Changes in daily extreme temperatures in the extra-carpathians regions of Romania

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 33: (2013) Published online 15 August 2012 in Wiley Online Library (wileyonlinelibrary.com) DOI: /joc.3567 Changes in daily extreme temperatures in the extra-carpathians regions of Romania Adina-Eliza Croitoru a * and Adrian Piticar b a Faculty of Geography, Physical and Technical Geography Department, Babes-Bolyai University, Cluj-Napoca, Romania b Faculty of Environmental Science and Engineering, Babes-Bolyai University, Cluj-Napoca, Romania ABSTRACT: Changes in daily extreme temperatures have been identified in many studies conducted at local, regional or global scales. For Romanian territory, only little research on this issue was done. In this article, the extra-carpathians regions of Romania located southward and eastward from the Carpathians Chain were considered. This study is focused on analyzing daily extreme temperature trends at a regional scale (eastern, southern and southeastern regions of Romania) across 50 years ( ). Data sets of daily minimum and maximum temperature recorded in 14 weather stations were analyzed. The main goal was to find changes in extreme daily temperatures using a set of 20 indices adopted from the core indices developed by ETCCDMI with appropriate modifications to suit to the analyzed territory. The main results suggest that regional temperature trends at the scale of extra-carpathians areas of Romania are similar to those calculated for global and European continental scales; the climate has become warmer during the last decades. It has been identified that both extreme daily maximum and minimum temperatures have increased in the analyzed areas. For all the indices related to hot temperature most trends are significantly positive. The strongest increase was detected for hot related extremes such as summer days and tropical nights as well as for maximum values of maximum and minimum daily temperatures. For indices related to cold there are different sign slopes, but negative slopes prevail, especially for number of days under a defined threshold. This is also an evidence of the important warming in the area. Generally, it was found that the daily maximum temperature is getting more extreme, whereas the minimum is getting less extreme. Copyright 2012 Royal Meteorological Society KEY WORDS daily extreme temperature indices; daily maximum and minimum temperatures; trend; Mann-Kendall test and Sen s slope; extra-carpathians areas of Romania Received 5 March 2012; Revised 23 June 2012; Accepted 30 June Introduction The increase of mean temperature over the last century has been largely demonstrated in many studies focused on different regions of the world. Although changes in the mean values of the climate variables have a bearing on the long-term climatic conditions, the changes in the extremes of the variables can have far-reaching economic and social consequences (Hundecha and Bardossy, 2005). Thus, recently, many efforts have been made to estimate not only changes in mean temperature series, but also changes in the frequency, intensity, and duration of extreme events (Easterling et al., 2000; Jones et al., 2001; Frich et al., 2002; Klein Tank and Konnen, 2003; Kostopoulou and Jones, 2005; Moberg and Jones, 2005; Vincent et al., 2005; Alexander et al., 2006; Moberg et al., 2006; Brown et al., 2008, El Kenawy et al., 2011). At the end of the 20th century, when studies conducted using daily data began, it has been remarked that the daily minimum temperature has increased more than the daily maximum temperature at planetary scale (Easterling Correspondence to: A.-E. Croitoru, Faculty of Geography, Physical and Technical Geography Department, Babes-Bolyai University, 5-7, Clinicilor Street, Cluj-Napoca, Romania. croitoru@geografie.ubbcluj.ro; adina04@yahoo.com et al., 1997). On the contrary, later studies, mainly published in the first decade of the 21st century or more recently, revealed the significant increase in maximum temperature extremes both in duration, frequency and intensity, while a significant decrease was registered in minimum temperature data sets. The results were reported by studies conducted at global scale (Alexander et al., 2006), at a broader spatial scale such as Europe, China, South America (Frich et al., 2002; Fan et al., 2012; Vincent et al., 2005) as well as Asia-Indo-Pacific region (Choi et al., 2009; Caesar et al., 2011; Wong et al., 2011; Revadekar et al., 2012). Other authors considered lower spatial scales like sub-continental, national, regional or local (Salinger and Griffiths, 2001; Hundecha and Bardossy, 2005; Nandintsetseg et al., 2007; Moberg and Jones, 2005; Bartholy and Pongracz, 2007; Rahimzadeh et al., 2009; Martinez et al., 2010; El Kenawy et al., 2011; Fernandez-Montes and Rodrigo, 2011; Revadekar et al., 2012; Toros, 2012). In Romania a great part of the previous studies on observed changes were focused mainly on the mean temperature rather than on the extremes (Micu and Micu, 2006; Micu, 2009; Busuioc et al., 2010; Croitoru et al., 2012a, 2012b). Some of them also identified threats to Copyright 2012 Royal Meteorological Society

2 1988 A.-E. CROITORU AND A. PITICAR specific economic sectors like agriculture (Croitoru et al., 2012a) or winter tourism activities (Micu, 2009). Even if in the last decades, in different areas of the world, including regions in Europe, the behavior of extreme temperatures was investigated, little research on the variability of temperature extremes is available for the Romanian territory until now. Some previous studies determined trends in monthly maximum and minimum temperatures in different regions of Romania (Croitoru et al., 2002; Hauer et al., 2003; Hauer, 2009). Other studies analyzed extreme temperature changes mainly on cold and/or heat waves (Busuioc et al., 2010; Micu, 2012). Few articles on daily extreme temperatures changes that covered larger areas as Europe (Klein Tank and Konnen, 2003; Moberg et al., 2006) or the Carpathian basin (Bartholy and Pongracz, 2007), considered also some weather stations from Romania. This study is focused on analyzing daily temperature trends at a small regional scale, extra-carpathians areas of Romania (eastern, southern and southeastern regions of Romania), across 50 years ( ). This study aimed to identify, if the weather in the considered areas is getting more extreme in terms of air temperature. The main goal is to find changes in extreme daily temperatures using a set of 20 indices adopted from the core indices developed by ETCCDMI with appropriate modifications to suit to the analyzed territory. 2. Data and methods 2.1. Study area The Carpathian Mountains divide the Romanian territory in two groups of regions: intra-carpathian regions and extra-carpathian regions. The first group includes the areas located inside the mountain chain (the Transylvanian Depression) and areas located westward from the mountains (the Western Plain and Hills). The second group consists of the extra-carpathians regions and cover the southern, eastern, and southeastern areas of the Romanian territory and they are located southward and eastward from the Carpathians (the Romanian Plain, the Moldavian and Dobrudja tablelands). The main reason for such a division is the spatial variability in climatic features of the two groups of regions. Thus, the first group is more dominated by western moist air masses, while the second one is more often under the influence of southern tropical or eastern continental air masses (Topor and Stoica, 1965; Badea et al., 1983; Bogdan and Niculescu, 1999). For this article, extra-carpathians regions of Romania were considered (Figure 1). The choice was made especially for two reasons. First, because the intensity of the extreme temperature events as identified in annual and monthly data series, is higher than in the other regions due to the continental climate less influenced by wet western air masses (Bogdan and Niculescu, 1999). Second, because extreme temperature events are very important, mainly during extreme seasons (summer and winter) for different social and economic sectors. They may generate serious health problems to population or may disturb transportation, construction and tourism activities. Also, these areas include the most important agricultural regions in Romania. Since agricultural crops and yields are very sensitive to extreme weather events, especially to those related to temperature and precipitation, we considered necessary to study the changes in daily extreme Figure 1. The area and the weather stations location.

3 EXTREME TEMPERATURE CHANGES IN EXTRA-CARPATHIANS AREAS OF ROMANIA 1989 Table I. Geographical coordinates of the weather stations considered. No. a Weather station b Latitude (degr.) Longitude (degr.) Altitude (m) Sub-region 1. Botosani East 2. Suceava East 3. Iasi East 4. Bacau East 5. Galati East 6. Buzau East 7. Tulcea South-East 8. Sulina South-East 9. Constanta South-East 10. Calarasi South 11. Bucuresti-Baneasa South 12. Rm. Valcea South 13. Craiova South 14. Drobeta Turnu Severin (DTS) South a For greater ease in reading and understanding, in the text the number will be used instead of the station name. b The stations are ranged clockwise around the arch-shaped Carpathian Range. temperature in the above-mentioned regions. Then, the southeastern region is located on the western Black Sea coast and represents the most important summer tourism area in Romania. The studied area extends on more than 4 of latitude (between and ) and on more than 7 of longitude (between E and E). Even if the topography is not very complex and the altitude ranges between 0 and 500 m, there are diverse climatic regimes in the area. Thus, dominating continental conditions are more specific to the eastern part, while the southeastern region is particularly affected by the Black Sea influences. At the same time, southwestern Romania, including the Western Romanian Plain, seems to be more influenced by the Mediterranean Sea than the Black Sea climatic conditions (Badea et al., 1983; Sandu et al., 2008) Data description Changes in temperature extremes indices were identified using daily time series recorded in 14 weather stations, covering a 50 years period ( ). In Figure 1 the spatial distribution of the weather stations considered is shown. As illustrated, we consider that the chosen weather stations have a reasonable spatial coverage, and therefore they may emphasize the regional behavior and variability of the temperature extremes in the extra-carpathians areas of Romania. The geographical coordinates of the stations are presented in Table I. To identify changes in extreme temperature events, daily maximum (TX) and daily minimum (TN) temperatures data series were used. The great majority of the data sets were freely provided by ECA&D project (Klein Tank et al., 2002b). For one station (2), data series were extracted from the National Meteorology Administration database. Inhomogeneities are more frequent in longer climatic time series due to changes in routine observation practices, changing measurement location, urbanization or to any other non-climatic origin factors. Most of them were found at the beginning of the weather stations set up, in the late 19th century, or during the 20th century, before the Second World War (Rebetez and Reinhard, 2008; Croitoru et al., 2012a). The period of 50 years was chosen in order to avoid as much as possible the inhomogeneities due to non-climatic factors. On 1 January 1961, the weather stations network in Romania was greatly reorganized and there were only few data missing from that point on, while the routine observation practices, timetable and the location of the observatories considered for this study have not changed until the end of the period. Nevertheless, some factors, which may introduce breakpoints in the data sets, remained, and the most important of them are the changing in the surrounding environments and the setting up of the automatic weather stations at the end of 1990s or the beginning of 2000s. In this case, in addition to data availability, the quality control and homogeneity of the temperature time series are prerequisites for the detailed attribution of extreme events (El Kenawy et al., 2011). In order to work with continuous and homogeneous data sets, the blend series were chosen from ECA&D project database for the present article. The daily ECA series of surface air temperature were statistically tested with respect to homogeneity for different intervals: 1851 last year, 1901 last year, 1951 last year, , , 1979 last year (Wijngaard et al., 2003). Thus, the homogeneity of the ECA data has been assessed (the blend subsets for the interval ), for those weather stations where daily data were available since 1951 or earlier (6, 7, 10, 11, and 14). The ECA blend data sets were also used by some previous study on temperature extreme events (Moberg et al., 2006; Micu, 2012). The methodology has been implemented in ECA&D (Project team ECA&D, 2011) and is presented in the chapter 2.3. For the locations where availability or recordings began in 1961 (weather stations 1, 3, 4,

4 1990 A.-E. CROITORU AND A. PITICAR 5, 8, 9, 12, and 13), as well as for weather station (2) with data provided by the National Meteorological Administration, the homogeneity was tested for the considered period using the same methods and software package (RhtestsV3) like in ECA&D project (Wang and Feng, 2010). The results are presented in chapter Methods Quality control Quality control is needed to assure the reliability of climatic data. Therefore, both maximum and minimum temperature raw data of the stations, which were extracted directly from the National Meteorological Administration database, were subject to quality control. The conditions to admit or reject a value were the same with those used by Project team ECA&D (2011) and are presented below. Thus, to pass the quality control, each value in the data sets of daily maximum temperature (TX) had to meet simultaneously six conditions: it must exceed 40.0 C; it must be less than 50.0 C, it must exceed or be equal to daily minimum temperature; it must not be repetitive (i.e. exactly the same) for 5 d in a row; it must be less than the long term average daily maximum temperature for that calendar day +5 times standard deviation (calculated for a 5 d window centered on each calendar day over the whole period) and it must exceed the long term average daily maximum temperature for that calendar day 5 times standard deviation (calculated for a 5 d window centered on each calendar day over the whole period). Similarly, to be accepted, each of the minimum daily temperature (TN) had to simultaneously carry out the next six conditions: it must exceed 40.0 C; it must be less than 50.0 C; it must be less or equal to daily maximum temperature; it must not be repetitive (i.e. exactly the same) for 5 d in a row; it must be less than the long term average daily minimum temperature for that calendar day +5 times standard deviation (calculated for a 5 d window centered on each calendar day over the whole period); it must exceed the long term average daily minimum temperature for that calendar day 5 times standard deviation (calculated for a 5 d window centered on each calendar day over the whole period) (adapted after Zang and Yang, 2004; Project team ECA&D, 2011) Homogeneity testing For the homogenization of the climatic data series, most techniques developed so far are suited for data at monthly or longer time scales. Few methods were also developed for the use with daily data (Klein Tank et al., 2002a; Vincent et al., 2002). An R-based toolkit RHtests (Wang and Feng, 2010) that uses a two-phase regression technique (Wang, 2003) for the detection and adjustment of inhomogeneity is also available. Wijngaard et al. (2003) statistically tested with good results the daily ECA series ( ) of surface air temperature with respect to homogeneity, and their methodology has been implemented in ECA&D project. A twostep approach is followed. First, the daily series are evaluated with four homogeneity tests: Standard Normal Homogeneity Test SNH (Alexandersson, 1986), Buishand Range test BHR (Buishand, 1982), Pettitt test PET (Pettitt, 1979) and Von Neumann Ratio test VON (Von Neumann, 1941). The use of derived annual variables avoids auto correlation problems with testing daily series. Second, the test results are condensed for each series into three classes: useful, doubtful and suspect. All four tests suppose under the null hypothesis that in the series of a testing variable, the values are independent with the same distribution (Wang and Feng, 2010). Under the alternative hypothesis the SNH, BHR, and PET tests assume that a step-wise shift in the mean (a break) is present. The year where a break is likely may be located with the three up-mentioned tests. The fourth test (VON) assumes under the alternative hypothesis that the series is not randomly distributed. This last test does not give information on the year of the break (Wang and Feng, 2010). In this article, we have tested for homogeneity the data sets recorded in locations 1, 2, 3, 4, 5, 9, 12, 13 using RHtestsV3 software developed in R (R version , 2011) and presented by Wang and Feng (2010). For further analysis we considered only useful data series. We employed the RHtestsV3 software because the data sets of the other six stations considered for this article were checked for homogeneity with the same software in the ECA&D project which is the source data for all the temperature series except station 2 (Suceava). Those four tests were indicated by Aguilar et al. (2003) as a valid approach to assess the homogeneity of daily time series. The same tests were recently employed with good results for Catalonia (Martinez et al., 2010). One or more of them were previously used in other studies focused on the changes in extreme weather indices in different regions of the world (Alexandersson and Moberg, 1997; Salinger and Griffiths, 2001; Tomozeiu et al., 2002; Gilles et al., 2006; Kysely and Domonkos, 2006; Ha and Ha, 2006; Lee et al., 2006; Serra et al., 2006; Brunet et al., 2007; Wong et al., 2011) Temperature indices In the general context of climate change, it is considered that extreme temperatures will be some of the most affected climatic parameters. The changes will imply frequency, intensity, and persistence. For this reason, in the last decades many researchers tried to define extreme temperature events in different ways (Zang and Yang, 2004; Alexander et al., 2006; IPCC, 2007; El Kenawy et al., 2011; Radinović andćurić, 2011). Even if inside the scientific community in the field of climatology there are reasons for using some or other indices, the general opinion is that the more indices are used, the better and more reliable image on the changes in the extreme temperatures is presented. For this study, we employed a set of 20 indicators relating to hot, cold and variability in extreme temperatures. Most of them were chosen from the list established

5 EXTREME TEMPERATURE CHANGES IN EXTRA-CARPATHIANS AREAS OF ROMANIA 1991 by the climatic community (coordinated by the joint WMO Commission for Climatology and the Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDMI) of the Climate Variability and Predictability (CLIVAR) project (World Climate Research Programme) (Kioutsioukis et al., 2010). During last decade, these indicators were widely used to assess climate changes of the extreme temperature for different regions of the world (Hundecha and Bardossy, 2005; Moberg et al., 2006; Bartholy and Pongracz, 2007; Choi et al., 2009; Martinez et al., 2010; Caesar et al., 2011; El Kenawy et al, 2011; Fernandez-Montes and Rodrigo, 2011; Fan et al., 2012; Toros, 2012; Revadekar et al., 2012). For the Romanian territory, these indices were used only in the studies focused on European continent for older periods (Klein Tank and Konnen, 2003; Moberg et al., 2006). One article that studied the Carpathian Basin (Bartholy and Pongracz, 2007) took into consideration three weather stations from Western Romania. Also, based on CSDI and WSDI indices, the cold and/or heat waves were studied by Busuioc et al. (2010) and Micu (2012). In this article, the indices were chosen primarily for the assessment of many aspects concerning a changing regional climate, including changes in the intensity and frequency of temperature events. They represent events that occur several times per season or year giving them more robust statistical properties than measures of extremes which are far enough into the tails of the distribution so as not to be observed during some years (Alexander et al., 2006). Their brief description is available in Table II. Considering the way the indices are computed, they may be divided into four categories: a. Indices based on fixed thresholds are indices defined on a certain fixed threshold of recorded temperature and they may vary according to the analyzed region (Hundecha and Bardossy, 2005). Calculation of temperature indices in this way is considered a straightforward and appropriate approach for climate impact assessment, particularly at detailed spatial scales. These definitions can be more valuable when the defined thresholds have physical, hydrological or biological meaning (Politano, 2008; El Kenawy et al., 2011). Thus, we have used seven fixed threshold defined indicators: summer days (SU25), tropical days (TRD30), hot days (HD35), tropical nights (TR20), frost days (FD0), frost nights (FN-10), and ice days (ID0). Even if some previous studies considered for the upper threshold the temperature of 25 C (Kioutsioukis et al., 2010; El Kenawy et al, 2011), in this study other two threshold for maximum temperature (30 and 35 C) were established because these temperatures seem to be not so rare in the analyzed area. They define tropical and hot days. For minimum temperature, one more threshold was added ( 10 C) in order to identify frost nights. All these three values were Table II. List of the ETCCDMI Climate Indices used in this article (after Zhang and Yang, 2004, completed). ID Index name Definitions UNITS Hot extremes SU25 Summer days Annual count when TX (daily maximum)>25 C Days TR20 Tropical nights Annual count when TN (daily minimum)>20 C Days TRD30 Tropical days Annual count when TX (daily maximum)>30 C Days HD35 Hot days Annual count when TX (daily maximum)>35 C Days TN90p Warm nights Percentage of days when TN>90 th percentile Days TX90p Warm days Percentage of days when TX>90 th percentile Days TXx Max Tmax Monthly maximum value of daily maximum temperature C TXn Min Tmax Monthly minimum value of daily maximum temperature C TX mean Mean Tmax Monthly mean value of daily maximum temperature C Cold extremes FD0 Frost days Annual count when TN (daily minimum)<0 C Days ID0 Ice days Annual count when TX (daily maximum)<0 C Days FN-10 Frost nights Annual count when TN (daily minimum)< 10 C Days TN10p Cool nights Percentage of days when TN<10 th percentile Days TX10p Cool days Percentage of days when TX<10 th percentile Days TNx Max Tmin Monthly maximum value of daily minimum temperature C TNn Min Tmin Monthly minimum value of daily minimum temperature C TNmean Mean Tmin Monthly mean value of daily minimum temperature (TN) C Variability extremes DTR Diurnal temperature Monthly mean difference between TX and TN C range ETR Intra-annual extreme Difference between the highest TX and the lowest TN in C temperature range a year GSL Growing season Length Annual count between first span of at least 6 days with TG>5 C and the first occurrence after 1st July of at least 6 consecutive days with TG<5 C Days

6 1992 A.-E. CROITORU AND A. PITICAR chosen because the National Meteorological Administration releases special weather alert messages in the case of surpassing these thresholds during summertime, and respectively under the threshold in case of wintertime. Fernandez-Montes and Rodrigo (2011) also used the threshold of 30 C for the Iberian Peninsula. For this kind of indices, changes in annual number of days were analyzed. b. Indices based on station-related thresholds are those indices defined on a percentile-based threshold. Generally, the percentile-based indicators are defined as days surpassing the warmest/coldest long-term percentiles (El Kenawy et al., 2011). This is a commonly used method to determine extreme values in climatology (Alexander et al., 2006; IPCC, 2007; Trenberth et al., 2007). These definitions are objective, siteindependent, and facilitate direct comparisons between different regions (Choi et al., 2009). In this article, four indices of this category were analyzed: cold days (TX10p), cold nights (TN10p), warm days (TX90p), and warm nights (TN90p). c. Non-threshold indices include those indices computed considering the absolute values recorded in the area without considering any threshold. They are focused on the monthly absolute values of the temperature (maximum and minimum), and not on the number of days with specific temperature values as in the previous two main categories. Six indices were considered: monthly maximum value of daily maximum temperature (TXx), monthly maximum value of daily minimum temperature (TNx), monthly minimum value of daily maximum temperature (TXn), monthly minimum value of daily minimum temperature (TNn), monthly mean value of daily maximum temperature (TXmean), and monthly mean value of daily minimum temperature (TNmean). d. Variability extremes or mixed temperature indices. Finally, some indices were included in the variability extremes category and they are used to analyze the relationship between maximum and minimum temperatures. This group includes three indicators: diurnal temperature range (DTR), annual temperature range (ETR) and growing season length (GSL). For those indices where monthly data series are considered for calculation (TN90p, TX90p, TXx, TXn, TX mean, TN10p, TX10p, TNx, TNn, TNmean, and DTR), also seasonal and annual series were generated in order to get a more detailed temporal analysis. In the list above, warm spell duration index (WSDI) and cold spell duration index (CSDI) were intentionally omitted because the study of these indicators using different ways of calculation, is the subject to a forthcoming project Trends calculation To detect and estimate trends in the time series of monthly, seasonal, and annual temperature values, the Excel template MAKESENS (Mann Kendall test for trend and Sen s slope estimates), developed by researchers of the Finnish Meteorological Institute (Salmi et al., 2002), was used. The procedure is based on the nonparametric Mann Kendall test for the trend and Sen s nonparametric method for the magnitude of the trend (Mann, 1945; Kendall, 1975). The Mann Kendall test is applicable to the detection of a monotonic trend of a time series. Sen s method uses a linear model to estimate the slope of the trend, and the variance of the residuals should be constant in time (Croitoru et al., 2012a). The MAKESENS software performs two types of statistical analyses: first, the presence of a monotonic increasing or decreasing trend is tested with the nonparametric Mann Kendall test, and then, the slope of a linear trend estimated with Sen s non-parametric method is computed (Gilbert, 1987). In MAKESENS, the tested significance levels α are 0.001, 0.01, 0.05, and 0.1. Both methods are used here in their basic forms. We employed this method mainly because it is robust to outliers and does not assume an underlying probability distribution of the data series (Moberg et al., 2006). As a result, this statistic has been widely used in climatological and hydrological applications (e.g. Zhang et al., 2005; Choi et al., 2009; Croitoru et al., 2011, 2012a; El Kenawy et al., 2011; Tabari et al., 2011). 3. Results 3.1. Homogeneity test Before conducting detailed analysis of the extreme indices, the metadata of the temperature data sets used for this study were examined for their homogeneity. In those locations where metadata were not available or where they were very poor, we made comparisons with neighboring stations to identify unrecorded like changes in the environment near the climate station site. This is one of the methods also used by Salinger and Griffiths (2001). We found that there were no important changes in surroundings of the analyzed stations. Testing for homogeneity the extreme temperature data sets of locations 1, 2, 3, 4, 5, 8, 9, 12, and 13, the most part of them were found to have no change points (Table III). However, change points were found in the minimum temperature data sets recorded in three stations (1, 8, and 9) with a statistical significance of We paid special attention to those three stations. We checked the metadata for the locations and we did not identify any specific non-climatic changes: no location change, no changes in the observation program. The instrumentation change from classic thermometers to automatic weather stations seems to have not an important influence, since there were not captured changes in the maximum temperature series, too. There is no information on changes of the surroundings, in terms of changing the vegetation, but the land use was maintained as agricultural land, in case of stations 1 and 9. In the last 20 years of the analyzed period, after the communist regime was removed,

7 EXTREME TEMPERATURE CHANGES IN EXTRA-CARPATHIANS AREAS OF ROMANIA 1993 Table III. Change points detected in the data sets over the period No. Weather station Maximum temperature Minimum temperature Date of the change point 1 Botosani /12/18 2 Suceava Iasi Bacau Galati Sulina /01/01 9 Constanta /11/ /01/03 12 Rm. Valcea Craiova in 1989, the urbanization has continuously developed and the limits of some cities became closer and closer to the weather station. In the same period, the local and regional pollution in Romania generated by the industry dramatically decreased due to Romanian economy collapse, but increased the pollution induced by the auto-traffic, especially in the urban and metropolitan areas. So, it is very difficult to evaluate the role of the non-climatic factors on the temperature increase, especially because the pollution data were not public before 1989 and sometimes they were modified. The weather stations 8 and 9 are located in the Southeast of the country, on the Black Sea western coastline. If urbanization and local pollution might be specific to station 9, in case of station 8, which is located on a pier entering the Black Sea more than 5 km, this change has to be excluded and should be considered that it was not influenced at all by the urbanization. Thus, it seems unlikely that the surrounding environment caused the change at the end of the 20th century. The setting up the automatic weather stations was done in 2004 for both stations. We also consider that non-climatic factors should have influenced also maximum daily temperature, not only minimum daily temperature data series, as it is the situation identified. In this context, we consider that the causes are rather connected to global change than local scale human-induced. It is more likely that the change points identified in both stations data series may be related to the increase in sea surface temperature, where a positive trend for the mean annual basin average of about 0.06 C/year was found during the period (Ginzburg et al., 2007). More, the change points identified at the end of the 1980s at stations 1 and 9 are in accordance with change points also identified in mean air temperature of the lower regions of Romania and seems to have natural causes (Busuioc et al., 2010) Extreme temperature changes This section gives an overview of trend results for each of the investigated indicators. Below, they are analyzed in three different categories: changes in hot extremes, changes in cold extremes, and changes in variability extremes. The results obtained after the application of the Mann Kendall test to all series of indices are summarized in Table IV, while the spatial distribution of the trend for each index both in terms of the direction (sign) and statistical significance is synthetically presented in next chapters. When analyzing the intensity of increasing and decreasing trends, three degrees were define: - high increasing/decreasing trends (large triangle in Figures 3, 5, and 7): >/<4.000 d decade 1 for SU25, TR30, TRD35, TR20, TN90p, TX90p, ID0, FD0, FN10, TN10p, TX10p, GSL, and ±0.601 to ±0.900 C/ decade for TXn, TXx, TNx, TNn, Txmean, TNmean, DTR, and ETR; - moderate increasing/decreasing (medium triangle in Figures 3, 5, and 7): ±2.001 to ±4.000 d decade 1, for SU25, TR30, TRD35, TR20, TN90p, TX90p, ID0, FD0, FN10, TN10p, TX10p, GSL, and ±0.301 to ±0.600 C/decade for TXn, TXx, TNx, TNn, TXmean, TNmean, DTR and ETR; - low increasing/decreasing (little triangle in Figures 3, 5, and 7): ±0.001 to ±2.000 d decade 1 for SU25, TR30, TRD35, TR20, TN90p, TX90p, ID0, FD0, FN10, TN10p, TX10p, GSL, and ±0.001 to ±0.300 C/ decade TXn, TXx, TNx, TNn, TXmean, TNmean, DTR, and ETR Changes in hot extremes The hot extremes analysis in the considered area indicates general positive trends. Thus, the percentage values reveal prevailing positive trends for the most part of the time series (between 73 and 100%) (Table IV, Figure 2). Further, for five of them, more than 90% of the data sets considered showed positive trends. It is about the indices related mostly to daytime and specific for warm period of the year (from May to September): SU25, TR20, TRD30, HD35, and WD. They are more frequent especially during summer months. Statistically significant positive trends vary for all of the hot extreme indices from 27 to more than 90%. The highest frequencies were also reached mostly by daytime indices (the tropical days, summer days, and warmest day). This finding demonstrates that temperature during summer months increased more rapidly in the daytime than that related to nighttime for the same months. Monthly maximum values of daily maximum temperature (TXx) and warm days (TX90p) have the lowest frequencies both for total positive and for statistically significant positive trends. At the same time, more of the TXx data sets (85.7 %) indicate positive trends with respect to TXn data series (76.1 %). In terms of significance, the difference is even higher: 47.9 and 27.3%, respectively. For indices TN90p and TX90p the situation is reverse; 5% more frequent for increasing trends in the case of

8 1994 A.-E. CROITORU AND A. PITICAR Table IV. Results of the trend test in extreme temperatures (%). Index No. of data series Positive trend Negative trend No-trend considered Total SS a Non-SS Total SS a Non-SS Total SS a Non-SS Hot extremes SU TR TRD HD TN90p TX90p TXx TXn TX mean WD Cold extremes FD ID FN TN10p TX10p TNx TNn TN mean CD Variability extremes DTR ETR GSL a Statistical significance: α = Figure 2. Frequency of trend types of hot extreme indices. minimum temperature than in the maximum, while the statistically significant positive trends are also less for TX90p. Some of the hot indices calculated for Romanian extra-carpathians areas seem to increase more compared to other regions in Europe (north-eastern Spain), where similar indices were computed for almost the same period ( ) (El Kenawy et al., 2011). Consequently, the negative trends have very low frequency compared to that of the positive ones, with maximum values lower than 22%. It is remarkable that the statistically significant negative trends do not exceed 3% of the total number of data sets considered for this study. An important frequency of no-trends data series (more than 18%) was recorded both for TN90p and TX90p indices, but for none of them a statistically significance was found. For hot extremes indices, the highest slopes were found for SU25 and TRD30 with overall average around 4 d decade 1 (Table V, Figure 3). The values for the other four indices with slope given in days/decade

9 EXTREME TEMPERATURE CHANGES IN EXTRA-CARPATHIANS AREAS OF ROMANIA 1995 Table V. Slopes for the hot extreme indices calculated for annual data series over the period (in the case of indices calculated for each month and for annual, only the annual value is presented in the table). Weather station SU25 TR20 TRD30 HD35 TN90p TX90p TXx TXn TXmean Slope Days/decade % of days/decade C/decade Suceava (1) a Botosani (2) Iasi (3) Bacau (4) Galati (5) Buzau (6) Tulcea (7) Sulina (8) Constanta (9) Calarasi (10) Bucuresti (11) Rm Valcea (12) Craiova (13) DTS (14) Overall average a Values in bold are statistically significant slopes at α = 0.1. Figure 3. Spatial distribution and intensity of hot extreme indices trends: the upward triangles are positive trends, downward triangles are negative trends, and circles are for no trend; the full symbol shapes are statistically significant slopes and empty symbol shapes are statistically insignificant slopes.

10 1996 A.-E. CROITORU AND A. PITICAR (TR20, HD35, TN90p, and TX90p) are between 0.95 and In the case of TN90p and TX90p, like in the situation of the frequency, the values of the slope are very closed, 1.65 and 1.61, respectively. There is a very important difference between the intensity of the increasing of TXx and TXn: an overall average of 0.44 C/decade for TXx and almost a stationary trend in case of TXn. More, the slopes calculated for each station indicate statistically insignificant values for TXn, while the great majority of the locations experienced statistically significant increasing trends for TXx. In terms of spatial pattern, a higher increase is specific to the southeastern region of Romania (weather stations 5, 9, and 10) as well as in the southwestern region (location 14; Figure 3). The southeastern areas are the most exposed to dry tropical air advection from the Arabian Peninsula, especially during summertime, while in the west, the tropical wet air coming from Mediterranean regions is more frequent (Topor and Stoica, 1965). The altitude and latitude seem to have no influence on the intensity of the increasing trends, most likely because of the small range in altitude and latitude of the chosen locations. Temporal variation during the year of the indices calculated for each month and then summarized for each season is presented in Table VI. It indicates that the intensity of the warming process is stronger during summer if percentile-based indices are considered (TN90p and TX90p), while if we look at the non-threshold indices, the warming seems to be more intense during winter for maximum and mean values of the maximum daily temperature (TXx, TXmean), as well as in spring and summer for minimum values of the maximum daily temperature (TXn). Among the maximum, minimum and mean values of maximum daily temperature, the highest slopes were calculated for maximum temperature (TXx). When compare slopes of pairs of indices like TN90p vs. TX90p, and TXx vs. TXn, respectively, the differences are more important for winter and for spring, while the values are very close for summer (Table VI). In autumn, the lowest slopes are specific, even with negative values for Table VI. The seasonal intensity of the changes in hot temperature extremes as overall mean values of the analyzed region. Season TN90p TX90p TXx TXn TXmean Slope % of days/decade C/decade DJF MAM JJA SON minimum and mean values of the daily maximum temperature (TXn and TXmean) Changes in cold extremes Changes in cold extreme were calculated for 8 indices: three indices with fixed threshold (FD0, ID0, and FN- 10), two indices related to percentile threshold (TN10p, TX10p), and three indices computed considering the absolute values recorded (TNx, TNn, and TNmean). Generally, the negative trends are more specific for these indices, especially for those computed based on a fixed threshold and percentile-based indices: frost days (FD0), frost nights (FN-10), ice days (ID0), cool nights (TN10p), and cool days (TX10p) (Table IV, Figure 4); some of them are statistically significant (Tables IV and VII). The positive trends have very low frequency for these indices, which is an important evidence for warming during winter time. The indices based on extreme minimum temperature records (TNx, TNn, and TNmean) also indicate, by their high frequency of positive trends, a warming process through the analyzed 50 years period. In terms of intensity, the most important decrease in number of days characterizes the frost days index (FD0), with an overall average considered of about 2.7 d decade 1 (Table VII, Figure 5). Most of the locations experienced significant decreasing slopes of more than Figure 4. Frequency of trend types of cold extreme indices.

11 EXTREME TEMPERATURE CHANGES IN EXTRA-CARPATHIANS AREAS OF ROMANIA 1997 Table VII. Slopes for the cold extreme indices calculated for annual data series for the period (in the case of indices calculated for each month and for annual, only the annual value is presented in this table). Weather station FD0 ID0 FN-10 TN10p TX10p TNx TNn TNmean CD Slope Days/decade % of days/decade C/decade Suceava (1) a Botosani (2) Iasi (3) Bacau (4) Galati (5) Buzau (6) Tulcea (7) Sulina (8) Constanta (9) Calarasi (10) Bucuresti (11) Rm Valcea (12) Craiova (13) DTS (14) Overall average a Values in bold are statistically significant at α = 0.1. Figure 5. Spatial distribution and intensity of cold extreme indices trends: the upward triangles are positive trends, downward triangles are negative trends, and circles are for no trend; the full symbol shapes are statistically significant slopes and empty symbol shapes are statistically insignificant slopes. 3.0 d decade 1. Only for one weather station a positive trend was calculated, and other three stations had negative trends, but all of them were detected as statistically insignificant. From the point of view of the spatial pattern, for FN- 10, the highest decrease in number of days was recorded for the northern stations (1, 2, 3, and 4), while no trends were identified for the south-eastern and western ones.

12 1998 A.-E. CROITORU AND A. PITICAR Table VIII. The seasonal intensity of the changes in hot temperature extremes as overall mean values of the analyzed region. Season TN10p TX10p TNx TNn TNmean Slope % of days/decade C/decade DJF MAM JJA SON Figure 6. Frequency of trend types of variability indices. This may be due to the locations near large water surface like the Black Sea (8, 9) and the Danube River (7) and the water body built on it (14). Even if the slopes values are not as high as in previously analyzed indicators, it is remarkable that for the annual 10-percentile based indices (TN10p, TX10p), strong statistically significant slopes were found for all locations in case of TN10p and for 12 out of 14, in case of TX10p. Among the second category of indices, TNx and TNmean indicate generalized statistically significant increase in minimum temperature for annual values with overall average slopes of C/decade and C/decade, respectively. For TNn the slope values vary from positive to negative, and also no-trend were found for few stations. Anyway, all of them are statistically insignificant. Similarly to hot temperature extreme indices, the temporal variation of the cold temperature extremes of these indices calculated for each month and then summarized for each season is presented in Table VIII. The warming based on cold temperature indicators is more obvious in extreme seasons with maximum values of slopes in winter (TN10p, TNx, and TNmean) or in summer (TX10p, TNn). The process is less visible during transition seasons, even if for some indices the second highest slope was calculated for spring (TX10p, TNn) or for autumn (TN10p) Changes in variability indices Positive trends prevail (72 100%) in all variability indices datasets with statistical significance identified for 14 43% of them (Table IV, Figure 6). The increase in DTR is in disagreement with some previous results presented for the global surface at the end of the 20th century (Easterling et al., 1997), which presented general decreasing trend of this index. The most important changes characterize the GSL index: all weather stations series were found to have positive trends and almost half of them were also statistically significant. Negative trends have the highest frequency for DTR, but only about the third part of them indicates statistical significance. Compared to hot extreme and cold extreme indices, no-trend series are Table IX. Slopes for the variability indices calculated for annual data series for the period (in the case of indices calculated for each month and for annual, only the annual value is presented in this table). Weather station DTR ETR GSL C/decade Slope a Days/decade Suceava (1) Botosani (2) Iasi (3) Bacau (4) Galati (5) Buzau (6) Tulcea (7) Sulina (8) Constanta (9) Calarasi (10) Bucuresti (11) Rm Valcea (12) Craiova (13) DTS (14) Overall average a Values in bold are statistically significant slopes. extremely rare in the case of variability indices (only 0.8% of DTR series). The intensity of the increase in temperature range as overall average value is more important for daily than for annual values (Table IX, Figure 7). Spatial analysis reveals that the highest increase for DTR is in the southeastern areas of Romania, reaching up to 0.46 C/decade. For ETR, the highest slopes are specific to eastern areas ( C/decade). During wintertime, these regions are the most exposed to the ultrapolar circulation, bringing extremely cold dry air (from East European High) while in summertime the area is largely covered by hot dry air masses from the Arabian Peninsula (continental tropical circulation) or by Mediterranean hot moist air masses (maritime tropical circulation). The GLS has the highest slopes (5 9 d decade 1 )in southern weather stations. Because they are the main

13 EXTREME TEMPERATURE CHANGES IN EXTRA-CARPATHIANS AREAS OF ROMANIA 1999 Figure 7. Spatial distribution and intensity of variability extreme indices trends: the upward triangles are positive trends, downward triangles are negative trends, and circles are for no trend; the full symbol shapes are statistically significant slopes and empty symbol shapes are statistically insignificant slopes. agricultural areas for cereal crops, the evolution of this indicator is very important both for agriculture managers and farmers in order to adopt appropriate measures to mitigate the impact of global warming on crops and to ensure the food security of the country. These findings are in accordance with some simulations for winter wheat that have already been done (Croitoru et al., 2012a). The analysis of DTR indicator across the year revealed the most important changes for spring (0.25 C/decade) followed by winter and summer with 0.21 C/decade and 0.19 C/decade, respectively, while for autumn a negative slope was calculated ( 0.14 C/decade). 4. Discussions and conclusions The main findings of this article suggest that regional temperature trends at the scale of extra-carpathians areas of Romania are similar to those calculated for global and continental scales; the climate has become warmer during the last decades. It has been identified that both the extreme daily maximum and minimum temperatures have increased in eastern, southern and southeastern Romania. The strongest increase was detected for hot related extremes such as summer days and tropical nights (SU25, TR20) as well as for maximum values of maximum and minimum daily temperatures (TXx and TNx). For all the indices related to hot temperature (SU25, TR20, TRD30, HD35, TN90p, TX90p, TXx, and TXmean), most trends are significantly positive. The only exceptions are TXn series, which have positive and negative trends, but both categories have statistically insignificant slopes. For indices related to cold there are different sign slopes; negative slopes prevail for FD0, ID0, FN-10, TN10p, and TX10p, while mainly positive slopes were detected for TNx and TNmean. But this does not mean that they indicate both warming and cooling. The decrease in number of days under a fixed threshold or a station-related threshold is also evidence for warming and not for cooling, since fewer days have lower temperatures. The significant increase in both maximum and mean annual values of minimum temperatures (TNx and TN mean) completes the warming frame in the analyzed area. Generally, it was found that the daily maximum temperature is getting more extreme, whereas the minimum is getting less extreme. The result is similar to that obtained for different European regions like western Romania (Bartholy and Pongracz, 2007), western Germany (Hundecha and Bardossy, 2005) or northeastern Spain (El Kenawy et al., 2011). At the same time, this finding is contrary to that presented for a global scale in an older study by Easterling et al. (1997). The warming found for almost all of the indices is in accordance with findings in different regions of Europe (Hundecha and Bardossy, 2005; Moberg and Jones, 2005; Kioutsioukis et al., 2010; Fernandez-Montes and Rodrigo, 2011) or in other continents of the Northern Hemisphere (Choi et al., 2009; Rahimzadeh et al., 2009; Toros, 2012). Most of the investigated temperature indices revealed seasonal variability, with maximum intensity during extreme seasons (summer and winter), and less average trends during spring and autumn. Acknowledgements The authors acknowledge the daily data freely provided by the European Climate Assessment & Dataset project initiated by the European Climate Support Network and supported by the Network of European Meteorological Services (Klein Tank et al., 2002a). This article was partially supported by POSDRU CUANTUMDOC Doctoral Studies for European Performances in Research and Inovation ID79407 Project, funded by the European Social Found and Romanian Government. The authors wish to thank the two reviewers for their useful critical comments on the earlier version of this manuscript. The authors particularly acknowledge PhD. Lecturer Raularian Rusu for