Performance of recalibration systems for GCM forecasts for southern Africa
International Journal of Climatology
Swaziland National Meteorological Service, Mbabane, Swaziland; Department of Geography Geoinformatics and Meteorology, University of Pretoria, Pretoria, South Africa; South Africa Weather Service, Pretoria, South Africa; International Research Institute for Climate and Society, Columbia University, New York, NY, United States; Royal Netherlands Meteorological Institute, P.O. Box 201, 3730 AE De Bilt, Netherlands
Two regression-based methods that recalibrate the ECHAM4.5 general circulation model (GCM) output during austral summer have been developed for southern Africa, and their performance assessed over a 12-year retroactive period 1989/90-2000/01. A linear statistical model linking near-global sea-surface temperatures (SSTs) to regional rainfall has also been developed. The recalibration technique is model output statistics (MOS) using principal components regression (PCR) and canonical correlation analysis (CCA) to statistically link archived records of the GCM to regional rainfall over much of Africa, south of the equator. The predictability of anomalously dry and wet conditions over each rainfall region during December-February (DJF) using the linear statistical model and MOS models has been quantitatively evaluated. The MOS technique outperforms the raw-GCM ensembles and the linear statistical model. Neither the PCR-MOS nor the CCA-MOS models show clear superiority over the other, probably because the two methods are closely related. The need to recalibrate GCM predictions at regional scales to improve their skill at smaller spatial scales is further demonstrated in this paper. Copyright © 2006 Royal Meteorological Society.
Atmospheric movements; Atmospheric temperature; Climate change; Correlation methods; Mathematical models; Principal component analysis; Rain; Regression analysis; Canonical correlation analysis; Climate variability; General circulation model (GCM); Model output statistics; Principal components regression; Sea-surface temperature; Seasonal climate prediction; Climatology; atmospheric modeling; calibration; climate variation; forecasting method; general circulation model; model validation; principal component analysis; regression analysis; sea surface temperature; Africa; South Africa; Southern Africa; Sub-Saharan Africa