IASI‐Derived Sea Surface Temperature Data Set for Climate Studies

Abstract 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 data set suitable for climate studies based on a single...

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Bibliographic Details
Published in:Earth and Space Science
Main Authors: Ana C. Parracho, Sarah Safieddine, Olivier Lezeaux, Lieven Clarisse, Simon Whitburn, Maya George, Pascal Prunet, Cathy Clerbaux
Format: Article in Journal/Newspaper
Language:English
Published: American Geophysical Union (AGU) 2021
Subjects:
climate data
climate trends
IASI
remote sensing
satellite data
sea surface temperature
Astronomy
QB1-991
Geology
QE1-996.5
Pacific
North Atlantic
Online Access:https://doi.org/10.1029/2020EA001427
https://doaj.org/article/c81e61dbaf5c4f849eb10207cbe2c600
Description
Summary:Abstract 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 data set suitable for climate studies based on a single instrument is still a challenge. In this work, we assess a homogeneous SST data set 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 data set 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 SD of difference around 0.3°C and a correlation coefficient over 0.99. In addition, the reprocessed data set shows a stable bias and SD, which is an advantage for climate studies. The interannual variability and trends were compared with other SST data sets: ERA5, Hadley Centre's SST (HadISST), and NOAA's Optimal Interpolation SST Analysis (OISSTv2). We found that the reprocessed SST data set is able to capture the patterns of interannual variability well, showing the same areas of high interannual 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 data set reproduces the same trend patterns found in the other data sets (i.e., cooling trend in the North Atlantic, warming trend over the Mediterranean).

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