Daily Atmospheric Circulation Patterns and Their Influence on Dry/Wet Events in Iran

Abstract

Analyzing atmospheric circulation patterns characterize prevailing weather in a region. The method of principal component analysis and clustering was used to classify daily atmospheric circulation patterns. The average daily geopotential height of 500 hPa with 0.5° resolution of the ECMWF (1990–2019) were extracted from the Middle East. The S array was used to identify air types, and k-means clustering was used to classify daily air types. All days were divided into eighteen groups. Then, the surface maps and moisture flux divergence at the 850-hPa level of each pattern were studied. The, the connection between circulation patterns and precipitation occurrence is investigated by the PI index. The existence of a variety of precipitation and temperature regimes and consequent dry/wet periods is related to the type and frequency of the circulation patterns. In patterns with south to southwesterly currents, the low-pressure surface center extends from the south of the Red Sea to southern Turkey and is associated with the mid-level trough, where the moisture fluxes converge in the south of the Red Sea, southwest/south of Iran, and east of the Mediterranean Sea. Therefore, according to the intensity of the patterns, the most precipitation falls in the country’s western half, and the Zagros Mountain’s wind side. With the eastward movement of the Cyclonic patterns, the rainfall area extends to the eastern half of the country. With the pattern that the thermal low surface pressure extends to 35 °N latitude and is associated with the mid-level subtropical high, almost no rain occurs in the country.

Variability in Future Atmospheric Circulation Patterns in the MPI-ESM1-2-HR Model in Iran

“…In classifying atmospheric patterns, researchers usually use GPH data of 500 hPa [17,23,24]. In the continuation, sea level pressure data was extracted from the same center.…”

“…In the present study, the k-means method selects the days of the sample to show the mean conditions of each weather type, and then the other days are determined to the nearest cluster based on its distance from the mean values of the sample days [23]. Therefore, clustering by the k-means method classifies weather types and indicates the dominant atmospheric circulation patterns.…”

“…After selecting the main components and determining the component scores, days with scores greater than 2 (for the positive phase) or less than −2 (for negative phase) in a certain component were chosen to identify the number of primary groups. Based on this information, all days (58,440), including both 40 years of the historical simulations and SSPs, were classified into 18 groups [23,27] during 1975-2054.…”