Abstract
Atmospheric desert-dust aerosol, primarily from north Africa, causes negative biases in remotely sensed climate data records of sea surface temperature (SST). Here, large-scale bias adjustments are deduced and applied to the v2 climate data record of SST from the European Space Agency Climate Change Initiative (CCI). Unlike SST from infrared sensors, SST measured in situ is not prone to desert-dust bias. An in-situ-based SST analysis is combined with column dust mass from the Modern-Era Retrospective analysis for Research and Applications, Version 2 to deduce a monthly, large-scale adjustment to CCI analysis SSTs. Having reduced the dust-related biases, a further correction for some periods of anomalous satellite calibration is also derived. The corrections will increase the usability of the v2 CCI SST record for oceanographic and climate applications, such as understanding the role of Arabian Sea SSTs in the Indian monsoon. The corrections will also pave the way for a v3 climate data record with improved error characteristics with respect to atmospheric dust aerosol.
Highlights
Sea surface temperature (SST) is an essential variable to observe for many oceanographic and climatological applications [1]
The global offset adjustment is applied to all SSTs in the Climate Change Initiative (CCI) analysis, except: (i) areas where the adjusted analysis SST registers less than 271.35 K which are reset to 271.35 K to avoid unphysical subfreezing values; (ii) there is a linear tapering of the adjustment during 1992 with zero adjustment made from 1993 onwards
The presence of desert-dust aerosol is a cause of bias in satellite infrared retrievals of sea surface temperature used in a multidecadal analysis, namely, the SST CCI analysis
Summary
Sea surface temperature (SST) is an essential variable to observe for many oceanographic and climatological applications [1]. For the large-scale post-hoc correction derived in this paper, the dust mass in a given month is the key predictor of the required SST adjustment This rest of this paper proceeds as follows. Since HadSST4 is unbiased with respect to variability in desert dust aerosol or satellite calibration, differences between the CCI analysis and HadSST4 are, at appropriate scales, used to derive empirical adjustments to the CCI SSTs. In Section 3, an adjustment is derived in the form of a time-dependent scaling of dust mass as represented in MERRA-2. This is shown to reduce spatial, seasonal and multiannual signatures of dust-related differences on large scales. The method normalizes the variability of CCI-HadSST4 differences prior to 1993 by global, monthly additive adjustments, such that the statistics of CCI-HadSST4 differences become consistent across the full timeseries
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