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A long-term IASI-derived sea surface temperature dataset for climate studies

Abstract : A rise in global surface temperature is often seen as an indication of climate change on Earth, and has been the subject of considerable public and scientific attention. Sea surface temperature (SST) measurements are scarce and satellite measurements offer the advantage of continuous coverage of SST over the remote oceans. The question remains of whether or not these measurements can have the accuracy and stability necessary for the study of climate trends and natural variability. In this work, we present our computed global sea surface temperature (SST) dataset, i.e. the skin temperature over the sea, from the calibrated radiance measurements obtained twice a day using the Infrared Atmospheric Sounding Interferometer (IASI) instruments. IASI sounds the Earth-atmosphere system in the thermal infrared and covers the spectral range from 645 to 2760 cm-1. Three instruments have been launched in 2006, 2012 and 2018 aboard the Metop satellites. The SSTs are computed from recently reprocessed L1C radiances data, using Planck’s function, at 100 radiative channels where the atmosphere is relatively transparent for cloud-free scenes. The channel selection method (based on jacobians analysis) allows us to rank the channels by highest to lowest sensitivity to SST. We analyze first the dependence of temperature on channel selection and we determine the ultimate number of channels that is necessary for proper SST use. The SST dataset covers the IASI sounding period between 2007 and 2018, under cloud-free conditions. Climate means, inter-annual variability and trends in global SST are computed. We divide them into seasons, and look separately into day and night statistics. These results are then compared with those obtained using SSTs from the state-of-the art ECMWF’s ERA5 analysis. The comparison with ERA5 allows for an assessment of the satellite-derived SST. A general excellent agreement is found between the datasets, with relatively low biases and high correlation coefficients. Regions of better and worse agreement are highlighted and studied further.
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Submitted on : Friday, August 9, 2019 - 12:47:25 PM
Last modification on : Saturday, December 12, 2020 - 12:10:12 PM


  • HAL Id : insu-02265314, version 1


Ana Claudia Parracho, Sarah Safieddine, Maya George, Cathy Clerbaux, Olivier Lezeaux, et al.. A long-term IASI-derived sea surface temperature dataset for climate studies. Living Planet Symposium 2019, May 2019, Milan, Italy. ⟨insu-02265314⟩



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