Based on the data of long-term instrumental observations of precipitation, the average long-term indicators (norms) and linear trends for the period 1966-2021 were calculated. Maps of the distribution of precipitation anomalies for each month and season were constructed. Linear trends of precipitation anomalies were calculated for the entire territory of Russia as well as for individual physical and geographical regions, such as the European part of Russia, Western Siberia, and the Far East. Based on the wavelet and cross-wavelet analysis, characteristic cycles of variability of atmospheric fluctuations were revealed and close connection and the coherence of fluctuations of atmospheric fluctuations with dangerous climatic indices were determined.
Based on the data of long-term instrumental observations of surface atmospheric pressure, the average long-term indicators (norms) and linear trends for the 1950-2021 period were calculated. Maps of the distribution of atmospheric pressure anomalies for each month and seasons were constructed. Linear trends of atmospheric pressure anomalies were calculated for the whole territory of Russia as well as for individual physical and geographical regions, such as the European part of Russia, Western Siberia and the Far East. For the European region, the Far East and the territory of Russia as a whole, a positive linear trend for surface atmospheric pressure was established. For Western Siberia this trend was found to be negative. Significant deviations of real atmospheric pressure from the calculated norm for 1950- 2019 were established for the entire territory of Russia in 1968, 1978, 1996, 2001, 2006, 2014, 2016 and 2020.
The present work is a continuation of an interdecadal climate change study based on the surface data from meteorological stations in the South Urals. Mean monthly air temperature and monthly atmospheric precipitation data from 17 stations covering 1936-2019 were used to determine anomalies' variability. The results show that positive temperature anomalies prevail in January, May, July, September, November, and December. Negative temperature anomalies are more common for February, March, April, June, August, October, specific seasons, and annual air temperature anomalies. An equal number of anomalies of both signs are characteristic for the annual precipitation, namely negative anomalies at most meteorological stations prevail in the winter season, positive anomalies outweigh in the summer season.
Local climate changes differ significantly from global trends and require tailored mitigation practices. Here, we analyze the surface temperature data from the network of meteorological stations in the Southern Urals and the numerical experiments over the period from 1979 to 2012. The isolated greenhouse effect was evaluated using the global atmospheric general circulation model (ECHAM5). Simulations with constant concentrations of CO2 and CH4 at the level of the 1980s describe the real values of surface temperature better than scenarios in which the concentration of greenhouse gases changed according to observations.
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