Projections for near-surface soil moisture content in Europe for the 21st century were derived from simulations performed with 26 CMIP5 global climate models (GCMs). Two Representative Concentration Pathways, RCP4.5 and RCP8.5, were considered. Unlike in previous research in general, projections were calculated separately for all four calendar seasons. To make the moisture contents simulated by the various GCMs commensurate, the moisture data were normalized by the corresponding local maxima found in the output of each individual GCM. A majority of the GCMs proved to perform satisfactorily in simulating the geographical distribution of recent soil moisture in the warm season, the spatial correlation with an satellitederived estimate varying between 0.4-0.8. In southern Europe, long-term mean soil moisture is projected to decline substantially in all seasons. In summer and autumn, pronounced soil drying also afflicts western and central Europe. In northern Europe, drying mainly occurs in spring, in correspondence with an earlier melt of snow and soil frost. The spatial pattern of drying is qualitatively similar for both RCP scenarios, but weaker in magnitude under RCP4.5. In general, those GCMs that simulate the largest decreases in precipitation and increases in temperature and solar radiation tend to produce the most severe soil drying. Concurrently with the reduction of time-mean soil moisture, episodes with an anomalously low soil moisture, occurring once in 10 years in the recent past simulations, become far more common. In southern Europe by the late 21st century under RCP8.5, such events would be experienced about every second year.

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Abstract.Values of the scavenging coefficient determined from observations of ultrafine particles (with diameters in the range 10-510 nm) during rain events at a boreal forest site in Southern Finland between 1996 and 2001 were reported by Laakso et al. (2003a). The estimated range of the median values of the scavenging coefficient was [7×10 −6 −4×10 −5 ] s −1 , which is generally higher than model calculations based only on below-cloud processes (Brownian diffusion, interception, and typical phoretic and charge effects).In the present study, in order to interpret these observed data on scavenging coefficients from Laakso et al. (2003a), we use a model that includes below-cloud scavenging processes, mixing of ultrafine particles from the boundary layer (BL) into cloud, followed by cloud condensation nuclei activation and in-cloud removal by rainfall. The range of effective scavenging coefficient predicted by the new model, corresponding to wide ranges of values of its input parameters, are compared with observations. Results show that ultrafine particle removal by rain depends on aerosol size, rainfall intensity, mixing processes between BL and cloud elements, in-cloud scavenged fraction, in-cloud collection efficiency, and in-cloud coagulation with cloud droplets.The scavenging coefficients predicted by the new model are found to be significantly sensitive to the choice of representation of: (1) mixing processes; (2) raindrop size distribution; (3) phoretic effects in aerosol-raindrop collisions; and (4) cloud droplet activation. Implications for future studies of BL ultrafine particles scavenging are discussed.

ABSTRACT:The annual and seasonal mean temperature of Finland was calculated for 162 years based on spatially interpolated monthly mean temperature records. The spatial interpolation method, known as kriging, was used with the following forcing parameters: the geographical coordinates, elevation of the terrain, and percentage share of lakes and sea. Homogenised data was used, and thus the most important factor affecting the accuracy of the interpolated data was the uneven distribution of the available observation stations both in time and space. The uncertainty due to the homogenisation adjustments made earlier was notably smaller. In the mid-1800s, the uncertainty in the annual and seasonal mean temperatures was large, with a maximum in winter of over ±2.0°C, but the accuracy improved quickly with time as the number of the observation stations increased. At the beginning of the 20th century, the uncertainty related to the limited station network was less than ±0.2°C, in winter less than ±0.4°C. According to the data, the rise in Finland's annual mean temperature has been statistically significant during the last 100, 50 and 30 years. During the last 100 years the increase in the mean temperature was largest during spring, but during the last 50 years winters have warmed up the most. The temperature time series obtained are compatible with grid point values picked from the global temperature data grids starting from the 1880s, though the global data sets tend to smooth the extremes.