The Impact of Teleconnection on Pressure, Temperature and Precipitation in Serbia

Transcription

1 International Journal of Remote Sensing Applications Volume 3 Issue 4, December 2013 doi: /ijrsa The Impact of Teleconnection on Pressure, Temperature and Precipitation in Serbia Nada Pavlovic Berdon Republic Hydrometeorological Service of Serbia Belgrade, Kneza Viseslava 66, Serbia ndberdon@yahoo.com Abstract Teleconection patterns show schedule in the space field, certain meteorological elements according to the global atmospheric circulation. Atmospheric oscillation is regarded as a measure of the global circulation influence of pressure, temperature, precipitation, humidity, wind, cloud cover in the local regions. The intensity and time of action teleconnection patterns can be determined by examining the correlation between indices of atmospheric oscillations and anomalies of meteorological quantities (pressure, temperature and precipitation).this paper examined the impact of 10 atmospheric oscillations and an oceanic oscillation on pressure anomalies, temperature anomalies and precipitation anomalies in Serbia, during the winter (DJF), from 1950 to 2010 years using a multidimensional PCA method.these results distinguished the dominant influence of certain oscillations in each of the meteorological elements. Keywords Atmospheric Oscillations; Pressure; Temperature; Precipitation; PCA Introduction Teleconnection in atmospheric sciences is related to climate anomalies that act on each other over long distances (often thousands of kilometers). Teleconnection was first noted British meteorologist Sir Gilbert Walker in the 19th century, calculating the correlation between time series of atmospheric pressure, temperature and precipitation. Higher quality of meteorological observations during the 1980s, allowed detecting teleconnection over long distances through the troposphere. There is a theory that such patterns can be understood through the dispersion of Rossby waves due to spherical geometry of the Earth. One mechanism teleconnection represent stationary waves. These waves are (somewhat insensitive to geographic features) persist in time and have a surprisingly accurate linear expansion of planetary waves in threedimensional seasonal various grounds of the ground state. Another mechanism teleconnection between tropical ocean regions and high latitudes has been discovered that is symmetrical along circles of latitude (e.g., "zonal"). It relies on the interaction between transient eddies in the sea and the average atmospheric currents which reinforce each other (nonlinear). It has been shown to explain some aspects of ENSO teleconnection for temperature and precipitation. Teleconnection is used in many papers to investigate the impact of the global circulation in some regions of the earth. In [8] mean monthly pressure of 500 mb geopotential height, the correlation method was compared with the pressure at any point on earth. Correlation matrix is the objective methods to identify and describe the most powerful models in teleconnection. Action centers are obtained based on 15 year period during the winter in the Northern Hemisphere. Teleconnection patterns were conducted by an independent data set. The already known forms (NAO, PNA, AO and NPO) were confirmed and some new were discovered that were not reproducible. In some works [2] EOF method and regression analysis are further used to find connections between the index of extreme rainfall and teleconnection forms. By using the method of canonical correlation the El Nino impact [1] in the European winter was considered. The results showed that the stratosphere plays an important role in the European response to the El Nino effect. The possible teleconnection patterns of atmospheric circulation in the Eastern Mediterranean were investigated using the daily geopotential height values from the NCEP/ NCAR network for the period , [3]. They used correlation analysis and PCA on a seasonal and monthly basis. Teleconnection pattern between the eastern Mediterranean and north Atlantic has been identified at 500 and 300 hp in the winter months. In the autumn teleconnection pattern becomes weaker, so the dipole centers move eastward. Teleconnection ENSO, NAO and AO on temperature and precipitation in Serbia have been analyzed in some works [5]. Correlations between NAOI and AOI and the daily precipitation in Serbia for the period

2 International Journal of Remote Sensing Applications Volume 3 Issue 4, December , [4], showed that the NAO and AO have a dominant influence on the precipitation regime, especially in the winter and the mountain station. It was stated that the AO teleconnection signal has a stronger influence on the NAO. Also the trends have been analyzed in daily rainfall for each oscillation. For selected cells which represent particular regions, regression equations were calculated to show a positive trend in rainfall from , mostly as a result of the positive AO phase. Data and Methodology The analysis used data for pressure, temperature and rainfall in the winter season (DJF) from 21 synoptic stations in Serbia in the period In analysis 10 atmospheric oscillations were utilized including: Artic Oscillation (AO), North Atlantic Oscillation (NAO), (West Mediterranean Oscillation (WeMO), Mediterranean Oscillation Index (MOI), Mediterranean Pressure Index (MPI), East Pacific/West Russia (EA / WR), West Pacific (WP), Polar Oscillation Index (POL), Pacific Decadal Oscillation (PDO), Multi-decadal ENSO Index (MEI) and an oceanic oscillation El Niño. Detailed explanations and interpretations teleconnection, EOF and SVD methods in climatology [7], helped to apply the method chosen to analyze the effects of different oscillations at selected meteorological parameters. Teleconnections samples can be seen from the table of correlation coefficients, using the PCA methods [6]. Statistical significance was tested with two-tallied Student's t-test. The analysis employed XLSTAT software. From the table 1, one can see the correlation coefficients for all 11 index oscillations and atmospheric pressure anomalies over Serbia at synoptic stations. Statistical significance at α = 0.05 showed the AO, NAO, WeMO, MPI and EA/WR. AOI has the highest positive correlation with the pressure and the maximum correlation is for Belgrade. The maps are plotted in Surfer program. Data sources are the databases RHS of Serbia and the International index of atmospheric oscillations (in Appendix). The data for 21 synoptic stations was used in Serbia and to the pressure, temperature and precipitation. Anomalies were calculated as deviations from the mean monthly values of the climatologically normal for the period Results First of all, the correlation of coefficients was analyzed between the atmospheric oscillations and meteorological parameters which characterized the climate in Serbia. The correlation between the NAOI and the pressure in Serbia is positive and the highest with Zlatibor (0.583). while WeMO has a negative correlation, with a maximum V. Gradiste and to The correlation between the PDO and pressure in Serbia is negative (Table 1), and for the POL, the El Niño and the MEI is combined, both positive and negative. MPI has the highest positive correlation with the Kraljevo, and to be 0.591, and EA/WR is the most correlated with Belgrade and to be TABLE I CORRELATION MATRIX (PEARSON CORRELATION COEFFICIENTS) BETWEEN THE OSCILLATION AND PRESSURE ANOMALIES FOR DJF IN SERBIA IN THE PERIOD (VALUES IN BOLD ARE DIFFERENT FROM 0 WITH A SIGNIFICANSE LEVEL ALPHA =0.05) Stations AO NAO WeMO ElNino MOI MPI EA/WR WP POL PDO MEI Beograd 0,776 0,548-0,610 0,045 0,327 0,503 0,628 0,161 0,049-0,022 0,032 Palic 0,693 0,493-0,621 0,026 0,327 0,531 0,570 0,295 0,008-0,043 0,045 Novi Sad 0,601 0,339-0,452 0,028 0,310 0,506 0,476 0,263 0,248-0,128 0,046 V.Gradiste 0,713 0,482-0,658 0,008 0,324 0,520 0,619 0,224-0,028-0,059 0,040 Zlatibor 0,741 0,583-0,396 0,050 0,426 0,583 0,499 0,306-0,108-0,062 0,089 Valjevo 0,638 0,417-0,596 0,066 0,355 0,543 0,605 0,165 0,008-0,018 0,086 Dimitrovgrad 0,721 0,506-0,614-0,008 0,326 0,513 0,593 0,201-0,013-0,082 0,046 S.Palanka 0,675 0,421-0,533-0,044 0,353 0,554 0,539 0,259 0,106-0,169-0,020 Kraljevo 0,750 0,556-0,565 0,009 0,390 0,591 0,571 0,299-0,028-0,071 0,047 Negotin 0,488 0,260-0,598-0,061 0,135 0,296 0,505 0,126 0,103-0,116-0,029 Nis 0,544 0,360-0,490 0,119 0,278 0,455 0,511 0,239-0,077 0,129 0,180 Sjenica 0,503 0,330-0,366 0,048 0,205 0,317 0,418 0,152 0,101-0,275 0,012 Vranje 0,689 0,545-0,485 0,036 0,392 0,580 0,493 0,375-0,134 0,108 0,110 Leskovac 0,600 0,344-0,410-0,051 0,289 0,485 0,477 0,131 0,141-0,300-0,066 Pozega 0,737 0,500-0,506-0,068 0,423 0,627 0,538 0,256 0,078-0,134-0,032 Loznica 0,556 0,364-0,597 0,087 0,231 0,397 0,567 0,247 0,061-0,102 0,069 Surcin 0,691 0,417-0,479-0,026 0,396 0,588 0,540 0,228 0,149-0,186-0,014 Vrsac 0,693 0,405-0,538-0,090 0,306 0,515 0,544 0,200 0,174-0,215-0,061 Cuprija 0,717 0,485-0,548-0,038 0,362 0,568 0,543 0,269 0,063-0,131-0,

3 International Journal of Remote Sensing Applications Volume 3 Issue 4, December TABLE II CORRELATION MATRIX (PEARSON CORRELATION COEFFICIENTS) BETWEEN THE OSCILLATION AND TEMPERATURE ANOMALIES FOR DJF IN SERBIA IN THE PERIOD (VALUES IN BOLD ARE DIFFERENT FROM 0 WITH A SIGNIFICANCE LEVEL ALPHA =0.05) Stations AO NAO WeMO ElNino MOI MPI EA/WR WP POL PDO MEI Beograd 0,115 0,234 0,314 0,101 0,227 0,156-0,166 0,231-0,285 0,075 0,120 Palic 0,299 0,344 0,222 0,014 0,251 0,240-0,073 0,303-0,273-0,023 0,044 Novi Sad 0,174 0,293 0,299 0,074 0,253 0,190-0,157 0,236-0,257 0,047 0,093 V. Gradiste 0,068 0,225 0,366 0,059 0,152 0,060-0,268 0,163-0,234 0,021 0,045 Zlatibor 0,061 0,147 0,261 0,066 0,026-0,048-0,216 0,264-0,218 0,067 0,065 Valjevo 0,054 0,171 0,343 0,131 0,148 0,064-0,201 0,224-0,204 0,044 0,117 Dimitrovgrad -0,104 0,043 0,439 0,064-0,023-0,152-0,328 0,118-0,160 0,069 0,039 Sm. Palanka 0,073 0,209 0,362 0,095 0,163 0,078-0,233 0,212-0,233 0,062 0,091 Kraljevo 0,029 0,130 0,327 0,122 0,089 0,022-0,227 0,215-0,220 0,064 0,113 Negotin 0,361 0,399 0,283 0,115 0,479 0,457 0,045 0,263-0,330 0,049 0,133 Nis -0,044 0,094 0,394 0,076 0,032-0,072-0,307 0,172-0,196 0,070 0,064 Sjenica -0,134-0,015 0,312 0,096-0,141-0,263-0,295 0,169-0,135 0,173 0,080 Vranje -0,097 0,022 0,363 0,062-0,085-0,197-0,306 0,127-0,128 0,059 0,030 Leskovac -0,010 0,135 0,277 0,162-0,003-0,126-0,148 0,137-0,265 0,046 0,150 Pozega 0,087 0,164 0,124 0,207 0,010-0,046-0,026 0,231-0,248 0,005 0,208 Loznica 0,147 0,209 0,270 0,116 0,208 0,137-0,089 0,233-0,268 0,046 0,127 Surcin 0,144 0,178 0,098 0,134 0,123 0,089 0,014 0,096-0,179-0,082 0,111 Vrsac 0,012 0,108 0,210 0,137 0,041-0,052-0,114 0,047-0,208-0,046 0,110 Ćuprija 0,074 0,220 0,268 0,147 0,124 0,032-0,123 0,147-0,317 0,028 0,143 TABLE III CORRELATION MATRIX (PEARSON CORRELATION COEFFICIENTS) BETWEEN THE OSCILLATION AND PRECIPITATION ANOMALIES FOR DJF IN SERBIA IN THE PERIOD (VALUES IN BOLD ARE DIFFERENT FROM 0 WITH A SIGNIFICANCE LEVEL ALPHA =0.05) Stations AO NAO WeMO ElNino MOI MPI EA/WR WP POL PDO MEI Beograd -0,586-0,455 0,344 0,046-0,184-0,274-0,386-0,203-0,114 0,128 0,058 Palic -0,735-0,505 0,409 0,023-0,389-0,558-0,458-0,162-0,110 0,300 0,009 Novi Sad -0,692-0,542 0,440 0,081-0,296-0,408-0,413-0,137-0,100 0,137 0,089 Loznica -0,459-0,330 0,318 0,053-0,180-0,243-0,311-0,140-0,102 0,087 0,050 Vel.Gradiste -0,439-0,461 0,243 0,022-0,273-0,325-0,359-0,034 0,032-0,025-0,027 Zlatibor -0,323-0,106 0,349 0,037 0,037-0,067-0,311-0,181-0,183 0,303 0,106 Valjevo -0,310-0,181 0,360 0,007-0,061-0,124-0,325-0,075-0,078 0,160 0,029 Dimitrovgrad -0,534-0,450 0,329 0,081-0,230-0,329-0,389-0,193 0,103 0,032 0,023 Sm.Palanka -0,469-0,301 0,366 0,068-0,189-0,303-0,398-0,233-0,086 0,224 0,010 Kraljevo -0,331-0,167 0,470-0,044 0,074-0,066-0,358-0,391 0,004 0,114-0,047 Negotin -0,459-0,274 0,173 0,055-0,304-0,382-0,343-0,285 0,034 0,053-0,018 Nis -0,553-0,431 0,368 0,010-0,256-0,371-0,445-0,245 0,033 0,098-0,019 Sjenica -0,134-0,015 0,312 0,096-0,141-0,263-0,295 0,169-0,135 0,173 0,080 Vranje -0,649-0,497 0,366-0,037-0,356-0,511-0,458-0,283 0,181 0,065-0,091 Leskovac -0,460-0,230 0,373 0,019-0,088-0,249-0,488-0,272-0,104 0,149-0,009 Požega -0,509-0,283 0,487 0,049-0,105-0,295-0,346-0,405 0,000 0,170 0,037 Loznica -0,459-0,330 0,318 0,053-0,180-0,243-0,311-0,140-0,102 0,087 0,050 Surcin -0,532-0,336 0,322 0,186-0,281-0,362-0,331-0,070-0,201 0,274 0,168 Vrsac -0,415-0,227 0,463 0,080-0,166-0,303-0,367-0,194-0,172 0,197 0,081 Ćuprija -0,355-0,148 0,471 0,090 0,056-0,092-0,401-0,236-0,142 0,138 0,035 The strongest influence on the pressure anomalies during the winter in Serbia carried the AO and the NAO, as a whole and EA/WR. The WeMO, MPI and MOI are to act as parts of the NAO oscillations. El Nino, PDO, MEI and POL have very little influence, which is not statistically significant. Table 2 shows that only WeMO having influence on the temperature anomalies is statistically significant and with the highest positive correlation coefficient (0.439 in Dimitrovgrad) in Serbia. The correlation coefficient between the NAO and temperature anomalies is the maximum for Negotin (0.399), but for most stations is not statistically significant. The POL shows a negative correlation of for Negotin. Negative correlation with temperature is the EA/WR, and partly the MPI and the MOI. The low correlation shows the El. Nino, PDO and MEI. The oscillations with low correlations mean that their influence is very weak and does not cause changes in temperature. Also, the joint analysis of the impact El Nino, PDO and MEI on the 187

4 International Journal of Remote Sensing Applications Volume 3 Issue 4, December 2013 temperature anomalies may indicate multicollinearity. Correlation of atmospheric oscillations and precipitation anomaly (Table 3) is mostly negative (AO, NAO, MOI, MPI, EA/WR, WP, POL, and partially MEI). Statistically significant correlations are AO, EA/WR, WeMO and partially for NAO. The highest correlation coefficients with anomalies of rainfall have AO (r=0.735 for Palic), NAO (r=0.542 for Novi Sad) and EA/WR (r=0.488 for Leskovac). ElNino, POL and MEI have negligible and not statistically significant influence on the precipitation anomalies. The first and second eigenvalues carry much of the variance (68%), meaning that over the two EOF-a will be represented field by linear combinations atmospheric oscillations and temperature anomalies. Figure 2 shows a positive deviation of monthly mean temperature from 1988 to Results of PCA method From principal component analysis of atmospheric oscillations and pressure anomalies, it was shown that 80% of the total variance was explained by four eigenvalues. The first eigenvalue (17.23) explains of the total variance, the second (2.78) explained 9.25% variance, the third (2.54) carries an 8.46% and the fourth (1.40) has 3.4 %. It is important to note that the first eigenvalue carries a very large portion of the variance, meaning that the first EOF over a very good field to be represented by linear combination of atmospheric oscillations and pressure anomalies. Figure 1 shows the time pattern of the first and second principal components of the pressure anomaly. The first component (57.4%) has significantly more variance than the other components and is the dominant telecconection pattern in the period of 61 years. FIGURE 2 TEMPORAL PATTERN OF THE INFLUENCE OF AN ATMOSPHERIC OSCILLATION (PC1 AND PC2) ON TEMPERATURE ANOMALY FOR THE PERIOD IN SERBIA By applying PCA to the precipitation anomalies results are less than those for the pressures and temperature anomalies. The first eigenvalue explains 44.8% variance, the second 10.5%, and 8.5% third, fourth 5.8%, in total 70% of the variance. To explain 80.4% of the total variance, it is 8 eigenvalue. Figure 3 shows that the negative deviations from the monthly mean precipitation ( normal) present more since 1988 than before. For amount of rainfall except atmospheric oscillation, the relief and other so-called latent factors were affected. FIGURE 1 TEMPORAL PATTERN OF THE INFLUENCE OF AN ATMOSPHERIC OSCILLATION (PC1 AND PC2) ON PRESSURE ANOMALY FOR THE PERIOD IN SERBIA Principal component analysis of atmospheric oscillation and temperature anomalies showed that 81% of the total variance was explained by four eigenvectors. The first eigenvector (18.53) explains 56.16% of the total variance, the second (3.85) explains 11.67% variance, the third (2.65) carries a 5.36% and the fourth (1.77) is 5, 36%. FIGURE 3 TEMPORAL PATTERN OF THE INFLUENCE OF AN ATMOSPHERIC OSCILLATION (PC1 AND PC2) ON PRECIPITATION ANOMALY FOR THE PERIOD IN SERBIA This paper presented the results of PCA analysis for each of the three meteorological elements, which in correlation with the examined oscillations was the largest. AO shows the highest correlation coefficient with pressure anomalies in Serbia (FIG. 4). The correlation 188

5 International Journal of Remote Sensing Applications Volume 3 Issue 4, December coefficient ranged from r = 0.49 (in the southwestern and southeastern Serbia) to r = 0.76 (in central Serbia and west Vojvodina). The correlation coefficient for the AO index and rainfall (FIG. 6) ranges from r = (in southwestern Serbia) and r = (in Vojvodina and southeast Serbia). FIGURE 4 CORRELATION COEFFICIENT BETWEEN AO INDEX AND PRESSURE ANOMALIES IN SERBIA FOR THE PERIOD The maximum correlation for WeMO index (FIG. 5) and temperature anomalies is in southeastern Serbia (r = 0.45), a minimum in western Serbia (r = 0.01) and the north of Vojvodina (r = 0.20). FIGURE 5 CORRELATION COEFFICIENT BETWEEN WEMO INDEX AND TEMPERATURE ANOMALIES IN SERBIA FOR THE PERIOD FIGURE 6 CORRELATION COEFFICIENT BETWEEN AO INDEX AND PRECIPITATION ANOMALIES IN SERBIA FOR THE PERIOD EOF1 for the influence of fluctuations of atmospheric pressure anomalies over Serbia (Fig 7a) shows that the strongest influence in central and eastern Serbia, Vojvodina, as shown by the correlation coefficient. EOF2 for the influence of fluctuations of atmospheric pressure anomalies over Serbia (Fig 7b) shows that the negative impact is present in east Vojvodina, central and western part of southern Serbia. Pressure anomalies are negatively correlated with WeMO and its oscillation effect is visible through the second EOF. EOF1 for the effect of atmospheric fluctuations of temperature anomalies over Serbia (Fig 8a) shows that the weakest impact is in the north and east of Vojvodina, in the southwestern, southern and eastern Serbia. EOF2 for temperature (Fig 8b) shows that the south parts of Serbia have opposing temperature changes of the index of atmospheric oscillations. The greatest influence on the temperature has WeMO and EA / WR (with opposite sign). EOF1 for the effect of atmospheric oscillations in rainfall anomalies over Serbia (Fig 9a) shows that the weakest impact is in southwest Serbia, a mountainous area. It is reasonable to expect that in addition to the impact of atmospheric oscillations in precipitation here altitude, relief and 189

6 International Journal of Remote Sensing Applications Volume 3 Issue 4, December 2013 some other so-called latent factors also affect it(can be seen through the communality, common variance, and the factors which they share). EOF2 for precipitation (Fig 9b) shows negative correlation coefficients of linear correlation for atmospheric fluctuations and precipitation anomalies in the analyzed period. The negative effect is strongest in the north of Vojvodina and the southern and eastern Serbia. EOF2 explains 10.5% of total variance. It can be seen that the mentioned parts of Serbia have the same trend in precipitation and oscillation indices. In combination with EOF1, it can be concluded that mountain parts south of the Zlatibor have more rainfall than other parts. This arrangement resembles the impact of AO on precipitation. This means that the temperature in southern parts of Serbia will be higer than that in other parts of Serbia, due to the influence of positive phase AO and NAO, while the temperature is lower due to influence of the negative phase AO and WeMO. The same applies to the part such as Vrsac and Veliko Gradiste. FIGURE 7 SPATIAL PATTERN OF THE INFLUENCE OF ATMOSPHERIC OSCILLATION ON PRESSURE ANOMALY, a) EOF1 (57.4% EXPLAINED VARIANCES) AND b) EOF2 (9.25% VARIANCES) FIGURE 8 SPATIAL PATTERN OF THE INFLUENCE OF ATMOSPHERIC OSCILLATION ON TEMPERATURE ANOMALY, a) EOF1 AND b) EOF2 190

7 International Journal of Remote Sensing Applications Volume 3 Issue 4, December Conclusion FIGURE 9 PRINCIPAL COMPONENTS OF PRECIPITATION ANOMALIES IN SERBIA FOR THE PERIOD , a)left FIGURE EOF1 AND b)right FIGURE EOF2 The influence of atmospheric oscillations AO, NAO, WeMO, EA/WR and POL is the most significant with anomalies of pressure, temperature and precipitation in Serbia. Teleconnection pattern of pressure anomalies has been identified for the AO (maximum correlation coefficient of 0.78), NAO (maximum correlation coefficient of 0.58) and WeMO (max correlation coefficient -0.66). At the temperature, anomalies have the strongest impact WeMO (max correlation coefficient 0.44), NAO (max correlation coefficient 0.4) and POL (max correlation coefficient ). For precipitation, teleconnection determines impacts of AO pattern (maximum correlation coefficient of 0.74), NAO (maximum correlation coefficient of 0.54) and EA/WR (max correlation coefficient 0.49). Overall, over the territory of Serbia is best seen teleconnection impact of AO and NAO oscillation. The next step would be to test teleconnection patterns of these oscillations using linear regression. PCA method gave the characteristic spatial (EOF modes) and temporally patterns (PC) that explain most of the variance. El Nino, PDO, MEI and POL have the least impact on analyzed meteorological elements in Serbia. This means that although their impact is very small, which can be completely ignored, but the use of other statistical methods is likely that their influence is evident. It is known that the observed oscillations (atmospheric and oceanographic) have positive, negative and neutral phase. This analysis is not concerned with these details, but it shows that they are important and need analysis conducted in phase s activities. In any case, this paper has highlighted some important teleconnections patterns and opened new fields of investigation of mutual dependence. ACKNOWLEDGMENT I thank to Brankica Drakula on technical data processing. Appendix The web-site of the teleconnection patterns: REFERENCES Bell CJ, Gray LJ, Charlton-Perez AJ, Joshi MM and Scaife AA., 2009: Stratospheric Communication of El Niño Teleconnections to European Winter, Journal of Climate, 22 (15). Casanueva A., Rodriguez-Puebla C. and Gonzalez-Reviriego N., 2010: The role of teleconnection patterns on extreme precipitation indices, Casanueva_ECAC. Hatzaki M., H.A Flocas, D.N.Asimakopoulos, P. Maheras, 2009: The Impact of the Eastern Mediterranean Teleconnection Pattern on the Mediterranean Climate, Jurnal of Climate, 22. Jovanovic G., Reljin I., and Reljin B., 2008 : The influence of 191

8 International Journal of Remote Sensing Applications Volume 3 Issue 4, December 2013 Arctic and North Atlantic Oscillation on precipitation regime in Serbia, XXIV th Conference of the Danubian countries on hydrological forecasting and hydrological bases of water management, Slovenia, Bled. Jovanovic G., Reljin I., and Reljin B., 2010: The influence of Dominant Global Climate Phenomena ENSO. NAO and AO on Climate in Serbia, European Conference on Applied Climatology. Switzerland. Navarra A., and V.Simoncini, 2010: A Guide to Empirical Orthogonal Functions for Climate Data Analysis. Springer Science+Business Media B.V. Von Storch H. and A.Navara, 1995: Analysis of Climate Variability: Application of statistical Techniques. Springer -Verlag, Berlin. Wallace M. John and Gutzler S. David, 1981: Teleconnections in the Geopotential Height Field during the Northern Hemisphere Winter. Mon. Wea. Rev., 109, Nada Pavlovic Berdon was born in Republic Croatia in 1954, and studied in Meteorology college in Belgrade in 1978, then she worked on cloud microphysics, weather modification and the physical and statistical evaluation of effectiveness of hail suppression and radar identification of the cloud. Hear first book was published in the cloud microphysics, and publisher was the RHMS of Serbia, Now, she mainly deals with climate forecasts. 192