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Future climate conditions for the Mediterranean region based on an ensemble of 16 Global Climate Models are expressed and mapped using three approaches, giving special attention to the intermodel uncertainty. (1) The scenarios of mean seasonal temperature and precipitation agree with the projections published previously by other authors. The results show an increase in temperature in all seasons and for all parts of the Mediterranean with good intermodel agreement. Precipitation is projected to decrease in all parts and all seasons (most significantly in summer) except for the northernmost parts in winter. The intermodel agreement for the precipitation changes is lower than for temperature. (2) Changes in drought conditions are represented using the Palmer Drought Severity Index and its intermediate Z-index product. The results indicate a significant decrease in soil moisture in all seasons, with the most significant decrease occurring in summer. The displayed changes exhibit high intermodel agreement. (3) The climate change scenarios are defined in terms of the changes in parameters of the stochastic daily weather generator calibrated with the modeled daily data; the emphasis is put on the parameters, which affect the diurnal and interdiurnal variability in weather series. These scenarios indicate a trend toward more extreme weather in the Mediterranean. Temperature maxima will increase not only because of an overall rise in temperature means, but partly (in some areas) because of increases in temperature variability and daily temperature range. Increased mean daily precipitation sums on wet days occurring in some seasons, and some parts of the Mediterranean may imply higher daily precipitation extremes, and decreased probability of wet day occurrence will imply longer drought spells all across the Mediterranean. [ABSTRACT FROM AUTHOR]

This study aims to evaluate soil climate quantitatively under present and projected climatic conditions across Central Europe (12.1°-18.9° E and 46.8°-51.1° N) and the U.S. Central Plains (90°-104° W and 37°-49° N), with a special focus on soil temperature, hydric regime, drought risk and potential productivity (assessed as a period suitable for crop growth). The analysis was completed for the baselines (1961-1990 for Europe and 1985-2005 for the U.S.) and time horizons of 2025, 2050 and 2100 based on the outputs of three global circulation models using two levels of climate sensitivity. The results indicate that the soil climate (soil temperature and hydric soil regimes) will change dramatically in both regions, with significant consequences for soil genesis. However, the predicted changes of the pathways are very uncertain because of the range of future climate systems predicted by climate models. Nevertheless, our findings suggest that the risk of unfavourable dry years will increase, resulting in greater risk of soil erosion and lower productivity. The projected increase in the variability of dry and wet events combined with the uncertainty (particularly in the U.S.) poses a challenge for selecting the most appropriate adaptation strategies and for setting adequate policies. The results also suggest that the soil resources are likely be under increased pressure from changes in climate. [ABSTRACT FROM AUTHOR]

The close links of climate, soil conditions, and agricultural productivity have been used in Central Europe for taxation purposes since the eighteenth century. Since agroclimatic conditions are variable, their fluctuations in the past centuries can provide a valuable context for analyzing changes expected in the coming decades. Here, historical agroclimatic conditions and future projections were constructed for key agricultural regions in Central Europe. The agroclimatic zoning method used in this study incorporates (1) the sum of temperatures for days with a mean temperature above 10°C during the frost-free period, (2) the water deficit during the summer period from June to August, defined as the difference between precipitation and reference evapotranspiration, and (3) information regarding the suitability of soil and terrain for agriculture production based on twentieth century soil surveys. Changes in selected agroclimatological indices were also analyzed. To produce a weather series representing climate conditions between 1803 and 2008 over the study area, we used a stochastic weather generator trained on high-quality daily observations from 52 representative meteorological stations during the baseline period from 1961 to 1990. To estimate the extent of agroclimatic zones and the values of selected agroclimatic indices, the parameters of the weather generator were perturbed by the deviations of the temperature and precipitation means from the baseline using a long-term climate series from 1803 to 2008, from Brno. To generate a weather series representing the climate in 2050, we used an approach known as 'pattern-scaling' in combination with outputs of three general circulation models. To our knowledge, this is the first study analyzing both continuous fluctuations in agroclimatic conditions over the past 200 years and expected shifts in the coming decades over Central Europe. In the study region, our results demonstrate that changes in climate factors since the second half of the twentieth century have favored the expansion of warmer and drier agroclimatic conditions in the most fertile areas, progressively endangering the sustainability of rain-fed agriculture. Conversely, the agroclimatic conditions of regions at higher elevations have improved over the past six decades, as witnessed by increases in maize production areas but also an influx of previously absent pests, e.g., the European corn borer. The length of the vegetation summer has been increasing and shows daily average temperature exceeding 15°C. The mean number of days with snow cover has decreased by up to 30 days since a peak in the late nineteenth century. In lowland areas, the date of the last frost, with a 20-year return period, has moved closer to beginning of the season. Our results show that the predicted rate of change is unprecedented in available agroclimatic records; thus, adaptation cannot rely on past. Consequently, agricultural producers in the region will be forced to significantly bolster their adaptive capacity and develop flexible procedures that reflect the rapidly changing agroclimatic conditions. [ABSTRACT FROM AUTHOR]

During the past few decades, the basic assumption of agroclimatic zoning, i.e., that agroclimatic conditions remain relatively stable, has been shattered by ongoing climate change. The first aim of this study was to develop a tool that would allow for effective analysis of various agroclimatic indicators and their dynamics under climate change conditions for a particular region. The results of this effort were summarized in the AgriClim software package, which provides users with a wide range of parameters essential for the evaluation of climate-related stress factors in agricultural crop production. The software was then tested over an area of 114,000 km in Central Europe. We have found that by 2020, the combination of increased air temperature and changes in the amount and distribution of precipitation will lead to a prolonged growing season and significant shifts in the agroclimatic zones in Central Europe; in particular, the areas that are currently most productive will be reduced and replaced by warmer but drier conditions in the same time the higher elevations will most likely experience improvement in their agroclimatic conditions. This positive effect might be short-lived, as by 2050, even these areas might experience much drier conditions than observed currently. Both the rate and the scale of the shift are amazing as by 2020 (assuming upper range of the climate change projections) only 20-38% of agriculture land in the evaluated region will remain in the same agroclimatic and by 2050 it might be less than 2%. On the other hand farmers will be able to take advantage of an earlier start to the growing season, at least in the lowland areas, as the proportion of days suitable for sowing increases. As all of these changes might occur within less than four decades, these issues could pose serious adaptation challenges for farmers and governmental policies. The presented results also suggest that the rate of change might be so rapid that the concept of static agroclimatic zoning itself might lose relevance due to perpetual change. [ABSTRACT FROM AUTHOR]

The third and fourth statistical moments, that is, skewness and kurtosis, are compared for daily maximum temperature in summer and daily minimum temperature in winter between observations, outputs of two global climate models, four versions of statistical downscaling, and weather generator. The comparison is performed at six stations in central Europe. None of the simulation models can be considered as superior to the others. Causes of a good correspondence with and differences from observations are identified e.g. in the treatment of physics in the models, imperfections in physical parameterizations, or a linear transfer of properties from predictors onto predictands in statistical downscaling. [ABSTRACT FROM AUTHOR]

Three-dimensional general circulation models (GCMs) are 'state-of-the-art' tools for projecting possible changes in climate. Scenarios constructed for the Czech Republic are based on daily outputs of the ECHAM-GCM in the central European region. Essential findings, derived from validating, procedures are summarized and changes in variables between the control and perturbed experiments are examined. The resulting findings have been used in selecting the most proper methods of generating climate change projections for assessing possible hydrological and agricultural impacts of climate change in selected exposure units. The following weather variables have been studied: Daily extreme temperatures, daily mean temperature, daily sum of global solar radiation, and daily precipitation amounts. Due to some discrepancies revealed, the temperature series for changed climate conditions (2×CO2 ) have been created with the help of temperature differences between the control and perturbed runs, and the precipitation series have been derived from an incremental scenario based on an intercomparison of the GCMs' precipitation performance in the region. Solar radiation simulated by the ECHAM was not available and, therefore, it was generated using regression techniques relating monthly means of daily extreme temperatures and global radiation sums. The scenarios published in the paper consist of monthly means of all temperatures, their standard deviations, and monthly means of solar radiation and precipitation amounts. Daily weather series, the necessary input to impact models, are created (i) by the additive or multiplicative modification of observed weather daily series or (ii) by generating synthetic time series with the help of a weather generator whose parameters have been modified in accord with the suggested climate change scenarios. [ABSTRACT FROM AUTHOR]

The paper deals with the probabilistic prediction of event occurrence with use of the binary decision tree which is grown from the learning sample. The tree growing algorithm consists in recursive partition of the predictor space by either single-predictor-based (SP) splits or by hyperplanes perpendicular to the best linear discriminant function (BLDF), and is intended to maximally effectively discriminate the elements of the learning sample with event occurrence from the elements without event occurrence. The predictand is the thunderstorm occurrence in the afternoon in Prague, the set of predictors includes variables derived from a midday single-station TEMP-A data (Perfect Prog approach), persistence predictors and predictors related to passages of the fronts across Prague. The experiments are designed to test the performance of the tree growing algorithm - with a stress upon indeterminateness following from the limited size of the learning sample - and to evaluate the predictive potential of the predictors for thunderstorm forecasting. The stability of the tree structure, the optimal size of the tree and the related prognostic skill score increase with increasing size of the learning sample. Employment of the BLDF splits allows quicker and more effective partition of the predictor space on the assumption that the predictor vector has lower dimension and is 'well behaved' (preferably normally distributed). The stability indices of Faust, Showalter and Adedokun were found to be the most effective predictors. Persistence and frontal predictors only slightly contribute to the total prediction skill of the decision tree. The optimally sized tree has only five splitting nodes and employs three thermodynamical predictors, one frontal and one persistence predictor. [ABSTRACT FROM AUTHOR]

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