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The ability to anticipate marine habitat shifts responding to climate variability has high scientific and socioeconomic value. Here we quantify interannual-to-decadal predictability of habitat shifts by combining trait-based aerobic habitat constraints with a suite of initialized retrospective Earth System Model forecasts, for diverse marine ecotypes in the North American Large Marine Ecosystems. We find that aerobic habitat viability, defined by joint constraints of temperature and oxygen on organismal energy balance, is potentially predictable in the upper-600 m ocean, showing a substantial improvement over a simple persistence forecast. The skillful multiyear predictability is dominated by the oxygen component in most ecosystems, yielding higher predictability than previously estimated based on temperature alone. Notable predictability differences exist among ecotypes differing in temperature sensitivity of hypoxia vulnerability, especially along the northeast coast with predictability timescale ranging from 2 to 10 years. This tool will be critical in predicting marine habitat shifts in face of a changing climate.

Abstract. This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, http://www.isimip.org, last access: 2 November 2023) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include high-resolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems. In a second set of ISIMIP3a experiments the participating impact models are forced by the same DHFs but a counterfactual set of atmospheric forcings and coastal water levels where observed trends have been removed. These experiments are designed to allow for the attribution of observed changes in natural, human, and managed systems to climate change, rising CH4 and CO2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6.

Motivated by a need for climate-informed living marine resource management, increased emphasis has been placed on regional end-to-end modeling frameworks designed to project climate impacts on marine ecosystems and evaluate the efficacy of potential management strategies under changing conditions. The ‘Future Seas’ project was initiated with a focus on three fisheries (Pacific sardine, swordfish, and albacore tuna) in the California Current System (CCS). This work leverages a suite of climate, ocean, ecosystem, and economic models to project physical, ecological, and socio-economic change, evaluate management strategies, and quantify uncertainty in model projections. Here we describe the components of the modeling framework, considerations underlying choices made in model development, engagement with stakeholders, and key physical, ecological, and socio-economic results to date, including projections to 2100. Our broad aims are to (i) synthesize a large body of climate and fisheries research that has been conducted, and continues, under the Future Seas umbrella, and (ii) provide insight and recommendations to those pursuing similar efforts for other applications and in other regions. In general, our results indicate that all three species will likely shift their distributions (predominantly poleward) in the future, which impacts accessibility to fishing fleets, spatial management, and quota allocation. For similar integrative climate-to-fisheries projections, we recommend attention is given to: recognizing potential biases arising from differences between the climate products used for ecological model fitting and those used for model projection; how sources of projection uncertainty are prioritized, incorporated, and communicated; and quantitatively linking scenarios – especially socio-economic scenarios – with climate and ecological projections.

Abstract: Aim: Theory predicts fish community biomass to decline with increasing temperature due to higher metabolic losses resulting in less efficient energy transfer in warm‐water food webs. However, whether these metabolic predictions explain observed macroecological patterns in fish community biomass is virtually unknown. Here, we test these predictions by examining the variation in demersal fish biomass across productive shelf regions. Location: Twenty one continental shelf regions in the North Atlantic and Northeast Pacific. Time Period: 1980–2015. Major Taxa Studied: Marine teleost fish and elasmobranchs. Methods: We compiled high‐resolution bottom trawl survey data of fish biomass containing 166,000 unique tows and corrected biomass for differences in sampling area and trawl gear catchability. We examined whether relationships between net primary production and demersal fish community biomass are mediated by temperature, food‐web structure and the level of fishing exploitation, as well as the choice of spatial scale of the analysis. Subsequently, we examined if temperature explains regional changes in fish biomass over time under recent warming. Results: We find that biomass per km2 varies 40‐fold across regions and is highest in cold waters and areas with low fishing exploitation. We find no evidence that temperature change has impacted biomass within marine regions over the time period considered. The biomass variation is best explained by an elementary trophodynamic model that accounts for temperature‐dependent trophic efficiency. Main Conclusions: Our study supports the hypothesis that temperature is a main driver of large‐scale cross‐regional variation in fish community biomass. The cross‐regional pattern suggests that long‐term impacts of warming will be negative on biomass. These results provide an empirical basis for predicting future changes in fish community biomass and its associated services for human wellbeing that is food provisioning, under global climate change.

Transfer efficiency is the proportion of energy passed between nodes in food webs. It is an emergent, unitless property that is difficult to measure, and responds dynamically to environmental and ecosystem changes. Because the consequences of changes in transfer efficiency compound through ecosystems, slight variations can have large effects on food availability for top predators. Here, we review the processes controlling transfer efficiency, approaches to estimate it, and known variations across ocean biomes. Both process-level analysis and observed macroscale variations suggest that ecosystem-scale transfer efficiency is highly variable, impacted by fishing, and will decline with climate change. It is important that we more fully resolve the processes controlling transfer efficiency in models to effectively anticipate changes in marine ecosystems and fisheries resources.

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.

Global climate change is expected to impact ocean ecosystems through increases in temperature, decreases in pH and oxygen, increased stratification, with subsequent declines in primary productivity. These impacts propagate through the food chain leading to amplified effects on secondary producers and higher trophic levels. Similarly, climate change may disproportionately affect different species, with impacts depending on their ecological niche. To investigate how global environmental change will alter fish assemblages and productivity, we used a spatially explicit mechanistic model of the three main fish functional types reflected in fisheries catches (FEISTY) coupled to an Earth system model (GFDL-ESM2M) to make projections out to 2100. We additionally explored the sensitivity of projections to uncertainties in widely used metabolic allometries and their temperature dependence. When integrated globally, the biomass and production of all types of fish decreased under a high emissions scenario (RCP 8.5) compared to mean contemporary conditions. Projections also revealed strong increases in the ratio of pelagic zooplankton production to benthic production, a dominant driver of the abundance of large pelagic fish vs. demersal fish under historical conditions. Increases in this ratio led to a “pelagification” of ecosystems exemplified by shifts from benthic-based food webs toward pelagic-based ones. The resulting pelagic systems, however, were dominated by forage fish, as large pelagic fish suffered from increasing metabolic demands in a warming ocean and from declines in zooplankton productivity that were amplified at higher trophic levels. Patterns of relative change between functional types were robust to uncertainty in metabolic allometries and temperature dependence, though projections of the large pelagic fish had the greatest uncertainty. The same accumulation of trophic impacts that underlies the amplification of productivity trends at higher trophic levels propagates to the projection spread, creating an acutely uncertain future for the ocean’s largest predatory fish.

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.

Illness caused by pathogenic strains of Vibrio bacteria incurs significant economic and health care costs in many areas around the world. In the Chesapeake Bay, the two most problematic species are V. vulnificus and V. parahaemolyticus, which cause infection both from exposure to contaminated water and consumption of contaminated seafood. We used existing Vibrio habitat models, four global climate models, and a recently developed statistical downscaling framework to project the spatiotemporal probability of occurrence of V. vulnificus and V. cholerae in the estuarine environment, and the mean concentration of V. parahaemolyticus in oysters in the Chesapeake Bay by the end of the 21st century. Results showed substantial future increases in season length and spatial habitat for V. vulnificus and V. parahaemolyticus, while projected increase in V. cholerae habitat was less marked and more spatially heterogeneous. Our findings underscore the need for spatially variable inputs into models of climate impacts on Vibrios in estuarine environments. Overall, economic costs associated with Vibrios in the Chesapeake Bay, such as incidence of illness and management measures on the shellfish industry, may increase under climate change, with implications for recreational and commercial uses of the ecosystem.

Illness caused by pathogenic strains of Vibrio bacteria incurs significant economic and health care costs in many areas around the world. In the Chesapeake Bay, the two most problematic species are V. vulnificus and V. parahaemolyticus, which cause infection both from exposure to contaminated water and consumption of contaminated seafood. We used existing Vibrio habitat models, four global climate models, and a recently developed statistical downscaling framework to project the spatiotemporal probability of occurrence of V. vulnificus and V. cholerae in the estuarine environment, and the mean concentration of V. parahaemolyticus in oysters in the Chesapeake Bay by the end of the 21st century. Results showed substantial future increases in season length and spatial habitat for V. vulnificus and V. parahaemolyticus, while projected increase in V. cholerae habitat was less marked and more spatially heterogeneous. Our findings underscore the need for spatially variable inputs into models of climate impacts on Vibrios in estuarine environments. Overall, economic costs associated with Vibrios in the Chesapeake Bay, such as incidence of illness and management measures on the shellfish industry, may increase under climate change, with implications for recreational and commercial uses of the ecosystem.

The capability to anticipate the exceptionally rapid warming of the Northwest Atlantic Shelf and its evolution over the next decade could enable effective mitigation for coastal communities and marine resources. However, global climate models have struggled to accurately predict this warming due to limited resolution; and past regional downscaling efforts focused on multi‐decadal projections, neglecting predictive skill associated with internal variability. We address these gaps with a high resolution (1/12°) ensemble of dynamically downscaled decadal predictions. The downscaled simulations accurately predicted past oceanic variability at scales relevant to marine resource management, with skill typically exceeding global coarse‐resolution predictions. Over the long term, warming of the Shelf is projected to continue; however, we forecast a temporary warming pause in the next decade. This predicted pause is attributed to internal variability associated with a transient, moderate strengthening of the Atlantic meridional overturning circulation and a southward shift of the Gulf Stream. Plain Language Summary: The Northwest Atlantic Shelf is experiencing a rapid rise in ocean temperatures, one of the fastest globally, impacting the region's marine resources. Global coupled models struggle to accurately simulate this regional warming and have large uncertainties associated with the future evolution of this warming. To address this issue, we developed a high‐resolution decadal prediction system for the Northwest Atlantic Shelf which downscales global decadal predictions using a high‐resolution regional ocean model. The downscaled simulations accurately predict past oceanic variability and forecast a temporary pause in warming over the next decade due to natural changes in the Atlantic meridional overturning circulation and the position of the Gulf Stream. This predictive capability could pave the way for the implementation of effective mitigation strategies, benefiting coastal communities and marine resources alike. Key Points: Dynamical downscaling enhances decadal prediction skill of Northwest Atlantic Shelf sea surface temperaturesA temporary respite from rapid Northwest Atlantic Shelf warming is forecast for the coming decadeThe forecast warming pause is attributed to a modest strengthening of the Atlantic meridional overturning circulation [ABSTRACT FROM AUTHOR]

Estimating global river solids, nitrogen (N), and phosphorus (P), in both quantity and composition, is necessary for understanding the development and persistence of many harmful algal blooms, hypoxic events, and other water quality issues in inland and coastal waters. This requires a comprehensive freshwater model that can resolve intertwined algae, solid, and nutrient dynamics, yet previous global watershed models have limited mechanistic resolution of instream biogeochemical processes. Here we develop the global, spatially explicit, and process-based Freshwater Algae, Nutrient, and Solid cycling and Yields (FANSY) model and incorporate it within the Land Model (LM3). The resulting model, LM3-FANSY v1.0, is intended as a baseline for eventual linking of global terrestrial and ocean biogeochemistry in next-generation Earth system models to project global changes that may challenge empirical approaches. LM3-FANSY explicitly resolves interactions between algae, N, P, and solid dynamics in rivers and lakes at 1° spatial and 30 min temporal resolution. Simulated suspended solids (SS), N, and P in multiple forms (particulate or dissolved, organic or inorganic) agree well with measurement-based yield (kg km -2 yr -1), load (kt yr -1), and concentration (mg L -1) estimates across a globally distributed set of large rivers, with an accuracy comparable to other global nutrient and SS models. Furthermore, simulated global river loads of SS, N, and P in different forms to the coastal ocean are consistent with published ranges, though regional biases are apparent. River N loads are estimated to contain approximately equal contributions by dissolved inorganic N (41 %) and dissolved organic N (39 %), with a lesser contribution by particulate organic N (20 %). For river P load estimates, particulate P, which includes both organic and sorbed inorganic forms, is the most abundant form (64 %), followed by dissolved inorganic and organic P (25 % and 11 %). Time series analysis of river solid and nutrient loads in large US rivers for the period ∼ 1963–2000 demonstrates that simulated SS and N loads in different N forms covary with variations of measurement-based loads. LM3-FANSY, however, has less capability to capture interannual variability of P loads, likely due to the lack of terrestrial P dynamics in LM3. Analyses of the model results and sensitivity to components, parameters, and inputs suggest that fluxes from terrestrial litter and soils, wastewater, and weathering are the most critical inputs to the fidelity of simulated river nutrient loads for observation-based estimates. Sensitivity analyses further demonstrate a critical role of algal dynamics in controlling the ratios of inorganic and organic nutrient forms in freshwaters. While the simulations are able to capture significant cross-watershed contrasts at a global scale, disagreement for individual rivers can be substantial. This limitation is shared by other global river models and could be ameliorated through further refinements in nutrient sources, freshwater model dynamics, and observations. Current targets for future LM3-FANSY development include the additions of terrestrial P dynamics, freshwater carbon, alkalinity, enhanced sediment dynamics, and anthropogenic hydraulic controls. [ABSTRACT FROM AUTHOR]

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Global Earth system models are often enlisted to assess the impacts of climate variability and change on marine ecosystems. In this study, we compare high frequency (daily) outputs of potential ecosystem stressors, such as sea surface temperature and surface pH, and associated variables from an Earth system model (GFDL ESM4.1) with high frequency time series from a global network of moorings to directly assess the capacity of the model to resolve local biogeochemical variability on time scales from daily to interannual. Our analysis indicates variability in surface temperature is most consistent between ESM4.1 and observations, with a Pearson correlation coefficient of 0.93 and bias of 0.40°C, followed by variability in surface salinity. Physical variability is reproduced with greater accuracy than biogeochemical variability, and variability on seasonal and longer time scales is more consistent between the model and observations than higher frequency variability. At the same time, the well‐resolved seasonal and longer timescale variability is a reasonably good predictor, in many cases, of the likelihood of extreme events. Despite limited model representation of high frequency variability, model and observation‐based assessments of the fraction of days experiencing surface T‐pH and T‐Ωarag multistressor conditions show reasonable agreement, depending on the stressor combination and threshold definition. We also identify circumstances in which some errors could be reduced by accounting for model biases. Plain Language Summary: Ocean ecosystems are under stress from changing temperature and acidity due to the human‐driven increase in global atmospheric carbon dioxide. Global Earth system models (ESMs) are used to study the effects of climate variability and change on marine ecosystems. However, computing power and storage constraints limit the level of detail represented by these simulations. Some short timescale variability present in the real world is missing from ESM output. Despite this inconsistency, we show that at an array of sites where daily observation data from ocean moorings is available, models accurately capture observed spatial patterns in estimates of the amount of time the locations experience combined temperature and acidification stress. We also demonstrate circumstances in which some model errors can be reduced through bias correction. Key Points: Physical and biogeochemical variability from moorings and GFDL ESM4.1 output is most consistent on seasonal and longer time scalesMore high frequency (daily to monthly) variability is present in observations than in ESM outputDespite missing high frequency variability, ESM output can be applied to accurately quantify multiple stressor events [ABSTRACT FROM AUTHOR]

The relative skill of 21 regional and global biogeochemical models was assessed in terms of how well the models reproduced observed net primary productivity (NPP) and environmental variables such as nitrate concentration (NO3), mixed layer depth (MLD), euphotic layer depth (Zeu), and sea ice concentration, by comparing results against a newly updated, quality-controlled in situ NPP database for the Arctic Ocean (1959-2011). The models broadly captured the spatial features of integrated NPP (iNPP) on a pan-Arctic scale. Most models underestimated iNPP by varying degrees in spite of overestimating surface NO3, MLD, and Zeu throughout the regions. Among the models, iNPP exhibited little difference over sea ice condition (ice-free versus ice-influenced) and bottom depth (shelf versus deep ocean). The models performed relatively well for the most recent decade and toward the end of Arctic summer. In the Barents and Greenland Seas, regional model skill of surface NO3 was best associated with how well MLD was reproduced. Regionally, iNPP was relatively well simulated in the Beaufort Sea and the central Arctic Basin, where in situ NPP is low and nutrients are mostly depleted. Models performed less well at simulating iNPP in the Greenland and Chukchi Seas, despite the higher model skill in MLD and sea ice concentration, respectively. iNPP model skill was constrained by different factors in different Arctic Ocean regions. Our study suggests that better parameterization of biological and ecological microbial rates (phytoplankton growth and zooplankton grazing) are needed for improved Arctic Ocean biogeochemical modeling.

Numerical models of ocean biogeochemistry are relied upon to make projections about the impact of climate change on marine resources and test hypotheses regarding the drivers of past changes in climate and ecosystems. In large areas of the ocean, iron availability regulates the functioning of marine ecosystems and hence the ocean carbon cycle. Accordingly, our ability to quantify the drivers and impacts of fluctuations in ocean ecosystems and carbon cycling in space and time relies on first achieving an appropriate representation of the modern marine iron cycle in models. When the iron distributions from 13 global ocean biogeochemistry models are compared against the latest oceanic sections from the GEOTRACES program, we find that all models struggle to reproduce many aspects of the observed spatial patterns. Models that reflect the emerging evidence for multiple iron sources or subtleties of its internal cycling perform much better in capturing observed features than their simpler contemporaries, particularly in the ocean interior. We show that the substantial uncertainty in the input fluxes of iron results in a very wide range of residence times across models, which has implications for the response of ecosystems and global carbon cycling to perturbations. Given this large uncertainty, iron fertilization experiments based on any single current generation model should be interpreted with caution. Improvements to how such models represent iron scavenging and also biological cycling are needed to raise confidence in their projections of global biogeochemical change in the ocean.