Vol. 124, No. 4 * Pages 427–560 * October - December 2020

This paper deals with the temperature projections of two regonal climate models, actually three scenarios by 2100: the A2 scenario of the EBU-POM model and the RCP4.5 and RCP8.5 scenarios, the latest projections of the ALADIN model. Kolašin was chosen, because the altitude of the place is the average height of the northern region of Montenegro (about 1000 m). A total of 22 temperature parameters for the period 2011–2100 were analyzed. The upward trend of projected seasonal and annual (TY, TYx, and TYn) mean, mean maximum, and mean minimum temperatures by 2100 is very significant. According to the RCP4.5 and RCP8.5 scenarios, in 2011-2100, the trend of projected mean winter (TW) temperatures will be from 0.2to 0.37 ^°C per decade, and the trend of projected mean summer (TSu) temperatures will be from 0.24 to 0.54 °C per decade. Compared to the base period (1981–2010), the average annual temperature in 2071–2100 is expected to be higher than 2.2 (RCP4.5) to 3.6 °C (A2 and RCP8.5). Also, by the end of the 21st century, a significant increase in the number of summer and tropical days (SD and TD) together with a decrease in the number of frost and ice days (FD and ID) are expected. During the instrumental period, a temperature higher than 37 °C was not recorded. According to projections, in the late 21st century, in summer, maximum temperatures of 40 °C are possible, even in the milder variant (RCP4.5) scenario. According to projections of the used models, Kolašin and the northern region of Montenegro expect a warmer future with more frequent extreme temperatures in a positive direction.

A characteristic symptom of climate change is the modified distribution of frost events that has fundamental effect on agricultural production. To learn these changes in Sopron region, Hungary, we investigated daily minimum and maximum temperature data of the period 1961–2016 provided by the Hungarian Meteorological Service. The time interval 1961–2016 was split into two (1961–1990 and 1991–2016) in order to compare nearly two climate cycles statistically. We analyzed the 56-year trends of the last frosty day of spring and the first frosty day of autumn as well as the length of the longest yearly frost-free period. As for the winter period (October 16 – February 28), the frequencies of four different strengths of frosty days were examined. We analyzed how often an uninterrupted frost-free period (T_min>0 °C) longer than 12 days developed between two frosty periods in spring and in autumn, exactly when it occurred (Julian day), and how many days it lasted for. Finally, we investigated the trends of the yearly numbers of spring frosty days and their variances.
Amongst others, we show that the length of the longest uninterrupted frost-free periods has increased significantly over the past 56 years, with 70% of years in the recent climate cycle (1991–2016) having frost-free periods of more than 200 days. As an average change per 10 years, the last spring frost period ended four days earlier, while the onset of autumn frost shifted 2 days towards the end of the year. The number of days with minimum temperatures below -15 °C, -10 °C, -5 °C, and below 0 °C decreased significantly during the dormant period. These changes mean a prolongation of the growing season, partly a reduction of the winter regeneration period, and the potential higher overwintering success of the pests. The number of spring frost days also decreased significantly, while their variability has grown markedly.

Thousands of billions of cubic meters of fresh water collected at great expense are evaporated annually from dams, and salts of evaporating water reduces water quality. In this study, the efficiency of the vector autoregressive model called VAR model has been examined on an annual scale using pan evaporation data in the salt lake basin, Iran, during the statistical period of 1996–2015. Since hydrologic modeling is concerned with the accuracy and efficiency of the model, therefore, we must try to evolve and improve the results of the models. In this study, VAR multivariable time series and nonlinear GARCH models have been used. The results of linear and nonlinear hybrid models in modeling the annual and monthly pan evaporation values of studied stations at the basin area of the salt lake indicated, that the pan evaporation values in the annual scale have the best fit with hybrid models. The results of the study of the accuracy of these models in modeling the pan evaporation values indicated, that the VAR-GARCH hybrid models have a high accuracy relative to the vector models and have been able to model the pan evaporation values with good accuracy and with the lowest error rate. Of the two models that have both annual nature (VAR and VAR-GARCH), the best model can be selected based on the estimation of the error values. In this study, we first examine the accuracy of the relatively new vector autoregressive model. The results of the estimation of error and efficiency of the model indicated the acceptable accuracy of this model in estimating the pan evaporation values in the annual scale. The 95% confidence interval confirmed the simulation results of the calibration step. Overall, the results showed that both VAR and VAR-GARCH models have high accuracy and correlation, and the model's performance criterion also confirms this. The percentage of improvement in the results from the model of the pan evaporation values in the annual scale using the VAR-GARCH model is about 4% relative to the VAR model. However, due to modeling the random section and reducing the uncertainty of the model, the results of modeling the pan evaporation values using the VAR-GARCH model are better than the VAR model. But due to the complexity of calculating the GARCH model, the VAR model can also be used.

Foehn is a warm, dry, and downslope wind blowing in the lee side of a mountain range. It is a well known example of a local atmospheric circulation. The foehn wind is also an extreme weather event, and its forecasting is an important task for the short-range weather forecaster. The foehn in Bulgaria is observed on the northern slopes of the mountains, as a result of warm air advection from the south and southwest. Its occurrence is highest north of the Vitosha and Balkan mountains. In this study, a synoptic classification of the meteorological conditions leading to foehn in the central meteorological station in Sofia for the period 1975–2014 is made. Foehn climatology is prepared, and in addition, an evaluation of the foehn as an extreme weather event by wind gust is presented. For the period 1975–2014, there were 298 days with foehn in Sofia, which resulted from 220 synoptic cases. A manual foehn classification was developed with four major types. Type I is associated with the Mediterranean cyclone with the highest frequency – 52% of the foehn days. Foehn climatology gives average annual number of 7.5 foehn days but with a large variance between decades. The lowest annual number of days (4.5) is registered for the 2005–2014 period, and it was associated with the lowest recorded wind gust (22 m/s).

The paper analyses, spatial and temporal patterns of precipitation over Montenegro. Data on mean monthly precipitation during the period 1961–2015 from 17 meteorological stations were used for the analysis. Four regions with different spatial precipitation regimes were identified by using the principal component analysis and the agglomerative hierarchical clustering method. A downward tendency in annual precipitation prevails over Montenegro. The most prominent reduction was present in the summer season. In contrast, precipitation increased during autumn. However, the majority of estimated trend values was low and statistically insignificant.

The prediction of global climate change using the values recorded in a statistical period requires a precise method that can accurately identify the fluctuations of these changes. By patterning these changes, the parameter values for the years or future periods are predicted, or the statistical gap can be eliminated. In this research, meteorological data of six stations in different climates of Iran were used to model and estimate the values of the dew point temperature (DPT). The stations studied are Ahvaz, Urmia, Kerman, Gorgan, Rasht, and Babolsar. In order to estimate the DPT values, support vector regression was used, and to optimize the parameters of the support vector regression model, the ant colony algorithm was used. In this study, four different input patterns of meteorological data have been investigated as input of the support vector regression model. Pattern I with seven inputs (monthly minimum, maximum, and average air temperatures, monthly precipitation, saturation vapor pressure, actual vapor pressure and relative humidity), Pattern II with three inputs (monthly average air temperature, saturation vapor pressure, and actual vapor pressure), Pattern III with two inputs (monthly minimum and maximum air temperatures), and Pattern IV with an input (monthly average air temperature) were used. It is recommended that if the number of inputs in the model is small, the model will be more user-friendly. Based on the results of analyzing different patterns, it can be concluded, that Pattern III is the suitable pattern for estimating DPT values at the stations studied in different climates of Iran based on the three criteria of root mean square error (RMSE), Nash–Sutcliffe model efficiency coefficient (NSE), and coefficient of determination (R²). Overall, the results showed that the selected pattern increases the accuracy of the model by up to 24% compared to the conventional model.

In the last decades, knowledge about the climate has increased significantly. Climate change today is the subject of many sciences, including meteorology, climatology, geology, geography, geophysics, astronomy, etc. The present predictions with updated meteorological data and with data of the number of particles of CO₂ in the troposphere may give satisfying results. Forecasting for industrial grains such as maize, soybean, and wheat will be essential for industry and everyday life. Within the last agreement of climate change in Paris, global temperatures will continuously be increasing by 2100. In this research, we used a synthetic grid with agroclimatological data which comprises predictions until 2100. These data were found in the sub-section called World Clim Version 1 or in the CMIP5 database. After numerical and geospatial GIS analysis, we got the following predictions: (i) slight- no temperature changes or changes including the increase of temperature by 0.5 °C, (ii) moderate- temperature increases by 2.0 °C, (iii) severe- temperature increases by 5.0 °C, and (iv) incredible- temperature increases to extreme values, incase of which the survival of plants will be endangered.