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Abstract Assessing the biological characteristics of high-latitude winter habitats of migratory marine predators is necessary for conservation and management in Antarctica. Tracking data from chinstrap penguins (Pygoscelis antarcticus) and southern elephant seals (Mirounga leonina), key Antarctic predators with different diets and foraging habits, indicate that some individuals undertake long-distance winter migrations to remote regions south of 55°S and west of 120°W. There, localized hotspots of increased use, with general reductions in mean swimming speed are evident. Presumably, these predators migrate to areas with higher productivity, however the marine productivity in this remote region during winter is unknown. Light limitation during winter precludes the use of optical satellite data to characterize marine productivity here, but biogeochemical-Argo floats can provide year-round chlorophyll data. These data inform the Biogeochemical Southern Ocean State Estimate (B-SOSE), which provides year-round estimates of marine productivity. The predator hotspots overlap with two areas with year-round elevated surface chlorophyll levels predicted by B-SOSE, consistent with previous studies indicating enhanced mixing in those areas. Our results suggest that persistent areas of elevated chlorophyll centered near 160°W and 120°W near the boundaries of the Ross Gyre and the southern boundary of the Antarctic Circumpolar Current support a productive food web capable of supporting the diverse foraging niches of pelagic species during winter. 

Abstract Basal melting of Antarctic ice shelves is primarily driven by heat delivery from warm Circumpolar Deep Water. Here we classify near-shelf water masses in an eddy-resolving numerical model of the Southern Ocean to develop a unified view of warm water intrusion onto the Antarctic continental shelf. We identify four regimes on seasonal timescales. In regime 1 (East Antarctica), heat intrusions are driven by easterly winds via Ekman dynamics. In regime 2 (West Antarctica), intrusion is primarily determined by the strength of a shelf-break undercurrent. In regime 3, the warm water cycle on the shelf is in antiphase with dense shelf water production (Adélie Coast). Finally, in regime 4 (Weddell and Ross seas), shelf-ward warm water inflow occurs along the western edge of canyons during periods of dense shelf water outflow. Our results advocate for a reformulation of the traditional annual-mean regime classification of the Antarctic continental shelf. 

This gridded hydrographic data set for Sermilik Fjord was created by objectively mapping (optimally interpolating) discrete hydrographic profile datasets from shipboard Conductivity Temperature Depth (CTD) and helicopter-deployed eXpendable CTDs (XCTDs). These data are all from the summer season (July - September) and cover the years 2009 - 2023 (excluding 2014 and 2020). Grids are standardized to 2 kilometer (km) (horizontal) x 5 meter (m) (depth) resolution grid stretching from 0 km (at Helheim Glacier terminus in 2019) to 106 km away from the terminus following the deepest pathway of bathymetry from the glacier to the shelf (thalweg section). CTD and XCTD profiles were combined to increase along-fjord coverage of the gridded fields. Appropriate gridding parameters and the a priori error were found through a series of manual tests to find a balance between smoothness and hydrographic feature representation (more information in Roth et al. (2025)). The same parameters were used for gridding all variables. Currently the conservative temperature (°C, celsius) and absolute salinity (g kg^-1 (gram per kilogram)) fields, along with their associated mapping relative error, are provided. Other hydrographic variables (eg. dissolved oxygen, nitrate) can be added in the future following the method in Roth et al. (2025) and future surveys of Sermilik Fjord can also be added to increase the time coverage. 

Abstract The Southern Ocean is a region of intense air–sea exchange that plays a critical role for ocean circulation, global carbon cycling, and climate. Subsurface chlorophyll‐a maxima, annually recurrent features throughout the Southern Ocean, may increase the energy flux to higher trophic levels and facilitate downward carbon export. It is important that model parameterizations appropriately represent the chlorophyll vertical structure in the Southern Ocean. Using BGC‐Argo chlorophyll profiles and the Biogeochemical Southern Ocean State Estimate (B‐SOSE), we investigate the sensitivity of chlorophyll vertical structure to model parameters. Based on the sensitivity analysis results, we estimate optimized parameters, which efficiently improve the model consistency with observations. We characterize chlorophyll vertical structure in terms of Empirical Orthogonal Functions and define metrics to compare model results and observations in a series of parameter perturbation experiments. We show that chlorophyll magnitudes are likely to respond quasi‐symmetrically to perturbations in the analyzed parameters, while depth and thickness of the subsurface chlorophyll maximum show an asymmetric response. Perturbing the phytoplankton growth tends to generate more symmetric responses than perturbations in the grazing rate. We identify parameters that affect chlorophyll magnitude, subsurface chlorophyll or both and discuss insights into the processes that determine chlorophyll vertical structure in B‐SOSE. We highlight turbulence, differences in phytoplankton traits, and grazing parameterizations as key areas for improvement in models of the Southern Ocean. 

Abstract Recent advances in explainable artificial intelligence (XAI) methods show promise for understanding predictions made by machine learning (ML) models. XAI explains how the input features are relevant or important for the model predictions. We train linear regression (LR) and convolutional neural network (CNN) models to make 1-day predictions of sea ice velocity in the Arctic from inputs of present-day wind velocity and previous-day ice velocity and concentration. We apply XAI methods to the CNN and compare explanations to variance explained by LR. We confirm the feasibility of using a novel XAI method [i.e., global layerwise relevance propagation (LRP)] to understand ML model predictions of sea ice motion by comparing it to established techniques. We investigate a suite of linear, perturbation-based, and propagation-based XAI methods in both local and global forms. Outputs from different explainability methods are generally consistent in showing that wind speed is the input feature with the highest contribution to ML predictions of ice motion, and we discuss inconsistencies in the spatial variability of the explanations. Additionally, we show that the CNN relies on both linear and nonlinear relationships between the inputs and uses nonlocal information to make predictions. LRP shows that wind speed over land is highly relevant for predicting ice motion offshore. This provides a framework to show how knowledge of environmental variables (i.e., wind) on land could be useful for predicting other properties (i.e., sea ice velocity) elsewhere. Significance StatementExplainable artificial intelligence (XAI) is useful for understanding predictions made by machine learning models. Our research establishes trustability in a novel implementation of an explainable AI method known as layerwise relevance propagation for Earth science applications. To do this, we provide a comparative evaluation of a suite of explainable AI methods applied to machine learning models that make 1-day predictions of Arctic sea ice velocity. We use explainable AI outputs to understand how the input features are used by the machine learning to predict ice motion. Additionally, we show that a convolutional neural network uses nonlinear and nonlocal information in making its predictions. We take advantage of the nonlocality to investigate the extent to which knowledge of wind on land is useful for predicting sea ice velocity elsewhere. 

Abstract Pine Island, Thwaites, Smith, and Kohler glaciers in the Amundsen Sea Embayment (ASE) sector of West Antarctica experience rapid mass loss and grounding line retreat due to enhanced ocean thermal forcing from Circumpolar Deep Water (CDW) reaching the grounding lines. We use simulated Lagrangian particles advected with a looping 1 year output from the Southern Ocean high‐resolution model to backtrack the transport and cooling of CDW to these glaciers. For the simulated year 2005–2006, we find that the median time needed to reach the grounding lines from the edge of the ASE is 3 years. In addition, the Antarctic Coastal Current contributes an equal number of particles as off‐shelf sources to the grounding lines of Pine Island and Thwaites. For CDW coming from off‐shelf, results from SOhi indicate that 25%–66% of the cooling occurs within ice shelf cavities. 

Southern Ocean air–sea fluxes are a critical component of the climate system but are historically undersampled due to the remoteness of the region. While much focus has been placed on interannual flux variability, it has become increasingly clear that high-frequency fluctuations, driven by processes like storms and (sub-)mesoscale eddies, play a nonnegligible role in longer-term changes. Therefore, collecting high-resolution in situ flux observations is crucial to better understand the dynamics operating at these scales, as well as their larger-scale impacts. Technological advancements, including the development of new uncrewed surface vehicles, provide the opportunity to increase sampling at small scales. However, determining where and when to deploy such vehicles is not trivial. This study, conceived by the Air–Sea Fluxes working group of the Southern Ocean Observing System, aims to characterize the statistics of high-frequency air–sea flux variability. Using statistical analyses of atmospheric reanalysis data, numerical model output, and mooring observations, we show that there are regional and seasonal variations in the magnitude and sign of storm- and eddy-driven air–sea flux anomalies, which can help guide the planning of field campaigns and deployment of uncrewed surface vehicles in the Southern Ocean. 

Abstract. As global atmosphere and ocean temperatures rise and the Greenland Ice Sheet loses mass, the glacial fjords of Kalaallit Nunaat/Greenland play an increasingly critical role in our climate system. Fjords are pathways for freshwater from ice melt to reach the ocean and for deep, warm, nutrient-rich ocean waters to reach marine–terminating glaciers, supporting abundant local ecosystems that Greenlanders rely upon. Research in Greenland fjords has become more interdisciplinary and more observations are being collected in fjords than in previous decades. However, there are few long-term (> 10 years) datasets available for single fjords. Additionally, observations in fjords are often spatially and temporally disjointed, utilize multiple observing tools, and datasets are rarely provided in formats that are easily used across disciplines or audiences. We address this issue by providing standardized, gridded summer season hydrographic sections for Sermilik Fjord in Southeast Greenland, from 2009–2023. Gridded data facilitate the analysis of coherent spatial patterns across the fjord domain, and are a more accessible and intuitive data product compared to discrete profiles. We combined ship-based conductivity, temperature, and depth (CTD) profiles with helicopter-deployed eXpendable CTD (XCTD) profiles from the ice mélange region to create objectively mapped (or optimally interpolated) along-fjord sections of conservative temperature and absolute salinity. From the gridded data, we derived a summer season climatological mean and root mean square deviation, summarizing typical fjord conditions and highlighting regions of variability. This information can be used by model and laboratory studies, biological and ecosystem studies in the fjord, and provides context for interpreting previous work. Additionally, this method can be applied to datasets from other fjords helping to facilitate fjord intercomparison studies. The gridded data and climatological products are available in netCDF format at https://doi.org/10.18739/A28G8FK6D (Roth et al., 2025a). All original profile observations, with unique DOIs for each field campaign, are available through the Sermilik Fjord Hydrography Data Portal (https://arcticdata.io/catalog/portals/sermilik, last access: 7 November 2025) hosted by the Arctic Data Center (Straneo et al., 2025). The code used has also been made available to facilitate continued updates to the Sermilik Fjord gridded section dataset and applications to other fjord systems. 

Abstract The Southern Ocean is rich in highly dynamic mesoscale eddies and substantially modulates global biogeochemical cycles. However, the overall surface and subsurface effects of eddies on the Southern Ocean biogeochemistry have not been quantified observationally at a large scale. Here, we co‐locate eddies, identified in the Meta3.2DT satellite altimeter‐based product, with biogeochemical Argo floats to determine the effects of eddies on the dissolved inorganic carbon (DIC), nitrate, and dissolved oxygen concentrations in the upper 1,500 m of the ice‐free Southern Ocean, as well as the eddy effects on the carbon fluxes in this region. DIC and nitrate concentrations are lower in anticyclonic eddies (AEs) and increased in cyclonic eddies (CEs), while dissolved oxygen anomalies switch signs above (CEs: positive, AEs: negative) and below the mixed layer (CEs: negative, AEs: positive). We attribute these anomalies primarily to eddy pumping (isopycnal heave), as well as eddy trapping for oxygen. Maximum anomalies in all tracers occur at greater depths in the subduction zone north of the Antarctic Circumpolar Current (ACC) compared to the upwelling region in the ACC, reflecting differences in background vertical structures. Eddy effects on air–sea exchange have significant seasonal variability, with additional outgassing in CEs in fall (physical process) and additional oceanic uptake in AEs and CEs in spring (biological and physical process). Integrated over the Southern Ocean, AEs contribute 0.01 Pg C (7 ) to the Southern Ocean carbon uptake, and CEs offset this by 0.01 Pg C (2 ). These findings underscore the importance of considering eddy impacts in observing networks and climate models. 

Abstract Direct observations of background diapycnal mixing rates in the Southern Ocean (SO) are limited spatially and temporally, making the choice of an appropriate value to parameterize this mixing in Earth system models a challenge. However, the deployment of Argo floats throughout the SO has provided an extensive range of observations of both physical and biogeochemical parameters. We use an ocean state estimate run with various background diapycnal mixing coefficients to assess if biogeochemical tracer observations can be used to better constrain SO diapycnal mixing rates. We find that vertical tracer distributions in the SO are highly sensitive to the rate of background diapycnal mixing and can provide an upper limit on background mixing rates. This demonstrates the importance of biogeochemical tracer observations throughout the full depth of the water column to validate ocean models.