This study deals with an analysis of the performance of a general circulation model (GCM) (HadCM2) in reproducing the large-scale circulation mechanisms controlling Swedish precipitation variability, and in estimating regional climate changes owing to increased CO 2 concentration by using canonical correlation analysis (CCA). Seasonal precipitation amounts at 33 stations in Sweden over the period are used. The large-scale circulation is represented by sea level pressure (SLP) over the Atlantic-European region.The link between seasonal Swedish precipitation and large-scale SLP variability is strong in all seasons, but especially in winter and autumn. For these two seasons, the link is a consequence of the North Atlantic Oscillation (NAO) pattern. In winter, another important mechanism is related to a cyclonic/anticyclonic structure centred over southern Scandinavia. In the past century, this connection has remained almost unchanged in time for all seasons except spring. The downscaling model that is built on the basis of this link is skilful in all seasons, but especially so in winter and autumn. This observed link is only partially reproduced by the HadCM2 model, while large-scale SLP variability is fairly well reproduced in all seasons. A concept about optimum statistical downscaling models for climate change purposes is proposed. The idea is related to the capability of the statistical downscaling model to reproduce low frequency variability, rather than having the highest skill in terms of explained variance. By using these downscaling models, it was found that grid point and downscaled climate signals are similar (increasing precipitation) in summer and autumn, while in winter, the amplitudes of the two signals are different. In spring, both signals show a slight increase in the northern and southern parts of Sweden.
The main characteristics of spatial and temporal variability of the precipitation regime in Sweden were studied by using the long-term monthly precipitation amount (1890-1990) at 33 stations. The data were filtered by using Empirical Orthogonal Function (EOF) analysis, which provides principal modes of both spatial variability and time coefficient series describing the dominant temporal variability. Canonical correlation analysis (CCA) was used to reveal association between the atmospheric circulation and the characteristics of the climate variability.
Differences in the mean atmospheric conditions during summer (June-August) extreme and non-extreme precipitation events in Sweden are analysed for cyclonic, anticyclonic and directional weather types based on the Lamb classification. Extreme and non-extreme events are defined as daily totals ≥40 mm and totals <40 mm and more than 1 mm respectively. The analyses are based on daily precipitation data from a data set consisting of 366 stations in Sweden, divided into 11 regions. The atmospheric conditions are described by daily National Centers for Environmental Prediction reanalysis data of various variables at nine levels (1000-200 hPa). The proportion of precipitation events that occurs during the cyclonic weather type is ∼45% for non-extreme events but increases to ∼70% for the extreme events. This can be related to higher mean vertical velocities for the cyclonic types compared with the directional and anticyclonic types, and higher mean specific humidity compared with the directional type.The atmospheric circulations of extreme and non-extreme events are compared through composites of wind and geopotential heights at 500 and 850 hPa. The significance of the differences in mean values of the atmospheric variables during the two types of event is examined using a two-tailed t-test. The composites of extreme events occurring during cyclonic weather types are characterized by a more pronounced ridge over western Russia at 500 hPa compared with the non-extreme events. For the directional weather type the flow at 500 hPa is, on average, basically zonal for the non-extreme events, whereas for the extreme events a ridge is positioned east of Sweden over Finland. Most of the variables analysed show significant differences in mean during extreme and non-extreme events, and, not surprisingly, specific humidity and vertical velocity can be regarded as the key variables when describing climatological differences between extremes and non-extremes. The extreme events are favoured by lower westerly wind flow and wind speed and stronger southerly winds compared with the non-extreme events.
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