Projecting spatiotemporal variations of sunshine duration with regard to climate change in Iran as a step toward clean energy

PAZH | Iran is a country with great potential for using solar energy. To manage the supply and demand for using solar energy technologies, proper knowledge regarding the areas with homogeneous sunshine duration is essential. Therefore, in this study, the monthly data anomalies were used to determine homogeneous climatic regions over two study periods.

The matrix grids were prepared for the base (1981–۲۰۱۸) and future (2041–۲۰۸۰) periods respectively as 456×۲۷۲ and 480×۲۷۲ (the number of months and stations). Notably, the future period model was prepared according to two separate Representative Concentration Pathway (RCP) scenarios (4.5 and 8.5). The S-mode input matrix was used to perform principal component analysis (PCA) and Ward’s method was utilized for cluster analysis (CA).

Subsequently, the stations were partitioned into different sunshine clusters. Findings showed that 86.6 % of the total variance was involved with seven leading PCs (7 clusters for the base period), 67.2 % with four initial RCP4.5 components, and 62.8 % with the first three RCP8.5 components (4 and 3 clusters for the future period, respectively). Concerning the sunshine duration, results indicated geographical latitude as the most important factor for the spatial distribution of different clusters.

Additionally, global warming was seen to have caused an increase in sunshine hours for 73 and 182.5 hr/year according to RCP4.5 and RCP8.5 compared to the base period. Therefore, an overview of how climate change could affect sunshine duration in Iran enhances the potential for efficient use of clean solar energy. Iran is a country with great potential for using solar energy. To manage the supply and demand for using solar energy technologies, proper knowledge regarding the areas with homogeneous sunshine duration is essential. Therefore, in this study, the monthly data anomalies were used to determine homogeneous climatic regions over two study periods.

The matrix grids were prepared for the base (1981–۲۰۱۸) and future (2041–۲۰۸۰) periods respectively as 456×۲۷۲ and 480×۲۷۲ (the number of months and stations). Notably, the future period model was prepared according to two separate Representative Concentration Pathway (RCP) scenarios (4.5 and 8.5). The S-mode input matrix was used to perform principal component analysis (PCA) and Ward’s method was utilized for cluster analysis (CA).

Subsequently, the stations were partitioned into different sunshine clusters. Findings showed that 86.6 % of the total variance was involved with seven leading PCs (7 clusters for the base period), 67.2 % with four initial RCP4.5 components, and 62.8 % with the first three RCP8.5 components (4 and 3 clusters for the future period, respectively).

Concerning the sunshine duration, results indicated geographical latitude as the most important factor for the spatial distribution of different clusters. Additionally, global warming was seen to have caused an increase in sunshine hours for 73 and 182.5 hr/year according to RCP4.5 and RCP8.5 compared to the base period. Therefore, an overview of how climate change could affect sunshine duration in Iran enhances the potential for efficient use of clean solar energy.