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Prediction skill of monthly SST in the North Atlantic Ocean in NCEP Climate Forecast System version 2

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Abstract

This work evaluates the skill of retrospective predictions of the second version of the NCEP Climate Forecast System (CFSv2) for the North Atlantic sea surface temperature (SST) and investigates the influence of El Niño-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) on the prediction skill over this region. It is shown that the CFSv2 prediction skill with 0–8 month lead displays a “tripole”-like pattern with areas of higher skills in the high latitude and tropical North Atlantic, surrounding the area of lower skills in the mid-latitude western North Atlantic. This “tripole”-like prediction skill pattern is mainly due to the persistency of SST anomalies (SSTAs), which is related to the influence of ENSO and NAO over the North Atlantic. The influences of ENSO and NAO, and their seasonality, result in the prediction skill in the tropical North Atlantic the highest in spring and the lowest in summer. In CFSv2, the ENSO influence over the North Atlantic is overestimated but the impact of NAO over the North Atlantic is not well simulated. However, compared with CFSv1, the overall skills of CFSv2 are slightly higher over the whole North Atlantic, particularly in the high latitudes and the northwest North Atlantic. The model prediction skill beyond the persistency initially presents in the mid-latitudes of the North Atlantic and extends to the low latitudes with time. That might suggest that the model captures the associated air-sea interaction in the North Atlantic. The CFSv2 prediction is less skillful than that of SSTA persistency in the high latitudes, implying that over this region the persistency is even better than CFSv2 predictions. Also, both persistent and CFSv2 predictions have relatively low skills along the Gulf Stream.

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Acknowledgments

We appreciate the helps of Mingyue Chen in getting and processing the hindcast data. Thanks also go to two reviewers and Yan Xue for their constructive comments and suggestions, which make the manuscript improved significantly. B. Huang and J. Zhu are supported by the COLA omnibus grant from NSF (ATM-0830068), NOAA (NA09OAR4310058) and NASA (NNX09AN50G).

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Authors and Affiliations

  1. Climate Prediction Center, NCEP/NWS/NOAA, 5200 Auth Road (Suite 605), Camp Springs, MD, 20746, USA

    Zeng-Zhen Hu, Arun Kumar, Wanqiu Wang & Caihong Wen

  2. Department of Atmospheric, Oceanic, and Earth Sciences, College of Science, Gorge Mason University, 4400 University Drive, Fairfax, VA, 22030, USA

    Bohua Huang

  3. Center for Ocean-Land-Atmosphere Studies, 4041 Powder Mill Road, #302, Calverton, MD, 20705, USA

    Bohua Huang & Jieshun Zhu

  4. WYLE Science, Technology and Engineering Group, McLean, VA, USA

    Caihong Wen

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  1. Zeng-Zhen Hu
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  2. Arun Kumar
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  3. Bohua Huang
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  4. Wanqiu Wang
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Correspondence to Zeng-Zhen Hu.

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Hu, ZZ., Kumar, A., Huang, B. et al. Prediction skill of monthly SST in the North Atlantic Ocean in NCEP Climate Forecast System version 2. Clim Dyn 40, 2745–2759 (2013). https://doi.org/10.1007/s00382-012-1431-z

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  • DOI: https://doi.org/10.1007/s00382-012-1431-z

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