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16 pages, 6 figures, 3 tables, supplementary data https://doi.org/10.1016/j.pocean.2021.102659.-- Code and data availability: The experimental protocol in this paper has no code associated with it. Forcing data from CMIP5 used for the protocol, and the FishMIP model outputs presented in this paper are available on the ISIMIP servers (https://www.isimip.org/) ; Climate change is warming the ocean and impacting lower trophic level (LTL) organisms. Marine ecosystem models can provide estimates of how these changes will propagate to larger animals and impact societal services such as fisheries, but at present these estimates vary widely. A better understanding of what drives this inter-model variation will improve our ability to project fisheries and other ecosystem services into the future, while also helping to identify uncertainties in process understanding. Here, we explore the mechanisms that underlie the diversity of responses to changes in temperature and LTLs in eight global marine ecosystem models from the Fisheries and Marine Ecosystem Model Intercomparison Project (FishMIP). Temperature and LTL impacts on total consumer biomass and ecosystem structure (defined as the relative change of small and large organism biomass) were isolated using a comparative experimental protocol. Total model biomass varied between −35% to +3% in response to warming, and -17% to +15% in response to LTL changes. There was little consensus about the spatial redistribution of biomass or changes in the balance between small and large organisms (ecosystem structure) in response to warming, an LTL impacts on total consumer biomass varied depending on the choice of LTL forcing terms. Overall, climate change impacts on consumer biomass and ecosystem structure are well approximated by the sum of temperature and LTL impacts, indicating an absence of nonlinear interaction between the models' drivers. Our results highlight a lack of theoretical clarity about how to represent fundamental ecological mechanisms, most importantly how temperature impacts scale from individual to ecosystem level, and the need to better understand the two-way coupling between LTL organisms and consumers. We finish by identifying future research needs to strengthen global marine ecosystem modelling and improve projections of climate change impacts ; JDE was funded by Australian Research Council Discovery Projects DP150102656 and DP190102293. MC, JS, NB and OM received financial support by the European Union's Horizon 2020 research and innovation programme under grant agreement No 817578 (Triatlas project). CH received funding from the Open Philanthropy Project. NB and OM also acknowledge the support of the French ANR project CIGOEF (grant ANR-17-CE32-0008-01). DPT acknowledges funding from the ISI-MIP project to support a workshop on this topic, and the Jarislowsky Foundation. EDG received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 682602, BIGSEA). RFH was funded by the Spanish Ministry of Science, Innovation and Universities through the Acciones de Programación Conjunta Internacional (PCIN-2017-115). MC acknowledges the 'Severo Ochoa Centre of Excellence' accreditation (CEX2019-000928-S) to the Institute of Marine Science (ICM-CSIC). TDE acknowledges funding from the ISIMIP project to support a workshop on this topic and the Fisheries and Oceans Canada Atlantic Fisheries Fund. All authors declare no conflict of interest with respect to this study. JAFS received funding from the European Union's Horizon 2020 FutureMARES project (#869300). ; Peer reviewed

6 pages, 5 figures, supporting information https://doi.org/10.1073/pnas.1900194116.-- All data reported in this paper are archived and publicly available at http://dataservices.gfz-potsdam.de/pik/showshort.php?id=escidoc:2956913. ; While the physical dimensions of climate change are now routinely assessed through multimodel intercomparisons, projected impacts on the global ocean ecosystem generally rely on individual models with a specific set of assumptions. To address these single-model limitations, we present standardized ensemble projections from six global marine ecosystem models forced with two Earth system models and four emission scenarios with and without fishing. We derive average biomass trends and associated uncertainties across the marine food web. Without fishing, mean global animal biomass decreased by 5% (±4% SD) under low emissions and 17% (±11% SD) under high emissions by 2100, with an average 5% decline for every 1 °C of warming. Projected biomass declines were primarily driven by increasing temperature and decreasing primary production, and were more pronounced at higher trophic levels, a process known as trophic amplification. Fishing did not substantially alter the effects of climate change. Considerable regional variation featured strong biomass increases at high latitudes and decreases at middle to low latitudes, with good model agreement on the direction of change but variable magnitude. Uncertainties due to variations in marine ecosystem and Earth system models were similar. Ensemble projections performed well compared with empirical data, emphasizing the benefits of multimodel inference to project future outcomes. Our results indicate that global ocean animal biomass consistently declines with climate change, and that these impacts are amplified at higher trophic levels. Next steps for model development include dynamic scenarios of fishing, cumulative human impacts, and the effects of management measures on future ocean biomass trends ; Financial support was provided by the German Federal Ministry of Education and Research through ISI-MIP (Grant01LS1201A1), the European Union's Horizon 2020 Research and Innovation Program (Grant 678193), and the Ocean Frontier Institute (Module G). We acknowledge additional financial support as follows: to H.K.L., W.W.L.C., and B.W. from the Natural Sciences and Engineering Research Council (NSERC) of Canada; to D.P.T. from the Kanne Rasmussen Foundation Denmark; to A.B.-B. from the NSERC Transatlantic Ocean Science and Technology Program; to W.W.L.C. and T.D.E. from the Nippon Foundation-Nereus Program; to E.D.G., M.C. and J. Steenbeek from the European Union's Horizon 2020 Re-search and Innovation Program (Grants 682602 and 689518); to E.A.F., J.L.B., andT.R. from Commonwealth Scientific and Industrial Research Organization and the Australian Research Council; to N.B., L.B., and O.M. from the French Agence Nationale de la Recherche and Pôle de Calcul et de Données pour la Mer; and to S.J. from the UK Department of Environment, Food and Rural Affairs ; Peer Reviewed

22 pages, 5 figures, 1 table, supplementary information https://doi.org/10.1038/s41558-021-01173-9.-- Data availabilityAll standardized forcing variables from the ESMs are available at https://doi.org/10.48364/ISIMIP.575744.1; all outputs from the MEMs are available via ISIMIP (https://www.isimip.org/gettingstarted/data-access/).-- Code availabilityAll code used to analyse simulations is available at https://github.com/Fish-MIP/CMIP5vsCMIP6 ; Projections of climate change impacts on marine ecosystems have revealed long-term declines in global marine animal biomass and unevenly distributed impacts on fisheries. Here we apply an enhanced suite of global marine ecosystem models from the Fisheries and Marine Ecosystem Model Intercomparison Project (Fish-MIP), forced by new-generation Earth system model outputs from Phase 6 of the Coupled Model Intercomparison Project (CMIP6), to provide insights into how projected climate change will affect future ocean ecosystems. Compared with the previous generation CMIP5-forced Fish-MIP ensemble, the new ensemble ecosystem simulations show a greater decline in mean global ocean animal biomass under both strong-mitigation and high-emissions scenarios due to elevated warming, despite greater uncertainty in net primary production in the high-emissions scenario. Regional shifts in the direction of biomass changes highlight the continued and urgent need to reduce uncertainty in the projected responses of marine ecosystems to climate change to help support adaptation planning ; This work was supported by the Jarislowsky Foundation (D.P.T.), the Natural Sciences and Engineering Research Council of Canada Discovery Grant programme (D.P.T., H.K.L., T.D.E., W.W.L.C., J.P.-A. and V.C.); Australian Research Council (ARC) Discovery Projects DP170104240 (J.L.B. and C.N.), DP190102293 (J.L.B., C.N., A.J.R., J.D.E. and D.P.T.) and DP150102656 (J.D.E.); the European Union's Horizon 2020 research and innovation programme under grant agreements 817578 (TRIATLAS) (M.C., J.S., L.S., O.M., L.B., Y.-J.S., N.B. and J.R.), 869300 (FutureMARES) (J.A.F.-S.,Y.-J.S. and M.C.) and 862428 (MISSION ATLANTIC (J.A.F.-S, Y.-J.S. and M.C.); the Spanish National Project ProOceans (PID2020-118097RB-I00) (M.C. and J.S.); the Open Philanthropy Project (C.S.H.); the United Kingdom Research and Innovation (UKRI) Global Challenges Research Fund (GCRF) One Ocean Hub (NE/S008950/1) (K.O.-C. and L.S.); the Simons Foundation (nos. 54993, 645921) (G.L.B.); the Belmont Forum and BiodivERsA under the BiodivScen ERA-Net COFUND programme (SOMBEE project, ANR-18-EBI4-0003-01) (Y.-J.S. and N.B.); the MEOPAR Postdoctoral Fellowship Award 2020–2021 and the Ocean Frontier Institute (Module G) (A.B.-B.); the French ANR project CIGOEF (grant ANR-17-CE32-0008-01) (O.M., L.B. and J.R.); the California Ocean Protection Council Grant C0100400, the Alfred P. Sloan Foundation and the Extreme Science and Engineering Discovery Environment (XSEDE) allocation TG-OCE170017 (D.B. and J.G.); the National Oceanographic and Atmospheric Association (NA20OAR4310441, NA20OAR4310442) (C.M.P.). M.C. acknowledges the Severo Ochoa Centre of Excellence accreditation (CEX2019-000928-S) to the Institute of Marine Science (ICM-CSIC) ; Peer reviewed