Statistical–Dynamical Predictions of Seasonal North Atlantic Hurricane Activity

Skillfullypredicting North Atlantichurricane activitymonths in advance is of potential societal significance and a useful test of our understanding of the factors controlling hurricane activity. In this paper, a statistical‐ dynamical hurricane forecasting system, based on a statistical hurricane model, with explicit uncertainty estimates,andbuiltfromasuiteofhigh-resolutionglobalatmosphericdynamicalmodelintegrationsspanning a broad range of climate states is described. The statistical model uses two climate predictors: the sea surface temperature (SST) in the tropical North Atlantic and SST averaged over the global tropics. The choice of predictorsis motivatedby physicalconsiderations, aswell astheresultsofhigh-resolutionhurricanemodeling and statistical modeling of the observed record. The statistical hurricane model is applied to a suite of initialized dynamical global climate model forecasts of SST to predict North Atlantic hurricane frequency, which peaks during the August‐October season, from different starting dates. Retrospective forecasts of the 1982‐2009 period indicate that skillful predictions can be made from as early as November of the previous year; that is, skillful forecasts for the coming North Atlantic hurricane season could be made as the current one is closing. Based on forecasts initialized between November 2009 and March 2010, the model system predictsthattheupcoming2010NorthAtlantichurricaneseasonwilllikelybemoreactivethanthe1982‐2009 climatology, with the forecasts initialized in March 2010 predicting an expected hurricane count of eight and a 50% probability of counts between six (the 1966‐2009 median) and nine.

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