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Arctic sea-ice loss is a consequence of anthropogenic global warming and can itself be a driver of climate change in the Arctic and at lower latitudes, with sea-ice minima likely favoring extreme events over Europe and North America. Yet the role that the sea-ice plays in ongoing climate change remains uncertain, partly due to a limited understanding of whether and how the exact geographical distribution of sea-ice loss impacts climate. Here we demonstrate that the climate response to sea-ice loss can vary widely depending on the pattern of sea-ice change, and show that this is due to the presence of an atmospheric feedback mechanism that amplifies the local and remote signals when broader scale sea-ice loss occurs. Our study thus highlights the need to better constrain the spatial pattern of future sea-ice when assessing its impacts on the climate in the Arctic and beyond. ; X.J.L. has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement H2020-MSCA-COFUND-2016-754433 and from the H2020 project APPLICATE (Grant 727862). I.C. was supported by Generalitat de Catalunya (Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement) through Beatriu de Pinós 2017 programme. M.G.D. and P.O. are grateful for funding by the Spanish Ministry for the Economy, Industry and Competitiveness, respectively, for the Grant references RYC-2017-22964 and RYC-2017-22772. E.T. has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 748750 (SPFireSD project). Experiments were completed on the Marenostrum IV supercomputer at the Barcelona Supercomputing Center (BSC), and support was provided by BSC's Computational Earth Sciences (CES) department. ; Peer Reviewed ; Postprint (published version)

Prediction skill of continental climate in the Northern Hemisphere (NH) midlatitudes is generally limited throughout the year in dynamical seasonal forecast systems. Such limitations narrow the range of possible applications by different stakeholders. Improving the predictive capacity in these regions has been a challenging task. Sea ice is a central component of the Arctic climate system and a local source of climate predictability, yet its state is often not fully constrained in dynamical forecast systems. Using the EC-Earth3 climate model, we study the added value of assimilating observed Arctic sea ice concentration on the NH extratropical climate in retrospective forecasts of summer and fall, initialized every spring over 1992–2019. Predictions in the North Atlantic and Eurasia benefit from better initialization of sea ice in the Atlantic sector of the Arctic in a two-step mechanism. Initially, sea ice influences the central North Atlantic Ocean through an atmospheric bridge that develops in the first forecast weeks, subsequently leading to preserved skill in the sea surface temperatures (SSTs) throughout summer and early fall. Secondly, these long-lasting SST improvements provide better surface boundary conditions for the atmosphere and lead to more skillful predictions of circulation and surface climate in the Euro-Atlantic and Asian regions. In addition, our findings suggest that fully coupled ocean-atmosphere-sea ice models are likely necessary to study linkages between Arctic sea ice and midlatitudes, by better representing the interactions and feedbacks between the different components of the climate system. ; The data that support the findings of this study are available upon reasonable request from the authors. This work was funded by the European Union projects APPLICATE (Grant 727862), INTAROS (Grant 727890), and ESA/CMUG-CCI3. J C A N received financial support from the Spanish Ministerio de Ciencia, Innovación y Universidades through a Juan de la Cierva personal grant (FJCI-2017-34027). J G-S ...

Simulations from seven global coupled climate models performed at high and standard resolution as part of the high resolution model intercomparison project (HighResMIP) are analyzed to study deep ocean mixing in the Labrador Sea and the impact of increased horizontal resolution. The representation of convection varies strongly among models. Compared to observations from ARGO-floats and the EN4 data set, most models substantially overestimate deep convection in the Labrador Sea. In four out of five models, all four using the NEMO-ocean model, increasing the ocean resolution from 1° to 1/4° leads to increased deep mixing in the Labrador Sea. Increasing the atmospheric resolution has a smaller effect than increasing the ocean resolution. Simulated convection in the Labrador Sea is mainly governed by the release of heat from the ocean to the atmosphere and by the vertical stratification of the water masses in the Labrador Sea in late autumn. Models with stronger sub-polar gyre circulation have generally higher surface salinity in the Labrador Sea and a deeper convection. While the high-resolution models show more realistic ocean stratification in the Labrador Sea than the standard resolution models, they generally overestimate the convection. The results indicate that the representation of sub-grid scale mixing processes might be imperfect in the models and contribute to the biases in deep convection. Since in more than half of the models, the Labrador Sea convection is important for the Atlantic Meridional Overturning Circulation (AMOC), this raises questions about the future behavior of the AMOC in the models. ; This work has been funded by the PRIMAVERA project, which is funded by the European Union's Horizon 2020 programme, Grant Agreement No. 641727PRIMAVERA. D. V. Sein was also supported by the state assignment of the Ministry of Science and Higher Education of Russia (theme No. 0128-2021-0014). PO was supported by the Spanish Ministry of Economy, Industry and Competiveness through the Ramon y Cajal grant (RYC-2017-22772). The global ocean heat flux and evaporation products were provided by the WHOI OAFlux project (http://oaflux.whoi.edu) funded by the NOAA Climate Observations and Monitoring (COM) program. ; Peer Reviewed ; "Article signat per 12 autors/es: Torben Koenigk, Ramon Fuentes-Franco, Virna L. Meccia, Oliver Gutjahr, Laura C. Jackson, Adrian L. New, Pablo Ortega, Christopher D. Roberts, Malcolm J. Roberts, Thomas Arsouze, Doroteaciro Iovino, Marie-Pierre Moine & Dmitry V. Sein " ; Postprint (published version)

El Niño-Southern Oscillation (ENSO) is a key mode of climate variability with worldwide climate impacts. Recent studies have highlighted the impact of other tropical oceans on its variability. In particular, observations have demonstrated that summer Atlantic Niños (Niñas) favor the development of Pacific Niñas (Niños) the following winter, but it is unclear how well climate models capture this teleconnection and its role in defining the seasonal predictive skill of ENSO. Here we use an ensemble of seasonal forecast systems to demonstrate that a better representation of equatorial Atlantic variability in summer and its lagged teleconnection mechanism with the Pacific relates to enhanced predictive capacity of autumn/winter ENSO. An additional sensitivity study further shows that correcting SST variability in equatorial Atlantic improves different aspects of forecast skill in the Tropical Pacific, boosting ENSO skill. This study thus emphasizes that new efforts to improve the representation of equatorial Atlantic variability, a region with long standing systematic model biases, can foster predictive skill in the region, the Tropical Pacific and beyond, through the global impacts of ENSO. ; E.E.'s work was supported by the FP7-PREFACE project (grant agreement number: 603521) and the H2020 project TRIATLAS (grant agreement number: 817578). C.P. was supported by a Spanish Juan de la Cierva (JCI-2016-30802) fellowship and P.O. by a Ramon y Cajal (RyC-2017-22772) fellowship. B.R.d.F., T.L., and I.P. are supported by the European Union Seventh Framework Program (FP7/2007-2013) under Grant Agreement 603521 (PREFACE-EU project) and the Spanish Project CGL2017-86415-R. We are grateful to Francisco J. Doblas Reyes for his valuable feedback. ; Peer Reviewed ; Postprint (published version)

The possibility that Arctic sea ice loss weakens mid-latitude westerlies, promoting more severe cold winters, has sparked more than a decade of scientific debate, with apparent support from observations but inconclusive modelling evidence. Here we show that sixteen models contributing to the Polar Amplification Model Intercomparison Project simulate a weakening of mid-latitude westerlies in response to projected Arctic sea ice loss. We develop an emergent constraint based on eddy feedback, which is 1.2 to 3 times too weak in the models, suggesting that the real-world weakening lies towards the higher end of the model simulations. Still, the modelled response to Arctic sea ice loss is weak: the North Atlantic Oscillation response is similar in magnitude and offsets the projected response to increased greenhouse gases, but would only account for around 10% of variations in individual years. We further find that relationships between Arctic sea ice and atmospheric circulation have weakened recently in observations and are no longer inconsistent with those in models. ; D.M.S., R.E., L.H., L.S.G., T.J., T.S., X.L., and P.O. were supported by the EU H2020 APPLICATE project (GA727862). The Met Office contribution was also supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra and by the UK-China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. J.A.S was supported by NERC grants NE/P006760/1, NE/R005125/1 and NE/V005855/1. G.M and Y.P. were supported by the US Department of Energy, grant number DE-SC0019407. L.S.G was also supported by the Research council of Norway INES project (270061), and the Norwegian e-infrastructure for Research and Education (UNINETT Sigma2) through projects NN2345K, NS2345K and NS9034K. E.M. and D.M. acknowledge the support of the German Federal Ministry of Education and Research through the JPI Climate/JPI Oceans NextG-Climate Science-ROADMAP (FKZ: 01LP2002A) project and of the European Union's Horizon 2020 Programme through the Blue-Action Project (GA727852); and the use of resources from the DKRZ bm0966 and bm1190 projects. C. Deser acknowledges support from the National Center for Atmospheric Research, which is a major facility sponsored by the US National Science Foundation under cooperative agreement 1852977. M.M. was supported by MEXT through the Integrated Research Program for Advancing Climate Models (JPMXD0717935457) and ArCS II (JPMXD1420318865) programs, and by the Environment Research and Technology Development Fund (JPMEERF20192004). J.G.-S. and P.O. were supported by the Spanish Ramón y Cajal' programme (RYC-2016-21181, RYC-2016-22772). B.H. was jointly funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19070404) and the National Natural Science Foundation of China (Grant Nos. 42030602, 91837101). G.G. was supported by the EU H2020 Blue–Action (GA727852) project and uses the HPC resources of TGCC under the allocations 2018-R0040110492 and 2019-A0060107732 made by GENCI. J.S. acknowledges the project L4 of the Collaborative Research Centre TRR 181 Energy Transfers in Atmosphere and Ocean funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Project 274762653. ; Peer Reviewed ; "Article signat per 31 autors/es: D. M. Smith, R. Eade, M. B. Andrews, H. Ayres, A. Clark, S. Chripko, C. Deser, N. J. Dunstone, J. García-Serrano, G. Gastineau, L. S. Graff, S. C. Hardiman, B. He, L. Hermanson, T. Jung, J. Knight, X. Levine, G. Magnusdottir, E. Manzini, D. Matei, M. Mori, R. Msadek, P. Ortega, Y. Peings, A. A. Scaife, J. A. Screen, M. Seabrook, T. Semmler, M. Sigmond, J. Streffing, L. Sun & A. Walsh " ; Postprint (published version)

Numerical systems used for weather and climate predictions have substantially improved over past decades. We argue that despite a continued need for further addressing remaining limitations of their key components, numerical prediction systems have reached a sufficient level of maturity to examine and critically assess the suitability of Earth's current observing systems – remote and in situ, for prediction purposes; and that they can provide evidence-based support for the deployment of future observational networks. We illustrate this point by presenting recent, co-ordinated international efforts focused on Arctic observing systems, led in the framework of the Year of Polar Prediction and the H2020 project APPLICATE. The Arctic, one of the world's most rapidly changing regions, is relatively poorly covered in terms of in situ data but richly covered in terms of satellite data. In this study, we demonstrate that existing state-of-the-art datasets and targeted sensitivity experiments produced with numerical prediction systems can inform us of the added value of existing or even hypothetical Arctic observations, in the context of predictions from hourly to interannual time-scales. Furthermore, we argue that these datasets and experiments can also inform us how the uptake of Arctic observations in numerical prediction systems can be enhanced to maximise predictive skill. Based on these efforts we suggest that (a) conventional in situ observations in the Arctic play a particularly important role in initializing numerical weather forecasts during the winter season, (b) observations from satellite microwave sounders play a particularly important role during the summer season, and their enhanced usage over snow and sea ice is expected to further improve their impact on predictive skill in the Arctic region and beyond, (c) the deployment of a small number of in situ sea-ice thickness monitoring devices at strategic sampling sites in the Arctic could be sufficient to monitor most of the large-scale sea-ice volume variability, and (d) sea-ice thickness observations can improve the simulation of both the sea ice and near-surface air temperatures on seasonal time-scales in the Arctic and beyond. ; This study was supported by the APPLICATE project (727862), which was funded by the European Union's Horizon 2020 research and innovation programme. It was also supported by the Norwegian Research Council project no. 280573 "Advanced models and weather prediction in the Arctic: enhanced capacity from observations and polar process representations (ALERTNESS)". ; Peer Reviewed ; Article signat per 23 autors/es: Irina Sandu (1), François Massonnet (2), Guillian van Achter (2), Juan C. Acosta Navarro (3), Gabriele Arduini (1), Peter Bauer (1), Ed Blockley (4), Niels Bormann (1), Matthieu Chevallier (5), Jonathan Day (1), Mohamed Dahoui (1), Thierry Fichefet (2), Daniela Flocco (6), Thomas Jung (7), Ed Hawkins (6), Stephane Laroche (8), Heather Lawrence (1,4), Jorn Kristianssen (9), Eduardo Moreno-Chamarro (3), Pablo Ortega (3), Emmanuel Poan (8), Leandro Ponsoni (2), Roger Randriamampianina (9) // (1) European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK (2) Université Catholique de Louvain, Brussels, Belgium (3) Barcelona Supercomputing Centre, Barcelona, Spain (4) Met Office, Exeter, UK (5) Meteo-France, Toulouse, France (6) University of Reading, Reading, UK (7) Alfred Wegener Institute, Bremerhaven, Germany (8) Environment and Climate Change, Gatineau, Quebec Canada (9) Met Norway, Oslo, Norway ; Postprint (published version)

The Earth system model EC-Earth3 for contributions to CMIP6 is documented here, with its flexible coupling framework, major model configurations, a methodology for ensuring the simulations are comparable across different high-performance computing (HPC) systems, and with the physical performance of base configurations over the historical period. The variety of possible configurations and sub-models reflects the broad interests in the EC-Earth community. EC-Earth3 key performance metrics demonstrate physical behavior and biases well within the frame known from recent CMIP models. With improved physical and dynamic features, new Earth system model (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. ; The development of EC-Earth3 was supported by the European Union's Horizon 2020 research and innovation program under project IS-ENES3, the third phase of the distributed e-infrastructure of the European Network for Earth System Modelling (ENES) (grant agreement no. 824084, PRIMAVERA grant no. 641727, and CRESCENDO grant no. 641816). Etienne Tourigny and Raffaele Bernardello have received funding from the European Union's Horizon 2020 research and innovation program under Marie Skłodowska-Curie grant agreement nos. 748750 (SPFireSD project) and 708063 (NeTNPPAO project). Ivana Cvijanovic was supported by Generalitat de Catalunya (Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement) through the Beatriu de Pinós program. Yohan Ruprich-Robert was funded by the European Union's Horizon 2020 research and innovation program in the framework of Marie Skłodowska-Curie grant INADEC (grant agreement 800154). Paul A. Miller, Lars Nieradzik, David Wårlind, Roland Schrödner, and Benjamin Smith acknowledge financial support from the strategic research area "Modeling the Regional and Global ...

Modes of climate variability affect global and regional climates on different spatio-temporal scales, and they have important impacts on human activities and ecosystems. As these modes are a useful tool for simplifying the understanding of the climate system, it is crucial that we gain improved knowledge of their long-term past evolution and interactions over time to contextualise their present and future behaviour. We review the literature focused on proxy-based reconstructions of modes of climate variability during the Holocene (i.e., the last 11.7 thousand years) with a special emphasis on i) proxy-based reconstruction methods; ii) available proxy-based reconstructions of the main modes of variability, i.e., El Niño Southern Oscillation, Pacific Decadal Variability, Atlantic Multidecadal Variability, the North Atlantic Oscillation, the Southern Annular Mode and the Indian Ocean Dipole; iii) major interactions between these modes; and iv) external forcing mechanisms related to the evolution of these modes. This review shows that modes of variability can be reconstructed using proxy-based records from a wide range of natural archives, but these reconstructions are scarce beyond the last millennium, partly due to the lack of robust chronologies with reduced dating uncertainties, technical issues related to proxy calibration, and difficulty elucidating their stationary impact (or not) on regional climates over time. While for each mode the available reconstructions tend to agree at mutidecadal timescales, they show notable disagreement on shorter timescales beyond the instrumental period. The reviewed evidence suggests that the intrinsic variability of modes can be modulated by external forcing, such as orbital, solar, volcanic, and anthropogenic forcing. The review also highlights some modes experience higher variability over the instrumental period, which is partly ascribed to anthropogenic forcing. These features stress the paramount importance of further studying their past variations using long climate-proxy records for the progress of climate science. ; A.H. is supported by a Beatriu de Pinós –Marie Curie Cofund programme fellowship (2016 BP 00023) and HOLMODRIVE - North Atlantic Atmospheric Patterns influence on Western Iberia Climate: From the Lateglacial to the Present (PTDC/CTA-GEO/29029/2017) project funded by the Fundação para a Ciência e a Tecnologia, Portugal (FCT). A.H., S.G., and S.P-R. thank the Spanish research project PaleoModes (CGL2016-75281-C2-1-R) which provided some of their financial support. E.M.-C. contribution was funded by the project PARAMOUR (30454083) from the EOS program by the F.R.SFNRS. CM-P is supported by the Royal Society (ref: DH150185). P.O. contribution has been supported by the Ramon y Cajal senior tenure programme from the Spanish Ministry of Economy and Competitiveness. L.C-B. acknowledges support from the ERC-funded project GC2.0 (Global Change 2.0: Unlocking the past for a clearer future, grant number 694481). The Spanish PTI PolarCSIC ( https://www.polarcsic.es ) is also acknowledged for providing partial financial support to S.G. R.M. was partially supported by the Swedish Research Council (grant DNR2013-8421). A. P.'s work was supported by a Science Foundation Ireland Career Development Award (17/CDA/4695), a research centre award (12/RC/2289_P2), an investigator award (16/IA/4520), and a Marine Research Programme funded by the Irish Government, cofinanced by the European Regional Development Fund (Grant-Aid Agreement No. PBA/CC/18/01). A.A.S. was supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra. J.S. is supported by the strategic research program of Modelling the Regional and Global Earth system (MERGE) hosted by the Faculty of Science at Lund University. D.S. is supported by Blue-Action (European Union's Horizon 2020 research and innovation program, Grant Number: 727852) and EUCP (European Union's Horizon 2020 research and innovation programme under grant agreement no 776613) projects as well as by the French national program LEFE/INSU with VADEMECUM project. G. X. thanks the funding from the Chinese Scholarship Council (201704910171). This study is partly based on discussions held during the joint workshop of the CLIMOVAR group and IBCC-lo2k project, Barcelona, 25–27 September 2013. PAGES and IBCC-lo2k are thanked for supporting this workshop. Finally, we also thank three anonymous reviewers for their helpful comments on an earlier version of the manuscript ; Peer Reviewed ; Postprint (author's final draft)

In this paper, we present and evaluate the skill of an EC-Earth3.3 decadal prediction system contributing to the Decadal Climate Prediction Project – Component A (DCPP-A). This prediction system is capable of skilfully simulating past global mean surface temperature variations at interannual and decadal forecast times as well as the local surface temperature in regions such as the tropical Atlantic, the Indian Ocean and most of the continental areas, although most of the skill comes from the representation of the external radiative forcings. A benefit of initialization in the predictive skill is evident in some areas of the tropical Pacific and North Atlantic oceans in the first forecast years, an added value that is mostly confined to the south-east tropical Pacific and the eastern subpolar North Atlantic at the longest forecast times (6–10 years). The central subpolar North Atlantic shows poor predictive skill and a detrimental effect of initialization that leads to a quick collapse in Labrador Sea convection, followed by a weakening of the Atlantic Meridional Overturning Circulation (AMOC) and excessive local sea ice growth. The shutdown in Labrador Sea convection responds to a gradual increase in the local density stratification in the first years of the forecast, ultimately related to the different paces at which surface and subsurface temperature and salinity drift towards their preferred mean state. This transition happens rapidly at the surface and more slowly in the subsurface, where, by the 10th forecast year, the model is still far from the typical mean states in the corresponding ensemble of historical simulations with EC-Earth3. Thus, our study highlights the Labrador Sea as a region that can be sensitive to full-field initialization and hamper the final prediction skill, a problem that can be alleviated by improving the regional model biases through model development and by identifying more optimal initialization strategies. ; The work in this paper was supported by the European Commission H2020 projects EUCP (grant no. 776613), APPLICATE (grant no. 727862), INTAROS (grant no. 727890) and PRIMAVERA (grant no. 641727); a Spanish project funded by the Spanish Ministry of Economy, Industry and Competitiveness (CLINSA, grant no. CGL2017-85791-R); a FRS-FNRS/FWO-funded Belgian project (PARAMOUR, grant no. EOS-30454083); and an ESA contract (grant no. CMUG-CCI3-TECHPROP). The climate simulations analysed in the paper were performed using the internal computing resources available at MareNostrum and additional resources from PRACE (HiResNTCP, project 3: grant no. 2017174177) and the Red Española de Supercomputación (AECT-2019-2-0003 and AECT-2019-3-0006 projects) as well as technical support provided by the Barcelona Supercomputing Center. In addition, several co-authors have been supported by personal grants: Yohan Ruprich-Robert, Etienne Tourigny and Simon Wild received funding from the European Union Horizon 2020 research and innovation programme (grant agreement nos. 800154, 748750 and 754433 respectively); Ivana Cvijanovic was supported by Generalitat de Catalunya (Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement) through the Beatriu de Pinós programme; Juan Acosta-Navarro was supported by the Spanish Ministry of Science, Innovation and Universities through a Juan de la Cierva personal grant (grant no. FJCI-2017-34027); Rubén Cruz-García was funded by the Spanish Ministry of Education, Culture and Sports with an FPU grant (grant no. FPU15/01511); and Markus Donat and Pablo Ortega were funded by the Spanish Ministry of Economy, Industry and Competitiveness through the Ramon y Cajal grants RYC-2017-22964 and RYC-2017-22772. We also want to thank Panos Athanasidis, Stephen Yeager and Gerald Meehl for their very helpful comments when reviewing the paper. ; Postprint (published version)

Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change1,2,3. Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain4. This leads to low confidence in regional projections, especially for precipitation, over the coming decades5,6. The chaotic nature of the climate system7,8,9 may also mean that signal uncertainties are largely irreducible. However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate model predictions of the past six decades and show that decadal variations in North Atlantic winter climate are highly predictable, despite a lack of agreement between individual model simulations and the poor predictive ability of raw model outputs. Crucially, current models underestimate the predictable signal (the predictable fraction of the total variability) of the North Atlantic Oscillation (the leading mode of variability in North Atlantic atmospheric circulation) by an order of magnitude. Consequently, compared to perfect models, 100 times as many ensemble members are needed in current models to extract this signal, and its effects on the climate are underestimated relative to other factors. To address these limitations, we implement a two-stage post-processing technique. We first adjust the variance of the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictable signal. We then select and use only the ensemble members with a North Atlantic Oscillation sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation. This approach greatly improves decadal predictions of winter climate for Europe and eastern North America. Predictions of Atlantic multidecadal variability are also improved, suggesting that the North Atlantic Oscillation is not driven solely by Atlantic multidecadal variability. Our results highlight the need to understand why the signal-to-noise ratio is too small in current climate models10, and the extent to which correcting this model error would reduce uncertainties in regional climate change projections on timescales beyond a decade. ; DMS, AAS, NJD, LH and RE were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra and by the European Commission Horizon 2020 EUCP project (GA 776613). FJDR, LPC, SW and RB also acknowledge the support from the EUCP project (GA 776613) and from the Ministerio de Econom´ıa y Competitividad (MINECO) as part of the CLINSA project (Grant No. CGL2017-85791-R). SW received funding from the innovation programme under the Marie Sk´lodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433 and PO from the Ramon y Cajal senior tenure programme of MINECO. The EC-Earth simulations were performed on Marenostrum 4 (hosted by the Barcelona Supercomputing Center, Spain) using Auto-Submit through computing hours provided by PRACE.WAM, HP, KMand KP were supported by the German FederalMinistry for Education and Research (BMBF) project MiKlip (grant 01LP1519A). NK, IB, FC and YW were supported by the Norwegian Research Council projects SFE (grant 270733) the Nordic Center of excellent ARCPATH (grant 76654) and the Trond Mohn Foundation, under the project number : BFS2018TMT01 and received grants for computer time from the Norwegian Program for supercomputing (NOTUR2, NN9039K) and storage grants (NORSTORE, NS9039K). JM, LFB and DS are supported by Blue-Action (European Union Horizon 2020 research and innovation program, Grant Number: 727852) and EUCP (European Union Horizon 2020 research and innovation programme under grant agreement no 776613) projects. The National Center for Atmospheric Research (NCAR) is a major facility sponsored by the US National Science Foundation (NSF) under Cooperative Agreement No. 1852977. NCAR contribution was partially supported by the National Oceanic and Atmospheric Administration (NOAA) Climate Program Office under Climate Variability and Predictability Program Grant NA13OAR4310138 and by the US NSF Collaborative Research EaSM2 Grant OCE-1243015. ; Peer Reviewed ; Postprint (author's final draft)