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Climate-induced changes in the world’s oceans will have implications for fisheries productivity and management. Using a model ensemble from the Fisheries and Marine Ecosystem Model Intercomparison Project (Fish-MIP), we analyzed future trajectories of climate-change impacts on marine animal biomass and associated environmental drivers across the North Atlantic Ocean and within the North west Atlantic Fisheries Organization (NAFO) convention area and evaluated potential consequences for fisheries productivity and management. Our ensemble results showed that the magnitude of projected biomass changes increased over time and from a low (RCP2.6) to high (RCP8.5) emissions scenario. Within individual NAFO divisions, however, projected biomass changes differed in the magnitude and sometimes direction of change between near (the 2030s) and far future (the 2090s) and contrasting emissions scenarios. By the 2090s, most NAFO divisions with historically (1990−1999) high fisheries landings were projected to experience biomass decreases of 5−40%, while Arctic and subarctic divisions with lower historical landings were projected to experience biomass increases between 20 and 70% under RCP8.5. Future trajectories of sea surface temperature and primary production corroborated that the far-future, high-emissions scenario poses the greatest risk to marine ecosystems and the greatest challenges to fisheries management. Our study summarizes future trends of marine animal biomass and underlying uncertainties related to model projections under contrasting climate-change scenarios. Understanding such climate-change impacts on marine ecosystems is imperative for ensuring that marine fisheries remain productive and sustainable in a changing ocean.

To understand and manage marine ecosystems for conservation, particle-tracking simulation based on a realistic ocean model is one of the most basic and essential scientific numerical techniques for a multidisciplinary approach. In Japanese waters, this technique was first used under somewhat simplified conditions in the 1980s, and then it continued to be developed, with the number of studies using this technique increasing drastically after the mid-2000s. At that time, mesoscale eddy-resolving ocean forecast systems moved into operational phase, and since then, those reanalysis or analysis products have been publicly shared among scientists. This article provides an overview of the history and current status of particle-tracking simulation for marine biology around Japan (e.g., target species, ocean models utilized, configurations and timescales of particle-tracking models, and related biological data) and summarizes the scientific resources to be maintained and some issues to be resolved in the next future step.

Marine Ecosystem Models (MEMs) are increasingly driven by Earth System Models (ESMs) to better understand marine ecosystem dynamics, and to analyze the effects of alternative management efforts for marine ecosystems under potential scenarios of climate change. However, policy and commercial activities typically occur on seasonal-to-decadal time scales, a time span widely used in the global climate modeling community but where the skill level assessments of MEMs are in their infancy. This is mostly due to technical hurdles that prevent the global MEM community from performing large ensemble simulations with which to undergo systematic skill assessments. Here, we developed a novel distributed execution framework constructed of low-tech and freely available technologies to enable the systematic execution and analysis of linked ESM/MEM prediction ensembles. We apply this framework on the seasonal-to-decadal time scale, and assess how retrospective forecast uncertainty in an ensemble of initialized decadal ESM predictions affects a mechanistic and spatiotemporal explicit global trophodynamic MEM. Our results indicate that ESM internal variability has a relatively low impact on the MEM variability in comparison to the broad assumptions related to reconstructed fisheries. We also observe that the results are also sensitive to the ESM specificities. Our case study warrants further systematic explorations to disentangle the impacts of climate change, fisheries scenarios, MEM internal ecological hypotheses, and ESM variability. Most importantly, our case study demonstrates that a simple and free distributed execution framework has the potential to empower any modeling group with the fundamental capabilities to operationalize marine ecosystem modeling.

As the urgency to evaluate the impacts of climate change on marine ecosystems increases, there is a need to develop robust projections and improve the uptake of ecosystem model outputs in policy and planning. Standardizing input and output data is a crucial step in evaluating and communicating results, but can be challenging when using models with diverse structures, assumptions, and outputs that address region‐specific issues. We developed an implementation framework and workflow to standardize the climate and fishing forcings used by regional models contributing to the Fisheries and Marine Ecosystem Model Intercomparison Project (FishMIP) and to facilitate comparative analyses across models and a wide range of regions, in line with the FishMIP 3a protocol. We applied our workflow to three case study areas‐models: the Baltic Sea Mizer, Hawai'i‐based Longline fisheries therMizer, and the southern Benguela ecosystem Atlantis marine ecosystem models. We then selected the most challenging steps of the workflow and illustrated their implementation in different model types and regions. Our workflow is adaptable across a wide range of regional models, from non‐spatially explicit to spatially explicit and fully‐depth resolved models and models that include one or several fishing fleets. This workflow will facilitate the development of regional marine ecosystem model ensembles and enhance future research on marine ecosystem model development and applications, model evaluation and benchmarking, and global‐to‐regional model comparisons. Plain Language Summary As the need to understand how climate change impacts marine ecosystems increases, it is crucial to develop reliable projections and improve how ecosystem model outputs are used in policy and planning. Standardizing data used in ecosystem models is essential for evaluating and communicating marine ecosystem model results. However, it can be difficult due to diverse model structures, assumptions, and outputs. We develop an implementation framework and workflow to standardize the climate and fishing data used by regional models participating in the Fisheries and Marine Ecosystem Model Intercomparison Project (FishMIP). We applied our framework to three case study models for the Baltic Sea, the Hawai'i‐based Longline fisheries and the southern Benguela ecosystem. By focusing on the most challenging steps of the workflow, our study shows that our workflow is adaptable and how it can be implemented across a wide range of regions and types of ecosystem models. Our workflow will support the development of regional marine ecosystem model ensembles and promote future research on model evaluation and comparisons. Key Points Develops a standardized protocol for detecting past ecosystem changes and simulating climate impacts by regional marine ecosystem models Details tools such as the Regional Climate Forcing Data Explorer Shiny application to access, visualize, and process climate forcing variables The protocol and tools are flexible and can be applied to the different marine ecosystem model types included in Fisheries and Marine Ecosystem Model Intercomparison Project

The FishErIes Size and functional TYpe model (FEISTY) is a mechanistic ecosystem model that fully integrates ecosystem structure across trophic levels through functional types. We present an R package that enables users to run simulations ranging from a 0D chemostat to full global scales. The library is written in Fortran90 with an R interface and provides a web application for visual exploration. We present and compare results from four core configurations across a range of depths, productivity and fishing levels, and we assess the convergence of solutions as the number of size classes is increased. The model has historically been coupled to biogeochemical models of mesozooplankton and detritus production, but it can also be applied in a stand‐alone version. We demonstrate the library to set up and simulate fish communities under varying productivity of mesozooplankton and benthos, and top‐down forcing from fishing. We outline three strategies for coupling FEISTY with biogeochemical model output and discuss future directions and open issues.

The seas of Southeast Asia are home to some of the world’s most diverse ecosystems and resources that support the livelihoods of millions of people. Climate change will bring temperature changes, acidification and other environmental change, with uncertain consequences for human and natural systems, but there has been little regional-scale climate modelling of the marine ecosystem. We present initial dynamically downscaled projections using a biogeochemical model suitable for coastal and shelf seas. A coupled physical-biogeochemical model with a resolution of 0.1° (approximately 11 km) was used to create projections of future environmental conditions under moderate (RCP4.5) and high (RCP8.5) greenhouse gas scenarios. Changes for different parts of the region are presented, including four sensitive coastal sites of key importance for biodiversity and sustainable development: UNESCO Biosphere Reserves at Cu Lao Cham-Hoi An in Vietnam, Palawan in the Philippines and Taka Bonerate-Kepulauan Selayar in Indonesia, and coastal waters of Sabah, Malaysia, which include several marine parks. The projections show a sea that is warming by 1.1 to 2.9°C through the 21st century, with dissolved oxygen decreasing by 5 to 13 mmol m -3 and changes in many other environmental variables. The changes reach all parts of the water column and many places are projected to experience conditions well outside the range seen at the start of the century. The resulting damage to coral reefs and altered species distribution would have consequences for biodiversity, the livelihoods of small-scale fishers and the food security of coastal communities. Further work using a range of global models and regional models with different biogeochemical components is needed to provide confidence levels, and we suggest some ways forward. Projections of this type serve as a key tool for communities and policymakers as they plan how they will adapt to the challenge of climate change.

Sinking detritus particles in the ocean help to regulate global climate by transporting organic carbon into deep waters where it is sequestered from the atmosphere. The rate at which bacteria remineralise detritus influences how deep particles sink and the length-scale of carbon sequestration. Conventional marine biogeochemical models typically represent particles as smooth spheres where remineralisation causes surface area (SA) to progressively shrink over time. In contrast, we propose that particle SA increases during degradation as microbial ectoenzymes cause a roughening of surfaces in a process similar to acid etching on previously smooth glass or metal surfaces. This concept is investigated using a novel model, SAMURAI (Surface Area Modelling Using Rubik As Inspiration), in which the biomass of individual particles is represented as a 3D matrix of cubical sub-units that degrades by progressive removal of sub-units that have faces in contact with the external environment. The model rapidly generates microscale rugosity (roughness) that profoundly increases total SA, giving rise to biomass-specific remineralisation rates that are approximately double those of conventional models. Faster remineralisation means less carbon penetrates the ocean’s interior, diminishing carbon sequestration in deep waters. Results indicate that both SA and microbial remineralisation are highly dynamic, as well as exhibiting large variability associated with particles of different porosities. Our work highlights the need for further studies, both observational and modelling, to investigate particle SA and related microbial dynamics in order to reliably represent the role of ocean biology in global biogeochemical models.

Climate change is affecting ocean temperature, acidity, currents, and primary production, causing shifts in species distributions, marine ecosystems, and ultimately fisheries. Earth system models simulate climate change impacts on physical and biogeochemical properties of future oceans under varying emissions scenarios. Coupling these simulations with an ensemble of global marine ecosystem models has indicated broad decreases of fish biomass with warming. However, regional details of these impacts remain much more uncertain. Here, we employ CMIP5 and CMIP6 climate change impact projections using two Earth system models coupled with four regional and nine global marine ecosystem models in 10 ocean regions to evaluate model agreement at regional scales. We find that models developed at different scales can lead to stark differences in biomass projections. On average, global models projected greater biomass declines by the end of the 21st century than regional models. For both global and regional models, greater biomass declines were projected using CMIP6 than CMIP5 simulations. Global models projected biomass declines in 86% of CMIP5 simulations for ocean regions compared to 50% for regional models in the same ocean regions. In CMIP6 simulations, all global model simulations projected biomass declines in ocean regions by 2100, while regional models projected biomass declines in 67% of the ocean region simulations. Our analysis suggests that improved understanding of the causes of differences between global and regional marine ecosystem model climate change projections is needed, alongside observational evaluation of modeled responses. Plain Language Summary Climate change is affecting the world's oceans, marine ecosystems, biodiversity, and the ecosystem services that they support, including fisheries that feed millions of people worldwide. Anticipating the impacts of climate change can help society and managers to prepare for, and adapt to, changes ahead. Present understanding of climate change impacts on the world's oceans based on global models indicates a 5% loss in animal biomass with every 1°C that the planet warms. Here, we compare potential future biomass on regional scales that are most relevant for management decisions about sustainable resource use. We used regional scale ecosystem models tailored to the species and fisheries they represent. We compared climate change projections of ocean biomass changes from these regional models to corresponding areas from global models to see how well they agreed. We found key differences in climate change projections of ocean biomass between global and regional models. In some cases, both global and regional models projected biomass declines, while in others global models suggested a decline and regional models an increase. Our study highlights that we need further exploration and understanding of the differences in ocean biomass change between global and regional marine ecosystem models. Key Points Global marine ecosystem models projected greater biomass declines with climate change than regional marine ecosystem models for many regions For both global and regional models, greater biomass declines were projected in CMIP6 than CMIP5 and in IPSL versus GFDL simulations Projected impacts of climate change on marine ecosystems at regional scales are currently less certain than at global scale

The El Niño–Southern Oscillation (ENSO) is a key driver of global climate variability, and its impact on phytoplankton concentrations in the eastern equatorial Pacific via nutrient supply changes is well established. However, the extent to which phytoplankton feedback influences ENSO remains unclear. Chlorophyll in phytoplankton warms the upper ocean by absorbing solar radiation, and this effect weakens during El Niño and strengthens during La Niña, yet its overall impact is not well quantified. Using a simple nitrogen-based Nitrate–Phytoplankton–Zooplankton–Detritus pelagic model, here we show that phytoplankton concentration anomalies significantly dampen ENSO by cooling sea surface temperature by 0.69 °C during El Niño development and warming it by 1.09 °C during La Niña, with mean amplitudes of 1.71 °C and 1.42 °C, respectively. This may partially contributes to the amplitude asymmetry of ENSO, and accounts for 16.8% of total shortwave radiation-related damping during El Niño and 17.4% during La Niña. Our offline modeling approach successfully isolates this direct heating effect by excluding indirect dynamical effects of phytoplankton to physical variables.

A marine ecosystem model that incorporates nitrous oxide (N 2 O) production processes (i.e., ammonium oxidation during nitrification and nitrite reduction during nitrifier denitrification) and N isotopomers was developed to estimate the sea–air N 2 O flux and to quantify N 2 O production processes. This model was applied to water above the depth of 220 m at two contrasting time series sites, a subarctic station (K2) and a subtropical station (S1) in the western North Pacific. The model was validated with observed N concentration and N isotopomer data sets, and successfully simulated the higher N 2 O concentrations, higher δ 15 N values, and higher site preference values for N 2 O at K2 compared with S1. The annual mean N 2 O emissions were estimated to be 32.3 mg N m −2  year −1 at K2 and 2.7 mg N m −2  year −1 at S1. The results of case studies based on this model estimated the ratios of in situ biological N 2 O production to nitrate production during nitrification to be ~0.22 % at K2 and ~0.06 % at S1. It is also suggested that N 2 O was mainly produced via ammonium oxidation at K2, but was produced via both ammonium oxidation and nitrite reduction at S1. A large fraction (~80 %) of the ammonium oxidation at K2 was carried out by archaea in the subsurface water. Isotope tracer incubation experiments using an archaeal activity inhibitor supported this hypothesis.