Abstract
There is high demand for complete satellite SST maps (or L4 SST analyses) of the Arctic regions to monitor the rapid environmental changes occurring at high latitudes. Although there are a plethora of L4 SST products to choose from, satellite-based products evolve constantly with the advent of new satellites and frequent changes in SST algorithms, with the intent of improving absolute accuracies. The constant change of these products, as reflected by the version product, make it necessary to do periodic validations against in situ data. Eight of these L4 products are compared here against saildrone data from two 2019 campaigns in the western Arctic, as part of the MISST project. The accuracy of the different products is estimated using different statistical methods, from standard and robust statistics to Taylor diagrams. Results are also examined in terms of spatial scales of variability using auto- and cross-spectral analysis. The three products with the best performance, at this point and time, are used in a case study of the thermal features of the Yukon–Kuskokwim delta. The statistical analyses show that two L4 SST products had consistently better relative accuracy when compared to the saildrone subsurface temperatures. Those are the NOAA/NCEI DOISST and the RSS MWOI SSTs. In terms of the spectral variance and feature resolution, the UK Met Office OSTIA product appears to outperform all others at reproducing the fine scale features, especially in areas of high spatial variability, such as the Alaska coast. It is known that L4 analyses generate small-scale features that get smoothed out as the SSTs are interpolated onto spatially complete grids. However, when the high-resolution satellite coverage is sparse, which is the case in the Arctic regions, the analyses tend to produce more spurious small-scale features. The analyses here indicate that the high-resolution coverage, attainable with current satellite infrared technology, is too sparse, due to cloud cover to support very high resolution L4 SST products in high latitudinal regions. Only for grid resolutions of ~9–10 km or greater does the smoothing of the gridding process balance out the small-scale noise resulting from the lack of high-resolution infrared data. This scale, incidentally, agrees with the Rossby deformation radius in the Arctic Ocean (~10 km).
Highlights
We looked at the simultaneous behavior of the normalized standard deviation (SD), from both the L4 and observations (i.e., NSDSAT = SD of the individual satellite products (SDSAT)/SDSAIL; NSDSAIL = SDSAIL/SDSAIL = 1), the normalized root-mean-square error (RMSE) (i.e., NRMSE = RMSE/SDSAIL), and their serial correlation, through a normalized Taylor diagram
We focus on three data sets: DOISST, OSTIA, and the MWIR SSTs
The purpose of the research is not to determine the best Group for High-Resolution Sea Surface Temperature (GHRSST) SST analysis for Arctic applications, but to show results in such a way that can lead to further improvements in satellite-derived SST products at high latitudes, where they play a critical role in monitoring changes in this part of the world’s oceans
Summary
Warming sea surface temperatures in the Arctic Seas have resulted from several factors, including earlier sea ice retreat in the presence of downward atmospheric heat fluxes [1]. This heat is released to the lower atmosphere in the fall [2], some of it can remain below the deepening winter mixed layer and influence surface conditions through. Ocean surface warming affects ecosystems in profound ways [6]. Ocean increased by 7% from 1936 to 1999 Such an increase has had a substantial impact on coastal ecosystems and biodiversity in the polar coastal regions. There is a substantial need for accurate and precise satellite-derived SST products in this critical part of the world’s oceans
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