P1.7
Sources of Seasonal Predictability for Daily Precipitation Extreme Statistics Over the Eastern US
Alexander Gershunov, SIO/University of California, La Jolla, CA; and T. J. Reichler and J. O. Roads
It has been shown that dynamical seasonal predictions of the large-scale atmospheric circulation can be used together with a statistical model to predict intraseasonal precipitation statistics (e.g. seasonal frequency of daily extremes) with significant skill over many parts of the conterminous United States. Beyond ENSO, however, the sources of this predictability remain uncertain, although both the north Pacific and north Atlantic oscillations are expected to contribute. In this work, hybrid dynamical-statistical methodology for seasonal climate prediction will be used to examine seasonal predictability of intraseasonal precipitation statistics due to ENSO, NPO and NAO forcing (separately and together). Four sets of 50-year 10-member ensemble runs with the NCEP AGCM are being performed. Global oceans, tropical Pacific, north Pacific and north Atlantic SST-forced (climatological seasonal cycle of SST everywhere else) AGCM runs will be analyzed as follows. The influence of global and regional SST variability on large-scale atmospheric circulation will be accessed through pairwise intra-ensemble correlation analysis. The seasonal- (January-March) and ensemble-average large-scale circulation anomalies will then be statistically related to observed precipitation over the northeastern US. A rigorous cross-validated estimation of predictive skill due to interannual- and interdecadal-scale modes residing in the tropical Pacific, north Pacific and north Atlantic oceans separately, as well as all global SST modes together, will be performed. Results of this experiment are expected to shed light on the origins of seasonal to interannual predictability in the eastern United States.
Poster Session 1, Natural Climate Variability Posters
Monday, 15 January 2001, 1:30 PM-3:30 PM
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