Stochastic Modeling of the Effects of Large-Scale Circulation on Daily Weather in the Southeastern U.S.

Abstract

Abstract. Statistical methodology is devised to model time series of daily weather at individual locations in the southeastern U.S. conditional on patterns in large-scale atmosphere-ocean circulation. In this way, weather information on an appropriate temporal and spatial scale for input to crop-climate models can be generated, consistent with the relationship between circulation and temporally and/or spatially aggregated climate data (an exercise sometimes termed 'downscaling'). The Bermuda High, a subtropical Atlantic circulation feature, is found to have the strongest contemporaneous correlation with seasonal mean temperature and total precipitation in the Southeast (in particular, stronger than for the El Niño-Southern Oscillation phenomenon). Stochastic models for time series of daily minimum and maximum temperature and precipitation amount are fitted conditional on an index indicating the average position of the Bermuda High. For precipitation, a multi-site approach involving a statistical technique known as 'borrowing strength' is applied, constraining the relationship between daily precipitation and the Bermuda High index to be spatially the same. In winter (the time of greatest correlation), higher daily maximum and minimum temperature means and higher daily probability of occurrence of precipitation are found when there is an easterly shift in the average position of the Bermuda High. Methods for determining aggregative properties of these stochastic models for daily weather (e.g., variance and spatial correlation of seasonal total precipitation) are also described, so that their performance in representing low frequency variations can be readily evaluated.

Tropical Cyclones and Drought Amelioration in the Gulf and Southeastern Coastal United States

“…The summer mean location of the Bermuda high also is significantly related to warmseason drought variability in the Southeast via its influence on moisture advection into the region (Stahle and Cleaveland 1993;Katz et al 2003;Diem 2006;Ortegren et al 2011). AMO1 is associated with increased drought frequency in the Atlantic coastal Southeast (Enfield et al 2001;McCabe et al 2004).…”

“…The NAO influences the tracks of TCs over the North Atlantic Ocean (Elsner and Kara 1999;Elsner and Kocher 2000;Keim et al 2004;Elsner et al 2006;Elsner and Jagger 2006;Dailey et al 2009). Additionally, there is evidence of reductions in winter precipitation in the southeastern United States during NAO2 phases (Stenseth et al 2002), although some studies indicate an insignificant relationship between the NAO and precipitation in the southeastern United States during other seasons (Stahle and Cleaveland 1992;Henderson and Vega 1996;Katz et al 2003;Tootle et al 2005;Hurrell and Deser 2009). Maxwell et al (2012) identified the NAO as the only significant variable affecting the likelihood of a TCDB impacting the southeast ACS during the period 1950-2008.…”

“…Regional variations in TC landfall likelihood are associated with low-frequency variability in the location of the steering winds on the south and west flanks of the Bermuda high. In a positive NAO phase (NAO1), the western ridge of the Bermuda high typically shifts farther north and east, and TC activity increases (relative to NAO2 phases) at higher latitudes on the U.S. East Coast (Elsner et al 2000;Katz et al 2003). When the Bermuda high shifts farther south and west (NAO2), TCs experience less recurving and are steered into the Gulf of Mexico, resulting in TC landfall frequency increases in the Gulf Coast states and decreases along the U.S. East Coast (Elsner et al 2000).…”

The annual cycle of heavy precipitation across the United Kingdom: a model based on extreme value statistics

“…In the latter case, c(t) is called a covariate (Coles, 2001;Katz et al, 2003). These new parameters, µ 0 and a µ (or the corresponding parameters for σ and ξ ), can be estimated in the same way as µ, σ and ξ using Equation 5.…”

“…The parametric (harmonic) form of the model reduces the uncertainty of the parameter estimates. This approach is based on a more general method described in Coles (2001) and Katz et al (2003), who include external influences as covariates in their extreme-value analysis. We analyse a set of 689 rain gauge records across the UK, revealing coherent spatial patterns of extreme precipitation characteristics and their annual cycle.…”