Suchergebnisse

The influence of the Atlantic multidecadal variability (AMV) on the North Atlantic storm track and eddy-driven jet in the winter season is assessed via a coordinated analysis of idealized simulations with state-of-the-art coupled models. Data used are obtained from a multimodel ensemble of AMV± experiments conducted in the framework of the Decadal Climate Prediction Project component C. These experiments are performed by nudging the surface of the Atlantic Ocean to states defined by the superimposition of observed AMV± anomalies onto the model climatology. A robust extratropical response is found in the form of a wave train extending from the Pacific to the Nordic seas. In the warm phase of the AMV compared to the cold phase, the Atlantic storm track is typically contracted and less extended poleward and the low-level jet is shifted toward the equator in the eastern Atlantic. Despite some robust features, the picture of an uncertain and model-dependent response of the Atlantic jet emerges and we demonstrate a link between model bias and the character of the jet response. ; The authors are thankful to three anonymous reviewers for valuable comments on an early version of the manuscript. PR, AB, DN, SG, ND, RE, LH, DS, and SW were supported by the H2020 EUCP project (Grant GA 776613). ND, RE, LH, and DS were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra. CC, ESG, and SQ would like to thank Marie-Pierre Moine for her help in model output post-processing and publication on CMIP6 database. BH acknowledges support from the NERC North Atlantic Climate System: Integrated Study (ACSIS) project. YRR was founded by the European Union's Horizon 2020 Research and Innovation Programme in the framework of the Marie Sklodowska-Curie Grant INADEC (Grant Agreement 800154). The National Center for Atmospheric Research (NCAR) is a major facility sponsored by the U.S. National Science Foundation (NSF) under Cooperative Agreement 1852977. The NCAR contribution was supported by the U.S. NSF under the Collaborative Research EaSM2 Grant OCE-1243015 and the U.S. National Oceanic and Atmospheric Administration (NOAA) Climate Program Office under the Climate Variability and Predictability Program Grant NA13OAR4310138. SW has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement H2020-MSCA-COFUND-2016-754433. ; Peer Reviewed ; "Article signat per 20 autors/es: Paolo Ruggieri, Alessio Bellucci, Dario Nicolí, Panos J. Athanasiadis, Silvio Gualdi, Christophe Cassou, Fred Castruccio, Gokhan Danabasoglu, Paolo Davini, Nick Dunstone, Rosemary Eade, Guillaume Gastineau, Ben Harvey, Leon Hermanson, Saïd Qasmi, Yohan Ruprich-Robert, Emilia Sanchez-Gomez, Doug Smith, Simon Wild, and Matteo Zampieri" ; Postprint (author's final draft)

The recent increase in Atlantic and Pacific ocean heat transports has led to a decrease in Arctic sea-ice area and volume. As the respective contributions from both oceans in driving sea-ice loss is still uncertain, our study explores this. We use the EC-Earth3 coupled global climate model and perform different sensitivity experiments to gain insights into the relationships between ocean heat transport and Arctic sea ice. In these model experiments, the sea-surface temperature is artificially increased in different regions of the North Atlantic and North Pacific Oceans and with different levels of warming. All the experiments lead to enhanced ocean heat transport, and consequently to a decrease in Arctic sea-ice area and volume. We show that the wider the domain in which the sea-surface temperature is increased and the larger the level of warming, the larger the increase in ocean heat transport and the stronger the decrease in Arctic sea-ice area and volume. We also find that for a same amount of ocean heat transport increase, the reductions in Arctic sea-ice area and volume are stronger when the sea-surface temperature increase is imposed in the North Pacific, compared to the North Atlantic. This is explained by the lower-salinity water at the Bering Strait and atmospheric warming of the North Atlantic Ocean in the Pacific experiments. Finally, we find that the sea-ice loss is mainly driven by reduced basal growth along the sea-ice edge and enhanced basal melt in the Central Arctic. This confirms that the ocean heat transport is the primary driver of Arctic sea-ice loss in our experiments. ; DD is supported by the OSeaIce project (https://cordis.europa.eu/project/id/834493), which has received funding from the European Union's (EU) Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 834493. TK and RFF are funded by the EU Horizon 2020 PRIMAVERA project, grant agreement No. 641727. MPK acknowledges support from the NordForsk research program Arctic Climate Predictions: Pathways to Resilient, Sustainable Societies (ARCPATH, No. 76654). YRR was funded by the EU Horizon 2020 research and innovation programme in the framework of the Marie Sklodowska-Curie grant INADEC (grant agreement No. 800154). ; Peer Reviewed ; Postprint (published version)

Atlantic multidecadal variability (AMV) has been linked to the observed slowdown of global warming over 1998–2012 through its impact on the tropical Pacific. Given the global importance of tropical Pacific variability, better understanding this Atlantic–Pacific teleconnection is key for improving climate predictions, but the robustness and strength of this link are uncertain. Analyzing a multi-model set of sensitivity experiments, we find that models differ by a factor of 10 in simulating the amplitude of the Equatorial Pacific cooling response to observed AMV warming. The inter-model spread is mainly driven by different amounts of moist static energy injection from the tropical Atlantic surface into the upper troposphere. We reduce this inter-model uncertainty by analytically correcting models for their mean precipitation biases and we quantify that, following an observed 0.26 °C AMV warming, the equatorial Pacific cools by 0.11 °C with an inter-model standard deviation of 0.03 °C. ; Y.R.-R. was founded by the European Union's Horizon 2020 Research and Innovation Program in the framework of the Marie Skłodowska-Curie grant INADEC (Grant agreement 800154). E.M.-C. acknowledges funding from the European Commission's Horizon 2020 projects PRIMAVERA (Grant Agreement 641727). X.L. has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433. A.B. and D.N. acknowledge funding from the European Commission's Horizon 2020 project EUCP (Grant agreement 776613). F.C. and G.D. were supported by the US National Science Foundation (NSF) under the Collaborative Research EaSM2 Grant OCE-1243015 to NCAR and by the US National Oceanic and Atmospheric Administration (NOAA) Climate Program Office under the Climate Variability and Predictability Program Grant NA13OAR4310138. NCAR is a major facility sponsored by the US NSF under Cooperative Agreement 1852977. Acknowledgments are made for the use of ECMWF's computing and archive facilities in this research, in particular, P.D. thanks ECMWF for providing computing time in the framework of the special project SPITDAVI. R.E., N.D., L.H., and D.S. were supported by the Met Office Hadley Center 522 Climate Program funded by BEIS and Defra and by the European Commission Horizon 2020 EUCP 523 project (GA 776613). J.L.-P. was funded by the European Union's Horizon 2020 Research and Innovation Program in the framework of the PRIMAVERA project (Grant Agreement 641727). J.R. and D.H. were funded by NERC via NCAS and the ACSIS project (NE/N018001/1), and JR was also funded by the NERC SMURPHS project (NE/N006054/1). M.M.-R. was funded by the European Union's Horizon 2020 Research and Innovation Program in the framework of the Marie Skłodowska-Curie grant FESTIVAL (Grant agreement 797236). E.T. has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 748750 (SPFireSD project). The analysis and plots of this paper were performed with the NCAR Command Language (Version 6.6.2; 2019)67. ; Peer Reviewed ; "Article signat per 25 autors/es: Yohan Ruprich-Robert, Eduardo Moreno-Chamarro, Xavier Levine, Alessio Bellucci, Christophe Cassou, Frederic Castruccio, Paolo Davini, Rosie Eade, Guillaume Gastineau, Leon Hermanson, Dan Hodson, Katja Lohmann, Jorge Lopez-Parages, Paul-Arthur Monerie, Dario Nicolì, Said Qasmi, Christopher D. Roberts, Emilia Sanchez-Gomez, Gokhan Danabasoglu, Nick Dunstone, Marta Martin-Rey, Rym Msadek, Jon Robson, Doug Smith & Etienne Tourigny " ; Postprint (author's final draft)

North Atlantic sea surface temperatures (SSTs) underwent pronounced multidecadal variability during the twentieth and early twenty-first century. We examine the impacts of this Atlantic Multidecadal Variability (AMV), also referred to as the Atlantic Multidecadal Oscillation (AMO), on climate in an ensemble of five coupled climate models at both low and high spatial resolution. We use a SST nudging scheme specified by the Coupled Model Intercomparision Project's Decadal Climate Prediction Project Component C (CMIP6 DCPP-C) to impose a persistent positive/negative phase of the AMV in the North Atlantic in coupled model simulations; SSTs are free to evolve outside this region. The large-scale seasonal mean response to the positive AMV involves widespread warming over Eurasia and the Americas, with a pattern of cooling over the Pacific Ocean similar to the Pacific Decadal Oscillation (PDO), together with a northward displacement of the inter-tropical convergence zone (ITCZ). The accompanying changes in global atmospheric circulation lead to widespread changes in precipitation. We use Analysis of Variance (ANOVA) to demonstrate that this large-scale climate response is accompanied by significant differences between models in how they respond to the common AMV forcing, particularly in the tropics. These differences may arise from variations in North Atlantic air-sea heat fluxes between models despite a common North Atlantic SST forcing pattern. We cannot detect a widespread effect of increased model horizontal resolution in this climate response, with the exception of the ITCZ, which shifts further northwards in the positive phase of the AMV in the higher resolution configurations. ; The Authors would like to acknowledge the use of the UKRI funded JASMIN data analysis facility which was essential to the analysis and storage of PRIMAVERA project data. Ongoing curation of project data has been supported by the IS-ENES3 project that has received funding from the European Union' Horizon 2020 research and innovation programme under Grant Agreement No. 824084. Authors DH, PM, JS, PD, YRR, CDR acknowledge funding from the PRIMAVERA project (www.primavera-h2020.eu), funded by the European Union's Horizon 2020 programme under Grant Agreement 641727. PM was supported by U.K.–China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. PD thanks ECMWF for providing computing time in the framework of the special projects SPITDAVI. YRR was funded by the European Union's Horizon 2020 Research and Inovation Programme in the framework of the Marie Skłodowska-Curie grant INADEC (Grant Agreement 80015400). Author DH would like to thank Nick Klingaman and Linda Hirons for their extensive help with the MetUM-GOML model. This article was written with support (DH) from National Environmental Research Council (NERC) national capability grant for the North Atlantic Climate System: Integrated study (ACSIS) program (Grants NE/N018001/1, NE/N018044/1, NE/N018028/1, and NE/N018052/1). MMR is supported by a Juan de la Cierva Incorporacion research contract of MICINN (Spain). The authors wish to acknowledge use of the Ferret program for analysis and graphics in this paper. Ferret is a product of NOAA's Pacific Marine Environmental Laboratory. (Information is available at http://ferret.pmel.noaa.gov/Ferret/) and also the CF-python analysis package http://ncas-cms.github.io/cf-python/. Assembly of MetUM-GOML and development of MC-KPP was supported by the National Centre for Atmospheric Science and led by Dr. Nicholas Klingaman. The authors would also like to thank the three anonymous reviewers whose comments contributed to a much improved final manuscript. ; Peer Reviewed ; "Article signat per 17 autors/es: Daniel L. R. Hodson, Pierre-Antoine Bretonnière, Christophe Cassou, Paolo Davini, Nicholas P. Klingaman, Katja Lohmann, Jorge Lopez-Parages, Marta Martín-Rey, Marie-Pierre Moine, Paul-Arthur Monerie, Dian A. Putrasahan, Christopher D. Roberts, Jon Robson, Yohan Ruprich-Robert, Emilia Sanchez-Gomez, Jon Seddon & Retish Senan" ; Postprint (published version)

We examine the weakening of the Atlantic Meridional Overturning Circulation (AMOC) in response to increasing CO2 at different horizontal resolutions in a state-of-the-art climate model and in a small ensemble of models with differing resolutions. There is a strong influence of the ocean mean state on the AMOC weakening: models with a more saline western subpolar gyre have a greater formation of deep water there. This makes the AMOC more susceptible to weakening from an increase in CO2 since weakening ocean heat transports weaken the contrast between ocean and atmospheric temperatures and hence weaken the buoyancy loss. In models with a greater proportion of deep water formation further north (in the Greenland-Iceland-Norwegian basin), deep-water formation can be maintained by shifting further north to where there is a greater ocean-atmosphere temperature contrast. We show that ocean horizontal resolution can have an impact on the mean state, and hence AMOC weakening. In the models examined, those with higher resolutions tend to have a more westerly location of the North Atlantic Current and stronger subpolar gyre. This likely leads to a greater impact of the warm, saline subtropical Atlantic waters on the western subpolar gyre resulting in greater dense water formation there. Although there is some improvement of the higher resolution models over the lower resolution models in terms of the mean state, both still have biases and it is not clear which biases are the most important for influencing the AMOC strength and response to increasing CO2. ; LJ, MR, DI, TK, VM, CR, YR-R were funded by the PRIMAVERA project, funded by the European Union's Horizon 2020 programme under grant agreement 641727. LJ, HH, MR, RW were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra (GA01101). We wish to thank two anonymous reviewers for their comments which improved this manuscript. ; Peer Reviewed ; Postprint (published version)

This study investigates tropical cyclone integrated kinetic energy, a measure which takes into account the intensity and the size of the storms and which is closely associated with their damage potential, in three different global climate models integrated following the HighResMIP protocol. In particular, the impact of horizontal resolution and of the ocean coupling are assessed. We find that, while the increase in resolution results in smaller and more intense storms, the integrated kinetic energy of individual cyclones remains relatively similar between the two configurations. On the other hand, atmosphere‐ocean coupling tends to reduce the size and the intensity of the storms, resulting in lower integrated kinetic energy in that configuration. Comparing cyclone integrated kinetic energy between a present and a future scenario did not reveal significant differences between the two periods. ; This research has been supported by the Horizon 2020 programme (PRIMAVERA, GA #641727). S. Wild received funding from the European Union Horizon 2020 research and innovation programme under the Marie Sklodowska- Curie grant agreement 2020-MSCA- COFUND-2016-754433 and financial support from the Spanish Agencia Estatal de Investigación (FJC2019- 041186-I/AEI/10.13039/501100011033). M. J. Roberts acknowledges the support from 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. Finally, the authors are most grateful to three anonymous reviewers for their helpful comments in improving a previous version of this manuscript. ; Peer Reviewed ; 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 ...

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)

11 pages, 6 figures, supplementary information https://doi.org/10.1038/s41612-021-00188-5.-- Data availability; The data generated and analyzed during the current study are available from the corresponding author YRR on reasonable request ; Atlantic multidecadal variability (AMV) has been linked to the observed slowdown of global warming over 1998–2012 through its impact on the tropical Pacific. Given the global importance of tropical Pacific variability, better understanding this Atlantic–Pacific teleconnection is key for improving climate predictions, but the robustness and strength of this link are uncertain. Analyzing a multi-model set of sensitivity experiments, we find that models differ by a factor of 10 in simulating the amplitude of the Equatorial Pacific cooling response to observed AMV warming. The inter-model spread is mainly driven by different amounts of moist static energy injection from the tropical Atlantic surface into the upper troposphere. We reduce this inter-model uncertainty by analytically correcting models for their mean precipitation biases and we quantify that, following an observed 0.26 °C AMV warming, the equatorial Pacific cools by 0.11 °C with an inter-model standard deviation of 0.03 °C ; Y.R.-R. was founded by the European Union's Horizon 2020 Research and Innovation Program in the framework of the Marie Skłodowska-Curie grant INADEC (Grant agreement 800154). E.M.-C. acknowledges funding from the European Commission's Horizon 2020 projects PRIMAVERA (Grant Agreement 641727). X.L. has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433. A.B. and D.N. acknowledge funding from the European Commission's Horizon 2020 project EUCP (Grant agreement 776613). F.C. and G.D. were supported by the US National Science Foundation (NSF) under the Collaborative Research EaSM2 Grant OCE-1243015 to NCAR and by the US National Oceanic and Atmospheric Administration (NOAA) Climate Program Office under the Climate Variability and Predictability Program Grant NA13OAR4310138. NCAR is a major facility sponsored by the US NSF under Cooperative Agreement 1852977. [.] R.E., N.D., L.H., and D.S. were supported by the Met Office Hadley Center 522 Climate Program funded by BEIS and Defra and by the European Commission Horizon 2020 EUCP 523 project (GA 776613). J.L.-P. was funded by the European Union's Horizon 2020 Research and Innovation Program in the framework of the PRIMAVERA project (Grant Agreement 641727). J.R. and D.H. were funded by NERC via NCAS and the ACSIS project (NE/N018001/1), and JR was also funded by the NERC SMURPHS project (NE/N006054/1). M.M.-R. was funded by the European Union's Horizon 2020 Research and Innovation Program in the framework of the Marie Skłodowska-Curie grant FESTIVAL (Grant agreement 797236). E.T. has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 748750 (SPFireSD project) ; With the funding support of the 'Severo Ochoa Centre of Excellence' accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI) ; Peer reviewed

Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. http://www.sciencemag.org/about/science-licenses-journal-article-reuse This is an article distributed under the terms of the Science Journals Default License. ; The El Niño–Southern Oscillation (ENSO), which originates in the Pacific, is the strongestand most well-known mode of tropical climate variability. Its reach is global, and it canforce climate variations of the tropical Atlantic and Indian Oceans by perturbing the globalatmospheric circulation. Less appreciated is how the tropical Atlantic and Indian Oceansaffect the Pacific. Especially noteworthy is the multidecadal Atlantic warming that began inthe late 1990s, because recent research suggests that it has influenced Indo-Pacificclimate, the character of the ENSO cycle, and the hiatus in global surface warming.Discovery of these pantropical interactions provides a pathway forward for improvingpredictions of climate variability in the current climate and for refining projections offuture climate under different anthropogenic forcing scenarios. ; W.C. is supportedby National Key R&D Program of China (2018YFA0605700).L.W., Xia.L., and B.G. are supported by the National NaturalScience Foundation of China (NSFC) projects (41490643,41490640, U1606402, and 41521091). W.C., G.W., B.N., and A.S.are supported by CSHOR and the Earth System and ClimateChange Hub of the Australian Government'sNationalEnvironment Science Program. CSHOR is a joint research Centrefor Southern Hemisphere Oceans Research between QNLM andCSIRO. M.L. is supported by the GOTHAM Belmont project(ANR-15-JCLI-0004-01) and the ARiSE ANR project. T.L. issupported by NSFC 41630423 and NSF AGS-1565653 grants.S.M. is supported by the Australian Research Council (ARC)through grant number FT160100162. J.-S.K. was supported bya National Research Foundation of Korea (NRF) grant fundedby the Korea government (MSIT) (NRF-2018R1A5A1024958).J.-Y.Y. is supported by NSF AGS-1505145 and AGS-1833075grants. M.F.S is supported by the Institute for Basic Science(project code IBS-R028-D1). Y.-G.H. is funded by the KoreaMeteorological AdministrationResearchandDevelopmentProgram under grant KMI2018-03214. M.J.M is supported byNOAA and PMEL contribution number 4838. Y.D. is supportedby the National Natural Science Foundation of China (41525019,41830538, and 41521005) and the State Oceanic Administrationof China (GASI-IPOVAI-02). D.D. is supported by ARC grantnumber CE170100023. M.M.-R. has been supported by theMORDICUS grant under contract ANR-13-762 SENV-0002-01and CGL2017-86415-R. Y.R.-R. is supported by an 800154-INADEC individual MSCA-IF-EF-ST grant. S.-P.X. is supportedby the NSF. J.B.K. was supported by the National EnvironmentResearch Council (NE/N005783/1).Author contributions:The manuscript was written as a group effort during two"Pantropical interbasin climate interactions"workshops heldat Xiamen University and Jeju Island. All authors contributedto the manuscript preparation, interpretations, and thediscussions that led to the final figure design. W.C. and L.W.designed the study and coordinated the writing. M.L., T.L., S.M.,J.-S.K., A.S., J.-Y.Y., Xic.L., M.F.S., Y.C., Y.-G.H., M.J.M., N.K.,Y.R.-R., and J.B.K. coordinated the discussion for varioussections.B.N.helpedtocollatecomments and prepare an initialversion.Competing interests:The authors declare nocompeting interests.Data and materials availability:Allobservation and model datasets used here are publiclyavailable or available on request. ; Peer Reviewed ; Postprint (author's final draft)

We present a new framework for global ocean–sea-ice model simulations based on phase 2 of the Ocean Model Intercomparison Project (OMIP-2), making use of the surface dataset based on the Japanese 55-year atmospheric reanalysis for driving ocean–sea-ice models (JRA55-do). We motivate the use of OMIP-2 over the framework for the first phase of OMIP (OMIP-1), previously referred to as the Coordinated Ocean–ice Reference Experiments (COREs), via the evaluation of OMIP-1 and OMIP-2 simulations from 11 state-of-the-science global ocean–sea-ice models. In the present evaluation, multi-model ensemble means and spreads are calculated separately for the OMIP-1 and OMIP-2 simulations and overall performance is assessed considering metrics commonly used by ocean modelers. Both OMIP-1 and OMIP-2 multi-model ensemble ranges capture observations in more than 80 % of the time and region for most metrics, with the multi-model ensemble spread greatly exceeding the difference between the means of the two datasets. Many features, including some climatologically relevant ocean circulation indices, are very similar between OMIP-1 and OMIP-2 simulations, and yet we could also identify key qualitative improvements in transitioning from OMIP-1 to OMIP-2. For example, the sea surface temperatures of the OMIP-2 simulations reproduce the observed global warming during the 1980s and 1990s, as well as the warming slowdown in the 2000s and the more recent accelerated warming, which were absent in OMIP-1, noting that the last feature is part of the design of OMIP-2 because OMIP-1 forcing stopped in 2009. A negative bias in the sea-ice concentration in summer of both hemispheres in OMIP-1 is significantly reduced in OMIP-2. The overall reproducibility of both seasonal and interannual variations in sea surface temperature and sea surface height (dynamic sea level) is improved in OMIP-2. These improvements represent a new capability of the OMIP-2 framework for evaluating process-level responses using simulation results. Regarding the sensitivity of individual models to the change in forcing, the models show well-ordered responses for the metrics that are directly forced, while they show less organized responses for those that require complex model adjustments. Many of the remaining common model biases may be attributed either to errors in representing important processes in ocean–sea-ice models, some of which are expected to be reduced by using finer horizontal and/or vertical resolutions, or to shared biases and limitations in the atmospheric forcing. In particular, further efforts are warranted to resolve remaining issues in OMIP-2 such as the warm bias in the upper layer, the mismatch between the observed and simulated variability of heat content and thermosteric sea level before 1990s, and the erroneous representation of deep and bottom water formations and circulations. We suggest that such problems can be resolved through collaboration between those developing models (including parameterizations) and forcing datasets. Overall, the present assessment justifies our recommendation that future model development and analysis studies use the OMIP-2 framework. ; This research has been supported by the Integrated Research Program for Advancing Climate Models (TOUGOU) of the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan (grant nos. JPMXD0717935457 and JPMXD0717935561), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) (grant no. 274762653), the Helmholtz Climate Initiative REKLIM (Regional Climate Change) and European Union's Horizon 2020 Research & Innovation program (grant nos. 727862 and 800154), the Research Council of Norway (EVA (grant no. 229771) and INES (grant no. 270061)), the US National Science Foundation (NSF) (grant no. 1852977), the National Natural Science Foundation of China (grant nos. 41931183 and 41976026), NOAA's Science Collaboration Program and administered by UCAR's Cooperative Programs for the Advancement of Earth System Science (CPAESS) (grant nos. NA16NWS4620043 and NA18NWS4620043B), and NOAA (grant no. NA18OAR4320123). ; Peer Reviewed ; Postprint (published version)