Dual-satellite sea level observations in the ice-covered Southern Ocean, 2002–2024
Isabelle White, Oana Dragomir, Alessandro Silvano, Alberto C. Naveira Garabato, Anna E. Hogg, Pierre Dutrieux, Laura Herráiz‐Borreguero, Jennifer Cocks
University of Southampton University of Leeds British Antarctic Survey Commonwealth Scientific and Industrial Research Organisation
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Long-term and spatially continuous data is hard to attain in the remote and harsh environment of the Southern Ocean. Using satellite altimetry, we have been able to measure the subpolar sea surface height (SSH) in the open ocean and sea ice zone since 2002 using Envisat and CrysoSat-2. Here we present a 22-year sea level anomaly (SLA) 1° (longitude) by 0.5° (latitude) gridded product from 50° S to the Antarctic coast. This is the longest SLA product to date for the seasonally ice-covered Southern Ocean. We correct for intra- and inter-satellite offsets for the full-time series and overlapping period 01/11/2010 to 01/03/2012, respectively, to level CryoSat-2 data to Envisat. Acquisition modes include synthetic aperture radar interferometry (SARIn), which leads to increased spatial cover and resolution near the coast and decreased product uncertainty. We calculate the product uncertainty using the standard error for offset climatologies, satellite crossover locations and the seasonal variability between four geoids. This results in an uncertainty of 0.78 cm. Validation of our product with the CMEMS reanalysis product, bottom pressure recorders, and other intersatellite products gives average correlations of 0.93, 0.67 and 0.93 (0.81) for the open ocean (sea ice zone), further validating the new product. This product has the potential to improve our ability to identify regional- and large-scale shifts in long-term sea level trends and relate these to climate forcing. It also provides a comprehensive error calculation for a merged Southern Ocean altimetry product.
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物理Oceanographic and Atmospheric Processes
Geophysics and Gravity Measurements · Ocean Waves and Remote Sensing