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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.

Ocean color satellites require a procedure known as System Vicarious Calibration (SVC) after launch as the pre-launch and on-orbit calibration accuracy is insufficient. The current approach for determination of post-launch SVC uses a single fixed measurement location and may be susceptible to unexpected biases in satellite processing algorithms. Here we describe a novel SVC program which is based on a high resolution and high accuracy radiometric system integrated with an autonomous profiling float (providing a buoyancy engine, physical observations, and communication). This float + radiometer (HyperNav) system can be shipped via air, land, ocean and is deployable from small boats. This SVC program relies on multiple deployment sites with associated facilities to collect a significant amount of SVC quality data in a relatively short time. It has centralized logistics and command-and-control centers ensuring easy access to information regarding the status of each asset and to ensure floats stay within a certain ocean area. The development of the program has been associated with the launch of NASA’s PACE satellite and has been executed by academic institutions in collaboration with an industrial partner. Other approaches for a future float-based operational SVC program are discussed.

A novel ocean profiling float system for calibrating and validating satellite-based ocean color observations has been developed and tested. The float-based radiometric sampling system, herein referred to as HyperNav, is complementary to traditional moored in-situ observing systems and provides additional capability due to the relatively small platform size and high radiometric accuracy that allows for opportunistic deployments at locations during seasons and conditions that are best for ocean color observations. The purpose of this study is to optimize the deployment locations of an array of HyperNav systems to support the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission by performing System Vicarious Calibration (SVC) observations. Specifically, we present the development of logistical and scientific criteria for selecting suitable sites for developing an SVC network of profiling-float-based radiometric systems capable of calibrating and validating ocean color observations. As part of the analyses described in this paper, we have synthetically deployed HyperNav at potential US-based and international sites, including: north of Crete island; south-east of Bermuda island; south of Puerto Rico island; southwest of Port Hueneme, CA; west of Monterey, CA; west of Kona, HI; and south-west of Tahiti island. The synthetic analyses identified Kona, Puerto Rico, Crete, and Tahiti as promising SVC sites. All sites considered are suitable for generating a significant number of validation match-ups. Optimally deploying HyperNav systems at these sites during the PACE post-launch SVC campaign is expected to cost-effectively provide a large number of SVC match-ups to fulfill the PACE calibration requirements.

Hyperspectral optical observations of the Earth’s surface oceans from space offer a means to improve our understanding of ocean biology and biogeochemistry. NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission, which includes a hyperspectral ocean color instrument (OCI), will provide radiometric observations of surface ocean with near continuous spectral resolution across the near UV to NIR range. Maintaining sufficient accuracy over the lifetime of satellite ocean color missions requires a robust program for system vicarious calibration (SVC) and product validation. The system vicarious calibration process combines satellite sensor data with in-situ radiometric/optical measurements to remove potential biases due to the combined errors from both satellite radiometric sensor calibration and atmospheric correction. As such, high accuracy, high-spectral resolution in-situ radiometric measurements are required to provide a principal source of truth for the satellite-derived products. To meet the requirements, a novel in-situ radiometric system, called HyperNav, has been developed, rigorously characterized and field tested. Key attributes of HyperNav are dual upwelling radiance heads coupled to individual spectrometers, spectral resolution of ∼2.2 nm (full width, half-maximum) across 320–900 nm, integrated shutter systems for dark measurements, and integrated tilt and pressure sensors. The HyperNav operational modes include traditional profiling and surface modes, as well as integration with an autonomous profiling float for unattended deployment, offering a new capability for a network of autonomous platforms to support the long-term needs for hyperspectral ocean color remote sensing observations. This paper describes the HyperNav design, in-situ operational modes, and field verification results.

Abstract 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.

Abstract 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.

Thick transiting ice blocked newly formed sea ice from exiting the Terra Nova Bay polynya during a shrinking event in 2013 and had a stronger influence on polynya area than katabatic winds or air temperature changes, according to an analysis of sea ice and meteorological data.

Abstract To deal with the great challenges in simulating the highly non‐linear physics of Antarctic sea ice, a multivariate balanced atmospheric ensemble forcing is developed based on the high‐resolution component of ERA5, which considers the relationship between different variables and adjacent times. To validate the performance of this new forcing, experiments were conducted from 1 January 2016 to 28 February 2017. Compared to simulations forced with the ensemble component of ERA5, a more reasonable ensemble of simulations is produced by the atmospheric ensemble forcing developed in this study, which suppresses the sea‐ice concentration (SIC) simulation errors and produces a better estimation of SIC simulation uncertainties. Further sea‐ice thickness budget analysis reveals that this impact of atmospheric ensemble forcing on sea ice simulation is due to a modulation of atmosphere‐ocean and sea ice‐ocean thermodynamic processes. These results lay the foundation for further improvements in Antarctic sea ice data assimilation and probabilistic prediction.

To improve Antarctic sea-ice simulations and estimations, an ensemble-based Data Assimilation System for the Southern Ocean (DASSO) was developed based on a regional sea ice–ocean coupled model, which assimilates sea-ice thickness (SIT) together with sea-ice concentration (SIC) derived from satellites. To validate the performance of DASSO, experiments were conducted from 15 April to 14 October 2016. Generally, assimilating SIC and SIT can suppress the overestimation of sea ice in the model-free run. Besides considering uncertainties in the operational atmospheric forcing data, a covariance inflation procedure in data assimilation further improves the simulation of Antarctic sea ice, especially SIT. The results demonstrate the effectiveness of assimilating sea-ice observations in reconstructing the state of Antarctic sea ice, but also highlight the necessity of more reasonable error estimation for the background as well as the observation.

Marine animals equipped with biological and physical electronic sensors have produced long-term data streams on key marine environmental variables, hydrography, animal behavior and ecology. These data are an essential component of the Global Ocean Observing System (GOOS). The Animal Borne Ocean Sensors (AniBOS) network aims to coordinate the long-term collection and delivery of marine data streams, providing a complementary capability to other GOOS networks that monitor Essential Ocean Variables (EOVs), essential climate variables (ECVs) and essential biodiversity variables (EBVs). AniBOS augments observations of temperature and salinity within the upper ocean, in areas that are under-sampled, providing information that is urgently needed for an improved understanding of climate and ocean variability and for forecasting. Additionally, measurements of chlorophyll fluorescence and dissolved oxygen concentrations are emerging. The observations AniBOS provides are used widely across the research, modeling and operational oceanographic communities. High latitude, shallow coastal shelves and tropical seas have historically been sampled poorly with traditional observing platforms for many reasons including sea ice presence, limited satellite coverage and logistical costs. Animal-borne sensors are helping to fill that gap by collecting and transmitting in near real time an average of 500 temperature-salinity-depth profiles per animal annually and, when instruments are recovered (∼30% of instruments deployed annually, n = 103 ± 34), up to 1,000 profiles per month in these regions. Increased observations from under-sampled regions greatly improve the accuracy and confidence in estimates of ocean state and improve studies of climate variability by delivering data that refine climate prediction estimates at regional and global scales. The GOOS Observations Coordination Group (OCG) reviews, advises on and coordinates activities across the global ocean observing networks to strengthen the effective implementation of the system. AniBOS was formally recognized in 2020 as a GOOS network. This improves our ability to observe the ocean’s structure and animals that live in them more comprehensively, concomitantly improving our understanding of global ocean and climate processes for societal benefit consistent with the UN Sustainability Goals 13 and 14: Climate and Life below Water. Working within the GOOS OCG framework ensures that AniBOS is an essential component of an integrated Global Ocean Observing System.

The Southern Ocean is disproportionately important in its effect on the Earth system, impacting climatic, biogeochemical, and ecological systems, which makes recent observed changes to this system cause for global concern. The enhanced understanding and improvements in predictive skill needed for understanding and projecting future states of the Southern Ocean require sustained observations. Over the last decade, the Southern Ocean Observing System (SOOS) has established networks for enhancing regional coordination and research community groups to advance development of observing system capabilities. These networks support delivery of the SOOS 20-year vision, which is to develop a circumpolar system that ensures time series of key variables, and delivers the greatest impact from data to all key end-users. Although the Southern Ocean remains one of the least-observed ocean regions, enhanced international coordination and advances in autonomous platforms have resulted in progress toward sustained observations of this region. Since 2009, the Southern Ocean community has deployed over 5700 observational platforms south of 40°S. Large-scale, multi-year or sustained, multidisciplinary efforts have been supported and are now delivering observations of essential variables at space and time scales that enable assessment of changes being observed in Southern Ocean systems. The improved observational coverage, however, is predominantly for the open ocean, encompasses the summer, consists of primarily physical oceanographic variables, and covers surface to 2000 m. Significant gaps remain in observations of the ice-impacted ocean, the sea ice, depths >2000 m, the air-ocean-ice interface, biogeochemical and biological variables, and for seasons other than summer. Addressing these data gaps in a sustained way requires parallel advances in coordination networks, cyberinfrastructure and data management tools, observational platform and sensor technology, two-way platform interrogation and data-transmission technologies, modeling frameworks, intercalibration experiments, and development of internationally agreed sampling standards and requirements of key variables. This paper presents a community statement on the major scientific and observational progress of the last decade, and importantly, an assessment of key priorities for the coming decade, toward achieving the SOOS vision and delivering essential data to all end-users.

There is a growing need for operational oceanographic predictions in both the Arctic and Antarctic polar regions. In the former, this is driven by a declining ice cover accompanied by an increase in maritime traffic and exploitation of marine resources. Oceanographic predictions in the Antarctic are also important, both to support Antarctic operations and also to help elucidate processes governing sea ice and ice shelf stability. However, a significant gap exists in the ocean observing system in polar regions, compared to most areas of the global ocean, hindering the reliability of ocean and sea ice forecasts. This gap can also be seen from the spread in ocean and sea ice reanalyses for polar regions which provide an estimate of their uncertainty. The reduced reliability of polar predictions may affect the quality of various applications including search and rescue, coupling with numerical weather and seasonal predictions, historical reconstructions (reanalysis), aquaculture and environmental management including environmental emergency response. Here, we outline the status of existing near-real time ocean observational efforts in polar regions, discuss gaps, and explore perspectives for the future. Specific recommendations include a renewed call for open access to data, especially real-time data, as a critical capability for improved sea ice and weather forecasting and other environmental prediction needs. Dedicated efforts are also needed to make use of additional observations made as part of the Year of Polar Prediction (YOPP; 2017–2019) to inform optimal observing system design. To provide a polar extension to the Argo network, it is recommended that a network of ice-borne sea ice and upper-ocean observing buoys be deployed and supported operationally in ice-covered areas together with autonomous profiling floats and gliders (potentially with ice detection capability) in seasonally ice covered seas. Finally, additional efforts to better measure and parameterize surface exchanges in polar regions are much needed to improve coupled environmental prediction.

Strengths and weakness of remotely sensed winds are discussed, along with the current capabilities for remotely sensing winds and stress. Future missions are briefly mentioned. The observational needs for a wide range of wind and stress applications are provided. These needs strongly support a short list of desired capabilities of future missions and constellations.

Air-sea and air-sea-ice fluxes in the Southern Ocean play a critical role in global climate through their impact on the overturning circulation and oceanic heat and carbon uptake. The challenging conditions in the Southern Ocean have led to sparse spatial and temporal coverage of observations. This has led to a “knowledge gap” that increases uncertainty in atmosphere and ocean dynamics and boundary-layer thermodynamic processes, impeding improvements in weather and climate models. Improvements will require both process-based research to understand the mechanisms governing air-sea exchange and a significant expansion of the observing system. This will improve flux parameterizations and reduce uncertainty associated with bulk formulae and satellite observations. Improved estimates spanning the full Southern Ocean will need to take advantage of ships, surface moorings, and the growing capabilities of autonomous platforms with robust and miniaturized sensors. A key challenge is to identify observing system sampling requirements. This requires models, Observing System Simulation Experiments (OSSEs), and assessments of the specific spatial-temporal accuracy and resolution required for priority science and assessment of observational uncertainties of the mean state and direct flux measurements. Year-round, high-quality, quasi-continuous in situ flux measurements and observations of extreme events are needed to validate, improve and characterize uncertainties in blended reanalysis products and satellite data as well as to improve parameterizations. Building a robust observing system will require community consensus on observational methodologies, observational priorities, and effective strategies for data management and discovery.

In 1999, the consortium on Estimating the Circulation and Climate of the Ocean (ECCO) set out to synthesize the hydrographic data collected by the World Ocean Circulation Experiment (WOCE) and the satellite sea surface height measurements into a complete and coherent description of the ocean, afforded by an ocean general circulation model. Twenty years later, the versatility of ECCO's estimation framework enables the production of global and regional ocean and sea-ice state estimates, that incorporate not only the initial suite of data and its successors, but nearly all data streams available today. New observations include measurements from Argo floats, marine mammal-based hydrography, satellite retrievals of ocean bottom pressure and sea surface salinity, as well as ice-tethered profiled data in polar regions. The framework also produces improved estimates of uncertain inputs, including initial conditions, surface atmospheric state variables, and mixing parameters. The freely available state estimates and related efforts are property-conserving, allowing closed budget calculations that are a requisite to detect, quantify, and understand the evolution of climate-relevant signals, as mandated by the Coupled Model Intercomparison Project Phase 6 (CMIP6) protocol. The solutions can be reproduced by users through provision of the underlying modeling and assimilation machinery. Regional efforts have spun off that offer increased spatial resolution to better resolve relevant processes. Emerging foci of ECCO are on a global sea level changes, in particular contributions from polar ice sheets, and the increased use of biogeochemical and ecosystem data to constrain global cycles of carbon, nitrogen and oxygen. Challenges in the coming decade include provision of uncertainties, informing observing system design, globally increased resolution, and moving toward a coupled Earth system estimation with consistent momentum, heat and freshwater fluxes between the ocean, atmosphere, cryosphere and land.