Clothing resistance and potential evapotranspiration as thermal climate indicators—The example of the Carpathian region

Abstract

Clothing resistance parameter rcl and potential evapotranspiration (PET), a major component of Thornthwaite type climate classifications, are used as thermal climate indicators for characterizing the thermal climate of the Carpathian region. rcl is simulated by a model based on clothed human body energy balance considerations. rcl refers to a walking human in outdoor conditions, whose somatotype can differ. Somatotype shapes are determined by applying the Heath–Carter somatotype method. PET is estimated using only air temperature and latitude as inputs. In addition rcl is linked to PET. The annual mean of rcl is statistically interconnected with annual sum of PET, and the annual fluctuation of rcl (drcl = rclmax − rclmin) with the annual fluctuation of PET (dPET = PETmax − PETmin). The Carpathian region's thermal climate is analysed by comparing PET results with rcl model results and rcl results obtained by statistical link. We showed that rcl model results are strongly sensitive to human body somatotype variations. It is also shown that the spatial heterogeneity of thermal climates is the lowest in the lowlands and the highest in the mountains. The spatial heterogeneity of rcl and drcl values obtained by statistical link is comparable to the spatial heterogeneity of PET, but is lower than that obtained from the rcl model. Similarly to rcl model results, rcl results obtained by statistical link are also sensitive to human body somatotype variations. All these results suggest that statistical connections between rcl and PET and drcl and dPET can be used as subunits in Thornthwaite type climate classifications to obtain human thermal climate information. Lastly, areas with the largest thermal contrast are reproduced in terms of both the annual sum of PET and the rcl, which is obtained by both the model and the statistical link.

Individual local human thermal climates in the Hungarian lowland: Estimations by a simple clothing resistance‐operative temperature model

“…In scientific practice, the former approach is widespread (e.g., Höppe, 1999; Blazejczyk et al ., 2013). It was not until recently that methods using the latter approach appeared (e.g., Thuomaala et al ., 2013; Zhou et al ., 2013; Schweiker et al ., 2018; Ács et al ., 2021b). In both approaches, human modelling involves modelling of (a) clothing, (b) metabolic heat flux density related to the activity, and (c) human thermal perception.…”

“…Ács et al . (2021b) also demonstrated that large somatotype differences are associated with large differences in metabolic heat production (M). To the best of our knowledge, interpersonal human thermal climate deviations have not yet been thoroughly investigated.…”

“…Skin temperature, core temperature, or the clothing's surface temperature are the variables to be calculated. For this, we usually say that the model works in “forward mode.” However, there are also models in which the state variables are known and the objective is to estimate the thermal insulation of the clothing, in such cases, we usually say that the model works in “backward mode.” Based on the terminology and the model types introduced, we can easily define our model type (Ács et al ., 2021b): it is a Fanger model (one‐segment, one‐node) used in “backward mode” (clothing resistance is not an input, but rather an output variable). It calculates the thermal insulation value of virtual clothing ( r cl ), which achieves the thermal equilibrium between the human body and the environment.…”

“…Other attempts have been made by Ács et al . (2021a, 2021b). In these studies, a person walking at a speed of 1.1 m∙s −1 is analysed.…”

Comparison of CMIP6 model performance in estimating human thermal load in Europe in the winter season

“…Holmér (1988, 2005) developed the IREQ index, which was used and evaluated in the studies of Griefahn and Forsthoff (1997), Gavhed and Holmér (1998) and Rissanen et al (2000). Based on the research of Campbell and Norman (1997), the clothing resistance index ( rcl index) was developed (Ács et al, 2019) and applied to estimate the human thermal climate of the Carpathian Basin (Ács et al, 2020, 2021b) using the CarpatClim dataset (Spinoni et al, 2015). By definition, these indices must be positive and can only be used in cold conditions, when thermal equilibrium can be achieved by eliminating the heat deficit of the body with clothing.…”