Deriving a sea surface temperature record suitable for climate change research from the along-track scanning radiometers

Merchant, C.J., Llewellyn-Jones, D., Saunders, R.W., Rayner, N.A., Kent, E.C., Old, C.P., Berry, D.I., Birks, A.R., Blackmore, T., Corlett, G.K., Embury, O., Jay, V., Kennedy, J., Mutlow, C.T., Nightingale, T.J., O'Carroll, A.G., Pritchard, M.J., Remedios, J.J. and Tett, S.B.F. (2008) Deriving a sea surface temperature record suitable for climate change research from the along-track scanning radiometers. Advances in Space Research, 41 (1), 1-11. (doi:10.1016/j.asr.2007.07.041).

Abstract

We describe the approach to be adopted for a major new initiative to derive a homogeneous record of sea surface temperature for 1991–2007 from the observations of the series of three along-track scanning radiometers (ATSRs). This initiative is called (A)RC: (Advanced) ATSR Re-analysis for Climate. The main objectives are to reduce regional biases in retrieved sea surface temperature (SST) to less than 0.1 K for all global oceans, while creating a very homogenous record that is stable in time to within 0.05 K decade-1, with maximum independence of the record from existing analyses of SST used in climate change research. If these stringent targets are achieved, this record will enable significantly improved estimates of surface temperature trends and variability of sufficient quality to advance questions of climate change attribution, climate sensitivity and historical reconstruction of surface temperature changes. The approach includes development of new, consistent estimators for SST for each of the ATSRs, and detailed analysis of overlap periods. Novel aspects of the approach include generation of multiple versions of the record using alternative channel sets and cloud detection techniques, to assess for the first time the effect of such choices. There will be extensive effort in quality control, validation and analysis of the impact on climate SST data sets. Evidence for the plausibility of the 0.1 K target for systematic error is reviewed, as is the need for alternative cloud screening methods in this context.

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