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Species distribution models, linked to climate projections, are widely used in extinction-risk assessment and conservation planning. However, the degree of confidence that we can place on future climate-change projections depends on global climate-model performance and involves uncertainties that need to be assessed rigorously via climate-model evaluation. Performance assessments are important because the choice of climate model influences projections of species' range movement and extinction risk. A consensus view from the climate modeling community is that no single climate model is superior in its ability to forecast key climatic features. Despite this, the advantages of using multi-model ensemble-averaged climate forecasts to account for climate-model uncertainties have not been recognized by ecologists. Here we propose a method to use a range of skill and convergence metrics to rank commonly used atmosphere-ocean general circulation models (AOGCMs) according to their skill in reproducing 20-year observed patterns of regional and global climates of interest, and to assess their consistency with other AOGCMs. By eliminating poorly performing models and averaging the remainder with equal weights, we show how downscaled annual multi-climate-model ensemble-averaged forecasts, which have a strong regional focus, can be generated. We demonstrate that: (1) model ranking (match of simulated to observed conditions) differs according to the skill metric used, as well as the climate variable and season considered; (2) although the multi-model averaged result tends to outperform single models at a global scale, at the continental scale at least some models can perform better than the multi-model average; and (3) forecasts for the Australian region, which are often based on a single AOGCM (CSIRO-3.0), show spatial patterns of change that differ noticeably from ensemble-average projections based on a subset of better-performing AOGCMs. Our suggested approach-novel in the ecology discipline-provides a straightforward, consistent, and defensible method for conservation practitioners and natural-resource managers to generate estimates of future climate change at a spatial resolution suitable for biodiversity impact studies.

This paper is concerned with a complete asymptotic analysis as These boundary layers, which are the main center of interest of the present paper, exhibit several types of peculiar behaviour. First, the size of the boundary layer on the western and eastern boundary, which had already been computed by several authors, becomes formally very large as one approaches northern and southern portions of the boudary, i.e. pieces of the boundary on which the normal is vertical. This phenomenon is known as geostrophic degeneracy. In order to avoid such singular behaviour, previous studies imposed restrictive assumptions on the domain Moreover, when the domain Eventually, the effect of boundary layers is non-local in several aspects. On the first hand, for algebraic reasons, the boundary layer equation is radically different on the west and east parts of the boundary. As a consequence, the Sverdrup equation is endowed with a Dirichlet condition on the East boundary, and no condition on the West boundary. Therefore western and eastern boundary layers have in fact an influence on the whole domain

This study addresses the boundary artifacts in machine‐learned (ML) parameterizations for ocean subgrid mesoscale momentum forcing, as identified in the online ML implementation from a previous study (Zhang et al., 2023, https://doi.org/10.1029/2023ms003697). We focus on the boundary condition (BC) treatment within the existing convolutional neural network (CNN) models and aim to mitigate the “out‐of‐sample” errors observed near complex coastal regions without developing new, complex network architectures. Our approach leverages two established strategies for placing BCs in CNN models, namely zero and replicate padding. Offline evaluations revealed that these padding strategies significantly reduce root mean squared error (RMSE) in coastal regions by limiting the dependence on random initialization of weights and restricting the range of out‐of‐sample predictions. Further online evaluations suggest that replicate padding consistently reduces boundary artifacts across various retrained CNN models. In contrast, zero padding sometimes intensifies artifacts in certain retrained models despite both strategies performing similarly in offline evaluations. This study underscores the need for BC treatments in CNN models trained on open water data when predicting near‐coastal subgrid forces in ML parameterizations. The application of replicate padding, in particular, offers a robust strategy to minimize the propagation of extreme values that can contaminate computational models or cause simulations to fail. Our findings provide insights for enhancing the accuracy and stability of ML parameterizations in the online implementation of ocean circulation models with coastlines. Plain Language Summary This study focuses on improving machine learning (ML) models used to predict ocean forces near coastlines, where errors arise because these models lack information in the area. We investigated how boundary conditions are handled in existing convolutional neural network models to reduce these errors without creating complex new architectures. By using two methods, that is, zero padding and replicate padding, we found that replicate padding significantly decreases prediction errors in coastal areas. While zero padding sometimes worsens issues in certain models, our results show that replicate padding is more reliable for effectively minimizing extreme value errors. This work highlights the importance of proper boundary condition treatment in ML models for coastal applications, ultimately aiming to enhance the accuracy and reliability of ocean circulation predictions. Key Points This study validates specialized boundary condition treatments in CNN models to reduce boundary artifacts in ocean parameterizations This approach can be applied directly to already trained CNN models to ensure accurate and stable implementation of mesoscale eddies parameterizations Replicate padding outperforms zero padding by minimizing boundary artifacts and preventing extreme values that compromise simulations

For endemic benthos inhabiting hydrothermal vent fields, larval recruitment is critical for population maintenance and colonization via migration among separated sites. The vent‐endemic limpet, Lepetodrilus nux, is abundant at deep‐sea hydrothermal vents in the Okinawa Trough, a back‐arc basin in the northwestern Pacific; nonetheless, it is endangered due to deep‐sea mining. This species is associated with many other vent species and is an important successor in these vent ecosystems. However, limpet genetic diversity and connectivity among local populations have not yet been examined. We conducted a population genetics study of L. nux at five hydrothermal vent fields (maximum geographic distance, ~545 km; depths ~700 m to ~1650 m) using 14 polymorphic microsatellite loci previously developed. Genetic diversity has been maintained among these populations. Meanwhile, fine population genetic structure was detected between distant populations, even within this back‐arc basin, reflecting geographic distances between vent fields. There was a significant, positive correlation between genetic differentiation and geographic distance, but no correlation with depth. Contrary to dispersal patterns predicted by an ocean circulation model, genetic migration is not necessarily unidirectional, based on relative migration rates. While ocean circulation contributes to dispersal of L. nux among vent fields in the Okinawa Trough, genetic connectivity may be maintained by complex, bidirectional dispersal processes over multiple generations.

Ocean circulation models do not generally exhibit equatorial deep jets (EDJs), even though EDJs are a recognised feature of the observed ocean circulation along the equator and they are thought to be important for tracer transport along the equator and even equatorial climate. EDJs are nevertheless found in nonlinear primitive equation models with idealised box geometry. Here we analyse several such model runs. We note that the variability of the zonal velocity in the model is dominated by the gravest linear equatorial basin mode for a wide range of baroclinic vertical normal modes and that the EDJs in the model are dominated by energy contained in vertical modes between 10 and 20. The emergence of the EDJs is shown to involve the linear superposition of several such neighbouring basin modes. Furthermore, the phase of these basin modes is set at the start of the model run and, in the case of the reference experiment, the same basin modes can be found in a companion experiment in which the amplitude of the forcing has been reduced by a factor of 1000. We also argue that following the spin-up, energy must be transferred between different vertical modes. This is because the model simulations are dominated by downward phase propagation following the spin-up whereas our reconstructions imply episodes of upward and downward propagation. The transfer of energy between the vertical modes is associated with a decadal modulation of the EDJs.

Understanding of the three-dimensional circulation in the South China Sea (SCS) is crucial for determining the transports of water masses, energy, and biogeochemical substances in the regional and adjacent larger oceans. The circulation’s kinematic and dynamic natures, however, are largely unclear. Results from a three-dimensional numerical ocean circulation model and geostrophic currents, derived from hydrographic data, reveal the existence of a unique, three-layer, cyclonic–anticyclonic–cyclonic (CAC) circulation in the upper (<750 m), middle (750–1500 m), and deep (>1500 m) layers in the SCS with differing seasonality. An inflow–outflow–inflow structure in Luzon Strait largely induces the CAC circulation, which leads to vortex stretching in the SCS basin because of a lateral planetary vorticity flux in each of the respective layers. The formation of joint effects of baroclinicity and relief (JEBAR) is an intrinsic dynamic response to the CAC circulation. The JEBAR arises from the CAC flow–topography interaction in the SCS.

During the last 15 years, operational oceanography systems have been developed in several countries around the world. These developments have been fostered primarily by the Global Ocean Data Assimilation Experiment (GODAE), which coordinated these activities, encouraged partnerships, and facilitated constructive competition. This multinational coordination has been very beneficial for the development of operational oceanography. Today, several systems provide routine, real-time ocean analysis, forecast, and reanalysis products. These systems are based on (1) state-of-the-art Ocean General Circulation Model configurations, either global or regional (basin-scale), with resolutions that range from coarse to eddy-resolving, and (2) data assimilation techniques ranging from analysis correction to advanced three- or four-dimensional variational schemes. These systems assimilate altimeter sea level anomalies, sea surface temperature data, and in situ profiles of temperature and salinity, including Argo data. Some systems have implemented downscaling capacities, which consist of embedding higher-resolution local systems in global and basin-scale models (through open boundary exchange of data), especially in coastal regions, where small scale-phenomena are important, and also increasing the spatial resolution for these regional/coastal systems to be able to resolve smaller scales (so-called downscaling). Others have implemented coupling with the atmosphere and/or sea ice. This paper provides a short review of these operational GODAE systems.

Antarctica has long been considered biologically isolated1. Global warming will make parts of Antarctica more habitable for invasive taxa, yet presumed barriers to dispersal—especially the Southern Ocean’s strong, circumpolar winds, ocean currents and fronts—have been thought to protect the region from non-anthropogenic colonizations from the north1,2. We combine molecular and oceanographic tools to directly test for biological dispersal across the Southern Ocean. Genomic analyses reveal that rafting keystone kelps recently travelled >20,000 km and crossed several ocean-front ‘barriers’ to reach Antarctica from mid-latitude source populations. High-resolution ocean circulation models, incorporating both mesoscale eddies and wave-driven Stokes drift, indicate that such Antarctic incursions are remarkably frequent and rapid. Our results demonstrate that storm-forced surface waves and ocean eddies can dramatically enhance oceanographic connectivity for drift particles in surface layers, and show that Antarctica is not biologically isolated. We infer that Antarctica’s long-standing ecological differences have been the result of environmental extremes that have precluded the establishment of temperate-adapted taxa, but that such taxa nonetheless frequently disperse to the region. Global warming thus has the potential to allow the establishment of diverse new species—including keystone kelps that would drastically alter ecosystem dynamics—even without anthropogenic introductions.

The ocean component of the Community Climate System Model version 4 (CCSM4) is described, and its solutions from the twentieth-century (20C) simulations are documented in comparison with observations and those of CCSM3. The improvements to the ocean model physical processes include new parameterizations to represent previously missing physics and modifications of existing parameterizations to incorporate recent new developments. In comparison with CCSM3, the new solutions show some significant improvements that can be attributed to these model changes. These include a better equatorial current structure, a sharper thermocline, and elimination of the cold bias of the equatorial cold tongue all in the Pacific Ocean; reduced sea surface temperature (SST) and salinity biases along the North Atlantic Current path; and much smaller potential temperature and salinity biases in the near-surface Pacific Ocean. Other improvements include a global-mean SST that is more consistent with the present-day observations due to a different spinup procedure from that used in CCSM3. Despite these improvements, many of the biases present in CCSM3 still exist in CCSM4. A major concern continues to be the substantial heat content loss in the ocean during the preindustrial control simulation from which the 20C cases start. This heat loss largely reflects the top of the atmospheric model heat loss rate in the coupled system, and it essentially determines the abyssal ocean potential temperature biases in the 20C simulations. There is also a deep salty bias in all basins. As a result of this latter bias in the deep North Atlantic, the parameterized overflow waters cannot penetrate much deeper than in CCSM3.

Oceanic submesoscale processes (submesoscales) with O(1–10) km horizontal scale can generate strong vertical buoyancy flux (VBF) that significantly modulate upper‐ocean stratification. Because submesoscales cannot be resolved by the prevailing ocean models, their VBFs have to be properly parameterized in order to improve model performance. Here, based on theoretical scaling analysis, we propose a new parameterization of submesoscale VBF by simultaneously considering mixed‐layer baroclinic instability (MLI) and strain‐induced frontogenesis, which are two leading generation mechanisms of submesoscales that typically co‐occur in open ocean. Compared with the parameterization of Fox‐Kemper et al. (2008, https://doi.org/10.1175/2007jpo3792.1; F08) that only considers the MLI, the new parameterization includes mesoscale strain rate and improves vertical structure function. Diagnostic validations based on submesoscale permitting simulation outputs suggest that the newly parameterized VBFs are more realistic than F08 in regard to three‐dimension distributions. How to incorporate this new parameterization into coarser‐grid ocean models, however, needs further studies. Plain Language Summary Oceanic submesoscale processes with spatial scale of O(1–10 km) can generate strong vertical buoyancy flux (VBF) in upper ocean and therefore, they significantly modulate the vertical density distribution (i.e., stratification). Because the prevailing ocean circulation models typically have horizontal resolutions of O(10–100 km), they are unable to resolve the submesoscale VBF. In order to accurately simulate the upper‐ocean stratification, the unresolved VBF needs to be expressed using the resolved larger‐scale quantities, which is called parameterization. Here, based on theoretical scaling analysis, we propose a new parameterization of submesoscale VBF by simultaneously considering contributions from mixed‐layer instability (MLI; baroclinic instability occurring in the mixed layer) and front sharpening induced by mesoscale strain, which are two important generation mechanisms of submesoscales. Compared with the previous parameterization by Fox‐Kemper et al. (2008, https://doi.org/10.1175/2007jpo3792.1; F08) that only includes MLI mechanism, the new parameterization has incorporated mesoscale strain rate and improved vertical structure function more realistically. Diagnostic analysis based on high‐resolution simulation outputs demonstrates that the newly parameterized VBFs are more realistic in aspect of both horizontal and vertical distributions than the F08 parameterization. The test and application of this parameterization in ocean models will be a focus of future studies. Key Points A new parameterization of submesoscale vertical buoyancy flux is proposed The parameterization considers simultaneously mixed‐layer baroclinic instability and strain‐induced frontogenesis Performance of the parameterization is diagnostically validated using high‐resolution simulation outputs