Yao Shuai-Lei, Luo Jing-Jia, Huang Gang
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China.
University of Chinese Academy of Sciences, Beijing, 100049, China.
PLoS One. 2016 Mar 1;11(3):e0149968. doi: 10.1371/journal.pone.0149968. eCollection 2016.
Regional climate projections are challenging because of large uncertainty particularly stemming from unpredictable, internal variability of the climate system. Here, we examine the internal variability-induced uncertainty in precipitation and surface air temperature (SAT) trends during 2005-2055 over East Asia based on 40 member ensemble projections of the Community Climate System Model Version 3 (CCSM3). The model ensembles are generated from a suite of different atmospheric initial conditions using the same SRES A1B greenhouse gas scenario. We find that projected precipitation trends are subject to considerably larger internal uncertainty and hence have lower confidence, compared to the projected SAT trends in both the boreal winter and summer. Projected SAT trends in winter have relatively higher uncertainty than those in summer. Besides, the lower-level atmospheric circulation has larger uncertainty than that in the mid-level. Based on k-means cluster analysis, we demonstrate that a substantial portion of internally-induced precipitation and SAT trends arises from internal large-scale atmospheric circulation variability. These results highlight the importance of internal climate variability in affecting regional climate projections on multi-decadal timescales.
区域气候预测具有挑战性,因为存在很大的不确定性,尤其是源于气候系统不可预测的内部变率。在此,我们基于社区气候系统模型第3版(CCSM3)的40个成员集合预测,研究了2005 - 2055年期间东亚降水和地表气温(SAT)趋势中由内部变率引起的不确定性。这些模型集合是使用相同的SRES A1B温室气体情景,从一系列不同的大气初始条件生成的。我们发现,与北半球冬季和夏季的预计地表气温趋势相比,预计的降水趋势受到的内部不确定性要大得多,因此置信度较低。冬季预计的地表气温趋势比夏季具有相对更高的不确定性。此外,低层大气环流的不确定性比中层更大。基于k均值聚类分析,我们证明,内部引起的降水和地表气温趋势的很大一部分源于内部大尺度大气环流变率。这些结果凸显了内部气候变率在多十年时间尺度上影响区域气候预测方面的重要性。