gchange  > 气候变化事实与影响
DOI: 10.1175/JCLI-D-13-00474.1
Scopus ID: 2-s2.0-84893853114
Title:
On the correspondence between mean forecast errors and climate errors in CMIP5 models
Author: Ma H.-Y.; Xie S.; Klein S.A.; Williams K.D.; Boyle J.S.; Bony S.; Douville H.; Fermepin S.; Medeiros B.; Tyteca S.; Watanabe M.; Williamson D.
Source Publication: Journal of Climate
ISSN: 8948755
Indexed By: EI ; SCI-E ; SCI ; Scopus
Publishing Year: 2014
Volume: 27, Issue:4
pages begin: 1781
pages end: 1798
Language: 英语
Subject in Chinese:
Subject: LEAD
English Abstract: The present study examines the correspondence between short- and long-term systematic errors in five atmospheric models by comparing the 16 five-day hindcast ensembles from the Transpose Atmospheric Model Intercomparison Project II (Transpose-AMIP II) for July-August 2009 (short term) to the climate simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and AMIP for the June- August mean conditions of the years of 1979-2008 (long term). Because the short-term hindcasts were conducted with identical climate models used in the CMIP5/AMIP simulations, one can diagnose over what time scale systematic errors in these climate simulations develop, thus yielding insights into their origin through a seamless modeling approach. The analysis suggests that most systematic errors of precipitation, clouds, and radiation processes in the long-term climate runs are present by day 5 in ensemble average hindcasts in all models. Errors typically saturate after few days of hindcasts with amplitudes comparable to the climate errors, and the impacts of initial conditions on the simulated ensemble mean errors are relatively small. This robust bias correspondence suggests that these systematic errors across different models likely are initiated by model parameterizations since the atmospheric large-scale states remain close to observations in the first 2-3 days. However, biases associated with model physics can have impacts on the large-scale states by day 5, such as zonal winds, 2-m temperature, and sea level pressure, and the analysis further indicates a good correspondence between shortand long-term biases for these large-scale states. Therefore, improving individual model parameterizations in the hindcast mode could lead to the improvement of most climate models in simulating their climate mean state and potentially their future projections. © 2014 American Meteorological Society.
Funding Project: DOE, U.S. Department of Energy ; DOE, U.S. Department of Energy
Citation statistics:
被引频次[WOS]:55   [查看WOS记录]     [查看WOS中相关记录]
Document Type: 期刊论文
Identifier: http://119.78.100.177/globalchange/handle/2HF3EXSE/50979
Appears in Collections:气候变化事实与影响

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Affiliation: Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, CA, United States; Met Office, Exeter, United Kingdom; L'Institut Pierre-Simon Laplace, Paris, France; Météo-France/CNRM, CNRS/GAME, Toulouse, France; National Center for Atmospheric Research, Boulder, CO, United States; Atmosphere and Ocean Research Institute, University of Tokyo, Tokyo, Japan

Recommended Citation:
Ma H.-Y.,Xie S.,Klein S.A.,et al. On the correspondence between mean forecast errors and climate errors in CMIP5 models[J]. Journal of Climate,2014-01-01,27(4)
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