Quantifying Local and Mesoscale Drivers of the Urban Heat Island of Moscow with Reference and Crowdsourced Observations

Abstract

Urban climate features, such as the urban heat island (UHI), are determined by various factors characterizing the modifications of the surface by the built environment and human activity. These factors are often attributed to the local spatial scale (hundreds of meters up to several kilometers). Nowadays, more and more urban climate studies utilize the concept of the local climate zones (LCZs) as a proxy for urban climate heterogeneity. However, for modern megacities that extend to dozens of kilometers, it is reasonable to suggest a significant contribution of the larger-scale factors to the temperature and UHI climatology. In this study, we investigate the contribution of local-scale and mesoscale driving factors of the nocturnal canopy layer UHI of the Moscow megacity in Russia. The study is based on air temperature observations from a dense network consisting of around 80 reference and more than 1,500 crowdsourced citizen weather stations for a summer and a winter season. For the crowdsourcing data, an advanced quality control algorithm is proposed. Based on both types of data, we show that the spatial patterns of the UHI are shaped both by local-scale and mesoscale driving factors. The local drivers represent the surface features in the vicinity of a few hundred meters and can be described by the LCZ concept. The mesoscale drivers represent the influence of the surrounding urban areas in the vicinity of 2–20 km around a station, transformed by diffusion, and advection in the atmospheric boundary layer. The contribution of the mesoscale drivers is reflected in air temperature differences between similar LCZs in different parts of the megacity and in a dependence between the UHI intensity and the distance from the city center. Using high-resolution city-descriptive parameters and different statistical analysis, we quantified the contributions of the local- and mesoscale driving factors. For selected cases with a pronounced nocturnal UHI, their respective contributions are of similar magnitude. Our findings highlight the importance of taking both local- and mesoscale effects in urban climate studies for megacities into account. Furthermore, they underscore a need for an extension of the LCZ concept to take mesoscale settings of the urban environment into account.

Evidence of horizontal urban heat advection in London using six years of data from a citizen weather station network

“…We chose to perform this simplification to avoid losing additional data after performing our quality-check and filtering. Nevertheless, micro-and mesoscale effects are known to affect measurements in air temperature at local scales (Fenner et al 2017, Skarbit et al 2017, Quanz et al 2018, Varentsov et al 2021-something that has to be kept in mind in our further analysis. Besides, we normalize the temperature observations by height, following Potgieter et al (2021), to get rid of the vertical thermal gradient.…”

“…High degrees of intra-LCZ variability under different wind conditions are thus observed, meaning that while the median anomaly can be positive, certain CWS in an area with similar land-use land-covers will measure a negative anomaly. This could potentially be explained by important micro-scale effects, undetected by the simplified LCZ classification (Fenner et al 2017, Skarbit et al 2017, Varentsov et al 2021. Highest degrees of intra-LCZ variability are observed in open low-rise (LCZ 6), and more natural LCZ, like sparsely built (LCZ 9), dense trees (LCZ A), sparse trees (LCZ B) and low vegetation (LCZ D).…”

“…For example, recent studies (e.g. Fenner et al 2017, Potgieter et al 2021, Varentsov et al 2021 have used CWS to improve our understanding of the impact of landuse and land-cover on urban temperatures through the perspective of Local Climate Zones (LCZ; Stewart and Oke 2012)-a land-use and land-cover classification specifically developed for urban climate studies. Others have used CWS to validate urban climate simulations (Hammerberg et al 2018), drive indoortemperatures in urban climate simulations (Jin et al 2021), and model air temperatures in European cities using machine learning (Venter et al 2020, 2021, Vulova et al 2020, Zumwald et al 2021.…”

CrowdQC+—A Quality-Control for Crowdsourced Air-Temperature Observations Enabling World-Wide Urban Climate Applications

“…All CWS available in the ROI were considered in the application of CrowdQC and CrowdQC+. For comparison between CWS and PRWS, an LCZ was assigned to each station following Fenner et al (2017) and Varentsov et al (2021), using the geographical position of each station and the LCZ maps. First, the nearest-pixel LCZ value was assigned to each station.…”

“…Second, for a buffer with a radius of 250 m around each station, the surface-cover fraction of the modal LCZ was calculated (using pixels of the LCZ map). Third, a weighted surface-cover LCZ fraction in the same buffer was calculated (Varentsov et al, 2021), applying "similarity weights" (Figure 3B in Bechtel et al, 2020) between the modal LCZ and all other grid points (LCZ pixels) within the buffer.…”