Northern Hemisphere forecast skill during extreme winter weather regimes

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Wednesday, 5 February 2014: 1:30 PM
Room C201 (The Georgia World Congress Center )
Ryan Maue, WeatherBELL Analytics, Tallahassee, FL; and R. H. Langland

From analysis of recent global deterministic and ensemble forecasts and analyses, multi-model forecast skill is shown to have similar variability on synoptic, monthly, and seasonal time scales. Often measured by the anomaly correlation coefficient (ACC) in 120-hr forecasts of 500 hPa geopotential height, Northern Hemisphere skill scores are significantly correlated with large-scale atmospheric flow anomalies defined by the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO). For example, intervals of exceptionally high ACC skill during the 2009–2010 and 2010–2011 winters were associated with periods in which the AO remained in a persistent negative phase pattern. Episodes of low ACC, including so-called ‘forecast skill dropouts' most frequently occur during transitions between negative and positive AO index and with positive AO index. It follows that atmospheric predictive skill receives a boost during periods of persistent extreme weather regimes including strong negative AO, and blocking. Part of the anomalously high skill is related to persistence of low wavenumber patterns which are preferentially weighted within the ACC metric. Conversely, active or extreme weather regimes associated with NAO or AO transitions often see forecast skill busts. Thus, in order to aid in the identification of potential low-skill forecast periods, a climatology of historical bust indicators is developed from the new ESRL/PSD GEFS Reforecast V2 from 1985-2013. The relationship between extreme winter weather regimes (as measured by large-scale teleconnections) and forecast skill is quantified in the medium-range where ensemble reforecast techniques can be leveraged.