In this study, we evaluated the climatology and interannual variability of marine cold-air outbreaks (MCAOs) in the Russian Arctic marginal seas (from the Barents to Chukchi seas). We used a simple index for identifying MCAOs based on the vertical potential temperature gradient between the sea surface and the 800 hPa level. We calculated the index using 6-hourly Era-Interim data for the 1979–2018 period. Given the index, we evaluated spatial and temporal variability of weak, medium, and strong MCAOs frequency as well as their dependence on sea-ice concentration using non-parametric tests. The most intense MCAOs were found in the Barents and Kara seas. The annual cycle maximum for the western Russian Arctic (WRA) were found in wintertime, while it was revealed in mid-late autumn for the eastern Russian Arctic (ERA). In the WRA, we found a statistically significant decrease in amount of strong MCAOs in winter and late autumn and a general strengthening of MCAOs in spring. Meanwhile, over the ERA region, increase of moderate and weak cold-air intrusions during October and November was revealed.

Mesoscale convective systems (MCSs) and associated hazardous meteorological phenomena cause considerable economic damage and even loss of lives in the mid-latitudes. The mechanisms behind the formation and intensification of MCSs are still not well understood due to limited observational data and inaccurate climate models. Improving the prediction and understanding of MCSs is a high-priority area in hydrometeorology. One may study MCSs either employing high-resolution atmospheric modeling or through the analysis of remote sensing images which are known to reflect some of the characteristics of MCSs, including high temperature gradients of cloud-top, specific spatial shapes of temperature patterns, etc. However, research on MCSs using remote sensing data is limited by inadequate (in size) databases of satellite-identified MCSs and poorly equipped automated tools for MCS identification and tracking. In this study, we present (a) the GeoAnnotateAssisted tool for fast and convenient visual identification of MCSs in satellite imagery, which is capable of providing AI-generated suggestions of MCS labels; (b) the Dataset of Mesoscale Convective Systems over the European Territory of Russia (DaMesCoS-ETR), which we created using this tool, and (c) the Deep Convolutional Neural Network for the Identification of Mesoscale Convective Systems (MesCoSNet), constructed following the RetinaNet architecture, which is capable of identifying MCSs in Meteosat MSG/SEVIRI data. We demonstrate that our neural network, optimized in terms of its hyperparameters, provides high MCS identification quality ($\mathrm{mAP}=0.75$, true positive rate $\mathrm{TPR}=0.61$) and a well-specified detection uncertainty (false alarm ratio $\mathrm{FAR}=0.36$). Additionally, we demonstrate potential applications of the GeoAnnotateAssisted labelling tool, the DaMesCoS-ETR dataset, and the MesCoSNet neural network in addressing MCS research challenges. Specifically, we present the climatology of axisymmetric MCSs over the European territory of Russia from 2014 to 2020 during summer seasons (May to September), obtained using MesCoSNet with Meteosat MSG/SEVIRI data. The automated identification of MCSs by the MesCoSNet artificial neural network opens up new avenues for previously unattainable MCS research topics.

The Arctic experiences remarkable changes in environmental parameters that affect fluctuations in the surface energy budget, including radiation and sensible and latent heat fluxes. Cold air masses and cloud transformations during marine cold air outbreaks (MCAOs) substantially influence the radiative fluxes, thereby shaping the link between large-scale dynamics, sea ice conditions, and the surface energy budget. In this study, we investigate various cloud characteristics during intense MCAOs over the Barents Sea from 2000 to 2018 using satellite data. We identify 72 intense MCAO events that propagated southward using reanalysis data of the surface temperature and potential temperature at the 800 hPa level. We investigate the macro- and microphysical parameters and radiative properties of clouds within selected MCAOs, their dependence on sea ice concentration, and their initial air mass properties using satellite data. A significant increase in low-level clouds near the ice edge (up to +25% anomalies) and a smooth transition to upper-level clouds is revealed. The total cloud top height during intense MCAOs is generally 500–700 m lower than under neutral conditions. MCAOs induce a positive net cloud radiative effect, which peaks at +20 W m⁻² (100 km from the ice edge) and gradually decreases towards the continent (−2.3 W m⁻² per 100 km). Our study provides evidence for the importance of changes in the cloud radiative effect within MCAOs, which should be accurately simulated in regional and global climate models.

The paper presents a comparative analysis of cloud properties based on regional model data and satellite observations over the Arctic (north of 65N). We estimate cloud fraction, cloud liquid, ice and total water path. We use model data from the Arctic-CORDEX regional models and CERES satellite data. The analysis is performed for cold and warm seasons. Cloud variables are calculated for different sea ice conditions depending on sea ice concentration (SIC), namely for open water (SIC<5%), transient sea ice concentration (5%<SIC95%). It is found that a number of models significantly overestimate the total cloud fraction (TCF) and tend to underestimate cloud liquid and ice water paths (CLWP and CIWP) compared to satellite observations. At the same time, while the models show a substantial difference for CLWP and CIWP values, they are in good agreement for surface downward longwave radiation (DLR). Models show much stronger interannual variability (both during cold and warm seasons, both for shortwave and longwave radiation fluxes, and both over ice and over water) than the observations. The largest biases are found for TCF over the solid sea ice during the winter season. The best agreement for both cloud and radiative properties was obtained for open water during the summer season.