气候变化领域集成服务门户(CCPortal): Origins of the IOD-like biases in CMIP multimodel ensembles: The atmospheric component and ocean-atmosphere coupling

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DOI	10.1175/JCLI-D-20-0346.1
	Origins of the IOD-like biases in CMIP multimodel ensembles: The atmospheric component and ocean-atmosphere coupling
	Long S.-M.; Li G.; Hu K.; Ying J.
发表日期	2020
ISSN	0894-8755
起始页码	10437
结束页码	10453
卷号	33 期号:24
英文摘要	Previous studies reveal that the last generation of coupled general circulation models (CGCMs) commonly suffer from the so-called Indian Ocean dipole (IOD)-like biases, lowering the models' ability in climate prediction and projection. The present study shows that such IOD-like biases are reduced insignificantly or even worsen in CGCMs from phase 5 to phase 6 of the Coupled Model Intercomparison Project (CMIP). The origins of the IOD-like biases in CGCMs are further investigated by comparing model outputs from CMIP and the Atmospheric Model Intercomparison Project (AMIP). The CGCMs' errors are divided into the biases from the AMIP simulation (AMIP biases) and ocean-atmosphere coupling (coupling biases). For the multimodel ensemble mean, the AMIP (coupling) biases account for about two-thirds (one-third) of the IOD-like CMIP biases. In AMIP simulations, the South Asian summer monsoon (SASM) is overly strong; therefore, it could advect overly large easterly momentum from the south Indian Ocean (IO) to the equator. The resultant equatorial easterly wind bias would initiate the convection-circulation feedback and develop large IOD-like AMIP biases. In contrast, the coupling biases weaken the SASM and hence generate warm SST error over the western IO during boreal summer. Such SST error persists to boreal autumn and triggers the Bjerknes feedback, developing the IOD-like coupling biases. Furthermore, the intermodel spread in the IOD-like CMIP biases is largely explained by the intermodel differences in the coupling biases rather than the AMIP biases. The results imply that substantial efforts should be respectively made on reducing the atmospheric models' intrinsic monsoon biases as well as advancing the simulations of ocean-atmosphere coupling processes. © 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).
英文关键词	Atmospheric thermodynamics; Climate models; Errors; Asian summer monsoons; Atmospheric components; Atmospheric model intercomparison projects; Coupled general circulation models; Coupled Model Intercomparison Project; Indian ocean dipoles; Multi-model ensemble; South Indian Ocean; Oceanography; air-sea interaction; atmosphere-ocean coupling; climate modeling; CMIP; ensemble forecasting; error analysis; Indian Ocean Dipole; monsoon; numerical model; Indian Ocean
语种	英语
来源期刊	Journal of Climate
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/171521
作者单位	College of Oceanography, Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Key Laboratory of Ministry of Education for Coastal Disaster and Protection, Hohai University, Nanjing, China; State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China; State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China; Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
推荐引用方式 GB/T 7714	Long S.-M.,Li G.,Hu K.,et al. Origins of the IOD-like biases in CMIP multimodel ensembles: The atmospheric component and ocean-atmosphere coupling[J],2020,33(24).
APA	Long S.-M.,Li G.,Hu K.,&Ying J..(2020).Origins of the IOD-like biases in CMIP multimodel ensembles: The atmospheric component and ocean-atmosphere coupling.Journal of Climate,33(24).
MLA	Long S.-M.,et al."Origins of the IOD-like biases in CMIP multimodel ensembles: The atmospheric component and ocean-atmosphere coupling".Journal of Climate 33.24(2020).