Reconstruction of Historical Sea Surface Temperatures Using Empirical Orthogonal Functions

Abstract

Studies of climate variability often rely on high quality sea surface temperature (SST) anomalies. Although the high-resolution National Centers for Environmental Prediction (formerly the National Meteorological Center) optimum interpolation (OI) SST analysis is satisfactory for these studies, the OI resolution cannot be maintained before November 1931 due to the lack of satellite data. Longer periods of SSTs have come from traditional analyses of in situ (ship and buoy) SST observations alone. A new interpolation method is developed using spatial patterns from empirical orthogonal functions (E0Fs)—that is, a principal component analysis—to improve analyses of SST anomalies from 1950 to 1981. The method uses the more accurate OI analyses from 1982 to 1993 to produce the spatial EOFs. The dominant EOF modes (which correspond to the largest variance) are used as basis functions and are fit, in a least squares sense, to the in situ data to determine the time dependence of each mode. A complete field... Abstract Studies of climate variability often rely on high quality sea surface temperature (SST) anomalies. Although the high-resolution National Centers for Environmental Prediction (formerly the National Meteorological Center) optimum interpolation (OI) SST analysis is satisfactory for these studies, the OI resolution cannot be maintained before November 1931 due to the lack of satellite data. Longer periods of SSTs have come from traditional analyses of in situ (ship and buoy) SST observations alone. A new interpolation method is developed using spatial patterns from empirical orthogonal functions (E0Fs)—that is, a principal component analysis—to improve analyses of SST anomalies from 1950 to 1981. The method uses the more accurate OI analyses from 1982 to 1993 to produce the spatial EOFs. The dominant EOF modes (which correspond to the largest variance) are used as basis functions and are fit, in a least squares sense, to the in situ data to determine the time dependence of each mode. A complete field...