This study is devoted to the problem of statistical downscaling of summer precipitation using the example of the Moscow region. We propose an approach to downscaling the local maximum of daily precipitation amounts within the region using machine learning (ML) models based on predictors characterizing large-scale meteorological processes. The study is based on 33 years of daily maximum precipitation data collected from 27 weather stations in the Moscow region used for ML model training and evaluation. We propose a set of physically justified precipitation predictors from ERA5 reanalysis (averaged over the region) including basic atmospheric variables (temperature, humidity, etc.) at different vertical levels as well as more complex indices characterizing convective instability, wind shear, humidity and circulation indicators. We evaluate three different ML models and several configurations of the feature selection and processing. The gradient boosting model with the daily averaged, standardized predictor set, including the reanalysis precipitation, demonstrated the best quality, with RMSE of 8.47 mm and R 2 of 0.6. The feature importance analysis revealed that the mean precipitation from reanalysis as well as several complex indices are the key influencing factors for precipitation maxima.

Observations of turbulent heat and momentum fluxes over urban landscapes are essential for understanding urban climate processes and improving meteorological models. However, flux data remain limited for cities in continental temperate and subarctic regions, where urbanization patterns and climatic conditions differ significantly from more studied urban areas.In this study, we present the process of data collection, processing, and database formation for urban heat and momentum fluxes in Tomsk, Western Siberia. The data are obtained from the regional eddy-covariance network Tomskfluxnet, which has been under development since 2022. The network includes two urban masts installed in different built environments and several rural sites, allowing for comparative urban-rural analysis of surface-atmosphere interactions.Measurements are conducted using Russian-made AMK-4 sonic anemometers, developed by the Institute of Monitoring of Climatic and Ecological Systems in Tomsk. The collected data undergoes strict quality control, calibration, and systematic processing to ensure accuracy and reliability. The resulting database is designed to support long-term studies on urban climate dynamics, energy balance variations, and the impact of anthropogenic activities on atmospheric processes in Siberian cities.This presentation will provide a detailed description of the data acquisition system, processing methodology, and database structure, emphasizing the challenges and solutions in monitoring heat fluxes in extreme climatic conditions.The study was supported by Russian Science Foundation Project no. 24-27-00300.

Urban green infrastructures (UGI) contribute to a quality of life in cities by provisioning cultural and regulating ecosystem services (ES). Planning and management of UGI needs transparent and spatially explicit indicators of the ES supply. In this study, we have used open-source geospatial data to quantify regulating (air pollution/noise and stormwater runoff regulation, climate mitigation), and cultural (aesthetic values, recreational activities, observation of flora and fauna) ES at three spatial levels within the Moscow megacity (municipal areas (districts), residential areas (quarters), and regular grid). For five regulating and two cultural ES we have developed six physically measurable indicators directly related to potential ES supply and an aggregated UGI index to reflect the cumulative impact of the investigated ES. The proposed UGI index has several important advantages: 1) it considers multiple scales; 2) it relies on CICES classification of ES, including regulating and cultural services; and 3) it is mainly based on open data sources. The developed approach allowed to explore spatial variation in UGI ES and to identify hotspots on the city map where these ES were undersupplied. The outcomes were verified thought comparison with other indices and rankings existing for Moscow. The revealed spatial disparity in UGI ES supply in the Moscow megacity (e.g., differences between central districts and suburbs or neighborhoods with higher and lower accessibility of the nearest urban forest) challenges the city’s environmental strategy, which aims to provide an equal access to green spaces for all citizens. To achieve this goal, several measures were proposed to reach at least half of the potential ES supply at the level of areal units.

This paper presents a technique for measuring the temperature of an inhomogeneous underlying surface using unmanned aerial vehicles (UAVs). To test the proposed technique, measurements over various landscapes are presented: dunes in an arid zone, a temperate swamp, a subarctic city, and a combination of natural and anthropogenic landscapes in the Arctic. A measuring complex based on a DJI Mavic 2 Zoom quadrocopter with a Flir TAU 2R thermal camera is used. Methods for correcting emerging hardware errors and artefacts have been developed. To obtain detailed data on the spatial distribution of the surface brightness temperature, the orthomosaic construction method is used. Thermal maps of surfaces with surface height inhomogeneities (dunes), moisture inhomogeneity (swamps), and urban areas in polar and subpolar conditions are obtained at different times of the day. It is shown that thermal contrasts can reach the first tens of degrees Celsius within an area of 10–20 ha, both under the background of daytime heating and nighttime cooling of the surface, and could have a significant effect on the spatial distribution of the heat transfer characteristics of the atmosphere and the underlying surface. These methods are recommended for constructing surface thermal maps using thermal imaging technology.

Urban areas are vulnerable to climate and weather extremes, including severe convective events. However, large cities can themselves intensify such events, although the magnitude of this intensification remains uncertain. In this study, we quantify the influence of Moscow on severe convective events by using simulations with the convection-permitting COSMO-CLM model with 1-km resolution for multiple summers for 2007-2016 and analyzing the difference between simulations with and without the urban canopy parameterization (URB and noURB, respectively). We found a significant difference between these simulations for extreme precipitation and wind characteristics. Within the city boundary, the difference is about 20% for 30 mm daily precipitation events and 70% for 50 mm events. Strong wind events are twice as frequent in the URB simulation. The urban canopy significantly increases the magnitude and variability of extreme quantiles for vertical velocities. We found more than a 50% increase in compound events with both wind and precipitation exceeding their 0.99 quantiles and in events with the 2-5 km updraft helicity exceeding critical values, which serve as a good proxy for mesocyclone formation. Our findings may help to refine the population bias and can be used to specify the predicted changes in convective hazards in mid-latitude cities.

Cities are highly interconnected systems where specific interactions between various urban environments occur due to the Urban Heat Island (UHI) and Urban Pollution Island (UPI) effects. Four compartments of the environment (atmospheric air, road dust, streamflow, and people) are discussed for Moscow city. Long-term meteorological, radiative, air quality, and precipitation measurements, the non -hydrostatic regional numerical COSMO model, and extensive hydrological and geochemical sampling were used. To characterize mortality and UPI interaction, a family of distributed lag non-linear models (DLNM) was applied. The study reveals increased aerosols concentrations which reduce the incoming solar radiation and increase the atmospheric longwave radiation. UHI strengthens the low -troposphere convergence due to urban breeze circulation and atmospheric circulation due to elevated surface roughness, the effect which leads to 11.6% heaviest precipitation increase compared to background values. Increased precipitation doubles streamflow rates and enhances the contribution of rain floods to annual flow. Similar geochemical associations with Sb, W, Zn, Cd, Pb, and Cu were found in aerosol PM 10 , indicating transport and road dust impact. Finally, associations between high temperatures and human mortality which are generally stronger at high levels of air pollution for both PM 10 and NO 2 , and for lag 1 day and 2-6 days are discussed.

Urban canopy models (UCMs) are widely used to parameterize surface-atmosphere interaction in weather and climate models. The physical processes described by UCMs include turbulent exchange and wind patterns within the urban canopy. This study compares several parameterizations of the vertical mean wind profile used in UCMs against simulations of the turbulent flow over an inhomogeneous urban-like surfaces with direct numerical simulation (DNS) model. The configurations of urban surface morphology are considered in a simplified form (columns, rows, and blocks) with different height, frontal area index and aspect ratio. The possibility of applying the considered approaches to wind profile parameterizations in single-layer urban canopy models are discussed. Evaluation showed that parameterizations which utilize more accurate turbulent length scales’ description and explicitly involve buildings density metrics are shown to be applicable for a wider range of urban canopy geometries.

The study presents the first results from the multi-platform observational campaign carried out at the Mukhrino peatland in June 2022. The focus of the study is the quantification of spatial contrasts of the surface heat budget terms and methane emissions across the peatland, which arise due to the presence of microlandscape heterogeneities. It is found that surface temperature contrasts across the peatland exceeded 10 °C for clear-sky conditions both during day and night. Diurnal variation of surface temperature was strongest over ridges and drier hollows and was smallest over the waterlogged hollows and shallow lakes. This resulted in strong spatial variations of sensible heat flux (H) and Bowen ratio, while the latent heat varied much less. During the clear-sky days, H over ryam exceeded the one over the waterlogged hollow by more than a factor of two. The Bowen ratio amounted to about unity over ryam, which is similar to values over forests. Methane emissions estimated using the static-chamber method also strongly varied between various microlandscapes, being largest at a hollow within a ridge-hollow complex and smallest at a ridge. A strong nocturnal increase in methane mixing ratio was observed and was used in the framework of the atmospheric boundary layer budget method to estimate nocturnal methane emissions, which were found to be in the same order of magnitude as daytime emissions. Finally, the directions for further research are outlined, including the verification of flux-aggregation techniques, parameterizations of surface roughness and turbulent exchange, and land-surface model evaluation and development.

Abstract Urban Land Surface Models (ULSMs) simulate energy and water exchanges between the urban surface and atmosphere. However, earlier systematic ULSM comparison projects assessed the energy balance but ignored the water balance, which is coupled to the energy balance. Here, we analyze the water balance representation in 19 ULSMs participating in the Urban‐PLUMBER project using results for 20 sites spread across a range of climates and urban form characteristics. As observations for most water fluxes are unavailable, we examine the water balance closure, flux timing, and magnitude with a score derived from seven indicators expecting better scoring models to capture the latent heat flux more accurately. We find that the water budget is only closed in 57% of the model‐site combinations assuming closure when annual total incoming fluxes (precipitation and irrigation) fluxes are within 3% of the outgoing (all other) fluxes. Results show the timing is better captured than magnitude. No ULSM has passed all water balance indicators for any site. Models passing more indicators do not capture the latent heat flux more accurately refuting our hypothesis. While output reporting inconsistencies may have negatively affected model performance, our results indicate models could be improved by explicitly verifying water balance closure and revising runoff parameterizations. By expanding ULSM evaluation to the water balance and related to latent heat flux performance, we demonstrate the benefits of evaluating processes with direct feedback mechanisms to the processes of interest.

Heat vulnerability in big cities is important because of the increase in heat wave frequency and thermal stress that is identified by Urban Heat Island. Our study investigated intra-urban heat vulnerability in Moscow, which strongly influenced by historic context in urban planning, with a focus on local disparities. We considered the vulnerability framework in terms of “exposure,” “sensitivity,” and “adaptive capacity,” and adopted the concept of a 15-minute city to evaluate spatial patterns on example of 2021 heat waves. We used high-resolution meteorological data from the regional meteorological model COSMOCLM and calculated the Physiologically Equivalent Temperature (PET) to assess thermal stress and define exposure. The data from OSM and other open sources were used to assess sensitivity and adaptive capacity through the proximity of green spaces, cooling centers, healthcare, and premium service facilities. The PET varied from 25.3 °C in the outskirts to 30.2 °C in Moscow centre; however, variations in thermal stress did not have adverse effects on the spatial patterns of vulnerability. The vulnerability indicator in the east was six times higher than in more prosperous areas of the center, north and southwest, due to historical development, mainly the transformation from former industrial areas into residential areas.

Extensive unforested sandy areas on the margins of floodplains and riverbeds, formed by dunes, barchans, and accumulation berms, are a ubiquitous feature across northern Eurasia and Alaska. These dynamic landscapes, which bear witness to the complex Holocene and modern climatic fluctuations, provide a unique opportunity to study ecosystem evolution. Within this heterogeneous assemblage, active dunes, characterized by their very sparse plant communities, contrast sharply with the surrounding taiga (boreal) forests common for the stabilized dunes. This juxtaposition makes these regions to natural laboratories to study vegetation succession and soil development. Through a comprehensive analysis of climate, geomorphology, vegetation, soil properties, and microbiome composition, we elucidate the intricacies of cyclic and linear ecosystem evolution within a representative sandy area located along the lower Nadym River in Siberia, approximately 100 km south of the Arctic Circle. The shift in the Holocene wind regime and the slow development of vegetation under harsh climatic conditions promoted cyclical ecosystem dynamics that precluded the attainment of a steady state. This cyclical trajectory is exemplified by Arenosols, characterized by extremely sparse vegetation and undifferentiated horizons. Conversely, accelerated vegetation growth within wind-protected enclaves on marginally stabilized soils facilitated sand stabilization and subsequent pedogenesis towards Podzols. Based on soil acidification due to litter input (mainly needles, lichens, and mosses) and the succession of microbial communities, we investigated constraints on carbon and nutrient availability during the initial stages of pedogenesis. In summary, the comprehensive study of initial ecosystem development on sand dunes within taiga forests has facilitated the elucidation of both common phases and spatiotemporal dynamics of vegetation and soil succession. This analysis has further clarified the existence of both cyclic and linear trajectories within the successional processes of ecosystem evolution.

The study of urban aerosol and its influence on radiation and meteorological regime is important due to the climate effect. Using COSMO-ART model with TERRA_URB parameterization, we estimated aerosols and their radiative and temperature response at different emission levels in Moscow. Mean urban aerosol optical depth (AOD) was about 0.029 comprising 20-30% of the total AOD. Urban black carbon mass concentration and urban PM10 accounted for 86% and 74% of their total amount, respectively. The urban AOD provided negative shortwave effective radiative forcing (ERF) of -0.9 W m(-2) at the top of the atmosphere (TOA) for weakly absorbing aerosol and positive ERF for highly absorbing aerosol. Urban canopy effects decreased surface albedo from 19.1% to 16.9%, which resulted in positive shortwave ERF at TOA, while for longwave irradiance negative ERF was observed due to additional emitting of urban heat. Air temperature at 2 m decreased independently on the ERF sign, partially compensating (up to 0.5 degrees C) for urban heat island effect (1.5 degrees C) during daytime. Mean radiative atmospheric absorption over the Moscow center in clear sky conditions reaches 4 W m(-2) due to urban AOD. The study highlights the role of urban aerosol and its radiative and temperature effects.

The 2010 summer heatwave in European Russia led to a notable increase in mortality due to extreme heat and associated wildfires. However, the diverse settlement patterns and the uneven impact of the heatwave in European Russia have left many geographical aspects of this event unexplored. For instance, the variations in excess mortality between major cities and smaller urban and rural areas remain unclear. According to our findings, during the 27–33 weeks of 2010, the total number of excess deaths was estimated at 56.0, with nearly 20% of them concentrated in Moscow. The age-standardized mortality rate in cities with more than one million inhabitants exceeded the expected values by 52% during the heatwave, while the excess mortality rate in rural areas was only 17%. The geographical area experiencing the highest excess mortality rate aligned with the zone of the greatest heatwave extent, as indicated by deviations from the climatic norm in temperatures and other measures of thermal stress. The risk of death from this increase in thermal stress more accurately represented by the Heat Index was found to be substantially higher in larger cities of 500,000 or more inhabitants, with the risk of death being especially high in major cities. Notably, air pollution was not found to be a significant modifier of excess mortality. It is important to note that the results obtained may have been influenced by the use of raster-based data from climate reanalysis, which may be expected to underrepresent local urban heat island effects, and consequently to underestimate risk exposure in urban areas.

From 2019 to 2022, for the first time in Svalbard, the rapid development of a thaw slump was observed in Hollendardalen Valley (Nordenskiold Land, West Spitsbergen), affecting an area of 6300 m2. Fast-paced thermokarst and thermo-erosion processes exposed massive ground ice, as well as thick ground ice veins within frozen silt strata. In the riverbed - in a non-carbonate, non-karstifying geological setting - thaw funnels appeared, swallowing part of the river flow, presumably via a local fault zone connecting to deep aquifers. The exposed ground ice has extremely low mineralization, dominated by Na+ and SO4 2-ions. The properties and morphology of the ice veins point to segregation origins. The broad middle reaches of the Hollendardalen Valley exhibit thermokarst depressions and lakes, tabular terrace remnants and traces of past thaw slumping. Such morphology represents a thermo-erosional plain, formed through the interplay of fluvial erosion and a series of fast-paced thermo-erosion and thermokarst events. The very presence of massive ground ice in places where its appearance was previously unexpected indicates the possibility of detecting further ground ice of various thicknesses in Svalbard. Thus, ongoing and future permafrost warming will likely accelerate rapid permafrost thaw in Svalbard, reshaping the surface morphology and subsurface hydrology.

Accurately predicting weather and climate in cities is critical for safeguarding human health and strengthening urban resilience. Multimodel evaluations can lead to model improvements; however, there have been no major intercomparisons of urban-focussed land surface models in over a decade. Here, in Phase 1 of the Urban-PLUMBER project, we evaluate the ability of 30 land surface models to simulate surface energy fluxes critical to atmospheric meteorological and air quality simulations. We establish minimum and upper performance expectations for participating models using simple information-limited models as benchmarks. Compared with the last major model intercomparison at the same site, we find broad improvement in the current cohort's predictions of short-wave radiation, sensible and latent heat fluxes, but little or no improvement in long-wave radiation and momentum fluxes. Models with a simple urban representation (e.g., 'slab' schemes) generally perform well, particularly when combined with sophisticated hydrological/vegetation models. Some mid-complexity models (e.g., 'canyon' schemes) also perform well, indicating efforts to integrate vegetation and hydrology processes have paid dividends. The most complex models that resolve three-dimensional interactions between buildings in general did not perform as well as other categories. However, these models also tended to have the simplest representations of hydrology and vegetation. Models without any urban representation (i.e., vegetation-only land surface models) performed poorly for latent heat fluxes, and reasonably for other energy fluxes at this suburban site. Our analysis identified widespread human errors in initial submissions that substantially affected model performances. Although significant efforts are applied to correct these errors, we conclude that human factors are likely to influence results in this (or any) model intercomparison, particularly where participating scientists have varying experience and first languages. These initial results are for one suburban site, and future phases of Urban-PLUMBER will evaluate models across 20 sites in different urban and regional climate zones.

The vulnerability of urban ecosystems to global climate change becomes a key issue in research and political agendas. Urban green infrastructures (UGIs) are widely considered as a nature-based solution to mitigate climate change and adapt to local urban climate anomalies in cities. However, UGI-induced cooling effect depends on the size, location and geometry of green spaces, and such dependencies remain overlooked. This research aimed to investigate the cooling effect of UGIs of different size under extreme conditions of 2021 summer heat wave for the case of Moscow megacity (Russia) using a numerical mesoclimatic model COSMO. UGIs objects were assigned to one of the four size categories (S, M, L and XL) based on their area. Their cooling effects at the local, non-local and city scales were evaluated based on comparison between the model outcomes for the realistic land cover and simulations for which UGI of a particular size category were replaced by the built-up areas typical for their surroundings. The highest cooling effect was observed for XL size UGIs, which reduced the local heat-wave-averaged air temperatures by up to 3.4 degrees C, whereas for the S size UGIs it did not exceed 2 degrees C. The cooling effectiveness for XL category was higher than for S category by 23 % inside the green spaces (locally), by 40-90 % in the buffer zones around the green space (non-locally) and by 35 % for the whole city. More effective cooling of large UGIs is partially explained by their stronger park breeze effect, i.e., impact on the airflow increasing the divergence over green spaces. However, when standardized to the population affected by cooling, the M size UGIs made the strongest contribution to the thermal environment where people live and work. The stronger non local cooling induced by the largest UGI objects cannot compensate for their remoteness from the built environment.

This study considers the problem of approximating the temporal dynamics of the urban-rural temperature difference (ΔT) in Moscow megacity using machine learning (ML) models and predictors characterizing large-scale weather conditions. We compare several ML models, including random forests, gradient boosting, support vectors, and multi-layer perceptrons. These models, trained on a 21-year (2001–2021) dataset, successfully capture the diurnal, synoptic-scale, and seasonal variations of the observed ΔT based on predictors derived from rural weather observations or ERA5 reanalysis. Evaluation scores are further improved when using both sources of predictors simultaneously and involving additional features characterizing their temporal dynamics (tendencies and moving averages). Boosting models and support vectors demonstrate the best quality, with RMSE of 0.7 K and R2 > 0.8 on average over 21 years. For three selected summer and winter months, the best ML models forced only by reanalysis outperform the comprehensive hydrodynamic mesoscale model COSMO, supplied by an urban canopy scheme with detailed city-descriptive parameters and forced by the same reanalysis. However, for a longer period (1977–2023), the ML models are not able to fully reproduce the observed trend of ΔT increase, confirming that this trend is largely (by 60–70%) driven by megacity growth. Feature importance assessment indicates the atmospheric boundary layer height as the most important control factor for the ΔT and highlights the relevance of temperature tendencies as additional predictors.

Many spectral indices have recently been developed for accurate extraction of impervious surfaces. Moreover, there are several 10-m global datasets available containing urban/impervious land cover class claiming to be of high accuracy. Up to date, there was no detailed analysis on the influence of easy-to-calculate spectral index and threshold on the final accuracy at large scale applied to Sentinel-2 scenes. Furthermore, the impact of growing season and the land-use type is unclear and the available global datasets must be validated in terms of their applicability for the accurate extraction of impervious surface for urban ecological applications. We show that the highest accuracy can be obtained by applying mNDVI and UCI thresholds (0.41 and -0.49 respectively) for summer median composites of Sentinel-2A/B acquisitions (highest R-2>0.82 and lowest RMSE<10%) if validated against true imperviousness on the areal basis. In cases, where the number of cloud-free scenes is insufficient, an established growing season shall be used. Small artificial patches possess the highest uncertainty at this resolution, but not exceeding 20%. Spectral unmixing applied to pixels extracted using the thresholds do not significantly improve the overall estimates. Only ESA Worldcover 10-m demonstrated the comparable R-2 and RMSE metrics among global datasets. Moreover, compared global datasets showed significant differences (up to tens of %) between the impervious surface estimates for selected ten cities, that highlights further evaluations of these data. Our results can successfully be implemented for mapping annual and even seasonal dynamics of imperviousness within the urban environment.

The effect of Moscow megapolis on precipitation of different intensity under contrasting physical–synoptic conditions was estimated. The analysis of long-term standard observations at weather stations in the Moscow Region and the data of high-resolution reanalysis ERA5 over 1988–2020 were used to demonstrate that the effect of the city on heavy precipitation is largest in the cases with higher static instability of the atmosphere, combined with a weak large-scale flow, high moisture content of the atmosphere, and the absence of pronounced frontal zones in the region. On the average over the study period, the excess of the total seasonal precipitation in Moscow relative to the background values over the Moscow region is 5.3%. It was found that the effect of the city on precipitation of various intensity is different: the precipitation of low and medium intensity was less in the city (statistically insignificant), while the heaviest precipitation (above 95 percentile) increased over the city by 11.6% above the background value.

Cities have a significant impact on the environment, forming microclimatic features such as urban heat island, an increase in the intensity of convective weather events, etc. Numerical models of the atmosphere with an integrated block that describes the interaction between the urbanized surface and the atmosphere—urban parameterization—are good at reproducing the meteorological features of the urban environment. Reviews of urban parameterizations are mostly outdated, and recent ones do not fully cover aspects of the methods used in the models to describe physical processes. This paper is dedicated to updating information on urban parameterizations, comparing the approaches used in them to describe physical processes, and forming proposals for their improvement. Based on the most common urban parameterizations of various levels of complexity, the main groups of physical processes describing “urban surface-atmosphere” interaction are identified. They are the surface energy balance, radiation heat transfer, surface moisture balance, turbulent heat and moisture exchange in the urban canopy, anthropogenic influence on heat and moisture fluxes, radiation, and turbulent interaction with urban vegetation. The main approaches to the parameterization of physical processes are defined within each block. Modern trends in the development of urban parameterizations are highlighted: (1) over the past 10 years, parameterizations have become more complicated due to the addition of the building energy model, a three-dimensional structure of urban vegetation, and vertical resolution when calculating turbulent fluxes; (2) at the same time, not much attention is paid to revising the original empirical formulas, often obtained on the basis of single field or laboratory experiments. Ways to improve urban parameterizations are proposed by clarifying the basic dependencies used mainly in the calculation of turbulent fluxes, particularly using the results of highly detailed large-eddy simulation modeling, which, with growing computational power, is increasingly used to simulate explicit heat transfer between the atmosphere and individual elements of the urban environment.