Climate

The Russian Far East is one of the regions of the country with the least investigated processes affecting the air quality and related climate changes of the region. In the current study 3D numerical modeling (WRF-Chem) together with the ground- and satellite-based observation data of the particular atmospheric pollutants (NO₂, CO, SO₂, O₃, aerosols) were applied to demonstrate how wildfires and transboundary pollution from China could influence air quality in the Far East of Russia (with focus on the Amur region) in July 2015 and January 2023. The WRF-Chem modeling system represents a near-surface air temperature with bias (compared to observations) of 0.5–2 °C and standard deviation, or STD, of 2–5 °C. In general the model overestimates near-surface wind speed—the bias varies in the range 0.8–1.9 m/s with STD of ~2 m/s. This fact should affect the model performance of near-surface gaseous and aerosol composition. Robust Pearson correlation coefficient (from ~0.5) in both periods was found only between modeled and observed near-surface NO₂ and CO. Significant correlation for O₃ (0.73) was found only in January. By using WRF-Chem regional modeling it was demonstrated that seasonal wildfires in the northern Amur region, Zabaykalsky Krai, and the Republic of Yakutia (July 2015) and transboundary pollution from northeastern China (January 2023) could cause the degradation of air quality in the Amur region. Additionally, the possible effect on air quality from the domestic anthropogenic emissions of the Amur region was found in January 2023. According to the modeling, in July 2015 monthly mean NO₂ concentration higher than state standards was found in the territory of the Amur region. The highest monthly mean near-surface NO₂ concentrations exceeding state standards were modeled in northeastern China (~0.05 ppm). The modeled concentrations of other pollutants in the Russian Far East fit the state norms in both periods. The effect of wildfires and transboundary pollution episodes on air quality in the Russian Far East can be considered for the evaluation in the future state air quality reports.

Isoprene (C₅H₈), the most abundant biogenic volatile organic compound (400–600 Tg C yr⁻¹), exerts complex NO_x-dependent influence on tropospheric ozone, yet its representation remains absent in many climate models. This study aims to quantify isoprene’s impact on tropospheric chemical composition using the Russian Earth system model INM-CM6.0 with newly implemented isoprene oxidation chemistry. Two 12-year experiments (2008–2019) were conducted: a control run without isoprene and an experiment with the Mainz Isoprene Mechanism (MIM1: 44 reactions, 16 species). Results reveal a NO_x-dependent two-layer vertical structure. In the tropical surface layer (0–5 km, 20° S–20° N), ozone decreases by 10–15 ppb through radical termination under low-NO_x (<100 ppt), with 15–30% OH reduction and 30–60% CO increase. In the middle troposphere (8–12 km), ozone increases by 10–15 ppb through thermal decomposition of vertically transported PAN and MPAN. In subtropics (20–35°) with elevated NO_x (>500 ppt), isoprene stimulates ozone formation at all altitudes (+3–12 ppb). Oxidation product distributions establish a spatial hierarchy: local (ISON, NALD: 0–5 km), regional (MPAN: to 8 km), and global (PAN: reaching high latitudes at 8–12 km). Comparison with CAMS, MERRA-2, and ERA5 reanalyses shows substantial improvement: tropical CO discrepancies decrease from 20–30% to 10–15%, OH by factors of 2–3, and ozone overestimation from 30–40% to 10–15%. These findings demonstrate that explicit isoprene chemistry is essential for accurate tropospheric composition simulation, particularly given the projected 21–57% emission increases by 2100 under climate warming.

The aim of this study is to quantify the impact of increased surface solar radiation on climate warming in Central Europe from 1915 to 2024 and to examine the relationship between CO₂ air concentrations and emissions on a global scale with an empirical approach. A statistical model with proxies for short-wave and long-wave radiation (sunshine duration SSD and CO₂ concentration) as independent variables and surface air temperature as the dependent variable was tested for validity and significance, and the results were presented for six long-term measuring stations in Central Europe. The statistical model fulfilled all tests (error probability, normal distribution of the residuals, autocorrelation, statistical power, multicollinearity) and showed that the increase in SSD in the entire year accounts for around 20% of the warming over the last 100 years; in the summer half-year (April–September) and summer (June–August) it is around 30%. The increase in CO₂ concentration accounts for the remainder portion of warming of 70–80%. Studies and models neglecting the influence of the increase in surface solar radiation are overestimating the influence of GHG on warming. A lifetime concept for CO₂ was evaluated empirically for the last 130 years: the development of CO₂ air concentration from industrialization until today can be mapped very well with a lifetime of 58 years. With this lifetime, reducing annual CO₂ emissions by around half would stabilize CO₂ concentrations. The contrasting concept of cumulative CO₂ emissions, according to which stabilization can only be achieved with ‘zero emissions’, is discussed in this context.