评估CMIP6模型和多模型对伊朗的温度和降雨的平均值
1Faculty of Civil, Water and Environmental Engineering, Shadid Beheshti University, Tehran, Iran.
Scientific reports
|October 15, 2024
概括
本研究评估了伊朗的40个合模型对比项目第6阶段 (CMIP6) 的全球气候模型. 偏差纠正模型的整体平均值显著提高了降水和温度预测的准确性.
科学领域:
- 气候科学 气候科学
- 大气科学 大气科学
- 地球系统科学 地球系统科学
背景情况:
- 准确的气候预测对于了解区域气候动态和影响至关重要.
- 结合模型相互比较项目第6阶段 (CMIP6) 全球气候模型 (GCMs) 提供了多样化的模拟,但性能不同.
- 根据观测数据对GCM性能进行评估对于在伊朗等特定地区进行可靠的气候变化评估至关重要.
研究的目的:
- 根据伊朗的观察数据,评估40个CMIP6 GCM的性能.
- 为了比较不同偏差校正方法和整体平均化策略的有效性.
- 为了确定伊朗地区降水和温度的高性能CMIP6模型.
主要方法:
- 评估40个CMIP6 GCM,使用各种统计标准与伊朗的同视站数据对比.
- 偏差校正技术的应用,包括量子映射和线性缩放,与贝叶斯模型平均值集成.
- 对单个模型,偏差纠正模型和多模型组合的性能指标 (RMSE,MAE,相关系数) 的比较.
主要成果:
- 在不同的气候条件和评估标准中,CMIP6 GCM 显示出不同的精度,在非静止性 R 标准中存在显著差异.
- 在降水方面,NorESM2-MM,AWI-ESM-1-1-LR和MPI-ESM1-2-LR的排名一直很高;在温度方面,MPI-ESM1-2-LR,TaiESM1,INM-CM4-8和IITM-ESM的排名一直很高.
- 量子映射优于线性缩放,但与时间变化的偏差作斗争;与单个模型相比,多模型组合平均值显著提高了准确性和减少了不确定性.
结论:
- 调整偏差的CMIP6模型的整体平均值提高了伊朗降水和温度的预测准确度.
- 偏差校正方法至关重要,但它们的有效性可能受到非静止偏差的限制.
- 具体的CMIP6模型显示了伊朗地区气候预测的卓越性能,为未来的气候研究和影响研究提供了信息.
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