Eric W. Leuliette

The routine measurement of sea ice, an essential climate variable, is urgently needed for improving seasonal sea ice forecasting and tracking ocean-ice-atmosphere interactions. Over the last few decades, ocean remote sensing techniques have revealed significant and rapid declines in both the extent and volume of Arctic sea ice, driven by accelerated warming at high northern latitudes. The Surface Water Ocean Topography (SWOT) mission delivers innovative wide-swath Ka-band synthetic aperture radar (SAR) interferometry and coincident Ku-band altimetry and provides, for the first time, swath mapping of sea ice from a space-based system. The novel SWOT technology can be exploited to advance knowledge of sea ice processes and properties. Although SWOT’s latitudinal limit of coverage is 78° N, this is nevertheless sufficient for mapping the seasonal sea ice zone in the Arctic which develops, grows and deforms in winter and typically reaches a maximum in the month of April. SWOT can obtain measurements across ~9-10 million square kilometers of Arctic sea ice in April. It's high-resolution swath-mapping approach captures sea ice surface topography in two dimensions, both along- and across-track. This new capability allows us to determine the areal fraction and shape of individual sea ice floes, map the 2D structure of leads, identify the ice edge location in higher fidelity than has heretofore been possible, and simultaneously measure sea ice height, from which sea ice freeboard and hence thickness may be derived. Here we show the results of our initial assessment of SWOT data collected during the 1-day fast repeat orbit phase. This particular sampling strategy allows us to track individual sea ice floes and derive high resolution estimates of ice drift velocity. SWOT backscatter signatures over sea ice are also used to discriminate between rough sea ice floes and smooth, specular leads. We evaluate SWOT backscatter using high-resolution measurements of sea ice roughness derived from spatially-coincident ICESat-2 laser altimetry data. We also show the feasibility of using the 2D swath-mapping capabilities of SWOT in concert with ICESat-2 sea ice height profiles to map the joint floe size-ice thickness distribution. Our results demonstrate the many benefits of swath mapping altimetry for polar sea ice studies.

The National Oceanic and Atmospheric Administration’s (NOAA) Center for Satellite Applications and Research (STAR) facilitates and enables societal benefits from satellite oceanography, supporting operational and experimental satellite missions, developing new and improved ocean observing capabilities, engaging users by developing and distributing fit-for-purpose data, applications, tools, and services, and curating, translating, and integrating diverse data products into information that supports informed decision making. STAR research, development, and application efforts span from passive visible, infrared, and microwave observations to active altimetry, scatterometry, and synthetic aperture radar (SAR) observations. These efforts directly support NOAA’s operational geostationary (GEO) and low Earth orbit (LEO) missions with calibration/validation and retrieval algorithm development, implementation, maintenance, and anomaly resolution, as well as leverage the broader international constellation of environmental satellites for NOAA’s benefit. STAR’s satellite data products and services enable research, assessments, applications, and, ultimately, decision making for understanding, predicting, managing, and protecting ocean and coastal resources, as well as assessing impacts of change on the environment, ecosystems, and climate. STAR leads the NOAA Coral Reef Watch and CoastWatch/OceanWatch/PolarWatch Programs, helping people access and utilize global and regional satellite data for ocean, coastal, and ecosystem applications.

In this study, we introduce the Multiparameter Eddy Significance Index (MESI) for use in conjunction with mesoscale eddy tracking to estimate the impact of mesoscale eddies on the upper ocean and marine ecosystems. MESI combines blended satellite observations of sea level anomalies, sea surface temperature, sea surface salinity, and ocean color chlorophyll‐a and is a normalized index. When used in conjunction with mesoscale eddy tracking, MESI can highlight those eddies which extend into the deeper ocean and have a greater effect on nutrient cycling. Preliminary analysis in the Gulf Stream region of the western North Atlantic shows that MESI covaries with model estimates of nitrate, phosphate, iron, and pH with cross‐correlation values between 76% and 95%. These results suggest that when MESI is used in concert with eddy tracking, it can be used as an indicator of eddies and areas that are more likely to impact nutrient cycling in the upper ocean. An additional analysis of MESI values inside eddies during Hurricane Dorian (2019) along the Carolina coast further suggests that MESI is most effective when used to enhance eddy tracking as it provides valuable insight into mesoscale eddy activity and the upper ocean circulation.

Coastal high water level events are increasing in frequency and severity as global sea‐levels rise, and are exposing coastlines to risks of flooding. Yet, operational seasonal forecasts of sea‐level anomalies are not made for most coastal regions. Advancements in forecasting climate variability using coupled ocean‐atmosphere global models provide the opportunity to predict the likelihood of future high water events several months in advance. However, the skill of these models to forecast seasonal sea‐level anomalies has not been fully assessed, especially in a multi‐model framework. Here, we construct a 10‐model ensemble of retrospective forecasts with future lead times of up to 11 months. We compare predicted sea levels from bias‐corrected forecasts with 20 years of observations from satellite‐based altimetry and shore‐based tide gauges. Forecast skill, as measured by anomaly correlation, tends to be highest in the tropical and subtropical open oceans, whereas the skill is lower in the higher latitudes and along some continental coasts. For most locations, multi‐model averaging produces forecast skill that is comparable to or better than the best performing individual model. We find that the most skillful predictions typically come from forecast systems with more accurate initializations of sea level, which is generally achieved by assimilating altimetry data. Having relatively higher horizontal resolution in the ocean is also beneficial, as such models seem to better capture dynamical processes necessary for successful forecasts. The multi‐model assessment suggests that skillful seasonal sea‐level forecasts are possible in many, though not all, parts of the global ocean.

Previous studies have noted the presence of interannual to multidecadal variability in tide gauge sea level records which is correlated with meteorological variability and which can overwhelm the signal associated with global sea level rise. This study examines the usefulness of using a set of seven ocean reanalysis and synthesis products in studies of sea level variability by comparing the tide gauges and reanalysis products at a representative set of 87 tide gauge station locations. The comparison is carried out for both a half‐century base period and a century long‐extended period. Treating the set of products as an ensemble of realizations obtained using different techniques, the results show generally good agreement for the half‐century period with ensemble average correlations of 0.57 and RMS differences of 2.2 cm, reducing to a correlation of 0.5 for the extended period. A significant fraction of the difference between tide gauge sea level and product sea level is associated with meteorological forcing. These results support the conclusion that much of the interannual to multidecadal variability that appears in the tide gauge records is meteorologically driven. This suggests that ocean products have potential to be used to isolate this variability from the signal associated with the underlying global sea level rise.

In the interest of improving the sea level climate data record, we compare the 19-year global mean sea level (GMSL) time series derived from TOPEX-Poseidon, Jason-1, and Jason-2 and produced by different research institutions. The GMSL time series are each produced using varying techniques and different applied corrections, and subsequently, the time series exhibit unique characteristics and sensitivity to seasonal and interannual signals. We find that the different applied corrections affect the time series variability much less than the technique for computing the GMSL from along-track versus gridded sea surface height anomalies and the choice of a minimum depth criteria.

The observation strategy for satellite radar altimetry includes both inter-satellite calibration and calibration with a network of tide gauges. These cross-comparisons are valuable tools for identifying sources of error and for establishing progress toward a sea level climate data record. Demonstrating the capabilities of the Radar Altimetry Database System (RADS) as a platform for climate data records, we show comparisons of Jason-2 with Jason-1 and Envisat that illustrate errors associated with solar intensity and tracker bias. A multi-mission tide gauge calibration shows a drift in the 17-year sea level record of –0.04 ± 0.4 mm/yr.