IASI‐derived sea surface temperature dataset for climate studies
Résumé
Sea surface temperature (SST) is an essential climate variable, that is directly used in climate monitoring. Although satellite measurements can offer continuous global coverage, obtaining a long‐term homogeneous satellite‐derived SST dataset suitable for climate studies based on a single instrument is still a challenge. In this work, we assess a homogeneous SST dataset derived from reprocessed Infrared Atmospheric Sounding Interferometer (IASI) level‐1 (L1C) radiance data. The SST is computed using Planck’s Law and simple atmospheric corrections. We assess the dataset using the ERA5 reanalysis and the Eumetsat‐released IASI level‐2 SST product. Over the entire period, the reprocessed IASI SST shows a mean global difference with ERA5 close to zero, a mean absolute bias under 0.5°C, with a standard deviation of difference around 0.3°C and a correlation coefficient over 0.99. In addition, the reprocessed dataset shows a stable bias and standard deviation, which is an advantage for climate studies. The inter‐annual variability and trends were compared with other SST datasets: ERA5, Hadley Centre's SST (HadISST) and NOAA’s Optimal Interpolation SST Analysis (OISSTv2). We found that the reprocessed SST dataset is able to capture the patterns of inter‐annual variability well, showing the same areas of high inter‐annual variability (>1.5°C), including over the tropical Pacific in January corresponding to the El Niño Southern Oscillation. Although the period studied is relatively short, we demonstrate that the IASI dataset reproduces the same trend patterns found in the other datasets (i.e.: cooling trend in the North Atlantic, warming trend over the Mediterranean).
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