Evaluation and projection of precipitation in Pakistan using the Coupled Model Intercomparison Project Phase 6 model simulations

This study aimed to evaluate the performance of global climate models (GCMs) from the family of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in the historical simulation of precipitation and select the best performing GCMs for future projection of precipitation in Pakistan under multipl...

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Vydané v:International journal of climatology Ročník 42; číslo 13; s. 6665 - 6684
Hlavní autori: Abbas, Adnan, Ullah, Safi, Ullah, Waheed, Waseem, Muhammad, Dou, Xin, Zhao, Chengyi, Karim, Aisha, Zhu, Jianting, Hagan, Daniel Fiifi Tawia, Bhatti, Asher Samuel, Ali, Gohar
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Chichester, UK John Wiley & Sons, Ltd 15.11.2022
Wiley Subscription Services, Inc
Predmet:
Annual precipitation
Annual variations
Climate
Climate models
Coupled Model Intercomparison Project Phase 6
Disaster management
Emergency preparedness
Food security
Future precipitation
Global climate
Global climate models
Interannual variability
Intercomparison
Mitigation
model evaluation
Modelling
Pakistan
Performance evaluation
Precipitation
precipitation projections
Resource management
Risk management
Simulation
Statistical analysis
Trends
Water management
Water resources
Water resources management
Water resources planning
Pakistan
ISSN:0899-8418, 1097-0088
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Popis
Shrnutí:This study aimed to evaluate the performance of global climate models (GCMs) from the family of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in the historical simulation of precipitation and select the best performing GCMs for future projection of precipitation in Pakistan under multiple shared socioeconomic pathways (SSPs). The spatiotemporal performance of GCMs was evaluated against the Climate Research Unit (CRU) data in simulating annual precipitation during 1951–2014, using the Taylor diagram and interannual variability skill (IVS). Moreover, the modified Mann–Kendall (mMK) and Sen's slope estimator (SSE) tests were employed to estimate significant trends in future precipitation for the period 2015–2100. Based on the comprehensive ranking index (CRI), the HadGEM3‐GC31‐MM model has the highest skill in simulating precipitation distributions followed by EC‐Earth3‐Veg‐LR, CNRM‐ESM2‐1, MPI‐ESM1‐2‐HR, CNRM‐CM6‐1, MRI‐ESM2‐0, CNRM‐CM6‐1‐HR, EC‐Earth3‐Veg, MCM‐UA‐1‐0, INM‐CM5‐0, KACE‐1‐0‐G, CAMS‐CSM1‐0, and HadGEM3‐GC31‐LL models. Furthermore, the projections of the best models ensemble mean (BMEM) showed that the study region will experience a substantial increase in precipitation under SSP3‐7.0 and SSP5‐8.5 but an indolent rise under SSP1‐2.6 and SSP2‐4.5 scenarios. The summer and annual precipitations exhibit a statistically significant increasing trend relative to the winter season under most scenarios. Moreover, the magnitude of monotonic trends in seasonal and annual precipitation progresses from low forcing scenario (SSP1‐2.6) to high forcing scenario (SSP5‐8.5). The findings of the study could provide a benchmark in selecting appropriate GCMs for future projection over a data scare region, like Pakistan. Moreover, the projected trends of future precipitation are crucial in devising adaption and mitigation actions towards sustainable planning of water resource management, food security, and disaster risk management. Simulation and projection of precipitations in Pakistan.
Bibliografia:Funding information
Innovative and Entrepreneurial Talent Program of Jiangsu Province, Grant/Award Number: R2020SC04; National Natural Science Foundation of China, Grant/Award Number: 42130405; Strategic Priority Research Program of the Chinese Academy of Sciences, Grant/Award Number: XDA2006030201
Adnan Abbas and Safi Ullah are first co‐authors and they contributed equally to this study.
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ISSN:0899-8418
1097-0088
DOI:10.1002/joc.7602