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The Sources of Uncertainty in the Projection of Global Land Monsoon Precipitation
Geophysical Research Letters ( IF 5.2 ) Pub Date : 2020-07-29 , DOI: 10.1029/2020gl088415
Tianjun Zhou 1, 2, 3 , Jingwen Lu 1, 2 , Wenxia Zhang 1 , Ziming Chen 1, 2
Affiliation  

Policy makers need reliable future climate projection for adaptation purposes. A clear separation of sources of uncertainty also helps narrow the projection uncertainty. However, it remains unclear for monsoon precipitation projections. Here we quantified the contributions of internal variability, model uncertainty, and scenario uncertainty to the ensemble spread of global land monsoon precipitation projections using Coupled Model Intercomparison Project Phase 5 (CMIP5) models and single‐model initial‐condition large ensembles (SMILEs). For mean precipitation, model uncertainty (contributing ~90%) dominates the projection uncertainty, while the contribution of internal variability (scenario uncertainty) decreases (increases) with time. The source of uncertainty for extreme precipitation differs from that of mean precipitation mainly in long‐term projection, with the contribution of scenario uncertainty comparable to model uncertainty. Reducing model uncertainty can effectively narrow the monsoon precipitation projection. The internal variability estimates differ slightly among models and methods, the uncertainty partitioning is robust in middle‐long term.

中文翻译:

全球陆地季风降水预测中的不确定性来源

决策者需要可靠的未来气候预测以适应气候变化。明确区分不确定性来源也有助于缩小预测不确定性。但是,目前尚不清楚季风降水的预测。在这里,我们使用耦合模型比较项目阶段5(CMIP5)模型和单模型初始条件大集合(SMILE)来量化内部变异性,模型不确定性和情景不确定性对全球陆地季风降水预测的总体分布的贡献。对于平均降水量,模型不确定性(约占90%)占主导地位,而内部变异性(情景不确定性)的贡献随时间减少(增加)。极端降水的不确定性来源与平均降水的不确定性主要在长期预测方面有所不同,情景不确定性的贡献可与模型不确定性相提并论。减少模型的不确定性可以有效地缩小季风降水预测。内部可变性估计在模型和方法之间略有不同,不确定性划分在中长期内是稳健的。
更新日期:2020-08-08
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