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识别和排名CMIP6全球气候模型,用于印度次大陆预测的温度变化
1Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati, 781039, Assam, India.
Scientific reports
|February 6, 2024
概括
这项研究对印度的温度预测进行了30种气候模型的排名. 它确定了未来气候变化影响评估的KIOST-ESM和E3SM-1-0等顶级模型.
科学领域:
- 气候科学 气候科学
- 水文学 水文学
- 环境科学 环境科学
背景情况:
- 准确的区域气候模型对于评估气候变化对水资源和极端天气事件的影响至关重要.
- 为了减少全球气候模型 (GCM) 模拟中的不确定性,需要识别和排名熟练的GCM.
研究的目的:
- 对印度次大陆的最高和最低温度进行30个合模型对比项目第6阶段 (CMIP6) GCM的批判性评估和排名.
- 确定最适合未来对该地区气候变化影响研究的GCM.
主要方法:
- 评估了30个CMIP6 GCM的1°分辨率的每日温度数据 (1965-2014).
- 使用的性能指标:纳什-萨克利夫效率 (NSE),相关系数 (CC),珀金斯技能得分 (PSS),规范化根平均平方误差 (NRMSE) 和绝对规范化平均偏差误差 (ANMBE).
- 采用了指标权重的方法和用于多标准决策的VIKOR方法,以单独和集体排名模型.
主要成果:
- 确定NRMSE和NSE分别是S1和S2场景中最重要的绩效指标;CC是最不重要的.
- 在最高温度方面,KIOST-ESM,MRI-ESM2-0,MIROC6,NESM3和CanESM5的排名最高.
- 在最低温度方面,E3SM-1-0,NESM3,CanESM5,GFDL-CM4,INM-CM5-0和CMCC-ESM2的排名最高.
结论:
- 这项研究为印度的温度模拟提供了CMIP6 GCM的强大排名.
- 鉴定出熟练的GCM可以减少对水文项目和极端高温事件气候变化影响评估的不确定性.
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