Evaluation of outdoor thermal comfort conditions in northern Russia over 30-year period: Arkhangelsk region

Abstract

The aim of the current paper is to evaluate spatial and temporal characteristics of the distribution of bioclimatic comfort within the Arkhangelsk region (Russian Federation) with two modern indices of thermal comfort: PET and UTCI. Its average values calculated for the modern climatic period (1981-2010) in the monthly mean give a clear picture of spatial heterogeneity for the warmest month (July) and for the coldest one (January). The spatial picture of both indices in July allows us to distinguish three large internal regions: the Arkhangelsk province, the continental part of the Nenets Autonomous Okrug (NAO) and Novaya Zemlya islands (NZ). Winter distribution of thermal discomfort is fundamentally different: the coldest regions (with extreme cold stress) are equally NZ and the Eastern half of NAO; intermediate position is occupied by the West of the NAO and the extreme northeast of the Arkhangelsk region, the highest winter UTCI values are observed in the rest of the region. In Archangelsk-city extreme cold stress in January has repeatability 6.7%, in February-4%, in December-2.2%, respectively. The average number of time points during the year at which thermal stress is not observed is only 19%. Obtained results will be the basis for planning relevant health measures and providing reliable forecasts of the effects of climate change in the Arctic region.

Universal thermal climate index in the Arctic in an era of climate change: Alaska and Chukotka as a case study

“…To evaluate trends in these time series, R 2 was obtained from Excel analysis. The Mann-Kendall test was used to check significance of correlations and trends with a threshold probability of 0.05 (Mann 1945;Kendall 1975).…”

“…Climate warming can seriously change bioclimate, potentially decreasing the period with exceptionally low temperatures on one hand, but improving the intervals with comfortable conditions, which are very important for both inhabitants (Mölders 2019;Konstantinov et al 2020) and tourists traveling to the Arctic (Huang et al 2021). This change in bioclimate is very important, especially given the growing demand for tourism development in the Arctic, and in particular in Alaska and Chukotka.…”

“…The UTCI has many advantages and shortcomings (Błażejczyk et al 2012;Błażejczyk and Kuchcik 2021). Among other considerations, the UTCI has important advantages: (i) it is expressed in °C-a unit of measurement that is understandable not only to specialists, but also to the broader public; and (ii) it can be used in a huge temperature range from below −50°C and up to higher than +50°C (Błażejczyk et al 2012;de Freitas and Grigorieva 2017;Błażejczyk and Kuchcik 2021) and applied around the world, enabling direct comparisons of results for different areas: in Europe (Antonescu et al 2021), in the Caribbean (Di Napoli et al 2022), in the Arctic (Mölders 2019;Shartova et al 2019;Huang et al 2021), China (Mi et al 2020;An et al 2021;Wang and Yi 2021;Liu et al 2022), Japan (Ohashi et al 2018), Russia (Shartova et al 2019;Konstantinov et al 2020;Vinogradova 2021). The UTCI has been used in a variety of applications (Krüger 2021), including medical science (Shartova et al 2019; Urban et al 2021;Romaszko et al 2022), tourism (Huang et al 2021), and in urban planning (Krüger 2021).…”

Evaluation of outdoor thermal comfort conditions: evidence from the Serbian major ski resort over the last 30 years

“…Numerous studies describe different thermophysiological indices for the quantification of thermal conditions (Höppe 1999 ; Matzarakis et al 2010 , etc.) and their use in different environments, climates, and regions (Mayer and Höppe 1987a , b ; Konstantinov et al 2020 ; Peñalba et al 2021 etc. ), as well as their influence on tourism (Amelung et al 2007 ; Roshan et al 2016 ; Kapetanakis et al 2022 ; etc.).…”

North Eurasian thermal comfort indices dataset (NETCID): new gridded database for the biometeorological studies

“…A human perception of the thermal environment is often neglected in strategic documents due to the need to calculate bioclimatic indices. Nevertheless, an examples of Yangtze River Delta urban agglomeration (Wang et al 2020), Wuhan (Dong et al 2020), Moscow (Zemtsov et al 2020), as well as northern regions (Konstantinov et al 2020(Konstantinov et al , 2021, and other areas (Bleta et al 2014, Ahmadi and Ahmadi 2017, Basarin et al 2018, Geletic ˇet al 2018 showed that bioclimatic indices can be used as tools for spatial thermal stress zoning at various territorial levels. The results of these studies can be highly important in order to protect socially vulnerable population.…”