TITLE:
Climatological Variability of Diurnal Temperature Range across Pakistan during Transitional Seasons and Its Link to Indian Ocean SST Anomalies (1980-2016)
AUTHORS:
Anas Farooq, Iqra Zainab, Hasnain Farooq, Aminu Dalhatu Datti, Hassnain Haider, Muhammad Asad Farooq, Khansa Amina
KEYWORDS:
Diurnal Temperature Range, Sea Surface Temperature, Autumn, Spring, Indian Ocean, Seasonal Variability, Pakistan
JOURNAL NAME:
Journal of Geoscience and Environment Protection,
Vol.13 No.5,
May
29,
2025
ABSTRACT: An investigation has been conducted on Diurnal Temperature Range (DTR) variations throughout Pakistan during spring (MAM) and autumn (SON) from 1980 to 2016 while exploring their relationship with Indian Ocean Sea Surface Temperature (SST) anomalies. Analysis of seasonal DTR patterns along with their atmospheric-oceanic drivers required the combination of temperature data (CRU TS4.08) with cloud cover (ERA5), SST anomalies (ERSST v5), and upper-level wind fields (NCEP/NCAR) through anomaly detection and Empirical Orthogonal Function (EOF) analysis and regression techniques. Research data indicates that southern Pakistan experiences higher DTR values than the northern regions primarily because of geographical features and atmospheric cover. The first EOF mode demonstrates its ability to account for 45.5% of the spring DTR dataset and 40.6% of autumn DTR values. Spring excessive SSTs registered in the western Arabian Sea produced an anti-correlated effect on daily temperature range by approximately –0.035 which reduced DTR by –1.2˚C for every ˚C rise in SST. The relationship between SST and DTR displayed weak but noticeable associations during autumn in 2010 and 2015 with r ≈ 0.011. The relationship between cloud cover and daily temperature range is clear in spring where the correlation reaches –0.72 and in autumn it shows moderate influence at –0.14. Indian Ocean SST anomalies play a substantial role in controlling Pakistan’s seasonal thermal patterns through transitional periods thus providing essential knowledge for future climate predictions and sectoral operational planning.