Introduction: Iran’s precipitation mainly affects by air masses that arrive to it from adjacent area directly or affects as teleconnection pattern due to remoteness from water sources. Due to irregular arrival of the air masses to Iran and far distance to teleconnection centers, occurrence of precipitation variations is one of the prominent characteristics of Iran’s climate. Also due to short period of instrumental data in the meteorological stations, there is no possibility for understanding of the climatic variations during the last centuries. Reconstruction of climatic variations based on current evidences, shows a more realistic understanding of the climatic variations of the region. Trees are the useful climatic evidences that experience different climatic situation of temperature and precipitation during their life period and the effects of these climatic variations is reflected in their tissues. In temperate climates, trees add a ring annually to their previous rings. Therefore, by studying of the tree-ring widths, useful climatic information can be obtained over the last centuries of the region. In the previous studies, relationship between the SOUTHERN OSCILLATION INDEX ((SOI)) and Iran’s precipitation only has been studied in a relatively short period due to the lack of instrumental data. In this study, we present a longer period of relationship between monthly precipitations of the region with SOUTHERN OSCILLATION INDEX ((SOI)) by reconstruction of precipitation based on Oak tree rings in the central Zagros region over the last centuries (1705-2010).Methodology: In the present paper, three sites of Oak species with a short distance to the meteorological stations were selected in Lorestan (Shineh), Kermanshah (Faryadras) and Ilam (Dalab) provinces. In Shineh and Faryadras sites Quercus infectoria species and in Dalab site Quercus Persica species used for sampling. Two increment cores at different sides of each tree were extracted by a Suunto increment borer at breast high. In each site 10 to 14 trees were sampled. After air drying, the surface of cores was prepared by a razor blade. The ring widths were measured with a LINTAB5 measuring system with a resolution of 0.01 mm, and all cores were cross-dated by visual and statistical tests (sign-test and t-test) using the software package TSAP-Win. Raw ring widths were standardized by ARSTAN program to remove non climatic signals. Three site chronologies were obtained from the three sites. To increase the length of the chronology and due to the same trends and high correlations between the three site chronologies, all growth curves were combined and the regional chronology was calculated. The values of mean sensitivity (MS), signal to noise ratio (SNR) and first auto correlation (AC1) were calculated for the regional chronology. Mean monthly precipitation of Khorramabad (1951-2010), Kermanshah (1951-2010) and Ilam (1987-2010) meteorological stations were used to calibrate the tree-ring/climate relationship. Due to the high correlation (p<0.01) between the regional chronology and mean monthly precipitation of the region, October-May, precipitation of the region was reconstructed using a linear regression model. For investigation of relationship between the reconstructed wet years and the El Nino events, Historical El Nino events from 18th century to present were obtained from NOAA database. The longest value of (SOI) (1876-2010) was obtained from Bureau meteorological station. Finally the relationship between October-May monthly values of (SOI) and the reconstructed October-May precipitation of central Zagros was investigated in the common period (1877-2010).Discussion: Similar growth patterns of trees in the three sites indicate that they influence by the same growth factors. Relationship and correlations between monthly precipitation and the regional chronology showed that precipitation has positive effect on tree growths in the region. The positive correlation with precipitation during the pre-growing season show the fact that precipitation during these months provides moisture availability in semi-arid regions and favors formation of early wood cells, which account for the majority of the total ring-width (Liu et al., 2011). Compression between the reconstructed precipitation and the actual precipitation of the central Zagros region shows some years with inconsistency between observed and reconstructed data. Most of the reconstructed wet years have been occurred coincidence with El Nino events. There is a negative relationship between the reconstructed October-May precipitation of central Zagros and SOUTHERN OSCILLATION INDEX ((SOI)). It should be noted that negative values of (SOI) indicate El Niño events and during the El Niño phases precipitation in most parts of Iran is above average. According to this, SOUTHERN OSCILLATION INDEX ((SOI)) during October to May has positive effect on precipitation in central Zagros. The highest correlation (p<0.01) between (SOI) and the reconstructed precipitation was found on November and December which is supported by the results of Azizi (2000), Yarahmadi and Azizi (2007) and Khoshakhlagh (1998). Also there is a significant correlation (p<0.05) between (SOI) and the reconstructed precipitation in April and May. It may be due to the far distance between the study region and the El Nino origin. Therefore the study region during April-May influence by El Nino with a lag time.Conclusion: The length of the regional chronology is 305 years (1705-2010). Total and monthly precipitations from October to May have positive effect on tree growths in the region. The results showed that dry conditions occurred in 1730s, 1760s-1790s, 1810s, 1840s-1850s, 1870s-1880s, 1900s, 1920s, 1940s, 1960s and 1980s. Comparison of wet years and historical El Nino events showed that 77 of reconstructed wet years have occurred with El Nino events simultaneously. Investigation of relationship between the SOUTHERN OSCILLATION INDEX ((SOI)) and the reconstructed October-May precipitation of the central Zagros region showed that (SOI) has positive effect on precipitation in the region. The highest negative correlation between the SOUTHERN OSCILLATION INDEX ((SOI)) and the reconstructed precipitation was found in December. The highest correlation (p<0.01) between (SOI) and the reconstructed precipitation of the region was found in November, December and January. There was a significant correlation (p<0.05) between (SOI) and the reconstructed precipitation in April and May as well.

In Iran, about 75% of national rice production is supplied in Gilan and Mazandaran proviences which have the highest amount of precipitation. Seasonal prediction of rainfall induces significant improvement on yield production and on preventing climate hazardz over these feritle areas. Canonical correlation analysis (CCA) model was carried out evaluates the possibility of the prediction of winter rainfall according to the states of ENSO events. The time series of (SOUTHERN oscilation INDEX ((SOI)) and SST (sea surface temperature) over Nino"s area (Nino"s SST) are used as the predictors, and precipitation in Bandar Anzali and Noushahr are used as the predictands. Emperical orthogonal functions (EOF) were applied for reducing the number of original predictors variables to fewer presumably essential orthogonal variables. Four modes of variations (EOF1, EOF2, EOF3, EOF4) which account for about 92% of total variance in predictors field were retained and the others were considered as noise. Based on the retained EOFs and precipitation time series, the canonical correlation analysis (CCA) was carried out to predict winter precipitation in Noushahr and Bandar Anzali. The results indicated that the predictors considered account for about 45% of total variance in the rainfall time series. The correlation coefficients between the simulated and observed time series were significant at 5% significant level. For 70% of events the anomalies of observed and simulated values have the same sign indicating the ability of the model for reasonable prediction of above or below normal values of precipitation. For rainfall prediction, the role of Nino"s SST (Nino4 in particular) was found to be around 10% more influential than (SOI).

In this paper, the effects of large scales climate signals on the low and high precipitation Spells in the southwestern part of Iran are investigated. Large scales climate signals are parameters that can play the important role on analysis variations of seasonal and annual precipitation. In this study monthly SOUTHERN OSCILLATION INDEX ((SOI)), North Atlantic OSCILLATION (NAO) and ENSO INDEX were applied in NINO4, NINO3, NINO1+2, NINo3.4 were used respectively. All data of above signals received from center analyzed data (NCEP) during 1960 to 2003. In order to determine the rate of importance of these parameters on quantity of precipitation was used multivariate regression method. Results of regression analysis show that ENSO INDEX in zone of NINO1+2, NINO3 and NINo3.4 strong correlations with the variations of precipitation. In this study long- Range precipitation prediction for the time period, 3 and 6 months was done. Analysis of artificial neural network model results in comparisons with observations show that the warm phases of ENSO are accompanied with more rainy periods and, cold phases of ENSO with less rainy periods.

Drought is one of the atmospheric events that causes great casualties. In this study, the main goal of this research is long rang prediction of drought as large scale climatic signals data were used. Large scale climatic signals are among parameters that are used in analysis of seasonal and annual variations of temperature and precipitation. In this research, data of monthly SOUTHERN OSCILLATION INDEX ((SOI)), Northern Atlantic OSCILLATION (NAO) INDEX and Nino indices like NINO4, NINO3, NINO3.4 and NINO1+2 with ENSO phenomenon were used. All data concerning the above signals for the period 1960-2000 were received from National Center of Environmental Prediction (NCEP). To determine the most important effective signals on precipitation in different regions of the country, multi-regression method was used. Results of such a regression analysis showed that the most important signals causing precipitation are NINO1+2, and NINO3 indices. In this research, using Artificial Neural Network (ANN) method, prediction of precipitation in simultaneous lead time periods of three and six months was done. Comparison of ANN model results with observed data showed that the wet periods correspond with warm phases of ENSO and negative NAO, whereas cold phases ENSO and positive NAO associate with drought years in Iran.

An investigation was conducted to detect the change-point years in the SOUTHERN OSCILLATION INDEX ((SOI)) and precipitation time series in Iran for the period 1951-1999 (49 years). Due to the unavailability of data, the record length of the precipitation time series was not consistent for all stations, varying from 34 to 49 years. The Pettitt-Mann-Whitney and Mann- Whitney-Wilcoxon tests were applied to determine the significance of the detected changes. The difference in (SOI) and precipitation amounts for the period before and after the change years was investigated. The coincidence of change-point years in the (SOI) time series and precipitation data was explored to evaluate the possible forcing effects of the El Nino-SOUTHERN OSCILLATION (ENSO) phenomenon on the suppression or enhancement of Iran’s hydrological cycle. The results indicated that the mid 1970s are the most probable change-point years in the time series of SOUTHERN OSCILLATION INDEX ((SOI)) data. The frequency and intensity of El Nino events have increased since then. Consistent with this finding, precipitation data from both south-western and northern parts of Iran have also shown significant change years in or around the mid 1970s. Compared to the period before 1975, annual precipitation over most of the studied regions has increased. This increase was found to be more considerable in SOUTHERN rather than northern districts. Seasonal precipitation amounts in SOUTHERN regions have generally increased during autumn and winter and decreased in spring. On the other hand, for northern regions, precipitation has increased during summer and autumn and decreased throughout winter and spring. The most enhanced portions of the hydrological cycle in the SOUTHERN and northern regions were centred on March and May, respectively.

Studying climatic processes provides plan makers with a suitable tool in different sectors to plan future policies for optimizing cost and maximizing productivity while bearing these studies in mind. Precipitation is associated with complex atmospheric processes including the El Niñ o– SOUTHERN OSCILLATION (ENSO) that is inextricably linked to the OSCILLATIONs in the surface water temperature of the Pacific Ocean. The objective of this study was to investigate the relationship between rainfall in wet seasons of the year (cold season for six months) and the El Niñ o SOUTHERN OSCILLATION (ENSO) in the Persian Gulf and Oman Sea. After conducting adequate investigations, 12 synoptic stations with appropriate statistical period were selected and the homogeneity and adequacy tests of the data collected were carried out on rainfall amounts. The SOUTHERN OSCILLATION INDEX ((SOI)) was used to investigate the effect of the El Niñ o SOUTHERN OSCILLATION. The warm and cold phase was determined using the (SOI) INDEX. Also, the longest droughts and wet years were identified and the wet and dry years associated with the warm phase (El Niñ o) and the cold phase (La Niñ a) was specified as well. The results showed that the wet years occurred in warm phase and droughts occurred during the cold phase so that at the same time as the dry years, rainfall in the stations under investigation was lower than normal, and the numerical value of the (SOI) INDEX was lower than normal. The numerical value of the (SOI) INDEX obtained in dry years was negative. While, at the same time as wet years, the amount of rainfall at the stations is more than the average annual precipitation of the investigation period and the numerical value of the (SOI) INDEX is also positive and indicates higher values than normal. Pearson correlation test was used to investigate the correlation between rainfall and (SOI) INDEX. The correlation was carried out on a monthly, seasonal, annual, and moving window basis. The results showed that on the monthly basis, the highest correlation was registered at Konarak station, on the seasonal basis, at Bandar Abbas, Bandar-e-Lengeh, Jask, Kish, Konarak, Bushehr and coastal Bushehr stations, and on the annual basis, at Abadan and coastal Bushehr stations at a significant level of 99%. It is also possible to conclude from the correlation of the moving window that the signal has the most effect on Bushehr, coastal Bushehr, Bandar Abbas, Kish, and Konarak stations in autumn and winter.

Seasonal prediction of dry and wet spells is critical for competent management of water resources in the rain-fed wheat region in western and northwestern parts of Iran. The present study has evaluated the effects of the Persian Gulf Sea Surface Temperatures (PGSSTs) on precipitation variability of these regions during warm (EI Nino) and cold (La Nina) phases of the EI Nino - SOUTHERN OSCILLATION (ENSO) phenomenon. The results indicated that, compared to single variable, the composite effects of the PGSSTs and ENSO enhances the prediction of winter precipitation. When warm ENSO phase is coincided with below normal PGSSTs, the probability of winter drought varies from 0.50 and 0.7. This probability drops to less than 0.33 if the PGSSTs are warmer than usual. Therefore, as EI Nino is approached, above and below normal PGSSTs imply less and more frequency of dry spells, respectively. The coincidence of La Nina and colder than usual PGSSTs promises the prevalence of wet condition (for winter) over most parts of the study area and a productive situation is anticipated for the rain-fed crops. On the other hand, these crops encounter a high risk of water shortage if La Nina concurs with warm PGSST epochs. 

Changing ozone layer which is measured as Total ozone (TO) OSCILLATION name, are one of the modern human concerns as one of the causes or impacts of climate change. In this research, Total ozone OSCILLATION in relationship with ENSO phenomena have been evaluated. To this aim, monthly mean data from Total Ozone Mapping Spectrometer (TOMS) in point by 1*1.25 degree of geographical distance and monthly mean SOUTHERN OSCILLATION INDEX ((SOI)) as defining as El Nino and La Nina have been used. The results show that total ozone variations have the best fit to (SOI) INDEX by Regression Cubic model and the total ozone could be estimated by this model on confidence level 99%. The relation between ozone values estimated by the model and the (SOI) INDEX is inverse, strong and confidence in level significant 99% in annual, seasonal and monthly time scale in all aresa of Iran except a small area in the North West in July. Correlation coefficients were the strongest in the Central Region, South and Southeast of the country and the poor relations in the North and North West respectively. The results show that the total ozone in El Nino\La Nina occurrence will increase\decrease in all Iran areas. The value of (TO) increase from the south to north and the fitted line slope in El Nino is more.