Summer daily precipitation variability over the East Anglian region of Great Britain

Abstract

Daily precipitation totals for five consecutive summers (1994)(1995)(1996)(1997)(1998) were obtained for 136 stations in East Anglia to explore geographic variations in rainfall. S-mode principal components analysis (PCA) was used to identify the main spatial modes of variability in the region's precipitation field. The components were interpreted physically by examining synoptic charts and the British Isles' circulation variables for the days with high PC scores. Whilst seven principal components have eigenvalues greater than 1, and collectively explain 86.3% of the variance, only the first 5 PCs have well defined rainfall patterns that can be attributed to specific circulation patterns. The most dominant pattern (PC 1) shows a gradient between wetter conditions in NE Norfolk and drier conditions in Cambridgeshire and Essex, which is favoured by a cyclonic northerly airflow. PC 2 is almost as dominant, and shows a west to east decay in rainfall totals that is typically associated with active zonal W-SW flows. PC 3 sees the highest totals occurring in Essex. Wetter conditions in the south of East Anglia tend to occur when areas of low pressure follow a southerly track along the English Channel and southern North Sea. PC 4 shows a maximum in convective activity that occurs furthest inland in SE Cambridgeshire and west Suffolk, with the convection being triggered or accentuated by the East Anglian Heights (the highest land in East Anglia). PC 5 shows the rainfall distribution under a cyclonic N-NW, whereby showers generated over The Wash penetrate inland and produce the most rainfall in west Norfolk. Finally, a summer precipitation regionalization was derived by applying agglomerative hierarchical cluster analysis to the loadings of PCs 1-7. It is concluded that a location's precipitation climatology is more strongly determined by the orientation of the nearest coastline rather than simply the site's distance from the North Sea.

Deriving mechanisms and thresholds for cliff retreat in soft-rock cliffs under changing climates: Rapidly retreating cliffs of the Suffolk coast, UK

“…The climate is more continental than elsewhere in the British Isles, with short duration, high intensity rainfall events generated by convective activity in the summer months (Crossley and Lofthouse, 1964;Mayes and Sutton, 1997). The long-term summer (June, July, and August) rainfall mean is 142 mm at Lowestoft but with considerable interannual variability (Neal and Phillips, 2009). Examples of extreme daily totals include a 100 mm event across Norfolk on 25 August 1912, and 205 mm registered at Brundall, near Norwich, and 147 mm near Bungay, Suffolk, both recorded on 1 September 1994 (http://www.…”

“…Annual rainfall totals on the Suffolk coast are typically 550 mm (Neal and Phillips, 2009). The rainfall regime is strongly influenced by the low relief (generally b60 m) and easterly location; orographic rainfall cannot be generated, while frontal rainfall is generally low in the rainshadow from westerly airstreams.…”

Autumn and Extended Winter Daily Precipitation Variability over Central and Southern Scotland

“…Eight years were deemed adequate in representing the area's mean climate and is also in line with previous studies (e.g. Bonell & Sumner 1992;Neal & Phillips 2009) that typically use between 5 and 10 years. The representativeness of the study period was investigated by comparing annual totals at Edinburgh and Glasgow over these eight years with the 1870-1991 mean ( Table 1).…”

Climate Zone Delineation: Evaluating Approaches for Use in Natural Resource Management

“…However, because the cluster analyses here do not rely on time series data, instead using only a matrix of monthly averages (normals), we used more traditional climate clustering methods (Fovell and Fovell 1993;Unal and others 2003) and instead employed bootstrapping (described below) to assess the sensitivity of our results to data artifacts. All cluster analyses calculated the distinctiveness of weather stations in multivariate space with the squared Euclidean Distance metric because it has performed better than alternatives in previous meteorological studies (Fovell and Fovell 1993;Unal and others 2003;Neal and Phillips 2009;Van Cooten and others 2009). Unless noted, all analyses were performed within the R statistical analysis platform (R Development Core Team 2009).…”