Global Sea Surface Temperature Analyses: Multiple Problemsand Their Implications for Climate Analysis, Modeling, and Reanalysis

A comprehensive comparison is made among four sea surface temperature (SST) datasets: the optimum interpolation (OI) and the empirical orthogonal function reconstructed SST analyses from the National Centers for Environmental Prediction (NCEP), the Global Sea-Ice and SST dataset (GISST, version 2.3b) from the United Kingdom Meteorological Office, and the optimal smoothing SST analysis from the Lamont-Doherty Earth Observatory (LDEO). Significant differences exist between the GISST and NCEP 1961–90 SST climatologies, especially in the marginal sea-ice zones and in regions of important small-scale features, such as the Gulf Stream, which are better resolved by the NCEP product. Significant differences also exist in the SST anomalies that relate strongly to the number of in situ observations available. In recent years, correlations between monthly anomalies are less than 0.75 south of about 10°N and are lower still over the southern oceans and parts of the tropical Pacific where root-mean-square differences exceed 0.6°C. While adequate for many purposes, the SST datasets all contain problems of one sort or another. Noise is evident in the GISST data and realistic temporal persistence of SST anomalies after 1981 is lacking. Trends in recent years are quite different between the GISST and NCEP analyses, and this can be partially traced to differences in the processing of in situ data and an increasing cold bias in the NCEP 01 data arising from incompletely corrected satellite data. Significant discrepancies also exist in centennial trends from the LDEO and GISST datasets, and these likely reflect the separate treatment of the very low frequency signal in the GISST analysis and questionable assumptions about the stationarity of statistics in the LDEO method. Ensembles of integrations with an atmospheric general circulation model (AGCM) are used with three of the SST datasets as lower boundary conditions to show that the differences among them imply physically important differences in the atmospheric circulation. Over the Tropics, where masking by internal atmospheric variability is small, SST differences affect moist convection and systematically produce strong responses in the local divergent circulation. A case study shows that analyzed SST differences in the tropical Pacific can be as large as for a moderate El Niño. Such large discrepancies induce local rainfall anomalies up to 8 mm day−1 and, in addition to the tropical circulation anomalies, are associated with global teleconnections that influence temperatures and precipitation around the world. Results also show the limitations to using AGCMs when forced by specified SSTs. The likely sources of the problems evident in the different SST products are identified and discussed. Several of the problems are being addressed by current efforts to reprocess the SST data, which is strongly recommended, but remaining problems demand further attention and attempts to resolve them should continue. The choice among SST analyses used for AGCM simulations, for the atmospheric reanalysis projects, for identifying climate signals, and for monitoring climate is important, as known flaws in the global analyses can compromise the results.