Articles | Volume 17, issue 8
https://doi.org/10.5194/essd-17-4185-2025
© 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/essd-17-4185-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An ensemble-based coupled reanalysis of the climate from 1860 to the present (CoRea1860+)
Nansen Environmental and Remote Sensing Center and Bjerknes Centre for Climate Research, Bergen, Norway
François Counillon
Nansen Environmental and Remote Sensing Center and Bjerknes Centre for Climate Research, Bergen, Norway
Lea Svendsen
Geophysical Institute, University of Bergen, and Bjerknes Centre for Climate Research, Bergen, Norway
Ping-Gin Chiu
Geophysical Institute, University of Bergen, and Bjerknes Centre for Climate Research, Bergen, Norway
Noel Keenlyside
Nansen Environmental and Remote Sensing Center and Bjerknes Centre for Climate Research, Bergen, Norway
Geophysical Institute, University of Bergen, and Bjerknes Centre for Climate Research, Bergen, Norway
Patrick Laloyaux
European Centre for Medium-Range Weather Forecasts, Reading, UK
Mariko Koseki
Geophysical Institute, University of Bergen, and Bjerknes Centre for Climate Research, Bergen, Norway
Eric de Boisseson
European Centre for Medium-Range Weather Forecasts, Reading, UK
Related authors
Zikang He, Yiguo Wang, Julien Brajard, Xidong Wang, and Zheqi Shen
The Cryosphere, 19, 3279–3293, https://doi.org/10.5194/tc-19-3279-2025, https://doi.org/10.5194/tc-19-3279-2025, 2025
Short summary
Short summary
Declining Arctic sea ice presents both risks and opportunities for ecosystems, communities, and economic activities. To address prediction errors in dynamical models, we apply machine learning for error correction during prediction (online) or post-processing (offline). Our results show that both methods enhance sea ice predictions, particularly from September to January, with offline corrections outperforming online corrections.
Zikang He, Julien Brajard, Yiguo Wang, Xidong Wang, and Zheqi Shen
EGUsphere, https://doi.org/10.5194/egusphere-2025-212, https://doi.org/10.5194/egusphere-2025-212, 2025
Short summary
Short summary
Climate prediction is challenging due to systematic errors in traditional climate models. We addressed this by training a machine learning model to correct these errors and then integrating it with the traditional climate model to form an AI-physics hybrid model. Our study demonstrates that the hybrid model outperforms the original climate model on both short-term and long-term predictions of the atmosphere and ocean.
Nicholas Williams, Yiguo Wang, and François Counillon
EGUsphere, https://doi.org/10.5194/egusphere-2025-104, https://doi.org/10.5194/egusphere-2025-104, 2025
Short summary
Short summary
We assimilate satellite observations of Arctic sea ice thickness to create a skillful initial sea ice state, assimilating ENVISAT-derived sea ice thickness for the first time. We produce a reanalysis and seasonal hindcasts showing that sea ice thickness and volume estimates are significantly improved in both reanalysis and prediction. Predictions of summer sea ice extent in our model are also substantially improved by reducing the high sea ice thickness bias.
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.
Zikang He, Yiguo Wang, Julien Brajard, Xidong Wang, and Zheqi Shen
The Cryosphere, 19, 3279–3293, https://doi.org/10.5194/tc-19-3279-2025, https://doi.org/10.5194/tc-19-3279-2025, 2025
Short summary
Short summary
Declining Arctic sea ice presents both risks and opportunities for ecosystems, communities, and economic activities. To address prediction errors in dynamical models, we apply machine learning for error correction during prediction (online) or post-processing (offline). Our results show that both methods enhance sea ice predictions, particularly from September to January, with offline corrections outperforming online corrections.
Ingo Richter, Ping Chang, Ping-Gin Chiu, Gokhan Danabasoglu, Takeshi Doi, Dietmar Dommenget, Guillaume Gastineau, Zoe E. Gillett, Aixue Hu, Takahito Kataoka, Noel S. Keenlyside, Fred Kucharski, Yuko M. Okumura, Wonsun Park, Malte F. Stuecker, Andréa S. Taschetto, Chunzai Wang, Stephen G. Yeager, and Sang-Wook Yeh
Geosci. Model Dev., 18, 2587–2608, https://doi.org/10.5194/gmd-18-2587-2025, https://doi.org/10.5194/gmd-18-2587-2025, 2025
Short summary
Short summary
Tropical ocean basins influence each other through multiple pathways and mechanisms, referred to here as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models but have obtained conflicting results. This may be partly due to differences in experiment protocols and partly due to systematic model errors. The Tropical Basin Interaction Model Intercomparison Project (TBIMIP) aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-509, https://doi.org/10.5194/egusphere-2025-509, 2025
Short summary
Short summary
The nextGEMS project developed two Earth system models that resolve processes of the order of 10 km, giving more fidelity to the representation of local phenomena, globally. In its fourth cycle, nextGEMS performed simulations with coupled ocean, land, and atmosphere 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.
Zikang He, Julien Brajard, Yiguo Wang, Xidong Wang, and Zheqi Shen
EGUsphere, https://doi.org/10.5194/egusphere-2025-212, https://doi.org/10.5194/egusphere-2025-212, 2025
Short summary
Short summary
Climate prediction is challenging due to systematic errors in traditional climate models. We addressed this by training a machine learning model to correct these errors and then integrating it with the traditional climate model to form an AI-physics hybrid model. Our study demonstrates that the hybrid model outperforms the original climate model on both short-term and long-term predictions of the atmosphere and ocean.
Nicholas Williams, Yiguo Wang, and François Counillon
EGUsphere, https://doi.org/10.5194/egusphere-2025-104, https://doi.org/10.5194/egusphere-2025-104, 2025
Short summary
Short summary
We assimilate satellite observations of Arctic sea ice thickness to create a skillful initial sea ice state, assimilating ENVISAT-derived sea ice thickness for the first time. We produce a reanalysis and seasonal hindcasts showing that sea ice thickness and volume estimates are significantly improved in both reanalysis and prediction. Predictions of summer sea ice extent in our model are also substantially improved by reducing the high sea ice thickness bias.
William Eric Chapman, Francine Schevenhoven, Judith Berner, Noel Keenlyside, Ingo Bethke, Ping-Gin Chiu, Alok Gupta, and Jesse Nusbaumer
EGUsphere, https://doi.org/10.5194/egusphere-2024-2682, https://doi.org/10.5194/egusphere-2024-2682, 2024
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.
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.
Nil Irvalı, Ulysses S. Ninnemann, Are Olsen, Neil L. Rose, David J. R. Thornalley, Tor L. Mjell, and François Counillon
Geochronology, 6, 449–463, https://doi.org/10.5194/gchron-6-449-2024, https://doi.org/10.5194/gchron-6-449-2024, 2024
Short summary
Short summary
Marine sediments are excellent archives for reconstructing past changes in climate and ocean circulation. Yet, dating uncertainties, particularly during the 20th century, pose major challenges. Here we propose a novel chronostratigraphic approach that uses anthropogenic signals, such as the oceanic 13C Suess effect and spheroidal carbonaceous fly-ash particles, to reduce age model uncertainties in high-resolution marine archives over the 20th century.
Lilian Garcia-Oliva, Alberto Carrassi, and François Counillon
EGUsphere, https://doi.org/10.5194/egusphere-2024-1843, https://doi.org/10.5194/egusphere-2024-1843, 2024
Short summary
Short summary
We used a simple coupled model and a data assimilation method to find the correct initialisation for climate predictions. We aim to clarify when weakly or strongly coupled data assimilation (WCDA or SCDA) is best, depending on the system's dynamical characteristics (spatio-temporal) and data coverage.
We found that WCDA is better in full data coverage. When we have a partially observed system, SCDA is better. This result depends on the temporal and spatial scale of the observed quantity.
We found that WCDA is better in full data coverage. When we have a partially observed system, SCDA is better. This result depends on the temporal and spatial scale of the observed quantity.
Eric de Boisséson and Magdalena Alonso Balmaseda
Ocean Sci., 20, 265–278, https://doi.org/10.5194/os-20-265-2024, https://doi.org/10.5194/os-20-265-2024, 2024
Short summary
Short summary
Marine heatwaves are long periods of extremely warm ocean surface temperatures. Predicting such events a few months in advance would help decision-making to mitigate their impacts on marine ecosystems. This work investigates how well operational seasonal forecasts can predict marine heatwaves. Results show that such events can be predicted a few months in advance in the tropics but that extending the predictability skill to other regions will require additional work on the forecast models.
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.
Jakob Simon Dörr, David B. Bonan, Marius Årthun, Lea Svendsen, and Robert C. J. Wills
The Cryosphere, 17, 4133–4153, https://doi.org/10.5194/tc-17-4133-2023, https://doi.org/10.5194/tc-17-4133-2023, 2023
Short summary
Short summary
The Arctic sea-ice cover is retreating due to climate change, but this retreat is influenced by natural (internal) variability in the climate system. We use a new statistical method to investigate how much internal variability has affected trends in the summer and winter Arctic sea-ice cover using observations since 1979. Our results suggest that the impact of internal variability on sea-ice retreat might be lower than what climate models have estimated.
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
Ammann, C. M., Meehl, G. A., Washington, W. M., and Zender, C. S.: A monthly and latitudinally varying volcanic forcing dataset in simulations of 20th century climate, Geophys. Res. Lett., 30, 1657, https://doi.org/10.1029/2003GL016875, 2003. a
Back, L. E. and Bretherton, C. S.: On the relationship between SST gradients, boundary layer winds, and convergence over the tropical oceans, J. Climate, 22, 4182–4196, https://doi.org/10.1175/2009JCLI2392.1, 2009. a
Balmaseda, M. A., Alves, O. J., Arribas, A., Awaji, T., Behringer, D. W., Ferry, N., Fujii, Y., Lee, T., Rienecker, M., Rosati, T., and Stammer, D.: Ocean initialization for seasonal forecasts, Oceanography, 22, 154–159, https://doi.org/10.5670/oceanog.2009.73, 2009. a
Bentsen, M., Bethke, I., Debernard, J. B., Iversen, T., Kirkevåg, A., Seland, Ø., Drange, H., Roelandt, C., Seierstad, I. A., Hoose, C., and Kristjánsson, J. E.: The Norwegian Earth System Model, NorESM1-M – Part 1: Description and basic evaluation of the physical climate, Geosci. Model Dev., 6, 687–720, https://doi.org/10.5194/gmd-6-687-2013, 2013. a, b, c, d, e
Bethke, I., Wang, Y., Counillon, F., Kimmritz, M., Langehaug, H., Bentsen, M., and Keenlyside, N.: Subtropical North Atlantic preconditioning key to skillful subpolar gyre prediction, in: Second International Conference on Seasonal to Decadal Prediction, Boulder, US, 17–21 September 2018, Abstract number: B2-10, https://www.wcrp-climate.org/images/WCRP_conferences/S2S_S2D_2018/pdf/Programme/orals/presentations/B2-10_Ingo-Bethke.pdf (last access: 20 August 2025), 2018. 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, c, d, e, f, g, h, i, j, k, l
Billeau, S., Counillon, F., Keenlyside, N., and Bertino, L.: Impact of changing the assimilation cycle: centered vs. staggered, snapshot vs monthly averaged, Zenodo, https://doi.org/10.5281/zenodo.7436884, 2016. a
Bleck, R., Rooth, C., Hu, D., and Smith, L. T.: Salinity-driven thermocline transients in a wind- and thermohaline-forced isopycnic coordinate model of the North Atlantic, J. Phys. Oceanogr., 22, 1486–1505, https://doi.org/10.1175/1520-0485(1992)022<1486:SDTTIA>2.0.CO;2, 1992. 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
Brayshaw, D. J., Hoskins, B., and Blackburn, M.: The basic ingredients of the North Atlantic storm track. Part II: Sea surface temperatures, J. Atmos. Sci., 68, 1784–1805, https://doi.org/10.1175/2011JAS3674.1, 2011. a
Brune, S., Nerger, L., and Baehr, J.: Assimilation of oceanic observations in a global coupled Earth system model with the SEIK filter, Ocean Model., 96, 254–264, https://doi.org/10.1016/j.ocemod.2015.09.011, 2015. a, b
Caesar, L., Rahmstorf, S., Robinson, A., Feulner, G., and Saba, V.: Observed fingerprint of a weakening Atlantic Ocean overturning circulation, Nature, 556, 191–196, https://doi.org/10.1038/s41586-018-0006-5, 2018. a, b
Cai, Q., Wang, J., Beletsky, D., Overland, J., Ikeda, M., and Wan, L.: Accelerated decline of summer Arctic sea ice during 1850–2017 and the amplified Arctic warming during the recent decades, Environ. Res. Lett., 16, 034015, https://doi.org/10.1088/1748-9326/abdb5f, 2021. a
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. a
Carrassi, A., Weber, R. J. T., Guemas, V., Doblas-Reyes, F. J., Asif, M., and Volpi, D.: Full-field and anomaly initialization using a low-order climate model: a comparison and proposals for advanced formulations, Nonlin. Processes Geophys., 21, 521–537, https://doi.org/10.5194/npg-21-521-2014, 2014. a, b, c
Carrassi, A., Bocquet, M., Bertino, L., and Evensen, G.: Data assimilation in the geosciences: an overview of methods, issues, and perspectives, WIREs Clim. Change, 9, e535, https://doi.org/10.1002/wcc.535, 2018. a
Carton, J. A. and Hakkinen, S.: Introduction to: Atlantic Meridional Overturning Circulation (AMOC), Deep-Sea Res. Pt. II, 58, 1741–1743, https://doi.org/10.1016/j.dsr2.2010.10.055, 2011. a
Cheng, W., Chiang, J. C. H., and Zhang, D.: Atlantic Meridional Overturning Circulation (AMOC) in CMIP5 models: RCP and historical simulations, J. Climate, 26, 7187–7197, https://doi.org/10.1175/JCLI-D-12-00496.1, 2013. a, b
Counillon, F., Bethke, I., Keenlyside, N., Bentsen, M., Bertino, L., and Zheng, F.: Seasonal-to-decadal predictions with the ensemble Kalman filter and the Norwegian Earth System Model: a twin experiment, Tellus A, 66, 1–21, https://doi.org/10.3402/tellusa.v66.21074, 2014. a
Counillon, F., Keenlyside, N., Bethke, I., Wang, Y., Billeau, S., Shen, M. L., and Bentsen, M.: Flow-dependent assimilation of sea surface temperature in isopycnal coordinates with the Norwegian Climate Prediction Model, Tellus A, 68, 1–17, https://doi.org/10.3402/tellusa.v68.32437, 2016. a, b, c, d, e, f, g, h, i, j, k, l, m, n
Counillon, F., Keenlyside, N., Toniazzo, T., Koseki, S., Demissie, T., Bethke, I., and Wang, Y.: Relating model bias and prediction skill in the equatorial Atlantic, Clim. Dynam., 56, 2617–2630, https://doi.org/10.1007/s00382-020-05605-8, 2021. a
Craig, A. P., Vertenstein, M., and Jacob, R.: A new flexible coupler for earth system modeling developed for CCSM4 and CESM1, Int. J. High Perform. Comput. Appl., 26, 31–42, https://doi.org/10.1177/1094342011428141, 2012. a
Dalaiden, Q., Rezsöhazy, J., Goosse, H., Thomas, E. R., Vladimirova, D. O., and Tetzner, D.: An unprecedented sea ice retreat in the Weddell Sea driving an overall decrease of the Antarctic sea-ice extent over the 20th century, Geophys. Res. Lett., 50, e2023GL104666, https://doi.org/10.1029/2023GL104666, 2023. a, b, c
Delworth, T. L., Zeng, F., Vecchi, G. A., Yang, X., Zhang, L., and Zhang, R.: The North Atlantic Oscillation as a driver of rapid climate change in the Northern Hemisphere, Nat. Geosci., 9, 509–512, https://doi.org/10.1038/ngeo2738, 2016. a, b
Desroziers, G., Berre, L., Chapnik, B., and Poli, P.: Diagnosis of observation, background and analysis-error statistics in observation space, Q. J. Roy. Meteor. Soc., 131, 3385–3396, https://doi.org/10.1256/qj.05.108, 2005. a, b
Ding, Q., Wallace, J. M., Battisti, D. S., Steig, E. J., Gallant, A. J. E., Kim, H.-J., and Geng, L.: Tropical forcing of the recent rapid Arctic warming in northeastern Canada and Greenland, Nature, 509, 209–212, https://doi.org/10.1038/nature13260, 2014. a
Divine, D. V., Divina, S., Bjørge, O. E., Isaksson, E., Jølle, H. D., Stokkeland, I., Vasquez Guzman, M., Wilkinson, S., and Wilkinson, C.: Southern Ocean sea ice, icebergs, and meteorological data from maritime sources for the period 1929 to 1940, Geosci. Data J., 11, 902–920, https://doi.org/10.1002/gdj3.265, 2024. a, b, c
Evensen, G.: The Ensemble Kalman Filter: theoretical formulation and practical implementation, Ocean Dynam., 53, 343–367, https://doi.org/10.1007/s10236-003-0036-9, 2003. a, b
Evensen, G.: Data Assimilation: The Ensemble Kalman Filter, Springer, Berlin, Germany, https://doi.org/10.1007/978-3-642-03711-5, 2009. 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
Fogt, R. L., Sleinkofer, A. M., Raphael, M. N., and Handcock, M. S.: A regime shift in seasonal total Antarctic sea ice extent in the twentieth century, Nat. Clim. Change, 12, 54–62, https://doi.org/10.1038/s41558-021-01254-9, 2022. a, b, c
Fu, Y., Lozier, M. S., Biló, T. C., Bower, A. S., Cunningham, S. A., Cyr, F., de Jong, M. F., deYoung, B., Drysdale, L., Fraser, N., Fried, N., Furey, H. H., Han, G., Handmann, P., Holliday, N. P., Holte, J., Inall, M. E., Johns, W. E., Jones, S., Karstensen, J., Li, F., Pacini, A., Pickart, R. S., Rayner, D., Straneo, F., and Yashayaev, I.: Seasonality of the Meridional Overturning Circulation in the subpolar North Atlantic, Communications Earth and Environment, 4, 181, https://doi.org/10.1038/s43247-023-00848-9, 2023. a
Fujii, Y., Nakaegawa, T., Matsumoto, S., Yasuda, T., Yamanaka, G., and Kamachi, M.: Coupled climate simulation by constraining ocean fields in a coupled model with ocean data, J. Climate, 22, 5541–5557, https://doi.org/10.1175/2009JCLI2814.1, 2009. a, b
Garcia-Oliva, L., Counillon, F., Bethke, I., and Keenlyside, N.: Intercomparison of initialization methods for seasonal-to-decadal climate predictions with the NorCPM, Clim. Dynam., 62, 5425–5444, https://doi.org/10.1007/s00382-024-07170-w, 2024. a
Gaspari, G. and Cohn, S. E.: Construction of correlation functions in two and three dimensions, Q. J. Roy. Meteor. Soc., 125, 723–757, https://doi.org/10.1002/qj.49712555417, 1999. a
Gavart, M. and Mey, P. D.: Isopycnal EOFs in the Azores Current region: a statistical tool for dynamical analysis and data assimilation, J. Phys. Oceanogr., 27, 2146–2157, https://doi.org/10.1175/1520-0485(0)027<2146:IEITAC>2.0.CO;2, 1997. a
Gent, P. R., Danabasoglu, G., Donner, L. J., Holland, M. M., Hunke, E. C., Jayne, S. R., Lawrence, D. M., Neale, R. B., Rasch, P. J., Vertenstein, M., Worley, P. H., Yang, Z. L., and Zhang, M.: The community climate system model version 4, J. Climate, 24, 4973–4991, https://doi.org/10.1175/2011JCLI4083.1, 2011. a, b
Giese, B. S. and Ray, S.: El Niño variability in simple ocean data assimilation (SODA), 1871–2008, J. Geophys. Res.-Oceans, 116, C02024, https://doi.org/10.1029/2010JC006695, 2011. a, b, c
Giese, B. S., Seidel, H. F., Compo, G. P., and Sardeshmukh, P. D.: An ensemble of ocean reanalyses for 1815–2013 with sparse observational input, J. Geophys. Res.-Oceans, 121, 6891–6910, https://doi.org/10.1002/2016JC012079, 2016. a, b, c
Gill, A. E.: Some simple solutions for heat-induced tropical circulation, Q. J. Roy. Meteor. Soc., 106, 447–462, https://doi.org/10.1002/qj.49710644905, 1980. 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, b
Goosse, H., Dalaiden, Q., Feba, F., Mezzina, B., and Fogt, R. L.: A drop in Antarctic sea ice extent at the end of the 1970s, Communications Earth and Environment, 5, 628, https://doi.org/10.1038/s43247-024-01793-x, 2024. a, b, c
Gulev, S. K., Latif, M., Keenlyside, N., Park, W., and Koltermann, K. P.: North Atlantic Ocean control on surface heat flux on multidecadal timescales, Nature, 499, 464–467, https://doi.org/10.1038/nature12268, 2013. a
Hand, R., Keenlyside, N., Omrani, N.-E., and Latif, M.: Simulated response to inter-annual SST variations in the Gulf Stream region, Clim. Dynam., 42, 715–731, https://doi.org/10.1007/s00382-013-1715-y, 2014. 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
Holland, M. M., Bailey, D. A., Briegleb, B. P., Light, B., and Hunke, E.: Improved sea ice shortwave radiation physics in CCSM4: the impact of melt ponds and aerosols on arctic sea ice, J. Climate, 25, 1413–1430, https://doi.org/10.1175/JCLI-D-11-00078.1, 2012. a
Houtekamer, P. L. and Mitchell, H. L.: A sequential ensemble Kalman filter for atmospheric data assimilation, Mon. Weather Rev., 129, 123–137, https://doi.org/10.1175/1520-0493(2001)129<0123:ASEKFF>2.0.CO;2, 2001. a
Hurrell, J. W.: Decadal trends in the North Atlantic oscillation: regional temperatures and precipitation, Science, 269, 676–679, https://doi.org/10.1126/science.269.5224.676, 1995. a, b
Hurrell, J. W., Holland, M. M., Gent, P. R., Ghan, S., Kay, J. E., Kushner, P. J., Lamarque, J.-F., Large, W. G., Lawrence, D., Lindsay, K., Lipscomb, W. H., Long, M. C., Mahowald, N., Marsh, D. R., Neale, R. B., Rasch, P., Vavrus, S., Vertenstein, M., Bader, D., Collins, W. D., Hack, J. J., Kiehl, J., and Marshall, S.: The Community Earth System Model: a framework for collaborative research, B. Am. Meteorol. Soc., 94, 1339–1360, https://doi.org/10.1175/BAMS-D-12-00121.1, 2013. a
Hurtt, G. C., Chini, L. P., Frolking, S., Betts, R. A., Feddema, J., Fischer, G., Fisk, J. P., Hibbard, K., Houghton, R. A., Janetos, A., Jones, C. D., Kindermann, G., Kinoshita, T., Klein Goldewijk, K., Riahi, K., Shevliakova, E., Smith, S., Stehfest, E., Thomson, A., Thornton, P., van Vuuren, D. P., and Wang, Y. P.: Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands, Climatic Change, 109, 117, https://doi.org/10.1007/s10584-011-0153-2, 2011. a
IPCC: Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, https://doi.org/10.1017/9781009157896, 2023. a
Jackson, L. C., Dubois, C., Forget, G., Haines, K., Harrison, M., Iovino, D., Köhl, A., Mignac, D., Masina, S., Peterson, K. A., Piecuch, C. G., Roberts, C. D., Robson, J., Storto, A., Toyoda, T., Valdivieso, M., Wilson, C., Wang, Y., and Zuo, H.: The mean state and variability of the North Atlantic Circulation: a perspective from ocean reanalyses, J. Geophys. Res.-Oceans, 124, 9141–9170, https://doi.org/10.1029/2019JC015210, 2019. a
Janicot, S., Moron, V., and Fontaine, B.: Sahel droughts and Enso dynamics, Geophys. Res. Lett., 23, 515–518, https://doi.org/10.1029/96GL00246, 1996. a
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Leetmaa, A., Reynolds, R., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Jenne, R., and Joseph, D.: The NCEP/NCAR 40-year reanalysis project, B. Am. Meteorol. Soc., 77, 437–471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996. a
Karspeck, A. R., Stammer, D., Köhl, A., Danabasoglu, G., Balmaseda, M., Smith, D. M., Fujii, Y., Zhang, S., Giese, B., Tsujino, H., and Rosati, A.: Comparison of the Atlantic meridional overturning circulation between 1960 and 2007 in six ocean reanalysis products, Clim. Dynam., 49, 957–982, https://doi.org/10.1007/s00382-015-2787-7, 2017. a
Keenlyside, N. S., Ba, J., Mecking, J., Omrani, N.-E., Latif, M., Zhang, R., and Msadek, R.: North Atlantic multi-decadal variability – mechanisms and predictability, Chap. 9, World Scientific Publishing, https://doi.org/10.1142/9789814579933_0009, 141–157, 2015. a
Kimmritz, M., Counillon, F., Bitz, C., Massonnet, F., Bethke, I., and Gao, Y.: Optimising assimilation of sea ice concentration in an Earth system model with a multicategory sea ice model, Tellus A, 70, 1435945, https://doi.org/10.1080/16000870.2018.1435945, 2018. a, b, c
Kimmritz, M., Counillon, F., Smedsrud, L. H., Bethke, I., Keenlyside, N., Ogawa, F., and Wang, Y.: Impact of ocean and sea ice initialisation on seasonal prediction skill in the Arctic, J. Adv. Model. Earth Sy., 11, 4147–4166, https://doi.org/10.1029/2019MS001825, 2019. a, b, c
Kirkevåg, A., Iversen, T., Seland, Ø., Hoose, C., Kristjánsson, J. E., Struthers, H., Ekman, A. M. L., Ghan, S., Griesfeller, J., Nilsson, E. D., and Schulz, M.: Aerosol–climate interactions in the Norwegian Earth System Model – NorESM1-M, Geosci. Model Dev., 6, 207–244, https://doi.org/10.5194/gmd-6-207-2013, 2013. a
Laloyaux, P., Balmaseda, M., Dee, D., Mogensen, K., and Janssen, P.: A coupled data assimilation system for climate reanalysis, Q. J. Roy. Meteor. Soc., 142, 65–78, https://doi.org/10.1002/qj.2629, 2016. a
Laloyaux, P., de Boisseson, E., Balmaseda, M., Bidlot, J.-R., Broennimann, S., Buizza, R., Dalhgren, P., Dee, D., Haimberger, L., Hersbach, H., Kosaka, Y., Martin, M., Poli, P., Rayner, N., Rustemeier, E., and Schepers, D.: CERA-20C: a coupled reanalysis of the twentieth century, J. Adv. Model. Earth Sy., 10, 1172–1195, https://doi.org/10.1029/2018MS001273, 2018. a, b, c, d, e, f, g, h, i
Lamarque, J.-F., Bond, T. C., Eyring, V., Granier, C., Heil, A., Klimont, Z., Lee, D., Liousse, C., Mieville, A., Owen, B., Schultz, M. G., Shindell, D., Smith, S. J., Stehfest, E., Van Aardenne, J., Cooper, O. R., Kainuma, M., Mahowald, N., McConnell, J. R., Naik, V., Riahi, K., and van Vuuren, D. P.: Historical (1850–2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: methodology and application, Atmos. Chem. Phys., 10, 7017–7039, https://doi.org/10.5194/acp-10-7017-2010, 2010. a, b
Larson, J. G., Thompson, D. W. J., and Hurrell, J. W.: Signature of the western boundary currents in local climate variability, Nature, 634, 862–867, https://doi.org/10.1038/s41586-024-08019-2, 2024. a
Lawrence, D. M., Oleson, K. W., Flanner, M. G., Thornton, P. E., Swenson, S. C., Lawrence, P. J., Zeng, X., Yang, Z.-L., Levis, S., Sakaguchi, K., Bonan, G. B., and Slater, A. G.: Parameterization improvements and functional and structural advances in version 4 of the community land model, J. Adv. Model. Earth Sy., 3, M03001, https://doi.org/10.1029/2011MS000045, 2011. a
Lean, J., Rottman, G., Harder, J., and Kopp, G.: SORCE contributions to new understanding of global change and solar variability, Sol. Phys., 230, 27–53, https://doi.org/10.1007/s11207-005-1527-2, 2005. a
Leathers, D. J., Yarnal, B., and Palecki, M. A.: The Pacific/North American teleconnection pattern and United States climate. Part I: Regional temperature and precipitation associations, J. Climate, 4, 517–528, https://doi.org/10.1175/1520-0442(1991)004<0517:TPATPA>2.0.CO;2, 1991. a, b
Lindzen, R. S. and Nigam, S.: On the role of sea surface temperature gradients in forcing low-level winds and convergence in the tropics, J. Atmos. Sci., 44, 2418–2436, https://doi.org/10.1175/1520-0469(1987)044<2418:OTROSS>2.0.CO;2, 1987. a
Liu, W. and Fedorov, A.: Interaction between Arctic sea ice and the Atlantic meridional overturning circulation in a warming climate, Clim. Dynam., 58, 1811–1827, https://doi.org/10.1007/s00382-021-05993-5, 2022. a
Liu, W., Fedorov, A. V., Xie, S.-P., and Hu, S.: Climate impacts of a weakened Atlantic Meridional Overturning Circulation in a warming climate, Science Advances, 6, eaaz4876, https://doi.org/10.1126/sciadv.aaz4876, 2020. a
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. a
Magnusson, L., Alonso-Balmaseda, M., Corti, S., Molteni, F., and Stockdale, T.: Evaluation of forecast strategies for seasonal and decadal forecasts in presence of systematic model errors, Clim. Dynam., 41, 2393–2409, https://doi.org/10.1007/s00382-012-1599-2, 2013. a
Minobe, S., Kuwano-Yoshida, A., Komori, N., Xie, S.-P., and Small, R. J.: Influence of the Gulf Stream on the troposphere, Nature, 452, 206–209, https://doi.org/10.1038/nature06690, 2008. a
Moat, B., Smeed, D., Rayner, D., Johns, W., Smith, R., Volkov, D., Elipot, S., Petit, T., Kajtar, J., Baringer, M., and Collins, J.: Atlantic meridional overturning circulation observed by the RAPID-MOCHA-WBTS (RAPID-Meridional Overturning Circulation and Heatflux Array-Western Boundary Time Series) array at 26N from 2004 to 2023 (v2023.1), British Oceanographic Data Centre [data set], https://doi.org/10.5285/223b34a3-2dc5-c945-e063-7086abc0f274, 2024. a, b, c
Nair, A., Counillon, F., and Keenlyside, N.: Improving subseasonal forecast skill in the Norwegian Climate Prediction Model using soil moisture data assimilation, Clim. Dynam., 62, 10483–10502, https://doi.org/10.1007/s00382-024-07444-3, 2024. a
Neale, R. B., Richter, J. H., Conley, A. J., Park, S., Lauritzen, P. H., Gettelman, A., Williamson, D. L., Rasch, P. J., Vavrus, S. J., Taylor, M. A., Collins, W. D., Zhang, M., and Lin, S.-J.: Description of the NCAR Community Atmosphere Model (CAM 4.0), Tech. Rep. NCAR/TN-485+STR, National Center for Atmospheric Research, Boulder, Colorado, USA, https://opensky.ucar.edu/system/files/2024-08/technotes_595.pdf (last access: 21 August 2025), 2010. a
Newman, M., Alexander, M. A., Ault, T. R., Cobb, K. M., Deser, C., Lorenzo, E. D., 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. a
Oldenburg, D., Kwon, Y.-O., Frankignoul, C., Danabasoglu, G., Yeager, S., and Kim, W. M.: The respective roles of ocean heat transport and surface heat fluxes in driving Arctic ocean warming and sea ice decline, J. Climate, 37, 1431–1448, https://doi.org/10.1175/JCLI-D-23-0399.1, 2024. a
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Flanner, M. G., Kluzek, E., Lawrence, P. J., Levis, S., Swenson, S. C., Thornton, P. E., Dai, A., Decker, M., Dickinson, R., Feddema, J., Heald, C. L., Hoffman, F., Lamarque, J.-F., Mahowald, N., Niu, G.-Y., Qian, T., Randerson, J., Running, S., Sakaguchi, K., Slater, A., Stöckli, R., Wang, A., Yang, Z.-L., Zeng, X., and Zeng, X.: Technical Description of version 4.0 of the Community Land Model (CLM), Tech. Rep. NCAR/TN-478+STR, National Center for Atmospheric Research, Boulder, Colorado, USA, https://doi.org/10.5065/D6FB50WZ, 2010. a
Omrani, N.-E., Ogawa, F., Nakamura, H., Keenlyside, N., Lubis, S. W., and Matthes, K.: Key role of the ocean western boundary currents in shaping the Northern Hemisphere climate, Sci. Rep.-UK, 9, 3014, https://doi.org/10.1038/s41598-019-39392-y, 2019. a
Omrani, N.-E., Keenlyside, N., Matthes, K., Boljka, L., Zanchettin, D., Jungclaus, J. H., and Lubis, S. W.: Coupled stratosphere-troposphere-Atlantic multidecadal oscillation and its importance for near-future climate projection, npj Climate and Atmospheric Science, 5, 59, https://doi.org/10.1038/s41612-022-00275-1, 2022. a, b, c
Outten, S., Li, C., King, M. P., Suo, L., Siew, P. Y. F., Cheung, H., Davy, R., Dunn-Sigouin, E., Furevik, T., He, S., Madonna, E., Sobolowski, S., Spengler, T., and Woollings, T.: Reconciling conflicting evidence for the cause of the observed early 21st century Eurasian cooling, Weather Clim. Dynam., 4, 95–114, https://doi.org/10.5194/wcd-4-95-2023, 2023. a
O'Kane, T. J., Sandery, P. A., Kitsios, V., Sakov, P., Chamberlain, M. A., Collier, M. A., Fiedler, R., Moore, T. S., Chapman, C. C., Sloyan, B. M., and Matear, R. J.: CAFE60v1: A 60-year large ensemble climate reanalysis. Part I: System design, model configuration, and data assimilation, J. Climate, 34, 5153–5169, https://doi.org/10.1175/JCLI-D-20-0974.1, 2021. a, b, c
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
Penny, S., Akella, S., Alves, O., Bishop, C., Buehner, M., Chevallier, M., Counillon, F., Draper, C., Frolov, S., Fujii, Y., Karspeck, A., Kumar, A., Laloyaux, P., Mahfouf, J.-F., Martin, M., Peña, M., de Rosnay, P., Subramanian, A., Tardif, R., Wang, Y., and Wu, X.: Coupled Data Assimilation for Integrated Earth System Analysis and Prediction: Goals, Challenges and Recommendations, Tech. Rep. WWRP 2017-3, World Meteorol. Org. (WMO), https://library.wmo.int/idurl/4/57666 (last access: 21 August 2025), 2017. a, b, c, d
Poli, P., Hersbach, H., Dee, D. P., Berrisford, P., Simmons, A. J., Vitart, F., Laloyaux, P., Tan, D. G. H., Peubey, C., Thépaut, J.-N., Trémolet, Y., Hólm, E. V., Bonavita, M., Isaksen, L., and Fisher, M.: ERA-20C: an atmospheric reanalysis of the twentieth century, J. Climate, 29, 4083–4097, https://doi.org/10.1175/JCLI-D-15-0556.1, 2016. a, b, c
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
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, c
Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L. V., Rowell, D. P., Kent, E. C., and Kaplan, A.: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century, J. Geophys. Res.-Atmos., 108, 4407, https://doi.org/10.1029/2002JD002670, 2003. a
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, https://doi.org/10.1175/1520-0442(2002)015<1609:AIISAS>2.0.CO;2, 2002. a
Richter, I.: Climate model biases in the eastern tropical oceans: causes, impacts and ways forward, WIREs Clim. Change, 6, 345–358, https://doi.org/10.1002/wcc.338, 2015. a, b, c, d
Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P., Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R., Gayno, G., Wang, J., Hou, Y.-T., Chuang, H.-Y., Juang, H.-M. H., Sela, J., Iredell, M., Treadon, R., Kleist, D., Delst, P. V., Keyser, D., Derber, J., Ek, M., Meng, J., Wei, H., Yang, R., Lord, S., Dool, H. V. D., Kumar, A., Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J.-K., Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou, C.-Z., Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds, R. W., Rutledge, G., and Goldberg, M.: The NCEP climate forecast system reanalysis, B. Am. Meteorol. Soc., 91, 1015–1057, https://doi.org/10.1175/2010BAMS3001.1, 2010. a
Sakov, P., Counillon, F., Bertino, L., Lisæter, K. A., Oke, P. R., and Korablev, A.: TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic, Ocean Sci., 8, 633–656, https://doi.org/10.5194/os-8-633-2012, 2012. a, b, c
Semenov, V. A., Aldonina, T. A., Li, F., Keenlyside, N. S., and Wang, L.: Arctic sea ice variations in the first half of the 20th century: a new reconstruction based on hydrometeorological data, Adv. Atmos. Sci., 41, 1483–1495, https://doi.org/10.1007/s00376-024-3320-x, 2024. a, b, c
Slivinski, L. C., Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Giese, B. S., McColl, C., Allan, R., Yin, X., Vose, R., Titchner, H., Kennedy, J., Spencer, L. J., Ashcroft, L., Brönnimann, S., Brunet, M., Camuffo, D., Cornes, R., Cram, T. A., Crouthamel, R., Domínguez-Castro, F., Freeman, J. E., Gergis, J., Hawkins, E., Jones, P. D., Jourdain, S., Kaplan, A., Kubota, H., Blancq, F. L., Lee, T.-C., Lorrey, A., Luterbacher, J., Maugeri, M., Mock, C. J., Moore, G. K., Przybylak, R., Pudmenzky, C., Reason, C., Slonosky, V. C., Smith, C. A., Tinz, B., Trewin, B., Valente, M. A., Wang, X. L., Wilkinson, C., Wood, K., and Wyszyński, P.: Towards a more reliable historical reanalysis: improvements for version 3 of the twentieth century reanalysis system, Q. J. Roy. Meteor. Soc., 145, 2876–2908, https://doi.org/10.1002/qj.3598, 2019. a, b
Slivinski, L. C., Compo, G. P., Sardeshmukh, P. D., Whitaker, J. S., McColl, C., Allan, R. J., Brohan, P., Yin, X., Smith, C. A., Spencer, L. J., Vose, R. S., Rohrer, M., Conroy, R. P., Schuster, D. C., Kennedy, J. J., Ashcroft, L., Brönnimann, S., Brunet, M., Camuffo, D., Cornes, R., Cram, T. A., Domínguez-Castro, F., Freeman, J. E., Gergis, J., Hawkins, E., Jones, P. D., Kubota, H., Lee, T. C., Lorrey, A. M., Luterbacher, J., Mock, C. J., Przybylak, R. K., Pudmenzky, C., Slonosky, V. C., Tinz, B., Trewin, B., Wang, X. L., Wilkinson, C., Wood, K., and Wyszyński, P.: An evaluation of the performance of the twentieth century reanalysis version 3, J. Climate, 34, 1417–1438, https://doi.org/10.1175/JCLI-D-20-0505.1, 2021. a, b, c, d, e
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
Stocker, T., Qin, D., Plattner, G.-K., Tignor, M., Allen, S., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. (Eds.): Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK and New York, USA, https://doi.org/10.1017/CBO9781107415324, 2013. a
Sun, C., Zhang, J., Li, X., Shi, C., Gong, Z., Ding, R., Xie, F., and Lou, P.: Atlantic Meridional Overturning Circulation reconstructions and instrumentally observed multidecadal climate variability: a comparison of indicators, Int. J. Climatol., 41, 763–778, https://doi.org/10.1002/joc.6695, 2021. a
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and the experiment design, B. Am. Meteorol. Soc., 93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012. 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, b
Wallace, J. M. and Gutzler, D. S.: Teleconnections in the geopotential height field during the Northern Hemisphere winter, Mon. Weather Rev., 109, 784–812, https://doi.org/10.1175/1520-0493(1981)109<0784:TITGHF>2.0.CO;2, 1981. a, b
Walsh, J. E., Fetterer, F., Scott Stewart, J., and Chapman, W. L.: A database for depicting Arctic sea ice variations back to 1850, Geogr. Rev., 107, 89–107, https://doi.org/10.1111/j.1931-0846.2016.12195.x, 2017. a, b
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. a
Wang, Y. and Counillon, F.: CoRea1860+: a coupled reanalysis of the climate from 1860 to the present, NIRD RDA [data set], https://doi.org/10.11582/2025.00009, 2025. a, b, c
Wang, Y., Counillon, F., and Bertino, L.: Alleviating the bias induced by the linear analysis update with an isopycnal ocean model, Q. J. Roy. Meteor. Soc., 142, 1064–1074, https://doi.org/10.1002/qj.2709, 2016. a, b
Wang, Y., Counillon, F., Bethke, I., Keenlyside, N., Bocquet, M., and Shen, M.-l.: Optimising assimilation of hydrographic profiles into isopycnal ocean models with ensemble data assimilation, Ocean Model., 114, 33–44, https://doi.org/10.1016/j.ocemod.2017.04.007, 2017. a, b
Wang, Y., Counillon, F., Keenlyside, N., Svendsen, L., Gleixner, S., Kimmritz, M., Dai, P., and Gao, Y.: Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF, Clim. Dynam., 53, 5777–5797, https://doi.org/10.1007/s00382-019-04897-9, 2019. a, b, c, d, e, f
Wang, Y., Counillon, F., Barthélémy, S., and Barth, A.: Benefit of vertical localization for sea surface temperature assimilation in isopycnal coordinate model, Frontiers in Climate, 4, 918572, https://doi.org/10.3389/fclim.2022.918572, 2022. a, b, c
Wang, Y., Wu, X., Jiang, L., Zheng, F., and Brune, S.: Editorial: recent advances in climate reanalysis, Frontiers in Climate, 5, 1158244, https://doi.org/10.3389/fclim.2023.1158244, 2023. a
Wang, Y.-M., Lean, J. L., and N. R. Sheeley, J.: Modeling the Sun's magnetic field and irradiance since 1713, Astrophys. J., 625, 522–538, https://doi.org/10.1086/429689, 2005. a
Weber, R. J. T., Carrassi, A., and Doblas-Reyes, F. J.: Linking the anomaly initialization approach to the mapping paradigm: a proof-of-concept study, Mon. Weather Rev., 143, 4695–4713, https://doi.org/10.1175/MWR-D-14-00398.1, 2015. a, b, c
Xiu, Y., Wang, Y., Luo, H., Garcia-Oliva, L., and Yang, Q.: Impact of ocean, sea ice or atmosphere initialization on seasonal prediction of regional Antarctic sea ice, J. Adv. Model. Earth Sy., 17, e2024MS004382, https://doi.org/10.1029/2024MS004382, 2025. a, b, c
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
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. a, b, c
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
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.
CoRea1860+ is a new climate dataset that reconstructs past climate conditions from 1860 to...
Altmetrics
Final-revised paper
Preprint