Global sea surface temperature and analyses: Multiple problems and 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 func tion 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, esp ecially in the marginal sea-ice zones and in regions of important small-sca le features, such as the Gulf Stream, which are better resolved by the NCEP product. Significant differences also exist in the SST anomalies that rela te strongly to the number of in situ observations available. In recent year s, correlations between monthly anomalies are less than 0.75 south of about 10 degrees N and are lower still over the southern oceans and parts of the tropical Pacific where root-mean-square differences exceed 0.6 degrees 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 tempo ral persistence of SST anomalies after 1981 is lacking. Trends in recent ye ars are quite different between the GISST and NCEP analyses, and this can b e partially traced to differences in the processing of in situ data and an increasing cold bias in the NCEP OI data arising from incompletely correcte d satellite data. Significant discrepancies also exist in centennial trends from the LDEO and GISST datasets, and these Likely reflect the separate tr eatment of the very low frequency signal in the GISST analysis and question able assumptions about the stationarity of statistics in the LDEO method. Ensembles of integrations with an atmospheric general circulation model (AG CM) are used with three of the SST datasets as lower boundary conditions to show that the differences among them imply physically important difference s in the atmospheric circulation. Over the Tropics, where masking by intern al atmospheric variability is small, SST differences affect moist convectio n and systematically produce strong responses in the local divergent circul ation. A case study shows that analyzed SST differences in the tropical Pac ific can be as large as for a moderate EI Nino. Such large discrepancies in duce local rainfall anomalies up to 8 mm day-L and, in addition to the trop ical circulation anomalies, are associated with global teleconnections that influence temperatures and precipitation around the world. Results also sh ow the limitations to using AGCMs when forced by specified SSTs. The likely sources of the problems evident in the different SST products ar e identified and discussed. Several of the problems are being addressed by current efforts to reprocess the SST data, which is strongly recommended, b ut 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, a nd for monitoring climate is important, as known flaws in the global analys es can compromise the results.