The influences of the Southern and North Atlantic Oscillations on climatic surface variables in Turkey

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1 HYDROLOGICAL PROCESSES Hydrol. Process. 19, (2005) Published online 8 December 2004 in Wiley InterScience ( DOI: /hyp.5560 The influences of the Southern and North Atlantic Oscillations on climatic surface variables in Turkey M. Çaǧatay Karabörk, 1, * Ercan Kahya 2 and Mehmet Karaca 3 1 Dumlupinar University, Civil Engineering Department, Kütahya, Turkey 2 Istanbul Technical University, Civil Engineering Department, Maslak, İstanbul Turkey 3 Istanbul Technical University, Eurasia Institute of Earth Sciences, Maslak, İstanbul, Turkey Abstract: In this study, Turkish climatic variables (precipitation, stream flow and maximum and minimum temperatures) were first analysed in association with both the Southern Oscillation (SO) and the North Atlantic Oscillation (NAO). The relationships between Turkish maximum and minimum monthly temperatures and the extreme phases of the SO (El Niño and La Niña events) were examined. The results of this analysis showed that relationships between Turkish monthly maximum temperatures and El Niño and La Niña contain some complexity still to be identified, because both events produce a signal indicating a correspondence with cold anomalies in the aggregate composites. A relationship between turkish minimum temperatures and El Niño was detected in western Anatolia, whereas there was no significant and consistent signal associated with La Niña. Moreover a series of cross-correlation analyses was carried out to demonstrate the teleconnections between the climatic variables and both the NAO and SO. The NAO during winter was found to influence precipitation and stream-flow patterns. In contrast temperature patterns appeared to be less sensitive to the NAO. Furthermore, lag-correlation results indicated a prediction potential for both precipitation and stream-flow variables in connection with the NAO. Simultaneous and time-lag correlations between the climatic variables and the SO index, in general, indicated weaker relationships in comparison with those for the NAO. These analyses also showed that the influences of the SO on Turkish temperature data are negligible. The outcomes were presented in conjunction with an explanation regarding physical mechanisms behind the implied teleconnections. Copyright 2004 John Wiley & Sons, Ltd. KEY WORDS Turkey; NAO; SO; correlation analysis; precipitation; stream flow; maximum and minimum temperatures INTRODUCTION Prediction potential of any water resource system requires a good understanding of the atmospheric physics and climatic processes underlying the system behaviour. In this context, it is plausible to consider that some of this potential can be found in understanding large-scale atmospheric oscillation patterns, such as the Southern Oscillation (SO) and the North Atlantic Oscillation (NAO), as has been done increasingly during the past two decades. It is well known that the extreme phases of the SO (El Niña and La Niña conditions) have essential impacts on global climate. The links between the SO (defined as a circulation of atmospheric mass between a persistent high pressure zone over the eastern South Pacific and a persistent low pressure zone above the western South Pacific) and climatic variables (i.e. precipitation, stream flow, temperature and drought index patterns) has been well established a in number of studies beginning from the 1980s (e.g. Ropelewski and Halpert, 1986, 1987, 1989; Kiladis and Diaz, 1989; Kahya and Dracup, 1993; Dracup and Kahya, 1994; Piechota and Dracup, 1996). In addition, some recent studies (e.g. Price et al., 1998; Cullen and demenocal, 2000; Karaca et al., 2000; Nazemosadat and Cordery, 2000) have concerned the relationships between the ENSO phenomena and climatic variables in the Middle East region. * Correspondence to: M. Çaǧatay Karabörk, Dumlupinar University, Civil Engineering Department, Kütahya, Turkey. mckarabork@hotmail.com Received 24 March 2003 Copyright 2004 John Wiley & Sons, Ltd. Accepted 3 January 2004

2 1186 M. Ç. KARABÖRK, E. KAHYA AND M. KARACA Recent hydrological variability studies in Turkey have concentrated mostly on the detection trends i.e. Kahya and Kalaycı, 2004; and spatial variation modes. Additionally the influence of the SO extreme events on stream flow and precipitation patterns of Turkey were examined from the hydroclimatological perspective. For example, Kahya and Karabörk (2001) showed that there are two core regions with positive streamflow anomalies associated with El Niño events in western and eastern Turkey, whereas La Niña events result in negative stream-flow anomalies in eastern Turkey. Karabörk and Kahya (2003) analysed Turkish precipitation patterns in relation with the extreme phases of SO and found similar results to those of stream flow: seasonal positive anomalies associated with El Niño events in western and eastern Turkey and a seasonal negative anomaly associated with La Niña events in eastern Turkey. The seasonal anomalies detected and their geographical extent in the core regions were found to be similar for stream flow and precipitation. The NAO is another well known large-scale oscillation of atmospheric mass. It has a meridional pattern that occurs between the centre of subtropical high surface pressure located near the Azores and the subpolar low surface pressure near Iceland (Marshall et al., 2001). It is one of the major modes of variability of the Northern Hemisphere atmosphere; exerting a strong control on the climate of the Northern Hemisphere, especially during winter. Although the SO is the first to be linked to global climatic anomalies the far reaching effects of the NAO can be considered to rival the SO phenomena according to Marshall et al. (2001), and have been analysed in order to understand the Northern Hemisphere climate. The NAO index (NAOI) can be defined simply as the sea-level pressure difference between Azores and Iceland. The positive NAOI phase implies a stronger than usual subtropical high pressure centre and a deeper than normal Icelandic low. This increased pressure difference causes stronger winter storms over the Atlantic Ocean on a more northerly track and this situation results in wetter than normal conditions in northern Europe whereas the Mediterranean sector experiences dry conditions. The negative NAOI defines the reduced pressure gradient resulting from a weak subtropical high and a weak Icelandic low. During this phase, the opposite conditions dominate over northern Europe and the Mediterranean. Some studies (e.g. Cullen and demenocal, 2000) have demonstrated the linkages between the NAO and recent drier conditions over western Greenland and the Mediterranean as well as wetter and warmer conditions in northern Europe. Mares et al. (2002) demonstrated the NAO impacts on summer moisture variability across Europe. Cullen and demenocal (2000) was the first to examine the North Atlantic influences on Turkish stream flow in the Tigris Euphrates river basins using limited data coverage. The far reaching effects of the NAO lately have been sought in the Caribbean region (Giannini et al., 2001) and the Tibetan plateau (Liu and Yin, 2001). The aims of this study are summarized in two groups: (i) to investigate the linkages between the extreme phases of the SO and maximum and minimum temperatures over Turkey by adapting the approach of Ropelewski and Halpert (1986), and (ii) to document the influences of the NAO and the SO on Turkish climatic variables (precipitation, stream flow, maximum and minimum temperatures) through correlation analysis using a large data set. It is reasonable to correlate the Turkish climatic data with the NAO and SO because both oscillations are not significantly correlated (Wang, 2002). It should be noted that other than the findings of Wang (2002) our earlier results showed that there is no significant simultaneous or time-lag correlations between the SOI and NAOI at seasonal and interannual time-scales. DATA Climatic variables In this study, monthly precipitation, stream flow and maximum and minimum temperature observations were used. The length of stream-flow records (expressed as monthly means), compiled from 76 stream-flow gauging stations, is 31 years ( ). Figure 1 depicts the distribution of these stations over Turkey. The length of precipitation records (expressed as monthly totals), collected from 94 climate stations, is 43 years ( ). Finally, the lengths of maximum and minimum temperatures (expressed as monthly means), compiled from 54 meteorology stations, are 44 years ( ). Figure 2 and Table I show the location

3 INFLUENCES OF OCEANIC PRESSURE OSCILLATIONS ON CLIMATIC SURFACE VARIABLES IN TURKEY 1187 of the precipitation and temperature stations. Previous studies (Kahya and Karabörk, 2001; Ünal et al., 2003) have already confirmed the homogeneity of climatic data used in this study. Indicators of the atmospheric oscillations In order to calculate the simultaneous and time-lag cross-correlations between large-scale atmospheric oscillation patterns and Turkish climatic variables, the Southern Oscillation index (SOI) and the North Atlantic Oscillation index (NAOI) were used as indicators of the large-scale oscillations. The SOI represents the state of the atmosphere in the equatorial Pacific and is the difference in the normalized sea-level atmospheric pressure between Tahiti, Society Island and Darwin, Australia. The monthly SOI series was supplied from Figure 1. Distribution of the stream-flow stations used in the study Figure 2. Distribution of the precipitation and temperature stations used in the study

4 1188 M. Ç. KARABÖRK, E. KAHYA AND M. KARACA Table I. List of the precipitation stations used in the study Adana a Cizre PIzmir a Rize a Adıyaman Çanakkale Kahramanmaraş Sakarya Afyon a Çorlu Karaman Salihli Aǧrı a Çorum a Kars Samsun a Akhisar a Dikili a Kastamonu a Sarıkamış Aksaray Diyarbakır a Kayseri a Siirt a Akşehir Dörtyol a Kilis Silifke Alanya Edirne a Kırklareli Simav Amasya Edremit Kırşehir Sinop a Ankara a Elazıǧ a Kocaeli a Sivas a Antakya a Erzincan a Konya a Siverek Antalya Erzurum a Kütahya a Sivrihisar Ardahan Eskişehir Lüleburgaz a Şanlıurfa a Aydın a Fethiye a Malatya a Şebinkarahisar Balıkesir a Florya a Manavgat Şile a Bandırma a Gaziantep a Manisa a Tekirdaǧ a Bayburt Giresun a Mardin a Tokat Biga Gümüşhane Mersin a Trabzon a Bilecik a Hınıs Merzifon a Uşak a Bitlis Iǧdır Muǧla a Van a Bodrum a Ilgın Muş Yozgat a Bolu a Islahiye a Niǧde a Zonguldak a Burdur a Isparta a Ordu Bursa a PIskenderun a Polatlı a Stations that have both precipitation and temperature records. CPC (Climate Prediction Center, USA). The NAOI is defined as the difference in the normalized sea-level atmospheric pressure between a station in Azores and a station in Iceland. The monthly NAOI series was supplied from CRU (Climatic Research Unit, UK). METHODS Ropelewski and Halpert s (1986) approach Kahya and Karabörk (2001) and Karabörk and Kahya (2003) described the relationships between Turkish stream flow and precipitation patterns and the extreme phases of the SO; following their approach, harmonic analysis was first applied to both maximum and minimum monthly mean temperatures in order to examine El Niño and La Niña-related behaviour. In this context, the timing, magnitude, consistency and geographical extent of possible variable responses were analysed. El Niño and La Niña years need to be identified at the beginning of the analysis. There are 10 El Niño events (1951, 1953, 1957, 1965, 1969, 1972, 1976, 1982, 1986 and 1991) and seven La Niña events (1955, 1964, 1970, 1971, 1973, 1975 and 1988) between 1951 and 1994 (Piechota and Dracup, 1996). Detailed explanations of the empirical methods used here can be found in Ropelewski and Halpert (1986), Kahya and Dracup (1993) and Kahya and Karabörk (2001). A brief explanation of the method is based only on the El Niño case, which is, of course, applicable to the La Niña case. As the first step, temperature data are expressed as percentiles (values of cumulative probability density function) of an appropriate distribution for each month at each station. In this study, as temperature values are not very skewed, a normal distribution was selected as an underlying probability function. This transformation of original data into percentiles is also necessary in order to place all stations having different statistical characteristics on an equal footing.

5 INFLUENCES OF OCEANIC PRESSURE OSCILLATIONS ON CLIMATIC SURFACE VARIABLES IN TURKEY 1189 A first harmonic is fitted to 24-month El Niño composites at each station. These 24-month composites start from July of the preceding year (denoted as ( ) year) and continue through to June of the following the event year (denoted as (C) year). The event year is denoted as (0) year. An El Niño composite is composed of the averaged climatic data on all of the El Niño years for each of 24 months (average of the 10 values for each of 24 months in this study). The results of the first harmonic fit for each station can be displayed on a vectorial map where the directions and the lengths of the vectors show the timing of maximum signal and the magnitude of the El Niño-related response, respectively (Figure 3). The term signal is used to define the forced response of the analysed climatological variable to the El Niño/La Niña forcings. On the map, the region in which harmonic vectors have similar directions is identified and named as a candidate region. To select a candidate region more realistically, the composite structure of stations included in that region can be checked for their similarity. Additionally, the concept of coherence is a numerical criterion for a group of stations having similar composite structures in a candidate region. In this study, the minimum value of coherence was taken as 0Ð80, following Kahya and Dracup (1993). In a candidate region, all El Niño composites are spatially averaged to form an aggregate El Niño composite in which a season defined by anomalies of the same sign for four or more consecutive months is detected as the El Niño signal period. The index time-series based on the detected season in a candidate region is formed by averaging percentiles of the signal season for all years of the record in order to see the consistency of El Niño signal. The temporal consistency rate of the signal was selected as at least 0Ð7 levels in this study. Furthermore, to make an evaluation about the reliability of consistency of the signal, a hypergeometric model is used to compute the cumulative probability (P) that at least m successes are obtained in n trials from a finite population of size N containing k successes (Haan, 1977). Two cases (A and B) are defined for hypergeometric testing. In case A, the occurrence of a wet (dry) signal season in any year with an anomaly sign that is positive (negative) in the index time-series is defined as a success. In case B, a success is defined as the occurrence of a wettest (driest) season in any year with an anomaly that is greater (lower) than the 90th (10th) percentile in the index time-series. Finally a decision is made for a candidate region as a core region after applying the procedures above. Cross-correlations and lag correlations between Turkish climatic variables and the atmospheric oscillations As the first stage of correlation analysis, because the NAO reaches its strongest mode in the Northern Hemisphere winter, the monthly NAOI and monthly Turkish precipitation, stream flow and maximum and Figure 3. Maximum temperature vectors based on the 24-month harmonic fitted to El Niño composites. Explanation for the direction of arrows: south, July ( ); west, January (0); north, July (0); and east, January (C). The magnitude of arrows is proportional with the amplitude of the harmonics

6 1190 M. Ç. KARABÖRK, E. KAHYA AND M. KARACA minimum temperatures at each station were averaged for the winter season (December, January, February and March; DJFM). Then cross-correlation coefficients between the seasonally averaged NAOI and Turkish climatic data were calculated at each station. In order to understand the forecasting potential and influences of the large-scale atmospheric oscillations during an entire year on Turkey s climatology, lag correlations between the SOI and the NAOI and Turkish climatic variables were also calculated from lag zero to lag six using 2-month, 3-month and 4-month seasonal averages. The details of forming a season are given in the next section. Original observations were used rather than percentiles for all correlation analysis. RESULTS Analysis of El Niño and Turkish maximum temperature relationships A vectorial map based on the first harmonic fitting to the El Niño composites of Turkish temperature stations is given in Figure 3. It is seen that almost all stations have the same direction, indicating that the El Niño related responses of all stations are similar. As the previous studies (Kahya and Karabörk, 2001; Karabörk and Kahya, 2003) identified two different core regions for both stream flow and precipitation, we expected to find two different candidate regions with different timings for El Niño related responses. In order to see whether the stations located over the west and east Anatolia have similar El Niño composites, the aggregate El Niño composites of the west and east Anatolia regions were formed separately (Figure 4a and b). As seen from Figure 4, the composites have almost the same anomaly patterns and therefore the analysis was carried out for all of Anatolia except for the eastern Black Sea shore line, because it has a different composite structure compared with other stations. The selected candidate region is shown on Figure 3 by a dashed line with a vectorial coherence of 0Ð85. The aggregate El Niño composite of this candidate region is given in Figure 5a. There is an evident negative anomaly run from January (0) to July (0). The index time series of January (0) to July (0) period was prepared and given in Figure 5b. The horizontal dotted lines in Figure 5a delineate the upper (90%) and lower (10%) limits of the distribution of index time-series values based on the Weibull plotting position formula. Seven out of ten El Niño events confirm the cold season, resulting in a temporal consistency value of 70%. Three out of the four coldest years correspond to El Niño events. Some seasons shown in Figure 5a, such as the positive anomaly season of September ( ) to December ( ), were also tested but were found to have an inadequate temporal consistency. The probabilities of the temporal consistencies in the relevant index time-series occurring at random were computed by the hypergeometric distribution. The chance of occurrence at random that the January (0) to July (0) season has a negative temperature anomaly duringanelniño year is 0Ð20. This rate of randomness is not low. For the case of lowest values (lower than the 10% level) in the relevant index time-series, this chance of occurrence at random becomes 0Ð001. Percentiles (%) Western Anatolia Percentiles (%) Eastern Anatolia (a) 20 JASONDJFMAMJJASONDJFMAMJ 20 (b) JASONDJFMAMJJASONDJFMAMJ Figure 4. El Niño aggregate composite for (a) western Anatolia (WA) region and (b) eastern Anatolia (EA) region. The months belonging to the El Niño year are boxed and designated as the (0) year

7 INFLUENCES OF OCEANIC PRESSURE OSCILLATIONS ON CLIMATIC SURFACE VARIABLES IN TURKEY 1191 Percentiles (%) (a) JASONDJFMAMJJASONDJFMAMJ Percentiles (%) (b) Figure 5. (a) El Niño aggregate composite for the candidate Anatolia region. The dotted vertical lines delineate the season of possible El Niño related responses (i.e. the negative signal from January (0) to July (0)). The months belonging to the El Niño year designated by the (0) year are boxed. (b) The index time-series (ITS) for the candidate region for the season previously detected. El Niño years are shown by solid bars. The dotted horizontal lines are the upper (90%) and lower (10%) limits for the distribution of ITS values Analysis of La Niña and Turkish maximum temperature relationships Figure 6 shows the harmonic vectors that were computed from the 24-month La Niña composites of maximum temperature values. The harmonic vectors have similar directions over all of Anatolia, indicating a similar La Niña related behaviour for all stations. Stations located at the eastern Black Sea shore line, however, were excluded from the selected candidate region owing to their different composite anomaly structures. This candidate region is the same as that for the El Niño and Turkish temperature relationship and resulted in a vectorial coherence of 0Ð90 (Figure 6). The aggregate La Niña composite (Figure 7a) reveals the August (0) to December (0) season with a dominant negative anomaly trend. A weaker positive anomaly season is observed for the April (0) to July (0) period. Figure 7b shows the index time-series of the August (0) to December (0) season. It is shown that all La Niña years confirm the cold season signal of the August (0) to December (0) period in the index time-series, referring to 100% consistency. The chance of occurring at random at this consistency rate is equal to 0Ð007 according to the hypergeometric distribution test. As seen from Figure 7b, none of the coldest years corresponds to La Niña years. In Figure 5, the January (0) to July (0) negative anomaly period was selected as the El Niño related season. Here, it must be noted that results of global-scale studies (i.e. Ropelewski and Halpert, 1987, 1989; Kiladis and Diaz, 1989) showed the reverse influences of El Niño and La Niña events on climatological variables. Figure 6. Maximum temperature vectors based on the 24-month harmonic fitted to La Niña composites. Details are the same as given in Figure 3

8 1192 M. Ç. KARABÖRK, E. KAHYA AND M. KARACA Percentiles (%) Percentiles (%) (a) J ASONDJ FMAMJ J ASONDJ FMAMJ (b) Figure 7. (a) La Niña aggregate composite for the candidate Anatolia region. (b) The index time-series (ITS) for the candidate region for the season previously detected. Details are the same as given in Figure As El Niño related to Turkish temperature response was selected as a colder season, expectation is toward a warmer season for La Niña signals. Consequently the weak warm anomaly sequence of April (0) to July (0) in Figure 7a was considered, however, its relevant index time-series showed a very low temporal consistency (three out of seven). The only season in association with La Niña events is the August (0) to December (0) season. Therefore the relationship between the extreme phases of the SO and Turkish temperature patterns could be considered more complex. Nevertheless, the La Niña and Turkish temperature relationships exhibited a more robust signal compared with that of El Niño events. Analysis of El Niño and Turkish minimum temperature relationships The timing and magnitude of the El Niño related response at each station were calculated by means of the first harmonic fit to the 24-month El Niño composite and presented as a harmonic vector in Figure 8. Unlike the El Niño and maximum temperature relationship, the vectorial map indicates two candidate regions located in western and eastern Anatolia. Selecting stations having similar El Niño composites enables us to identify the borders of the candidate regions shown in Figure 8. Western Anatolia (WA). The vectorial coherence for this region is 0Ð87. The aggregate composite and index time-series of this region are given in Figure 9a and b. The cold period considered as a signal season is the Figure 8. Minimum temperature vectors based on the 24-month harmonic fitted to El Niño composites. Details are the same as given in Figure 3

9 INFLUENCES OF OCEANIC PRESSURE OSCILLATIONS ON CLIMATIC SURFACE VARIABLES IN TURKEY (a) Percentiles (%) Western Anatolia JASONDJFMAMJJASONDJFMAMJ Western Anatolia (b) Percentiles (%) Figure 9. (a) El Niño aggregate composite for the candidate western Anatolia (WA) region. (b) The index time-series (ITS) for the candidate western Anatolia region for the season previously detected. Details are the same as given in Figure 5 October (0) to January (C) period in the aggregate composite. Other positive or negative trends have short periods to be selected as a signal season. In the relevant index time-series the temporal consistency rate is found to be 70% (Figure 9b). The chance of occurrence at random for this temporal consistency rate is 0Ð09 for the October (0) to January (C) signal season. Two out of four coldest years in the index time-series coincide with El Niño years with a randomness probability of 0Ð04. Eastern Anatolia (EA). This region has a vectorial coherence of 0Ð85. The October (0) to January (C) season was selected as a signal season from the aggregate composite (Figure 10a). From the index time-series of this region (Figure 10b), the temporal consistency for this season is computed as low as equal to 0Ð60. As there is no other satisfying period as a signal season, we decided that eastern Anatolia region has not a consistent signal associated with the El Niño events. Analysis of La Niña and Turkish minimum temperature relationships The amplitudes and phases of 56 first harmonics are computed from 24-month La Niña composites, which were formed using the temperature percentiles based on the normal distribution (Figure 11). The selected candidate region based on vectorial coherence and composite similarity is shown in Figure 11. It should be noted that although the directions of the vectors in eastern and far western Turkey suggest different phasing of thelaniña signal, the stations were included in the same candidate region because their La Nina composite structures are similar. The eastern Black Sea region was again not included in the candidate region. The vectorial coherence of the candidate region is 0Ð89. In the aggregate composite (Figure 12a), there are two dominant positive anomaly seasons. As a first trial, the March (C) to June (C) season was selected as a signal season and its index time-series (Figure 12b) indicates a fairly low temporal consistency (57%). As (a) Percentiles (%) Eastern Anatolia J ASOND J FMAMJ J ASOND J FMAMJ Eastern Anatolia (b) Percentiles (%) Figure 10. (a) El Niño aggregate composite for the candidate eastern Anatolia (EA) region. (b) The index time-series (ITS) for the candidate eastern Anatolia region for the season previously detected. Details are the same as given in Figure 5

10 1194 M. Ç. KARABÖRK, E. KAHYA AND M. KARACA Figure 11. Minimum temperature vectors based on the 24-month harmonic fitted to La Niña composites. Details are the same as given in Figure 3 Percentiles (%) (a) J ASOND J FMAMJ J ASOND J FMAMJ Percentiles (%) (b) Figure 12. (a) La Niña aggregate composite for the candidate region. (b) The index time series (ITS) for the candidate region for the season previously detected. Details are the same as given in Figure 5 the second trial, the September ( ) to January (0) season with positive anomalies was tested and found to have an identical temporal consistency (57%). It is therefore decided that the La Nina and Turkish minimum temperatures relationship is not significant and consistent in the region. Analysis of the relationships between the NAO and Turkish climatic variables during the Northern Hemisphere winter season The monthly time-series of each variable (namely, the NAOI, precipitation, stream flow, maximum temperature and minimum temperature) was averaged for the Northern Hemisphere winter season (DJFM). For each individual precipitation, stream flow and temperature station, the cross-correlations relative to the NAOI were calculated for the winter season. As the SOI and NAOI series are not significantly correlated to each other, significant correlations between the SOI and Turkish climatic variables will be sought independently in this study. Simultaneous cross-correlations between Turkish precipitation and the NAO during the winter season. The NAOI and precipitation records were found to be negatively correlated across Turkey. Seventy out of 94 stations have significant correlations in association with the NAOI at the D 0Ð10 or lower significance level (Figure 13). Twenty-five out of these 70 stations exhibit correlation values significant at the D 0Ð01 or lower

11 INFLUENCES OF OCEANIC PRESSURE OSCILLATIONS ON CLIMATIC SURFACE VARIABLES IN TURKEY 1195 Figure 13. Locations of the precipitation stations that have significant correlations in association with the NAOI series for the winter season. Circles denote the 0Ð01 significance level (jrj > 0Ð389); squares denote 0Ð05 significance level (jrj > 0Ð301) and triangles denote 0Ð10 significance level (jrj > 0Ð254) level. As seen in Figure 13, stations with a higher correlation value are densely located in western Anatolia and all correlation values appear to be significant at the D 0Ð01 level, except the Aegean Sea coast line. As the threshold value of the correlation coefficients at the 0Ð01 significance level is 0Ð389, it can be said that if a precipitation station is correlated with the NAO at the 0Ð01 or lower levels, about 15% of the variance at that station is explained by the NAO. In eastern Turkey, the correlations become less significant (mostly at the D 0Ð05 level), indicating less sensitivity to the North Atlantic influences compared with western Anatolia. As a noticeable point, none of stations located along the Black Sea coasts have a significant correlation with the NAOI. The southern coasts of the country demonstrate a less sensitive pattern to the NAO, as the Aegean coast. Simultaneous cross-correlations between Turkish stream flow and the NAO during the winter season. Of 76 stream-flow stations, 50 had a negative significant correlation with the NAOI (Figure 14) and mostly took place in western and southern Anatolia regions. Fourteen of these stations were correlated with the NAOI at the D 0Ð01 or lower significance level and mostly are located in north-western Anatolia (i.e. Sakarya basin). Stream flow along the Black Sea coast does not show a correlation with the NAOI, except three stations with a significance level that is not high ( D 0Ð10), located at the far east Black Sea coast. The findings concerning the Black Sea coastal region coincide with those between precipitation and the NAO. Simultaneous cross-correlations between Turkish maximum and minimum temperatures and the NAO during the winter season. The cross-correlations between Turkish maximum and minimum temperature patterns and the NAO appear weaker compared with those for the precipitation and NAO and the stream flow and NAO relationships, as seen in Figures 15 and 16. For the case of the maximum temperature and NAO relationship, 18 out of 56 stations have a negative significant correlation with the NAOI and display an irregular distribution in the study domain. Hence the correlation structure is weak, only three of 18 stations have a significant correlation at the D 0Ð01 or lower level. In contrast to the previous indications, the Black Sea coastal region seems to be correlated with the NAOI, but not at a satisfactory significance level. For the minimum temperature and NAO relationship in Turkey, 18 out of 56 stations possess a negative correlation value at the D 0Ð10 or lower significance level. Three out of those 18 correlations were found to be significant at the D 0Ð01 level. None of the stations located in northern and north-western Turkey seem to be correlated with the NAOI. Consequently these results clearly reveal less sensitive behaviour of

12 1196 M. Ç. KARABÖRK, E. KAHYA AND M. KARACA Figure 14. Same as in Figure 13, except for stream flow stations. Circles denote the 0Ð01 significance level (jrj > 0Ð456); squares denote 0Ð05 significance level (jrj > 0Ð355) and triangles denote 0Ð10 significance level (jrj > 0Ð301) Figure 15. Same as in Figure 13, except for maximum temperature stations. Circles denote the 0Ð01 significance level (jrj > 0Ð384); squares denote 0Ð05 significance level (jrj > 0Ð297) and triangles denote 0Ð10 significance level (jrj > 0Ð251) Turkish temperature patterns against the NAO forcings and somewhat different responses from precipitation and stream flow. Analysis of the time-lag correlations between the NAO and Turkish climatic variables In order to examine the North Atlantic influences on Turkish climatic variables as well as to obtain the highest correlation structures, cross-correlation analysis was performed between the time-series of precipitation, stream flow, maximum temperature and minimum temperature and the time-series of the NAOI based on 4-month, 3-month and 2-month seasonal averages at several time-lags. Using the seasonal mean values of the atmospheric circulation index and Turkish climatic variables, the correlations for each season from lag-0 to lag-k (k should be taken as 2 for the 4-month season, as 3 for the 3-month season and as 4 for the 2-month season) were determined at each station. The lag-k correlation implies that the seasonal mean

13 INFLUENCES OF OCEANIC PRESSURE OSCILLATIONS ON CLIMATIC SURFACE VARIABLES IN TURKEY 1197 Figure 16. Same as in Figure 13, except for minimum temperature stations values are correlated with the variable records k season(s) in advance. Furthermore, the entire analysis was similarly carried out for the relationships between the SOI and Turkish climatic variables. Results for each variable will be presented separately. Precipitation. In the case of the 4-month averaged series, three seasons were defined by averaging monthly precipitation totals and the NAOI values as: Season 1, which consists of January, February, March and April, and so on. As a result, precipitation values were found to be negatively correlated with the NAOI series from zero to two lags (Table II). The significance of these correlation coefficients was tested by the t-test. Stations with significant correlations at the D 0Ð10 or lower level were noted, as a result of this, 82 out of 94 stations are determined to exhibit significant correlations at various time-lags (Table II). Most of the stations have significant negative correlations with the NAOI for Season 1 at lag-0. All stations located in western Anatolia have a significant correlation with the NAO at the D 0Ð01 level. Correlations tend to be weakening from west to east. The stations located at the eastern Black Sea coast do not have a significant correlation with the NAOI with the exception of few stations for Seasons 2 and 3. It should be noted that Season 3 (the 4-month season formation) partially corresponds to the winter season. As a noticeable result, some stations have a significant correlation with the NAOI (mostly at the D 0Ð05 level) for Season 1 at lag-2. This implies an important prediction potential because the seasonal mean NAOI values are correlated with the seasonal mean precipitation values two seasons in advance. In the case of three-month averaged series, four seasons were formed by averaging monthly precipitation totals and the NAOI values as: Season 1 containing January, February and March; Season 2 containing April, May and June, and so on. Most of the stations are significantly correlated with the NAOI for Season 1 at lag-0 (Table III). Correlations become weaker from the west to east direction. Unlike the four-month season, the stations located in south-eastern Turkey do not have a significant correlation associated with the NAOI for Season 1 at lag-0. The stations located at the eastern Black Sea coast remain uncorrelated with the NAO. Most stations with a significant correlation with the NAO for season 1 at lag-0 are correlated with the NAO for Season 4 at lag-0. By definition, Season 4 corresponds to winter. In Table III, the lag-2 correlation values of some stations for Season 1 indicate a prediction potential because the seasonal mean NAOI values are correlated with the seasonal mean precipitation values two seasons in advance. Some stations have a significant correlation with the NAO for Seasons 2 and 3 at lag-0, reflecting the North Atlantic influences on Turkish precipitation during the whole year.

14 1198 M. Ç. KARABÖRK, E. KAHYA AND M. KARACA Table II. For the 4-month seasons, precipitation stations having a significant negative correlation with the NAOI series at different lags Station Season number Station Season number Lag 0 Lag 1 Lag 2 Lag 0 Lag 1 Lag 2 Ş.karahisar 1 ŁŁ Mersin 1 Ł 1 ŁŁ Bolu 1 ŁŁŁ Silifke 1 Ł 1 ŁŁŁ Çorum 1 ŁŁ Islahiye 1 ŁŁŁ 1 Ł Giresun 2 ŁŁ Alanya 1 ŁŁ 1 ŁŁ Kastamonu 3 ŁŁ Burdur 1 ŁŁŁ 1 ŁŁ Bayburt 2 Ł Fethiye 1 ŁŁŁ 1 ŁŁŁ Zonguldak 1 ŁŁ Antakya 1 ŁŁŁ 1 ŁŁ Tokat 1 ŁŁ PIskenderun 1 ŁŁ Amasya 1 ŁŁ K.Maraş 1 ŁŁŁ 1 Ł Bursa 1 ŁŁŁ Manavgat 1 ŁŁ 1 ŁŁŁ Çanakkale 1 ŁŁŁ Sivrihisar 1 ŁŁŁ 1 Ł Edirne 1 ŁŁŁ Sivas 1 ŁŁŁ Bilecik 1 ŁŁŁ Polatlı 1 ŁŁŁ 1 Ł Kocaeli 3 Ł Konya 1 ŁŁŁ Tekirdaǧ 1 ŁŁŁ Kırşehir 1 ŁŁ 1 Ł Bandırma 1 ŁŁŁ Karaman 1 Ł Biga 1 ŁŁŁ Ilgın 1 ŁŁŁ 1 ŁŁ Kırklareli 1 ŁŁŁ Eskişehir 1 ŁŁŁ Çorlu 1 ŁŁŁ Ankara 1 ŁŁŁ Florya 1 ŁŁŁ Niǧde 1 ŁŁŁ 1 Ł Sakarya 3 Ł Kayseri 1 ŁŁŁ Balıkesir 1 ŁŁŁ Aksaray 1 ŁŁŁ 1 Ł Şile 1 ŁŁ Akşehir 1 ŁŁŁ 1 Ł Lüleburgaz 1 ŁŁŁ Yozgat 3 ŁŁ Afyon 1 ŁŁŁ 1 Ł Erzurum 1 ŁŁ Akhisar 1 ŁŁŁ Kars 1 ŁŁ Aydın 1 ŁŁŁ 1 Ł Elazıǧ 1 ŁŁŁ 1 ŁŁ PIzmir 1 ŁŁŁ Malatya 1 ŁŁ 1 ŁŁ Kütahya 1 ŁŁŁ Sarıkamış 1 Ł Manisa 1 ŁŁŁ Erzincan 1 ŁŁ Muǧla 1 ŁŁŁ 1 Ł Aǧrı 1 ŁŁ Uşak 1 ŁŁŁ Hınıs 1 ŁŁ 1 ŁŁ Bodrum 1 ŁŁŁ 1 ŁŁŁ Iǧdır 1 ŁŁ 1 Ł Simav 1 ŁŁŁ Diyarbakır 1 ŁŁ Salihli 1 ŁŁŁ Siirt 1 Ł Dikili 1 ŁŁŁ Kilis 1 ŁŁŁ 1 ŁŁ Edremit 1 ŁŁŁ Gaziantep 1 ŁŁŁ Adana 1 Ł 1 ŁŁ Şanlıurfa 1 ŁŁ 1 ŁŁ Antalya 1 ŁŁŁ 1 ŁŁ Adıyaman 1 Ł Dörtyol 1 ŁŁ Mardin 1 ŁŁ 1 ŁŁ Isparta 1 ŁŁŁ 1 ŁŁ Siverek 1 ŁŁ 1 ŁŁ Ł 0Ð10 significance level; ŁŁ 0Ð05 significance level; ŁŁŁ 0Ð01 significance level. In the case of two-month averaged series; six seasons were obtained by averaging monthly values of the both variables, such as Season 1 containing January and February, and so on. Possible correlations were calculated from lag-0 to lag-4. The results of this analysis (not shown here) show a similar pattern with, of course, more details than those for the 4-month and 3-month seasons.

15 INFLUENCES OF OCEANIC PRESSURE OSCILLATIONS ON CLIMATIC SURFACE VARIABLES IN TURKEY 1199 Table III. For the 3-month seasons, precipitation stations having significant negative correlations associated with the NAOI series at different lags Station Season number Station Season number Station Season number Lag 0 Lag1 Lag 2 Lag3 Lag 0 Lag 1 Lag 2 Lag 3 Lag 0 Lag 1 Lag 2 Lag 3 Ş.Karahisar 1 ŁŁ Edirne 1 ŁŁŁ Aydın 3 ŁŁ Ş.Karahisar 2 Ł Bilecik 1 ŁŁŁ PIzmir 1 ŁŁ Samsun 4 Ł Bilecik 4 ŁŁŁ PIzmir 4 Ł Trabzon 4 ŁŁ Kocaeli 1 Ł Kütahya 1 ŁŁŁ Bolu 1 ŁŁ Kocaeli 4 ŁŁŁ Kütahya 4 ŁŁŁ Bolu 4 ŁŁ Tekirdaǧ 1 ŁŁŁ Manisa 1 ŁŁŁ Çorum 1 ŁŁŁ Tekirdaǧ 4 Ł 4 ŁŁ Manisa 4 ŁŁ Çorum 2 Ł 2 Ł Bandırma 1 ŁŁŁ Muğla 1 ŁŁŁ 3 Ł Çorum 4 ŁŁ Bandırma 4 ŁŁŁ Uşak 1 ŁŁŁ Giresun 4 ŁŁ 3 ŁŁŁ Biga 1 ŁŁŁ Uşak 4 ŁŁ Kastamonu 4 ŁŁŁ 4 ŁŁ 2 ŁŁŁ Biga 3 ŁŁ Bodrum 1 ŁŁŁ Ordu 4 ŁŁ 3 ŁŁŁ Biga 4 ŁŁŁ Simav 1 ŁŁŁ Merzifon 1 ŁŁŁ Kırklareli 1 ŁŁŁ Simav 4 ŁŁŁ Merzifon 2 Ł 4 ŁŁ Çorlu 1 ŁŁŁ Salihli 1 ŁŁŁ Rize 2 ŁŁ Çorlu 4 ŁŁ 4 Ł Salihli 4 ŁŁŁ Rize 4 ŁŁ Florya 1 ŁŁŁ Dikili 1 ŁŁŁ Bayburt 1 ŁŁŁ Florya 4 ŁŁŁ Edremit 1 ŁŁŁ Zonguldak 1 Ł Sakarya 4 ŁŁ Edremit 3 ŁŁ Zonguldak 4 Ł Balıkesir 3 ŁŁŁ Adana 1 ŁŁŁ Tokat 4 Ł Şile 4 ŁŁŁ 4 Ł Adana 2 Ł Amasya 1 ŁŁŁ 2 Ł Lüleburgaz 1 ŁŁŁ Antalya 1 ŁŁŁ 1 Ł Amasya 4 Ł 4 ŁŁ Afyon 1 ŁŁŁ Dörtyol 1 Ł Bursa 1 ŁŁŁ Afyon 4 ŁŁŁ Dörtyol 3 ŁŁŁ Bursa 4 ŁŁŁ Akhisar 1 ŁŁŁ Isparta 1 ŁŁŁ 4 ŁŁ Çanakkale 1 ŁŁŁ Akhisar 4 ŁŁ Mersin 2 ŁŁŁ Çanakkale 3 ŁŁ Aydın 1 ŁŁŁ Silifke 3 Ł Islahiye 1 ŁŁŁ Ilgın 1 Ł 1 Ł Muş 1 ŁŁ Islahiye 2 ŁŁ Ilgın 2 ŁŁ Ardahan 1 Ł Islahiye 3 ŁŁ 3 ŁŁ Ilgın 4 ŁŁŁ Erzincan 1 ŁŁŁ Burdur 1 ŁŁŁ Eskişehir 1 ŁŁŁ 2 Ł Erzincan 2 ŁŁ Fethiye 1 ŁŁŁ Eskişehir 4 ŁŁŁ Bitlis 3 Ł Antakya 1 ŁŁ Ankara 1 ŁŁŁ Aǧrı 1 Ł Antakya 3 Ł Ankara 2 ŁŁ Aǧrı 2 Ł PIskenderun 1 Ł 1 ŁŁ Ankara 4 ŁŁŁ Hınıs 2 Ł 1 ŁŁŁ 3 Ł K.Maraş 1 ŁŁŁ Niǧde 1 ŁŁ 1 ŁŁ Iddır 1 Ł 1 ŁŁ Manavgat 1 Ł Niǧde 2 ŁŁ 2 ŁŁ Iddır 2 ŁŁ Sivrihisar 1 ŁŁŁ Kayseri 1 ŁŁŁ Siirt 3 Ł Sivrihisar 4 ŁŁŁ Kayseri 2 ŁŁ Kilis 1 Ł Sivas 1 ŁŁŁ Aksaray 1 ŁŁŁ 1 ŁŁ Kilis 2 ŁŁ 2 Ł Sivas 2 Ł Aksaray 2 ŁŁ Gaziantep 1 ŁŁ Sivas 4 ŁŁ Akşehir 1 Ł 1 Ł Gaziantep 2 ŁŁ 3 ŁŁ Polatlı 1 ŁŁŁ Yozgat 2 ŁŁ 3 ŁŁ Gaziantep 4 Ł Polatlı 2 ŁŁŁ Yozgat 4 ŁŁ Şanlıurfa 2 ŁŁ Polatlı 4 ŁŁŁ Erzurum 1 ŁŁ Şanlıurfa 4 Ł Konya 1 ŁŁ Kars 1 ŁŁ 1 ŁŁ Adıyaman 1 Ł Konya 2 ŁŁ Elazıǧ 1 Ł 1 Ł Adıyaman 2 Ł Konya 3 ŁŁ Elazıǧ 2 Ł Adıyaman 3 ŁŁ Konya 4 Ł Malatya 2 ŁŁ 1 ŁŁŁ Mardin 1 ŁŁ Kırşehir 1 ŁŁŁ Malatya 3 Ł Siverek 4 Ł 3 Ł Kırşehir 4 Ł Sarıkamış 1 Ł 1 ŁŁ Cizre 1 Ł Karaman 2 Ł Van 1 Ł Ł 0Ð10 significance level; ŁŁ 0Ð05 significance level; ŁŁŁ 0Ð01 significance level.

16 1200 M. Ç. KARABÖRK, E. KAHYA AND M. KARACA Stream flow. As a result of this analysis, stream flow values were found to be negatively correlated with the NAOI series from zero to two lags. The significance of these correlation coefficients were tested by the t-test and stations containing significant correlations at the D 0Ð10 or lower were selected. Seventy-three out of 76 stations exhibit statistically significant correlations at various time lags (Table IV). In general, the western Anatolia region contains stations with a correlation value at zero to two lags of D 0Ð10 or lower level for Seasons 1, 2 and 3. Most of the stations located in western Anatolia have strong linear relationships with the NAOI series based on significant correlations at the D 0Ð01 or lower level. For the western Anatolia region, stream-flow averages during Season 1 have a strong relationship with the NAOI series simultaneously; during Season 2 at lag-1 and finally during Season 3 at lag-2. These results clearly indicate that the NAOI values during Season 1 (corresponding partially to both winter and spring seasons) have strong relations with stream flow in western Anatolia. For the middle and eastern parts of Turkey, although some stations have a significant correlation for Season 1 at lag-0, significant correlations tend to increase during Seasons 2 and 3 at both lag-0 and lag-1. This region has an elevated topography and larger precipitation amounts in the form of snow more often in winter as opposed to western Anatolia. In particular, snowmelt is an important component of stream flow in eastern Anatolia. This fact possibly causes some time lags for the NAO-related response in middle and eastern Anatolia (as confirmed by the values of lag-0 and lag-1 correlations corresponding to Seasons 2 and 3, respectively). In the case of 3-month averaged series, four seasons were formed by averaging monthly stream flow and the NAOI values. Table V presents the results of this analysis and an accordance with the relationships is evident for western Turkey when comparing with the case of 4-month averaged series. The majority of stations located in western Anatolia possess a strong relationship with the NAOI series, having correlation values significant at the D 0Ð01 or lower level. In general, the NAO during Season 1 (covering winter season) has a strong influence on stream flow in western Anatolia during the entire year. This potential enables one to predict stream flow based on the NAOI values during Season 1. Stream flow in the middle and eastern parts of Turkey showed similar responses to the NAO as in western Turkey for the 3-month seasons. In general, Season 1 and Season 4 (covering the winter season) have significant correlations with the NAOI at lag-0. This relationship, however, is not valid for the other seasons at some stations in middle and eastern Anatolia. These results imply weaker influences of the NAO on stream flow in eastern Anatolia in comparison with western Anatolia. In the case of 2-month averaged series, six seasons were established by averaging monthly values of both variables, such as Season 1 containing January and February, and so on. For the both western and eastern Anatolia regions, similar correlation structure is valid in a more detailed manner (not shown here). Stream flow is highly correlated with winter NAOI values that coincide with Seasons 1, 2 and 6. Maximum and minimum temperatures. The correlations between Turkish maximum and minimum temperatures and the NAOI are given in Tables VI IX, which imply that Turkish temperature patterns are less sensitive to the NAO as opposed to Turkish precipitation and stream-flow patterns. Tables VIII and IX show that the Turkish temperature and NAO relationships are noticeable during winter because significant correlations appear mostly during Seasons 1 and 4 for the 3-month season formation. In contrast with precipitation and stream-flow patterns, temperature patterns seem to be affected by the NAO at the Black Sea coast. Analysis of the time-lag correlations between the SOI and Turkish climatic variables Precipitation. For this analysis, 4-month seasons were first formed for the SOI and then lag correlations between the 4-month averaged SOI and precipitation series were calculated from lag-0 to lag-2 at each station (Table X). As a result, 43 out of 94 precipitation stations were found to be negatively correlated with the SOI at the D 0Ð10 or lower significance level at various lags. The precipitation stations mostly have a significant correlation at the D 0Ð05 level for Season 2 at lag-0. The locations of the station having a significant correlation, being denser in the eastern parts of the country. These results are in good agreement with the results of Karabörk and Kahya (2003), who concluded that eastern Anatolia was identified as a region having

17 INFLUENCES OF OCEANIC PRESSURE OSCILLATIONS ON CLIMATIC SURFACE VARIABLES IN TURKEY 1201 Table IV. For the 4-month seasons, stream-flow stations having a significant negative correlation with the NAOI series at different lags Station Season number Station Season number Lag 0 Lag 1 Lag 2 Lag 0 Lag 1 Lag ŁŁŁ Ł 3 ŁŁ ŁŁŁ 2 ŁŁ ŁŁ ŁŁŁ 2 ŁŁ 3 ŁŁ Ł ŁŁŁ Ł ŁŁŁ 2 Ł Ł 1 ŁŁ ŁŁŁ 2 ŁŁ Ł ŁŁ ŁŁ Ł ŁŁ ŁŁŁ ŁŁ ŁŁŁ 2 Ł ŁŁ ŁŁŁ 2 ŁŁŁ 3 Ł Ł 2 ŁŁ ŁŁ Ł ŁŁ ŁŁ ŁŁŁ 3 Ł Ł ŁŁ 2 ŁŁ 3 ŁŁŁ ŁŁŁ 2 ŁŁ 3 Ł ŁŁ Ł ŁŁŁ 2 ŁŁŁ 3 ŁŁ ŁŁ ŁŁŁ 2 ŁŁŁ 3 ŁŁŁ Ł ŁŁŁ 2 ŁŁŁ 3 ŁŁŁ ŁŁ ŁŁŁ 2 ŁŁŁ 1 ŁŁ ŁŁ ŁŁŁ Ł 2 Ł ŁŁŁ 2 ŁŁ 3 ŁŁ Ł ŁŁŁ 2 ŁŁŁ 3 ŁŁ ŁŁ 2 Ł 1 Ł ŁŁ 2 ŁŁ ŁŁ Ł ŁŁ 2 ŁŁ Ł ŁŁ ŁŁŁ ŁŁ 2 ŁŁ ŁŁŁ 2 ŁŁŁ 3 ŁŁŁ Ł 3 ŁŁ ŁŁ 2 Ł 3 ŁŁ ŁŁ ŁŁ 2 Ł ŁŁ 1 ŁŁ Ł Ł 2 ŁŁ 3 ŁŁ ŁŁŁ 2 ŁŁŁ 3 ŁŁŁ ŁŁ 3 Ł ŁŁ 2 ŁŁŁ 3 Ł Ł 1 ŁŁ ŁŁ Ł 1 Ł ŁŁŁ 2 Ł Ł 2 Ł Ł Ł 3 ŁŁ ŁŁŁ 2 ŁŁŁ 3 ŁŁŁ ŁŁ 2 ŁŁ Ł Ł Ł ŁŁ Ł Ł 3 Ł Ł Ł ŁŁ Ł 1 Ł Ł 3 Ł Ł Ł ŁŁ Ł 0Ð10 significance level; ŁŁ 0Ð05 significance level; ŁŁŁ 0Ð01 significance level. consistent and coherent El Niño and La Niña signals. It is reasonable to expect negative correlations between the SOI and precipitation values because positive precipitation anomalies have been found to be associated

18 1202 M. Ç. KARABÖRK, E. KAHYA AND M. KARACA Table V. For the 3-month seasons, stream-flow stations having significant negative correlations associated with the NAOI series at different lags Station Season number Station Season number Station Season number Station Season number Lag 0 Lag 1 Lag 2 Lag 3 Lag 0 Lag 1 Lag 2 Lag 3 Lag 0 Lag 1 Lag 2 Lag 3 Lag 0 Lag 1 Lag 2 Lag ŁŁŁ 2 ŁŁ ŁŁŁ 2 ŁŁ 3 ŁŁ Ł Ł 2 ŁŁ 3 Ł 2 Ł ŁŁŁ 2 ŁŁ 3 ŁŁŁ ŁŁ 2 Ł 3 Ł Ł ŁŁ ŁŁŁ 2 ŁŁŁ 3 ŁŁ ŁŁ 4 Ł ŁŁ Ł 2 Ł 3 ŁŁ ŁŁŁ 3 ŁŁ ŁŁ ŁŁ ŁŁŁ Ł ŁŁŁ Ł 3 Ł 4 Ł ŁŁ 2 ŁŁ 3 ŁŁ 4 ŁŁ Ł ŁŁŁ 2 ŁŁŁ 3 ŁŁŁ 4 ŁŁŁ ŁŁ Ł ŁŁŁ 2 Ł ŁŁŁ 2 Ł 4 ŁŁ ŁŁ ŁŁ 1 ŁŁŁ ŁŁ Ł Ł ŁŁ 1 Ł 3 ŁŁ 4 ŁŁ ŁŁ 2 ŁŁŁ 3 ŁŁŁ ŁŁ 2 ŁŁ ŁŁ Ł Ł ŁŁŁ Ł ŁŁ 3 ŁŁ ŁŁ 2 Ł ŁŁŁ 2 ŁŁŁ 3 ŁŁŁ 4 ŁŁŁ Ł ŁŁ ŁŁŁ ŁŁŁ 2 Ł 3 ŁŁŁ ŁŁŁ ŁŁ ŁŁŁ 2 Ł ŁŁŁ 2 ŁŁ Ł ŁŁ 1 Ł 3 ŁŁ 4 Ł Ł ŁŁ Ł Ł ŁŁ 2 ŁŁ ŁŁŁ Ł 4 Ł ŁŁ 3 ŁŁ ŁŁŁ 2 ŁŁŁ Ł Ł 2 Ł ŁŁ Ł 3 Ł ŁŁŁ 2 ŁŁŁ 3 ŁŁŁ 4 ŁŁŁ ŁŁ 3 Ł ŁŁ ŁŁ 2 Ł 3 Ł Ł ŁŁŁ ŁŁ Ł 2 Ł 3 ŁŁ ŁŁ ŁŁŁ Ł ŁŁ 2 Ł 4 Ł Ł ŁŁŁ 3 ŁŁŁ ŁŁ ŁŁ 2 ŁŁ 3 ŁŁŁ 4 Ł Ł ŁŁ 4 Ł ŁŁ Ł 2 Ł ŁŁ Ł ŁŁ 1 Ł ŁŁ 2 ŁŁ 3 ŁŁŁ Ł 4 ŁŁ ŁŁ Ł 1 Ł ŁŁŁ 2 ŁŁŁ 3 ŁŁŁ 4 ŁŁŁ ŁŁŁ 4 ŁŁ Ł ŁŁŁ 1 Ł ŁŁŁ 2 ŁŁŁ 3 ŁŁŁ 4 ŁŁŁ ŁŁ 4 ŁŁ 2 ŁŁ Ł Ł ŁŁ 3 ŁŁŁ 4 ŁŁ Ł Ł ŁŁ ŁŁ 2 Ł Ł Ł 2 ŁŁ 1 ŁŁ 2 ŁŁ ŁŁ ŁŁŁ 2 ŁŁ 3 ŁŁ 4 Ł Ł ŁŁ Ł 0Ð10 significance level; ŁŁ 0Ð05 significance level; ŁŁŁ 0Ð01 significance level.