CMIP5多模式对东亚地区地面气温年际变率的回报研究
投稿时间: 2016-03-16  最后修改时间: 2017-06-06  点此下载全文
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智协飞 南京信息工程大学 气象灾害预报预警与评估协同创新中心/气象灾害教育部重点实验室, 江苏 南京 210044;南京大气科学联合研究中心, 江苏 南京 210008 zhi@nuist.edu.cn 
周红梅 南京信息工程大学 气象灾害预报预警与评估协同创新中心/气象灾害教育部重点实验室, 江苏 南京 210044  
王姝苏 南京信息工程大学 气象灾害预报预警与评估协同创新中心/气象灾害教育部重点实验室, 江苏 南京 210044  
胡航菲 河南省安阳市气象局, 河南 安阳 455000  
朱寿鹏 南京信息工程大学 气象灾害预报预警与评估协同创新中心/气象灾害教育部重点实验室, 江苏 南京 210044  
基金项目:北极阁开放研究基金南京大气科学联合研究中心重点项目(NJCAR2016ZD04);国家自然科学基金资助项目(41575104);国家重大科学研究计划项目(2012CB955204);江苏高校优势学科建设工程资助项目(PAPD)
中文摘要:对CMIP5全球气候模式中年代际回报试验的气温资料及其简单集合平均(Multi-model ensemble mean,EMN)和贝叶斯模式平均的结果(Bayesian Model Averaging,BMA)进行经验正交函数(Empirical Orthogonal Function,EOF)分解和Morlet小波分析,检验评估各个模式及其EMN和BMA对东亚地面气温的方差、气温时空分布特征及周期变化的回报能力。结果表明,10个模式、EMN、BMA都能很好地回报出1981-2010年东亚地面气温的方差分布,其中BMA回报效果最好。EOF分析表明,BMA能较好地回报出东亚地面气温第一模态的时空分布。MIROC5能较好地回报出第二模态的趋势变化,但却不能回报出气温的年际变率。绝大多数模式和EMN、BMA虽然能回报出东亚地面气温的变化趋势,但是对气温年际变率的回报仍然是比较困难的。CMCC-CM对气温变化主模态的3~5 a的周期变化特征回报效果最好,和NCEP资料的结果最为接近。
中文关键词:地面气温  CMIP5  贝叶斯模式平均  年际变率  回报
 
Prediction of interannual variability of East Asian surface air temperature based on CMIP5 data
Abstract:At present,the global climate models have some limitations on the ability to simulate and hindcast the climate characteristics of the East Asian monsoon region,as well as the complex terrain.The numerical simulation and prediction using the fully coupled model is a promising method in the study of climate change.Coupled Model Intercomparison Project Phase 5(CMIP5),includes a fully coupled model of decadal return testing,which is the latest version of the major international advanced models,much better than the previous model.The return performance of the model is mainly reflected in two aspects:climate mean and climate variability,in which the model of climate change can be reasonably rewarded in order to predict the future climate effectively.Most of these studies focused on the assessment of climate change trends and climate averages.Ensemble forecasting can effectively cope with the uncertainty of model prediction.Some studies have pointed out that the multi-model ensemble has more reliable simulation ability than the single model in the contemporary climate of East Asia.BMA is a pre-processing method of ensemble forecasting,which can provide more accurate prediction,and studies have shown that BMA is better than simple multi-mode ensemble average.Based on the CMIP5 which runs of 10 climate system models for the surface air temperature hindcast,the multi-model ensemble(EMN) and Bayesian model averaging(BMA) prediction of the surface air temperature have been conducted.In terms of the variance distribution,the spatial and temporal distribution of the surface air temperature variation as well as the periodic oscillation characteristics,the skills of single models,EMN,BMA hindcasts are reasonably good by using EOF analysis and Morlet wavelet analysis.The results show that each single model,EMN and BMA can hindcast the variance distribution quite well for the period of 1981-2010.Among them BMA provides the best hindcast.EOF analysis indicates that each single model,EMN and BMA can hindcast two leading modes of the surface air temperature anomalies over East Asia.The models MIROC5 can not only hindcast the trend,but also hindcast the detrended variability fairly well.Although most single models and their multi-model ensembles are able to hindcast the trend of the surface air temperature over East Asia,it is still difficult to hindcast the interannual and interdecadal variability.Wavelet analysis suggests that CMCC-CM model systems can hindcast the quasi-quadrennial oscillation reasonably well,which is associated with that of the NCEP data.Multimodel ensemble mean and BMA are not able to hindcast the periodic variation characteristics of the surface air temperature principal components.
keywords:surface air temperature  CMIP5  BMA  interannual variability  hindcast
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