Modern climatic changes in cloud cover over Russia are analyzed from standard ground-based meteorological observations in 1951-2000 and information on cloudiness obtained from the actinometrical observations in 1976-2000. In addition to studies of changes in total cloud amount, a special attention is paid to the distribution of basic forms of cloudiness with respect to their frequency. A spatial generalization of the results for large-scale regions (the European and Asian parts of Russia) allowed revealing dominant tendencies in cloud cover changes that are slightly related to seasons of year and reflect large-scale changes in cloud characteristics in the second half of the 20th century.

No abstract

Different means to estimate changes in fire-hazard conditions in the Russian forests are analyzed in connection with climate changes. Climate data are considered as based upon standard observations and calculated characteristics obtained from the atmosphere-ocean general circulation models. Space distributions are calculated of changes in extreme conditions for fire hazard at the end of the 21st century, as compared with those at the end of the 20th century.

No abstract

Possible changes in temperature extremes and wildfires in the 21st century in the regions of Russia are analyzed using a regional climate model (RCM) with high space-time resolution. The IPCC A2 scenario of greenhouse gas and aerosol concentration increase in the atmosphere is used. It is shown that changes in extreme wildfires appear to be in the 21st century most pronounced relative to changes in moderate and high wildfires. In some areas, along with decreased moderate and high wildfires, a significant increase in extreme wildfires is expected. Further studies should be associated both with RCM development, aimed at improving simulation of the wildfire atmospheric condition, and with enlarging the parameter set for wildfire analysis. Not only seasonal mean, but also monthly mean wildfire conditions should be studied, along with the use of statistical methods of model data interpretation to analyze intraseasonal and interannual variability of wildfire characteristics.