Gridded model assessments require at least one climatic and one soil database for carrying out the simulations. There are several parallel soil and climate database development projects that provide sufficient, albeit considerably different, observation based input data for crop model based impact studies. The input database related uncertainty of the Biome-BGCMuSo agro-environmental model outputs was investigated using three and four different gridded climatic and soil databases, respectively covering an area of nearly 100.000 km 2 with 1104 grid cells. Spatial, temporal, climate and soil database selection related variances were calculated and compared for four model outputs obtained from 30-year-long simulations. The choice of the input database introduced model output variability that was comparable to the variability the year-to-year change of the weather or the spatial heterogeneity of the soil causes. Input database selection could be a decisive factor in carbon sequestration related studies as the soil carbon stock change estimates may either suggest that the simulated ecosystem is a carbon sink or to the contrary a carbon source on the long run. Careful evaluation of the input database quality seems to be an inevitable and highly relevant step towards more realistic plant production and carbon balance simulations.

A human body-clothing-atmosphere environment system energy balance model is constructed to evaluate individual human thermal climates in the Carpathian Basin. The analysis is performed in terms of clothing resistance and operative temperature for the period 1971-2000. The model's main strength is that it simulates the metabolic activity rate M as simply as possible taking into account interpersonal variations. Non-sweating, walking humans are considered in natural outdoor conditions at a walking speed of 4 kmÁh −1. Atmospheric data are used from the CarpatClim dataset; human data are taken from a Hungarian human dataset. The dataset reveals that the interpersonal variations of M of walking humans can reach 40-50 WÁm −2. According to the results, the variability of individual human thermal climates can be significant. This variability increases towards cold climates and is less in the comfortable thermal zone, when the operative temperature is between 23 and 28 C. It should be mentioned that summer is thermally neutral in the Little Hungarian Plain, the Great Hungarian Plain and in larger parts of the Transylvanian Plateau, irrespective of the person considered. The warmest areas in the Carpathian Basin can be found in Bačka and Banat. In terms of thermal sensation, the results obtained agree well with the results referring to the human considered in the Physiological Equivalent Temperature index model.

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.

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.

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 r cl and operative temperature. The model's main strength is that it simulates metabolic rate M as simply as possible. r cl 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. r cl deviations caused by wind speed variations can be especially large in large heat excess cases. Further model tests are needed for more extreme conditions.