Trend analysis of precipitation time series in Greece and their relationship with circulation using surface and satellite data:

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1 Theor. Appl. Climatol. 87, (2007) DOI /s Department of Geography, University of the Aegean, Mytilene, Greece 2 Division of Meteorology Climatology, Department of Geology, Aristotle University of Thessaloniki, Thessaloniki, Greece 3 Division of Statistics and Operational Research, Department of Mathematics, Aristotle University of Thessaloniki, Thessaloniki, Greece Trend analysis of precipitation time series in Greece and their relationship with circulation using surface and satellite data: H. Feidas 1, Ch. Noulopoulou 1, T. Makrogiannis 2, and E. Bora-Senta 3 With 11 Figures Received March 7, 2005; revised December 12, 2005; accepted January 7, 2006 Published online June 14, 2006 # Springer-Verlag 2006 Summary In this study, the trends of annual and seasonal precipitation time series were examined on the basis of measurements of 22 surface stations in Greece for the period , and satellite data during the period For this purpose, two statistical tests based on the least square method and one based on the Mann-Kendall test, which is also capable of detecting the starting year of possible climatic discontinuities or changes, are applied. Greece, in general, presents a clear significant downward trend in annual precipitation for the period , which is determined by the respective decreasing trend in winter precipitation. Both winter and annual series exhibit a downward trend with a starting year being Satellite-derived precipitation time series could be an alternative means for diagnosing the variability of precipitation in Greece and detecting trends provided that they have been adjusted by surface measurements in the wider area of interest. The relationship between precipitation variability in Greece and atmospheric circulation was also examined using correlation analysis with three circulation indices: the well-known North Atlantic Oscillation Index (NAOI), a Mediterranean Oscillation Index (MOI) and a new Mediterranean Circulation Index (MCI). NAOI is the index that presented the most interesting correlation with winter, summer and annual precipitation in Greece, whereas the MOI and MCI were found to explain a significant proportion of annual and summer precipitation variability, respectively. The observed downward trend in winter and annual precipitation in Greece is linked mainly to a rising trend in the hemispheric circulation modes of the NAO, which are connected with the Mediterranean Oscillation Index. 1. Introduction The analysis of precipitation data at global scales with respect to climate change indicates that land surface precipitation continues to increase in much the Northern Hemisphere mid and high latitudes, but over much of the tropics conditions have become drier (IPCC, 2001). Although regional differences are relatively high, annual precipitation trends in the 20 th century, for a number of stations in Europe, exhibit a clear general increase for northern Europe, with the exception of Finland, and a decrease for southern Europe and the Mediterranean (ECSN, 1995; IPCC, 1996, 2001). Many recent studies have contributed to the analysis of precipitation variability over the Mediterranean. Most of these analyses have only been carried out for part of the region, and most often conducted for individual stations (Maheras,

2 156 H. Feidas et al. 1988; Ben-Gai et al., 1994; Amanatidis et al., 1993; De Luis et al., 1998, 2000; Sch onwiese et al., 1997; Gonzalez-Hidalgo et al., 2001; Moisselin et al., 2002; Piervitali and Colacino, 2003; T urkes, 2003; ECA&D, 2004; Norrant and Douguedroit, 2003b). Precipitation trends for the whole basin have rarely been studied, examples include, Douguedroit and Norrant (2003), Norrant and Douguedroit (2003a), and D unkeloh and Jacobeit (2003). The results from these studies indicate a non-significant decrease in precipitation or the lack of a linear trend, whether during the whole last century or a shorter period (De Luis et al., 1998; Lana and Burgue~no, 2000a; Klein Tank et al., 2002; Douguedroit and Norrant, 2003; Norrant and Douguedroit, 2003a, b; ECA&D, 2004). Significant decreases have been found across the 20 th century for one of the seasons for a region or for a city (Amanatidis et al., 1993; Piervitali et al., 1997, 1998; Buffoni et al., 1999; Matari et al., 1999; Brunetti et al., 2000; De Luis et al., 2000; Lana and Burgue~no, 2000b; Xoplaki et al., 2000; Ventura et al., 2002; Tomozeiu et al., 2002; Maheras and Anagnostopoulou, 2003; Piervitali and Colacino, 2003; Esteban-Parra et al., 2003; Douguedroit and Norrant, 2003; Norrant and Douguedroit, 2003a; T urkes, 2003). The majority of the Mediterranean region has tended toward decreasing winter precipitation during the last few decades, mostly starting in the 1970s and proceeding to an accumulation of dry years in the 1980s and 1990s (Sch onwiese et al., 1994; Palutikof et al., 1996; Piervitali et al., 1997; Sch onwiese and Rapp, 1997). The westcentral Mediterranean area has experienced a precipitation decrease during the last 50 years (IPCC, 1996; Piervitali et al., 1997). Decreasing precipitation is also evident in large parts of the eastern Mediterranean area. Sch onwiese et al. (1994, 1998) reported a pronounced significant trend towards a drier winter climate in the eastern Mediterranean area, for the period A general drying is also discernible over most of the southeastern Mediterranean and Greece, which is prominent and statistically significant during the second half of 20 th century and especially in the southern part of the region (Kandylis et al., 1989; Hatzioannou et al., 1998; Paz et al., 1998). Time series from stations across Greece show changes towards drier conditions during the last 20 years of the period , following wetter conditions during the previous two decades (Amanatidis et al., 1993; Kandylis et al., 1994; Kutiel et al., 1996). A decrease in winter precipitation over Greece was found by Xoplaki et al. (2000), although significant only over the northern and eastern parts and in the western mountainous regions. Similar developments have been reported for the Mediterranean coast of Turkey (Kadioglou, 2000; T urkes, 1996). Large-scale circulation, has been found to play an important role in precipitation variability in Mediterranean. According to the results of Krichak and Alpert (2005) the precipitation decline over the Mediterranean region during the last decades of the past century can be explained by the positive trend in the East Atlantic Western Russia (EAWR) pattern, which was induced by the positive trend of the North Atlantic Oscillation. Cullen and de Menocal (2000) detected a connection between the North Atlantic sector and southeastern Mediterranean, which is the easternmost limit of the North Atlantic Oscillation influence on Mediterranean climate. The study of D unkeloh and Jacobeit (2003) dealt with coupled variations of circulation and precipitation by attributing rainfall trends in different Mediterranean regions to the temporal evolution of particular large-scale circulation modes. Maheras et al. (1999) tried to interpret the spatial distribution of the positive and negative precipitation anomalies in terms of mean circulation over the Mediterranean. The relation of circulation with extreme rainfall conditions in the eastern Mediterranean during the 20 th century was investigated by Kutiel et al. (1996). Concerning Greece, several climatologists have studied the relations between precipitation and multi-scale atmospheric circulation or air temperature regimes over Greece (Metaxas and Kallos, 1980; Maheras, 1982a, b; Maheras and Kolyva- Machera, 1993; Metaxas et al., 1993; Rizou and Karakostas, 1993; Lutterbacher et al., 1998; Xoplaki et al., 2000; Kutiel et al., 2002; Nastos et al., 2002, 2003; Maheras et al., 2004). The relationships, however, between precipitation and atmospheric circulation indices over the Mediterranean have not been well identified: only a few studies have been made on this topic and most of them have focussed on limited areas and seasons. Piervitali et al. (1999) found a weak

3 Trend analysis of precipitation time series in Greece 157 negative correlation between annual Mediterranean precipitation and the North Atlantic Oscillation Index (NAOI). They proposed the existence of the Mediterranean Oscillation (MO) which produces a better correlation with precipitation than the NAOI for the eastern Iberian Peninsula and Italy. With regard to winter precipitation, Quantrelli et al. (2001) observed that the largest fraction of variance, in the Mediteranean area for the period , is explained by the NAOI, while smaller but still substantial fractions are explained by the Eastern Atlantic pattern and the Euro-Atlantic blocking (EAB). The variability of winter precipitation at most stations in Turkey was found by T urkes and Erlat (2005) to be significantly correlated with the variability of three different NAO indices. Brunetti et al. (2002) examined the relationship between precipitation in Italy and atmospheric circulation using five indices. They found that the indices that show the most interesting correlation with total precipitation and number of wet days in Italy are the Western European Zonal Circulation Index (WEZCI) and the Mediterranean Circulation Index (MCI). The Mediterranean Oscillation was also found by D unkeloh and Jacobeit (2003) to be the most important coupled pattern explaining a large part of precipitation variability in Mediterranean throughout the whole year. Kutiel et al. (2002) identified an upper level atmospheric teleconnection between the North Sea and the northern Caspian, and defined the North Sea Caspian pattern index (NCPI), which is positively correlated with below normal temperatures and above normal precipitation in the Balkans and the Middle East during positive phases. The few studies which focus on the trend analysis of precipitation and drought phenomena in Greece are based either on a single test or on extended time series of rainfall for a limited number of stations without extending the analysis for all seasons. Moreover, the relationship between atmospheric circulation indices and precipitation in Greece has not been well defined. The primary aim of this study is to examine the trends in the mean annual and seasonal precipitation time series for all available stations in Greece (24 stations), for the longest common time period of homogenous precipitation data (namely ), using three statistical tests. The same methodology was applied by Feidas et al. (2004) to examine the trends in air temperature time series in Greece. Taking into account the size of the Greek region and the even spatial distribution of these 24 stations, this number may be considered as sufficient to also account for the local differences in Greece. The study is also complemented by an investigation of the possibility of using precipitation estimates derived by satellite measurements for trend analysis of precipitation in Greece. The secondary objective of this study is to look for a circulation index, aimed at characterizing the link between Greek precipitation variability and atmospheric circulation. For this reason, correlation analysis is used to relate precipitation variability in Greece with air-pressure patterns over the Mediterranean. Atmospheric circulation is represented by three indices in this study: the well known North Atlantic Oscillation Index (NAOI), a Mediterranean Oscillation Index (MOI) based on the 500 hpa geopontential record for Algiers, and a new Mediterranean Circulation Index (MCI) constructed from Marseille and Jerusalem surface pressure records. 2. Data and methodology 2.1 Precipitation gauge data A set of monthly precipitation series from 24 stations in Greece for the period has been used. This period is the maximum common time period of precipitation data recorded at Fig. 1. Map of Greece with the locations of the 24 stations used in the study

4 158 H. Feidas et al. Table 1. Geographical coordinates and altitude of the 24 stations Station Latitude Longitude Altitude (m) 1. Aghialos N E Agrinio N E Alexandroupoli N E 4 4. Araxos N E Florina N E Heliniko N E Ierapetra N E Ioannina N E Iraklio N E Kalamata N E Kerkyra N E Kozani N E Kythira N E Larisa N E Methoni N E Milos N E Mytilene N E Nat. Obs. Athens N E Naxos N E Rodos N E Samos N E Skyros N E Thessaloniki N E Tripoli N E 663 all the 24 stations. Figure 1 presents the location of the stations and Table 1 shows their coordinates and altitudes. A preliminary examination of the monthly data showed that the records of 11 stations had missing data of not more than 2 per cent of the total number of monthly values. In this case, the normal ratio method (Singh, 1992) was used to complete the precipitation record. In this estimating procedure, the recorded values in nearby reference stations, with the highest correlation (r between 0.65 and 0.8 at 95% level of significance), were chosen. Finally, seasonal and annual precipitation totals were estimated for each of the 24 stations and were converted into percentages of normal precipitation (mean of the period). Annual precipitation totals were conventionally made to correspond to the period from September 1 st to August 31 th and dated by the year in which the January occurred, to ensure the rainy season is retained as one continuous period that ends with the dry summer season. Winter precipitation refers to the interval December January February (DJF), spring as March April May (MAM), summer as June July August (JJA) and autumn as September October November (SON). Records of the annual and seasonal precipitation totals have been examined in order to obtain homogeneous records and to ensure a good geographical representation where possible. A preliminary examination of the homogeneity of the precipitation data was first accomplished by selecting stations where the monitoring site has not been changed during the period of the record. Four statistical tests were applied to evaluate the homogeneity of the data for the remaining stations. In general, a combination of statistical methods and the examination of metadata information is considered to be most effective was to identify inhomogeneities. It is recommended (e.g. Peterson et al., 1998) that homogeneity tests be applied relatively, i.e. testing with respect to a neighbouring station that is assumed to be homogeneous. If the two series are sufficiently correlated, relative tests are considered to be more powerful than absolute tests, which use only the single station series. Inhomogeneities are thus more easily distinguished from the real climate variations. However, for the precipitation dataset used in this study, relative testing is not appropriate, given the sparse spatial density of the station network. Therefore, we currently restrict the homogeneity analysis to absolute tests based on the statistical analysis of station data, making the distinction between artificial biases and true climatic fluctuations more difficult. The four test methods selected to test the departure of homogeneity in the annual and seasonal time series are: the short-cut Bartlett test (Mitchell et al., 1966), the Buishand range test (Buishand, 1982), the Von Newmann ratio test (Von Newmann, 1941) and the Levene test (Levene, 1960). All four tests assume, under the null hypothesis, that precipitation values Y i of a series are independent and identically distributed. Under the alternative hypothesis, the Buishand range test assumes that a step-wise shift in the mean (a break) is present. This test is capable of locating the year where a break is likely. The other three tests, the short-cut Bartlett test, the Von Newmann ratio test and the Levene test assume, under the alternative hypothesis, that the series is not randomly distributed. These tests do not identify the year of the break. The Levene test

5 Trend analysis of precipitation time series in Greece 159 is complementary to the other three tests because it is less sensitive to departures from normality. The second step in the homogeneity assessment of each precipitation dataset is an overall evaluation of the four tests. The outcomes of the four tests for each precipitation series are grouped together. As in Sch onwiese and Rapp (1997), a classification is made depending on the number of tests rejecting the null hypothesis at the 5% level. According to this classification, a series is labelled suspect when three or four tests reject the null hypothesis. In this case, it is likely that an inhomogeneity is present of a magnitude that exceeds the level expressed by the inter-annual (inter-seasonal) standard deviation of the precipitation series. Marginal results of trend should be regarded as spurious. Only very large trends may be related to a climatic signal. It is clear that series labelled suspect lack credibility and should not be used in the analysis of trend. Only in exceptional cases, where it is plausible that a real climatic signal rather than an artificial break triggered the test results, can the original station series be used in further analysis. Only the annual time series of two stations (Florina and Skyros) were identified as suspect and as such, both stations are regarded as possessing an inhomogeneity problem. As a result, both stations were eliminated from the analysis. In conclusion, the homogeneity test results demonstrate that the precipitation series for the remaining 22 stations are homogeneous. 2.2 Precipitation satellite data Satellite precipitation data for the area of Greece (20 28 E and N) for the period , from the Global Precipitation Climatology Project (GPCP) Version 2 combined precipitation data set (Huffman et al., 1997; Adler et al., 2003), were also used. This dataset is constructed as part of the Global Precipitation Climatology Project, an international project of the Global Energy and Water Cycle Experiment (GEWEX) of the World Climate Research program (WCRP) designed to provide improved long-term record estimates of precipitation over the globe. The current data set provides as a final product, the combined satellite-gauge precipitation estimate which consists of monthly mean precipitation data on a latitude longitude grid. The complete data set, which includes the input and intermediate data files, contains a suite of 12 products providing monthly, global gridded values of precipitation totals for the period January 1979 to present. The GPCP has accomplished this by merging infrared and microwave satellite estimates of precipitation with rain gauge data from more than 6,000 stations. Infrared precipitation estimates are obtained from GOES (United States), GMS (Japan) and Meteosat (European Community) geostationary satellites and National Oceanic and Atmospheric Administration (NOAA) operational polar orbiting satellites. Microwave estimates are obtained from the U.S. Defense Meteorological Satellite Program (DMSP) satellites using the Special Sensor Microwave Imager (SSM=I). Since no single satellite data source spans the entire data record, the product draws upon many different sources covering different time periods within the entire data record. Substantial attempts have been made to ensure consistency among the different available input sources. In this study, two products of the GPCP Version 2 combined precipitation data set were used to examine its capacity in diagnosing the variability of a regional climate: the multi-satellite and the combined satellite-gauge precipitation product. This is particularly useful in areas such as Greece with relatively sparse rain gauge network and with the alternation of mountainous terrain and sea. The multi-satellite precipitation estimates are an intermediate step to the final satellite-gauge precipitation product and is a weighted combination of single satellite data obtained from many different sources covering different time periods within the entire data record. The combined satellite-gauge precipitation estimates are the final product of the GPCP Version 2 dataset, derived by the calibration of the multisatellite precipitation product with the rain gauge measurements in a 5 5 deg gridbox template. Finally, seasonal and annual precipitation percentages of the mean of the period were also estimated from the satellite data averaged over the area of Greece using the same method as with the ground measurements. It should be mentioned that since satellite data are available from January 1979, it is not possible to calculate the annual, winter and autumn value of

6 160 H. Feidas et al. 1979; hence the period under examination is limited to Statistical tests for trend analysis In order to examine any possible trend in the time series, this study has adopted the procedure proposed by Feidas et al. (2004) for the analysis of temperature series in Greece. This procedure uses three statistical tests in order to strengthen the confidence in the existence of a significant deterministic trend. The following three tests were applied: i. The first test is the basic linear regressionbased model. Under the usual regression assumptions, that is, when the residuals are independent and normally distributed, the rejection of the null hypothesis H 0 : b ¼ 0is determined using the t-test according to the value of the statistic t ¼ ^b=bs s 1 ð^bþ, where b is the slope of the regression line and sbs 1 ð^bþ is its standard error. ii. The second test is a stricter application of the basic linear regression-based model, since it assumes that residuals are not normally distributed and there is autocorrelation among them that has to be taken into account in the regression analysis. In this case Grenander (1954), Cryer (1986) and Bloomfield and Nychka (1992) suggest a different method for estimating the standard error of ^b that is not based on the independent error assumptions of regression analysis, but it rather assumes that errors are intercorrelated. iii. The third test applied is the Mann-Kendall rank statistic test, as is proposed by Sneyers (1990). In particular, the Mann-Kendall rank statistic calculates all u(d i ), 1 i n, through a formula similar to the one referred to in Michell et al. (1966). The null hypothesis H 0 : b ¼ 0 is rejected when the final value u(d n ) of the u(d i ) statistics for i ¼ n is greater, in absolute value, than 1.96 for a two-tailed test at the 95% significance level, and 2.58 for a two-tailed test at the 99% significance level, where n is the size of the time series. The graphical representation of all u(d i ), 1 i n, is denoted as C 1. The Mann-Kendall rank statistic is considered the most appropriate (Goosens and Berger, 1986) for the analysis of trends in climatological time series or for the detection of a climatic discontinuity, which according to Michell et al. (1966), is a climatic change that consists of a rather abrupt and permanent change during the period of record from one average value to another. In order to localize the beginning of the change, the same principle applied for the u(d i ) statistic is adapted to the retrograde series. The graphical representation of the retrograde series u 0 (d i ), is denoted as C 2. In the case of a significant trend, the intersection of these curves localizes the change and allows the identification of the year when the trend or change starts. 2.4 Pressure indices Three circulation indices were used with the aim of relating the variability of precipitation with airpressure patterns over the Mediterranean. The indices used in this study were the NAOI, the MOI and the MCI. The NAOI is derived from the principal components (PC) time series of the leading (usually regional) empirical orthogonal function (EOF) of sea level pressure (SLP). The MCI is obtained on a monthly basis as differences between standardized SLP station anomalies whereas the index used to define the MOI is the height of the 500 hpa surface recorded in Algiers. a. NAOI. The North Atlantic Oscillation (NAO) is one of the large-scale modes of climate variability in the Nothern Hemisphere. It defines the largescale meridional oscillation of atmospheric mass between the centre of the subtropical high surface pressure located near the Azores and the subpolar low surface pressure near Iceland. Synchronous strengthening (positive NAO state) and weakening (negative NAO state) have been shown to result in distinct, dipole-like climate change patterns between western Greenland=Mediterranean and northern Europe=Scandinavia (Walker, 1924; Walker and Bliss, 1932; van Loon and Rogers, 1978; Rogers and van Loon, 1979). In particular, a positive (negative) NAO is the result of a strong (weak) meridional pressure gradient leading to a colder, dryer (warmer, wetter) Greenland= Mediterranean sector and a warmer, wetter (colder, dryer) northern Europe=Scandinavia sector. A positive NAO implies a more meridional storm track while a negative NAO implies a more

7 Trend analysis of precipitation time series in Greece 161 zonal storm track, which ultimately penetrates into the Mediterranean Sea (Alpert et al., 1990). Accounting for greater than one-third of the total variance of the SLP field over the North Atlantic, the NAO is most pronounced during the winter months because of an increased sea-air temperature contrast (Barnston and Livezey, 1987). An NAO index (NAOI) was originally defined as the difference in pressure between Ponta Delgada (in the Azores, Portugal) and Reykjavic (Iceland). Later studies used Lisbon instead of Ponta Delgada (Hurrell, 1995) or Gibraltar (Jones et al., 1997). The main reason for changing the southern point was to construct a longer time series, but this change also led to an increase of the negative correlation with the northern point. Many climate researchers have utilized the previous station-based NAOIs generated by using different normalization approaches and periods. T urkes and Erlat (2005) investigated the relationship between precipitation in Turkey and three NAOIs and found that the variability of winter precipitation at most stations is significantly correlated with the variability of the three NAOIs. Another usual way of defining the NAOI is through the principal component (PC) time series corresponding to a pressure field principal component pattern (Rogers, 1990; Thompson and Wallace, 2000, 2001). NAO indices determined by this approach have been used by Krichak and Alpert (2005) and Tomozeiu et al. (2005) to examine the variability of winter precipitation and its links to the large-scale atmospheric circulation patterns in eastern Mediterranean and Romania, respectively. A more complete discussion of different NAOIs can be found in Osborn et al. (1999) and Hurrell et al. (2003). In this study, annual and seasonal (DJF, MAM, JJA, SON) indices of the NAO, based on the PC time series of the leading EOF of normalized SLP since 1899, were provided by the Climate Analysis Section of National Center of Atmospheric Research (NCAR) website cgd.ucar.edu=jhurrell=indices.html, and are an update of the time series published in Hurrell (1995). The leading eigenvectors of the crosscovariance matrix are calculated from seasonal SLP anomalies in the North Atlantic sector (20 70 N; 90 W 40 E). This index is advantageous compared to other NAO indices based on pressure differences at fixed locations because it always follows the main centres of action and thereby represents the full meridional pressure gradient. In addition, the PC method of obtaining the NAO index is appropriate for assessing a seasonally-varying signal since the pressure systems are subject to a seasonally-induced shift. Thus, the evident time-dependent shift of the centres of action (Ulbrich and Christoph, 1998) is taken into account. This feature is very important in the context of the present study as our analysis of circulation indices are related to precipitation, not only on an annual, but also on a seasonal basis. An additional disadvantage of the stationbased indices is that they are fixed in space. Given the movement of the NAO centers of action through the annual cycle, such indices can only adequately capture NAO variability for parts of the year. Moreover, individual station pressures are significantly affected by small-scale and transient meteorological phenomena not related to the NAO and, thus, contain noise. b. MOI. Conte et al. (1989) first described the Mediterranean Oscillation (MO) as a teleconnection pattern with opposite pressure and rainfall anomalies between the central-western and eastern Mediterranean area. This dipolar oscillation in pressure patterns was further observed by several other researchers (Colacino and Conte, 1993; Piervitali et al., 1999; Kutiel et al., 1996; Douguedroit, 1998; Maheras et al., 1999; Maheras and Kutiel, 1999). They introduced the MOI to describe this oscillation. It better explains the annual precipitation variability across the Central- Western Mediterranean basin than the NAOI. The MOI is constructed from the 500 hpa surface height monitored by soundings at stations in Algiers and Cairo and are assumed to be representative of the Western and Eastern basin, respectively. When the pressure increases in the Western basin a decrease is found in the East, and vice versa. This seesaw characterizes the Mediterranean Oscillation (MO). Here we use the index constructed by Piervitali et al. (1999), which defines the MO by the height of 500 hpa surface recorded in Algiers for the period There are however, drawbacks as it is available only for the last 50 years and it describes a bipolar pattern using only one station s series (the 500 hpa geopotential record for Algiers).

8 Table 2. Results of the application of the linear regression analysis and of the three trend analysis tests for the seasonal and annual precipitation over the period a (statistically significant cases at 95% level of significance, provided by at least two tests, are presented in bold) Station Winter Spring Summer Autumn Annual b b t 1 t 2 u(d n ) b b t 1 t 2 u(d n ) b b t 1 t 2 u(d n ) b b t 1 t 2 u(d n ) b b t 1 t 2 u(d n ) Aghialos Agrinio Alexandroupoli Araxos Helliniko Ierapetra Ioannina Iraklio Kalamata Kerkyra Kozani Kythira Larisa Methoni Milos Mytilene Nat. Obs Athens Naxos Rodos Samos Thessaloniki Tripoli Greece a b (percentage contribution=decade) is the slope of the linear regression line, b is the associated error, t 1, t 2 the statistics of the first and second test and u(d n ) the final value of the Mann- Kendall test

9 H. Feidas et al.: Trend analysis of precipitation time series in Greece 163 c. MCI. In order to obtain an index more suitable for the central Mediterranean, Brunetti et al. (2002) defined a new index, the Mediterranean Circulation Index (MCI). This is the normalized sea level pressure difference between one station lying in the northwestern Mediterranean and another in the southeastern Mediterranean. According to the quality (homogeneity and length) of the series, they selected the stations of Marseille and Jerusalem. MCI was found to present the most interesting correlation with total precipitation and number of wet days in Italy. In this study, the original MCI dataset covering the period was used (Brunetti et al., 2002). 2.5 Pressure data In addition, time-series of monthly mean gridded 500 hpa height data on a 5 latitude 5 longitude grid for the North Hemisphere were used to demonstrate the pressure pattern over the Mediterranean for two representative cases. This grid was computed by the Data Support Section (DSS) of the National Center for Atmospheric Research (NCAR) from the daily grids of various operational models and meteorological projects. 3. Results 3.1 Precipitation trends in surface observations The three statistical tests were applied to the annual and seasonal time series of the 22 stations for the period The slope of the regression line was estimated by the linear regression analysis of the first test. Table 2 provides the magnitude of the linear trends expressed as percentages of normal precipitation and the values of the statistics t 1, t 2 and u(d n ) for the annual and Fig. 2. Observed, 5-year moving average and trend line of seasonal and annual precipitation at the station of Mytilene for the period Precipitation is expressed as percentages of the mean of the period

10 164 H. Feidas et al. Fig. 3. Spatial distribution of the slope b (percentage contribution=decade) and significance levels (light grey levels indicate a significance level greater than 95%) of the regression analysis for the annual and seasonal totals ( ) Fig. 4. Observed values, 5-year moving average and trend line of seasonal and annual precipitation in Greece for the period Precipitation is expressed as percentages of the mean of the period

11 Trend analysis of precipitation time series in Greece 165 seasonal totals. Trends are considered statistically significant at the 95% level presented in bold and underlined characters in the table when identified as such by the regression-based model and by at least one of the other two tests. In this case, it is assumed that the results of the second and third test confirm a trend identified by the regression-based model. It is worth noticing that the last two tests are in agreement with the regression-based model, in every case this test results in a statistically significant trend. This agreement in all three tests supports the hypothesis of the existence of a significant trend in these series. The seasonal and annual precipitation time series, along with the 5-year moving average and the linear trend line, were graphically represented for each station but only the plots for Mytilene are presented here (Fig. 2). Figure 3 presents the spatial distribution of the slope b (in percentage contribution=decade) of the regression analysis for the annual and seasonal totals. The light grey areas indicate a significance level greater than 95%. Analysis of the full range of time series plots as well as Table 2 and Fig. 3, shows the decreasing or increasing trend at each station. An overall decreasing trend is evident in the annual time series of all stations for the period This is significant across the whole country with the exception of the central Aegean Sea and Peloponnesus in southern continental Greece (11 stations out of the 22). A similar decreasing trend pattern is also present in the winter time series; statistically significant cases, however, are limited over western, northwestern and northeastern continental Greece as well as in the northern and southeastern Fig. 5. Graphical representation of the series u(d i ) and the retrograde series u 0 (d i ) (denoted as C 1 and C 2 ) of the sequential version of Mann-Kendall, for seasonal and annual precipitation at the station of Ioannina for the period

12 166 H. Feidas et al. Aegean Sea (7 stations out of the 22). For the same period, the precipitation trends in spring and autumn exhibit a similar pattern with only a few significant decreasing trends distributed mainly in northwestern Greece. No any distinct overall trend pattern was found for the summer time series in which no statistically significant trends are present. The high values for slope b and the associated errors b results from the low and variable summer precipitation in Greece. According to these results, it is clear that an overall decreasing trend is evident in annual and seasonal precipitation series. However, statistically significant cases are prominent only in winter which determine to a great extent the respective significant decreasing trends found in the annual series in many areas of Greece. This was expected since maximum precipitation in Greece is observed in winter whereas thesummerseasonisdry.anexceptionisthe significant downward trends of northern and central Greece which are attributed to the decrease in the spring and autumn precipitation time series. It is interesting to examine the trends of the annual and seasonal regional mean series for the area of Greece since these regional series can represent the region as a whole. Thus, seasonal and annual precipitation percentages of normal precipitation for each of the 22 Greek stations were averaged to derive the corresponding time series at the national scale. This makes merging much easier and prevents the signal from being dominated by series with greater variability. The presence of trends in each series was examined by applying the three statistical tests used in the previous analysis and the results are presented in the last row of Table 2 for the seasonal and annual series and plotted in Fig. 4. Table 2 indicates that annual precipitation averaged over Greece presents a clear significant downward trend, which is determined by the respective decreasing trend in winter precipitation series. Finally, there is no distinct overall trend in the other seasons. Table 3. Approximate year of beginning of the increasing or decreasing trend according to the sequential version of Mann- Kendall rank statistic for annual and seasonal precipitation ( ) Station Winter Spring Summer Autumn Annual Aghialos Agrinio Alexandroupoli Araxos 1957 (1999þ) Helliniko 1959þ (1998 ) 1971þ 1970þ Ierapetra Ioannina Iraklio Kalamata Kerkyra Kozani 1958þ (1982 ) Kythira Larisa Methoni (2000þ) Milos 1958 (1991þ) Mytilene (1988þ) 1957 (1999þ) 1983 Nat. Obs. Athens (1999þ) Naxos þ (2000 ) Rodos (1993þ) Samos þ (1992 ) 1981 Thessaloniki 1961 (1999þ) 1988 Tripoli 1962þ (2000 ) 1956 (1993þ) Greece No abrupt change at the 95% level of significance þ Means a increasing trend Means a decreasing trend ( ) Approximate year of a new change in a trend which is not statistically significant

13 Trend analysis of precipitation time series in Greece 167 Considering the 5-year moving average line, precipitation for Greece, for both annual and winter series, are characterized by two major wet periods ( and ), and two major dry periods ( , and with the exception of the 4-year wet break of ). After the marked wet spell of the period, dry conditions dominated and reached a countrywide peak in severity in The driest year of the countrywide annual series for the period under examination was 1990 with a below normal precipitation of 57%, whereas the wettest year was 1963 with an above normal precipitation of 148%. Winter precipitation for the same years, 1990 and 1963, were also extreme with precipitation percentages of 42% and 141%, respectively, contributing, to a large extent, to the corresponding extreme annual values. A graphical analysis, similar to that for the precipitation time series, was applied to the u(d i )and u 0 (d i ) statistics (curves C 1 and C 2, respectively) of each station, in order to identify the intersection of the curves and thus enable the detection of the beginning of the trend or the change, where present. Plots for the Ioannina station are given in Fig. 5. An analysis of the full range of figures shows the decreasing or increasing trend per station, as well as the approximate year when an abrupt precipitation change occurred. This information is summarized in Table 3 for the annual and seasonal series. Increasing and decreasing trends are represented by (þ) and( ), respectively; each station is characterized by a year which reflects the initiation of an upward or a downward trend. In some stations a second year is added, reflecting a year when a new change in a trend was observed. Brackets in this year indicate that the change is not statistically significant at the 95% level. In addition, the asterisk in some stations denotes that there is no climatic change for the period (at the 95% level of significance). An inspection of Table 3 reveals a clear picture only for the annual and winter series. In particular, a decreasing trend for annual values began during the period in almost half of the stations. There are two stations in which the Fig. 6. Graphical representation of the series u(d i ) and the retrograde series u 0 (d i ) (denoted as C 1 and C 2 ) of the sequential version of Mann-Kendall, for seasonal and annual precipitation (percentages from the mean of the period) in Greece for the period

14 168 H. Feidas et al. decreasing trend initiated earlier, at about 1970, and reversed at the 1990s. Similarly, a corresponding decreasing trend in winter values began in several stations during the same period with the exception of two stations where a negative trend followed an increasing trend beginning around the late 1950s. It is worth noticing that the majority of these stations were found to exhibit a statistically significant decreasing trend for annual and winter values over the entire period. As a consequence, this significant decreasing trend seems to be caused more by the dry conditions in the last 25 years rather than by a regular negative trend. No explicit conclusion can be drawn regarding spring and summer precipitation due to too few series displaying clear climatic discontinuities. In contrast, there are some stations exhibiting a decreasing trend in autumn with a starting year in the period ; reversals in this decreasing trend, though not statistically significant, are observed during the 1990s. These findings are also supported by the results of the sequential Mann-Kendall analysis when applied to the seasonal and annual precipitation percentages of the normal averaged over Greece (see last row of Table 3 and Fig. 6). In particular, both winter and annual series exhibit a downward trend with a starting year in 1984, a fact that confirms the main finding of the previous analysis, namely, that the decreasing trend found in the annual series is determined, to a great extent, by the respective downward trend in the winter series. In summary, it seems that after 1984, Greece entered a dry period which is responsible for the significant decreasing trend found for the whole period Precipitation trends in satellite data series As a first step, the precipitation time series derived by the GPCP Version 2 precipitation data Fig. 7. Observed values (gauges) and GPCP estimates (multi-satellite and satellite-gauges) of seasonal and annual precipitation in Greece for the period Precipitation is expressed as percentages of the mean of the period

15 Trend analysis of precipitation time series in Greece 169 Table 4. Comparison statistics of GPCP precipitation series with gauge records for Greece and the respective slope of the linear regression line over the period a. Statistically significant trends at 95% level of significance, are presented in bold r MAE RMSE b b a. Annual Gauges 8 5 Multi-satellite Satellite-gauges b. Winter Gauges 8 9 Multi-satellite Satellite-gauges c. Spring Gauges 8 10 Multi-satellite Satellite-gauges d. Summer Gauges Multi-satellite Satellite-gauges e. Autumn Gauges 6 8 Multi-satellite Satellite-gauges a r is the correlation coefficient, MAE and RMSE is the mean absolute error and the root mean squared error, respectively (percentage contribution), b (percentage contribution= decade) is the slope of the linear regression line and b is the associated error set was compared with those recorded by the rain gauge network in Greece. Seasonal and annual precipitation time series for both surface observations and GPCP estimates (multi-satellite and satellite-gauges) were graphically represented in Fig. 7, for the period Correlation coefficients and statistical measures of closeness such as mean absolute error (MAE) and root mean squared error (RMSE) are presented in Table 4, along with the slope of the regression trend line. According to these results, the multisatellite precipitation estimates represent only a small proportion (26 45%) of the variance of the precipitation recorded at the surface. Satellite rainfall retrieval techniques failed completely to reproduce the low and variable summer precipitation in Greece. In contrast, as expected, the gauge-adjusted satellite precipitation estimates were found to reproduce a significant proportion of annual, winter and spring precipitation variability (85 90%), which is reduced, however, for autumn (74%) and, particularly, for summer precipitation (60%). Best results with the higher correlation coefficient and lower errors were obtained with the annual time series. The previous findings are supported by the trend analysis results for the period in Greece, which indicate a general decreasing trend in all series of the satellite-gauge GPCP dataset with the exception of the summer series which is in agreement, not only qualitatively but also quantitatively, with the respective negative trend found in the surface observations. This is more pronounced in the annual, spring and autumn series. As a final conclusion, it seems that the final product of the GPCP Version 2 precipitation data set (the satellite-gauge precipitation estimates) could be an alternative means for diagnosing the variability of precipitation in Greece and detecting trends, especially in annual series. 3.3 Circulation indices and precipitation in Greece Table 5 shows the correlation coefficients between annual and seasonal precipitation totals, spatially averaged over Greece and the three circulation indices, NAOI, MOI and MCI. Results for the NAOI refer to the period , whereas correlation coefficients for the MCI and MOI were calculated for the period Given that original annual circulation indices were calculated to correspond to the period from December to November and dated by Table 5. Seasonal and yearly correlation of precipitation in Greece (percentages of the mean of the period) with circulation indices. Associated error is also indicated. Boldface values have a significant level of 95% Winter Spring Summer Autumn Year NAOI MOI MCI

16 170 H. Feidas et al. the year in which January occurred, the corresponding annual precipitation totals are accumulated using the same convention. The NAOI seems to play an important role in the precipitation regime of Greece by presenting a statistically significant negative correlation with the annual and winter precipitation. The proportion of explained precipitation variance is up to 16% for both periods. The most intriguing finding, however, is the significant positive correlation between the NAOI with summer precipitation in Greece, which accounts for 16% of the precipitation variance. The MOI explains the same variance in the annual precipitation series as the NAOI. The MCI is negatively correlated only with summer precipitation; it explains, however, a negligible proportion of precipitation variance (9%) in Greece. The previous results indicate that the NAO has a significant influence on the precipitation regime in Greece. This connection, however, is not only developed during winter, as expected, but is reactivated during summer with an opposite correlation sign. The Mediterranean Oscillation in pressure patterns at mid-tropospheric levels, is as representative of annual precipitation variability in Greece as the NAO and should be considered as a climatic forcing factor. The equivalent correlation coefficients found for the NAOI and MOI imply that both indices are appropriate for studying the relationship between annual precipitation in Greece and pressure patterns. Nevertheless, the Mediterranean Oscillation pattern must not be seen as an independent large-scale circulation, since it correlates significantly with the Northern Hemispheric modes of the NAO (D unkeloh and Jacobeit, 2003). On the other hand, it does not coincide with the NAO, but rather comprises the part of the NAO which is linked with Mediterranean precipitation variability. Finally, summer precipitation variability is explained better by using the synoptic-scale pressure patterns of the NAO than the Mediterranean circulation. The link between precipitation variability over Greece and regional (MO) and synoptic-scale (NAO) oscillation modes is very reasonable from a physical point of view. In years with high MOI values, strong positive geopotential height anomalies at the 500 hpa surface prevail over western-central Mediterranean, whereas negative anomalies dominate over the eastern Fig. 8. (a) Annual and (b) seasonal mean of winter (DJF) 500 hpa geopotential heights (gpm) in 1990 Fig. 9. (a) Annual and (b) seasonal mean of winter (DJF) 500 hpa geopotential heights (gpm) in 1963