Satellite Infrared Retrievals of Sea Surface Temperature at High Latitudes

• Main Author: Jia, Chong
• Other Authors: Peter J. Minnett, Roland Romeiser, Malgorzata D. Szczodrak, Chelle L. Gentemann
• Format: Other/Unknown Material
• Language: unknown
• Published: Scholarly Repository 2019
• Subjects: Algorithm; Arctic; Emissivity; MODIS; Sea surface temperature; Climate change
• Online Access: https://scholarlyrepository.miami.edu/oa_theses/788; https://scholarlyrepository.miami.edu/cgi/viewcontent.cgi?article=1807&context=oa_theses

Climate change is amplified in the Arctic region (north of 60 ° N) relative to elsewhere. By analyzing climate model simulations, it has been found that the largest contribution to Arctic amplification comes from temperature feedbacks, due to both the different warming profile in low and high latitudes and a larger temperature increase in longwave emission per unit of warming at colder background temperatures compared to tropical conditions. Satellite remote sensing offers the best way of deriving sea surface temperature (SST) in the Arctic, but given that the retrieval algorithms in the infrared (IR) are designed to compensate for the effects of the atmosphere, mainly water vapor, IR satellite-derived SSTs have larger uncertainties at high latitudes because the atmosphere is very dry and cold. So, the motivation of this study is to improve the algorithms to obtain more accurate SSTs which can be used to research the feedback mechanisms. To undertake the study, the matchup database (MUDB) for MODIS (Moderate resolution Imaging Spectroradiometer) on Aqua has been analyzed to characterize the differences between collocated and simultaneous satellite retrieved skin SSTs and in situ buoy temperatures, and to identify the main causes of the discrepancies. According to the radiative transfer simulations, the sea surface emissivity is proven to be significant in satellite SST retrievals at high latitudes due to the low atmospheric water vapor content, especially during winter. An Emissivity introduced Brightness Temperature Difference (EBΔT) is introduced to correct this emissivity effect in the algorithm. Furthermore, the reference temperature as a weight factor for brightness temperature (BT) difference between MODIS bands 31 and 32 has also been adjusted. The satellite SST biases and standard deviation are reduced by 0.41 K and 0.11 K after the corrections. The approach presented in this study is capable to make more appropriate atmospheric corrections for MODIS SST retrieval algorithm, leading to regional optimization of the SST retrievals. We report on the progress towards improving the satellite-derived SST with the expectation that the near two-decadal time series of MODIS SST fields will contribute to studying climate change in the Arctic.