Near-Global Sea Surface Temperature Anomalies as Predictors of Australian Seasonal Rainfall

Abstract

An operational system for the prediction of Australian seasonal rainfall variations using sea surface temperature anomaly (SSTA) patterns over the Indian and Pacific Oceans is described. The SSTA patterns are represented by rotated principal components, with individual monthly values at 1- and 3-month lead times used as predictors; for example, November and January SSTAs are used to forecast March‐May seasonal rainfall. The historical seasonal rainfall is also represented by rotated principal components of a gridded 1 8 rainfall dataset, with the principal component loadings used as weights to project the forecasts back to the original 1 8 grid points. Forecasts of seasonal rainfall in two (above/below median) or three categories (terciles) are produced using linear discriminant analysis. Hindcast skill, measured by the linear error in probability space (LEPS) skill score has been assessed using cross validation. Experiments were also performed using a double or nested crossvalidation procedure to select the best model or combination of predictors. The model chosen for operational seasonal forecasts uses the first two rotated SSTA components lagged by 1 and 3 months as predictors for every season and location, to maintain continuity of forecast probabilities between the overlapping 3-month seasons. Current values of the principal component amplitudes are calculated by projecting either the Bureau of Meteorology’s or the National Centers for Environmental Prediction’s SST analysis onto the set of SST principal components. The hindcasts and experimental real-time forecasts over the 5-yr period from January‐March 1994 to December‐February 1998/99 indicate improved skill over parts of southern Australia during the autumn period using the SST-based schemes when compared with forecasts using the Southern Oscillation index alone.