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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

Abstract Future changes in tropical cyclone properties are an important component of climate change impacts and risk for many tropical and midlatitude countries. In this study we assess the performance of a multimodel ensemble of climate models, at resolutions ranging from 250 to 25 km. We use a common experimental design including both atmosphere-only and coupled simulations run over the period 1950–2050, with two tracking algorithms applied uniformly across the models. There are overall improvements in tropical cyclone frequency, spatial distribution, and intensity in models at 25 km resolution, with several of them able to represent very intense storms. Projected tropical cyclone activity by 2050 generally declines in the South Indian Ocean, while changes in other ocean basins are more uncertain and sensitive to both tracking algorithm and imposed forcings. Coupled models with smaller biases suggest a slight increase in average TC 10 m wind speeds by 2050. ; M. J. R. and J. C. acknowledge 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. M. J. R., J. S., P. L. V., K. H., B. V., R. H., A. B., E. S., L.‐ P. C., L. T., C. D. R., R. S., and D. P. acknowledge funding from the PRIMAVERA project, funded by the European Union's Horizon 2020 Framework Programme under Grant 641727. J. M. acknowledges funding from the Blue‐Action project, funded by the European Union's Horizon 2020 Framework Programme under Grant 727852. Funding for P. U. and C. Z. to support the use of the TempestExtremes suite was provided under National Aeronautics and Space Administration (NASA) Award NNX16AG62G and the U.S. Department of Energy Office of Science Award DE‐SC0016605. C. K. and Y. Y. acknowledge funding from the Environment Research and Technology Development Fund (2RF‐1701) by the Environmental Restoration and Conservation Agency (ERCA) of Japan and from the Integrated Research Program for Advancing Climate Models (TOUGOU) Grant JPMXD0717935457 by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan. The CESM1.3 simulations are completed through the International Laboratory for High‐Resolution Earth System Prediction (iHESP)—a collaboration among QNLM, TAMU, and NCAR, from which D. F., Q. Z., G. D., N. R., H. W., and L. W. acknowledge funding. NCAR is a major facility sponsored by the U.S. National Science Foundation under Cooperative Agreement 1852977. The CESM1.3 simulations were performed on Frontera at the Texas Advanced Computing Center. ; Peer Reviewed ; Postprint (published version)

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)