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This paper discusses some results of the study of eddy heat fluxes in the vicinity of a subtropical jet stream. Many large-scale dynamical phenomena in the Earth’s atmosphere are associated with Rossby wave propagation and collapse processes. Here we focus on regions of countergradient eddy heat fluxes in the region of the subtropical jet stream in the Northern Hemisphere associated with Rossby wave overturning. In these regions, we observe meridional energy transfer on the northern flank of the jet stream in the equatorial direction from the ERA-5 reanalysis data and simulation data with the INM-CM4-8 climate model of the Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences (INM RAS). The entropy production due to horizontal heat transfer becomes negative, because heat is transferred against the temperature gradient, but this is not a violation of the second law of thermodynamics, since the main part of entropy production occurs due to the processes of vertical heat transfer, such as convection, and other irreversible processes. Entropy production is sensitive to land cover, the entropy balance being most related to radiation at the surface. Quantifying the thermodynamic balance of entropy and entropy production is a useful metric for evaluating the interactions of the atmosphere–surface system. Some estimates of entropy production by the surface are presented in this paper. The traditional approach to studying the climate system focuses on the dynamic mechanisms and physical processes responsible for the conversion of energy from one form to another, but an approach based on analyzing the entropy balance of the climate system and especially entropy production is also important.

The processes of planetary wave breaking (Rossby Wave Breaking – RWB) significantly contribute to variability in stratospheric circulation. Employing a previously developed method for identifying RWB, adapted for stratospheric circulation, this study analyzes the climatology and long-term variability of RWB processes in the middle stratosphere. The method is based on the analysis of potential vorticity (PV) contour geometry at the 850-K level using ERA5 data within the PV range 0–400 PVU (Potential Vorticity Units) determined based on PV field climatology. It was demonstrated that RWB processes exhibit intraseasonal peculiarities. Most frequently, waves break in the northern regions of East Asia and the Pacific Ocean from October to December and in April to March. In January and February, no areas with prevailing RWB processes were identified. We obtained a statistically significant increase in the number of RWB for the first half of winter (October–December) and for the end of the winter period (March and April). For midwinter (January and February), insignificant negative trends were obtained. The results of this work can be used to analyze the long-term variations in stratospheric circulation and, in particular, the occurrence of stratospheric anomalies preceding sudden stratospheric warmings.

The paper describes the configuration of the COSMO-Ru6Sib system with a horizontal grid spacing of 6.6 km adapted to regional conditions. The COSMO-Ru2Sib configuration with a horizontal grid spacing of 2.2 km is also considered. The results of modifying this configuration using weather radar data assimilation as well as urban canopy parameterization are presented and discussed. The results of numerical experiments for the COSMO-Ru6Sib with the COSMO and ICON-LAM models as compute kernels are also presented.

This paper presents results of numerical estimates of vertical temperature profiles in the COSMO and ICON NWP models. Data from two vertical profilers, an MTP-5 microwave temperature profiler and a classic AVK-type radiosonde, are used for the estimates. Mean errors, correlation coefficients, and root-mean-square errors are calculated for different seasons. The simulation results are analysed depending on configurations of the horizontal resolution of the models and between the models. The estimates are plotted, and comparisons are made between the models using each of the measurement types: between different measurements for each model. The response to spatial variability in the measurements and model grid cells is assessed. Conclusions are formulated by comparing the quality of reproduction of the atmospheric boundary layer between the models and the model configurations. The findings provide useful information for the interpretation of modeling results by forecasters.

The changes in the response of air temperature to the variations in the instantaneous blocking frequency (IBF) ( ) between the periods 1979–1999 and 2000–2020 are studied. Blocking patterns, which are the spatial distributions of the coefficients of correlation between the IBF and the 1000 hPa temperature field, are used as the main characteristic. The blocking frequency is calculated in ten longitudinal sectors in the eastern direction from the west of the North Atlantic region to the eastern borders of Siberia and the Pacific Ocean. It is found that blocking patterns moved northward over the Atlantic region (November, December, and February), the Ural region and Siberia (November, January, and February) in 2000–2020. In addition, they were characterized by more pronounced areas of negative correlations over Eurasia and North America as compared to 1979–1999. Along with the pronounced correlation pattern of the Atlantic region, the patterns of the Ural region and Western Siberia (U-WS) proved to be the most significant. It is assumed that the IBF variability over the U-WS sector is an important indicator of the development of the most significant blocking processes over Eurasia in terms of the temperature regime anomalies. The influence of U-WS blockings was the least noticeable in 2000–2020 for December and in 1979–1999 for January, when the response of the temperature field to the blocking changes was similar to the response of the negative phase of the North Atlantic Oscillation.

This paper presents preliminary results of the effectiveness analysis of an air quality forecasting system for the city of Novosibirsk with replenishment of the missing information on emission sources by solving an inverse problem with urban monitoring network data. In solving the inverse problem, a priori information about the location and mode of the sources is used. To simulate concentration distributions, the WRF-Chem model is used, and a simplified model of chemical transport is applied to solving the inverse problem. These models are offline coupled in a hybrid forecast system in order to improve the initial information about the spatial distribution of emission intensity and air quality forecast, respectively. The results of numerical experiments and their analysis are presented. The influence of an urban parameterization on the results of the forecast is shown.

Three-dimensional transport and transformation models make it possible to take into account the vertical heterogeneity of atmospheric processes. However, their use requires setting a large number of parameters and significant computing resources, especially when solving inverse and data assimilation problems. A new data assimilation algorithm for a three-dimensional transport and transformation model with unknown emission sources is presented. The algorithm uses an approach based on sensitivity operators and ensembles of solutions of adjoint equations implemented in the IMDAF inverse modeling system for distributed memory computers. When tested in a realistic Baikal region scenario, the algorithm, based on the data of integrated vertical measurements simulating remote sensing data, enabled reducing the error in the concentration field by 15%. With the given vertical level of the source location, the errors in the concentration field and in the source were reduced by 93% and 85%, respectively.

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.