Prediction of sea surface temperature from the Global Historical Climatology Network data

This article describes a spatial prediction method that predicts the monthly sea surface temperature (SST) anomaly field from the land only data. The land data are from the Global Historical Climatology Network (GHCN). The prediction period is 1880-1999 and the prediction ocean domain extends from 60degreesS to 60degreesN with a spatial resolution 5degrees x 5degrees. The prediction method is a regression over the basis of empirical orthogonal functions (EOFs), The EOFs are computed from the following data sets: (a) the Climate Prediction Center's optimally interpolated sea surface temperature (OI/SST) data (1982-1999); (b) the National Climatic Data Center's blended product of land-surface air temperature (1992-1999) produced from combining the Special Satellite Microwave Imager and GHCN: and (c) the National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis data (1982-1999). The optimal prediction method minimizes the first-M-mode mean square error between the true and predicted anomalies over both land and ocean. In the optimization process, the data errors of the GHCN boxes are used, and their contribution to the prediction error is taken into account. The area-averaued root mean square error of prediction is calculated. Numerical experiments demonstrate that this EOF prediction method can accurately recover the global SST anomalies during some circulation patterns and add value to the SST bias correction in the early history of SST observations and the validation of general circulation models. Our results show that (i) the land only data can accurately predict the SST anomaly in the El Nino months when the temperature anomaly structure has very large correlation scales, and (ii) the predictions for La Nina, neutral. or transient months require more EOF modes because of the presence of the small scale structures in the anomaly field. Copyright (C) 2004 John Wiley Sons, Ltd.