IASI‐Derived Sea Surface Temperature Data Set for Climate Studies. Issue 5 (17th May 2021)

• Record Type: Journal Article
• Authors: Parracho, Ana C.; Safieddine, Sarah; Lezeaux, Olivier; Clarisse, Lieven; Whitburn, Simon; George, Maya; Prunet, Pascal; Clerbaux, Cathy
• Is Part Of: Earth and space science. Volume 8:Issue 5(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-05-17
• Subjects: climate data -- climate trends -- IASI -- remote sensing -- satellite data -- sea surface temperature; Space sciences -- Periodicals; Geophysics -- Periodicals; 500.5
• DOI: 10.1029/2020EA001427
• Languages: English
• ISSNs: 2333-5084

Abstract

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). Plain Language Summary: Sea surface temperature (SST) is an essential variable for monitoring climate, as defined by the Global Climate Observing System (GCOS; https://gcos.wmo.int/en/essential-climate-variables/sst ). Satellite measurements can offer global continuous SST measurements, but their stability over the time needs to be assured. In this work, we present a new data set derived from the Infrared Atmospheric Sounding Interferometer, IASI (flying aboard the Metop satellites), and compare it with other available data sets. This comparison shows that our data set produces similar means, variability and trends as other data sets, with the advantage that it is derived with a single algorithm from a single well‐calibrated instrument. This assures there are no substantial changes to the instrument characteristics over time that might result in artificial trends. Key Points: First IASI algorithm focused on sea surface temperature (SST) suitable for climate studies The IASI‐derived SST data set is compared with other available data sets Climate variability and trends are shown and compared to other data sets