Performance comparison of some dynamical and empirical downscaling methods for South Africa from a seasonal climate modelling perspective

• Published in: International journal of climatology 2009-09, Vol.29 (11), p.1535-1549
• Main Authors: Landman, Willem A., Kgatuke, Mary‐Jane, Mbedzi, Maluta, Beraki, Asmerom, Bartman, Anna, Piesanie, Annelise du
• Format: Article
• Language: English
• Subjects: Climatology. Bioclimatology. Climate change; downscaling; Earth, ocean, space; Exact sciences and technology; External geophysics; general circulation model; Geophysics. Techniques, methods, instrumentation and models; Meteorology; model output statistics; regional climate model; South Africa
• ISSN: 0899-8418; 1097-0088
• DOI: 10.1002/joc.1766

The ability of advanced state‐of‐the‐art methods of downscaling large‐scale climate predictions to regional and local scale as seasonal rainfall forecasting tools for South Africa is assessed. Various downscaling techniques and raw general circulation model (GCM) output are compared to one another over 10 December‐January‐February (DJF) seasons from 1991/1992 to 2000/2001 and also to a baseline prediction technique that uses only global sea‐surface temperature (SST) anomalies as predictors. The various downscaling techniques described in this study include both an empirical technique called model output statistics (MOS) and a dynamical technique where a finer resolution regional climate model (RCM) is nested into the large‐scale fields of a coarser GCM. The study addresses the performance of a number of simulation systems (no forecast lead‐time) of varying complexity. These systems' performance is tested for both homogeneous regions and for 963 stations over South Africa, and compared with each other over the 10‐year test period. For the most part, the simulations method outscores the baseline method that uses SST anomalies to simulate rainfall, therefore providing evidence that current approaches in seasonal forecasting are outscoring earlier ones. Current operational forecasting approaches involve the use of GCMs, which are considered to be the main tool whereby seasonal forecasting efforts will improve in the future. Advantages in statistically post‐processing output from GCMs as well as output from RCMs are demonstrated. Evidence is provided that skill should further improve with an increased number of ensemble members. The demonstrated importance of statistical models in operation capacities is a major contribution to the science of seasonal forecasting. Although RCMs are preferable due to physical consistency, statistical models are still providing similar or even better skill and should still be applied. Copyright © 2008 Royal Meteorological Society