action, but there is no annual tracking of the level of human-induced warming. We build on the Intergovernmental Panel on Climate Change assessment reports that are authoritative but published infrequently to create a set of key global climate indicators that can be tracked through time. Our hope is that this becomes an important annual publication that policymakers, media, scientists and the public can refer to.
Guillaume Gastineau, Claude Frankignoul, Yongqi Gao, Yu-Chiao Liang, Young-Oh Kwon, Annalisa Cherchi, Rohit Ghosh, Elisa Manzini, Daniela Matei, Jennifer Mecking, Lingling Suo, Tian Tian, Shuting Yang, and Ying Zhang
The Cryosphere, 17, 2157–2184, https://doi.org/10.5194/tc-17-2157-2023, https://doi.org/10.5194/tc-17-2157-2023, 2023
Short summary
Short summary
Snow cover variability is important for many human activities. This study aims to understand the main drivers of snow cover in observations and models in order to better understand it and guide the improvement of climate models and forecasting systems. Analyses reveal a dominant role for sea surface temperature in the Pacific. Winter snow cover is also found to have important two-way interactions with the troposphere and stratosphere. No robust influence of the sea ice concentration is found.
Qi Shu, Qiang Wang, Chuncheng Guo, Zhenya Song, Shizhu Wang, Yan He, and Fangli Qiao
Geosci. Model Dev., 16, 2539–2563, https://doi.org/10.5194/gmd-16-2539-2023, https://doi.org/10.5194/gmd-16-2539-2023, 2023
Short summary
Short summary
Ocean models are often used for scientific studies on the Arctic Ocean. Here the Arctic Ocean simulations by state-of-the-art global ocean–sea-ice models participating in the Ocean Model Intercomparison Project (OMIP) were evaluated. The simulations on Arctic Ocean hydrography, freshwater content, stratification, sea surface height, and gateway transports were assessed and the common biases were detected. The simulations forced by different atmospheric forcing were also evaluated.
Nicola Maher, Robert C. Jnglin Wills, Pedro DiNezio, Jeremy Klavans, Sebastian Milinski, Sara C. Sanchez, Samantha Stevenson, Malte F. Stuecker, and Xian Wu
Earth Syst. Dynam., 14, 413–431, https://doi.org/10.5194/esd-14-413-2023, https://doi.org/10.5194/esd-14-413-2023, 2023
Short summary
Short summary
Understanding whether the El Niño–Southern Oscillation (ENSO) is likely to change in the future is important due to its widespread impacts. By using large ensembles, we can robustly isolate the time-evolving response of ENSO variability in 14 climate models. We find that ENSO variability evolves in a nonlinear fashion in many models and that there are large differences between models. These nonlinear changes imply that ENSO impacts may vary dramatically throughout the 21st century.
Felix Pithan, Marylou Athanase, Sandro Dahlke, Antonio Sánchez-Benítez, Matthew D. Shupe, Anne Sledd, Jan Streffing, Gunilla Svensson, and Thomas Jung
Geosci. Model Dev., 16, 1857–1873, https://doi.org/10.5194/gmd-16-1857-2023, https://doi.org/10.5194/gmd-16-1857-2023, 2023
Short summary
Short summary
Evaluating climate models usually requires long observational time series, but we present a method that also works for short field campaigns. We compare climate model output to observations from the MOSAiC expedition in the central Arctic Ocean. All models show how the arrival of a warm air mass warms the Arctic in April 2020, but two models do not show the response of snow temperature to the diurnal cycle. One model has too little liquid water and too much ice in clouds during cold days.
Pengyang Song, Dmitry Sidorenko, Patrick Scholz, Maik Thomas, and Gerrit Lohmann
Geosci. Model Dev., 16, 383–405, https://doi.org/10.5194/gmd-16-383-2023, https://doi.org/10.5194/gmd-16-383-2023, 2023
Short summary
Short summary
Tides have essential effects on the ocean and climate. Most previous research applies parameterised tidal mixing to discuss their effects in models. By comparing the effect of a tidal mixing parameterisation and tidal forcing on the ocean state, we assess the advantages and disadvantages of the two methods. Our results show that tidal mixing in the North Pacific Ocean strongly affects the global thermohaline circulation. We also list some effects that are not considered in the parameterisation.
Sergei Kirillov, Igor Dmitrenko, David G. Babb, Jens K. Ehn, Nikolay Koldunov, Søren Rysgaard, David Jensen, and David G. Barber
Ocean Sci., 18, 1535–1557, https://doi.org/10.5194/os-18-1535-2022, https://doi.org/10.5194/os-18-1535-2022, 2022
Short summary
Short summary
The sea ice bridge usually forms during winter in Nares Strait and prevents ice drifting south. However, this bridge has recently become unstable, and in this study we investigate the role of oceanic heat flux in this decline. Using satellite data, we identify areas where sea ice is relatively thin and further attribute those areas to the heat fluxes from the warm subsurface water masses. We also discuss the potential role of such an impact on ice bridge instability and earlier ice break up.
Jan Streffing, Dmitry Sidorenko, Tido Semmler, Lorenzo Zampieri, Patrick Scholz, Miguel Andrés-Martínez, Nikolay Koldunov, Thomas Rackow, Joakim Kjellsson, Helge Goessling, Marylou Athanase, Qiang Wang, Jan Hegewald, Dmitry V. Sein, Longjiang Mu, Uwe Fladrich, Dirk Barbi, Paul Gierz, Sergey Danilov, Stephan Juricke, Gerrit Lohmann, and Thomas Jung
Geosci. Model Dev., 15, 6399–6427, https://doi.org/10.5194/gmd-15-6399-2022, https://doi.org/10.5194/gmd-15-6399-2022, 2022
Short summary
Short summary
We developed a new atmosphere–ocean coupled climate model, AWI-CM3. Our model is significantly more computationally efficient than its predecessors AWI-CM1 and AWI-CM2. We show that the model, although cheaper to run, provides results of similar quality when modeling the historic period from 1850 to 2014. We identify the remaining weaknesses to outline future work. Finally we preview an improved simulation where the reduction in computational cost has to be invested in higher model resolution.
Takaya Uchida, Julien Le Sommer, Charles Stern, Ryan P. Abernathey, Chris Holdgraf, Aurélie Albert, Laurent Brodeau, Eric P. Chassignet, Xiaobiao Xu, Jonathan Gula, Guillaume Roullet, Nikolay Koldunov, Sergey Danilov, Qiang Wang, Dimitris Menemenlis, Clément Bricaud, Brian K. Arbic, Jay F. Shriver, Fangli Qiao, Bin Xiao, Arne Biastoch, René Schubert, Baylor Fox-Kemper, William K. Dewar, and Alan Wallcraft
Geosci. Model Dev., 15, 5829–5856, https://doi.org/10.5194/gmd-15-5829-2022, https://doi.org/10.5194/gmd-15-5829-2022, 2022
Short summary
Short summary
Ocean and climate scientists have used numerical simulations as a tool to examine the ocean and climate system since the 1970s. Since then, owing to the continuous increase in computational power and advances in numerical methods, we have been able to simulate increasing complex phenomena. However, the fidelity of the simulations in representing the phenomena remains a core issue in the ocean science community. Here we propose a cloud-based framework to inter-compare and assess such simulations.
Xiaoxu Shi, Martin Werner, Carolin Krug, Chris M. Brierley, Anni Zhao, Endurance Igbinosa, Pascale Braconnot, Esther Brady, Jian Cao, Roberta D'Agostino, Johann Jungclaus, Xingxing Liu, Bette Otto-Bliesner, Dmitry Sidorenko, Robert Tomas, Evgeny M. Volodin, Hu Yang, Qiong Zhang, Weipeng Zheng, and Gerrit Lohmann
Clim. Past, 18, 1047–1070, https://doi.org/10.5194/cp-18-1047-2022, https://doi.org/10.5194/cp-18-1047-2022, 2022
Short summary
Short summary
Since the orbital parameters of the past are different from today, applying the modern calendar to the past climate can lead to an artificial bias in seasonal cycles. With the use of multiple model outputs, we found that such a bias is non-ignorable and should be corrected to ensure an accurate comparison between modeled results and observational records, as well as between simulated past and modern climates, especially for the Last Interglacial.
Dmitry V. Sein, Anton Y. Dvornikov, Stanislav D. Martyanov, William Cabos, Vladimir A. Ryabchenko, Matthias Gröger, Daniela Jacob, Alok Kumar Mishra, and Pankaj Kumar
Earth Syst. Dynam., 13, 809–831, https://doi.org/10.5194/esd-13-809-2022, https://doi.org/10.5194/esd-13-809-2022, 2022
Short summary
Short summary
The effect of the marine biogeochemical variability upon the South Asian regional climate has been investigated. In the experiment where its full impact is activated, the average sea surface temperature is lower over most of the ocean. When the biogeochemical coupling is included, the main impacts include the enhanced phytoplankton primary production, a shallower thermocline, decreased SST and water temperature in subsurface layers.
Herminia Torelló-Sentelles and Christian L. E. Franzke
Hydrol. Earth Syst. Sci., 26, 1821–1844, https://doi.org/10.5194/hess-26-1821-2022, https://doi.org/10.5194/hess-26-1821-2022, 2022
Short summary
Short summary
Drought affects many regions worldwide, and future climate projections imply that drought severity and frequency will increase. Hence, the impacts of drought on the environment and society will also increase considerably. Monitoring and early warning systems for drought rely on several indicators; however, assessments on how these indicators are linked to impacts are still lacking. Our results show that meteorological indices are best linked to impact occurrences.
Matthias Gröger, Christian Dieterich, Cyril Dutheil, H. E. Markus Meier, and Dmitry V. Sein
Earth Syst. Dynam., 13, 613–631, https://doi.org/10.5194/esd-13-613-2022, https://doi.org/10.5194/esd-13-613-2022, 2022
Short summary
Short summary
Atmospheric rivers transport high amounts of water from subtropical regions to Europe. They are an important driver of heavy precipitation and flooding. Their response to a warmer future climate in Europe has so far been assessed only by global climate models. In this study, we apply for the first time a high-resolution regional climate model that allow to better resolve and understand the fate of atmospheric rivers over Europe.
Sara Pasqualetto, Luisa Cristini, and Thomas Jung
Geosci. Commun., 5, 87–100, https://doi.org/10.5194/gc-5-87-2022, https://doi.org/10.5194/gc-5-87-2022, 2022
Short summary
Short summary
Many projects in their reporting phase are required to provide a clear plan for evaluating the results of those efforts aimed at translating scientific results to a broader audience. This paper illustrates methodologies and strategies used in the framework of a European research project to assess the impact of knowledge transfer activities, both quantitatively and qualitatively, and provides recommendations and hints for developing a useful impact plan for scientific projects.
Alba de la Vara, Iván M. Parras-Berrocal, Alfredo Izquierdo, Dmitry V. Sein, and William Cabos
Earth Syst. Dynam., 13, 303–319, https://doi.org/10.5194/esd-13-303-2022, https://doi.org/10.5194/esd-13-303-2022, 2022
Short summary
Short summary
We study with the regionally coupled climate model ROM the impact of climate change on the Tyrrhenian Sea circulation, as well as the possible mechanisms and consequences in the NW Mediterranean Sea. Our results show a shift towards the summer circulation pattern by the end of the century. Also, water flowing via the Corsica Channel is more stratified and smaller in volume. Both factors may contribute to the interruption of deep water formation in the Gulf of Lions in the future.
Patrick Scholz, Dmitry Sidorenko, Sergey Danilov, Qiang Wang, Nikolay Koldunov, Dmitry Sein, and Thomas Jung
Geosci. Model Dev., 15, 335–363, https://doi.org/10.5194/gmd-15-335-2022, https://doi.org/10.5194/gmd-15-335-2022, 2022
Short summary
Short summary
Structured-mesh ocean models are still the most mature in terms of functionality due to their long development history. However, unstructured-mesh ocean models have acquired new features and caught up in their functionality. This paper continues the work by Scholz et al. (2019) of documenting the features available in FESOM2.0. It focuses on the following two aspects: (i) partial bottom cells and embedded sea ice and (ii) dealing with mixing parameterisations enabled by using the CVMix package.
Keith B. Rodgers, Sun-Seon Lee, Nan Rosenbloom, Axel Timmermann, Gokhan Danabasoglu, Clara Deser, Jim Edwards, Ji-Eun Kim, Isla R. Simpson, Karl Stein, Malte F. Stuecker, Ryohei Yamaguchi, Tamás Bódai, Eui-Seok Chung, Lei Huang, Who M. Kim, Jean-François Lamarque, Danica L. Lombardozzi, William R. Wieder, and Stephen G. Yeager
Earth Syst. Dynam., 12, 1393–1411, https://doi.org/10.5194/esd-12-1393-2021, https://doi.org/10.5194/esd-12-1393-2021, 2021
Short summary
Short summary
A large ensemble of simulations with 100 members has been conducted with the state-of-the-art CESM2 Earth system model, using historical and SSP3-7.0 forcing. Our main finding is that there are significant changes in the variance of the Earth system in response to anthropogenic forcing, with these changes spanning a broad range of variables important to impacts for human populations and ecosystems.
Vera Fofonova, Tuomas Kärnä, Knut Klingbeil, Alexey Androsov, Ivan Kuznetsov, Dmitry Sidorenko, Sergey Danilov, Hans Burchard, and Karen Helen Wiltshire
Geosci. Model Dev., 14, 6945–6975, https://doi.org/10.5194/gmd-14-6945-2021, https://doi.org/10.5194/gmd-14-6945-2021, 2021
Short summary
Short summary
We present a test case of river plume spreading to evaluate coastal ocean models. Our test case reveals the level of numerical mixing (due to parameterizations used and numerical treatment of processes in the model) and the ability of models to reproduce complex dynamics. The major result of our comparative study is that accuracy in reproducing the analytical solution depends less on the type of applied model architecture or numerical grid than it does on the type of advection scheme.
Qiang Wang, Sergey Danilov, Longjiang Mu, Dmitry Sidorenko, and Claudia Wekerle
The Cryosphere, 15, 4703–4725, https://doi.org/10.5194/tc-15-4703-2021, https://doi.org/10.5194/tc-15-4703-2021, 2021
Short summary
Short summary
Using simulations, we found that changes in ocean freshwater content induced by wind perturbations can significantly affect the Arctic sea ice drift, thickness, concentration and deformation rates years after the wind perturbations. The impact is through changes in sea surface height and surface geostrophic currents and the most pronounced in warm seasons. Such a lasting impact might become stronger in a warming climate and implies the importance of ocean initialization in sea ice prediction.
Dirk Barbi, Nadine Wieters, Paul Gierz, Miguel Andrés-Martínez, Deniz Ural, Fatemeh Chegini, Sara Khosravi, and Luisa Cristini
Geosci. Model Dev., 14, 4051–4067, https://doi.org/10.5194/gmd-14-4051-2021, https://doi.org/10.5194/gmd-14-4051-2021, 2021
Tingfeng Dou, Cunde Xiao, Jiping Liu, Qiang Wang, Shifeng Pan, Jie Su, Xiaojun Yuan, Minghu Ding, Feng Zhang, Kai Xue, Peter A. Bieniek, and Hajo Eicken
The Cryosphere, 15, 883–895, https://doi.org/10.5194/tc-15-883-2021, https://doi.org/10.5194/tc-15-883-2021, 2021
Short summary
Short summary
Rain-on-snow (ROS) events can accelerate the surface ablation of sea ice, greatly influencing the ice–albedo feedback. We found that spring ROS events have shifted to earlier dates over the Arctic Ocean in recent decades, which is correlated with sea ice melt onset in the Pacific sector and most Eurasian marginal seas. There has been a clear transition from solid to liquid precipitation, leading to a reduction in spring snow depth on sea ice by more than −0.5 cm per decade since the 1980s.
Kyung-Sook Yun, Axel Timmermann, and Malte F. Stuecker
Earth Syst. Dynam., 12, 121–132, https://doi.org/10.5194/esd-12-121-2021, https://doi.org/10.5194/esd-12-121-2021, 2021
Short summary
Short summary
Changes in the Hadley and Walker cells cause major climate disruptions across our planet. What has been overlooked so far is the question of whether these two circulations can shift their positions in a synchronized manner. We here show the synchronized spatial shifts between Walker and Hadley cells and further highlight a novel aspect of how tropical sea surface temperature anomalies can couple these two circulations. The re-positioning has important implications for extratropical rainfall.
Dipayan Choudhury, Axel Timmermann, Fabian Schloesser, Malte Heinemann, and David Pollard
Clim. Past, 16, 2183–2201, https://doi.org/10.5194/cp-16-2183-2020, https://doi.org/10.5194/cp-16-2183-2020, 2020
Short summary
Short summary
Our study is the first study to conduct transient simulations over MIS 7, using a 3-D coupled climate–ice sheet model with interactive ice sheets in both hemispheres. We find glacial inceptions to be more sensitive to orbital variations, whereas glacial terminations need the concerted action of both orbital and CO2 forcings. We highlight the issue of multiple equilibria and an instability due to stationary-wave–topography feedback that can trigger unrealistic North American ice sheet growth.
Xavier Fettweis, Stefan Hofer, Uta Krebs-Kanzow, Charles Amory, Teruo Aoki, Constantijn J. Berends, Andreas Born, Jason E. Box, Alison Delhasse, Koji Fujita, Paul Gierz, Heiko Goelzer, Edward Hanna, Akihiro Hashimoto, Philippe Huybrechts, Marie-Luise Kapsch, Michalea D. King, Christoph Kittel, Charlotte Lang, Peter L. Langen, Jan T. M. Lenaerts, Glen E. Liston, Gerrit Lohmann, Sebastian H. Mernild, Uwe Mikolajewicz, Kameswarrao Modali, Ruth H. Mottram, Masashi Niwano, Brice Noël, Jonathan C. Ryan, Amy Smith, Jan Streffing, Marco Tedesco, Willem Jan van de Berg, Michiel van den Broeke, Roderik S. W. van de Wal, Leo van Kampenhout, David Wilton, Bert Wouters, Florian Ziemen, and Tobias Zolles
The Cryosphere, 14, 3935–3958, https://doi.org/10.5194/tc-14-3935-2020, https://doi.org/10.5194/tc-14-3935-2020, 2020
Short summary
Short summary
We evaluated simulated Greenland Ice Sheet surface mass balance from 5 kinds of models. While the most complex (but expensive to compute) models remain the best, the faster/simpler models also compare reliably with observations and have biases of the same order as the regional models. Discrepancies in the trend over 2000–2012, however, suggest that large uncertainties remain in the modelled future SMB changes as they are highly impacted by the meltwater runoff biases over the current climate.
Cited articles
Adler, R. F., Gu, G., and Huffman, G. J.: Estimating Climatological Bias Errors for the Global Precipitation Climatology Project (GPCP), J. Appl. Meteorol. Clim., 51, 84–99, https://doi.org/10.1175/JAMC-D-11-052.1, 2012.
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., and Nelkin, E.: The version 2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present), J. Hydrometeorol., 4, 1147–1167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2, 2003.
Ahn, M.-S., Kim, D., Sperber, K. R., Kang, I.-S., Maloney, E., Waliser, D., and Hendon, H.: MJO simulation in CMIP5 climate models: MJO skill metrics and process-oriented diagnosis, Clim. Dynam., 49, 4023–4045, https://doi.org/10.1007/s00382-017-3558-4, 2017.
Ahn, M. S., Kim, D., Kang, D., Lee, J., Sperber, K. R., Gleckler, P. J., Jiang, X. N., Ham, Y. G., and Kim, H.: MJO Propagation Across the Maritime Continent: Are CMIP6 Models Better Than CMIP5 Models?, Geophys. Res. Lett., 47, e2020GL08725, https://doi.org/10.1029/2020gl087250, 2020.
Almazroui, M., Saeed, F., Saeed, S., Islam, M. N., Ismail, M., Klutse, N. A. B., and Siddiqui, M. H.: Projected Change in Temperature and Precipitation Over Africa from CMIP6, Earth Syst. Environ., 4, 455–475, https://doi.org/10.1007/s41748-020-00161-x, 2020.
Balsamo, G., Viterbo, P., Beljaars, A., van den Hurk, B., Hirschi, M., Betts, A. K., and Scipal, K.: A Revised Hydrology for the ECMWF Model: Verification from Field Site to Terrestrial Water Storage and Impact in the Integrated Forecast System, J. Hydrometeorol., 10, 623–643, https://doi.org/10.1175/2008jhm1068.1, 2009.
Barbi, D., Gierz, P., Andrés-Martínez, M., Ural, D., and Cristini, L.: Esm_tools_release3_as_used_by_AWI-CM3_paper, Zenodo [code], https://doi.org/10.5281/zenodo.6335309, 2022.
Barker, P. A., Allen, G., Gallagher, M., Pitt, J. R., Fisher, R. E., Bannan, T., Nisbet, E. G., Bauguitte, S. J.-B., Pasternak, D., Cliff, S., Schimpf, M. B., Mehra, A., Bower, K. N., Lee, J. D., Coe, H., and Percival, C. J.: Airborne measurements of fire emission factors for African biomass burning sampled during the MOYA campaign, Atmos. Chem. Phys., 20, 15443–15459, https://doi.org/10.5194/acp-20-15443-2020, 2020.
Beech, N., Rackow, T., Semmler, T., Danilov, S., Wang, Q., and Jung, T.: Long-term evolution of ocean eddy activity in a warming world, Nat. Clim. Change, 12, 910–917, https://doi.org/10.1038/s41558-022-01478-3, 2022.
Bellucci, A., Gualdi, S., and Navarra, A.: The Double-ITCZ Syndrome in Coupled General Circulation Models: The Role of Large-Scale Vertical Circulation Regimes, J. Climate, 23, 1127–1145, https://doi.org/10.1175/2009jcli3002.1, 2010.
Biastoch, A., Böning, C. W., Schwarzkopf, F. U., and Lutjeharms, J. R. E.: Increase in Agulhas leakage due to poleward shift of Southern Hemisphere westerlies, Nature, 462, 495-498, https://doi.org/10.1038/nature08519, 2009.
Bidlot, J.-R., Prates, F., Ribas, R., Mueller-Quintino, A., and Crepulja M., V., F.: Enhancing tropical cyclone wind forecasts, ECMWF Newsletter, 164, 33–37, https://www.ecmwf.int/en/elibrary/81178-newsletter-no-164-summer-2020 (last access: 27 September 2024), 2020.
Biswas, R. R. and Rahman, A.: Adaptation to climate change: A study on regional climate change adaptation policy and practice framework, J. Environ. Manage, 336, 117666, https://doi.org/10.1016/j.jenvman.2023.117666, 2023.
Bock, L. and Lauer, A.: Cloud properties and their projected changes in CMIP models with low to high climate sensitivity, Atmos. Chem. Phys., 24, 1587–1605, https://doi.org/10.5194/acp-24-1587-2024, 2024.
Bock, L., Lauer, A., Schlund, M., Barreiro, M., Bellouin, N., Jones, C., Meehl, G. A., Predoi, V., Roberts, M. J., and Eyring, V.: Quantifying Progress Across Different CMIP Phases With the ESMValTool, J. Geophys. Res.-Atmos., 125, e2019JD03232, https://doi.org/10.1029/2019jd032321, 2020.
Bojariu, R. and Giorgi, F.: The North Atlantic Oscillation signal in a regional climate simulation for the European region, Tellus A, 57, 641–653, https://doi.org/10.3402/tellusa.v57i4.14709, 2005.
Bouvier, C., van den Broek, D., Ekblom, M., and Sinclair, V. A.: Analytical and adaptable initial conditions for dry and moist baroclinic waves in the global hydrostatic model OpenIFS (CY43R3), Geosci. Model Dev., 17, 2961–2986, https://doi.org/10.5194/gmd-17-2961-2024, 2024.
Brönnimann, S.: Impact of El Nino Southern Oscillation on European climate, Rev. Geophys., 45, RG3003, https://doi.org/10.1029/2006rg000199, 2007.
Caesar, L., Rahmstorf, S., Robinson, A., Feulner, G., and Saba, V.: Observed fingerprint of a weakening Atlantic Ocean overturning circulation, Nature, 556, 191–196, https://doi.org/10.1038/s41586-018-0006-5, 2018.
Cai, W. J. and Cowan, T.: Trends in Southern Hemisphere circulation in IPCC AR4 models over 1950–99: Ozone depletion versus greenhouse forcing, J. Climate, 20, 681–693, https://doi.org/10.1175/jcli4028.1, 2007.
Cai, W. J., Wang, G. J., Dewitte, B., Wu, L. X., Santoso, A., Takahashi, K., Yang, Y., Carreric, A., and McPhaden, M. J.: Increased variability of eastern Pacific El Niño under greenhouse warming, Nature, 564, 201–206, https://doi.org/10.1038/s41586-018-0776-9, 2018.
Cai, W. J., Santoso, A., Collins, M., Dewitte, B., Karamperidou, C., Kug, J. S., Lengaigne, M., McPhaden, M. J., Stuecker, M. F., Taschetto, A. S., Timmermann, A., Wu, L. X., Yeh, S. W., Wang, G. J., Ng, B., Jia, F., Yang, Y., Ying, J., Zheng, X. T., Bayr, T., Brown, J. R., Capotondi, A., Cobb, K. M., Gan, B., Geng, T., Ham, Y.-G., Jin, F.-F., Jo, H.-S., Li, X., Lin, X., McGregor, S., Park, J.-H., Stein, K., Yang, K., Zhang L., and Zhong, W. X.: Changing El Nino-Southern Oscillation in a warming climate, Nat. Rev. Earth & Environment, 2, 628–644, https://doi.org/10.1038/s43017-021-00199-z, 2021.
Caldwell, P. M., Mametjanov, A., Tang, Q., Van Roekel, L. P., Golaz, J. C., Lin, W. Y., Bader, D. C., Keen, N. D., Feng, Y., Jacob, R., Maltrud, M. E., Roberts, A. F., Taylor, M. A., Veneziani, M., Wang, H. L., Wolfe, J. D., Balaguru, K., Cameron-Smith, P., Dong, L., A., S., Klein, L., Leung, L. R., Li, H.-Y., Li, Q., Liu, X., Neale, R. B., Pinheiro, M., Qian, Y., Ullrich, P. A., Xie, S., Yang, Y., Zhang, Y., Zhang, K., and Zhou, T.: The DOE E3SM Coupled Model Version 1: Description and Results at High Resolution, J. Adv. Model. Earth Sy., 11, 4095–4146, https://doi.org/10.1029/2019ms001870, 2019.
Camargo, S. J.: Global and Regional Aspects of Tropical Cyclone Activity in the CMIP5 Models, J. Climate, 26, 9880–9902, https://doi.org/10.1175/jcli-d-12-00549.1, 2013.
Carmack, E. C., Yamamoto-Kawai, M., Haine, T. W. N., Bacon, S., Bluhm, B. A., Lique, C., Melling, H., Polyakov, I. V., Straneo, F., Timmermans, M. L., and Williams, W. J.: Freshwater and its role in the Arctic Marine System: Sources, disposition, storage, export, and physical and biogeochemical consequences in the Arctic and global oceans, J. Geophys. Res.-Biogeo., 121, 675–717, https://doi.org/10.1002/2015jg003140, 2016.
Chang, C.-W. J., Tseng, W.-L., Hsu, H.-H., Keenlyside, N., and Tsuang, B.-J.: The Madden-Julian Oscillation in awarmer world Geophys. Res. Lett., 42, 6034–6042, https://doi.org/10.1002/2015GL065095, 2015.
Chang, P., Zhang, S. Q., Danabasoglu, G., Yeager, S. G., Fu, H. H., Wang, H., Castruccio, F. S., Chen, Y. H., Edwards, J., Fu, D., Jia, Y. L., Laurindo, L. C., Liu, X., Rosenbloom, N., Small, R. J., Xu, G. P., Zeng, Y. H., Zhang, Q. Y., Bacmeister, J., Bailey, D. A., Duan, X., DuVivier, A. K., Li, D., Li, Y., Neale, R., Stössel, A., Wang, L., Zhuang, Y., Baker, A., Bates, S., Dennis, J., Diao, X., Gan, B., Gopal, A., Jia, D., Jing, Z., Ma, X., Saravanan, R., Strand, W. G., Tao, J., Yang, H., Wang, X., Wei, Z., and Wu, L. X.: An Unprecedented Set of High-Resolution Earth System Simulations for Understanding Multiscale Interactions in Climate Variability and Change, J. Adv. Model. Earth Sy., 12, e2020MS00229, https://doi.org/10.1029/2020ms002298, 2020.
Chen, G. W., Ling, J., Zhang, R. W., Xiao, Z. N., and Li, C. Y.: The MJO From CMIP5 to CMIP6: Perspectives From Tracking MJO Precipitation, Geophys. Res. Lett., 49, e2021GL095241, https://doi.org/10.1029/2021gl095241, 2022.
Chu, J. E., Lee, S. S., Timmermann, A., Wengel, C., Stuecker, M. F., and Yamaguchi, R.: Reduced tropical cyclone densities and ocean effects due to anthropogenic greenhouse warming, Sci. Adv., 6, eabd5109, https://doi.org/10.1126/sciadv.abd5109, 2020.
Chung, E. S., Ha, K. J., Timmermann, A., Stuecker, M. F., Bodai, T., and Lee, S. K.: Cold-Season Arctic Amplification Driven by Arctic Ocean-Mediated Seasonal Energy Transfer, Earth's Future, 9, e2020EF001898, https://doi.org/10.1029/2020ef001898, 2021.
Collins, M., An, S. I., Cai, W. J., Ganachaud, A., Guilyardi, E., Jin, F. F., Jochum, M., Lengaigne, M., Power, S., Timmermann, A., Vecchi, G., and Wittenberg, A.: The impact of global warming on the tropical Pacific ocean and El Nino, Nat. Geosci., 3, 391–397, https://doi.org/10.1038/ngeo868, 2010.
Craig, A., Valcke, S., and Coquart, L.: Development and performance of a new version of the OASIS coupler, OASIS3-MCT_3.0, Geosci. Model Dev., 10, 3297–3308, https://doi.org/10.5194/gmd-10-3297-2017, 2017 (code available at: https://oasis.cerfacs.fr/en/downloads/, last access: 25 June 2025).
Cui, J. and Li, T.: Changes in MJO Characteristics and Impacts in the Past Century, J. Climate, 35, 577–590, https://doi.org/10.1175/JCLI-D-21-0306.1, 2022.
Danabasoglu, G., Lamarque, J. F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R., Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System Model Version 2 (CESM2), J. Adv. Model. Earth Sy., 12, e2019MS00191, https://doi.org/10.1029/2019ms001916, 2020.
Danilov, S., Sidorenko, D., Wang, Q., and Jung, T.: The Finite-volumE Sea ice–Ocean Model (FESOM2), Geosci. Model Dev., 10, 765–789, https://doi.org/10.5194/gmd-10-765-2017, 2017.
Danilov, S., Wang, Q., Timmermann, R., Iakovlev, N., Sidorenko, D., Kimmritz, M., Jung, T., and Schröter, J.: Finite-Element Sea Ice Model (FESIM), version 2, Geosci. Model Dev., 8, 1747–1761, https://doi.org/10.5194/gmd-8-1747-2015, 2015.
Dawson, A. and Palmer, T. N.: Simulating weather regimes: impact of model resolution and stochastic parameterization., Clim. Dynam., 44, 2177–2193, https://doi.org/10.1007/s00382-014-2238-x, 2015.
Drouin, K. L., Lozier, M. S., and Johns, W. E.: Variability and Trends of the South Atlantic Subtropical Gyre, J. Geophys. Res.-Oceans, 126, e2020JC016405, https://doi.org/10.1029/2020jc016405, 2021.
Eden, C. and Timmermann, A.: The influence of the Galapagos Islands on tropical temperatures, currents and the generation of tropical instability waves, Geophys. Res. Lett., 31, L15308, https://doi.org/10.1029/2004GL020060, 2004.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
Fereday, D. R., Chadwick, R., Knight, J. R., and Scaife, A. A.: Tropical Rainfall Linked to Stronger Future ENSO-NAO Teleconnection in CMIP5 Models, Geophys. Res. Lett., 47, e2020GL088664, https://doi.org/10.1029/2020gl088664, 2020.
Fraedrich, K.: An ENSO impact on Europe? – A review, Tellus A, 46, 541–552, https://doi.org/10.1034/j.1600-0870.1994.00015.x, 1994.
Gentilucci, M., Domenicucci, S., Barbieri, M., Hamed, Y., Hadji, R., Missaoui, R., and Pambianchi, G.: Spatial Effects of NAO on Temperature and Precipitation Anomalies in Italy, Water, 15, 1–28, https://doi.org/10.3390/w15213727, 2023.
Giorgi, F. and Gutowski, W. J.: Regional Dynamical Downscaling and the CORDEX Initiative, in: Annu. Rev. Env. Resour., edited by: Gadgil, A., and Tomich, T. P., Annu. Rev. Env. Resour., 40, 467–490, https://doi.org/10.1146/annurev-environ-102014-021217, 2015.
Giorgi, F., Coppola, E., Solmon, F., Mariotti, L., Sylla, M. B., Bi, X., Elguindi, N., Diro, G. T., Nair, V., Giuliani, G., Turuncoglu, U. U., Cozzini, S., Güttler, I., O'Brien, T. A., Tawfik, A. B., Shalaby, A., Zakey, A. S., Steiner, A. L., Stordal, F., Sloan, L. C., and Brankovic, C.: RegCM4: model description and preliminary tests over multiple CORDEX domains, Clim. Res., 52, 7–29, https://doi.org/10.3354/cr01018, 2012.
Golaz, J. C., Caldwell, P. M., Van Roekel, L. P., Petersen, M. R., Tang, Q., Wolfe, J. D., Abeshu, G., Anantharaj, V., Asay-Davis, X. S., Bader, D. C., Baldwin, S. A., Bisht, G., Bogenschutz, P. A., Branstetter, M., Brunke, M. A., Brus, S. R., Burrows, S. M., Cameron-Smith, P. J., Donahue, A. S., Deakin, M., Easter, R. C., Katherine J. E., Feng, Y., Flannerr, M., Foucar, J. G., Fyke, J. G., Griffin, B. M., Hannay, C., Harrop, B. E., Hoffman, M. J., Hunke, E. C., Jacob, R. L., Jacobsen, D. W., Jeffery, N., Jones, P. W., Keen, N. D., Klein, S. A., Larson, V. E., Leung, L. R., Li, H.-Y., Lin, W., Lipscomb, W. H., Ma, P.-L., Mahajan, S., Maltrud, M. E., Mametjanov, A., McClean, J. L., McCoy, R. B., Neale, R. B., Price, S. F., Qian, Y., Rasch, P. J., Eyre, J. E. J. R., Riley, W. J., Ringler, T. D., Roberts, A. F., Roesler, E. L., Salinger, A. G., Shaheen, Z., Shi, X., Singh, B., Tang, J., Taylor, M. A., Thornton, P. E., Turner, A. K., Veneziani, M., Wan, H., Wang, H., Wang, S., Williams, D. N., Wolfram, P. J., Worley, P. H., Xie, S., Yang, Y., Yoon, J.-H., Zelinka, M. D., Zender, C. S., Zeng, X., Zhang, C., Zhang, K., Zhang, Y., Zheng, X., Zhou, T., and Zhu, Q.: The DOE E3SM Coupled Model Version 1: Overview and Evaluation at Standard Resolution, J. Adv. Model. Earth Sy., 11, 2089–2129, https://doi.org/10.1029/2018ms001603, 2019.
Good, S. A., Martin, M. J., and Rayner, N. A.: EN4: quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates, J. Geophys. Res.-Oceans, 118, 6704–6716, https://doi.org:10.1002/2013JC009067, 2013.
Gutowski Jr., W. J., Giorgi, F., Timbal, B., Frigon, A., Jacob, D., Kang, H.-S., Raghavan, K., Lee, B., Lennard, C., Nikulin, G., O'Rourke, E., Rixen, M., Solman, S., Stephenson, T., and Tangang, F.: WCRP COordinated Regional Downscaling EXperiment (CORDEX): a diagnostic MIP for CMIP6, Geosci. Model Dev., 9, 4087–4095, https://doi.org/10.5194/gmd-9-4087-2016, 2016.
Haarsma, R. J., Roberts, M. J., Vidale, P. L., Senior, C. A., Bellucci, A., Bao, Q., Chang, P., Corti, S., Fučkar, N. S., Guemas, V., von Hardenberg, J., Hazeleger, W., Kodama, C., Koenigk, T., Leung, L. R., Lu, J., Luo, J.-J., Mao, J., Mizielinski, M. S., Mizuta, R., Nobre, P., Satoh, M., Scoccimarro, E., Semmler, T., Small, J., and von Storch, J.-S.: High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6, Geosci. Model Dev., 9, 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, 2016.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 monthly averaged data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.f17050d7, 2023.
Hohenegger, C., Korn, P., Linardakis, L., Redler, R., Schnur, R., Adamidis, P., Bao, J., Bastin, S., Behravesh, M., Bergemann, M., Biercamp, J., Bockelmann, H., Brokopf, R., Brüggemann, N., Casaroli, L., Chegini, F., Datseris, G., Esch, M., George, G., Giorgetta, M., Gutjahr, O., Haak, H., Hanke, M., Ilyina, T., Jahns, T., Jungclaus, J., Kern, M., Klocke, D., Kluft, L., Kölling, T., Kornblueh, L., Kosukhin, S., Kroll, C., Lee, J., Mauritsen, T., Mehlmann, C., Mieslinger, T., Naumann, A. K., Paccini, L., Peinado, A., Praturi, D. S., Putrasahan, D., Rast, S., Riddick, T., Roeber, N., Schmidt, H., Schulzweida, U., Schütte, F., Segura, H., Shevchenko, R., Singh, V., Specht, M., Stephan, C. C., von Storch, J.-S., Vogel, R., Wengel, C., Winkler, M., Ziemen, F., Marotzke, J., and Stevens, B.: ICON-Sapphire: simulating the components of the Earth system and their interactions at kilometer and subkilometer scales, Geosci. Model Dev., 16, 779–811, https://doi.org/10.5194/gmd-16-779-2023, 2023.
Holmes, R. M., McGregor, S., Santoso, A., and England, M. H.: Contribution of tropical instability waves to ENSO irregularity, Clim. Dynam., 52, 1837–1855, https://doi.org/10.1007/s00382-018-4217-0, 2019.
Huang, B., Liu, C., Banzon, V., Freeman, E., Graham, G., Hankins, B., Smith, T., and Zhang, H.-M. : Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1, J. Climate, 34, 2923–2939, https://doi.org/10.1175/JCLI-D-20-0166.1, 2021a (data available at: https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html, last access: 21 April 2024).
Huang, L., Lee, S. S., and Timmermann, A.: Caspian Sea and Black Sea Response to Greenhouse Warming in a High-Resolution Global Climate Model, Geophys. Res. Lett., 48, e2020GL09027, https://doi.org/10.1029/2020gl090270, 2021b.
Huijnen, V., Le Sager, P., Köhler, M. O., Carver, G., Rémy, S., Flemming, J., Chabrillat, S., Errera, Q., and van Noije, T.: OpenIFS/AC: atmospheric chemistry and aerosol in OpenIFS 43r3, Geosci. Model Dev., 15, 6221–6241, https://doi.org/10.5194/gmd-15-6221-2022, 2022.
Hurrell, J. W. and Deser, C.: North Atlantic climate variability: The role of the North Atlantic Oscillation, J. Marine Syst., 79, 231–244, https://doi.org/10.1016/j.jmarsys.2009.11.002, 2009.
IBS Center for Climate Physics: CESM high-resolution data request form, https://ibsclimate.org/research/ultra-high-resolution-climate-simulation-project/cesm-high-resolution-data-request-form, last access: 15 April 2025.
IPCC: Annex I: Atlas of Global and Regional Climate Projections, Cambridge University Press, 1311–1394, https://doi.org/10.1017/CBO9781107415324.029, 2014.
IPCC: Chapter 3: Human Influence on the Climate System, Cambridge University Press, 423–552, https://doi.org/10.1017/9781009157896.005, 2023.
Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer, L. M., Braun, A., Colette, A., Déqué, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A., Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kröner, N. Kotlarski, S., Kriegsmann, A., Martin, E., van Meijgaard, E., Moseley, C.,, Pfeifer, S., Preuschmann, S., Radermacher, C., Radtke, K., Rechid, D., Rounsevell, M., Samuelsson, P., Somot, S., Soussana, J.-F., Teichmann, C., Valentini, R., Vautard, R., Weber, B., and Yiou, P.: EURO-CORDEX: new high-resolution climate change projections for European impact research, Reg. Environ. Change, 14, 563–578, https://doi.org/10.1007/s10113-013-0499-2, 2014.
Jebeile, J.: From regional climate models to usable information, Climate Change, 177, 53, https://doi.org/10.1007/s10584-024-03693-7, 2024.
Jerez, S., Trigo, R. M., Vicente-Serrano, S. M., Pozo-Vázquez, D., Lorente-Plazas, R., Lorenzo-Lacruz, J., Santos-Alamillos, F., and Montávez, J. P.: The Impact of the North Atlantic Oscillation on Renewable Energy Resources in Southwestern Europe, J. Appl. Meteorol. Clim., 52, 2204–2225, https://doi.org/10.1175/jamc-d-12-0257.1, 2013.
John, A., Douville, H., Ribes, A., and Yiou, P.: Quantifying CMIP6 model uncertainties in extreme precipitation projections, Weather Clim. Extremes, 36, 100435, https://doi.org/10.1016/j.wace.2022.100435, 2022.
Jung, T., Miller, M. J., Palmer, T. N., Towers, P., Wedi, N., Achuthavarier, D., Adams, J. M., Altshuler, E. L., Cash, B. A., Kinter, J. L., Marx, L., Stan, C., and Hodges, K. I.: High-Resolution Global Climate Simulations with the ECMWF Model in Project Athena: Experimental Design, Model Climate, and Seasonal Forecast Skill, J. Climate, 25, 3155–3172, https://doi.org/10.1175/jcli-d-11-00265.1, 2012.
Kato, S., Rose, F. G., Rutan, D. A., Thorsen, T. J., Loeb, N. G., Doelling, D. R., Huang, X. L., Smith, W. L., Su, W. Y., and Ham, S. H.: Surface Irradiances of Edition 4.0 Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Data Product, J. Climate, 31, 4501–4527, https://doi.org/10.1175/jcli-d-17-0523.1, 2018.
Kay, J., Holland, M., and Jahn, A.: Inter-annual to multi-decadal Arctic sea ice extent trends in a warming world, Geophys. Res. Lett., 38, L15708, https://doi.org/10.1029/2011GL048008, 2011.
Keil, P., Mauritsen, T., Jungclaus, J., Hedemann, C., Olonscheck, D., and Ghosh, R.: Multiple drivers of the North Atlantic warming hole, Nat. Clim. Change, 10, 667–671, https://doi.org/10.1038/s41558-020-0819-8, 2020.
Kim, H., Timmermann, A., Lee, S. S., and Schloesser, F.: Rainfall and Salinity Effects on Future Pacific Climate Change, Earth's Future, 11, e2022EF003457, https://doi.org/10.1029/2022ef003457, 2023.
Kinter, J. L., Cash, B., Achuthavarier, D., Adams, J., Altshuler, E., Dirmeyer, P., Doty, B., Huang, B., Jin, E. K., Marx, L., Manganello, J., Stan, C., Wakefield, T., Palmer, T., Hamrud, M., Jung, T., Miller, M., Towers, P., Wedi, N., Satoh, M., Tomita, H., Kodama, C., Nasuno, T., Oouchi, K., Yamada, Y., Taniguchi, H., Andrews, P., Baer, T., Ezell, M., Halloy, C., John, D., Loftis, B., Mohr, R., and Wong, K.: Revolutionizing Climate Modeling with Project Athena: A Multi-Institutional, International Collaboration, B. Am. Meteorol. Soc., 94, 231–245, https://doi.org/10.1175/bams-d-11-00043.1, 2013.
Kirschbaum, D., Kapnick, S. B., Stanley, T., and Pascale, S.: Changes in Extreme Precipitation and Landslides Over High Mountain Asia, Geophys. Res. Lett., 47, e2019GL085347, https://doi.org/10.1029/2019gl085347, 2020.
Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., and Neumann, C. J.: The International Best Track Archive for Climate Stewardship (IBTrACS), B. Am. Meteorol. Soc., 91, 363–376, https://doi.org/10.1175/2009bams2755.1, 2010.
Knutson, T. R., Sirutis, J. J., Zhao, M., Tuleya, R. E., Bender, M., Vecchi, G. A., Villarini, G., and Chavas, D.: Global Projections of Intense Tropical Cyclone Activity for the Late Twenty-First Century from Dynamical Downscaling of CMIP5/RCP4.5 Scenarios, J. Climate, 28, 7203–7224, https://doi.org/10.1175/jcli-d-15-0129.1, 2015.
Koldunov, N. V., Aizinger, V., Rakowsky, N., Scholz, P., Sidorenko, D., Danilov, S., and Jung, T.: Scalability and some optimization of the Finite-volumE Sea ice–Ocean Model, Version 2.0 (FESOM2), Geosci. Model Dev., 12, 3991–4012, https://doi.org/10.5194/gmd-12-3991-2019, 2019a.
Koldunov, N. V., Danilov, S., Sidorenko, D., Hutter, N., Losch, M., Goessling, H., Rakowsky, N., Scholz, P., Sein, P., Wang, Q., and Jung, T.: Fast EVP solutions in a high-resolution sea ice model, J. Adv. Model. Earth Sy., 11, 1269–1284, https://doi.org/10.1029/2018MS001485, 2019b.
Komen, G., Cavaleri, L., Donelan, M., Hasselmann, K., Hasselmann, S., and Janssen, P.: Dynamics and modelling of ocean waves, Cambridge University Press, UK, https://doi.org/10.1017/CBO9780511628955, 1996.
Krebs, U. and Timmermann, A.: Tropical air-sea interactions accelerate the recovery of the Atlantic Meridional Overturning Circulation after a major shutdown, J. Climate, 20, 4940–4956, https://doi.org/10.1175/jcli4296.1, 2007.
Lang, S. T. K., Dawson, A., Diamantakis, M., Dueben, P., Hatfield, S., Leutbecher, M., Palmer, T., Prates, F., Roberts, C. D., Sandu, I., and Wedi, N.: More accuracy with less precision, Q. J. Roy. Meteor. Soc., 147, 4358–4370, https://doi.org/10.1002/qj.4181, 2021.
Lavergne, T. and Down, E.: A climate data record of year-round global sea-ice drift from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF), Earth Syst. Sci. Data, 15, 5807–5834, https://doi.org/10.5194/essd-15-5807-2023, 2023.
Lavergne, T., Sørensen, A. M., Kern, S., Tonboe, R., Notz, D., Aaboe, S., Bell, L., Dybkjær, G., Eastwood, S., Gabarro, C., Heygster, G., Killie, M. A., Brandt Kreiner, M., Lavelle, J., Saldo, R., Sandven, S., and Pedersen, L. T.: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records, The Cryosphere, 13, 49–78, https://doi.org/10.5194/tc-13-49-2019, 2019.
Le, P. V. V., Guilloteau, C., Mamalakis, A., and Foufoula-Georgiou, E.: Underestimated MJO Variability in CMIP6 Models, Geophys. Res. Lett., 48, e2020GL092244, https://doi.org/10.1029/2020gl092244, 2021.
Lee, C. Y., Tippett, M. K., Sobel, A. H., and Camargo, S. J.: Rapid intensification and the bimodal distribution of tropical cyclone intensity, Nat. Commun., 7, 10625, https://doi.org/10.1038/ncomms10625, 2016.
Lesnikowski, A., Ford, J., Biesbroek, R., Berrang-Ford, L., and Heymann, S. J.: National-level progress on adaptation, Nat. Clim. Change, 6, 261–264, https://doi.org/10.1038/nclimate2863, 2016.
Li, G. and Xie, S. P.: Tropical Biases in CMIP5 Multimodel Ensemble: The Excessive Equatorial Pacific Cold Tongue and Double ITCZ Problems, J. Climate, 27, 1765–1780, https://doi.org/10.1175/jcli-d-13-00337.1, 2014.
Li, L., Dong, L., Xie, J., Tang, Y., Xie, F., Guo, Z., Liu, H., Feng, T., Wang, L., Pu, Y., Sun, W., Xia, K., Liu, L., Xie, Z., Wang, Y., Wang, L., Shi, X., Jia, B., Liu, J., and Wang, B.: The GAMIL3: Model description and evaluation, J. Geophys. Res.-Atmos., 125, e2020JD032574, https://doi.org/10.1029/2020jd032574, 2020.
Lin, J.-L.: The double-ITCZ problem in IPCC AR4 coupled GCMs: Ocean-atmosphere feedback analysis, J. Climate, 20, 4497–4525, https://doi.org/10.1175/jcli4272.1, 2007.
Lindzen, R. S. and Nigam, S.: On the Role of Sea Surface Temperature Gradients in Forcing Low-Level Winds and Convergence in the Tropics, J. Atmos. Sci., 44, 2418–2436, https://doi.org/10.1175/1520-0469(1987)044<2418:otross>2.0.co;2, 1987.
Liu, Z., Lee, S. S., Nellikkattil, A. B., Lee, J. Y., Dai, L., Ha, K. J., and Franzke, C. L. E.: The East Asian Summer Monsoon Response to Global Warming in a High Resolution Coupled Model: Mean and Extremes, Asia-Pac. J. Atmos. Sci., 59, 29–45, https://doi.org/10.1007/s13143-022-00285-2, 2023.
Lumpkin, R. and Flament, P. J.: Extent and Energetics of the Hawaiian Lee Countercurrent, Oceanography, 26, 58–65, https://doi.org/10.5670/oceanog.2013.05, 2013.
Luo, R., Ding, Q., Baxter, I., Chen, X., Wu, Z., Bushuk, M., and Wang, H.: Uncertain role of clouds in shaping summertime atmosphere-sea ice connections in reanalyses and CMIP6 models, Clim. Dynam., 61, 1973–1994, https://doi.org/10.1007/s00382-023-06785-9, 2023.
Majumdar, S. J., Magnusson, L., Bechtold, P., Bidlot J.-R., and Doyle, J. D.: Advanced Tropical Cyclone Prediction Using the Experimental Global ECMWF and Operational Regional COAMPS-TC Systems, Mon. Weather Rev., 151, 2029–2048, https://doi.org/10.1175/MWR-D-22-0236.1, 2023.
Malardel, S., Wedi, N., Deconinck, W., Diamantakis, M., Kuehnlein, C., Mozdzynski, G., Mats Hamrud, and Smolarkiewicz, P.: A new grid for the IFS, ECMWF Newsletter, 146, 23–28, https://doi.org/10.21957/zwdu9u5i, 2015.
Martínez-Moreno, J., Hogg, A. M., England, M. H., Constantinou, N. C., Kiss, A. E., and Morrison, A. K.: Global changes in oceanic mesoscale currents over the satellite altimetry record, Nat. Clim. Change, 11, 397–403, https://doi.org/10.1038/s41558-021-01006-9, 2021.
Maximenko, N. A., Melnichenko, O. V., Niiler, P. P., and Sasaki, H.: Stationary mesoscale jet-like features in the ocean, Geophys. Res. Lett., 35, L08603, https://doi.org/10.1029/2008gl033267, 2008.
McKenna, C. M. and Maycock, A. C.: The role of the North Atlantic Oscillation for projections of winter mean precipitation in Europe, Geophys. Res. Lett., 49, e2022GL099083, https://doi.org/10.1029/2022GL099083, 2022.
Meehl, G. A., Senior, C. A., Eyring, V., Flato, G., Lamarque, J. F., Stouffer, R. J., Taylor, K. E., and Schlund, M.: Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models, Sci. Adv., 6, eaba1981, https://doi.org/10.1126/sciadv.aba1981, 2020.
Meinshausen, M., Vogel, E., Nauels, A., Lorbacher, K., Meinshausen, N., Etheridge, D. M., Fraser, P. J., Montzka, S. A., Rayner, P. J., Trudinger, C. M., Krummel, P. B., Beyerle, U., Canadell, J. G., Daniel, J. S., Enting, I. G., Law, R. M., Lunder, C. R., O'Doherty, S., Prinn, R. G., Reimann, S., Rubino, M., Velders, G. J. M., Vollmer, M. K., Wang, R. H. J., and Weiss, R.: Historical greenhouse gas concentrations for climate modelling (CMIP6), Geosci. Model Dev., 10, 2057–2116, https://doi.org/10.5194/gmd-10-2057-2017, 2017.
Meurdesoif, Y.: XIOS 2.0 (Revision 1297), Zenodo [code], https://doi.org/10.5281/zenodo.4905653, 2017.
Minobe, S., Kuwano-Yoshida, A., Komori, N., Xie, S. P., and Small, R. J.: Influence of the Gulf Stream on the troposphere, Nature, 452, 206–209, https://doi.org/10.1038/nature06690, 2008.
Moon, J.-Y., Streffing, J., Lee, S.-S., Semmler, T., Andreìs-Martiìnez, M., Chen, J., Cho, E.-B., Chu, J.-E., Franzke, C., Gärtner, J. P.,, Ghosh, R., Hegewald, J., Hong, S., Koldunov, N., Lee, J.-Y., Lin, Z., Liu, C., Loza, S., Park, W., Roh, W., Sein, D. V., Sharma, S., Sidorenko, D., Son, J.-H., Stuecker, M. F., Wang, Q., Yi, G., Zapponini, M., Jung, T., and Timmermann, A.: Code and data for reproducing figures with AW-CM3 HR simulations in J.-Y. Moon et al. “Earth's Future Climate and its Variability simulated at 9 km global resolution”, Climate data server of the IBS Center for Climate Physics [data set], https://doi.org/10.22741/ICCP.20240001, 2024a.
Moon, J.-Y., Streffing, J., Lee, S.-S., Semmler, T., Andreìs-Martiìnez, M., Chen, J., Cho, E.-B., Chu, J.-E., Franzke, C., Gärtner, J. P.,, Ghosh, R., Hegewald, J., Hong, S., Koldunov, N., Lee, J.-Y., Lin, Z., Liu, C., Loza, S., Park, W., Roh, W., Sein, D. V., Sharma, S., Sidorenko, D., Son, J.-H., Stuecker, M. F., Wang, Q., Yi, G., Zapponini, M., Jung, T., and Timmermann, A.: Supplementary videos with AW-CM3 HR simulations in J.-Y. Moon et al. “Earth's Future Climate and its Variability simulated at 9 km global resolution”, Climate data server of the IBS Center for Climate Physics [video], https://doi.org/10.22741/ICCP.20240002, 2024b.
Murtugudde, R., Seager, R., and Busalacchi, A.: Simulation of the tropical oceans with an ocean GCM coupled to an atmospheric mixed-layer model, J. Climate, 9, 1795–1815, https://doi.org/10.1175/1520-0442(1996)009<1795:sottow>2.0.co;2, 1996.
Nellikkattil, A. B., Lee, J. Y., Guan, B., Timmermann, A., Lee, S. S., Chu, J. E., and Lemmon, D.: Increased amplitude of atmospheric rivers and associated extreme precipitation in ultra-high-resolution greenhouse warming simulations, Commun. Earth Env., 4, 313, https://doi.org/10.1038/s43247-023-00963-7, 2023.
NOAA PSL: NOAA Interpolated OLR data, Boulder, Colorado, USA, https://psl.noaa.gov, last access: 20 July 2024.
Notz, D. and Community, S.: Arctic Sea Ice in CMIP6, Geophys. Res. Lett., 47, e2019GL086749, https://doi.org/10.1029/2019gl086749, 2020.
Pacchetti, M. B., Dessai, S., Bradley, S., and Stainforth, D. A.: Assessing the Quality of Regional Climate Information, B. Am. Meteorol. Soc., 102, E476–E491, https://doi.org/10.1175/bams-d-20-0008.1, 2021.
Petzold, J., Hawxwell, T., Jantke, K., Gresse, E. G., Mirbach, C., Ajibade, I., Bhadwal, S., Bowen, K., Fischer, A. P., Joe, E. T., Kirchhoff, C. J., Mach, K. J., Reckien, D., Segnon, A. C., Singh, C., Ulibarri, N., Campbell, D., Cremin, E., Färber, L., Hegde, G., Jeong, J., Nunbogu, A. M., Pradhan, H. K., Schröder, L. S., Shah, M. A. R., Reese, P., Sultana, F., Tello, C., Xu, J., The Global Adaptation Mapping Initiative Team, and Garschagen, M. , and Global Adaptation, M.: A global assessment of actors and their roles in climate change adaptation, Nat. Clim. Change, 13, 1250–1257, https://doi.org/10.1038/s41558-023-01824-z, 2023.
Pfahl, S., O'Gorman, P. A., and Fischer, E. M.: Understanding the regional pattern of projected future changes in extreme precipitation, Nat. Clim. Change, 7, 423–427, https://doi.org/10.1038/nclimate3287, 2017.
Pozo-Vázquez, D., Esteban-Parra, M. J., Rodrigo, F. S., and Castro-Díez, Y.: The association between ENSO and winter atmospheric circulation and temperature in the North Atlantic region, J. Climate, 14, 3408–3420, https://doi.org/10.1175/1520-0442(2001)014<3408:tabeaw>2.0.co;2, 2001.
Raavi, P. H., Chu, J. E., Timmermann, A., Lee, S. S., and Walsh, K. J. E.: Moisture control of tropical cyclones in high-resolution simulations of paleoclimate and future climate, Nat. Commun., 14, 6426, https://doi.org/10.1038/s41467-023-42033-8, 2023.
Rackow, T., Pedruzo-Bagazgoitia, X., Becker, T., Milinski, S., Sandu, I., Aguridan, R., Bechtold, P., Beyer, S., Bidlot, J., Boussetta, S., Deconinck, W., Diamantakis, M., Dueben, P., Dutra, E., Forbes, R., Ghosh, R., Goessling, H. F., Hadade, I., Hegewald, J., Jung, T., Keeley, S., Kluft, L., Koldunov, N., Koldunov, A., Kölling, T., Kousal, J., Kühnlein, C., Maciel, P., Mogensen, K., Quintino, T., Polichtchouk, I., Reuter, B., Sármány, D., Scholz, P., Sidorenko, D., Streffing, J., Sützl, B., Takasuka, D., Tietsche, S., Valentini, M., Vannière, B., Wedi, N., Zampieri, L., and Ziemen, F.: Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5 and NEMOv3.4, Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, 2025.
Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L. V., Rowell, D. P., Kent, E. C., and Kaplan, A.: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century, J. Geophys. Res.-Atmos., 108, 4407, https://doi.org/10.1029/2002JD002670, 2003 (data available at: https://www.metoffice.gov.uk/hadobs/hadisst, last access: 21 April 2024).
Reichler, T. and Kim, J.: How Well Do Coupled Models Simulate Today's Climate?, B. Am. Meteorol. Soc., 89, 303–312, https://doi.org/10.1175/BAMS-89-3-303, 2008.
Richards, K. J., Maximenko, N. A., Bryan, F. O., and Sasaki, H.: Zonal jets in the Pacific Ocean, Geophys. Res. Lett., 33, L03605, https://doi.org/10.1029/2005gl024645, 2006.
Richter, I. and Xie, S. P.: Moisture transport from the Atlantic to the Pacific basin and its response to North Atlantic cooling and global warming, Clim. Dynam., 35, 551–566, https://doi.org/10.1007/s00382-009-0708-3, 2010.
Roberts, M. J., Camp, J., Seddon, J., Vidale, P. L., Hodges, K., Vanniere, B., Mecking, J., Haarsma, R., Bellucci, A., Scoccimarro, E., Caron, L. P., Chauvin, F., Terray, L., Valcke, S., Moine, M. P., Putrasahan, D., Roberts, C., Senan, R., Zarzycki, C., and Ullrich, P.: Impact of Model Resolution on Tropical Cyclone Simulation Using the HighResMIP-PRIMAVERA Multimodel Ensemble, J. Climate, 33, 2557–2583, https://doi.org/10.1175/jcli-d-19-0639.1, 2020a.
Roberts, M. J., Camp, J., Seddon, J., Vidale, P. L., Hodges, K., Vannière, B., Mecking, J., Haarsma, R., Bellucci, A., Scoccimarro, E., Caron, L. P., Chauvin, F., Terray, L., Valcke, S., Moine, M. P., Putrasahan, D., Roberts, C. D., Senan, R., Zarzycki, C., Ullrich, P., Yamada, Y., Mizuta, R., Kodama, C., Fu, D., Zhang, Q., Danabasoglu, G., Rosenbloom, N., Wang, H., and Wu, L.: Projected Future Changes in Tropical Cyclones Using the CMIP6 HighResMIP Multimodel Ensemble, Geophys. Res. Lett., 47, e2020GL088662, https://doi.org/10.1029/2020gl088662, 2020b.
Rodgers, K. B., Lee, S.-S., Rosenbloom, N., Timmermann, A., Danabasoglu, G., Deser, C., Edwards, J., Kim, J.-E., Simpson, I. R., Stein, K., Stuecker, M. F., Yamaguchi, R., Bódai, T., Chung, E.-S., Huang, L., Kim, W. M., Lamarque, J.-F., Lombardozzi, D. L., Wieder, W. R., and Yeager, S. G.: Ubiquity of human-induced changes in climate variability, Earth Syst. Dynam., 12, 1393–1411, https://doi.org/10.5194/esd-12-1393-2021, 2021.
Russell, J. L., Dixon, K. W., Gnanadesikan, A., Stouffer, R. S., and Toggweiler, J. R.: The Southern Hemisphere Westerlies in a Warming World: Propping Open the Door to the Deep Ocean, J. Climate, 19, 6382–6390, https://doi.org/10.1175/JCLI3984.1, 2006.
Sasaki, H., Xie, S. P., Taguchi, B., Nonaka, M., and Masumoto, Y.: Seasonal variations of the Hawaiian Lee Countercurrent induced by the meridional migration of the trade winds, Ocean Dynam., 60, 705–715, https://doi.org/10.1007/s10236-009-0258-6, 2010.
Savita, A., Kjellsson, J., Pilch Kedzierski, R., Latif, M., Rahm, T., Wahl, S., and Park, W.: Assessment of climate biases in OpenIFS version 43r3 across model horizontal resolutions and time steps, Geosci. Model Dev., 17, 1813–1829, https://doi.org/10.5194/gmd-17-1813-2024, 2024.
Scholz, P., Sidorenko, D., Gurses, O., Danilov, S., Koldunov, N., Wang, Q., Sein, D., Smolentseva, M., Rakowsky, N., and Jung, T.: Assessment of the Finite-volumE Sea ice-Ocean Model (FESOM2.0) – Part 1: Description of selected key model elements and comparison to its predecessor version, Geosci. Model Dev., 12, 4875–4899, https://doi.org/10.5194/gmd-12-4875-2019, 2019.
Scholz, P., Sidorenko, D., Koldunov, N., Hegewald, J., Rakowski, N., Streffing, J., Rackow, T., Gürses, Ö., Goessling, H., Guibert, D., Wang, Q., Wekerle, C., van den Oord, G., Berlin, J., Andrés-Martínez, M., Gierz, P., and Cheedela, S. K.: FESOM/fesom2: AWI-CM3 version 3.1 (AWI-CM3_v3.1), Zenodo [code], https://doi.org/10.5281/zenodo.6714838, 2022.
Shi, J., Stepanek, C., Sein, D., Streffing, J., and Lohmann, G.: East Asian summer precipitation in AWI-CM3: Comparison with observations and CMIP6 models, Int. J. Climatol., 43, 4083–4098, https://doi.org/10.1002/joc.8075, 2023.
Small, R. J., Xie, S. P., and Wang, Y. Q.: Numerical simulation of atmospheric response to Pacific tropical instability waves, J. Climate, 16, 3723–3741, https://doi.org/10.1175/1520-0442(2003)016<3723:nsoart>2.0.co;2, 2003.
Stein, K., Timmermann, A., Schneider, N., Jin, F.-F., and Stuecker, M. F.: ENSO Seasonal Synchronization Theory, J. Climate, 27, 5285–5310, https://doi.org/10.1175/JCLI-D-13-00525.1, 2014.
Stommel, H.: Thermohaline Convection with Two Stable Regimes of Flow, Tellus, 13, 224–230, 1961.
Stowasser, M. and Hamilton, K.: Relationship between Shortwave Cloud Radiative Forcing and Local Meteorological Variables Compared in Observations and Several Global Climate Models, J. Climate, 19, 4344–4359, https://doi.org/10.1175/JCLI3875.1, 2006.
Streffing, J. and Fladich, U.: Modifications to use OpenIFS CY43R3V1 for AWI-CM3 version 3.0, Zenodo [code], https://doi.org/10.5281/zenodo.6335498, 2022.
Streffing, J., Sidorenko, D., Semmler, T., Zampieri, L., Scholz, P., Andrés-Martínez, M., Koldunov, N., Rackow, T., Kjellsson, J., Goessling, H., Athanase, M., Wang, Q., Hegewald, J., Sein, D. V., Mu, L., Fladrich, U., Barbi, D., Gierz, P., Danilov, S., Juricke, S., Lohmann, G., and Jung, T.: AWI-CM3 coupled climate model: description and evaluation experiments for a prototype post-CMIP6 model, Geosci. Model Dev., 15, 6399–6427, https://doi.org/10.5194/gmd-15-6399-2022, 2022.
Timmermann, A., An, S. I., Kug, J. S., Jin, F. F., Cai, W. J., Capotondi, A., Cobb, K. M., Lengaigne, M., McPhaden, M. J., Stuecker, M. F., Stein, K., Wittenberg, A. T., Yun, K. S., Bayr, T., Chen, H. C., Chikamoto, Y., Dewitte, B., Dommenget, D., Grothe, P., Guilyardi, E., Ham, Y.-G., Hayashi, M., Ineson, S., Kang, D., Kim, S., Kim, W. M., Lee, J.-Y., Li, T., Luo, J.-J., McGregor, S., Planton, Y., Power, S., Rashid, H., Ren, H.-L., Santoso, A., Takahashi, K., Todd, A., Wang, G., Wang, G., Xie, R., Yang, W.-H., Yeh, S.-W., Yoon, J., Zeller, E., and Zhang, X. B.: El Nino-Southern Oscillation complexity, Nature, 559, 535–545, https://doi.org/10.1038/s41586-018-0252-6, 2018.
Timmermann, A., Lee, S.-S., and Chu, J. E.: Ultra-high-resolution climate simulation project, Climate data server of the IBS Center for Climate Physics [data set], https://doi.org/10.22741/iccp.20200001, 2020.
Tory, K. J., Chand, S. S., Dare, R. A., and McBride, J. L.: An Assessment of a Model-, Grid-, and Basin-Independent Tropical Cyclone Detection Scheme in Selected CMIP3 Global Climate Models, J. Climate, 26, 5508–5522, https://doi.org/10.1175/jcli-d-12-00511.1, 2013a.
Tory, K. J., Chand, S. S., Dare, R. A., and McBride, J. L.: The Development and Assessment of a Model-, Grid-, and Basin-Independent Tropical Cyclone Detection Scheme, J. Climate, 26, 5493–5507, https://doi.org/10.1175/jcli-d-12-00510.1, 2013b.
Trepte, Q. Z., Minnis, P., Sun-Mack, S., Yost, C. R., Chen, Y., Jin, Z. H., Hong, G., Chang, F. L., Smith, W. L., Bedka, K. M., and Chee, T. L.: Global Cloud Detection for CERES Edition 4 Using Terra and Aqua MODIS Data, IEEE T. Geosci. Remote., 57, 9410–9449, https://doi.org/10.1109/tgrs.2019.2926620, 2019.
Vecchi, G. A., Delworth, T. L., Murakami, H., Underwood, S. D., Wittenberg, A. T., Zeng, F. R., Zhang, W., Baldwin, J. W., Bhatia, K. T., Cooke, W., He, J., Kapnick, S. B., Knutson, T. R., Villarini, G., van der Wiel, K., Anderson, W., Balaji, V., Chen, J., Dixon, K. W., Gudgel, R., Harris, L. M., Jia, L., Johnson, N. C., Lin, S.-J., Liu, M., Ng, C. H. J., Rosati, A., Smith J. A., and Yang, X.: Tropical cyclone sensitivities to CO2 doubling: roles of atmospheric resolution, synoptic variability and background climate changes, Clim. Dynam., 53, 5999–6033, https://doi.org/10.1007/s00382-019-04913-y, 2019.
Visbeck, M. H., Hurrell, J. W., Polvani, L., and Cullen, H. M.: The North Atlantic Oscillation: Past, present, and future, P. Natl. Acad. Sci. USA, 98, 12876–12877, https://doi.org/10.1073/pnas.231391598, 2001.
Wang, Q., Wekerle, C., Danilov, S., Koldunov, N., Sidorenko, D., Sein, D., Rabe, B., and Jung, T.: Arctic Sea Ice Decline Significantly Contributed to the Unprecedented Liquid Freshwater Accumulation in the Beaufort Gyre of the Arctic Ocean, Geophys. Res. Lett., 45, 4956–4964, https://doi.org/10.1029/2018GL077901, 2018.
Wanner, H., Brönnimann, S., Casty, C., Gyalistras, D., Luterbacher, J., Schmutz, C., Stephenson, D. B., and Xoplaki, E.: North Atlantic Oscillation – Concepts and studies, Surv. Geophys., 22, 321–382, https://doi.org/10.1023/a:1014217317898, 2001.
Wekerle, C., Wang, Q., Danilov, S., Jung, T., and Schröter, J.: The Canadian Arctic Archipelago throughflow in a multiresolution global model: Model assessment and the driving mechanism of interannual variability, J. Geophys. Res.-Oceans, 118, 4525–4541, https://doi.org/10.1002/jgrc.20330, 2013.
Wengel, C., Lee, S. S., Stuecker, M. F., Timmermann, A., Chu, J. E., and Schloesser, F.: Future high-resolution El Nino/Southern Oscillation dynamics, Nat. Clim. Change, 11, 758–765, https://doi.org/10.1038/s41558-021-01132-4, 2021.
Wielicki, B. A., Barkstrom, B. R., Harrison, E. F., Lee, R. B., Smith, G. L., and Cooper, J. E.: Clouds and the earth's radiant energy system (CERES): An earth observing system experiment, B. Am. Meteorol. Soc., 77, 853–868, https://doi.org/10.1175/1520-0477(1996)077<0853:catere>2.0.co;2, 1996.
Wyser, K.: EC-Earth community runoff-mapper scheme, Zenodo [code], https://doi.org/10.5281/zenodo.6335474, 2022.
Xie, S. P.: Oceanic response to the wind forcing associated with the Intertropical Convergence Zone in the northern hemisphere, J. Geophys. Res.-Oceans, 99, 20393–20402, https://doi.org/10.1029/94jc01653, 1994.
Xu, G., Chang, P., Ramachandran, S., Danabasoglu, G., Yeager, S., Small, J., Zhang, Q., Jing, Z., and Wu, L.: Impacts of Model Horizontal Resolution on Mean Sea Surface Temperature Biases in the Community Earth System Model, J. Geophys. Res.-Oceans, 127, e2022JC019065, https://doi.org/10.1029/2022JC019065, 2022.
Yang, H., Lohmann, G., Wei, W., Dima, M., Ionita, M., and Liu, J. P.: Intensification and poleward shift of subtropical western boundary currents in a warming climate, J. Geophys. Res.-Oceans, 121, 4928–4945, https://doi.org/10.1002/2015jc011513, 2016.
Yang, L. C., Franzke, C. L. E., and Fu, Z. T.: Power-law behaviour of hourly precipitation intensity and dry spell duration over the United States, Int. J. Climatol., 40, 2429–2444, https://doi.org/10.1002/joc.6343, 2020.
Ying, J., Collins, M., Cai, W. J., Timmermann, A., Huang, P., Chen, D. K., and Stein, K.: Emergence of climate change in the tropical Pacific, Nat. Clim. Change, 12, 356–364, https://doi.org/10.1038/s41558-022-01301-z, 2022.
Yun, K. S., Lee, J. Y., Timmermann, A., Stein, K., Stuecker, M. F., Fyfe, J. C., and Chung, E. S.: Increasing ENSO-rainfall variability due to changes in future tropical temperature-rainfall relationship, Commun. Earth Env., 2, 43, https://doi.org/10.1038/s43247-021-00108-8, 2021.
Zapponini, M. and Goessling, H. F.: Atmospheric destabilization leads to Arctic Ocean winter surface wind intensification, Commun. Earth Env., 5, 262, https://doi.org/10.1038/s43247-024-01428-1, 2024.
Zhang, C. D.: MADDEN-JULIAN OSCILLATION Bridging Weather and Climate, B. Am. Meteorol. Soc., 94, 1849–1870, https://doi.org/10.1175/bams-d-12-00026.1, 2013.
Zuo, H., Balmaseda, M. A., Tietsche, S., Mogensen, K., and Mayer, M.: The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment, Ocean Sci., 15, 779–808, https://doi.org/10.5194/os-15-779-2019, 2019 (data available at: https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis, last access: 21 April 2024).
Short summary
Based on a series of storm-resolving greenhouse warming simulations conducted with the AWI-CM3 model at 9 km global atmosphere and 4–25 km ocean resolution, we present new projections of regional climate change, modes of climate variability, and extreme events. The 10-year-long high-resolution simulations for the 2000s, 2030s, 2060s, and 2090s were initialized from a coarser-resolution transient run (31 km atmosphere) which follows the SSP5-8.5 greenhouse gas emission scenario from 1950–2100 CE.
Based on a series of storm-resolving greenhouse warming simulations conducted with the AWI-CM3...
Altmetrics
Final-revised paper
Preprint