In 2019, the southern region of Eastern Siberia (located between 45° N and 60° N) experienced heavy floods, while the northern region (between 60° N and 75° N) saw intense forest fires that lasted for almost the entire summer, from 25 June to 12 August. To investigate the causes of these natural disasters, we analyzed the large-scale features of atmospheric circulation, specifically the Rossby wave breaking and atmospheric blocking events. In the summer of 2019, two types of Rossby wave breaking were observed: a cyclonic type, with a wave breaking over Siberia from the east (110° E–115° E), and an anticyclonic type, with a wave breaking over Siberia from the west (75° E–90° E). The sequence of the Rossby wave breaking and extreme weather events in summer, 2019 are as follows: 24–26 June (cyclonic type, extreme precipitation, flood), 28–29 June and 1–2 July (anticyclonic type, forest fires), 14–17 July (both types of breaking, forest fires), 25–28 July (cyclonic type, extreme precipitation, flood), 2 and 7 August (anticyclonic type, forest fires). Rossby wave breaking occurred three times, resulting in the formation and maintenance of atmospheric blocking over Eastern Siberia: 26 June–3 July, 12–21 July and 4–10 August. In general, the scenario of the summer events was as follows: cyclonic Rossby wave breaking over the southern part of Eastern Siberia (45° N–60° N) caused extreme precipitation (floods) and led to low gradients of potential vorticity and potential temperature in the west and east of Lake Baikal. The increased wave activity flux from the Europe–North Atlantic sector caused the anticyclonic-type Rossby wave breaking to occur west of the area of a low potential vorticity gradient and north of 60° N. This, in turn, contributed to the maintenance of blocking anticyclones in the north of Eastern Siberia, which led to the intensification and expansion of the area of forest fires. These events were preceded by an increase in the amplitude of the quasi-stationary wave structure over the North Atlantic and Europe during the first half of June.

The correlation of precipitation anomalies over Eurasia with atmospheric blocking events was examined with ERA Interim reanalysis data. We found that, regardless of the frequency of the atmospheric blocking events, they significantly affect the distribution of rainfall over all Eurasian regions in summer, due to both the change in the westerly transport and the dominant dipole blocking structure. It is important that, depending on the blocking positions in Asia, there are heavy rainfalls in an arid zone which includes Kazakhstan, Mongolia, Northern China, and the Trans-Baikal Territory.

This paper studies the long-term variability of frequency of atmospheric blockings over Western Siberia in 1948–2015 based on the data of three reanalysis projects. We revealed a decreasing blocking...

Data from ERA5 reanalysis and observations at meteorological stations are used to estimate the spatiotemporal variations of extreme values of climatic and dangerous meteorological phenomena for the territory of Western Siberia over 1979–2020, and to identify the tendencies of their future variations during the late twenty-first century against the background of global climate change. The combined analysis of the derived estimates made it possible to identify the centers of “risk” in these areas and to determine the trends in their development. The air temperature is found to increase in the period 2011–2020 throughout Western Siberia, while the amount of precipitation and wind speed do so predominantly in its northern areas. Extreme precipitation amounts increase during summer in the entire area, except in mountainous areas in its southeastern part. In addition, areas affected by heavy rainfall are becoming smaller in the region and are located mainly in the south and along its western boundary. The results of the INM-CM5.0 model calculations revealed the response of the regional climate system to the ongoing global changes: the increasing trend in the number of extreme events in the region will persist until 2100.

We describe a new intermediate complexity model (PlaSim-ESM-ICMMG-v.1.0) constructed by coupling the Planet Simulator [1-2] with ocean and sea-ice models. We demonstrate the results of climate simulation using PlaSim-ICMMG-v.1.0 considering global fields of surface air temperature, precipitation, sea surface temperature, and ocean circulation, and make comparisons with the results obtained using the original PlaSim version. PlaSim-ICMMG-v.1.0 reproduces the main features of the climate system reasonably well and demonstrates that it is useful for climate system modeling. Due to the rapid warming in the Arctic, there are major challenges associated with the mechanisms that regulate the dynamics of weather in the mid-latitudes. The new earth's system model of intermediate complexity PlaSim-ICMMG-v.1.0 can be used to deal with these challenges.

A comparative statistical analysis of the spatiotemporal variability of atmospheric precipitation characteristics (mean and extreme values) in Western Siberia was performed based on data acquired from meteorological stations, global precipitation datasets such as the project of Asian Precipitation—Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) and from Global Precipitation Climatology Centre (GPCC), and reanalysis archives, including from National Centers of Environmental Prediction (NCEP-DOE) and the European Center for Medium Range Weather Forecasts (ERA5) for the period 1979–2018. The best agreement of the values from the observational data was observed with the values from GPCC. This archive also represented the periodicities in the time series of observational data from meteorological stations, especially in the short-period part of the spectrum. Underestimated values were revealed for the APHRODITE archive, while overestimated ones were found for the NCEP reanalysis data. In comparison with GPCC, the ERA5 dataset reproduced the general variability but with a smaller amplitude (the correlation coefficient was up to 0.9). In general, the median estimates of the precipitation amount derived from the meteorological stations’ data, as well from the reanalysis data, were in better agreement with each other rather than their extreme values. However, their temporal variability can be effectively described by other datasets.