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Large scale flood risk analyses are fundamental to many applications requiring national or international overviews of flood risk. While large‐scale climate patterns such as teleconnections and climate change become important at this scale, it remains a challenge to represent the local hydrological cycle over various watersheds in a manner that is physically consistent with climate. As a result, global models tend to suffer from a lack of available scenarios and flexibility that are key for planners, relief organizations, regulators, and the financial services industry to analyze the socioeconomic, demographic, and climatic factors affecting exposure. Here we introduce a data‐driven, global, fast, flexible, and climate‐consistent flood risk modeling framework for applications that do not necessarily require high‐resolution flood mapping. We use statistical and machine learning methods to examine the relationship between historical flood occurrence and impact from the Dartmouth Flood Observatory (1985–2017), and climatic, watershed, and socioeconomic factors for 4,734 HydroSHEDS watersheds globally. Using bias‐corrected output from the NCAR CESM Large Ensemble (1980–2020), and the fitted statistical relationships, we simulate 1 million years of events worldwide along with the population displaced in each event. We discuss potential applications of the model and present global flood hazard and risk maps. The main value of this global flood model lies in its ability to quickly simulate realistic flood events at a resolution that is useful for large‐scale socioeconomic and financial planning, yet we expect it to be useful to climate and natural hazard scientists who are interested in socioeconomic impacts of climate.

International audience; Fisheries and aquaculture make a crucial contribution to global food security, nutrition and livelihoods. However, the UN Sustainable Development Goals separate marine and terrestrial food production sectors and ecosystems. To sustainably meet increasing global demands for fish, the interlinkages among goals within and across fisheries, aquaculture and agriculture sectors must be recognized and addressed along with their changing nature. Here, we assess and highlight development challenges for fisheries-dependent countries based on analyses of interactions and trade-offs between goals focusing on food, biodiversity and climate change. We demonstrate that some countries are likely to face double jeopardies in both fisheries and agriculture sectors under climate change. The strategies to mitigate these risks will be context-dependent, and will need to directly address the trade-offs among Sustainable Development Goals, such as halting biodiversity loss and reducing poverty. Countries with low adaptive capacity but increasing demand for food require greater support and capacity building to transition towards reconciling trade-offs. Necessary actions are context-dependent and include effective governance, improved management and conservation, maximizing societal and environmental benefits from trade, increased equitability of distribution and innovation in food production, including continued development of low input and low impact aquaculture.

Iron isotopes have been widely applied to investigate the redox evolution of Earth’s surface environments. However, it is still unclear whether iron cycling in the water column or during diagenesis represents the major control on the iron isotope composition of sediments and sedimentary rocks. Interpretation of isotopic data in terms of oceanic redox conditions is only possible if water column processes dominate the isotopic composition, whereas redox interpretations are less straightforward if diagenetic iron cycling controls the isotopic composition. In the latter scenario, iron isotope data is more directly related to microbial processes such as dissimilatory iron reduction. Here we present bulk rock iron isotope data from late Proterozoic marine shales from Svalbard, northwestern Canada, and Siberia, to better understand the controls on iron isotope fractionation in late Proterozoic marine environments. Bulk shales span a δ 56Fe range from −0.45 ‰ to +1.04 ‰ . Although δ 56Fe values show significant variation within individual stratigraphic units, their mean value is closer to that of bulk crust and hydrothermal iron in samples post-dating the ca. 717–660 Ma Sturtian glaciation compared to older samples. After correcting for the highly reactive iron content in our samples based on iron speciation data, more than 90% of the calculated δ 56Fe compositions of highly reactive iron falls in the range from ca. −0.8 ‰ to +3 ‰ . An isotope mass-balance model indicates that diagenetic iron cycling can only change the isotopic composition of highly reactive iron by 1 ‰ , suggesting that water column processes, namely the degree of oxidation of the ferrous seawater iron reservoir, control the isotopic composition of highly reactive iron. Considering a long-term decrease in the isotopic composition of the iron source to the dissolved seawater Fe(II) reservoir to be unlikely, we offer two possible explanations for the Neoproterozoic δ 56Fe trend. First, a decreasing supply of Fe(II) to the ferrous seawater iron reservoir could have caused the reservoir to decrease in size, allowing a higher degree of partial oxidation, irrespective of increasing environmental oxygen levels. Alternatively, increasing oxygen levels would have led to a higher proportion of Fe(II) being oxidized, without decreasing the initial size of the ferrous seawater iron pool. We consider the latter explanation as the most likely. According to this hypothesis, the δ 56Fe record reflects the redox evolution of Earth’s surface environments. δ 56Fe values in pre-Sturtian samples significantly heavier than bulk crust and hydrothermal iron imply partial oxidation of a ferrous seawater iron reservoir. In contrast, mean δ 56Fe values closer to that of hydrothermal iron in post-Sturtian shales reflects oxidation of a larger proportion of the ferrous seawater iron reservoir, and by inference, higher environmental oxygen levels. Nevertheless, significant iron isotopic variation in post-Sturtian shales suggest redox heterogeneity and possibly a dominantly anoxic deep ocean, consistent with results from recent studies using iron speciation and redox sensitive trace metals. However, the interpretation of generally increasing environmental oxygen levels after the Sturtian glaciation highlights the need to better understand the sensitivity of different redox proxies to incremental changes in oxygen levels to enable us to reconcile results from different paleoredox proxies.

Future climate impacts and their consequences are increasingly being explored using multi-model ensembles that average across individual model projections. Here we develop a statistical framework that integrates projections from coupled ecosystem and earth-system models to evaluate significance and uncertainty in marine animal biomass changes over the 21st century in relation to socioeconomic indicators at national to global scales. Significant biomass changes are projected in 40%–57% of the global ocean, with 68%–84% of these areas exhibiting declining trends under low and high emission scenarios, respectively. Given unabated emissions, maritime nations with poor socioeconomic statuses such as low nutrition, wealth, and ocean health will experience the greatest projected losses. These findings suggest that climate-driven biomass changes will widen existing equity gaps and disproportionally affect populations that contributed least to global CO2 emissions. However, our analysis also suggests that such deleterious outcomes are largely preventable by achieving negative emissions (RCP 2.6)., Numerous marine ecosystem models are used to project animal biomass over time but integrating them can be challenging. Here the authors develop a test for statistical significance in multi-model ensemble trends, and thus relate future biomass trends to current patterns of ecological and socioeconomic status.

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

Climate change is shifting the abundance and distribution of marine species with consequences for ecosystem functioning, seafood supply, management and conservation. Several approaches for future projection exist but these have never been compared systematically to assess their variability. We conducted standardized ensemble projections including 6 global fisheries and marine ecosystem models, forced with 2 Earth-system models and 4 emission scenarios in a fished and unfished ocean, to derive average trends and associated uncertainties. Without fishing, mean global animal biomass decreased by 5% (±4%) under low and 17% (±11%) under high emissions by 2100, primarily driven by increasing temperature and decreasing primary production. These climate-change effects were slightly weaker for larger animals and in a fished ocean. Considerable regional variation ranged from strong biomass increases in high latitudes to strong decreases in mid-low latitudes, with good model agreement on the direction of change but variable magnitude. Uncertainties due to differences among ecosystem or Earth-system models were similar, suggesting equal need for model improvement. Our ensemble projections provide the most comprehensive outlook on potential climate-driven ecological changes in the ocean to date. Realized future trends will largely depend on how fisheries and management adapt to these changes in a changing climate.

The global wild marine fish harvest increased fourfold between 1950 and a peak value near the end of the 20th century, reflecting interactions between anthropogenic and ecological forces. Here, we examine these interactions in a bio-energetically constrained, spatially and temporally resolved model of global fisheries. We conduct historical hindcasts with the model, which suggest that technological progress can explain most of the 20th century increase of fish harvest. In contrast, projections extending this rate of technological progress into the future under open access suggest a long-term decrease in harvest due to over-fishing. Climate change is predicted to gradually decrease the global fish production capacity, though our model suggests that this is of secondary importance to social and economic factors. Our study represents a novel way to integrate human-ecological interactions within a single model framework for long-term simulations., Global marine fish harvest increased over the 20th century, reaching a peak in the 1990s. Here, Galbraith and colleagues analyse a model combining both ecological and economic drivers to weigh the factors most likely to contribute to long-term changes in fish harvests.

Unidad de excelencia María de Maeztu MdM-2015-0552 Human exploitation of marine resources is profoundly altering marine ecosystems, while climate change is expected to further impact commercially-harvested fish and other species. Although the global fishery is a highly complex system with many unpredictable aspects, the bioenergetic limits on fish production and the response of fishing effort to profit are both relatively tractable, and are sure to play important roles. Here we describe a generalized, coupled biological-economic model of the global marine fishery that represents both of these aspects in a unified framework, the BiOeconomic mArine Trophic Size-spectrum (BOATS) model. BOATS predicts fish production according to size spectra as a function of net primary production and temperature, and dynamically determines harvest spectra from the biomass density and interactive, prognostic fishing effort. Within this framework, the equilibrium fish biomass is determined by the economic forcings of catchability, ex-vessel price and cost per unit effort, while the peak harvest depends on the ecosystem parameters. Comparison of a large ensemble of idealized simulations with observational databases, focusing on historical biomass and peak harvests, allows us to narrow the range of several uncertain ecosystem parameters, rule out most parameter combinations, and select an optimal ensemble of model variants. Compared to the prior distributions, model variants with lower values of the mortality rate, trophic efficiency, and allometric constant agree better with observations. For most acceptable parameter combinations, natural mortality rates are more strongly affected by temperature than growth rates, suggesting different sensitivities of these processes to climate change. These results highlight the utility of adopting large-scale, aggregated data constraints to reduce model parameter uncertainties and to better predict the response of fisheries to human behaviour and climate change.

Unidad de excelencia María de Maeztu MdM-2015-0552 Environmental change and the exploitation of marine resources have had profound impacts on marine communities, with potential implications for ocean biogeochemistry and food security. In order to study such global-scale problems, it is helpful to have computationally efficient numerical models that predict the first-order features of fish biomass production as a function of the environment, based on empirical and mechanistic understandings of marine ecosystems. Here we describe the ecological module of the BiOeconomic mArine Trophic Size-spectrum (BOATS) model, which takes an Earth-system approach to modelling fish biomass at the global scale. The ecological model is designed to be used on an Earth-system model grid, and determines size spectra of fish biomass by explicitly resolving life history as a function of local temperature and net primary production. Biomass production is limited by the availability of photosynthetic energy to upper trophic levels, following empirical trophic efficiency scalings, and by well-established empirical temperature-dependent growth rates. Natural mortality is calculated using an empirical size-based relationship, while reproduction and recruitment depend on both the food availability to larvae from net primary production and the production of eggs by mature adult fish. We describe predicted biomass spectra and compare them to observations, and conduct a sensitivity study to determine how they change as a function of net primary production and temperature. The model relies on a limited number of parameters compared to similar modelling efforts, while retaining reasonably realistic representations of biological and ecological processes, and is computationally efficient, allowing extensive parameter-space analyses even when implemented globally. As such, it enables the exploration of the linkages between ocean biogeochemistry, climate, and upper trophic levels at the global scale, as well as a representation of fish biomass for idealized studies of fisheries.