Articles | Volume 18, issue 9
https://doi.org/10.5194/gmd-18-2587-2025
© 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/gmd-18-2587-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
12 May 2025
Model experiment description paper |
| 12 May 2025
| 12 May 2025
The Tropical Basin Interaction Model Intercomparison Project (TBIMIP)
Ingo Richter
CORRESPONDING AUTHOR
Research Institute for Value-Added-Information Generation, Japan Agency for Marine-Earth Science and Technology, Yokohama, 236-0001, Japan
Ping Chang
Department of Oceanography, Texas A&M University, College Station, TX, USA
Ping-Gin Chiu
Geophysical Institute, University of Bergen and Bjerknes Centre for Climate Research, Bergen, 5007, Norway
Gokhan Danabasoglu
Climate and Global Dynamics Laboratory, US National Science Foundation National Center for Atmospheric Research, Boulder, CO, USA
Takeshi Doi
Research Institute for Value-Added-Information Generation, Japan Agency for Marine-Earth Science and Technology, Yokohama, 236-0001, Japan
Dietmar Dommenget
ARC Centre of Excellence for Climate Extremes, School of Earth Atmosphere and Environment, Monash University, Clayton, VIC, 3800, Australia
Guillaume Gastineau
UMR LOCEAN, Sorbonne Université/CNRS/IRD/MNHN, Paris, France
Zoe E. Gillett
Climate Change Research Centre and ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, Australia
Climate and Global Dynamics Laboratory, US National Science Foundation National Center for Atmospheric Research, Boulder, CO, USA
Takahito Kataoka
Research Center for Environmental Modeling and Application, Japan Agency for Marine-Earth Science and Technology, Yokohama, 236-0001, Japan
Noel S. Keenlyside
Geophysical Institute, University of Bergen and Bjerknes Centre for Climate Research, Bergen, 5007, Norway
Nansen Environmental and Remote Sensing Center, Bergen, 5007, Norway
Fred Kucharski
Earth System Physics, Abdus Salam International Centre for Theoretical Physics, Trieste, Italy
Yuko M. Okumura
Institute for Geophysics, Jackson School of Geosciences, University of Texas at Austin, Austin, TX, USA
Wonsun Park
IBS Center for Climate Physics and Department of Climate System, Pusan National University, Busan, South Korea
Malte F. Stuecker
Department of Oceanography and International Pacific Research Center, University of Hawai`i at Mānoa, Honolulu, HI, USA
Andréa S. Taschetto
Climate Change Research Centre and ARC Centre of Excellence for the 21st Century Weather, University of New South Wales, Sydney, Australia
Chunzai Wang
State Key Laboratory of Tropical Oceanography, Global Ocean and Climate Research Center, Guangdong Key Laboratory of Ocean Remote Sensing, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China
Stephen G. Yeager
Climate and Global Dynamics Laboratory, US National Science Foundation National Center for Atmospheric Research, Boulder, CO, USA
Sang-Wook Yeh
Department of Marine Sciences and Convergent Engineering, Hanyang University, Ansan, South Korea
Related authors
No articles found.
Dakuan Yu, Dietmar Dommenget, Holger Pohlmann, and Wolfgang A. Müller
Geosci. Model Dev., 19, 5531–5552, https://doi.org/10.5194/gmd-19-5531-2026, https://doi.org/10.5194/gmd-19-5531-2026, 2026
Short summary
Short summary
We developed a new method to improve how a leading climate model simulates El Niño, a major driver of global weather extremes. By testing how the model responds to small changes in key atmospheric settings, we identified which processes matter most and adjusted them systematically. This approach makes the model’s behavior closer to observations and shows a promising path for building more reliable climate predictions.
Gokhan Danabasoglu, Frederic S. Castruccio, Burcu Boza, Alice M. Barthel, Arne Biastoch, Adam Blaker, Alexandra Bozec, Diego Bruciaferri, Frank O. Bryan, Eric P. Chassignet, Yao Fu, Ian Grooms, Catherine Guiavarc'h, Hakase Hayashida, Andrew McC. Hogg, Ryan M. Holmes, Doroteaciro Iovino, Andrew E. Kiss, M. Susan Lozier, Gustavo Marques, Alex Megann, Franziska U. Schwarzkopf, Dave Storkey, Luke van Roekel, Jon Wolfe, Xiaobiao Xu, and Rong Zhang
Geosci. Model Dev., 19, 5071–5117, https://doi.org/10.5194/gmd-19-5071-2026, https://doi.org/10.5194/gmd-19-5071-2026, 2026
Short summary
Short summary
A comparison of simulated and observed overturning transports across the Overturning in the Subpolar North Atlantic Program sections is presented. Eighteen ocean simulations participate in the study with resolutions ranging from coarse to eddy-resolving. The simulated transports are in general agreement with observations, with better agreement at high resolution. Analyzing overturning circulations in both depth and density space together provides a more complete picture.
Sayantani Ojha, Joakim Kjellsson, Torge Martin, Léon Chafik, Eric Maisonnave, and Wonsun Park
EGUsphere, https://doi.org/10.5194/egusphere-2026-1481, https://doi.org/10.5194/egusphere-2026-1481, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
We study how model resolution affects the climate of the North Atlantic Ocean, with a focus on heat transport. Using a new climate model that improves interactions between the ocean and atmosphere, we compare simulations with different resolutions. Higher ocean resolution improves sea surface temperature and ocean circulation. Increasing both ocean and atmospheric resolution produces a more realistic balance of heat transport due to matching grid resolutions.
Mina Kim, Rokjin J. Park, Jingi Jung, Sang-Ik Oh, Eunjo S. Ha, Jaein I. Jeong, and Sang-Wook Yeh
Atmos. Chem. Phys., 26, 6869–6888, https://doi.org/10.5194/acp-26-6869-2026, https://doi.org/10.5194/acp-26-6869-2026, 2026
Short summary
Short summary
Given that East Asia has the highest CO2 emissions in the world, understanding the region’s carbon uptake is essential for planning mitigation strategies. We estimated natural carbon sinks in East Asia from 2010 to 2019 using satellite observations and a chemical transport model within a Bayesian inversion framework. Natural sinks offset 13.6 % of fossil fuel emissions, indicating that emissions exceed the region's carbon uptake capacity and highlighting the need for stronger mitigation efforts.
Didier Swingedouw, Laura Jackson, Aixue Hu, Anastasia Romanou, Nicole C. Laureanti, Wilbert Weijer, Sina Loriani, Bette Otto-Bliesner, Ayako Abe-Ouchi, Lucas Almeida, Alessio Bellucci, Reyk Börner, Gokhan Danabasoglu, Donovan P. Dennis, Marion Devilliers, Sybren Drijfhout, Jonathan Donges, Friederike Fröb, Thomas L. Frölicher, Guillaume Gastineau, Heiko Goelzer, Chuncheng Guo, Urs Hofmann, Anna Höse, Colin Jones, Torben Koenigk, Ann Kristin Klose, Valerio Lembo, Jose Licon-Salaiz, Ken Mankoff, Virna Meccia, Irina Melnikova, Oliver Mehling, Laurie Menviel, Juliette Mignot, Jon I. Robson, Gavin A. Schmidt, Robin Smith, Yuchen Sun, Irene Trombini, Matteo Willeit, Richard Wood, Fanghua Wu, Lin Zhaohui, and Ricarda Winkelmann
EGUsphere, https://doi.org/10.5194/egusphere-2026-1698, https://doi.org/10.5194/egusphere-2026-1698, 2026
Short summary
Short summary
This study presents a plan for climate model experiments to better understand how changes in freshwater in the North Atlantic affect major ocean currents. We designed coordinated simulations to test their response to warming, added freshwater, and possible recovery after weakening. Comparing results across models and past climate evidence helps improve confidence in projections and assess risks of large ocean circulation changes.
Yinglin Mu, Jason P. Evans, Andréa S. Taschetto, and Chiara Holgate
Geosci. Model Dev., 19, 1367–1385, https://doi.org/10.5194/gmd-19-1367-2026, https://doi.org/10.5194/gmd-19-1367-2026, 2026
Short summary
Short summary
Lagrangian approaches have been increasingly employed due to their suitability for extreme events and climatological studies in finding moisture sources of precipitation. However, as these approaches track independent air parcels carrying moisture – rather than simulate processes based on governing physical equations – they rely on several underlying assumptions. This study tests these assumptions and refines the approaches to enhance their broader applicability.
Marianne Williams-Kerslake, Helene R. Langehaug, Ragnheid Skogseth, Frank Nilsen, Annette Samuelsen, Silvana Gonzalez, and Noel Keenlyside
Ocean Sci., 22, 587–607, https://doi.org/10.5194/os-22-587-2026, https://doi.org/10.5194/os-22-587-2026, 2026
Short summary
Short summary
Marine heatwaves – periods of extreme ocean temperatures – are increasing globally, posing a threat to marine ecosystems. One region where a high number of marine heatwave events per year has been observed is around Svalbard. This study characterises past marine heatwave events in this region, including their extent in terms of both distance and depth. We identified eight events west of Svalbard that were largely driven by the movement of warmer water into the region by ocean currents.
Yun-Soo Na, Sang-Wook Yeh, and Jong-Seong Kug
EGUsphere, https://doi.org/10.5194/egusphere-2026-353, https://doi.org/10.5194/egusphere-2026-353, 2026
Short summary
Short summary
Terrestrial ecosystems in East Asia play a significant role in carbon dioxide uptake, but their response remains uncertain. We demonstrate that ecosystem productivity sensitivity to carbon dioxide has weakened since the late 1990s. This reduction is mainly driven by soil moisture sensitivity in non-monsoon region and decreased photosynthetically active radiation in monsoon region, emphasizing the need to consider regional climate and vegetation differences when predicting future carbon cycles.
Wolfgang A. Müller, Stephan Lorenz, Trang V. Pham, Andrea Schneidereit, Renate Brokopf, Victor Brovkin, Nils Brüggemann, Fatemeh Chegini, Dietmar Dommenget, Kristina Fröhlich, Barbara Früh, Veronika Gayler, Helmuth Haak, Stefan Hagemann, Moritz Hanke, Tatiana Ilyina, Johann Jungclaus, Martin Köhler, Peter Korn, Luis Kornblueh, Clarissa A. Kroll, Julian Krüger, Karel Castro-Morales, Ulrike Niemeier, Holger Pohlmann, Iuliia Polkova, Roland Potthast, Thomas Riddick, Manuel Schlund, Tobias Stacke, Roland Wirth, Dakuan Yu, and Jochem Marotzke
Geosci. Model Dev., 18, 9385–9415, https://doi.org/10.5194/gmd-18-9385-2025, https://doi.org/10.5194/gmd-18-9385-2025, 2025
Short summary
Short summary
We provide a new Earth System model configuration framed into the ICON architecture, which provides the baseline for the next generation of climate predictions and projections (hereafter ICON XPP). Two resolutions of ICON XPP are presented that show high runtime performances making it suitable to run long integrations and large-ensemble experiments. ICON XPP similarly perform to CMIP6-class of climate models making it a good basis for climate forecasts and projections, and climate research.
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.
Carmine Donatelli, Christopher M. Little, Rui M. Ponte, and Stephen G. Yeager
Ocean Sci., 21, 2367–2377, https://doi.org/10.5194/os-21-2367-2025, https://doi.org/10.5194/os-21-2367-2025, 2025
Short summary
Short summary
Assessing the spatiotemporal properties of intrinsic sea level variability is vital to improving predictions of coastal sea level changes. Here, we examined intrinsic sea level variability along the Southeast United States coast, an area of high and increasing societal vulnerability to sea level change, using numerical modeling. Our findings reveal that intrinsic coastal sea level variability is not negligible as previously thought and may exhibit predictability despite its chaotic nature.
Colin Jones, Isaline Bossert, Donovan P. Dennis, Hazel Jeffery, Chris D. Jones, Torben Koenigk, Sina Loriani, Benjamin Sanderson, Roland Séférian, Klaus Wyser, Shuting Yang, Manabu Abe, Sebastian Bathiany, Pascale Braconnot, Victor Brovkin, Friedrich A. Burger, Patrica Cadule, Frederic S. Castruccio, Gokhan Danabasoglu, Andrea Dittus, Jonathan F. Donges, Friederike Fröb, Thomas Frölicher, Goran Georgievski, Chuncheng Guo, Aixue Hu, Peter Lawrence, Paul Lerner, José Licón-Saláiz, Bette Otto-Bliesner, Anastasia Romanou, Elena Shevliakova, Yona Silvy, Didier Swingedouw, Jerry Tjiputra, Jeremy Walton, Andy Wiltshire, Ricarda Winkelmann, Richard Wood, Tokuta Yokohata, and Tilo Ziehn
EGUsphere, https://doi.org/10.5194/egusphere-2025-3604, https://doi.org/10.5194/egusphere-2025-3604, 2025
Short summary
Short summary
We introduce a new Earth system model experiment protocol to help researchers understand how Earth might respond to positive, zero, and negative carbon emissions. This protocol enables different models to be compared following similar warming and cooling rates. Researchers use the models to explore how the Earth reacts to different climate futures, including the risk of tipping points being exceeded and whether changes can be reversed. The results will support improved long-term climate policy.
William E. Chapman, Francine Schevenhoven, Judith Berner, Noel Keenlyside, Ingo Bethke, Ping-Gin Chiu, Alok Gupta, and Jesse Nusbaumer
Geosci. Model Dev., 18, 5451–5465, https://doi.org/10.5194/gmd-18-5451-2025, https://doi.org/10.5194/gmd-18-5451-2025, 2025
Short summary
Short summary
We introduce the first state-of-the-art atmosphere-connected supermodel, where two advanced atmospheric models share information in real time to form a new dynamical system. By synchronizing the models, particularly in storm track regions, we achieve better predictions without losing variability. This approach maintains key climate patterns and reduces bias in some variables compared to traditional models, demonstrating a useful technique for improving atmospheric simulations.
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.
Yiguo Wang, François Counillon, Lea Svendsen, Ping-Gin Chiu, Noel Keenlyside, Patrick Laloyaux, Mariko Koseki, and Eric de Boisseson
Earth Syst. Sci. Data, 17, 4185–4211, https://doi.org/10.5194/essd-17-4185-2025, https://doi.org/10.5194/essd-17-4185-2025, 2025
Short summary
Short summary
CoRea1860+ is a new climate dataset that reconstructs past climate conditions from 1860 to today. By using advanced modelling techniques and incorporating sea surface temperature observations, it provides a consistent picture of long-term climate variability. The dataset captures key ocean, sea ice, and atmosphere changes, helping scientists understand past climate changes and variability.
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.
Ja-Yeon Moon, Jan Streffing, Sun-Seon Lee, Tido Semmler, Miguel Andrés-Martínez, Jiao Chen, Eun-Byeoul Cho, Jung-Eun Chu, Christian L. E. Franzke, Jan P. Gärtner, Rohit Ghosh, Jan Hegewald, Songyee Hong, Dae-Won Kim, Nikolay Koldunov, June-Yi Lee, Zihao Lin, Chao Liu, Svetlana N. Loza, Wonsun Park, Woncheol Roh, Dmitry V. Sein, Sahil Sharma, Dmitry Sidorenko, Jun-Hyeok Son, Malte F. Stuecker, Qiang Wang, Gyuseok Yi, Martina Zapponini, Thomas Jung, and Axel Timmermann
Earth Syst. Dynam., 16, 1103–1134, https://doi.org/10.5194/esd-16-1103-2025, https://doi.org/10.5194/esd-16-1103-2025, 2025
Short summary
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.
Ricarda Winkelmann, Donovan P. Dennis, Jonathan F. Donges, Sina Loriani, Ann Kristin Klose, Jesse F. Abrams, Jorge Alvarez-Solas, Torsten Albrecht, David Armstrong McKay, Sebastian Bathiany, Javier Blasco Navarro, Victor Brovkin, Eleanor Burke, Gokhan Danabasoglu, Reik V. Donner, Markus Drüke, Goran Georgievski, Heiko Goelzer, Anna B. Harper, Gabriele Hegerl, Marina Hirota, Aixue Hu, Laura C. Jackson, Colin Jones, Hyungjun Kim, Torben Koenigk, Peter Lawrence, Timothy M. Lenton, Hannah Liddy, José Licón-Saláiz, Maxence Menthon, Marisa Montoya, Jan Nitzbon, Sophie Nowicki, Bette Otto-Bliesner, Francesco Pausata, Stefan Rahmstorf, Karoline Ramin, Alexander Robinson, Johan Rockström, Anastasia Romanou, Boris Sakschewski, Christina Schädel, Steven Sherwood, Robin S. Smith, Norman J. Steinert, Didier Swingedouw, Matteo Willeit, Wilbert Weijer, Richard Wood, Klaus Wyser, and Shuting Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1899, https://doi.org/10.5194/egusphere-2025-1899, 2025
Preprint archived
Short summary
Short summary
The Tipping Points Modelling Intercomparison Project (TIPMIP) is an international collaborative effort to systematically assess tipping point risks in the Earth system using state-of-the-art coupled and stand-alone domain models. TIPMIP will provide a first global atlas of potential tipping dynamics, respective critical thresholds and key uncertainties, generating an important building block towards a comprehensive scientific basis for policy- and decision-making.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
Short summary
Short summary
Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
Short summary
Short summary
HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
Chenrui Diao, Yangyang Xu, Aixue Hu, and Zhili Wang
Atmos. Chem. Phys., 25, 2167–2180, https://doi.org/10.5194/acp-25-2167-2025, https://doi.org/10.5194/acp-25-2167-2025, 2025
Short summary
Short summary
Industrial aerosol increases in Asia and reductions in North America and Europe in 1980–2020 influenced climate changes over the Pacific Ocean differently. Asian aerosols caused El Niño-like temperature patterns and slightly weakened the natural variation in the North Pacific, while reduced emissions of western countries led to extensive warming in middle–high latitudes of the North Pacific. Human impacts on the Pacific climate may change when emission reduction occurs over Asia in the future.
Hendrik Großelindemann, Frederic S. Castruccio, Gokhan Danabasoglu, and Arne Biastoch
Ocean Sci., 21, 93–112, https://doi.org/10.5194/os-21-93-2025, https://doi.org/10.5194/os-21-93-2025, 2025
Short summary
Short summary
This study investigates the Agulhas Leakage and examines its role in the global ocean circulation. It utilises a high-resolution Earth system model and a preindustrial climate to look at the response of the Agulhas Leakage to the wind field and the Atlantic Meridional Overturning Circulation (AMOC) and its evolution under climate change. The Agulhas Leakage could influence the stability of the AMOC, whose possible collapse would impact the climate in the Northern Hemisphere.
Shunya Koseki, Lander R. Crespo, Jerry Tjiputra, Filippa Fransner, Noel S. Keenlyside, and David Rivas
Biogeosciences, 21, 4149–4168, https://doi.org/10.5194/bg-21-4149-2024, https://doi.org/10.5194/bg-21-4149-2024, 2024
Short summary
Short summary
We investigated how the physical biases of an Earth system model influence the marine biogeochemical processes in the tropical Atlantic. With four different configurations of the model, we have shown that the versions with better SST reproduction tend to better represent the primary production and air–sea CO2 flux in terms of climatology, seasonal cycle, and response to climate variability.
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.
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.
Roberto Bilbao, Pablo Ortega, Didier Swingedouw, Leon Hermanson, Panos Athanasiadis, Rosie Eade, Marion Devilliers, Francisco Doblas-Reyes, Nick Dunstone, An-Chi Ho, William Merryfield, Juliette Mignot, Dario Nicolì, Margarida Samsó, Reinel Sospedra-Alfonso, Xian Wu, and Stephen Yeager
Earth Syst. Dynam., 15, 501–525, https://doi.org/10.5194/esd-15-501-2024, https://doi.org/10.5194/esd-15-501-2024, 2024
Short summary
Short summary
In recent decades three major volcanic eruptions have occurred: Mount Agung in 1963, El Chichón in 1982 and Mount Pinatubo in 1991. In this article we explore the climatic impacts of these volcanic eruptions with a purposefully designed set of simulations from six CMIP6 decadal prediction systems. We analyse the radiative and dynamical responses and show that including the volcanic forcing in these predictions is important to reproduce the observed surface temperature variations.
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.
Franco Molteni, Fred Kucharski, and Riccardo Farneti
Weather Clim. Dynam., 5, 293–322, https://doi.org/10.5194/wcd-5-293-2024, https://doi.org/10.5194/wcd-5-293-2024, 2024
Short summary
Short summary
We describe some new features of an intermediate-complexity coupled model, including a three-layer thermodynamic ocean model suitable to explore the extratropical response to tropical ocean variability. We present results on the model climatology and show that important features of interdecadal and interannual variability are realistically simulated in a
pacemakercoupled ensemble of 70-year runs, where portions of the tropical Indo-Pacific are constrained to follow the observed variability.
Akhilesh Sivaraman Nair, François Counillon, and Noel Keenlyside
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-217, https://doi.org/10.5194/gmd-2023-217, 2024
Publication in GMD not foreseen
Short summary
Short summary
This study demonstrates the importance of soil moisture (SM) in subseasonal-to-seasonal predictions. To addess this, we introduce the Norwegian Climate Prediction Model Land (NorCPM-Land), a land data assimilation system developed for the NorCPM. NorCPM-Land reduces error in SM by 10.5 % by assimilating satellite SM products. Enhanced land initialisation improves predictions up to a 3.5-month lead time for SM and a 1.5-month lead time for temperature and precipitation.
Lina Boljka, Nour-Eddine Omrani, and Noel S. Keenlyside
Weather Clim. Dynam., 4, 1087–1109, https://doi.org/10.5194/wcd-4-1087-2023, https://doi.org/10.5194/wcd-4-1087-2023, 2023
Short summary
Short summary
This study examines quasi-periodic variability in the tropical Pacific on interannual timescales and related physics using a recently developed time series analysis tool. We find that wind stress in the west Pacific and recharge–discharge of ocean heat content are likely related to each other on ~1.5–4.5-year timescales (but not on others) and dominate variability in sea surface temperatures on those timescales. This may have further implications for climate models and long-term prediction.
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.
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.
Laura C. Jackson, Eduardo Alastrué de Asenjo, Katinka Bellomo, Gokhan Danabasoglu, Helmuth Haak, Aixue Hu, Johann Jungclaus, Warren Lee, Virna L. Meccia, Oleg Saenko, Andrew Shao, and Didier Swingedouw
Geosci. Model Dev., 16, 1975–1995, https://doi.org/10.5194/gmd-16-1975-2023, https://doi.org/10.5194/gmd-16-1975-2023, 2023
Short summary
Short summary
The Atlantic meridional overturning circulation (AMOC) has an important impact on the climate. There are theories that freshening of the ocean might cause the AMOC to cross a tipping point (TP) beyond which recovery is difficult; however, it is unclear whether TPs exist in global climate models. Here, we outline a set of experiments designed to explore AMOC tipping points and sensitivity to additional freshwater input as part of the North Atlantic Hosing Model Intercomparison Project (NAHosMIP).
Zhiang Xie and Dietmar Dommenget
EGUsphere, https://doi.org/10.5194/egusphere-2023-370, https://doi.org/10.5194/egusphere-2023-370, 2023
Preprint archived
Short summary
Short summary
Using numeric modelling, the global interaction between the climate system and ice sheets are examined in this study. The results show the existence of ice sheets slows the response of the climate system to external forcings and enhances the response in high latitude in Northern Hemisphere. Some interactions amplify the climate response, such as the ice-albedo, ice latent heat and topography feedbacks, while others damp or shift the climate response, such as snowfall and sea level feedbacks.
Stephen G. Yeager, Nan Rosenbloom, Anne A. Glanville, Xian Wu, Isla Simpson, Hui Li, Maria J. Molina, Kristen Krumhardt, Samuel Mogen, Keith Lindsay, Danica Lombardozzi, Will Wieder, Who M. Kim, Jadwiga H. Richter, Matthew Long, Gokhan Danabasoglu, David Bailey, Marika Holland, Nicole Lovenduski, Warren G. Strand, and Teagan King
Geosci. Model Dev., 15, 6451–6493, https://doi.org/10.5194/gmd-15-6451-2022, https://doi.org/10.5194/gmd-15-6451-2022, 2022
Short summary
Short summary
The Earth system changes over a range of time and space scales, and some of these changes are predictable in advance. Short-term weather forecasts are most familiar, but recent work has shown that it is possible to generate useful predictions several seasons or even a decade in advance. This study focuses on predictions over intermediate timescales (up to 24 months in advance) and shows that there is promising potential to forecast a variety of changes in the natural environment.
Zhiang Xie, Dietmar Dommenget, Felicity S. McCormack, and Andrew N. Mackintosh
Geosci. Model Dev., 15, 3691–3719, https://doi.org/10.5194/gmd-15-3691-2022, https://doi.org/10.5194/gmd-15-3691-2022, 2022
Short summary
Short summary
Paleoclimate research requires better numerical model tools to explore interactions among the cryosphere, atmosphere, ocean and land surface. To explore those interactions, this study offers a tool, the GREB-ISM, which can be run for 2 million model years within 1 month on a personal computer. A series of experiments show that the GREB-ISM is able to reproduce the modern ice sheet distribution as well as classic climate oscillation features under paleoclimate conditions.
Seungmok Paik, Seung-Ki Min, Seok-Woo Son, Soon-Il An, Jong-Seong Kug, and Sang-Wook Yeh
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-187, https://doi.org/10.5194/acp-2022-187, 2022
Revised manuscript not accepted
Short summary
Short summary
This paper investigates Earth’s surface climate response to volcanic eruptions at different latitudes. By analyzing last millennium ensemble simulations of a coupled climate model, we have identified physical processes associated with the diverse impacts of volcanic eruption latitudes, focusing on the tropical ocean surface warming and the stratospheric polar vortex intensification. Our results provide important global implications for atmospheric responses to future volcanic aerosols.
Koffi Worou, Hugues Goosse, Thierry Fichefet, and Fred Kucharski
Earth Syst. Dynam., 13, 231–249, https://doi.org/10.5194/esd-13-231-2022, https://doi.org/10.5194/esd-13-231-2022, 2022
Short summary
Short summary
Over the Guinea Coast, the increased rainfall associated with warm phases of the Atlantic Niño is reasonably well simulated by 24 climate models out of 31, for the present-day conditions. In a warmer climate, general circulation models project a gradual decrease with time of the rainfall magnitude associated with the Atlantic Niño for the 2015–2039, 2040–2069 and 2070–2099 periods. There is a higher confidence in these changes over the equatorial Atlantic than over the Guinea Coast.
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.
Ingo Bethke, Yiguo Wang, François Counillon, Noel Keenlyside, Madlen Kimmritz, Filippa Fransner, Annette Samuelsen, Helene Langehaug, Lea Svendsen, Ping-Gin Chiu, Leilane Passos, Mats Bentsen, Chuncheng Guo, Alok Gupta, Jerry Tjiputra, Alf Kirkevåg, Dirk Olivié, Øyvind Seland, Julie Solsvik Vågane, Yuanchao Fan, and Tor Eldevik
Geosci. Model Dev., 14, 7073–7116, https://doi.org/10.5194/gmd-14-7073-2021, https://doi.org/10.5194/gmd-14-7073-2021, 2021
Short summary
Short summary
The Norwegian Climate Prediction Model version 1 (NorCPM1) is a new research tool for performing climate reanalyses and seasonal-to-decadal climate predictions. It adds data assimilation capability to the Norwegian Earth System Model version 1 (NorESM1) and has contributed output to the Decadal Climate Prediction Project (DCPP) as part of the sixth Coupled Model Intercomparison Project (CMIP6). We describe the system and evaluate its baseline, reanalysis and prediction performance.
Cited articles
Alexander, M. A., Bladeì, I., Newman, M., Lanzante, J. R., Lau, N.-C., and Scott, J. D.: The atmospheric bridge: the influence of ENSO teleconnections on air–sea interaction over the global oceans, J. Climate, 15, 2205–2231, 2002.
Alexander, M. A., Shin, S.-I., and Battisti, D. S.: The influence of the trend, basin interactions, and ocean dynamics on tropical ocean prediction. Geophys. Res. Lett., 49, e2021GL096120, https://doi.org/10.1029/2021GL096120, 2022.
Amaya, D. J.: The Pacific meridional mode and ENSO: A review, Curr. Climate Change Rep., 5, 296–307, https://doi.org/10.1007/s40641-019-00142-x, 2019.
Ashok, K., Chan, W.-L., Motoi, T., and Yamagata, T.: Decadal variability of the Indian Ocean dipole, Geophys. Res. Lett., 31, L24207, https://doi.org/10.1029/2004GL021345, 2004.
Behera, S. K. and Yamagata, T.: Influence of the Indian Ocean dipole on the Southern Oscillation, J. Meteorol. Soc. Jpn., 81, l69–177, 2003.
Behera, S. K., Luo, J.-J., Masson, S., Rao, S. A., Sakuma, H., and Yamagata, T.: A CGCM study on the interaction between IOD and ENSO, J. Climate, 19, 1688–1705, 2006.
Beverley, J. D., Newman, M., and Hoell, A.: Climate model trend errors are evident in seasonal forecasts at short leads, npj Clim. Atmos. Sci., 7, 285, https://doi.org/10.1038/s41612-024-00832-w, 2024.
Bi, D., Wang, G., Cai, W., Santoso, A., Sullivan, A., Ng, B., and Jia, F.: Improved simulation of ENSO variability through feedback from the equatorial Atlantic in a pacemaker experiment, Geophys. Res. Lett., 49, e2021GL096887. https://doi.org/10.1029/2021GL096887, 2022.
Bjerknes, J.: Atmospheric teleconnections from the equatorial Pacific, Mon. Weather Rev., 97, 163–172, 1969.
Boer, G. J., Smith, D. M., Cassou, C., Doblas-Reyes, F., Danabasoglu, G., Kirtman, B., Kushnir, Y., Kimoto, M., Meehl, G. A., Msadek, R., Mueller, W. A., Taylor, K. E., Zwiers, F., Rixen, M., Ruprich-Robert, Y., and Eade, R.: The Decadal Climate Prediction Project (DCPP) contribution to CMIP6, Geosci. Model Dev., 9, 3751–3777, https://doi.org/10.5194/gmd-9-3751-2016, 2016.
Bommer, P. L., Kretschmer, M., Hedström, A., Bareeva, D., and Höhne, M. M.: Finding the right XAI method – A guide for the evaluation and ranking of explainable AI methods in climate science, Artif. Intell. Earth Syst., 3, e230074, https://doi.org/10.1175/AIES-D-23-0074.1, 2024.
Brunton, S. L., Proctor, J. L., and Kutz, J. N.: Discovering governing equations from data by sparse identification of nonlinear dynamical systems, P. Natl Acad. Sci. USA, 113, 3932–3937, 2016.
Cai, W., Wu, L., Lengaigne, M., Li, T., McGregor, S., Kug, J.-S., Yu, J.-Y., Stuecker, M. F., Santoso, A., Li, X., Ham, Y.-G., Chikamoto, Y., Ng, B., McPhaden, M. J., Du, Y., Dommenget, D., Jia, F., Kajtar, J. B., Keenlyside, N., Lin, X., Luo, J.-J., Martín-Rey, M., Ruprich-Robert, Y., Wang, G., Xie, S.-P., Yang, Y., Kang, S. M., Choi, J.-Y., Gan, B., Kim, G.-I., Kim, C.-E., Kim, S., Kim, J.-H., and Chang, P.: Pantropical climate interactions, Science, 363, eaav4236, https://doi.org/10.1126/science.aav4236, 2019.
Capotondi, A., McGregor, S., McPhaden, M.J., Cravatte, S., Holbrook, N.J., Imada, Y., Sanchez, S. C., Sprintall, J., Stuecker, M.F., Ummenhofer, C. C., Zeller, M., Farneti, R., Graffino, G., Hu, S., Karnauskas, K. B., Kosaka, Y., Kucharski, F., Mayer, M., Qiu, B., Santoso, A., Taschetto, A. S., Wang, F., Zhang, X., Holmes, R. M., Luo, J.-J., Maher, N., Martinez-Villalobos, C., Meehl, G. A., Naha, R., Schneider, N., Stevenson, S., Sullivan, A., van Rensch, P., and Xu, T.: Mechanisms of tropical Pacific decadal variability, Nat. Rev. Earth Environ., 4, 754–769, 2023.
Chambers, D. P., Tapley, B. D., and Stewart, R. H.: Anomalous warming in the Indian ocean coincident with El Niño, J. Geophys. Res., 104, 3035–3047, https://doi.org/10.1029/1998jc900085, 1999.
Chang, P., Ji, L., and Li, H.: A decadal climate variation in the tropical Atlantic Ocean from thermodynamic air-sea interactions, Nature, 385, 516–518, 1997.
Chang, P., Yamagata, T., Schopf, P., Behera, S. K., Carton, J., Kessler, W. S., Meyers, G., Qu, T., Schott, F., Shetye, S., and Xie, S. P.: Climate Fluctuations of Tropical Coupled Systems – The Role of Ocean Dynamics, J. Climate, 19, 5122–5174, 2006a.
Chang, P., Fang, Y., Saravanan, R., Ji, L., and Seidel, H.: The cause of the fragile relationship between the Pacific El Niño and the Atlantic Niño, Nature, 443, 324–328, https://doi.org/10.1038/nature05053, 2006b.
Chiang, J. C. H. and Vimont, D. J.: Analogous Pacific and Atlantic Meridional Modes of Tropical Atmosphere–Ocean Variability, J. Climate, 17, 4143–4158, https://doi.org/10.1175/JCLI4953.1, 2004.
Chikamoto, Y., Mochizuki, T., Timmermann, A., Kimoto, M., and Watanabe, M.: Potential tropical Atlantic impacts on Pacific decadal climate trends, Geophys. Res. Lett., 43, 7143–7151, https://doi.org/10.1002/2016GL069544, 2016.
Cobb, K. M., Charles, C. D., and Hunter, D. E.: A central tropical Pacific coral demonstrates Pacific, Indian, and Atlantic decadal climate connections, Geophys. Res. Lett., 28, 2209–2212, 2001.
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.
Diaz, H. F., Hoerling, M. P., and Eischeid, J. K.: ENSO variability, teleconnections and climate change, Int. J. Climatol., 21, 1845–1862, 2001.
Di Capua, G., Kretschmer, M., Donner, R. V., van den Hurk, B., Vellore, R., Krishnan, R., and Coumou, D.: Tropical and mid-latitude teleconnections interacting with the Indian summer monsoon rainfall: a theory-guided causal effect network approach, Earth Syst. Dynam., 11, 17–34, https://doi.org/10.5194/esd-11-17-2020, 2020.
Ding, H. and Alexander, M. A.: Multi-year predictability of global sea surface temperature using model-analogs, Geophys. Res. Lett., 50, e2023GL104097, https://doi.org/10.1029/2023GL104097, 2023.
Ding, H., Keenlyside, N. S., and Latif, M.: Impact of the equatorial Atlantic on the El Niño Southern Oscillation, Clim. Dynam., 38, 1965–1972, 2012.
Ding, H., Greatbatch, R. J., Park, W., Latif, M., Semenov, V. A., and Sun, X.: The variability of the East Asian summer monsoon and its relationship to ENSO in a partially coupled climate model, Clim. Dynam., 42, 367–379, https://doi.org/10.1007/s00382-012-1642-3, 2014.
Dommenget, D. and Hutchinson, D.: El Niño Southern Oscillation and Tropical Basin Interaction in Idealized Worlds, Clim. Dynam., in review, 2025.
Drouard, M. and Cassou, C.: A modeling- and process-oriented study to investigate the projected change of ENSO-forced wintertime teleconnectivity in a warmer world, J. Climate, 32, 8047–8068, https://doi.org/10.1175/JCLI-D-18-0803.1, 2019.
Durack, P. J. and Taylor, K. E.: PCMDI AMIP SST and sea-ice boundary conditions (various versions). Version 1-1-9, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/input4MIPs.10449, 2016 (data available at: https://aims2.llnl.gov/search/input4mips/, last access: 19 May 2024).
Enfield, D. B. and Mayer, D. A.: Tropical Atlantic sea surface temperature variability and its relation to El Niño–Southern Oscillation, J. Geophys. Res., 102, 929–945, https://doi.org/10.1029/96JC03296, 1997.
Enfield, D. B. and Mestas-Nuñez, A. M.: Multiscale variability in global sea surface temperatures and their relationship with tropospheric climate patterns, J. Climate, 12, 2719–2733, 1999.
Exarchou, E., Ortega, P., Rodríguez-Fonseca, B., Losada, T., Polo, I., and Prodhomme, C.: Impact of equatorial Atlantic variability on ENSO predictive skill, Nat. Commun., 12, 1612, https://doi.org/10.1038/s41467-021-21857-2, 2021.
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 (data available at: https://esgf-node.llnl.gov/search/cmip6/, last access: 19 May 2024).
Feng, M., McPhaden, M., Xie, S.-P., and Hafner, J.: La Niña forces unprecedented Leeuwin Current warming in 2011, Sci. Rep., 3, 1277, https://doi.org/10.1038/srep01277, 2013.
Frankignoul, C.: Sea surface temperature anomalies, planetary waves and air–sea feedback in the middle latitudes, Rev. Geophys., 23, 357–390, 1985.
Frankignoul, C., Czaja, A., and L'Heveder, B.: Air–sea feedback in the North Atlantic and surface boundary conditions for ocean models, J. Climate, 11, 2310–2324, 1998.
Gastineau, G., Friedman, A. R., Khodri, M., and Vialard, J.: Global ocean heat content redistribution during the 1998–2012 Interdecadal Pacific Oscillation negative phase, Clim. Dynam., 53, 1187-1208, 2019.
Gibson, P. B., Chapman, W. E., Altinok, A., Delle Monache, L., DeFlorio, M. J., and Waliser, D. E.: Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts, Commun. Earth Environ., 2, 159, https://doi.org/10.1038/s43247-021-00225-4, 2021.
Gill, A. E.: Some simple solutions for heat-induced tropical circulations, Q. J. Roy. Meteor. Soc., 106, 447–462, 1980.
Gillett, N. P., Shiogama, H., Funke, B., Hegerl, G., Knutti, R., Matthes, K., Santer, B. D., Stone, D., and Tebaldi, C.: The Detection and Attribution Model Intercomparison Project (DAMIP v1.0) contribution to CMIP6, Geosci. Model Dev., 9, 3685–3697, https://doi.org/10.5194/gmd-9-3685-2016, 2016.
Ham, Y.-G., Kug, J.-S., Park, J.-Y., and Jin, F.-F.: Sea surface temperature in the north tropical Atlantic as a trigger for El Niño/Southern Oscillation events, Nat. Geosci., 6, 112–116, https://doi.org/10.1038/ngeo1686, 2013a.
Ham, Y.-G., Kug, J.-S., and Park, J.-Y.: Two distinct roles of Atlantic SSTs in ENSO variability: North tropical Atlantic SST and Atlantic Niño, Geophys. Res. Lett., 40, 4012–4017, https://doi.org/10.1002/grl.50729, 2013b.
Ham, Y. G., Kim, J. H., and Luo, J.-J.: Deep learning for multi-year ENSO forecasts, Nature, 573, 568–572, https://doi.org/10.1038/s41586-019-1559-7, 2019.
Han, W., Vialard, J., McPhaden, M. J., Lee, T., Masumoto, Y., Feng, M., and de Ruijter, W. P.: Indian Ocean Decadal Variability: A Review, B. Am. Meteorol. Soc., 95, 1679–1703, https://doi.org/10.1175/BAMS-D-13-00028.1, 2014.
Hasselmann, K.: PIPs and POPs: The reduction of complex dynamical systems using principal interaction and oscillation patterns, J. Geophys. Res., 93, 11015–11021, https://doi.org/10.1029/JD093iD09p11015, 1988.
Hastenrath, S. and Heller, L.: Dynamics of climate hazards in Northeast Brazil, Q. J. Roy. Meteor. Soc., 103, 77–92, 1977.
Held, I.: The gap between simulation and understanding in climate modeling, B. Am. Meteorol. Soc., 86, 1609–1614, https://doi.org/10.1175/BAMS-86-11-1609, 2005.
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 pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), https://doi.org/10.24381/cds.6860a573, 2023.
Horel, J. D. and Wallace, J. M.: Planetary-scale atmospheric phenomena associated with the Southern Oscillation, Mon. Weather Rev., 109, 813–829, 1981.
Jansen, M. F., Dommenget, D., and Keenlyside, N.: Tropical atmosphere–ocean interactions in a conceptual framework, J. Climate, 22, 550–567, https://doi.org/10.1175/2008JCLI2243.1, 2009.
Jeevanjee, N., Hassanzadeh, P., Hill, S., and Sheshadri, A.: A perspective on climate model hierarchies, J. Adv. Model. Earth Sy., 9, 1760–1771, 2017.
Jiang, F., Zhang, W., Jin, F.-F., Stuecker, M. F., Timmermann, A., McPhaden, M. J., Boucharel, J., and Wittenberg, A. T.: Resolving the tropical Pacific/Atlantic interaction conundrum, Geophys. Res. Lett., 50, e2023GL103777, https://doi.org/10.1029/2023GL103777, 2023.
Jin, F.-F.: An equatorial ocean recharge paradigm for ENSO. Part I: Conceptual model, J. Atmos. Sci., 54, 811–829, https://doi.org/10.1175/1520-0469(1997)054<0811:AEORPF>2.0.CO;2, 1997.
Jin, Y., Meng, X., Zhang, L., Zhao, Y., Cai, W., and Wu, L.: The Indian Ocean the ENSO spring predictability barrier: role of the Indian Ocean Basin and dipole modes, J. Climate, 36, 8331–8345, 2023.
Kajtar, J. B., Santoso, A., England, W. H., and Cai, W.: Tropical climate variability: Interactions across the Pacific, Indian and Atlantic Oceans, Clim. Dynam., 48, 2173–2190, 2017.
Kajtar, J. B., Santoso, A., McGregor, S., England, M. H., and Baillie, Z.: Model under-representation of decadal Pacific trade wind trends and its link to tropical Atlantic bias, Clim. Dynam., 50, 1471–1484, https://doi.org/10.1007/s00382-017-3699-5, 2018.
Karmouche, S., Galytska, E., Runge, J., Meehl, G. A., Phillips, A. S., Weigel, K., and Eyring, V.: Regime-oriented causal model evaluation of Atlantic–Pacific teleconnections in CMIP6, Earth Syst. Dynam., 14, 309–344, https://doi.org/10.5194/esd-14-309-2023, 2023.
Karoly, D.: Southern Hemisphere circulation features associated with El Niño–Southern Oscillation, J. Climate, 2, 1239–1252, 1989.
Kataoka, T., Masson, S., Izumo, T., Tozuka, T., and Yamagata, T.: Can Ningaloo Niño/Niña develop without El Niño–Southern oscillation?, Geophys. Res. Lett., 45, 7040–7048, https://doi.org/10.1029/2018GL078188, 2018.
Keenlyside, N., Latif, M., Botzet, M., Jungclaus, J., and Schulzweida, U.: A coupled method for initialising ENSO forecasts using SST, Tellus A, 57, 340–356, 2005.
Keenlyside, N. S., Ding, H., and Latif, M.: M. Potential of equatorial Atlantic variability to enhance El Niño prediction, Geophys. Res. Lett., 40, 2278–2283, 2013.
Keenlyside, N. S., Ba, J., Mecking, J., Omrani, N.-O., Latif, M., Zhang, R., and Msadek, R.: North Atlantic multi-decadal variability – mechanisms and predictability, in: Climate Change: Multidecadal and Beyond, edited by: Chang, C.-P., Ghil, M., Latif, M., and Wallace, M., World Scientific Publishing Company, Singapore, ISBN 978-9814579926, 2015.
Keenlyside, N., Kosaka, Y., Vigaud, N., Robertson, A., Wang, Y., Dommenget, D., Luo, J.-J., and Matei, D.: Basin Interactions and Predictability, in: Interacting Climates of Ocean Basins: Observations, Mechanisms, Predictability, and Impacts, edited by: Mechoso, C. R., Cambridge University Press, https://doi.org/10.1017/9781108610995, 2019.
Kido, S., Richter, I., Tozuka, T., and Chang, P.: Understanding the interplay between ENSO and related tropical SST variability using linear inverse models, Clim. Dynam., 61, 1029–1048, https://doi.org/10.1007/s00382-022-06484-x, 2022.
Kiladis, G. N. and Diaz, H. F.: Global climatic anomalies associated with extremes in the Southern Oscillation, J. Climate, 2, 1069–1090, 1989.
Kim, W. M., Yeager, S., and Danabasoglu, G.: Atlantic multidecadal variability and associated climate impacts initiated by ocean thermohaline dynamics, J. Climate, 33, 1317–1334, https://doi.org/10.1175/JCLI-D-19-0530.1, 2020.
Klein, S. A., Soden, B. J., and Lau, N. C.: Remote sea surface temperature variations during ENSO: Evidence for a tropical atmospheric bridge, J. Climate, 12, 917–932, https://doi.org/10.1175/1520-0442(1999)012<0917:RSSTVD>2.0.CO;2, 1999.
Kosaka, Y. and Xie, S.-P.: Recent global-warming hiatus tied to equatorial Pacific surface cooling, Nature, 501, 403–407, https://doi.org/10.1038/nature12534, 2013.
Kucharski, F., Ikram, F., Molteni, F., Farneti, R., Kang, I.-S., No, H.-H., King, M.-P., Giuliani, G., and Morgensen, K.: Atlantic forcing of Pacific decadal variability, Clim. Dynam., 46, 2337–2351, https://doi.org/10.1007/s00382-015-2705-z, 2016a.
Kucharski, F., Parvin, A., Rodriguez-Fonseca, B., Farneti, R, Martin-Rey, M., Polo, I., Mohino, E., Losada, T., and Mechoso, C. R.: The teleconnection of the tropical Atlantic to Indo-Pacific sea surface temperatures on inter-annual to centennial time scales: A review of recent findings, Atmosphere, 7, 29, https://doi.org/10.3390/atmos7020029, 2016b.
Kushnir, Y.: Interdecadal variations in the North Atlantic sea surface temperature and associated atmospheric conditions, J. Climate, 7, 141–157, https://doi.org/10.1175/1520-0442(1994)007<0141:IVINAS>2.0.CO;2, 1994.
Leduc, G., Vidal, L., Tachikawa, K., Rostek, F., Sonzogni, C., Beaufort, L., and Bard, E.: Moisture transport across Central America as a positive feedback on abrupt climatic changes, Nature, 445, 908–911, 2007.
Li, X., Xie, S.-P., Gille, S. T., and Yoo, C.: Atlantic-induced pan-tropical climate change over the past three decades, Nat. Clim. Change, 6, 275–279, https://doi.org/10.1038/nclimate2840, 2016.
Liao, H., Wang, C., and Song, Z.: ENSO phase-locking biases from the CMIP5 to CMIP6 models and a possible explanation, Deep-Sea Res. II, 189–190, 104943, https://doi.org/10.1016/j.dsr2.2021.104943, 2021.
Liguori, G., McGregor, S., Singh, M., Arblaster, J., and Di Lorenzo, E.: Revisiting ENSO and IOD contributions to Australian precipitation, Geophys. Res. Lett., 49, e2021GL094295, https://doi.org/10.1029/2021GL094295, 2022.
Liu, S., Chang, P., Wan, X., Yeager, S. G., and Richter, I.: Role of the Maritime Continent in the remote influence of Atlantic Niño on the Pacific, Nat. Commun. 14, 3327, https://doi.org/10.1038/s41467-023-39036-w, 2023.
Locarnini, R. A., Mishonov, A. V., Antonov, J. I., Boyer, T. P., Garcia, H. E., Baranova, O. K., Zweng, M. M., and Johnson, D. R.: World Ocean Atlas 2009, Volume 1: Temperature, edited by: Levitus, S., NOAA Atlas NESDIS 68, U.S. Government Printing Office, Washington, D.C., 184 pp., https://data.nodc.noaa.gov/woa/WOA18/DOC/woa18_vol1.pdf (last access: 5 May 2025), 2010.
Lübbecke, J. F. and McPhaden, M. J.: On the inconsistent relationship between Pacific and Atlantic Niños, J. Climate, 25, 4294–4303, https://doi.org/10.1175/JCLI-D-11-00553.1, 2012.
Lübbecke, J. F., Rodríguez-Fonseca, B., Richter, I., Martín-Rey, M., Losada, T., Polo, I., and Keenlyside, N.: Equatorial Atlantic variability – Modes, mechanisms, and global teleconnections, WIRES Clim. Change, 9, e527, https://doi.org/10.1002/wcc.527, 2018.
Luo, J.-J., Masson, S., Behera, S., Shingu, S., and Yamagata, T.: Seasonal climate predictability in a coupled OAGCM using a different approach for ensemble forecasts, J. Climate, 18, 4474–4497, https://doi.org/10.1175/JCLI3526.1, 2005.
Luo, J.-J., Zhang, R., Behera, S. K., Masumoto, Y., Jin, F.-F., Lukas, R., and Yamagata, T.: Interaction between El Niño and extreme Indian Ocean dipole, J. Climate, 23, 726–742, https://doi.org/10.1175/2009JCLI3104.1, 2010.
Luo, J.-J.., Liu, G., Hendon, H., Alves, O., and Yamagata, T.: Inter-basin sources for two-year predictability of the multi-year La Niña event in 2010–2012, Sci. Rep., 7, 2276, https://doi.org/10.1038/s41598-017-01479-9, 2017.
Mantua, N. J. and Hare, S. R.: The Pacific Decadal Oscillation, J. Oceanogr., 58, 35–44, https://doi.org/10.1023/A:1015820616384, 2002.
Mao, Y., Zou, Y., Alves, L. M., Macau, E. E. N., Taschetto, A. S., Santoso, A., and Kurths, J.: Phase coherence between surrounding oceans enhances precipitation shortages in Northeast Brazil, Geophys. Res. Lett., 49, e2021GL097647, https://doi.org/10.1029/2021GL097647, 2022.
Martín-Rey, M., Rodríguez-Fonseca, B., Polo, I., and Kucharski, F.: On the Atlantic–Pacific Niños connection: A multidecadal modulated mode, Clim. Dynam., 43, 3163–3178, https://doi.org/10.1007/s00382-014-2305-3, 2014.
Martin-Rey, M., Rodriguez-Fonseca, B., and Polo, I.: Atlantic opportunities for ENSO prediction, Geophys. Res. Lett., 42, 6802–6810, https://doi.org/10.1002/2015GL065062, 2015.
McCreary, J. P.: Eastern tropical ocean response to changing wind systems: with application to El Niño, J. Phys. Oceanogr., 6, 632–645, 1976.
McCreary, J. P. and Anderson, D. L. T.: A simple model of El Niño and the Southern Oscillation, Mon. Weather Rev., 112, 934–946, 1984.
McGregor, S., Stuecker, M. F., Kajtar, J. B., England, M. H., and Collins, M.: Model tropical Atlantic biases underpin diminished Pacific decadal variability, Nat. Clim. Change, 8, 493–498, https://doi.org/10.1038/s41558-018-0163-4, 2018.
Merle, J.: Annual and interannual variability of temperature in the eastern equatorial Atlantic Ocean – hypothesis of an Atlantic El Nino, Oceanol. Acta, 3, 209–220, 1980.
Molteni, F.: Atmospheric simulations using a GCM with simplified physical parametrizations. I: Model climatology and variability in multi-decadal experiments, Clim. Dynam., 20, 175–191, 2003.
Molteni, F., Kucharski, F., and Farneti, R.: Multi-decadal pacemaker simulations with an intermediate-complexity climate model, Weather Clim. Dynam., 5, 293–322, https://doi.org/10.5194/wcd-5-293-2024, 2024.
Moore, D., Hisard, P., McCreary, J. P., Merlo, J., O'Brien, J. J., Picaut, J., Verstraete, J. M., and Wunsch, C.: Equatorial adjustment in the eastern Atlantic, Geophys. Res. Lett., 5, 637–640, 1978.
Najar, M. A., Almar, R., Bergsma, E. W. J., Delvit, J.-M., and Wilson, D. G.: Improving a shoreline forecasting model with Symbolic Regression. Tackling Climate Change with Machine Learning, ICLR 2023, May 2023, Kigali, Rwanda, https://hal.science/hal-04281530 (last access: 27 October 2024), 2023.
Newman, M., Alexander, M. A., Ault, T. R., Cobb, K. M., Deser, C., Di Lorenzo, E., Mantua, N. J., Miller, A. J., Minobe, S., Nakamura, H. Schneider, N., Vimont, D. J., Phillips, A. S., Scott, J. D., and Smith, C. A.: The Pacific decadal oscillation, revisited, J. Climate, 29, 4399–4427, https://doi.org/10.1175/JCLI-D-15-0508.1, 2016.
National Geophysical Data Center: 5-minute Gridded Global Relief Data (ETOPO5), National Geophysical Data Center [data set], NOAA, https://doi.org/10.7289/V5D798BF, 1993.
Oettli, P., Yuan, C., and Richter, I.: The other coastal Niño/Niña – The Benguela, California and Dakar Niños/Niñas, Tropical and Extra-tropical Air-Sea Interactions, edited by: Behera, S. K., Elsevier, 237–266, ISBN 9780128181560, 2021.
O'Reilly, C. H., Patterson, M., Robson, J., Monerie, P. A., Hodson, D., and Ruprich-Robert, Y.: Challenges with interpreting the impact of Atlantic Multidecadal Variability using SST-restoring experiments, npj Clim. Atmos. Sci., 6, 14, https://doi.org/10.1038/s41612-023-00335-0, 2023.
Penland, C. and Magorian, T.: Prediction of Niño 3 sea surface temperatures using linear inverse modelling, J. Climate, 6, 1067–1076, https://doi.org/10.1175/1520-0442(1993)006<1067:PONSST>2.0.CO;2, 1993.
Penland, C. and Sardeshmukh, P. D.: The optimal growth of tropical sea surface temperature anomalies, J. Climate, 8, 1999–2024, https://doi.org/10.1175/1520-0442(1995)008<1999:TOGOTS>2.0.CO;2, 1995.
Philander, S. G.: El Niño and La Niña, J. Atmos. Sci., 42, 2652–2662, 1985.
Polo, I., Martin-Rey, M., Rodriguez-Fonseca, B., Kucharski, F., and Mechoso, C. R.: Processes in the Pacific La Niña onset triggered by the Atlantic Niño, Clim. Dynam., 44, 115–131, https://doi.org/10.1007/s00382-014-2354-7, 2015.
Power, S., Lengaigne, M., Capotondi, A., Khodri, M., Vialard, J., Jebri, B., Guilyardi, E., McGregor, S., Kug, J. S., Newman, M., McPhaden, M. J., Meehl, G., Smith, D., Cole, J., Emile-Geay, J., Vimont, D., Wittenberg, A. T., Collins, M., Kim, G.-I., Cai, W., Okumura, Y., Chung, C., Cobb, K. M., Delage, F., Planton, Y. Y., Levine, A., Zhu, F. Sprintall, J., Di Lorenzo, E., Zhang, X., Luo, J.-J., Lin, X., Balmaseda, M., Wang, G., and Henly, B. J.: Decadal climate variability in the tropical Pacific: Characteristics, causes, predictability, and prospects, Science, 374, eaay9165, https://doi.org/10.1126/science.aay9165, 2021.
Rasmusson, E. M. and Carpenter, T. H.: Variations in tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El Niño, Mon. Weather Rev., 110, 354–384, 1982.
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., 108, 4407, https://doi.org/10.1029/2002JD002670, 2003 (data available at: https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html, last access: 19 May 2024).
Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C., and Wang, W.: An improved in situ and satellite SST analysis for climate, J. Climate, 15, 1609–1625, 2002 (data available at: https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.html, last access: 19 May 2024).
Richter, I.: Protocol of coordinated climate model experiments for studying tropical basin interaction, Zenodo [data set], https://doi.org/10.5281/zenodo.13864935, 2024a.
Richter, I.: Ocean basin mask for coordinated climate model experiments to explore tropical basin interaction, Zenodo [data set], https://doi.org/10.5281/zenodo.13865022, 2024b.
Richter, I.: Processing and plotting routines for manuscript “The Tropical Basin Interaction Model Intercomparison Project (TBIMIP)”, Zenodo [code], https://doi.org/10.5281/zenodo.14000123, 2024c.
Richter, I. and Doi, T.: Estimating the role of SST in atmospheric surface wind variability over the tropical Atlantic and Pacific, J. Climate, 32, 3899–3915, https://doi.org/10.1175/JCLI-D-18-0468.1, 2019.
Richter, I. and Tokinaga, H.: An overview of the performance of CMIP6 models in the tropical Atlantic: Mean state, variability, and remote impacts, Clim. Dynam., 55, 2579–2601, https://doi.org/10.1007/s00382-020-05409-w, 2020.
Richter, I. and Tokinaga, H.: The Atlantic Niño: Dynamics, thermodynamics, and teleconnections, Tropical and Extra-Tropical Air–Sea Interactions, edited by: Behera, S. K., Elsevier, 171–206, ISBN 9780128181560, 2021.
Richter, I., Xie, S.-P., Wittenberg, A. T., and Masumoto, Y.: Tropical Atlantic biases and their relation to surface wind stress and terrestrial precipitation, Clim. Dynam., 38, 985–1001, https://doi.org/10.1007/s00382-011-1038-9, 2012.
Richter, I., Tokinaga, H., Kosaka, Y., Doi, T., and Kataoka, T.: Revisiting the tropical Atlantic influence on El Niño–Southern Oscillation, J. Climate, 34, 8533–8548, https://doi.org/10.1175/JCLI-D-21-0088.1, 2021.
Richter, I., Kosaka, Y., Kido, S., and Tokinaga, H.: The tropical Atlantic as a negative feedback on ENSO, Clim. Dynam., 61, 309–327, https://doi.org/10.1007/s00382-022-06582-w, 2023.
Richter, I., Kido, S., Tozuka, T., Kosaka, Y., Tokinaga, H., and Chang, P.: Revisiting the inconsistent influence of El Niño-Southern Oscillation on the equatorial Atlantic, J. Climate, 38, 481–496, https://doi.org/10.1175/JCLI-D-24-0182.1, 2024.
Rodríguez-Fonseca, B., Polo, I., García-Serrano, J., Losada, T., Mohino, E., Mechoso, C. R., and Kucharski, F.: Are Atlantic Niños enhancing Pacific ENSO events in recent decades?, Geophys. Res. Lett., 36, L20705, https://doi.org/10.1029/2009GL040048, 2009.
Ruggieri, P., Abid, M. A., García-Serrano, J., Grancini, C., Kucharski, F., Pascale, S., and Volpi, D.:SPEEDY-NEMO: performance and applications of a fully-coupled intermediate-complexity climate model, Clim. Dynam., 62, 3763–3781, https://doi.org/10.1007/s00382-023-07097-8, 2024.
Ruprich-Robert, Y., Msadek, R., Castruccio, F., Yeager, S.,Delworth, T., and Danabasoglu, G.: Assessing the climate impacts of the observed Atlantic multidecadal variability using the GFDL CM2.1 and NCAR CESM1 global coupled models, J. Climate, 30, 2785–2810, https://doi.org/10.1175/JCLI-D-16-0127.1, 2017.
Saji, N. H., Goswami, B. N., Vinayachandran, P. N., and Yamagata, T.: A dipole mode in the tropical Indian Ocean, Nature, 401, 360–363, 1999.
Schott, F. A., Xie, S.-P., and McCreary Jr., J. P.: Indian Ocean circulation and climate variability, Rev. Geophys., 47, RG1002, https://doi.org/10.1029/2007RG000245, 2009.
Servonnat, J., Mignot, J., Guilyardi, E., Swingedouw, D., Séférian, R., and Labetoulle, S.: Reconstructing the subsurface ocean decadal variability using surface nudging in a perfect model framework, Clim. Dynam., 44, 315–338, 2015.
Shannon, L. V., Boyd, A. J., Bundrit, G. B., and Taunton-Clark, J.: On the existence of an El Niño-type phenomenon in the Benguela system, J. Mar. Sci., 44, 495–520, 1986.
Shin, N., Ham, Y., Kim, J., Cho, M., and Kug, J.: Application of Deep Learning to Understanding ENSO Dynamics, Artif. Intell. Earth Syst., 1, e210011, https://doi.org/10.1175/AIES-D-21-0011.1, 2022.
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.
Stuecker, M. F.: Revisiting the Pacific Meridional Mode, Sci. Rep., 8, 3216, https://doi.org/10.1038/s41598-018-21537-0, 2018.
Stuecker, M. F.: The climate variability trio: stochastic fluctuations, El Niño, and the seasonal cycle, Geosci. Lett., 10, 51, https://doi.org/10.1186/s40562-023-00305-7, 2023.
Stuecker, M. F., Jin, F.-F., Timmermann, A., and McGregor, S.: Combination mode dynamics of the anomalous northwest Pacific anticyclone, J. Climate, 28, 1093–1111, https://doi.org/10.1175/JCLI-D-14-00225.1, 2015.
Stuecker, M. F., Timmermann, A., F. F. Jin, F.-F., Chikamoto, Y., Zhang, W.-J., Wittenberg, A. T., Widiasih, E., and Zhao, S.: Revisiting ENSO/Indian Ocean dipole phase relationships, Geophys. Res. Lett., 44, 2481–2492, https://doi.org/10.1002/2016GL072308, 2017a.
Stuecker, M. F., Bitz, C. M., and Armour, K. C.: Conditions leading to the unprecedented low Antarctic sea ice extent during the 2016 austral spring season, Geophys. Res. Lett., 44, 9008–9019, https://doi.org/10.1002/2017GL074691, 2017b.
Su, H., Neelin, J. D., and Meyerson, J. E.: Mechanisms for lagged atmospheric response to ENSO SST forcing, J. Climate, 18, 4195–4215, 2005.
Sullivan, A., Luo, J.-J., Hirst, A. C., Bi, D., Cai, W., and He, J.: Robust contribution of decadal anomalies to the frequency of central-Pacific El Niño, Sci. Rep., 6, 38540, https://doi.org/10.1038/srep38540, 2016.
Sun, C., Kucharski, F., Li, J., Jin, F.-F., Kang, I.-S., and Ding, R.: Western tropical Pacific multidecadal variability forced by the Atlantic multidecadal oscillation, Nat. Commun., 8, 15998, https://doi.org/10.1038/ncomms15998, 2017.
Timmermann, A., An, S. I., Kug, J. S., Jin, F.-F., Cai, W., 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., 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.: El Niño–Southern Oscillation complexity, Nature, 559, 535–545, https://doi.org/10.1038/s41586-018-0252-6, 2018.
Tokinaga, H., Richter, I., and Kosaka, Y.: ENSO influence on the Atlantic Niño, revisited: Multi-year versus single-year ENSO events, J. Climate, 32, 4585–4600, https://doi.org/10.1175/JCLI-D-18-0683.1, 2019.
Tozuka, T., Feng, M., Han, W., Kido, S., and Zhang, L.: The Ningaloo Niño/Niña: Mechanisms, relation with other climate modes and impacts, Tropical and Extratropical Air–Sea Interactions, edited by: Behera, S. K., Elsevier, 207–219, ISBN 9780128181560, 2021.
Voldoire, A., Exarchou, E., Sanchez‐Gomez, E., Demissie, T., Deppenmeier, A.-L., Frauen, C., Goubanova, K., Hazeleger, W., Keenlyside, N., Koseki, S., Prodhomme, C., Shonk, J., Toniazzo, T., and Traoré, A.-K.: Role of wind stress in driving SST biases in the tropical Atlantic, Clim. Dynam., 53, 3481–3504, https://doi.org/10.1007/s00382-019-04717-0, 2019.
von Storch, H., Bürger, G., Schnur, R., and von Storch, J.-S.: Principal oscillation patterns: A review, J. Climate, 8, 377–400, https://doi.org/10.1175/1520-0442(1995)008<0377:POPAR>2.0.CO;2, 1995.
Wang, B., Wu, R., and Fu, X.: Pacific–East Asian Teleconnection: How Does ENSO Affect East Asian Climate?, J. Climate, 13, 1517–1536, https://doi.org/10.1175/1520-0442(2000)013<1517:PEATHD>2.0.CO;2, 2000.
Wang, B., Ding, Q., Fu, X., Kang, I.-S., Jin, K., Shukla, J., and Doblas-Reyes, F.: Fundamental challenge in simulation and prediction of summer monsoon rainfall, Geophys. Res. Lett., 32, L15711, https://doi.org/10.1029/2005GL022734, 2005.
Wang, C.: Three-ocean interactions and climate variability: A review and perspective. Clim. Dynam., 53, 5119–5136, https://doi.org/10.1007/s00382-019-04930-x, 2019.
Wang, G., Cai, W., Santoso, A., Abram, N., Ng, B., Yang, K., Geng, T., Doi, T., Du, Y., Izumo, T., Ashok, K., Li, J., Li, T., McKenna, S., Sun, S., Tozuka, T., Zheng, X., Liu, Y., Wu, L., Jia, F., Hu, S., and Li, X.: The Indian Ocean Dipole in a warming world, Nat. Rev. Earth Environ., 5, 588–604, https://doi.org/10.1038/s43017-024-00573-7, 2024b.
Wang, R., He, J., Luo, J.-J., and Chen, L.: Atlantic warming enhances the influence of Atlantic Niño on ENSO, Geophys. Res. Lett., 51, e2023GL108013, https://doi.org/10.1029/2023GL108013, 2024a.
Webster, P. J., Moore, A. M., Loschnigg, J. P., and Leben, R. R.: Coupled ocean–atmosphere dynamics in the Indian Ocean during 1997-98, Nature, 401, 356–360, 1999.
Wilks, D. S.: Resampling hypothesis tests for autocorrelated fields, J. Climate, 10, 65–82, 1997.
Wills, R. C. J., Dong, Y., Proistosecu, C., Armour, K. C., and Battisti, D. S.: Systematic Climate Model Biases in the Large-Scale Patterns of Recent Sea-Surface Temperature and Sea-Level Pressure Change, Geophys. Res. Lett., 49, e2022GL100011, https://doi.org/10.1029/2022GL100011, 2022.
Wu, J., Fan, H., Lin, S., Zhong, W., He, S., Keenlyside, N., and Yang, S.: Boosting effect of strong western pole of the Indian Ocean Dipole on the decay of El Niño events, npj Clim. Atmos. Sci., 7, 6, https://doi.org/10.1038/s41612-023-00554-5, 2024.
Xie, S.-P. and Carton, J. A.: Tropical Atlantic variability: Patterns, mechanisms, and impacts. Earth Climate: The Ocean-Atmosphere Interaction, Geophys. Monogr. Ser., 147, 121–142, https://doi.org/10.1029/147GM07, 2004.
Yu, J., Kao, P., Paek, H., Hsu, H., Hung, C., Lu, M., and An, S.: Linking Emergence of the Central Pacific El Niño to the Atlantic Multidecadal Oscillation, J. Climate, 28, 651–662, https://doi.org/10.1175/JCLI-D-14-00347.1, 2015.
Zebiak, S. E.: Air–sea interaction in the equatorial Atlantic region, J. Climate, 6, 1567–1586, 1993.
Zebiak, S. E. and Cane, M. A.: A model El Niño-Southern Oscillation, Mon. Weather Rev., 115, 2262–2278, 1987.
Zhang, L., Wang, G., Newman, M., and Han, W.: Interannual to decadal variability of tropical Indian Ocean sea surface temperature: Pacific influence versus local internal variability, J. Climate, 34, 2669–2684, https://doi.org/10.1175/JCLI-D-20-0807.1, 2021.
Zhang, R., Sutton, R., Danabasoglu, G., Kwon, Y.-O., Marsh, R., Yeager, S. G., Amrhein, D. E., and Little, C. M.: A review of the role of the Atlantic Meridional Overturning Circulation in Atlantic Multidecadal Variability and associated climate impacts, Rev. Geophys., 57, 316–375, https://doi.org/10.1029/2019RG000644, 2019.
Zhang, W., Jiang, F., Stuecker, M. F., Jin, F.-F., and Timmermann, A.: Spurious North Tropical Atlantic precursors to El Niño, Nat. Commun., 12, 3096, https://doi.org/10.1038/s41467-021-23411-6, 2021.
Zhang, Y., Wallace, J. M., and Battisti, D. S.: ENSO-like interdecadal variability. J. Climate, 10, 1004–1020, 1997.
Zhao, Y. and Capotondi, A.: The role of the tropical Atlantic in tropical Pacific climate variability, npj Clim. Atmos. Sci., 7, 140, https://doi.org/10.1038/s41612-024-00677-3, 2024.
Zhao, Y., Jin, Y., Capotondi, A., Li, J., and Sun, D.: The role of tropical Atlantic in ENSO predictability barrier, Geophys. Res. Lett., 50, e2022GL101853, https://doi.org/10.1029/2022GL101853, 2023.
Zhao, S., Jin, F.-F., Stuecker, M. F., Thompson, P. R., Kug, J.-S., McPhaden, M. J., Cane, M. A., Wittenberg, A. T., and Cai, W.: Explainable El Niño predictability from climate mode interactions, Nature, 630, 891–898, https://doi.org/10.1038/s41586-024-07534-6, 2024.
Zhou, L. and Zhang, R.-H.: A self-attention–based neural network for three-dimensional multivariate modeling and its skillful ENSO predictions, Sci. Adv., 9, eadf282, https://doi.org/10.1126/sciadv.adf2827, 2023.
Zhou, T., Turner, A. G., Kinter, J. L., Wang, B., Qian, Y., Chen, X., Wu, B., Wang, B., Liu, B., Zou, L., and He, B.: GMMIP (v1.0) contribution to CMIP6: Global Monsoons Model Inter-comparison Project, Geosci. Model Dev., 9, 3589–3604, https://doi.org/10.5194/gmd-9-3589-2016, 2016.
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.
Tropical ocean basins influence each other through multiple pathways and mechanisms, referred to...
