1. Identifying hotspots cities vulnerable to climate change in Pakistan under CMIP5 climate projections.
- Author
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Ali, Shaukat, Kiani, Rida S., Reboita, Michelle S., Dan, Li, Eum, Hyung‐Il, Cho, Jaepil, Dairaku, K., Khan, Firdos, and Shreshta, Madan L.
- Subjects
CLIMATE change ,ATMOSPHERIC temperature ,PROBABILITY density function ,ATMOSPHERIC models ,DOWNSCALING (Climatology) - Abstract
In this study, an ensemble of statistically downscaled 14 multi‐global climate models for RCP4.5 and RCP8.5 emission scenarios was employed to implement a comprehensive assessment of climate change impacts over Pakistan in order to identify the future hotspots cities in terms of changes in temperature and precipitation. The analyses focused on the minimum, maximum and average temperature and precipitation in three time‐slices: 2006–2035, 2041–2070, and 2071–2,100. Average temperature is projected to increase by 2.6°C under RCP4.5 while 5.1°C under RCP8.5 by the end of this century with the north side of Pakistan (mainly over North Pakistan—NP, Monsoon Region—MR and Khyber Pakhtunkhwa—KP) presenting the highest changes in the temperatures. Wetter conditions are expected in the future over Pakistan, mainly over the MR. In general, air temperature and precipitation showed linear positive correlation over Pakistan in both RCP scenarios. Hotspot cities where extreme climate, that is, the hottest, dryer and wetter, exists were also identified. Hyderabad will likely become the hottest city of Pakistan by end century with the highest average temperature reaching 29.9°C under RCP4.5 and 32.0°C under RCP8.5 followed by Jacobabad, Bahawalnagar, and Bahawalpur. Most of the hottest cities are detected in areas on the southern side of Pakistan. On the other hand, the wettest cities, Murree, Balakot and Muzaffarabad, are located in the MR. Dry conditions are likely to be prevalent in Dalbandin followed by Khanpur and Jacobabad under both RCPs. The uncertainties of the projections were also evaluated. For precipitation, for example, there are a large number of outliers indicating the high variability/uncertainties of the projections. These uncertainties are clearer when the probability density functions are analysed for individual sub‐domains in Pakistan. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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