HR: 1340h
AN: H13E-1619    [Abstracts]
TI: Advantage of Multi-scenario Ensembling for AGCM Seasonal Climate Forecast
AU: * Li, S
EM: shuhua@iri.columbia.edu
AF: International Research Institute for Climate and Society, The Earth Institute at Columbia University, 61 Route 9W, Palisades, NY 10964, United States
AU: Goddard, L
EM: goddard@iri.columbia.edu
AF: International Research Institute for Climate and Society, The Earth Institute at Columbia University, 61 Route 9W, Palisades, NY 10964, United States
AU: DeWitt, D G
EM: daved@iri.columbia.edu
AF: International Research Institute for Climate and Society, The Earth Institute at Columbia University, 61 Route 9W, Palisades, NY 10964, United States
AB: The benefit of multi-model ensembling in seasonal forecasting has been increasingly employed in research and operations. In this presentation we examine skill of retrospective forecasts using the ECHAM4.5 atmospheric general circulation model (AGCM) forced with predicted sea surface temperatures (SSTs) from methods of varying complexity. The SST fields are predicted in three ways: persisted observed SST anomalies; empirically predicted SSTs and predicted SSTs from a dynamically coupled ocean-atmosphere model. Here we focus on the skill of seasonal precipitation given its importance in influencing streamflow variability. Analysis of the anomaly correlation skill for precipitation indicates that dynamically predicted SSTs generally improve upon persisted and empirically predicted SSTs when they are used as boundary forcing in the AGCM predictions. The skill differences in these experiments are ascribed to the skill of SST predictions in the tropical ocean basins. The multi-scenario forecast by averaging the results of the three retrospective experiments performs, overall, as well as or better than the best of the three individual experiments in specific seasons and regions. The advantage of multi-scenario forecast manifests both in the deterministic skill measured by the anomaly correlations based on the ensemble mean, and the probabilistic skill measured by the reliability and resolution of the ensemble distribution. In particular, the multi-scenario precipitation forecast for the December-February season demonstrates better skill than the best of the three scenarios over several regions, such as the western US and southeastern South America. These results suggest the potential value in producing super-ensembles spanning different SST prediction scenarios.
DE: 1854 Precipitation (3354)
DE: 3333 Model calibration (1846)
DE: 3337 Global climate models (1626, 4928)
SC: Hydrology [H]
MN: 2007 Fall Meeting