The northern Eurasian regions and Arctic Ocean will very likely undergo substantial changes during the next decades. The Arctic–boreal natural environments play a crucial role in the global climate via albedo change, carbon sources and sinks as well as atmospheric aerosol production from biogenic volatile organic compounds. Furthermore, it is expected that global trade activities, demographic movement, and use of natural resources will be increasing in the Arctic regions. There is a need for a novel research approach, which not only identifies and tackles the relevant multi-disciplinary research questions, but also is able to make a holistic system analysis of the expected feedbacks. In this paper, we introduce the research agenda of the Pan-Eurasian Experiment (PEEX), a multi-scale, multi-disciplinary and international program started in 2012 (https://www.atm.helsinki.fi/peex/). PEEX sets a research approach by which large-scale research topics are investigated from a system perspective and which aims to fill the key gaps in our understanding of the feedbacks and interactions between the land–atmosphere–aquatic–society continuum in the northern Eurasian region. We introduce here the state of the art for the key topics in the PEEX research agenda and present the future prospects of the research, which we see relevant in this context.

The observed ozone layer depletion is influenced by continuous anthropogenic activity. This fact enforced the regular ozone monitoring globally. Information on spatial-temporal variations in total ozone columns (TOCs) derived by various observational methods and models can differ significantly due to measurement and modelling errors, differences in ozone retrieval algorithms, etc. Therefore, TOC data derived by different means should be validated regularly. In the current study, we compare TOC variations observed by ground-based (Bruker IFS 125 HR, Dobson, and M-124) and satellite (OMI, TROPOMI, and IKFS-2) instruments and simulated by models (ERA5 and EAC4 re-analysis, EMAC and INM RAS—RSHU models) near St. Petersburg (Russia) between 2009 and 2020. We demonstrate that TOC variations near St. Petersburg measured by different methods are in good agreement (with correlation coefficients of 0.95–0.99). Mean differences (MDs) and standard deviations of differences (SDDs) with respect to Dobson measurements constitute 0.0–3.9% and 2.3–3.7%, respectively, which is close to the actual requirements of the quality of TOC measurements. The largest bias is observed for Bruker 125 HR (3.9%) and IKFS-2 (−2.4%) measurements, whereas M-124 filter ozonometer shows no bias. The largest SDDs are observed for satellite measurements (3.3–3.7%), the smallest—for ground-based data (2.3–2.8%). The differences between simulated and Dobson data vary significantly. ERA5 and EAC4 re-analysis data show slight negative bias (0.1–0.2%) with SDDs of 3.7–3.9%. EMAC model overestimates Dobson TOCs by 4.5% with 4.5% SDDs, whereas INM RAS-RSHU model underestimates Dobson by 1.4% with 8.6% SDDs. All datasets demonstrate the pronounced TOC seasonal cycle with the maximum in spring and minimum in autumn. Finally, for 2004–2021 period, we derived a significant positive TOC trend near St. Petersburg (~0.4 ± 0.1 DU per year) from all datasets considered.

The application of the radiative data inversion technique based on artificial neural networks (ANN) for the meteorological satellite sounding of the atmosphere is described. To increase the...

For the first time in Russia, using ground-based measurements of direct solar infrared radiation, we derived data on ozone content in different layers of the atmosphere. The measurements were conducted with the help of a Bruker IFS-125HR Fourier spectrometer in 2009–2012 in Petergof, which is 30 km west of the center of St. Petersburg. The errors in determining the ozone content by this method in the troposphere (0–12 km), in the stratosphere (12–50 km), in the layers of 10–20 and 20–50 km, and in the layers of 12–18, 18–25, and 25–50 km were ~4, 3, 3–5, and 4–7% (taking into account the instrumental and methodological errors, as well as the errors in specifying the temperature profile), respectively. The seasonal variation of tropospheric ozone content in the layer of 12–18 km is characterized by a clearly expressed maximum in March and a minimum in November, with amplitudes of 30 and 40%, respectively. For the layer of 18–25 km, the maximum and minimum are reached in the winter-spring period and late summer, respectively; the amplitude of the seasonal variation is ~20%. The amplitude of the annual variation in ozone content in the layer of 25–50 km is around 30%, with a maximum close to the summer solstice and a minimum close to the winter solstice. Over the three years of observations, the growth in the ozone content in this layer was ~10% per year of its value averaged over the time period. Comparisons of ground-based measurements with satellite measurements (by the IASI instrument) of tropospheric ozone revealed a discrepancy of (3.4 ± 17)% for both ensembles. The correlation between the two ensembles is 0.76–0.84 (depending on the season). Comparisons between ground-based and satellite measurements (by the MLS instrument) of stratospheric ozone revealed no systematic discrepancies of the two ensembles. The rms errors were 13, 6, and 5% for the layers of 10–20, 20–50, and 10–50 km, respectively; the coefficients of correlations between the two types of measurements were 0.82–0.94.

We present a joint analysis of data obtained by Fourier transform infrared measurements of CH₄ and EMAC model calculations for Petergof station (St. Petersburg State University) in 2009–2012. The systematic differences between observed and calculated data are 1.3% and 0.3% for the values of total column and atmospheric column-averaged mole fraction of methane, respectively. The high correlation for experimental and model data of the total column (r = 0.8) indicates that EMAC reproduces the total variability of the methane total column in the atmosphere due to meteorological processes. Using model data, we have analyzed the effect of meteorological conditions typical for Fourier transform IR observations on the resulting estimates of the mean values of total column and column-averaged mole fraction of CH4. We have shown that there can be systematic shifts (up to ~0.4%) in the experimental estimates for the mean value relative to the “true” value. This fact should be taken into account in comparing the climatological or model data with the results of Fourier transform IR measurements, especially for stations with a relatively small number of observation days.