چكيده لاتين :
. Introduction Climatic variation is one of the inherent features of the climate system. The components of the climate system are diverse and complex, so that these components interact with each other in a Interweaving way, so that the change in each component eventually changes other components as well. The climate indicators are defined to describe the status of the climate system and its changes. Each climatic index describes some aspects of the climate based on certain parameters. Therefore, various climate indicators have been proposed and used in many studies. Climatic indices are measurable and computable and correlate with some of the elements of the climate in different regions. Some atmospheric variables such as pressure, temperature, precipitation and radiation, as well as non-atmospheric parameters such as sea surface temperature (SST) or ice cover, are among the factors to be considered for climate forcing in different parts of the world. The large water resources, such as seas and oceans, are among the most important climatic operators. These resources are capable of storing a large part of the solar energy and also, due to their fluid nature, are capable of transporting energy to other parts of the planet in various ways (surface flow, subsurface flow, convection, and moisture advection). Changes in ocean behavior, therefore, cause changes in atmospheric patterns, which can further change the short and long-term climatic conditions in different regions. For this reason, ocean surface temperature can be considered as one of the important indicators affecting climatic abnormalities. All patterns of teleconnection as natural phenomena's, are resulting from the turbulent nature of the atmosphere and its internal energy resources. These patterns represent macro-scale variations in atmospheric wave patterns and jetstream flows, and affect the distribution of temperature, precipitation, storm paths, and the status and pattern and speed of the jetstream in large areas. For this reason, the patterns of teleconnection lead to abnormalities that occur simultaneously in very distant areas (Asakere, 2007; 48). In fact, the variability of the behavior of the atmosphere is a result of the set of behaviors and interactions between the ocean and the atmosphere. Hence, indicators that explain the abnormal behaviors of the ocean and therefore the atmosphere can help to identify the causes and nature of the occurrence of short and long-term climate abnormalities in a region. The study of air temperature anomalies in the warm season in Iran in relation to the most important oceanographic and atmospheric indices is the main aim of this research. 2. Material and Methods In this study, two different databases were used including the data of the IRIMO stations and indexes data of oceanographic and atmospheric teleconnection of the NOAA Data Center, affiliated to the U.S. Center for Oceanography Studies. The data of the IRIMO stations consist of 30 synoptic stations with a period of 50 years of data (1961-2010). In the first step, the standardized temperature of each station was calculated per each month during the warm period of the year (from May to September). Then, for the purpose of detecting anomalies, a function was defined in Excel macro as; -0.5 >¬x> +0.5, and from among the 250 months examined the anomalies (at least 20 stations from 30 stations), 57 cases with anomalies among whole months were selected in the study period, and then by the Pearson correlation method, a relation was calculated between the 17 selected atmospheric¬-oceanic indicators and the air temperature. The indicators used in this study are the most important indicators introduced in international studies. Also, by using multivariate regression, optimal parameters and regression functions are presented in order to explain and predict the relationship between indices and temperature anomalies in the warm season in the whole of Iran. 3. Results and Discussion The air temperature of Iran shows a significant relationship with the teleconnection indexes. According to the tests performed in selective stations, in general, NINO3, NINO1¬+¬2, NINO3.4, NINO4, GBI, CAR, PACEFIC WARM POOL and GLOBAL MEAN TEMP indexes were have a significant correlation in 90% confidence level. In terms of time in calculations with monthly synchronous steps at selected stations, the best indexes are GBI, NINO1 + 2, NINO3 and NINO3.4, with correlations of 0.8, -0.8, -0.57 and -0.4, respectively. In terms of a previous step, the GBI, NINO1¬+¬2 and NINO3 indexes had the highest correlation values of 0.8, -0.8 and -0.5, respectively. The temporal pattern of the impact of some indicators, such as NINO, which was mostly strong and inversely in the same month, was directly and significantly in the two and three months earlier. Based on the results obtained from the multivariate modeling, the correlation between the selected teleconnection indexes such as GLOBAL MEAN TEMP, GBI, NINO 1¬¬+¬2 with thermal anomalies in the warm season of Iran are 0.94; as the best temperature predictions, and at the same time a month earlier, the NINO3 index was added to the above-¬mentioned indexes. In general, the indexes of NINO3-4, NINO3, NINO1¬+¬2, NINO4, and GBI are the best atmospheric and oceanographic indicators that predict Iran's temperature anomalies. 4. Conclusion According to numerical correlation analysis between the selective indexes and the temperature anomalies of the selective stations in the warm season in Iran showed that NINO3, NINO1 + 2, NINO3.4, NINO4, GBI and GLOBAL MEAN TEMPERATURE indexes are the most important oceanic-atmospheric predictors. Also, in this paper, linear regression functions for the relationship between indices and monthly temperature anomalies are presented, which can explain and predict the temperature changes in Iran. The correctness of these functions is confirmed by using the actual and modeled data (estimating R correlation values, RMSE and MBE values) with an acceptable error rate. It should be noted as long as the intervals of predicting are prolonged, apparently the importance of atmospheric indexes is reduced and contradictory the number and reliability of ocean indexes are increased. In total, using the above mentioned indices and using multivariate regression method in each step of time (simultaneously, one, two and three months earlier), the linear regression function for the relationship between indexes and monthly temperature anomalies of Iran has been presented, which by using it the Iran's temperature changes can be predicted finally. It should be noted that the functions obtained here are to predict the average temperature of selected stations in Iran, and therefore for each station the calculations must be made individually.
