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West Antarctic Ice Sheet mass loss is a major source of uncertainty in sea level projections. The primary driver of this melting is oceanic heat from Circumpolar Deep Water originating offshore in the Antarctic Circumpolar Current. Yet, in assessing melt variability, open ocean processes have received considerably less attention than those governing cross-shelf exchange. Here, we use Lagrangian particle release experiments in an ocean model to investigate the pathways by which Circumpolar Deep Water moves toward the continental shelf across the Pacific sector of the Southern Ocean. We show that Ross Gyre expansion, linked to wind and sea ice variability, increases poleward heat transport along the gyre’s eastern limb and the relative fraction of transport toward the Amundsen Sea. Ross Gyre variability, therefore, influences oceanic heat supply toward the West Antarctic continental slope. Understanding remote controls on basal melt is necessary to predict the ice sheet response to anthropogenic forcing.

 

Open-ocean polynyas formed over the Maud Rise, in the Weddell Sea, during the winters of 2016–2017. Such polynyas are rare events in the Southern Ocean and are associated with deep convection, affecting regional carbon and heat budgets. Using an ocean state estimate, we found that during 2017, early sea ice melting occurred in response to enhanced vertical mixing of heat, which was accompanied by mixing of salt. The melting sea ice compensated for the vertically mixed salt, resulting in a net buoyancy gain. An additional salt input was then necessary to destabilize the upper ocean. This came from a hitherto unexplored polynya-formation mechanism: an Ekman transport of salt across a jet girdling the northern flank of the Maud Rise. Such transport was driven by intensified eastward surface stresses during 2015–2018. Our results illustrate how highly localized interactions between wind, ocean flow and topography can trigger polynya formation in the open Southern Ocean.

 

The Southern Ocean plays a major role in controlling the evolution of Antarctic glaciers and in turn their impact on sea level rise. We present the Southern Ocean high‐resolution (SOhi) simulation of the MITgcm ocean model to reproduce ice‐ocean interaction at 1/24° around Antarctica, including all ice shelf cavities and oceanic tides. We evaluate the model accuracy on the continental shelf using Marine Mammals Exploring the Oceans Pole to Pole data and compare the results with three other MITgcm ocean models (ECCO4, SOSE, and LLC4320) and the ISMIP6 temperature reconstruction. Below 400 m, all the models exhibit a warm bias on the continental shelf, but the bias is reduced in the high‐resolution simulations. We hypothesize some of the bias is due to an overestimation of sea ice cover, which reduces heat loss to the atmosphere. Both high‐resolution and accurate bathymetry are required to improve model accuracy around Antarctica.

 

Given the role played by the historical and extensive coverage of sea ice concentration (SIC) observations in reconstructing the long‐term variability of Antarctic sea ice, and the limited attention given to model‐dependent parameters in current sea ice data assimilation studies, this study focuses on enhancing the performance of the Data Assimilation System for the Southern Ocean in assimilating SIC through optimizing the localization and observation error estimate, and two assimilation experiments were conducted from 1979 to 2018. By comparing the results with the sea ice extent of the Southern Ocean and the sea ice thickness in the Weddell Sea, it becomes evident that the experiment with optimizations outperforms that without optimizations due to achieving more reasonable error estimates. Investigating uncertainties of the sea ice volume anomaly modeling reveals the importance of the sea ice‐ocean interaction in the SIC assimilation, implying the necessity of assimilating more oceanic and sea‐ice observations.

 

The Madagascar Basin is the primary pathway for Antarctic Bottom Water to ventilate the entire western Indian Ocean as part of the Global Overturning Circulation. The only way for this water mass to reach this basin is by crossing the Southwest Indian Ridge through its deep fracture zones. However, due to the scarcity of observations, the Antarctic Bottom Water presence has only been well‐established in the Atlantis II fracture zone. In May 2023, the Deep Madagascar Basin Experiment deployed three Deep SOLO Argo floats in the exit of the fracture zones that were more likely to transport Antarctic Bottom Water: Atlantis II, Novara, and Melville. These floats have been collecting temperature and salinity profiles every 3–5 days with high vertical resolution in the deep ocean. In the present paper, we use the first 7 months of float data to characterize the Antarctic Bottom Water in the deep fracture zone area, revisiting a half‐century puzzle about the Melville contribution. We also collected shipboard‐based profiles to calibrate float salinity and show it is within the Deep Argo program target accuracy. We find Antarctic Bottom Water in both Melville and Novara fracture zones, not only in Atlantis II. This is the first time the Novara contribution has been revealed. The floats also uncover their distinct properties, which may result from the different mixing histories.

 

The Southern Ocean (SO) connects major ocean basins and hosts large air‐sea carbon fluxes due to the resurfacing of deep nutrient and carbon‐rich waters. While wind‐induced turbulent mixing in the SO mixed layer is significant for air‐sea fluxes, the importance of the orders‐of‐magnitude weaker background mixing below is less well understood. The direct impact of altering background mixing on tracers, as opposed to the response due to a longer‐term change in large‐scale ocean circulation, is also poorly studied. Topographically induced upward propagating lee waves, wind‐induced downward propagating waves generated at the base of the mixed layer, shoaling of southward propagating internal tides, and turbulence under sea ice are among the processes known to induce upper ocean background turbulence but typically are not represented in models. Here, we show that abruptly altering the background mixing in the SO over a range of values typically used in climate models (m² s⁻¹–m² s⁻¹) can lead to a ∼70% change in annual SO air‐sea CO₂fluxes in the first year of perturbations, and around a ∼40% change in annual SO air‐sea CO₂fluxes over the 6‐year duration of the experiment, with even greater changes on a seasonal timescale. This is primarily through altering the temperature and the dissolved inorganic carbon and alkalinity distribution in the surface water. Given the high spatiotemporal variability of processes that induce small‐scale background mixing, this work demonstrates the importance of their representation in climate models for accurate simulation of global biogeochemical cycles.

 

Abstract Coastal polynyas in Antarctica are a window of air-sea energy exchange and an important source of Antarctic Bottom Water production. However, the relationship between the polynya area variation and the surrounding marine environment is yet to be fully understood. Here we quantify the influence of the volume of transiting consolidated ice on the Terra Nova Bay Polynya area with ice thickness data. Changes in transiting consolidated ice volume are shown to dominate the evolution and variation of the polynya during a typical polynya shrinking event that occurred between 19 June to 03 July, 2013, rather than katabatic winds or air temperature, which are commonly assumed to be the main drivers. Over the cold seasons from 2013 to 2020, the Terra Nova Bay Polynya area is highly correlated to the transiting consolidated ice volume. We demonstrate that thick transiting ice limits the polynya area by blocking the newly-formed sea ice from leaving. 

Physics-based simulations of Arctic sea ice are highly complex, involving transport between different phases, length scales, and time scales. Resultantly, numerical simulations of sea ice dynamics have a high computational cost and model uncertainty. We employ data-driven machine learning (ML) to make predictions of sea ice motion. The ML models are built to predict present-day sea ice velocity given present-day wind velocity and previous-day sea ice concentration and velocity. Models are trained using reanalysis winds and satellite-derived sea ice properties. We compare the predictions of three different models: persistence (PS), linear regression (LR), and a convolutional neural network (CNN). We quantify the spatiotemporal variability of the correlation between observations and the statistical model predictions. Additionally, we analyze model performance in comparison to variability in properties related to ice motion (wind velocity, ice velocity, ice concentration, distance from coast, bathymetric depth) to understand the processes related to decreases in model performance. Results indicate that a CNN makes skillful predictions of daily sea ice velocity with a correlation up to 0.81 between predicted and observed sea ice velocity, while the LR and PS implementations exhibit correlations of 0.78 and 0.69, respectively. The correlation varies spatially and seasonally: lower values occur in shallow coastal regions and during times of minimum sea ice extent. LR parameter analysis indicates that wind velocity plays the largest role in predicting sea ice velocity on 1-day time scales, particularly in the central Arctic. Regions where wind velocity has the largest LR parameter are regions where the CNN has higher predictive skill than the LR. 

We assess the Southern Ocean CO₂uptake (1985–2018) using data sets gathered in the REgional Carbon Cycle Assessment and Processes Project Phase 2. The Southern Ocean acted as a sink for CO₂with close agreement between simulation results from global ocean biogeochemistry models (GOBMs, 0.75 ± 0.28 PgC yr⁻¹) andpCO₂‐observation‐based products (0.73 ± 0.07 PgC yr⁻¹). This sink is only half that reported by RECCAP1 for the same region and timeframe. The present‐day net uptake is to first order a response to rising atmospheric CO₂, driving large amounts of anthropogenic CO₂(C_ant) into the ocean, thereby overcompensating the loss of natural CO₂to the atmosphere. An apparent knowledge gap is the increase of the sink since 2000, withpCO₂‐products suggesting a growth that is more than twice as strong and uncertain as that of GOBMs (0.26 ± 0.06 and 0.11 ± 0.03 Pg C yr⁻¹ decade⁻¹, respectively). This is despite nearly identicalpCO₂trends in GOBMs andpCO₂‐products when both products are compared only at the locations wherepCO₂was measured. Seasonal analyses revealed agreement in driving processes in winter with uncertainty in the magnitude of outgassing, whereas discrepancies are more fundamental in summer, when GOBMs exhibit difficulties in simulating the effects of the non‐thermal processes of biology and mixing/circulation. Ocean interior accumulation of C_antpoints to an underestimate of C_antuptake and storage in GOBMs. Future work needs to link surface fluxes and interior ocean transport, build long overdue systematic observation networks and push toward better process understanding of drivers of the carbon cycle.