عنوان مقاله English
نویسندگان English
Global warming is one of the most challenging climatic phenomena of the current era, accompanied by a rise in the average temperature of the Earth's oceans and land over the past few decades. This study provides a comprehensive analysis of the temporal and spatial changes in Iran's maximum temperature over the last four decades, using ERA5 reanalysis data.
Daily maximum temperature data was extracted from ERA5 products in HDF5 file format and processed using the Python programming language. For data analysis, Principal Component Analysis (PCA) was used for dimensionality reduction, and Ward's clustering method was employed to determine homogeneous climatic regions based on the similarity of weather elements. The long-term mean maximum temperature for the country during this period was estimated at 24.2∘C, with a range of variation from −0.4∘C to 53.7∘C. The results from smoothing the data's time series showed a significant temperature jump around the year 1998, which led to an increase in the mean maximum temperature from 23∘C to 25∘C. In the spatial dimension, the maximum temperature was directly influenced by topography, altitude, and latitude. The southern and southeastern regions were identified as the warmest, while the high-altitude areas of the west, north, and northeast were the coolest. The results of the PCA showed that the first two components explained more than 78% of the spatial variance and 93% of the temporal variance, respectively. This indicates the existence of consistent and interpretable climatic patterns. Additionally, the Ward's clustering analysis, which divided the data into five distinct clusters, reflected the diversity of temporal behavior of the maximum temperature across the country.
کلیدواژهها English
