Abstract
This study provided a quantitative evaluation of convectively coupled Kelvin waves (CCKWs) over the Indian Ocean and the Pacific Ocean simulated by 20 coupled climate models that participated in Coupled Model Intercomparison Project phase 5. The two leading empirical orthogonal function (EOF) modes of filtered daily precipitation anomalies are used to represent the eastward propagating CCKWs in both observations and simulations. The eigenvectors and eigenvalues of the EOF modes represent the spatial patterns and intensity of CCKWs respectively, and the lead–lag relationship between the two EOF principle components describe the phase propagation of CCKWs. A non-dimensional metric was designed in consideration of all the three factors (i.e., pattern, amplitude and phase propagation) for evaluation. The relative rankings of the models based on the skill scores calculated by the metric are conducted for the Indian Ocean and the Pacific Ocean, respectively. Two models (NorESM1-M and MPI-ESM-LR) are ranked among the best 20 % for both the regions. Three models (inmcm4, MRI-CGCM3 and HadGEM2-ES) are ranked among the worst 20 % for both the regions. While the observed CCKW amplitude is greater north of the equator in the Pacific, some models overestimate the CCKW ampliutde in the Southern Hemisphere. This bias is related to the mean state precipitation bias along the south Pacific convergence zone.
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Acknowledgments
This study is jointly supported by China National 973 Project 2015CB453200, NSFC Grant 41475084, ONR Grant N00014-16-12260, Jiangsu Natural Science Foundation Key project (BK20150062), Jiangsu Shuang-Chuang Team (R2014SCT001), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) and the International Pacific Research Center sponsored partially by the Japan Agency for Marine-Earth Science and Technology (JAMSTEC). This is SOEST Contribution Number 9609, IPRC Contribution Number 1184 and ESMC Number 100.
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Wang, L., Li, T. Convectively coupled Kelvin waves in CMIP5 coupled climate models. Clim Dyn 48, 767–781 (2017). https://doi.org/10.1007/s00382-016-3109-4
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DOI: https://doi.org/10.1007/s00382-016-3109-4