CHANGES IN SEASONAL MEAN MAXIMUM AIR TEMPERATURE IN ROMANIA AND THEIR CONNECTION WITH LARGE-SCALE CIRCULATION

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 22: (22) Published online 2 July 22 in Wiley InterScience ( DOI: 1.12/joc.785 CHANGES IN SEASONAL MEAN MAXIMUM AIR TEMPERATURE IN ROMANIA AND THEIR CONNECTION WITH LARGE-SCALE CIRCULATION RODICA TOMOZEIU, a,b, * ARISTITA BUSUIOC b and SABINA STEFAN c a ARPA Servizio Meteorologico Regionale, Viale Silvani 6, 4122 Bologna, Italy b National Institute of Meteorology and Hydrology, Sos. Bucuresti-Ploiesti, 97, sector 1, Bucharest, Romania c University of Bucharest, Faculty of Physics, PO Box MG-11, Bucharest, Romania Received 14 May 21 Revised 22 January 22 Accepted 23 January 22 ABSTRACT This paper investigates the temporal and spatial variability of the seasonal mean of maximum air temperature in Romania and its links with the large scale atmospheric circulation. The Romanian data sets are represented by time series at 14 stations. The large-scale parameters are represented by the observed sea-level pressure (SLP) and geopotential height at 5 hpa (Z5). The period analysed was for winter and for all seasons. Before analysis, the original temperature data were tested to detect for inhomogeneity using the standard normal homogeneity test. Empirical orthogonal functions (EOFs) were used to analyse the spatial and temporal variability of the local and large-scale parameters and to eliminate noise from the original data set. The time series associated with the first EOF pattern of the SLP and mean maximum temperature in Romania were analysed from trend and shifts point of view using the Pettitt and Mann Kendall tests respectively. The covariance map computed using the Z5 and the seasonal mean of maximum temperature in Romania were used as additional methods to identify the large-scale circulation patterns influencing the local variability. Significant increasing trends were found for winter and summer mean maximum temperature in Romania, with upward shifts around 1947 and 1985 respectively. During autumn, a decreasing trend with a downward shift around 1969 was detected. These changes seem to be real, since they are connected to similar changes in the large-scale circulation. So, the intensification of the southwesterly circulation over Europe since 1933 overlapped with the enhancement of westerly circulation after the 194s could be the reason for the change in winter mean maximum temperature. The slight weakening of the southwesterly circulation during autumn could be one of the reasons for the decrease in the regime of the mean maximum temperature for autumn seasons. Additionally, the covariance map technique reveals the influence of the North Atlantic oscillation in winter, East Atlantic Jet in summer and Scandinavian (or Euroasia-1) circulation pattern in autumn upon mean maximum air temperature. Copyright 22 Royal Meteorological Society. KEY WORDS: spatio-temporal variability; seasonal maximum temperature; East Atlantic Jet; Scandinavian pattern; Romania 1. INTRODUCTION Temperature and precipitation are perceived as the key elements of climate. Studies of changes in the evolution of these parameters have become the subjects of increasing concern in the scientific community. Analysis of the observed mean surface temperature and precipitation over the last century has revealed significant changes. The global mean surface air temperature has increased by between about.3 and.6 C since the late 19th century, and precipitation over land has generally increased in the extra-tropical areas with a tendency for rainfall decline in the subtropics (Houghton et al., 1996). Some important features of the temperature field can be revealed by analysing the extreme values of minimum and maximum temperature. Karl et al. (1993), using monthly mean maximum and minimum temperatures for over 5% of the Northern * Correspondence to: Rodica Tomozeiu, ARPA Servizio Meteorologico Regionale, Viale Silvani, 6, 4122 Bologna, Italy; r.tomozeiu@smr.arpa.emr.it Copyright 22 Royal Meteorological Society

2 1182 R. TOMOZEIU, A. BUSUIOC AND S. STEFAN Hemisphere landmass, found that the rise in the minimum temperature that has occurred is three times that of the maximum temperature in the period Similar results were obtained at a regional scale. Brazdil et al. (1996), analysing the trends of maximum and minimum temperatures in central and southeastern Europe for the period , found that the increase in the annual maximum temperature in central Europe is slightly lower than that of the minimum temperature. Some seasonal characteristics can be remarked upon. Winter maximum temperature exhibits a significant increase in central Europe, Slovenia and Hungary. In summer and autumn, maximum temperature also increases in Germany, mid-switzerland and Austria. The present study seeks to fill the existing gaps (Romania) concerning the variability of the mean maximum air temperature in southeastern Europe. Considerable progress has been made recently in understanding the causes that determine changes in the mean regime of the main climatic parameters. These changes can be determined by natural forcing, human activity, or both. In order to establish whether the changes are part of the natural variability of the climate system or are a response to anthropogenic forcing, information is needed on climate variability on a relevant time scale. The human influence on climate was shown, for example, among others, by Schönwiese et al. (199) and Kaufmann and Stern (1997), who analysed temperature records covering different periods of time. Numerous studies have proved the influence of general circulation mechanisms on the temperature or precipitation regimes (Werner and von Storch, 1993; Fu et al., 1999; Yin et al., 2; Quadrelli et al., 21). Similar studies were performed in Romania for mean air temperature and precipitation (Busuioc, 1996; Busuioc and von Storch, 1996), though extreme temperatures have been less well analysed (Busuioc and Tomozeiu, 1998). In the present study, an analysis of the seasonal maximum air temperature variability, including trend and shift point analysis, over Romania is carried out by examining the observed data collected at 14 stations, for the period covering the interval Taking into account that the time series have some missing values, the analysis was also performed for a common period for all seasons (196 98). The second aim of this study is to detect the causes that determine the changes in the seasonal mean of maximum air temperature. The large-scale circulation pattern influencing the mean maximum temperature variability in Romania was identified by means of covariance maps. A correlation between the seasonal mean of maximum air temperature and large-scale circulation indices was also performed. The paper is organized as follows. After this introduction, Section 2 presents the data set and a brief description of the methods used in this study. The characteristics of spatial and temporal variability of the seasonal mean of maximum air temperature from Romania are presented in Section 3. Section 4 includes a short description of the spatial and temporal variability of the large-scale parameter, represented here by the sea-level pressure (SLP). The influence of the large-scale circulation upon the mean maximum temperature, investigated by means of covariance maps, is also described in that section. The conclusions are presented in Section DATA AND METHODS The Romanian data used in this paper are the observed seasonal means of maximum air temperature during winter (December February), spring (March May), summer (June August) and autumn (September November) at 14 stations, uniformly distributed over the territory (Figure 1), between 1922 and In Figure 1 the shaded areas represent the Carpathian Mountains. As quite a lot of data were missing in the data set, the analysis was carried out over a common period, , for all seasons, and for the winter season the analysis was also performed over the interval. The large-scale parameters are represented by the SLP at the European scale (3 55 N; 5 5 E), resolution 5 5, and geopotential height at 5 hpa (Z5), which covers the latitudinal belt between 2 N and 9 N (resolution ). The pressure data are provided by the National Center for Atmospheric Research (NCAR), for the period (Trenberth and Paolino, 198), and geopotential height are taken from the operational reanalysis of the National Centers for Environmental Predictions (NCEP). The NCEP reanalysis data set used in this paper covers the period In addition, some major indices have been Copyright 22 Royal Meteorological Society Int. J. Climatol. 22: (22)

3 ROMANIAN TEMPERATURE CHANGES AND ATMOSPHERIC CIRCULATION Km BAIA MARE BISTRITA OCNASUGATAG IASI ROMAN TIMISOARA SIBIU TG-JIU TURNU SEVERIN BRASOV BUCURESTI FILARET CALARASI SULINA CONSTANTA Figure 1. Map of Romania showing the position of the 14 stations used in this study used to describe well-known large-scale patterns: North Atlantic oscillation (NAO), East-Atlantic Jet (EAJ) and Scandinavian (SCAN) pattern. The indices time series used in this paper are from the web pages at http.// Taking into account that the climatological studies need long homogeneous time series where variations are caused only by variation of weather or climate, an important problem is to analyse the homogeneity of the data. The standard normal homogeneity test (SNHT) developed by Alexandersson and Moberg (1997a,b) is the principal tool used to detect inhomogeneities in the seasonal maximum temperature. The SNHT for single shifts and trends was applied to the seasonal maximum temperature. The SNHT for temperature is based upon the assumption that the difference between temperature at the station being tested (i.e. test station) and the reference series is fairly constant in time. The method used for the selection of reference stations takes into account the reference stations that are best correlated with the test station. The correlation coefficient is calculated using the successive increments of the data, instead of the values themselves. This minimizes the risk of making poor estimates of correlation between the test station and a reference station, if one or both of them have in-homogeneities within the common time period used for calculation of the correlation coefficients. The reference series has been constructed as a weighted mean of the series selected as reference stations, with the correlation coefficient between the test station and the reference station being the weight. The results provided by the statistical test were completed with information obtained from metadata. In Romania, a station history archive exists, but it is not complete, such that a time series was classified as inhomogeneous if one of the following criteria was satisfied: the series contains an inhomogeneity significant at the 5% level if it is detected by both (single shift and trend) tests the series contains an inhomogeneity significant at the 1% level if it is detected by at least one test and is explainable by metadata (Hanssen-Bauer et al., 1991). The SNHT test was used with the following constraint: if a significant break occurred within the first or last 5 years then no correction was made because there are too few years to be able to obtain a stable correction factor (Hanssen-Bauer et al., 1991). Various methods are used to analyse spatial and temporal variability. The trend of each time series was detected by applying the Mann Kendall test (Sneyers, 1975), whereas changes of the seasonal mean of maximum temperature were detected by the Pettitt test (Pettitt, 1979). Copyright 22 Royal Meteorological Society Int. J. Climatol. 22: (22)

4 1184 R. TOMOZEIU, A. BUSUIOC AND S. STEFAN The Pettitt test says that the series X 1,...,X T has a change point at τ moment if X t for t = 1,...,τ has a common distribution function F 1 (x), andx t for t = τ + 1,...,T as a common distribution function F 2 (x), and F 1 (x) F 2 (x). The null hypothesis of no change is tested against the alternative hypothesis of change using the non-parametric statistic (Pettitt, 1979): k + T = max U t,t for downward shift 1 t T and where k T = min U t,t for upward shift 1 t T U t,t = t T i=1 j=t+1 D i,j with D i,j = sgn(x i X j ) On the null hypothesis, the distribution of U t,t is symmetric about zero for each t. The significance levels associated with the value k + or k are given approximately by the formula (Pettitt, 1979): p ± = exp [ 6 ( k ±) 2 /( T 3 + T 2)] After finding a main change point, the time series were divided into two sub-series, each of them being tested separately, looking for new change points. The analysis is repeated until the sub-series produced becomes stationary. Only the period without missing values was selected. This period covers the interval for the winter season and a common period for all seasons, namely It should mentioned that Pettitt s test is sensitive to the presence of trends, which causes the test to reject the null hypothesis too often (Busuioc and von Storch, 1996). To exclude such artificial signals, the application of Pettitt s test to pairs of physically linked variables is recommended. This procedure will be applied in this paper. To investigate the spatial variability of the seasonal mean of maximum temperature, an empirical orthogonal function (EOF) analysis was carried out. This procedure provides a method for studying the spatial and temporal variability of long time series over large areas. The method splits the temporal variance of the original data into orthogonal spatial patterns called empirical eigenvectors (EOFs) (Wilks, 1995; von Storch, 1995). Each eigenvector is associated with a series of time coefficients that describes the time evolution (PCs) of the particular spatial mode. In this paper, the EOFs are defined as the eigenvectors of the correlation matrix derived from the anomalies (computed by subtracting the long-term mean from the data) of the original data set. The contribution of any component to the total variance in the field is given by the associated eigenvalue, which provides a measure of its relative importance (Wilks, 1995). The temporal variability of the time series associated with the main EOF modes (PCs) was investigated, the results being compared with those obtained at each station. In this way the performance of the EOF analysis to extract the signal from data set is proved. Therefore, in the further analysis the time series associated with a few EOFs are used instead of time series at the 14 stations. In order to detect the reasons, which could lead to changes in the seasonal mean of maximum temperature in Romania, the EOF analysis was performed for the large-scale parameter represented by the SLP. The time series (PCs) associated with the first two EOF patterns were analysed from the trend and shift point of view, the results being compared with those obtained by analysing the temperature field. If simultaneous changes in the local and large-scale parameters were found, this could lead to the idea that the changes at the large scale determine the changes at the local scale. Copyright 22 Royal Meteorological Society Int. J. Climatol. 22: (22)

5 ROMANIAN TEMPERATURE CHANGES AND ATMOSPHERIC CIRCULATION 1185 The relationship between regional surface data and large-scale upper-air fields has been investigated by means of covariance maps computed using the PCs of the seasonal anomalies of mean maximum temperature and the geopotential height at 5 hpa (Z5) from 196 to The second method used to detect the main large-scale circulation patterns, which could influence the seasonal mean of maximum temperature in Romania, is based on the correlation between the temperature PCs and the indices associated with the pattern provided by the covariance maps. One of the most important patterns controlling the climate in the Atlantic European region is the NAO. This pattern, which is most clearly identified during the cold months, was firstly identified in the SLP field. It has also been found in the middle troposphere (Barnston and Livezey, 1987). The NAO combines parts of the East Atlantic and West Atlantic patterns, and consists of a north south dipole of anomalies. One centre is located over Greenland and the other one of opposite sign spans the central latitudes of the North Atlantic between 35 N and 4 N. The EAJ pattern is another important mode of low-frequency variability found over the North Atlantic, appearing between April and August. This pattern consists of a north south dipole structure of anomaly centres, with one main centre located over the high latitudes of the eastern North Atlantic and Scandinavia, and the other centre located over Northern Africa and the Mediterranean Sea (http.// SCA, or Euroasia-1 as referred to by Barnston and Livezey (1987), is a prominent mode of low-frequency variability that appears in all months except June and July. This pattern consists of a primary centre, which spans Scandinavia and large portions of the Arctic Ocean north of Siberia. Two additional weaker centres with opposite sign to the Scandinavian centre are located over Western Europe and over the Mongolia area. 3. TEMPORAL AND SPATIAL CHARACTERISTICS OF THE SEASONAL MAXIMUM AIR TEMPERATURE Before starting the trend analysis, the homogeneity of the 14 mean maximum temperature time series was tested. The SNHT test was applied for each season and for two sub-intervals: and The data over the interval are homogeneous, except data from Ocna Sugatag station, which presents an inhomogeneity (.5 significance level) in the winter The inhomogeneity year detected at the Ocna Sugatag station is confirmed by metadata, which mentioned that in 194 the station was removed. Taking into account the above result, the winter maximum series from Ocna Sugatag was adjusted, this new series then being used in the study. The seasonal maximum temperatures from the second period (196 98) are homogeneous, such that the original series were used in analysis Trends and changes in the seasonal maximum air temperature Trend analysis for the seasonal mean of maximum temperature in Romania was performed using the Mann Kendall test. In order to capture the real variability of the temperature fields the time series as long as possible were analysed. Table I presents the Mann Kendall statistics and the linear regression coefficients ( C/decade) for winter maximum temperature, the significant results (at least.5 significance level) being in bold type. An increasing trend in the winter mean of maximum air temperature was detected when the time series over the period were analysed. The results are generally significant in the southern part of Romania (Table I). Similar results were obtained when the analysis was repeated for the interval (Table II), but these were less significant than in the period. As can be observed, the spring maximum temperatures (Table II, columns 4 and 5) do not show a significant trend. Summer time series are characterized by a positive trend during the period, and were more significant from a statistical point of view in the southern part of country (Table II). A significant negative trend was found in the autumn maximum air temperature. The regression coefficients ( C/decade) computed for each season are also presented in Table II. Copyright 22 Royal Meteorological Society Int. J. Climatol. 22: (22)

6 1186 R. TOMOZEIU, A. BUSUIOC AND S. STEFAN Table I. The results of trend analysis (Mann Kendall statistics) of the winter mean of maximum air temperature in Romania during the period. The significant results are marked in bold type (.5 significance level) Station Winter Period Mann-Kendall statistics Trend ( C/decade) Baia Mare Bistrita Brasov Bucuresti Calarasi Constanta Iasi Ocna Sugatag Roman Sibiu Sulina Timisoara Tg.Jiu Turnu Severin Taking into account the results presented above, a change point analysis (Pettitt test) was performed for the seasons that showed a significant trend, namely: winter, summer and autumn. A significant upward shift was detected in winter around 1947 at all stations, except Baia Mare, Bistrita and Sulina. Summer mean maximum temperature exhibits an upward shift around 1985 (less significant at Bistrita and Iasi stations), and autumn reveals a significant downward shift point around An example of a change point detected in winter (a), summer (b) and autumn (c) at Calarasi station is presented in Figure 2. Using the shift point detected by Pettitt-test-like criteria, each seasonal time series was divided into two sub-series and the Pettitt test was applied again. The analysis performed for the sub-series did not reveal significant changes Spatial variability of seasonal maximum air temperature in Romania The characteristics of spatial variability of seasonal mean maximum temperature are provided by the EOF analysis. Taking into account the results obtained by analysing the seasonal temperature time series for each station (Section 3.1), the EOF technique was applied only for seasons with significant changes (winter, summer and autumn). The EOF analysis was performed over the interval for winter temperature and over the for all seasons using the complete data set. In order to test the robustness of the EOF decomposition, the method was also applied for summer and autumn temperatures over the long period ( ), but for less stations because of missing data. The patterns for both periods are very similar, confirming their stability. The patterns provided by this method show the main spatial features of the mean maximum temperature variability (namely the areas with the same climate variability), whereas their coefficient time series (PCs) describe the dominant time variability in the data set. Table III presents the variance explained by the first three EOFs corresponding to the winter, summer and autumn seasons. The first EOF pattern is characterized by the same sign in all seasons, with higher values (that means higher variability) in the region situated inside of the Carpathian chain (intra-carpathian area). The second EOF pattern presents a dipolar structure in all seasons, showing the topography influence (Carpathian Mountains) on the temperature distribution. Thus, two regions, intra-carpathian and extra-carpathian, placed inside and outside of the Carpathian chain, with opposite sign of variability are revealed. As an example, the patterns of the first two EOFs for the autumn mean maximum temperature are presented in Figure 3. Similar results (not shown) were obtained for winter and summer mean maximum temperatures. Copyright 22 Royal Meteorological Society Int. J. Climatol. 22: (22)

7 ROMANIAN TEMPERATURE CHANGES AND ATMOSPHERIC CIRCULATION 1187 Table II. The results of trend analysis (Mann Kendall statistics) of the seasonal mean of maximum air temperature in Romania during the period. The significant results are marked in bold type (.5 significance level) Station Winter Spring Summer Autumn Mann-Kendall statistics Trend ( C/decade) Mann-Kendall statistics Trend ( C/decade) Mann-Kendall statistics Trend ( C/decade) Mann-Kendall statistics Trend ( C/decade) Baia Mare Bistrita Brasov Bucuresti Calarasi Constanta Iasi Ocna Sugatag Roman Sibiu Sulina Timisoara Tg.Jiu Turnu Severin Copyright 22 Royal Meteorological Society Int. J. Climatol. 22: (22)

8 1188 R. TOMOZEIU, A. BUSUIOC AND S. STEFAN (a) T( C) (b) T( C) (c) T( C) Figure 2. Shift points in the mean maximum air temperature at Calarasi station during winter (a), summer (b), and autumn (c) seasons Table III. The variance explained by the first three EOFs corresponding to winter, summer autumn seasons Season Period of analysis EOF 1 (%) EOF 2 (%) EOF 3 (%) Winter Summer Autumn The time coefficient series (PC1 and PC2) associated with the first two EOF patterns corresponding to winter, summer and autumn were analysed from the trend and change points of view. Figure 4 displays the temporal evolution of the PC1 for winter (a), summer (b) and autumn (c). A significant increasing trend (Mann Kendall test) was detected in winter and summer, whereas in the autumn season a significant decreasing trend is noted. The Pettitt test applied to the above PCs shows shifts similar to those presented for Copyright 22 Royal Meteorological Society Int. J. Climatol. 22: (22)

9 ROMANIAN TEMPERATURE CHANGES AND ATMOSPHERIC CIRCULATION 1189 (a) OCNASUGATAG BAIA MARE BISTRITA IASI ROMAN TIMISOARA SIBIU TG-JIU 1.9 TURNU SEVERIN.9 BRASOV SULINA.9 BUCURESTI FILARET 1.1 CALARASI CONSTANTA (b) OCNASUGATAG BAIA MARE BISTRITA IASI ROMAN.4 TIMISOARA SIBIU TG-JIU.14 TURNU SEVERIN BRASOV SULINA BUCURESTI FILARET CALARASI CONSTANTA Figure 3. The first (a) and second (b) EOF pattern of the mean maximum air temperature during the autumn season the individual station time series. A similar analysis was performed for PC2 corresponding to each season, the results being statistically insignificant. In order to find a physical explanation for the changes detected in the mean maximum temperature in Romania during winter, summer and autumn, the large-scale SLP variability was investigated. Therefore, the EOF analysis for the SLP on a European scale for the above seasons was also done. The results are presented in Section CONNECTION BETWEEN SEASONAL MAXIMUM TEMPERATURE IN ROMANIA AND LARGE-SCALE CIRCULATION 4.1. Spatial variability of seasonal SLP at European scale provided by EOF technique The EOF analysis was performed for European SLP, in winter, summer and autumn seasons. The first EOF pattern in wintertime over the interval (not shown), which explains 51% of the total variance, represents a southwesterly circulation over Romania. The PC1 associated with this pattern reveals an increasing trend with an upward shift around This result leads to the idea that, starting in 1933, the southwesterly circulation over Europe was more frequent. The results are in agreement with those obtained by Busuioc and von Storch (1996), where the SLP from was analysed. In a previous paper, Busuioc and Tomozeiu (1998), using canonical correlation analysis, suggested a physically plausible mechanism to explain Copyright 22 Royal Meteorological Society Int. J. Climatol. 22: (22)

10 119 R. TOMOZEIU, A. BUSUIOC AND S. STEFAN (a) 2 1 PC1_winter (b) PC1_summer (c) PC1_autumn Figure 4. Coefficient time series of the first EOF (PC1) for: winter (a), summer (b) and autumn (c). The means of two sub-intervals determined by the shift point are also marked the changes in the winter mean of maximum temperature in Romania, namely an increase of the frequency of the westerly circulations around the 194s. This mechanism was enhanced by that mentioned above (i.e. an intensification of the southwesterly circulation after the 193s as well), which mostly affects the extra-carpathian areas, and may justify the warming being more marked in these areas. The first EOF pattern of the summer SLP, computed for the period , which explains 38% of the total variance, reveals a easterly northeasterly circulation over the Romanian territory (Figure 5(a)) given by the positive anomaly field over Europe. The analysis of the time series associated with this pattern (PC1) reveals a slightly increasing trend (Figure 5(b)). The first EOF pattern of the autumn SLP, which explains 47% of the total variance (Figure 6(a)), suggests a southwesterly circulation over Romania. The PC1 associated with the above configuration (Figure 6(b)) presents a slightly decreasing trend and a downward shift point around 1968 (.1 significance level). Copyright 22 Royal Meteorological Society Int. J. Climatol. 22: (22)

11 ROMANIAN TEMPERATURE CHANGES AND ATMOSPHERIC CIRCULATION 1191 (a) 54N 52N 5N 48N 46N 44N 42N 4N 38N 36N 34N 32N N 5E 1E 15E 2E 25E 3E 35E 4E 45E 5E (b) PC Figure 5. The pattern of the first EOF (a) and the temporal evolution of the PC1 (b) computed for summer SLP field Therefore, considering the explanation presented above, the increasing trend detected in the summer mean maximum temperature could be due to a slight intensification of the easterly northeasterly circulation (carrying a warm and dry continental air mass). The downward shift point in the autumn mean maximum temperature detected around 1969 could be due to a slight decrease in the frequency of the southwesterly circulation, especially after These conclusions are further corroborated when the link between PC1 of summer/autumn mean maximum temperature and circulation indices is analysed. These results are presented below. The changes in seasonal mean maximum temperature in Romania (winter, summer and autumn) presented in this section were explained by changes in the first mode of the atmospheric circulation variability on the European scale given by the EOF analysis. For the winter season an additional explanation was given by Busuioc and Tomozeiu (1998) using canonical correlation analysis, which selects optimum correlated spatial patterns. In the following, the changes detected in seasonal maximum temperature are additionally explained by investigating the relationship between seasonal maximum temperature and upper air fields by means of covariance maps computed using the first two temperature PCs and the 5 hpa geopotential height. The method is also applied for winter season, even if the period could not catch the shift point detected around The idea is to identify the main patterns that could influence the variability of maximum winter temperature in Romania. An interesting result is related by the covariance map of PC1 and winter Z5 anomaly (Figure 7). This pattern closely resembles one of the dominant European patterns, namely the NAO. A good correlation was found between the winter NAO index and the PC1 temperature ( period), namely.54 (.2 significance level); this confirms the possible influence on the winter Romanian climate. Hurrell (1995) analysed the decadal trends in the NAO and revealed a strong positive NAO index in recent decades. This is connected to a reinforcement of the westerlies in the northeast Atlantic and a warmer winter than normal. Copyright 22 Royal Meteorological Society Int. J. Climatol. 22: (22)

12 1192 R. TOMOZEIU, A. BUSUIOC AND S. STEFAN (a) 54N 52N 5N 48N 46N 44N 42N 4N 38N 36N 34N 32N 3 32N 5E 1E 15E 2E 25E 3E 35E 4E 45E 5E (b) 3 2 PC Figure 6. As Figure 5, but for autumn season 14 E 16 E 18 E 15 E 14 E 1 12 W 12 E 1 1 W 1 E 8 W 8 E 6 N 6 W N N 6 W 2 W 2 E 4 E 6 E Figure 7. The covariance map between PC1 and Z5 winter season Copyright 22 Royal Meteorological Society Int. J. Climatol. 22: (22)

13 ROMANIAN TEMPERATURE CHANGES AND ATMOSPHERIC CIRCULATION 1193 (a) 14 W 16 W E 14 E 12 W 12 E 4 1 W 4 1 E 8 W 8 N 4 8 E 6 N 4 6 W 4 N 6 E 2 N 4 W 2 W 2 E 4 E (b) EAJ_index Figure 8. (a) The covariance map between PC1 and Z5 summer season; (b) trends and shift point in the EAJ index time series Figure 8(a) presents the covariance map between the Z5 (summer) and the first PC of mean maximum air temperature. This structure bears some resemblance to the positive phase of the EAJ, with one (negative) centre over the far eastern North Atlantic and Scandinavia and the other one (positive) over Eastern Europe, influencing also the Mediterranean area. Romania is included in the area with high positive coefficients; this means that large values of mean maximum temperature in Romania tend to be associated with large values of Z5 in southern Europe and small values of Z5 in the eastern North Atlantic and Scandinavia. The correlation coefficient computed between the EAJ summer index and PC1 of the summer maximum air temperature is.5. The trend test (Mann Kendall) applied to the EAJ summer index (Figure 8(b)) shows a significant increasing trend (.5 significance level) and the Pettitt test reveals a significant upward shift around 1984 (.5 significance level). The period is characterized by the positive phase of the EAJ (Figure 8(b)). The persistence of the positive phase of the EAJ pattern reflects an intensification of westerlies over central latitudes in the eastern North Atlantic and over almost the whole of Europe. Therefore, the increase detected in the summer mean maximum temperature, and the corresponding change point around 1985, could be due to more frequent positive phases of the EAJ, especially after Copyright 22 Royal Meteorological Society Int. J. Climatol. 22: (22)

14 R. TOMOZEIU, A. BUSUIOC AND S. STEFAN The covariance map between the autumn Z5 and the first PC of mean maximum temperature (Figure 9(a)) closely resembles the positive phase of the SCAN pattern, or Euroasia-1 as referred to by Barnston and Livezey (1987). The pattern has one centre situated at 6 7 N, 3 5 E. Two additional weaker centres with opposite sign to those of the SCAN ones are located at 4 5 N, 7 11 E and 5 55 N, 1 E 2 W. The correlation coefficient between the SCAN index and PC1 of autumn maximum temperature is.45. The positive phase of this pattern is associated with positive height anomalies, sometimes reflecting major blocking anticyclones over Scandinavia and western Russia, whereas the negative phase of the pattern is associated with negative height anomalies over these regions. The index of the SCAN pattern (Figure 9(b)) reveals a significant negative trend during the period and a downward change point around 1968, similar to those obtained for the mean maximum temperature in Romania. Therefore, changes in the mean maximum air temperature in Romania during the autumn season could be due to more frequent negative phases of the SCAN pattern after (a) 14 E 16 E 18 E 15 Ε 14 E 12 W E W E 8 N 8 W E 6 N W 4 N 6 E 2 N 4 W 2 W 2 E 4 E (b) 2. Scandinavian index Figure 9. (a) As in Figure 7, but for autumn season; (b) trends and shift point in the SCAN index time series Copyright 22 Royal Meteorological Society Int. J. Climatol. 22: (22)

15 ROMANIAN TEMPERATURE CHANGES AND ATMOSPHERIC CIRCULATION CONCLUSIONS AND REMARKS Some conclusions may be drawn from this study with respect to the physical mechanisms responsible for the changes in the seasonal mean maximum temperature in Romania. These mechanisms were investigated in two ways. Firstly, the similarity between changes in the large-scale circulation represented by the SLP field and changes in the seasonal mean of maximum temperature in Romania were analysed. Before analysis, the EOF technique was used as a data filtering procedure to eliminate noise. Thus, the main characteristics of the spatial and temporal variability of the SLP and mean maximum Romanian temperature were revealed. The main modes of the SLP variability represented by circulation patterns were also identified by the EOF analysis. Therefore, the time series associated with the first EOF pattern (PC1) of the SLP and mean maximum temperature in Romania were analysed from the trend and shift points of view using the Pettitt and Mann Kendall tests respectively. Significant increasing trends were found for winter and summer mean maximum temperatures in Romania, with upward shifts around 1947 and 1985 respectively. During autumn, a decreasing trend with a downward shift around 1969 was detected. These changes seem to be real, since similar changes in the large-scale circulation were found. For the winter case, the intensification of the southwesterly circulation over Europe (as the principal mode of the SLP variability) since 1933 could be one reason. This mechanism is consistent with the findings of Busuioc and Tomozeiu (1998), who used canonical correlation analysis; namely an enhancement of the westerly circulation after the 194s, could be the real reason behind the increasing trend in the winter maximum temperature in Romania. These results are in agreement with those presented by Busuioc (1996) for winter mean temperature in Romania. The intensification of the westerlies is also confirmed by the frequently positive phase of the NAO during recent decades. The slight intensification of the easterly circulation during summer and a slight weakening of the southwesterly circulation during autumn could be the reasons for the increase/decrease in the regime of the mean maximum temperature for the summer/autumn seasons. The covariance map, computed using the geopotential height at 5 hpa and the first PC of temperature, was the second method used to identify the connection with the large-scale circulation patterns. The covariance maps revealed the influence of the EAJ and SCAN pattern on the mean maximum temperature in Romania, during summer and autumn respectively. The significant upward shift around 1984 detected in the EAJ index justifies the similar shift detected in the summer mean maximum temperature in Romania, and the downward shift around 1968 of the SCAN index justifies similar changes in the autumn mean maximum temperature. The trend results presented in this paper are in agreement with those presented by Brazdil et al. (1996) for central Europe and some parts of Southern Europe. This leads to the conclusion that the changes in the mean maximum temperature in Romania are controlled by real large-scale physical mechanisms. ACKNOWLEDGEMENTS NCEP reanalysis data were provided by the NOAA CIRES Climate Diagnostics Center, Boulder, CO, USA, from their website at One of the authors, Rodica Tomozeiu, undertook this work with the support of the ICTP Programme for Training and Research in Italian Laboratories, Trieste, Italy. The helpful comments of two anonymous reviewers are appreciated. REFERENCES Alexandersson H, Moberg A. 1997a. Homogenization of Swedish temperature data. Part I. A homogeneity test for linear trends. International Journal of Climatology 17: Alexandersson H, Moberg A. 1997b. Homogenization of Swedish temperature data. Part II. Homogenized gridded air temperature compared with a subset of global gridded air temperature since International Journal of Climatology 17: Barnston A, Livezey R Classification, seasonality persistence of low-frequency atmospheric circulation patterns. Monthly Weather Review 115: Brázdil R, Budikovà M, AuerI, Böhm R, Cegnar T, Faško P, Lapin M, Gajic-Čapka M, Zaninović K, KolevaE, Niedźwiedź T, Ustrnul Z, Szalai S, Weber RO Trends of maximum and minimum daily temperatures in central southeastern Europe. International Journal of Climatology 16: Copyright 22 Royal Meteorological Society Int. J. Climatol. 22: (22)

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