Significance Climate change can affect the distribution and abundance of marine life, with consequences for goods and services provided to people. Because different models can lead to divergent conclusions about marine futures, we present an integrated global ocean assessment of climate change impacts using an ensemble of multiple climate and ecosystem models. It reveals that global marine animal biomass will decline under all emission scenarios, driven by increasing temperature and decreasing primary production. Notably, climate change impacts are amplified at higher food web levels compared with phytoplankton. Our ensemble projections provide the most comprehensive outlook on potential climate-driven ecological changes in the global ocean to date and can inform adaptive management and conservation of marine resources under climate change.

Abstract 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.

Climate change is altering the abundance and distribution of marine species (1⇓⇓⇓–5), with consequences for ocean ecosystem structure and functioning, seafood supply, and marine management and conservation (6⇓–8). Quantifying future trends and uncertainties is critical to inform ongoing global assessments (1), including the Intergovernmental Panel for Climate Change (IPCC) and Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, and guide viable pathways toward achieving key policy objectives, such as the United Nations Sustainable Development Goals (SDGs). Various modeling approaches exist to assess current and future impacts on marine ecosystems (8⇓⇓⇓–12), yet each individual model is necessarily an incomplete simplification of the natural world, with different assumptions, structures, and processes (13). One approach to overcoming any single-model limitations is to force a suite of models with standardized climate change scenarios and combine them into ensemble projections to estimate mean future trends and associated intermodel spread (13). Such model intercomparison projects (MIPs) have become a “gold standard” in climate science and have proven critical for enhancing credibility and understanding of the physical and biochemical climate change projections (14) and associated impacts on Earth’s terrestrial biosphere (15⇓–17), yet can only now be attempted for the global ocean ecosystem (13).

Over the past decade, a number of global fisheries and marine ecosystem models (MEMs) have been developed (13). Some of these have been used individually to project future changes in species distribution, biomass, or potential fisheries catch (8⇓⇓⇓–12), but it remains unclear how consistent and comparable these results are, and thus how applicable for providing robust insight and advice. The Fisheries and Marine Ecosystem Model Intercomparison Project (Fish-MIP; ref. 13) was created to bring these various models and modeling groups together to produce ensemble projections under standardized climate change scenarios.

Here we assess projected changes in global marine animal biomass over the 21st century through ensemble projections with six published global MEMs from Fish-MIP, forced with standardized outputs from two contrasting Earth system models (ESMs) and four emission scenarios [Representative Concentration Pathways (RCPs)]. The MEMs range from size-structured [Bioeconomic Marine Trophic Size-spectrum (BOATS), Macroecological] and trait-based [Dynamic Pelagic Benthic Model (DPBM), Apex Predators EcoSystem Model (APECOSM)] to species distribution [Dynamic Bioclimate Envelope Model (DBEM)] and trophodynamic models (EcoOcean) (SI Appendix, Tables S1 and S2). The ESMs span the range of available Coupled Model Intercomparison Project Phase 5 (CMIP5) projections, from low [Geophysical Fluid Dynamics Laboratory Climate Model (GFDL-ESM2M)] to high Institute Pierre Simon Laplace Climate Model (IPSL-CM5A-LR) increases in sea surface temperature (SST) and associated changes in net primary production (NPP), while other drivers were more similar (ref. 14 and SI Appendix, Figs. S1 and S2). The RCPs range from a low-emission strong mitigation scenario (RCP2.6) to a high-emission business-as-usual scenario (RCP8.5), with two intermediate scenarios (RCP4.5 and RCP6.0). All climate change scenarios were run for historical (1970–2005) and future (2006–2100) periods without fishing to isolate the climate signal, and with fishing to evaluate how climate responses differ in an ocean fished at current levels of intensity (13). The six MEMs generated standardized outputs of total animal biomass (except zooplankton) and biomass of animals of >10 cm and >30 cm. Since not all MEMs could run the full set of scenarios, due to MEM or ESM limitations, we analyzed all available runs for each scenario, and performed sensitivity analyses on subsets, which revealed similar results (SI Appendix, Table S3).

The goals of this study were to examine the consistency of projections across MEMs over a range of climate change scenarios globally and regionally from 1970 to 2100. We also evaluated how ocean animal biomass changes correspond with those in the physical environment and the extent to which projected climate impacts on primary producers and zooplankton (18) are transmitted to higher food web levels.

Conclusions Our ensemble projections demonstrate that global ocean animal biomass consistently declines with climate change, and that impacts are amplified at higher trophic levels. Our hindcasts support recent empirical work that shows ongoing climate impacts on fish biomass (30, 37), and project elevated climate-driven declines in ocean ecosystems, with magnitudes dependent on emission pathways. Amplification of biomass declines for higher trophic levels represents a particular challenge for human society, including meeting the SDGs for food security (SDG2), livelihoods (SDG1), and well-being (SDG3) for a growing human population while also sustaining life below water (SDG14). Our ensemble projections indicate the largest decreases in animal biomass at middle to low latitudes, where many nations depend on seafood and fisheries, and where marine biodiversity is already threatened by multiple human activities (6, 38). In turn, the largest increases are projected at high latitudes, highlighting new opportunities for—and potential conflict over—resource use, but also an urgent need for protecting sensitive species and rapidly changing ecosystems. Overall, our results clearly highlight the benefits to be gained from climate change mitigation, as all impacts were substantially reduced under a strong mitigation (RCP2.6) compared with the business-as-usual (RCP8.5) scenario. By providing estimates of global biomass changes and associated uncertainties, our ensemble projections represent the most comprehensive outlook on the future of marine animal biomass to date. Our results are robust in terms of the direction of change, yet the substantial spread in the magnitude of projections illustrates considerable uncertainty in both ESMs and MEMs. The challenge is to address these uncertainties and improve our ability to predict marine ecosystem responses to climate change at different temporal and spatial scales. Projections based on global models are often less certain for coastal and polar regions but may be improved through regional downscaling to incorporate higher-resolution climate and ecosystem features (7, 39). The next round of CMIP6 projections with improved representation of biochemical parameters may also advance future ensemble projections (13, 40). The expansion of global observational datasets provides further opportunities to better constrain and validate models. The incorporation of additional MEMs based on novel paradigms or reflecting alternative structures and processes may also be informative (13), along with regional ecosystem or fish stock models that more accurately capture processes at management-relevant scales (21, 41, 42). Future MEMs could also further explore how species interactions and potential acclimatization or adaptation of marine organisms modify projected distribution and abundance. Finally, a large component of future change will depend on the trajectories of fisheries, aquaculture, and other human impacts on the ocean (6, 11, 29, 38). Incorporating standardized temporally and spatially resolved scenarios of human activities and alternative management approaches will improve our understanding of the future of marine animals and ocean ecosystems (13, 23), and identify the points of greatest leverage for mitigating impacts. Improved dynamic and adaptive ecosystem-based management may mitigate some future climate change impacts and maintain ecosystem health and service provision (6, 21, 22, 43). However, this can only happen if the international community, including national and regional bodies, strengthens the required institutions and management approaches (6, 44).

Acknowledgments We thank the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) and CMIP5 for providing data and logistical support, and we thank C. Free for sharing data. Financial support was provided by the German Federal Ministry of Education and Research through ISI-MIP (Grant 01LS1201A1), 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 Research and Innovation Program (Grants 682602 and 689518); to E.A.F., J.L.B., and T.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.

Footnotes Author contributions: H.K.L., D.P.T., T.D.E., W.W.L.C., E.D.G., M. Barange, J.L.B., L.B., V.C., E.A.F., S.J., O.M., and J. Schewe designed research; H.K.L., D.P.T., A.B.-B., T.D.E., W.W.L.C., E.D.G., M. Barange, N.B., D.B., J.L.B., M. Büchner, C.M.B., D.A.C., V.C., M.C., E.A.F., S.J., M.C.J., S.M., O.M., S.N., R.O.-R., J.A.F., Y.-J.S., T.A.M.S., J. Steenbeek, P.V., N.D.W., and B.W. performed research; L.B., J.P.D., T.R., J. Schewe, C.A.S., and J.V. contributed new reagents/analytic tools; H.K.L., D.P.T., A.B.-B., T.D.E., T.R., J.V., and B.W. analyzed data; and H.K.L., D.P.T., A.B.-B., T.D.E., W.W.L.C., E.D.G., M. Barange, N.B., D.B., J.L.B., L.B., M. Büchner, C.M.B., D.A.C., V.C., M.C., J.P.D., E.A.F., S.J., M.C.J., S.M., O.M., S.N., R.O.-R., T.R., J.A.F., J. Schewe, Y.-J.S., T.A.M.S., J. Steenbeek, C.A.S., P.V., J.V., N.D.W., and B.W. wrote the paper.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

Data deposition: All data reported in this paper are archived and publicly available at http://dataservices.gfz-potsdam.de/pik/showshort.php?id=escidoc:2956913.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1900194116/-/DCSupplemental.