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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. 

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 Deep-reaching warming along the boundary of the Antarctic Circumpolar Current and the subtropical gyre is a consistent feature of multidecadal observational estimates and projections of future climate. In the Indian basin, the maximum ocean heat content change is collocated with the powerful Agulhas Return Current (ARC) in the west and Subantarctic Front (SAF) in the east, forming a southeastward band we denote as the ARC–SAF. We find that this jet-confined warming is linked to a poleward shift of these strong currents via the thermal wind relation. Using a suite of idealized ocean-only and partially coupled climate model experiments, we show that strong global buoyancy flux anomalies consistently drive a poleward shift of the ARC–SAF circulation and the associated heat content change maximum. To better understand how buoyancy addition modifies this circulation in the absence of wind stress change, we next apply buoyancy perturbations only to certain regions. Buoyancy addition across the Indian and Pacific Oceans (including the ARC–SAF) gives rise to a strong baroclinic circulation response and modest poleward shift. In contrast, buoyancy addition in the North Atlantic drives a vertically coherent poleward shift of the ARC–SAF, which we suggest is associated with an ocean heat content perturbation communicated to the Southern Ocean via planetary waves and advected eastward along the ARC–SAF. Whereas poleward-shifting circulation and banded warming under climate change have been previously attributed to poleward-shifting winds in the Southern Ocean, we show that buoyancy addition can drive this circulation change in the Indian sector independent of changing wind stress. Significance StatementThis research aims to identify which changes at the atmosphere–ocean interface cause ocean warming localized within strong Southern Ocean currents under climate change. Whereas previous regional studies have emphasized the role of changes in Southern Hemisphere winds, we show that these currents are also sensitive to additional heat and freshwater input into the ocean—even in the faraway North Atlantic. Adding heat and freshwater shifts the currents southward, which is dynamically tied to the “band” of ocean warming seen in both long-term observations and climate change projections. We demonstrate that the warming climate will modify ocean circulation in unexpected ways; the consequences for the ocean’s ability to continue removing anthropogenic heat and carbon from the atmosphere remain poorly understood. 

Abstract The Southern Ocean is an important region for both heat and carbon uptake, due in large part to wind-driven circulation. This region also continually experiences strong winds associated with the passage of synoptic storms, which influence the upper ocean through strong fluxes of momentum, heat, freshwater, and gases. While studies have found that storms can induce strong carbon outgassing, their role in the combined heat and carbon uptake remains unknown. In this work, we explore the climatological impact of storms on the Southern Ocean combined heat and carbon uptake through two preindustrial coupled climate model runs with contrasting seasonal carbon fluxes. We use a feature tracking system to identify storms and create composites for storm-following and post-storm anomalous fluxes of heat and carbon. Storms induce a net anomalous release of heat and carbon from the ocean throughout the year, with clear seasonality in the magnitude of the fluxes that coincide with the background seasonal cycles. We find a strong model dependency for the storm-driven anomalous carbon fluxes, both in terms of the seasonal range and timing of maximum outgassing. Storm-induced anomalous fluxes are dampened on the order of days after the storm passes, with a small continued release of heat that is most persistent in the winter. Our study underlines the high uncertainty about the seasonal nature of storm impacts on the ocean and suggests that evolving atmospheric and oceanic conditions could impose opposing shifts in the future seasonality of storm impacts. 

Abstract Southern Ocean (SO) phytoplankton chlorophyll is highly variable on sub‐seasonal time scales. Although the SO is the windiest ocean basin globally, it is not conclusively understood how storms impact SO phytoplankton dynamics. Much of our existing knowledge stems from satellites, but biases due to data gaps from cloud cover and low solar angles remain unquantified. Here, we use ocean–sea‐ice simulations with the Community Earth System Model to quantify the climatological 1997–2018 imprint of storms on chlorophyll and phytoplankton dynamics in the ice‐free SO. Additionally, by comparing the full‐field model output to synthetic satellite observations, we quantify sampling biases in satellite‐derived estimates. We find that both the sign and the magnitude of the average surface chlorophyll imprint vary substantially across storms but last for at least 4 days after the storm passing. Based on our analysis, more than one third of the storms explain the majority of local non‐seasonal chlorophyll variability, but satellite‐derived storm imprints are often too large in magnitude. On the day of the storm passing, changes in vertical mixing predominantly cause surface chlorophyll anomalies, and reduced light availability due to enhanced cloud cover outweighs the enhanced nutrient availability due to entrainment. Interestingly, storms imprint differently on total net primary production than on surface chlorophyll, demonstrating the difficulty to derive carbon‐cycle impacts from a surface‐chlorophyll assessment. With SO future storm activity projected to increase, complementing satellite observations with other observing technologies, for example, profiling floats, is necessary to better constrain how storms impact biological carbon cycling in the SO. 

Abstract The Southern Ocean is a region of high surface nutrient content, reflecting an inefficient biological carbon pump. The variability, predictability, and causes of changes in these nutrient levels on interannual to decadal time scales remain unclear. We employ a deep learning approach, specifically a Temporal Convolution Attention Neural Network (TCANN), to conduct multi‐year forecasting of surface based on oceanic physical drivers. The TCANN successfully replicates testing data with a prediction skill extending to at least 4 years with the GFDL‐ESM4‐driven model and 1 year with the observation‐driven model. To benchmark the results, we compare the prediction skill of TCANN with a simple persistence model and two regression methods, a linear regression and a ridge regression. The TCANN model was able to predict variability with a higher skill than persistence and the two regression methods indicating that non‐linearities present in the system become too high to predict inter‐annual variability with traditional regression methods. To enhance the interpretability of the predictions, we explore three explainable AI techniques: occlusion analysis, integrated gradients, and Gradient Shap. The outcomes suggest a crucial role played by salinity processes and buoyancy/potential density fluxes on the prediction of on annual time scales. The deep learning tools' ability to provide skillful forecasts well into the future presents a promising avenue for gaining insights into how the Southern Ocean's surface nutrients respond to climate change based on physical quantities. 

Abstract Climate models generally overestimate observed Southern Ocean surface warming trends over the past three decades. This discrepancy could be due to biased surface freshwater fluxes in climate models, which underestimate observed precipitation increases and do not account for Antarctic Ice Sheet and shelf mass loss. Though past modeling experiments show surface cooling in response to freshwater perturbations, sea surface temperature (SST) responses vary widely across models. To address these ambiguities, we compute linear SST response functions for standardized freshwater flux increases across a subset of CMIP6 models. For 1990–2021, underestimated freshwater fluxes can explain up to 60% of the model‐observation SST trend difference. The response functions reveal that Southern Ocean SST trends are more sensitive to freshwater fluxes concentrated along the Antarctic margin versus more spatially distributed fluxes. Our results quantify, for the first time, the impact of missing freshwater forcing on Southern Ocean SST trends across a multi‐model ensemble. 

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 Water Mass Transformation (WMT) theory provides conceptual tools that in principle enable innovative analyses of numerical ocean models; in practice, however, these methods can be challenging to implement and interpret, and therefore remain under‐utilized. Our aim is to demonstrate the feasibility of diagnosing all terms in the water mass budget and to exemplify their usefulness for scientific inquiry and model development by quantitatively relating water mass changes, overturning circulations, boundary fluxes, and interior mixing. We begin with a pedagogical derivation of key results of classical WMT theory. We then describe best practices for diagnosing each of the water mass budget terms from the output of Finite‐Volume Generalized Vertical Coordinate (FV‐GVC) ocean models, including the identification of a non‐negligible remainder term as the spurious numerical mixing due to advection scheme discretization errors. We illustrate key aspects of the methodology through the analysis of a polygonal region of the Greater Baltic Sea in a regional demonstration simulation using the Modular Ocean Model v6 (MOM6). We verify the convergence of our WMT diagnostics by brute‐force, comparing time‐averaged (“offline”) diagnostics on various vertical grids to timestep‐averaged (“online”) diagnostics on the native model grid. Finally, we briefly describe a stack of xarray‐enabled Python packages for evaluating WMT budgets in FV‐GVC models (culminating in the newxwmbpackage), which is intended to be model‐agnostic and available for community use and development. 

Abstract Marine phytoplankton biomass and chlorophyll-a concentration are often estimated from pigment fluorescence measurements, which have become routine despite known variability in the fluorescent response for a given amount of chlorophyll-a. Here, we present a near-global, monthly climatology of chlorophyll-a fluorescence measurements from profiling floats combined with ocean color satellite estimates of chlorophyll-a concentration to illuminate seasonal biases in the fluorescent response and expand upon previously observed regional patterns in this bias. Global biases span over an order of magnitude, and can vary seasonally by a factor of 10. An independent estimate of chlorophyll-a from light attenuation shows similar global patterns in the chlorophyll-fluorescence bias when compared to biases derived from satellite estimates. Without accounting for these biases, studies or models using fluorescence-estimated chlorophyll-a will inherit the seasonal and regional biases described here.