The Carpathian Basin climate in the time period 1971–2000 is analyzed in terms of the results obtained by the Köppen method and a clothing resistance scheme. A clothing resistance scheme is based on human body energy balance considerations taking into account human interperson variations as simply as possible. Interperson variations are considered by estimating human body somatotypes using the Heath-Carter somatotype classification method. Non-sweating, walking humans in outdoor conditions are treated. Environmental and human data are taken from the CarpatClim dataset and a Hungarian human dataset, respectively. Though the biophysical bases of the methods are completely different, the spatial structure of thermal climates expressed in terms of Köppen climate types and the clothing resistance parameter r_cl are basically similar. A clothing resistance scheme creates more information than the Köppen method not only in mountain, plateau areas but also in lowlands. It is shown that more human thermal climate categories can refer to one Köppen climate formula irrespective of which Köppen formulae are considered. The magnitude and area heterogeneity of r_cl is strongly sensitive to human somatotype changes. A clothing resistance scheme cannot be used in classroom applications; it needs to be drastically simplified while maintaining its sensitivity to somatotype changes in order to be competitive with the Köppen method.

Atmospheric teleconnections are characteristic to the climate system and exert major impacts on the global and regional climate. Accurate representation of teleconnections by general circulation models (GCMs) is indispensable given their fundamental role in the large scale circulation patterns. In this study a statistical method is introduced to evaluate historical GCM outputs of the fifth phase of the Coupled Model Intercomparison Project (CMIP5) with respect to teleconnection patterns. The introduced method is based on the calculation of correlations between gridded time series of the 500 hPa geopotential height fields in the Northern Hemisphere. GCMs are quantified by a simple diversity index. Additionally, potential action centers of the teleconnection patterns are identified on which the local polynomial regression model is fitted. Diversity fields and regression curves obtained from the GCMs are compared against the NCEP/NCAR Reanalysis 1 and the ERA-20C reanalysis datasets. The introduced method is objective, reproducible, and reduces the number of arbitrary decisions during the analysis. We conclude that major teleconnection patterns are positioned in the GCMs and in the reanalysis datasets similarly, however, spatial differences in their intensities can be severe in some cases that could hamper the applicability of the GCM results for some regions. Based on the evaluation method, best-performing GCMs can be clearly distinguished. Evaluation of the GCMs based on the introduced method might help the modeling community to choose GCMs that are the most applicable for impact studies and for regional downscaling exercises.

A new clothing resistance model for estimating outdoor thermal load is proposed and its behavior is analyzed in different weather conditions. It is based on clothed human body energy balance considerations; the human treated is a walking human in outdoor conditions. Weather and human data are taken from the internet site of the Hungarian Meteorological Service and from a Hungarian human dataset, respectively. Environmental thermal load is characterized in terms of clothing resistance rcl and operative temperature. The model's main strength is that it simulates metabolic rate M as simply as possible. rcl deviations caused by personal variations of M are the largest in extreme cold and warm conditions, in the comfort zone this effect is non-essential. rcl deviations caused by wind speed variations can be especially large in large heat excess cases. Further model tests are needed for more extreme conditions.

Three different clothing thermal resistance (r cl) schemes are compared using meteorological and human data collected in Martonvásár. Model 1 is the most complex, it is energy balance based. Model 3 is the simplest, it is the UTCI-clothing model used as submodel in the UTCI (Universal Thermal Climate Index) scheme. It uses air temperature as sole input. Model 2 uses more data than model 3, the data used are the thermal insulation values of the clothing worn. Meteorological data refer to the town Martonvásár. The data were collected in the period August 9, 2016-May 23, 2018. The main result is that the r cl values obtained by model 1 differ significantly in most of the cases from the results obtained by models 3 or 2. The fact that the results of model 1 rarely match the results of model 3 or model 2 suggests that the energy balance between human body and environment is rarely achieved, merely this is the case in approximately 10 percent of the cases.

Weather at different locations in the Hungarian lowland in different seasons (winter, summer) and times of day (morning, noon) is investigated from the human biometeorological point of view. Human thermal load characteristics of weather are described in terms of clothing resistance and operative temperature. Individual human thermal load–thermal sensation relationships have been estimated to study weather variation in the cities of Sopron (cooler part of Hungary) and Szeged (warmer part of Hungary). In the clothing resistance model, the humans are walking at a speed of 1.1 ms⁻¹ in outdoor conditions without sweating. The main findings are as follows. (1) In the early summer mornings, the weather is sensed as “neutral” or “cool”, in these cases the inter-person variation effect is very small. (2) At noon in summer, heat stresses (clothing resistance parameter values less than −2 clo) are registered. In these cases, high temperature and irradiation, as well as low or moderate wind, characterized the atmospheric environment. Then, the inter-person variation effect is clearly visible. (3) The strength of summer heat excess at noon seems to be larger than the strength of winter heat deficit in the early morning. (4) Clothing resistance differences caused by inter-person variations and by weather variations between the cities of Sopron and Szeged are comparable in the majority of cases. When they are not comparable, the site variation effect is much larger than the inter-person variation effect. The clothing resistance model is constructed for individual use and it can be equally applied on both weather and climate data.

We characterized the thermal load of a person walking and/or standing in the fog by analyzing the thermal resistance of clothing, r_cl, and operative temperature, T_o. The r_cl–T_o model applies to individuals using weather data. The body mass index and basal metabolic flux density values of the person analyzed in this study are 25 kg m⁻² and 40 W m⁻², respectively. Weather data are taken from the nearest automatic weather station. We observed 146 fog events in the period 2017–2024 in Martonvásár (Hungary’s Great Plain region, Central Europe). The main results are as follows: (1) The r_cl and T_o values were mostly between 2 and 0.5 clo and −4 and 16 °C during fog events, respectively. (2) The largest and smallest r_cl and T_o values were around 2.5 and 0 clo and −7 and 22 °C, respectively. (3) The r_cl differences resulting from interpersonal and wind speed variability are comparable, with a maximum value of around 0.5–0.7 clo. (4) Finally, r_cl values are significantly different for standing and walking persons. At the very end, we can emphasize that the thermal load of the fog depends noticeably on the person’s activity and anthropometric characteristics.