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Climate change no longer involves and affects just a few people or communities. However, most of them need climate change detection studies to adapt to the current and future climate conditions efficiently. The present research aimed to detect climate changes by considering the shift in climate conditions from one region to another over different periods based on a similarity index in the Carpathians basin using the new ClimShift toolbox, specially created for this purpose. Developed in R, based on the cosine similarity index and using a set of 32 climate indices (temperature and precipitation), ClimShift uses NC raster format (NetCDF files) as input data. The application is compatible with Microsoft and Unix/Linux environments. The toolbox allows the detection of forward and backward climate shifts. The results can be employed as a Climate Service and are extremely helpful for an efficient process of adaption to climate changes at a local/regional scale. A user‐friendly interface and a tutorial on how to use the toolbox are also available. The toolbox was tested for four locations in the Carpathians Basin (Vienna, Bekes, Cluj‐Napoca and Kosice) using 1961–1990 as a base period and 1991–2021 as an analysis period for the forward climate shift analysis. For Cluj‐Napoca, the application was also tested for the backward climate shift, using 1991–2021 as the base period and 1961–1990 as the analysis period, identifying the region where present climate conditions were specific during the older period. The scientific results indicated a significant shift towards the east and northeast from the older period to the most recent one and a low percentage (6%–10%) in the overlapping area with highly similar conditions between the two periods. [ABSTRACT FROM AUTHOR]

This research aims to assess the climatic temporal suitability over the year and identify the appropriate season for open-air tourism in ten Romanian cities. It was evaluated using the Enhanced Tourism Climatic Index on a temporal scale of one day and then aggregated to 10 days over 61 years (1961–2021). Daily mean and maximum temperature, mean and minimum relative humidity, wind speed, precipitation, and sunshine hours were employed in the investigation. The Mann–Kendall test and Sen's slope were used for trend detection in the frequency, season duration, and first/last suitable day during the year for outdoor tourism. Acceptable or better weather conditions usually begin in the last part of April and end in mid-October, with Good or better conditions lasting between 260 and 310 days/year. The trend shows a shift of Good conditions earlier in the year (0.3–9.0 days/decade), resulting in a longer season duration (0.8–13.0 days/decade) for open-air activities. The trend is statistically significant mainly for the extra-Carpathian regions. Big differences in open-air events number during the climatically suitable season have been identified among the cities considered (2–19 events/year). This study is useful for better planning open-air events and activities for tourism and recreation. [ABSTRACT FROM AUTHOR]

The region of Romania is characterized in terms of continental temperate climate with oceanic influences. Thus, the precipitation regime and the thermal regime in this part of Europe are strongly influenced by the frequency of cyclones whose trajectories cross Europe from west to east coming from the Atlantic Ocean. The present study analyzes the variation of the amount of precipitation in Romania depending on the frequency and variation of the geographical position of the trajectories of the North Atlantic cyclones in the area of the European continent. Using the classic method of identification and tracking, 1189 cyclones were found that crossed Europe north of the Alps in a period of 30 years between December 1, 1985 - November 30, 2015, of which 71% generated precipitation in Romania. Their influence on the amount recorded at meteorological stations in Romania varies between 43% in the northwest of the country to 13% in the southern regions. [ABSTRACT FROM AUTHOR]

The objective of this research is to develop a set of vulnerability indicators and to analyze the effect of climate factors on social vulnerability. While the main aim of the study is to improve the existing methodology by quantifying the effects of climate change on social vulnerability, it also represents a novel scientific contribution in the field, as it delimits for the first time in the Romanian literature the most vulnerable areas from this point of view. This study aims to facilitate the decision-making processes and planning efforts targeting the increase of resilience and adaptive capacity of local communities. By applying the principal component analysis, we have selected 45 variables and have constructed four aggregated indexes. The Climate-Related Social Vulnerability index (CleSoVI) has pointed out that the largest impact on the current vulnerability of settlements in the test region (Cluj County) can be attributed to the lack of adaptive capacity and increased poverty, the most vulnerable areas being represented by the eastern and north-western parts of the county. From a socio-economic point of view, local authorities' efforts should concentrate on reducing the vulnerability of these regions and preparing them to cope with- and adapt to the impact of climate change. [ABSTRACT FROM AUTHOR]

Land surface temperature is one of the most important parameters related to global warming. It depends mainly on soil type, discontinuous vegetation cover, or lack of precipitation. The main purpose of this paper is to investigate the relationship between high LST, synoptic conditions and air masses trajectories, vegetation cover, and soil type in one of the driest region in Romania. In order to calculate the land surface temperature and normalized difference vegetation index, five satellite images of LANDSAT missions 5 and 7, covering a period of 26 years (1986-2011), were selected, all of them collected in the month of June. The areas with low vegetation density were derived from normalized difference vegetation index, while soil types have been extracted from Corine Land Cover database. HYSPLIT application was employed to identify the air masses origin based on their backward trajectories for each of the five study cases. Pearson, logarithmic, and quadratic correlations were used to detect the relationships between land surface temperature and observed ground temperatures, as well as between land surface temperature and normalized difference vegetation index. The most important findings are: strong correlation between land surface temperature derived from satellite images and maximum ground temperature recorded in a weather station located in the area, as well as between areas with land surface temperature equal to or higher than 40.0 °C and those with lack of vegetation; the sandy soils are the most prone to high land surface temperature and lack of vegetation, followed by the chernozems and brown soils; extremely severe drought events may occur in the region. [ABSTRACT FROM AUTHOR]

At present, research in climatology is mainly focusing on climate change and especially on global warming. Since climate change is already affecting large areas worldwide, it is important to study in details these changes at regional and local scale and to reduce its negative impact. The aim of this study is to analyze changes on extreme temperature indices over the periods 2041-2070 and 2071-2100 in Cluj-Napoca city using a set of 21 indices recommended by Expert Team for Climate Change Detection Monitoring and Indices. Extreme temperature indices for observed and modeled data were calculated by employing ClimPACT2 software. Modeled data for daily minimum and maximum temperature was extracted from EURO-CORDEX Project database. RCP4.5 and RCP8.5 scenarios of three regional climate models (RACMO22E, RCA4, and WRF331F) were considered. Next we compared the average values of the historical period (1981-2010) with the 2041-2070 and 2071-2100 periods for each scenario (RCP 4.5 and RCP 8.5) in order to obtain the future changes. The result of this study shows major changes for all the analyzed indices. The period 2071- 2100 presents the highest changes under both analyzed scenarios. [ABSTRACT FROM AUTHOR]