-This paper presents a study of the diurnal variability of CO2 and CH4 concentrations in the surface layer of the atmosphere. The analysis takes into account meteorological parameters under urban and background conditions of the Middle Urals for 2022-2024 using cluster analysis. Statistically significant groups of measurements reflecting typical and elevated greenhouse gas levels throughout the day are identified. Based on numerous samples of hourly average measurements collected over a 24-h period, two statistically significantly different classes of diurnal variability in greenhouse gas concentrations are identified. These classes are characterized by typical and elevated levels in the surface air layer. The main daily CO2 concentrations for the cluster with typical variability were approximately 417.18 ppm at urban site and 425.51 ppm at the background site during summer and 437.84 and 432.83 ppm, respectively, in winter. For the cluster of elevated CO2 concentrations, mean daily values reached 433.74 ppm (urban) and 454.32 ppm (background site) in summer and 469.51 and 454.32 ppm, respectively, in winter. For CH4, typical mean daily concentrations were 2.06 ppm (urban) and 2.00 ppm (background site) in summer and 2.10 and 2.06 ppm, respectively, in winter. Elevated mean daily CH4 concentrations were observed at 2.14 ppm (urban) and 2.06 ppm (background site) in summer, reaching 2.29 ppm and 2.16 ppm, respectively, in winter. A combined analysis of CO2 and CH4 concentrations with meteorological parameters in the surface layer revealed the conditions for the formation of elevated greenhouse gas concentrations, contributing to improved climate forecasts for the Middle Urals region.
-The results of continuous monitoring of surface CH4 concentration using cavity ring-down spectroscopy at the Cape Baranov Ice Base high-latitude station in 2015-2020 are presented. The average annual values, interannual increase, seasonal variation amplitudes, and the day of occurrence of annual maximums and minimums of methane concentration are calculated. The obtained results are compared with the data from other high-latitude stations-Barrow, Alert, and Pallas-and show good agreement. The average annual values exhibit an almost continuous increase in the CH4 concentration at all considered stations. A significant increase in the average annual CH4 concentration growth rate was recorded in 2020 for the Cape Baranov Ice Base and Alert stations (21.63 +/- 0.10 and 18.29 +/- 0.08 ppb/year, respectively). At all stations but Alert, the second period of maximum CH4 concentration can be distinguished; it occurs at the beginning of autumn, probably due to the influence of tundra ecosystems and forest fires. The first period falls on the winter months. Using the HYSPLIT trajectory model, areas of potential regional methane source were identified for the region under consideration. These include swamped tundra areas of the Taimyr Peninsula and Eastern Siberia, possible gas hydrate accumulations, and areas with developed industry.
This paper describes a method for estimating the amplitude of seismogenic seiche oscillations in enclosed bodies of water on land. The method is based on an analytical solution to the problem within the framework of linear potential wave theory. For a rectangular channel filled with an ideal fluid and moving under the influence of an earthquake according to an arbitrary law in the horizontal direction, explicit expressions are obtained for calculating the displacement of the free surface of the fluid, the flow velocity field, and the potential and kinetic energy. The response of the fluid in the channel to a model “seismic” movement and to a real seismic event—the Chilean earthquake of September 16, 2015 (Mw = 8.2)—has been analyzed. The input data are horizontal velocity components recorded by four South American stations of the IRIS network which are located at epicentral distances from 10.3° to 40.5°. Simple formulas are proposed for estimating the amplitude of seiche oscillations based on seismic records and reservoir parameters.
Intense convective systems like super typhoons are major sources of atmospheric gravity waves, which can influence stratospheric ozone distribution. However, the general mechanism by which typhoon-induced gravity waves affect ozone has not been thoroughly investigated. This study presents a comparative analysis of two super typhoons, Lekima (2019) and Mawar (2023), during their oceanic phases to identify the common and repeatable patterns linking typhoon intensity, gravity wave activity, and ozone variability. High-frequency brightness temperature data from the Fengyun-4A (FY-4A) satellite are used to quantify gravity wave disturbances, while cloud-penetrating total column ozone (TCO) data from Aura/Microwave Limb Sounder (MLS) are employed to detect ozone anomalies relative to long-term averages. Results show that both typhoons triggered strong gravity wave activity, with peak disturbances aligning with periods of their rapid intensification. Notably, a consistent lagged ozone response was observed in both cases. Approximately 3–5 days after peak wave activity, TCO exhibited significant fluctuations. Time-series analyses reveal a quasi-symmetric “double-peak” pattern in ozone anomalies associated with each typhoon’s passage. This robust and repeatable pattern across two independent events provides compelling evidence that the lagged, “double-peak” ozone response induced by gravity waves is a fundamental feature of stratospheric modulation by intense marine typhoons.
The TerM Land Surface Model (LSM) is based on equations of the land surface block of the INMCM Earth system model. One of the main areas in developing the TerM model is improving the description of hydrological processes. This paper analyzes reproducing the dynamics of snow water storage in the TerM model in the northern part of the East European Plain. The modeled snow water storage is compared with the observational data from snow courses in open terrain (field landscapes). The dynamics of the snow water storage is reproduced using a model configuration with a resolution of 0.5 × 0.5°. The numerical experiments were performed with a time step of 3 h using observational data from weather stations as atmospheric forcing and ERA5 reanalysis data as radiation forcing. The possible sources of modeling errors are analyzed. Based on the results of the analysis, improvements are proposed for the TerM model in terms of the system of equations for snow cover dynamics, parameterization of snow cover roughness, and parameterization of the subgrid distribution of the snow cover. The improved version of the TerM model correctly reproduces the dynamics of the snow water storage in the area under study.
