https://www.ijew.io/paper/projecting-future-temperature-using-cmip5-gcms-over-transboundary-gomal-river-basin

A. Khan, M. S. Khattak, M. Khan, Saadia Rehman
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Abstract

Temperature is a key driving force in hydrological cycle, defining the extent of climate change. It causes the alteration in hydrological cycle process, limiting and intensifying the rainfall, increasing the rate of evapotranspiration and changes the crop pattern and duration over a region. Its future analysis is utmost to cope with negative effects of climate change over a specified region. This study also investigates, the future temperature pattern over Gomal River Basin (GRB). To undertake the study, downscaled daily temperature data of four General Circulation Models (GCMS) namely; bcc_csm1_1_m, mpi_esm_mr, ncar_ccsm4 and ncc_noresm1_m and their ensemble mean were first compared and validated with observed data for the period of 1980-2005. After that, temperature was projected for the mid-century (2020-2060) for the Representative Concentration Pathways (RCPs) 4.5. The analysis were carried out based on the four seasons; winter (December-February), spring (March-May), summer (June-August) and autumn (September-November). The results indicate that, the basin temperature was accurately predicted by the ensemble mean of the four GCMs with R value of 0.9. All the GCMs projected a warming in future in all seasons. Winter warming is more compared to other seasons. Proper adaptation strategies are needed to cope with the adverse impacts of global warming in the basin. Keywords— Temperature, Climate Change, CMIP5, GCMs, GRB
https://www.ijew.io/paper/projecting-future-temperature-using-cmip5-gcms-over-transboundary-gomal-river-basin
温度是水文循环的关键驱动力,决定了气候变化的程度。它引起水循环过程的改变,限制和加强降雨,增加蒸散速率,改变一个地区的作物格局和持续时间。它的未来分析是最大限度地应对气候变化对特定地区的负面影响。本文还研究了戈马尔河流域(GRB)未来的温度格局。为了进行这项研究,我们将四个大气环流模式(GCMS)的日气温数据按比例缩小,即;首先将Bcc_csm1_1_m、mpi_esm_mr、ncar_ccsm4和ncc_noresm1_m及其集合平均值与1980—2005年的观测资料进行比较和验证。在此之后,根据代表性浓度路径(rcp) 4.5预测了本世纪中叶(2020-2060年)的温度。基于四季进行分析;冬季(12 - 2月)、春季(3 - 5月)、夏季(6 - 8月)和秋季(9 - 11月)。结果表明,4种gcm的集合平均值能较好地预测流域温度,R值为0.9。所有gcm都预测了未来所有季节的变暖。与其他季节相比,冬季变暖更多。为应对全球变暖对流域的不利影响,需要采取适当的适应策略。关键词:温度,气候变化,CMIP5, gcm, GRB
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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