Atmospheric Seasonal Predictability and Estimates of Ensemble Size

Abstract

Results from a set of nine-member ensemble seasonal integrations with a T63L19 version of the European Centre for Medium-Range Weather Forecasts (ECMWF) model are presented. The integrations are made using observed specified sea surface temperature (SST) from the 5-year period 1986?90, which included both warm and cold El Niño?Southern Oscillation (ENSO) events. The distributions of ensemble skill scores and internal ensemble consistency are studied. For years in which ENSO was strong, the model generally exhibits a relative high skill and high consistency in the Tropics. In the northern extratropics, the highest skill and consistency are found for the northern Pacific?North American region in winter, whereas for the northern Atlantic?European region the spring season appears to be both skillful and consistent. For years in which ENSO was weak, the distributions of ensemble skill and consistency are relatively broad and no clear distinction between Tropics and extratropics can be made. Applying a t test to interannual fluctuations over various tropical and extratropical regions, estimates of a minimum useful ensemble size are made. Explicit calculations are done with ensemble size varying between three and nine members; estimates for larger sizes are made by extrapolating the t values. Based on an analysis of 2-m temperature and precipitation, the use of relatively large (approximately 20 members) ensembles for extratropical predictions is likely to be required; in the Tropics, smaller-sized ensembles may be adequate during years in which ENSO is strong, particularly for regions such as the Sahel. The role of the SST forcing in a seasonal timescale ensemble is to bias the probability distribution function (PDF) of atmospheric states. Such PDFs can, in addition, be a convenient way of condensing a vast amount of data usually obtained from ensemble predictions. Interannual variability in PDFs of monsoon rainfall and regional geopotential height probabilities is discussed.