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Comparison of future and base precipitation anomalies by SimCLIM statistical projection through ensemble approach in Pakistan

Authors:

Asad Amin a, Wajid Nasim a,b,c, Muhammad Mubeen a, Dildar Hussain Kazmi d, Zhaohui Lin e,
Abdul Wahid f, Syeda Refat Sultanaa, Jim Gibbsg, Shah Fahadh

 

a Department of Environmental Sciences, COMSATS Institute of Information Technology (CIIT), Vehari 61100, Pakistan
b CIHEAM-Institut Agronomique Méditerranéen de Montpellier (IAMM), 3191 route de Mende, 34090 Montpellier, France
c CSIRO Sustainable Ecosystems, National Agricultural Research Flagship, Toowoomba, QLD 4350, Australia
d National Agromet Centre, Pakistan Meteorological Department, Islamabad, Pakistan
e International Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China.
f Department of Environmental Sciences, Bahauddin Zakariya University, Multan, Pakistan
g Livestock Health and Production, Department of Animal Science, Lincoln University, Lincoln 7647, Christchurch 85084, New Zealand
h College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China


 

Keywords:

GCM,Climate change,Mann-Kendall,Future projections,RCPs,Sen's slop,Meteorological stations

 

Abstract:

Unpredictable precipitation trends have largely influenced by climate change which prolonged droughts orfloods in South Asia. Statistical analysis of monthly, seasonal, and annual precipitation trend carried out fordifferent temporal (1996–2015 and 2041–2060) and spatial scale (39 meteorological stations) in Pakistan.Statistical downscaling model (SimCLIM) was used for future precipitation projection (2041–2060) and analyzedby statistical approach. Ensemble approach combined with representative concentration pathways (RCPs) atmedium level used for future projections. The magnitude and slop of trends were derived by applying MannKendal and Sen's slop statistical approaches. Geo-statistical application used to generate precipitation trendmaps. Comparison of base and projected precipitation by statistical analysis represented by maps and graphicalvisualization which facilitate to detect trends. Results of this study projects that precipitation trend wasincreasing more than 70% of weather stations for February, March, April, August, and September represented asbase years. Precipitation trend was decreased in February to April but increase in July to October in projectedyears. Highest decreasing trend was reported in January for base years which was also decreased in projectedyears. Greater variation in precipitation trends for projected and base years was reported in February to April.Variations in projected precipitation trend for Punjab and Baluchistan highly accredited in March and April.Seasonal analysis shows large variation in winter, which shows increasing trend for more than 30% of weatherstations and this increased trend approaches 40% for projected precipitation. High risk was reported in base yearpre-monsoon season where 90% of weather station shows increasing trend but in projected years this trenddecreased up to 33%. Finally, the annual precipitation trend has increased for more than 90% of meteorologicalstations in base (1996–2015) which has decreased for projected year (2041–2060) up to 76%. These resultrevealed that overall precipitation trend is decreasing in future year which may prolonged the drought in 14% ofweather stations under study.


 

Citation:

Asad Amin , Wajid Nasim , Muhammad Mubeen , Dildar Hussain Kazmi , Zhaohui Lin ,Abdul Wahid, Syeda Refat Sultana,Jim Gibbs,Shah Fahad,Comparison of future and base precipitation anomalies by SimCLIM statistical projection through ensemble approach in Pakistan,Atmospheric Research 194 (2017) 214–225,http://dx.doi.org/10.1016/j.atmosres.2017.05.002

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