Abstract
The ability of coupled general circulation models (CGCMs) to simulate a newly-discovered climate mode called Ningaloo Niño/Niña is examined using outputs from 17 CGCMs that participated in the Coupled Model Inter-comparison Project, phase 5. It is shown that 8 out of 17 models reproduce sea surface temperature (SST) anomalies associated with Ningaloo Niño/Niña with a pattern correlation coefficient of 0.7 or higher, but the location of peak SST anomalies is very different from the observation in 2 models. Also, fundamental features such as the seasonality and associated anomalous atmospheric circulation and precipitation are fairly well reproduced in most models, but the amplitude varies significantly among the models. The inter-model difference in the amplitude is found to be mainly due to that in the magnitude of remote influences from the El Niño-Southern Oscillation via oceanic and atmospheric teleconnections. On the other hand, the strength of local air-sea interaction does not contribute much to the inter-model difference. This study may pave the way for an improved representation of Ningaloo Niño/Niña in CGCMs.
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Acknowledgments
Constructive comments from two anonymous reviewers helped us to improve our manuscript. The first author is supported by Research Fellowship of Leading Graduate Course for Frontiers of Mathematical Sciences and Physics, MEXT, Japan. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. For CMIP, the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. NCAR Command Language (NCL) is extensively used for data processing and visualization.
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Kido, S., Kataoka, T. & Tozuka, T. Ningaloo Niño simulated in the CMIP5 models. Clim Dyn 47, 1469–1484 (2016). https://doi.org/10.1007/s00382-015-2913-6
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DOI: https://doi.org/10.1007/s00382-015-2913-6