Articles | Volume 16, issue 4
https://doi.org/10.5194/esd-16-1001-2025
© 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-16-1001-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A multi-model analysis of the decadal prediction skill for the North Atlantic ocean heat content
Teresa Carmo-Costa
CORRESPONDING AUTHOR
Instituto Dom Luiz, Faculdade de Ciências da Universidade de Lisboa, Lisbon, Portugal
Roberto Bilbao
Barcelona Supercomputing Center, Barcelona, Spain
Jon Robson
National Centre for Atmospheric Science, University of Reading, Reading, UK
Ana Teles-Machado
Instituto Dom Luiz, Faculdade de Ciências da Universidade de Lisboa, Lisbon, Portugal
Instituto Português do Mar e da Atmosfera, Lisbon, Portugal
Barcelona Supercomputing Center, Barcelona, Spain
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Eneko Martin-Martinez, Amanda Frigola, Eduardo Moreno-Chamarro, Daria Kuznetsova, Saskia Loosveldt-Tomas, Margarida Samsó Cabré, Pierre-Antoine Bretonnière, and Pablo Ortega
Earth Syst. Dynam., 16, 1343–1364, https://doi.org/10.5194/esd-16-1343-2025, https://doi.org/10.5194/esd-16-1343-2025, 2025
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We investigate the impact of model resolution on different processes in the North Atlantic using three different resolutions of the same climate model. The higher resolutions allow for the explicit simulation of smaller-scale processes. We found differences across resolutions in how denser waters are formed and transported southward, impacting the large-scale circulation of the Atlantic Ocean.
Florian Sauerland, Pierre-Vincent Huot, Sylvain Marchi, Thierry Fichefet, Hugues Goosse, Konstanze Haubner, François Klein, François Massonnet, Bianca Mezzina, Eduardo Moreno-Chamarro, Pablo Ortega, Frank Pattyn, Charles Pelletier, Deborah Verfaillie, Lars Zipf, and Nicole van Lipzig
EGUsphere, https://doi.org/10.5194/egusphere-2025-2889, https://doi.org/10.5194/egusphere-2025-2889, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
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We simulated the Antarctic climate from 1985 to 2014. Our model is driven using the ERA-5 reanalysis for one simulation and the EC-Earth global climate model for three others. Most of the simulated trends, such as sea ice extent and precipitation over land, have opposite signs for the two drivers, but agree between the three EC-Earth driven simulations. We conclude that these opposing trends must be due to the different drivers, and that the climate over land is less predictable than over sea.
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).
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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.
Carlos Delgado-Torres, Markus G. Donat, Núria Pérez-Zanón, Verónica Torralba, Roberto Bilbao, Pierre-Antoine Bretonnière, Margarida Samsó-Cabré, Albert Soret, and Francisco J. Doblas-Reyes
EGUsphere, https://doi.org/10.5194/egusphere-2025-3674, https://doi.org/10.5194/egusphere-2025-3674, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
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We explored how to provide consistent climate forecasts from months to years ahead. Our approach combines short-term forecasts with long-term climate information to create more reliable and regular predictions. We found that this method performs almost as well as more complex forecasts but is easier and cheaper to produce. This can help climate services deliver better guidance for planning in agriculture, water, and disaster risk.
Wah Kin Michael Lai, Jon Robson, Laura Wilcox, Nick Dunstone, and Rowan Sutton
EGUsphere, https://doi.org/10.5194/egusphere-2025-2598, https://doi.org/10.5194/egusphere-2025-2598, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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In a climate model at two different resolutions, anthropogenic aerosols induce a fast cooling followed by a delayed warming in the subpolar North Atlantic. The delayed warming is stronger at higher resolution due to a stronger Atlantic Meridional Overturning Circulation (AMOC) response. This difference is due to the lower resolution model having more sea ice which insulates the ocean. This result show that the North Atlantic response to external forcing is sensitive to regional differences.
Mehdi Pasha Karami, Torben Koenigk, Shiyu Wang, René Navarro Labastida, Tim Kruschke, Aude Carreric, Pablo Ortega, Klaus Wyser, Ramon Fuentes Franco, Agatha M. de Boer, Marie Sicard, and Aitor Aldama Campino
EGUsphere, https://doi.org/10.5194/egusphere-2025-2653, https://doi.org/10.5194/egusphere-2025-2653, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
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This study uses a high-resolution global climate model to simulate future climate, focusing on the Arctic and North Atlantic. The model captures observed sea ice loss and Atlantic circulation trends, projecting a nearly ice-free Arctic by 2040. It introduces a new method to quantify deep water formation, revealing how different ocean regions contribute to the weakening of overturning circulation in a warming climate.
Pedro José Roldán-Gómez, Pablo Ortega, and Markus G. Donat
EGUsphere, https://doi.org/10.5194/egusphere-2025-1784, https://doi.org/10.5194/egusphere-2025-1784, 2025
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The overshoot scenarios, in which temperatures exceed the targets of the Paris Agreement and are brought back afterwards with a net-negative emission strategy, are known to activate irreversible processes in the climate system. This work analyses in detail the impact of some of these mechanisms, with a particular focus on those associated with ocean circulation and sea ice changes.
Alba Santos-Espeso, María Gonçalves Ageitos, Pablo Ortega, Carlos Pérez García-Pando, Markus G. Donat, Margarida Samso Cabré, and Saskia Loosveldt Tomas
EGUsphere, https://doi.org/10.5194/egusphere-2025-1286, https://doi.org/10.5194/egusphere-2025-1286, 2025
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Short-lived air pollutants (e.g., aerosols and ozone) affect climate differently than greenhouse gases. Using climate models, we found that during 1950–2014, these pollutants caused global cooling, stronger in the Arctic, increased vertical mixing in the Labrador Sea, and southward displacement of the tropical rain belt. These regional impacts oppose those of greenhouse gases. Hence, future reductions in pollution for better air quality must be accompanied by stricter greenhouse gas mitigation.
Rashed Mahmood, Markus G. Donat, Roberto Bilbao, Pablo Ortega, Vladimir Lapin, Etienne Tourigny, and Francisco Doblas-Reyes
EGUsphere, https://doi.org/10.5194/egusphere-2025-1208, https://doi.org/10.5194/egusphere-2025-1208, 2025
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We present 30 year long initialized climate predictions run with the EC-Earth3 model. The predictions show high skill in most regions for near-surface temperatures, with some added skill from initialization for the first decade, but only very limited added skill beyond. The predictions exhibit drift associated with a persistent slowdown in Atlantic Meridonial Overturning Circulation , leaving the initialised predictions in a different climate state than the historical climate simulations.
Roberto Bilbao, Thomas J. Aubry, Matthew Toohey, and Pablo Ortega
EGUsphere, https://doi.org/10.5194/egusphere-2025-609, https://doi.org/10.5194/egusphere-2025-609, 2025
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Large volcanic eruptions are unpredictable and can have significant climatic impacts. If one occurs, operational decadal forecasts will become invalid and must be rerun including the volcanic forcing. By analyzing the climate response in EC-Earth3 retrospective predictions, we show that idealised forcings produced with two simple models could be used in operational decadal forecasts to account for the radiative impacts of the next major volcanic eruption.
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
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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.
Amanda Frigola, Eneko Martin-Martinez, Eduardo Moreno-Chamarro, Margarida Samsó, Saskia Loosvelt-Tomas, Pierre-Antoine Bretonnière, Daria Kuznetsova, Xia Lin, and Pablo Ortega
EGUsphere, https://doi.org/10.5194/egusphere-2025-547, https://doi.org/10.5194/egusphere-2025-547, 2025
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We examine the performance of coupled climate models at unprecedented resolutions, capable of resolving ocean eddies in extensive areas of the North Atlantic. Eddy-resolving models present more realistic density profiles and stronger deep water convection in the subpolar North Atlantic. The strength and structure of the Gulf Stream, North Atlantic Current, and subpolar gyre are also improved at high resolution, and so is the Atlantic Meridional Overturning Circulation.
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
Geosci. Model Dev., 18, 461–482, https://doi.org/10.5194/gmd-18-461-2025, https://doi.org/10.5194/gmd-18-461-2025, 2025
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We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10–15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100 km and a 25 km grid. The three models are compared with observations to study the improvements thanks to the increased resolution.
Raffaele Bernardello, Valentina Sicardi, Vladimir Lapin, Pablo Ortega, Yohan Ruprich-Robert, Etienne Tourigny, and Eric Ferrer
Earth Syst. Dynam., 15, 1255–1275, https://doi.org/10.5194/esd-15-1255-2024, https://doi.org/10.5194/esd-15-1255-2024, 2024
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The ocean mitigates climate change by absorbing about 25 % of the carbon that is emitted to the atmosphere. However, ocean CO2 uptake is not constant in time, and improving our understanding of the mechanisms regulating this variability can potentially lead to a better predictive capability of its future behavior. In this study, we compare two ocean modeling practices that are used to reconstruct the historical ocean carbon uptake, demonstrating the abilities of one over the other.
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
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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.
Rashed Mahmood, Markus G. Donat, Pablo Ortega, Francisco J. Doblas-Reyes, Carlos Delgado-Torres, Margarida Samsó, and Pierre-Antoine Bretonnière
Earth Syst. Dynam., 13, 1437–1450, https://doi.org/10.5194/esd-13-1437-2022, https://doi.org/10.5194/esd-13-1437-2022, 2022
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Near-term climate change projections are strongly affected by the uncertainty from internal climate variability. Here we present a novel approach to reduce such uncertainty by constraining decadal-scale variability in the projections using observations. The constrained ensembles show significant added value over the unconstrained ensemble in predicting global climate 2 decades ahead. We also show the applicability of regional constraints for attributing predictability to certain ocean regions.
Amélie Simon, Guillaume Gastineau, Claude Frankignoul, Vladimir Lapin, and Pablo Ortega
Weather Clim. Dynam., 3, 845–861, https://doi.org/10.5194/wcd-3-845-2022, https://doi.org/10.5194/wcd-3-845-2022, 2022
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The influence of the Arctic sea-ice loss on atmospheric circulation in midlatitudes depends on persistent sea surface temperatures in the North Pacific. In winter, Arctic sea-ice loss and a warm North Pacific Ocean both induce depressions over the North Pacific and North Atlantic, an anticyclone over Greenland, and a stratospheric anticyclone over the Arctic. However, the effects are not additive as the interaction between both signals is slightly destructive.
Oscar Dimdore-Miles, Lesley Gray, Scott Osprey, Jon Robson, Rowan Sutton, and Bablu Sinha
Atmos. Chem. Phys., 22, 4867–4893, https://doi.org/10.5194/acp-22-4867-2022, https://doi.org/10.5194/acp-22-4867-2022, 2022
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This study examines interactions between variations in the strength of polar stratospheric winds and circulation in the North Atlantic in a climate model simulation. It finds that the Atlantic Meridional Overturning Circulation (AMOC) responds with oscillations to sets of consecutive Northern Hemisphere winters, which show all strong or all weak polar vortex conditions. The study also shows that a set of strong vortex winters in the 1990s contributed to the recent slowdown in the observed AMOC.
Ralf Döscher, Mario Acosta, Andrea Alessandri, Peter Anthoni, Thomas Arsouze, Tommi Bergman, Raffaele Bernardello, Souhail Boussetta, Louis-Philippe Caron, Glenn Carver, Miguel Castrillo, Franco Catalano, Ivana Cvijanovic, Paolo Davini, Evelien Dekker, Francisco J. Doblas-Reyes, David Docquier, Pablo Echevarria, Uwe Fladrich, Ramon Fuentes-Franco, Matthias Gröger, Jost v. Hardenberg, Jenny Hieronymus, M. Pasha Karami, Jukka-Pekka Keskinen, Torben Koenigk, Risto Makkonen, François Massonnet, Martin Ménégoz, Paul A. Miller, Eduardo Moreno-Chamarro, Lars Nieradzik, Twan van Noije, Paul Nolan, Declan O'Donnell, Pirkka Ollinaho, Gijs van den Oord, Pablo Ortega, Oriol Tintó Prims, Arthur Ramos, Thomas Reerink, Clement Rousset, Yohan Ruprich-Robert, Philippe Le Sager, Torben Schmith, Roland Schrödner, Federico Serva, Valentina Sicardi, Marianne Sloth Madsen, Benjamin Smith, Tian Tian, Etienne Tourigny, Petteri Uotila, Martin Vancoppenolle, Shiyu Wang, David Wårlind, Ulrika Willén, Klaus Wyser, Shuting Yang, Xavier Yepes-Arbós, and Qiong Zhang
Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, https://doi.org/10.5194/gmd-15-2973-2022, 2022
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The Earth system model EC-Earth3 is documented here. Key performance metrics show physical behavior and biases well within the frame known from recent models. With improved physical and dynamic features, new ESM components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.
Charles Pelletier, Thierry Fichefet, Hugues Goosse, Konstanze Haubner, Samuel Helsen, Pierre-Vincent Huot, Christoph Kittel, François Klein, Sébastien Le clec'h, Nicole P. M. van Lipzig, Sylvain Marchi, François Massonnet, Pierre Mathiot, Ehsan Moravveji, Eduardo Moreno-Chamarro, Pablo Ortega, Frank Pattyn, Niels Souverijns, Guillian Van Achter, Sam Vanden Broucke, Alexander Vanhulle, Deborah Verfaillie, and Lars Zipf
Geosci. Model Dev., 15, 553–594, https://doi.org/10.5194/gmd-15-553-2022, https://doi.org/10.5194/gmd-15-553-2022, 2022
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We present PARASO, a circumpolar model for simulating the Antarctic climate. PARASO features five distinct models, each covering different Earth system subcomponents (ice sheet, atmosphere, land, sea ice, ocean). In this technical article, we describe how this tool has been developed, with a focus on the
coupling interfacesrepresenting the feedbacks between the distinct models used for contribution. PARASO is stable and ready to use but is still characterized by significant biases.
Pablo Ortega, Jon I. Robson, Matthew Menary, Rowan T. Sutton, Adam Blaker, Agathe Germe, Jöel J.-M. Hirschi, Bablu Sinha, Leon Hermanson, and Stephen Yeager
Earth Syst. Dynam., 12, 419–438, https://doi.org/10.5194/esd-12-419-2021, https://doi.org/10.5194/esd-12-419-2021, 2021
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Deep Labrador Sea densities are receiving increasing attention because of their link to many of the processes that govern decadal climate oscillations in the North Atlantic and their potential use as a precursor of those changes. This article explores those links and how they are represented in global climate models, documenting the main differences across models. Models are finally compared with observational products to identify the ones that reproduce the links more realistically.
Roberto Bilbao, Simon Wild, Pablo Ortega, Juan Acosta-Navarro, Thomas Arsouze, Pierre-Antoine Bretonnière, Louis-Philippe Caron, Miguel Castrillo, Rubén Cruz-García, Ivana Cvijanovic, Francisco Javier Doblas-Reyes, Markus Donat, Emanuel Dutra, Pablo Echevarría, An-Chi Ho, Saskia Loosveldt-Tomas, Eduardo Moreno-Chamarro, Núria Pérez-Zanon, Arthur Ramos, Yohan Ruprich-Robert, Valentina Sicardi, Etienne Tourigny, and Javier Vegas-Regidor
Earth Syst. Dynam., 12, 173–196, https://doi.org/10.5194/esd-12-173-2021, https://doi.org/10.5194/esd-12-173-2021, 2021
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This paper presents and evaluates a set of retrospective decadal predictions with the EC-Earth3 climate model. These experiments successfully predict past changes in surface air temperature but show poor predictive capacity in the subpolar North Atlantic, a well-known source region of decadal climate variability. The poor predictive capacity is linked to an initial shock affecting the Atlantic Ocean circulation, ultimately due to a suboptimal representation of the Labrador Sea density.
Irene Polo, Keith Haines, Jon Robson, and Christopher Thomas
Ocean Sci., 16, 1067–1088, https://doi.org/10.5194/os-16-1067-2020, https://doi.org/10.5194/os-16-1067-2020, 2020
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AMOC variability controls climate and is driven by wind and buoyancy forcing in the Atlantic. Density changes there are expected to connect to tropical regions. We develop methods to identify boundary density profiles at 26° N which relate to the AMOC. We found that density anomalies propagate equatorward along the western boundary, eastward along the Equator and then poleward up the eastern boundary with 2 years lag between boundaries. Record lengths of more than 26 years are required.
Cited articles
Athanasiadis, P. J., Yeager, S., Kwon, Y.-O., Bellucci, A., Smith, D. W., and Tibaldi, S.: Decadal predictability of North Atlantic blocking and the NAO, npj Climate and Atmospheric Science, 3, 20, https://doi.org/10.1038/s41612-020-0120-6, 2020. a, b
Balaguru, K., Foltz, G. R., and Leung, L. R.: Increasing Magnitude of Hurricane Rapid Intensification in the Central and Eastern Tropical Atlantic, Geophys. Res. Lett., 45, 4238–4247, https://doi.org/10.1029/2018GL077597, 2018. a
Balmaseda, M. A., Mogensen, K., and Weaver, A. T.: Evaluation of the ECMWF ocean reanalysis system ORAS4, Q. J. Roy. Meteor. Soc., 139, 1132–1161, https://doi.org/10.1002/qj.2063, 2013. a
Bethke, I., Wang, Y., Counillon, F., Keenlyside, N., Kimmritz, M., Fransner, F., Samuelsen, A., Langehaug, H., Svendsen, L., Chiu, P.-G., Passos, L., Bentsen, M., Guo, C., Gupta, A., Tjiputra, J., Kirkevåg, A., Olivié, D., Seland, Ø., Solsvik Vågane, J., Fan, Y., and Eldevik, T.: NorCPM1 and its contribution to CMIP6 DCPP, Geosci. Model Dev., 14, 7073–7116, https://doi.org/10.5194/gmd-14-7073-2021, 2021. a, b
Bilbao, R., Wild, S., Ortega, P., Acosta-Navarro, J., Arsouze, T., Bretonnière, P.-A., Caron, L.-P., Castrillo, M., Cruz-García, R., Cvijanovic, I., Doblas-Reyes, F. J., Donat, M., Dutra, E., Echevarría, P., Ho, A.-C., Loosveldt-Tomas, S., Moreno-Chamarro, E., Pérez-Zanon, N., Ramos, A., Ruprich-Robert, Y., Sicardi, V., Tourigny, E., and Vegas-Regidor, J.: Assessment of a full-field initialized decadal climate prediction system with the CMIP6 version of EC-Earth, Earth Syst. Dynam., 12, 173–196, https://doi.org/10.5194/esd-12-173-2021, 2021. a, b, c, d, e
Bilbao, R. A. F., Gregory, J. M., Bouttes, N., Palmer, M. D., and Stott, P.: Attribution of ocean temperature change to anthropogenic and natural forcings using the temporal, vertical and geographical structure, Clim. Dynam., 53, 5389–5413, https://doi.org/10.1007/s00382-019-04910-1, 2019. a
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. a, b, c
Bonnet, R., Boucher, O., Deshayes, J., Gastineau, G., Hourdin, F., Mignot, J., Servonnat, J., and Swingedouw, D.: Presentation and Evaluation of the IPSL-CM6A-LR Ensemble of Extended Historical Simulations, J. Adv. Model. Earth Sy., 13, e2021MS002565, https://doi.org/10.1029/2021MS002565, 2021a. a
Bonnet, R., Swingedouw, D., Gastineau, G., Boucher, O., Deshayes, J., Hourdin, F., Mignot, J., Servonnat, J., and Sima, A.: Increased risk of near term global warming due to a recent AMOC weakening, Nat. Commun., 12, 6108, https://doi.org/10.1038/s41467-021-26370-0, 2021b. a
Borchert, L. F., Müller, W. A., and Baehr, J.: Atlantic ocean heat transport influences interannual-to-decadal surface temperature predictability in the North Atlantic Region, J. Climate, 31, 6763–6782, https://doi.org/10.1175/JCLI-D-17-0734.1, 2018. a, b
Buckley, M. W. and Marshall, J.: Observations, inferences, and mechanisms of the Atlantic Meridional Overturning Circulation: A review, Rev. Geophys., 54, 5–63, https://doi.org/10.1002/2015RG000493, 2016. a
Buckley, M. W., DelSole, T., Lozier, M. S., and Li, L.: Predictability of North Atlantic Sea surface temperature and upper-ocean heat content, J. Climate, 32, 3005–3023, https://doi.org/10.1175/JCLI-D-18-0509.1, 2019. a
Caesar, L., McCarthy, G. D., Thornalley, D. J., Cahill, N., and Rahmstorf, S.: Current Atlantic Meridional Overturning Circulation weakest in last millennium, Nat. Geosci., 14, 118–120, https://doi.org/10.1038/s41561-021-00699-z, 2021. a
Carmo-Costa, T., Bilbao, R., Ortega, P., Teles-Machado, A., and Dutra, E.: Trends, variability and predictive skill of the ocean heat content in North Atlantic: an analysis with the EC-Earth3 model, Clim. Dynam., 58, 1311–1328, https://doi.org/10.1007/s00382-021-05962-y, 2021. a, b, c, d
Chang, Y. S., Zhang, S., Rosati, A., Delworth, T. L., and Stern, W. F.: An assessment of oceanic variability for 1960–2010 from the GFDL ensemble coupled data assimilation, Clim. Dynam., 40, 775–803, https://doi.org/10.1007/s00382-012-1412-2, 2013. a
Cherchi, A., Fogli, P. G., Lovato, T., Peano, D., Iovino, D., Gualdi, S., Masina, S., Scoccimarro, E., Materia, S., Bellucci, A., and Navarra, A.: Global Mean Climate and Main Patterns of Variability in the CMCC-CM2 Coupled Model, J. Adv. Model. Earth Sy., 11, 185–209, https://doi.org/10.1029/2018MS001369, 2019. a
Delgado-Torres, C., Donat, M. G., Gonzalez-Reviriego, N., Caron, L.-P., Athanasiadis, P. J., Bretonnière, P.-A., Dunstone, N. J., Ho, A.-C., Nicoli, D., Pankatz, K., Paxian, A., Pérez-Zanón, N., Cabré, M. S., Solaraju-Murali, B., Soret, A., and Doblas-Reyes, F. J.: Multi-Model Forecast Quality Assessment of CMIP6 Decadal Predictions, J. Climate, 35, 4363–4382, https://doi.org/10.1175/JCLI-D-21-0811.1, 2022. a, b
Devilliers, M., Swingedouw, D., Mignot, J., Deshayes, J., Garric, G., and Ayache, M.: A realistic Greenland ice sheet and surrounding glaciers and ice caps melting in a coupled climate model, Clim. Dynam., 57, 2467–2489, https://doi.org/10.1007/s00382-021-05816-7, 2021. a
Doblas-Reyes, F. J., Andreu-Burillo, I., Chikamoto, Y., García-Serrano, J., Guemas, V., Kimoto, M., Mochizuki, T., Rodrigues, L. R. L., and van Oldenborgh, G. J.: Initialized near-term regional climate change prediction, Nat. Commun., 4, 1–9, https://doi.org/10.1038/ncomms2704, 2013. a
Döscher, R., Acosta, M., Alessandri, A., Anthoni, P., Arsouze, T., Bergman, T., Bernardello, R., Boussetta, S., Caron, L.-P., Carver, G., Castrillo, M., Catalano, F., Cvijanovic, I., Davini, P., Dekker, E., Doblas-Reyes, F. J., Docquier, D., Echevarria, P., Fladrich, U., Fuentes-Franco, R., Gröger, M., v. Hardenberg, J., Hieronymus, J., Karami, M. P., Keskinen, J.-P., Koenigk, T., Makkonen, R., Massonnet, F., Ménégoz, M., Miller, P. A., Moreno-Chamarro, E., Nieradzik, L., van Noije, T., Nolan, P., O'Donnell, D., Ollinaho, P., van den Oord, G., Ortega, P., Prims, O. T., Ramos, A., Reerink, T., Rousset, C., Ruprich-Robert, Y., Le Sager, P., Schmith, T., Schrödner, R., Serva, F., Sicardi, V., Sloth Madsen, M., Smith, B., Tian, T., Tourigny, E., Uotila, P., Vancoppenolle, M., Wang, S., Wårlind, D., Willén, U., Wyser, K., Yang, S., Yepes-Arbós, X., and Zhang, Q.: The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6, Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, 2022. a
Drijfhout, S., van Oldenborgh, G. J., and Cimatoribus, A.: Is a decline of AMOC causing the warming hole above the North Atlantic in observed and modeled warming patterns?, J. Climate, 25, 8373–8379, https://doi.org/10.1175/JCLI-D-12-00490.1, 2012. a, b
Durack, P. J., Gleckler, P. J., Purkey, S. G., Johnson, G. C., Lyman, J. M., and Boyer, T. P.: Ocean Warming: From the Surface to the Deep in Observations and Models, Oceanography, 31, 41–51, https://doi.org/10.5670/oceanog.2018.227, 2018. a
Eden, C. and Jung, T.: North Atlantic interdecadal variability: Oceanic response to the North Atlantic oscillation (1865–1997), J. Climate, 14, 676–691, https://doi.org/10.1175/1520-0442(2001)014<0676:NAIVOR>2.0.CO;2, 2001. a
Enfield, D. B., Mestas-Nunez, A. M., and Trimble, P. J.: The Atlantic Multidecadal Oscillation and its relation to rainfall and river flows in the continental U.S., Geophys. Res. Lett., 28, 2077–2080, https://doi.org/10.1029/2000GL012745, 2001. a
ESGF: CMIP6, Earth System Grid Federation [data set], https://aims2.llnl.gov/search, last access: 9 July 2025. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a, b
Gastineau, G. and Frankignoul, C.: Influence of the North Atlantic SST Variability on the Atmospheric Circulation during the Twentieth Century, J. Climate, 28, 1396–1416, https://doi.org/10.1175/JCLI-D-14-00424.1, 2015. a
Gleckler, P. J., Santer, B. D., Domingues, C. M., Pierce, D. W., Barnett, T. P., Church, J. A., Taylor, K. E., AchutaRao, K. M., Boyer, T. P., Ishii, M., and Caldwell, P. M.: Human-induced global ocean warming on multidecadal timescales, Nat. Clim. Change, 2, 524–529, https://doi.org/10.1038/nclimate1553, 2012. a
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. a
Gouretski, V. and Reseghetti, F.: On depth and temperature biases in bathythermograph data: Development of a new correction scheme based on analysis of a global ocean database, Deep-Sea Res. Pt. II, 57, 812–833, https://doi.org/10.1016/j.dsr.2010.03.011, 2010. a
Guemas, V. and Salas, M. D.: Simulation of the Atlantic meridional overturning circulation in an atmosphere-ocean global coupled model. Part I: A mechanism governing the variability of ocean convection in a preindustrial experiment, Clim. Dynam., 31, 29–48, https://doi.org/10.1007/s00382-007-0336-8, 2008. a
Guemas, V., Manubens, N., Garcia-Serrano, J., Fuckar, N., Caron, L.-P., Bellprat, O., Rodrigues, L., Torralba, V., Hunter, A., Prodhomme, C., Menegoz, M., Manubens, D., Ardilouze, C., Batte, L., Lienert, F., Giner, J., Baudouin, J.-P., Gonzalez, N., Auger, L., Cortesi, N., Exarchou, E., Cruz, R., Andreu-Burillo, I., Saurral, R., Manubens, D., Lienert, F., Garcia-Serrano, J., Batte, L., Caron, L.-P., Rodrigues, L., Menegoz, M., Fuckar, N., Manubens, N., Bellprat, O., Torralba, V., and Guemas, V.: Package 's2dverification': Set of Common Tools for Forecast Verification, Tech. rep., BSC, Barcelona, 2019. a
Häkkinen, S., Rhines, P. B., and Worthen, D. L.: Heat content variability in the North Atlantic Ocean in ocean reanalyses, Geophys. Res. Lett., 42, 2901–2909, https://doi.org/10.1002/2015GL063299, 2015. a
Hazeleger, W., Guemas, V., Wouters, B., Corti, S., Andreu-Burillo, I., Doblas-Reyes, F. J., Wyser, K., and Caian, M.: Multiyear climate predictions using two initialization strategies, Geophys. Res. Lett., 40, 1794–1798, https://doi.org/10.1002/grl.50355, 2013. a, b
Hegerl, G. C., Ballinger, A. P., Booth, B. B. B., Borchert, L. F., Brunner, L., Donat, M. G., Doblas-Reyes, F. J., Harris, G. R., Lowe, J., Mahmood, R., Mignot, J., Murphy, J. M., Swingedouw, D., and Weisheimer, A.: Toward Consistent Observational Constraints in Climate Predictions and Projections, Frontiers in Climate, 3, 678109, https://doi.org/10.3389/fclim.2021.678109, 2021. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J. N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R.J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: Complete ERA5 from 1940: Fifth generation of ECMWF atmospheric reanalyses of the global climate, Copernicus Climate Change Service (C3S) Data Store (CDS) [data set], https://doi.org/10.24381/cds.143582cf, 2017. a
Hurrell, J. W.: Decadal Trends in the North Atlantic Oscillation: Regional Temperatures and Precipitation, Science, 269, 7–10, https://doi.org/10.1126/science.269.5224.676, 1995. a
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, in press, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, https://doi.org/10.1017/9781009157896, 2021. a
Johnson, G. C. and Lyman, J. M.: Warming trends increasingly dominate global ocean, Nat. Clim. Change, 10, 757–761, https://doi.org/10.1038/s41558-020-0822-0, 2020. a, b
Josey, S. A., Hirschi, J. J.-M., Sinha, B., Duchez, A., Grist, J. P., and Robert Marsh: The Recent Atlantic Cold Anomaly: Causes, Consequences, and Related Phenomena, Annu. Rev. Mar. Sci., 10, 475–501, https://doi.org/10.1146/annurev-marine-121916-063102, 2018. a, b
Kay, G., Dunstone, N. J., Smith, D. M., Betts, R. A., Cunningham, C., and Scaife, A. A.: Assessing the chance of unprecedented dry conditions over North Brazil during El Niño events, Environ. Res. Lett., 17, 064016, https://doi.org/10.1088/1748-9326/ac6df9, 2022. a
Keenlyside, N. S., Latif, M., Jungclaus, J., Kornblueh, L., and Roeckner, E.: Advancing decadal-scale climate prediction in the North Atlantic sector, Nature, 453, 84–88, https://doi.org/10.1038/nature06921, 2008. a
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. a, b
Kim, H.-J., An, S.-I., Park, J.-H., Sung, M.-K., Kim, D., Choi, Y., and Kim, J.-S.: North Atlantic Oscillation impact on the Atlantic Meridional Overturning Circulation shaped by the mean state, npj Climate and Atmospheric Science, 6, 25, https://doi.org/10.1038/s41612-023-00354-x, 2023a. a
Kim, W. M., Yeager, S. G., Danabasoglu, G., and Chang, P.: Exceptional multi-year prediction skill of the Kuroshio Extension in the CESM high-resolution decadal prediction system, npj Climate and Atmospheric Science, 6, 118, https://doi.org/10.1038/s41612-023-00444-w, 2023b. a
Knight, J. R., Allan, R. J., Folland, C. K., Vellinga, M., and Mann, M. E.: A signature of persistent natural thermohaline circulation cycles in observed climate, Geophys. Res. Lett., 32, 1–4, https://doi.org/10.1029/2005GL024233, 2005. a
Kröger, J., Pohlmann, H., Sienz, F., Marotzke, J., Baehr, J., Köhl, A., Modali, K., Polkova, I., Stammer, D., Vamborg, F. S., and Müller, W. A.: Full-field initialized decadal predictions with the MPI earth system model: an initial shock in the North Atlantic, Clim. Dynam., 51, 2593–2608, https://doi.org/10.1007/s00382-017-4030-1, 2018. a, b, c
Kwon, Y. O., Seo, H., Ummenhofer, C. C., and Joyce, T. M.: Impact of multidecadal variability in Atlantic SST on winter atmospheric blocking, J. Climate, 33, 867–892, https://doi.org/10.1175/JCLI-D-19-0324.1, 2020. a
Langehaug, H. R., Ortega, P., Counillon, F., Matei, D., Maroon, E., Keenlyside, N., Mignot, J., Wang, Y., Swingedouw, D., Bethke, I., Yang, S., Danabasoglu, G., Bellucci, A., Ruggieri, P., Nicoli, D., and Orthun, M.: Propagation of Thermohaline Anomalies and Their Predictive Potential along the Atlantic Water Pathway, J. Climate, 35, 2111–2131, https://doi.org/10.1175/JCLI-D-20-1007.1, 2022. a
Latif, M., Sun, J., Visbeck, M., and Hadi Bordbar, M.: Natural variability has dominated Atlantic Meridional Overturning Circulation since 1900, Nat. Clim. Change, 12, 455–460, https://doi.org/10.1038/s41558-022-01342-4, 2022. a
Levitus, S., Antonov, J. I., Boyer, T. P., and Stephens, C.: Warming of the World Ocean, Science, 287, 2225–2229, https://doi.org/10.1126/science.287.5461.2225, 2000. a
Levitus, S., Antonov, J. I., Boyer, T. P., Baranova, O. K., Garcia, H. E., Locarnini, R. A., Mishonov, A. V., Reagan, J. R., Seidov, D., Yarosh, E. S., and Zweng, M. M.: World ocean heat content and thermosteric sea level change (0–2000 m), 1955–2010, Geophys. Res. Lett., 39, 1–5, https://doi.org/10.1029/2012GL051106, 2012. a
Li, H., Ilyina, T., Müller, W. A., and Landschützer, P.: Predicting the variable ocean carbon sink, Sci. Adv., 5, 1–9, https://doi.org/10.1126/sciadv.aav6471, 2019. a
Manubens, N., Caron, L. P., Hunter, A., Bellprat, O., Exarchou, E., Fučkar, N. S., Garcia-Serrano, J., Massonnet, F., Ménégoz, M., Sicardi, V., Batté, L., Prodhomme, C., Torralba, V., Cortesi, N., Mula-Valls, O., Serradell, K., Guemas, V., and Doblas-Reyes, F. J.: An R package for climate forecast verification, Environ. Modell. Softw., 103, 29–42, https://doi.org/10.1016/j.envsoft.2018.01.018, 2018. a
Maroon, E. A., Yeager, S. G., Danabasoglu, G., and Rosenbloom, N.: Was the 2015 North Atlantic Subpolar Cold Anomaly Predictable?, J. Climate, 34, 5403–5423, https://doi.org/10.1175/JCLI-D-20-0750.1, 2021. a
Mecking, J. V., Drijfhout, S. S., Hirschi, J. J., and Blaker, A. T.: Ocean and atmosphere influence on the 2015 European heatwave, Enviro. Res. Lett., 14, 114035, https://doi.org/10.1088/1748-9326/ab4d33, 2019. a
Meehl, G. A., Moss, R., Taylor, K. E., Eyring, V., Stouffer, R. J., Bony, S., and Stevens, B.: Climate model intercomparisons: Preparing for the next phase, Eos, 95, 77–78, https://doi.org/10.1002/2014EO090001, 2014. a
Menary, M. B. and Hermanson, L.: Limits on determining the skill of North Atlantic Ocean decadal predictions, Nat. Commun., 9, 1694, https://doi.org/10.1038/s41467-018-04043-9, 2018. a
Menary, M. B., Hodson, D. L., Robson, J. I., Sutton, R. T., Wood, R. A., and Hunt, J. A.: Exploring the impact of CMIP5 model biases on the simulation of North Atlantic decadal variability, Geophys. Res. Lett., 42, 5926–5934, https://doi.org/10.1002/2015GL064360, 2015. a
Met Office: Quality-controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates, Met Office [data set], https://www.metoffice.gov.uk/hadobs/en4/download-en4-2-2.html, last access: 11 March 2022. a
Met Office: HadISST2.2.0.0: Monthly global sea ice concentration and sea surface temperature data, Met Office [data set], https://www.metoffice.gov.uk/hadobs/hadisst2, last access: 30 July 2024. a
Mignot, J., García-Serrano, J., Swingedouw, D., Germe, A., Nguyen, S., Ortega, P., Guilyardi, E., and Ray, S.: Decadal prediction skill in the ocean with surface nudging in the IPSL-CM5A-LR climate model, Clim. Dynam., 47, 1225–1246, https://doi.org/10.1007/s00382-015-2898-1, 2016. a
Milinski, S., Maher, N., and Olonscheck, D.: How large does a large ensemble need to be?, Earth Syst. Dynam., 11, 885–901, https://doi.org/10.5194/esd-11-885-2020, 2020. a
Moat, B. I., Sinha, B., Berry, D. I., Drijfhout, S. S., Fraser, N., Hermanson, L., Jones, D. C., Josey, S. A., King, B., Macintosh, C., Megann, A., Oltmanns, M., Sanders, R., and Williams, S.: Ocean Heat Convergence and North Atlantic Multidecadal Heat Content Variability, J. Climate, 37, 4723–4742, https://doi.org/10.1175/JCLI-D-23-0370.1, 2024. a
Müller, W. A., Jungclaus, J. H., Mauritsen, T., Baehr, J., Bittner, M., Budich, R., Bunzel, F., Esch, M., Ghosh, R., Haak, H., Ilyina, T., Kleine, T., Kornblueh, L., Li, H., Modali, K., Notz, D., Pohlmann, H., Roeckner, E., Stemmler, I., Tian, F., and Marotzke, J.: A Higher-resolution Version of the Max Planck Institute Earth System Model (MPI-ESM1.2-HR), J. Adv. Model. Earth Sy., 10, 1383–1413, https://doi.org/10.1029/2017MS001217, 2018. a
Nicolì, D., Bellucci, A., Ruggieri, P., Athanasiadis, P. J., Materia, S., Peano, D., Fedele, G., Hénin, R., and Gualdi, S.: The Euro-Mediterranean Center on Climate Change (CMCC) decadal prediction system, Geosci. Model Dev., 16, 179–197, https://doi.org/10.5194/gmd-16-179-2023, 2023. a
Oldenburg, D., Wills, R. C. J., Armour, K. C., Thompson, L., and Jackson, L. C.: Mechanisms of Low-Frequency Variability in North Atlantic Ocean Heat Transport and AMOC, J. Climate, 34, 4733–4755, https://doi.org/10.1175/JCLI-D-20-0614.1, 2021. a
Ortega, P., Montoya, M., González-Rouco, F., Mignot, J., and Legutke, S.: Variability of the Atlantic meridional overturning circulation in the last millennium and two IPCC scenarios, Clim. Dynam., 38, 1925–1947, https://doi.org/10.1007/s00382-011-1081-6, 2012. a
Ortega, P., Mignot, J., Swingedouw, D., Sévellec, F., and Guilyardi, E.: Reconciling two alternative mechanisms behind bi-decadal variability in the North Atlantic, Prog. Oceanogr., 137, 237–249, https://doi.org/10.1016/j.pocean.2015.06.009, 2015. a
Ortega, P., Robson, J. I., Menary, M., Sutton, R. T., Blaker, A., Germe, A., Hirschi, J. J.-M., Sinha, B., Hermanson, L., and Yeager, S.: Labrador Sea subsurface density as a precursor of multidecadal variability in the North Atlantic: a multi-model study, Earth Syst. Dynam., 12, 419–438, https://doi.org/10.5194/esd-12-419-2021, 2021. a, b
Palmer, M. D., Haines, K., Tett, S. F. B., and Ansell, T. J.: Isolating the signal of ocean global warming, Geophys. Res. Lett., 34, L23610, https://doi.org/10.1029/2007GL031712, 2007. a
Palmer, M. D., Roberts, C. D., Balmaseda, M., Chang, Y. S., Chepurin, G., Ferry, N., Fujii, Y., Good, S. A., Guinehut, S., Haines, K., Hernandez, F., Köhl, A., Lee, T., Martin, M. J., Masina, S., Masuda, S., Peterson, K. A., Storto, A., Toyoda, T., Valdivieso, M., Vernieres, G., Wang, O., and Xue, Y.: Ocean heat content variability and change in an ensemble of ocean reanalyses, Clim. Dynam., 49, 909–930, https://doi.org/10.1007/s00382-015-2801-0, 2017. a
Passos, L., Langehaug, H. R., Årthun, M., Eldevik, T., Bethke, I., and Kimmritz, M.: Impact of initialization methods on the predictive skill in NorCPM: an Arctic – Atlantic case study, Clim. Dynam., 60, 2061–2080, https://doi.org/10.1007/s00382-022-06437-4, 2023. a
Piecuch, C. G., Ponte, R. M., Little, C. M., Buckley, M. W., and Fukumori, I.: Mechanisms underlying recent decadal changes in subpolar North Atlantic Ocean heat content, J. Geophys. Res.-Oceans, 122, 7181–7197, https://doi.org/10.1002/2017JC012845, 2017. a
Pohlmann, H., Jungclaus, J. H., Köhl, A., Stammer, D., and Marotzke, J.: Initializing decadal climate predictions with the GECCO oceanic synthesis: Effects on the North Atlantic, J. Climate, 22, 3926–3938, https://doi.org/10.1175/2009JCLI2535.1, 2009. a
Polkova, I., Brune, S., Kadow, C., Romanova, V., Gollan, G., Baehr, J., Glowienka-Hense, R., Greatbatch, R. J., Hense, A., Illing, S., Köhl, A., Kröger, J., Müller, W. A., Pankatz, K., and Stammer, D.: Initialization and Ensemble Generation for Decadal Climate Predictions: A Comparison of Different Methods, J. Adv. Model. Earth Sy., 11, 149–172, https://doi.org/10.1029/2018MS001439, 2019. a
Polkova, I., Swingedouw, D., Hermanson, L., Köhl, A., Stammer, D., Smith, D., Kröger, J., Bethke, I., Yang, X., Zhang, L., Nicolì, D., Athanasiadis, P. J., Karami, M. P., Pankatz, K., Pohlmann, H., Wu, B., Bilbao, R., Ortega, P., Yang, S., Sospedra-Alfonso, R., Merryfield, W., Kataoka, T., Tatebe, H., Imada, Y., Ishii, M., and Matear, R. J.: Initialization shock in the ocean circulation reduces skill in decadal predictions of the North Atlantic subpolar gyre, Frontiers in Climate, 5, 1273770, https://doi.org/10.3389/fclim.2023.1273770, 2023. a, b, c
Qin, M., Dai, A., and Hua, W.: Quantifying Contributions of Internal Variability and External Forcing to Atlantic Multidecadal Variability Since 1870, Geophys. Res. Lett., 47, e2020GL089504, https://doi.org/10.1029/2020GL089504, 2020. a
Rahmstorf, S., Box, J. E., Feulner, G., Mann, M. E., Robinson, A., Rutherford, S., and Schaffernicht, E. J.: Exceptional twentieth-century slowdown in Atlantic Ocean overturning circulation, Nat. Clim. Change, 5, 475–480, https://doi.org/10.1038/nclimate2554, 2015. a, b
Righi, M., Andela, B., Eyring, V., Lauer, A., Predoi, V., Schlund, M., Vegas-Regidor, J., Bock, L., Brötz, B., de Mora, L., Diblen, F., Dreyer, L., Drost, N., Earnshaw, P., Hassler, B., Koldunov, N., Little, B., Loosveldt Tomas, S., and Zimmermann, K.: Earth System Model Evaluation Tool (ESMValTool) v2.0 – technical overview, Geosci. Model Dev., 13, 1179–1199, https://doi.org/10.5194/gmd-13-1179-2020, 2020. a
Robson, J., Ortega, P., and Sutton, R.: A reversal of climatic trends in the North Atlantic since 2005, Nat. Geosci., https://doi.org/10.1038/ngeo2727, 2016. a, b
Robson, J., Polo, I., Hodson, D. L., Stevens, D. P., and Shaffrey, L. C.: Decadal prediction of the North Atlantic subpolar gyre in the HiGEM high-resolution climate model, Clim. Dynam., 50, 921–937, https://doi.org/10.1007/s00382-017-3649-2, 2018. a
Robson, J. I., Sutton, R. T., and Smith, D. M.: Initialized decadal predictions of the rapid warming of the North Atlantic Ocean in the mid 1990s, Geophys. Res. Lett., 39, 1–6, https://doi.org/10.1029/2012GL053370, 2012. a
Sellar, A. A., Walton, J., Jones, C. G., Wood, R., Abraham, N. L., Andrejczuk, M., Andrews, M. B., Andrews, T., Archibald, A. T., de Mora, L., Dyson, H., Elkington, M., Ellis, R., Florek, P., Good, P., Gohar, L., Haddad, S., Hardiman, S. C., Hogan, E., Iwi, A., Jones, C. D., Johnson, B., Kelley, D. I., Kettleborough, J., Knight, J. R., Köhler, M. O., Kuhlbrodt, T., Liddicoat, S., Linova-Pavlova, I., Mizielinski, M. S., Morgenstern, O., Mulcahy, J., Neininger, E., O'Connor, F. M., Petrie, R., Ridley, J., Rioual, J. C., Roberts, M., Robertson, E., Rumbold, S., Seddon, J., Shepherd, H., Shim, S., Stephens, A., Teixiera, J. C., Tang, Y., Williams, J., Wiltshire, A., and Griffiths, P. T.: Implementation of U.K. Earth System Models for CMIP6, J. Adv. Model. Earth Sy., 12, 1–27, https://doi.org/10.1029/2019MS001946, 2020. a
Siegert, S., Bellprat, O., Ménégoz, M., Stephenson, D. B., and Doblas-Reyes, F. J.: Detecting improvements in forecast correlation skill: Statistical testing and power analysis, Mon. Weather Rev., 145, 437–450, https://doi.org/10.1175/MWR-D-16-0037.1, 2017. a
Smith, D. M., Eade, R., and Pohlmann, H.: A comparison of full-field and anomaly initialization for seasonal to decadal climate prediction, Clim. Dynam., 41, 3325–3338, https://doi.org/10.1007/s00382-013-1683-2, 2013. a
Smith, D. M., Eade, R., Scaife, A. A., Caron, L.-P., Danabasoglu, G., DelSole, T. M., Delworth, T., Doblas-Reyes, F. J., Dunstone, N. J., Hermanson, L., Kharin, V., Kimoto, M., Merryfield, W. J., Mochizuki, T., Müller, W. A., Pohlmann, H., Yeager, S., and Yang, X.: Robust skill of decadal climate predictions, npj Climate and Atmospheric Science, 2, 1–10, https://doi.org/10.1038/s41612-019-0071-y, 2019. a
Smith, D. M., Scaife, A. A., Eade, R., Athanasiadis, P., Bellucci, A., Bethke, I., Bilbao, R., Borchert, L. F., Caron, L. P., Counillon, F., Danabasoglu, G., Delworth, T., Doblas-Reyes, F. J., Dunstone, N. J., Estella-Perez, V., Flavoni, S., Hermanson, L., Keenlyside, N., Kharin, V., Kimoto, M., Merryfield, W. J., Mignot, J., Mochizuki, T., Modali, K., Monerie, P. A., Müller, W. A., Nicolí, D., Ortega, P., Pankatz, K., Pohlmann, H., Robson, J., Ruggieri, P., Sospedra-Alfonso, R., Swingedouw, D., Wang, Y., Wild, S., Yeager, S., Yang, X., and Zhang, L.: North Atlantic climate far more predictable than models imply, Nature, 583, 796–800, https://doi.org/10.1038/s41586-020-2525-0, 2020. a
Sospedra-Alfonso, R., Merryfield, W. J., Boer, G. J., Kharin, V. V., Lee, W.-S., Seiler, C., and Christian, J. R.: Decadal climate predictions with the Canadian Earth System Model version 5 (CanESM5), Geosci. Model Dev., 14, 6863–6891, https://doi.org/10.5194/gmd-14-6863-2021, 2021. a
Sutton, R. T. and Hodson, D. L. R.: Atlantic Ocean Forcing of North American and European Summer Climate, Science, 309, 115–118, https://doi.org/10.1126/science.1109496, 2005. a
Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Hanna, S., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Sigmond, M., Solheim, L., von Salzen, K., Yang, D., and Winter, B.: The Canadian Earth System Model version 5 (CanESM5.0.3), Geosci. Model Dev., 12, 4823–4873, https://doi.org/10.5194/gmd-12-4823-2019, 2019. a
Tietsche, S., Balmaseda, M., Zuo, H., Roberts, C., Mayer, M., and Ferranti, L.: The importance of North Atlantic Ocean transports for seasonal forecasts, Clim. Dynam., 55, 1995–2011, https://doi.org/10.1007/s00382-020-05364-6, 2020. a
Titchner, H. A. and Rayner, N. A.: The Met Office Hadley Centre sea ice and sea surface temperature data set, version 2: 1. Sea ice concentrations, J. Geophys. Res.-Atmos., 119, 2864–2889, https://doi.org/10.1002/2013JD020316, 2014. a
Volpi, D., Guemas, V., and Doblas-Reyes, F. J.: Comparison of full field and anomaly initialisation for decadal climate prediction: towards an optimal consistency between the ocean and sea-ice anomaly initialisation state, Clim. Dynam., 49, 1181–1195, https://doi.org/10.1007/s00382-016-3373-3, 2017. a, b, c
Volpi, D., Meccia, V. L., Guemas, V., Ortega, P., Bilbao, R., Doblas-Reyes, F. J., Amaral, A., Echevarria, P., Mahmood, R., and Corti, S.: A Novel Initialization Technique for Decadal Climate Predictions, Frontiers in Climate, 3, 1–14, https://doi.org/10.3389/fclim.2021.681127, 2021. a
Yashayaev, I. and Loder, J. W.: Recurrent replenishment of Labrador Sea Water and associated decadal-scale variability, J. Geophys. Res.-Oceans, 121, 8095–8114, https://doi.org/10.1002/2016JC012046, 2016. a, b
Yeager, S.: The abyssal origins of North Atlantic decadal predictability, Clim. Dynam., 55, 2253–2271, https://doi.org/10.1007/s00382-020-05382-4, 2020. a
Yeager, S., Kim, W., and Robson, J.: What caused the Atlantic Cold Blob of 2015?, US CLIVAR Var, 14, 24–31, 2016. a
Yeager, S. G., Danabasoglu, G., Rosenbloom, N. A., Strand, W., Bates, S. C., Meehl, G. A., Karspeck, A. R., Lindsay, K., Long, M. C., Teng, H., and Lovenduski, N. S.: Predicting near-term changes in the earth system: A large ensemble of initialized decadal prediction simulations using the community earth system model, B. Am. Meteorol. Soc., 99, 1867–1886, https://doi.org/10.1175/BAMS-D-17-0098.1, 2018. a
Yeager, S. G., Chang, P., Danabasoglu, G., Rosenbloom, N., Zhang, Q., Castruccio, F. S., Gopal, A., Cameron Rencurrel, M., and Simpson, I. R.: Reduced Southern Ocean warming enhances global skill and signal-to-noise in an eddy-resolving decadal prediction system, npj Climate and Atmospheric Science, 6, 107, https://doi.org/10.1038/s41612-023-00434-y, 2023. a
Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S., Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yabu, S., Yoshimura, H., Shindo, E., Mizuta, R., Obata, A., Adachi, Y., and Ishii, M.: The meteorological research institute Earth system model version 2.0, MRI-ESM2.0: Description and basic evaluation of the physical component, J. Meteorol. Soc. Jpn, 97, 931–965, https://doi.org/10.2151/jmsj.2019-051, 2019. a
Zanna, L., Khatiwala, S., Gregory, J. M., Ison, J., and Heimbach, P.: Global reconstruction of historical ocean heat storage and transport, P. Natl. Acad. Sci. USA, 116, 1126–1131, https://doi.org/10.1073/pnas.1808838115, 2019. a
Zhang, R. and Delworth, T. L.: Impact of Atlantic multidecadal oscillations on India/Sahel rainfall and Atlantic hurricanes, Geophys. Res. Lett., 33, L17712, https://doi.org/10.1029/2006GL026267, 2006. a
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. a
Short summary
Climate models can be used to skilfully predict decadal changes in North Atlantic ocean heat content. However, significant regional differences among these models reveal large uncertainties in the influence of external forcings. This study examines eight climate models to understand the differences in their predictive capacity for the North Atlantic, investigating the importance of external forcings and key model characteristics such as ocean stratification and the local atmospheric forcing.
Climate models can be used to skilfully predict decadal changes in North Atlantic ocean heat...
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