Using wavelet transforms to estimate surface temperature trends and dominant periodicities in Iran based on gridded reanalysis data

• Temperature data (55 years) in Iran were used to detect trends and dominant periodicities.
• This was done via discrete wavelet transform (DWT) and Mann–Kendall (MK) test.
• For monthly data analysis, 2 and 4 month periodic components were dominant.
• Seasonal and annual analyses showed that each region has a different periodic pattern.
• Spring and summer have more important roles in annual positive trend creation.

In this paper, the discrete wavelet transform (DWT), the Mann–Kendall (MK) trend test, and the sequential Mann–Kendall test are applied to temperature series at different time scales in order to detect the long-term trends (1956–2010) in synoptic-scale surface temperatures in Iran, as well as the dominant time scales affecting these temperature time series. The relevant data was extracted from a gridded data file of the region (44°E to 63.5°E, 25°N to 40°N) and divided into 12 regular zones (of dimensions 5 × 5°), each of which was analyzed as an individual unit. The results of this research show that at the monthly, seasonal and annual time scales, the trends in temperature were significant and positive in all of the study zones. In addition, the 2-month and 4-month components were dominant at the monthly time scale, the 48-month component dominant at the seasonal time scale, and the 8-year and 16-year components dominant at the annual time scale. Also, the temperature trends in the northern, and especially central, regions of the study zones increased from west to east, and these increasing trends in temperature were most prominent for the spring and summer seasons. The methodology applied here is generally applicable and quite useful for studying both trends and the dominant time scales affecting climatic data series and could find significant applications in related fields.