Investigating the predictability of precipitation in northeastern Iran with a teleconection approach and using the analysis of changes in sea level pressure patterns on a planetary scale

Introduction

The atmosphere is a fluid and interconnected system. Any change by known and unknown factors in a limited part of the atmosphere can cause larger changes in the general circulation of the atmosphere with the passage of time, this itself causes a change in the water share of each region of the water cycle.

The researches that have been carried out in order to predict rainfall in different areas of Iran have mostly focused on atmospheric indicators, which were initially made to predict rainfall or study climate changes in areas other than Iran. In fact, in these researches, it shows the effect of special atmospheric circulation patterns, such as Walker, North Atlantic Oscillation, on rainfall of Iran. On the other hand, pressure changes in limited areas have been analyzed to find a suitable predictor (index) for seasonal rainfall of Iran. It is necessary to carry out complete and comprehensive research (sea level pressure patterns on a planetary scale) to investigate the relationship between sea level pressure changes and precipitation in different climatic regions of Iran.

Materials and methods

The study area is considered for research and investigation of the explanation of the changes in the time series of precipitation in the annual time scales by the spatial changes of the sea level pressure in the time lags of one to six months, in the northeastern region of Iran (Khorasan Razavi, Northern Khorasan, and Southern Khorasan provinces).

Precipitation data for the 35-year statistical period (1987-2021) on a daily basis for the selected synoptic stations including of Mashhad, Sarkhs, Qouchan, Torbat Haidarieh, Sabzevar, Kashmer, Gonabad, Birjand, Nehbandan, Tabas, Ferdous, Qain, Beshravieh and Bojnoord was obtained from the Iranian Meteorological Organization.

In order to investigate the relationship between changes in sea level pressure and annual rainfall in northeastern Iran, monthly data of sea level pressure were used. These data are presented and updated on the website of the National Center for Atmospheric and Oceanographic Studies affiliated with the United States of America since 1979. In general, regression and correlation methods are used to determine the presence or absence of a relationship between variables. The correlation index shows the relationship between two variables that are both affected by common factors. Both of these variables are random and other unknown factors jointly affect their occurrence. Monthly time series of sea level pressure data for the time period from 1987 to 2021 were made for 10512 cells (2.5 by 2.5 degrees of arc). The months of September, August, July, June, May and April respectively correspond to the delays are one to six months. In order to be able to investigate the quantitative and spatial changes of precipitation with sea level pressure in the six months before the onset of precipitation, correlation maps were drawn for each of the six months (time delay of one to six months). In the second step, the significance of the correlation was investigated using inferential statistics (hypothesis testing) and the points whose correlation was significant on the map was drawn In the third step, for the practical use of sea level pressure changes to predict precipitation, the points whose correlation was significant (places with maximum correlation) were selected as predictors (independent variable). Using the data of these selected points and multivariable regression, rainfall forecasting models were fitted. The performance of these models in rainfall forecasting was evaluated using indices such as correlation coefficient, explanatory coefficient and root mean square errors. Models with the best performance in predicting rainfall with a delay of one to six months were selected. The places where the prediction models had the best performance in different months (September, August, July, June, May and April) using the data of those regions were introduced as key regions in the prediction.

Results and discussion

The results obtained from the correlation study show that there is a significant correlation between the patterns of changes in sea level pressure and precipitation (12 months from October to September of the following year) with a delay of one to six months. In fitting precipitation models based on sea level pressure changes using selected points from the centers with maximum correlation, the obtained results show that the pressure changes in the eastern and western Pacific Ocean, especially in the eastern ocean, in all annual rainfall forecasting models as a predictor has been selected. Changes in sea level pressure in the Pacific Ocean seem to play an important role in predicting annual precipitation.

According to the independent variables, Southeast Asia and regions from the Southeast China Sea and Guam Island to northern Australia can be other important indicator regions in predicting one-year rainfall. In examining the performance of forecasting models in delays of one to six months, it was found that fitting models with a delay of 5 and 6 months have better performance than other models. So that the 5 and 6 month delay models explain 70% and 65% of the rainfall changes, respectively.

Conclusion

The findings of this research show that it is possible to obtain favorable results from changes in sea level pressure patterns in the centers with maximum correlation for rainfall forecasting (12 months from October to September of the following year). Since sea level pressure data are available from various sources, it makes us somewhat independent from the data of physical models of atmospheric circulation. It also provides the possibility that we can have a proper prediction of the amount of precipitation for a year a few months before the beginning of rainfall. Since there is no 12-month rainfall forecast for one-year water resource management policies in the agriculture, industry, drinking water, natural resources and environment sectors, and on the other hand, due to the large range of annual rainfall changes in the northeast Using average rainfall cannot be useful and effective, the results of this research can be helpful.