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Skill of CMIP5 climate models to reproduce the stability indices in identifying thunderstorms over the Gangetic West Bengal

机译:CMIP5气候模型的技能来重现识别难以达到难以置信的难忘

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The skills of present generation Global Climate Models (GCMs) have been evaluated in reproducing the climatology of stability indices for the identification of thunderstorm over the Gangetic West Bengal (GWB) region. Three different stability Indices viz., K-Index (KI), Boyden Index (BI), and Total Totals Index (TT) have been used in this study. Estimated values of TT and BI from reanalysis data have shown significant (at 10%) declining trends in pre-monsoon season however, they have a similar month-to-month variation of trends as simulated by the GCMs. Similar declining trends as reanalysis data were also noticed in March. On the other hand, most GCMs were able to reproduce the mean climatology of BI and TT but failed to show better skill for KI. The values of correlation between GCMs simulated TT and KI have shown decreasing trends from March to May months which was almost similar to the reanalysis data. In general, the frequency of occurrence of different types of thunderstorms was more in May and less in March as per the analysis of KI and TT indices from the reanalysis data. It was found that few GCMs were able to discriminate the occurrences of thunderstormon-thunderstorm like the reanalysis data. Over the study region, either of KI or TT or a combination of both may be used to study the occurrence of thunderstormon-thunderstorms. However, based on the analysis of climatological mean, trend, and threshold values, TT has shown better performance than KI. It is concluded that the present generation of GCMs has shown a mixed response in simulating the stability indices to identify thunderstormon-thunderstorms over the GWB.
机译:目前一代全球气候模型(GCMS)的技能已在再现稳定指标的气候学中,以识别浑浊西孟加拉邦(GWB)地区的雷暴。本研究中使用了三种不同的稳定性指数。,K-Index(Ki),Boyden指数(BI)和总总计指数(TT)。从重新分析数据的TT和BI的估计值表现出显着的(以10%)在季风季节季节下降趋势,但它们具有与GCMS模拟的趋势相似的月份变化。与Reanalysis数据在3月份也发现类似的趋势。另一方面,大多数GCM能够再现BI和TT的平均气候学,但未能表现出更好的KI技能。 GCMS模拟TT和KI之间的相关性值显示了3月至5月几个月的趋势,几乎类似于再分析数据。通常,根据来自再分析数据的KI和TT指数的分析,3月份,3月份不同类型雷暴发生的发生频率越来越少。发现很少有GCMS能够区分雷暴/非雷暴的发生,如重新分析数据。在研究区,可以使用Ki或TT的组合或两者的组合来研究雷暴/非雷暴的发生。但是,基于气候均值的趋势平均值,趋势和阈值的分析,TT表现出比ki更好的性能。得出结论,目前的GCMS在模拟稳定性指标上显示了混合响应,以识别GWB上的雷暴/非雷暴。

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