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Evaluating the MJO prediction skill from different configurations of NCEP GEFS extended forecast

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Abstract

NOAA is accelerating its efforts to improve the numerical guidance and prediction capability for extended range (weeks 3 and 4) prediction in its seamless forecast system. Madden Julian Oscillation (MJO) is the dominant mode of sub-seasonal variability in tropics and prediction skill of MJO is investigated in this paper. We used different configurations of the NCEP Global Ensemble Forecast System (GEFS) to perform the experiments. The configurations include (1) the operational version of the stochastic perturbation forced with operational Sea Surface Temperatures (SSTs); (2) an updated stochastic physics forced with operational SSTs; (3) an updated stochastic physics forced with bias-corrected SSTs that are from Climate Forecast System (Version 2); and (4) as in (3) but with the addition of a scale aware-convection scheme. We evaluated MJO prediction skill from the experiments using Wheeler–Hendon indices and also examined the performance of the forecast system on prediction of key MJO components. We found that using the updated stochastic scheme improved the MJO prediction lead-time by about 4 days. Further updating the underlying SSTs with the bias corrected CFSv2 forecast increased the MJO prediction lead time by another 1.7 days. The best configuration of the four experiments is the last configuration which extends forecast lead time by ~ 9 days. Further investigation shows that upper and lower level zonal wind over the tropics has larger improvement than the outgoing longwave radiation (OLR). The improvement of the MJO prediction skill appears to be related primarily to the improvement in the representation associated circulations and OLR over the tropical West Pacific.

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b and e are from Fig.7a and b in Zhu et al. 2018

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Acknowledgements

The authors would like to thank Drs. Walter Kolczynski and Bing Fu for their help on stochastic physics perturbation settings. We really appreciate Dr. Shuguang Wang for the valuable discussion and constructive suggestion on the figures. We thank Drs. Qin Zhang, Wanqiu Wang and Ping Liu for providing valuable discussion on MJO. The authors are grateful to Dr. Xingren Wu on SST experiment discussion. We also thank Drs. Partha Bhattacharjee and Jack Kain for the EMC internal review and the two anonymous reviewers for their insightful comments that helped to improve this manuscript. This study is partially supported through NWS OSTI and NOAA’s Climate Program Office (CPO)’s Modeling, Analysis, Predictions, and Projections (MAPP) program.

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Authors and Affiliations

  1. EMC, NCEP, NWS, NOAA, College Park, MD, 20740, USA

    Yuejian Zhu & Dingchen Hou

  2. IMSG at EMC, NCEP, NWS, NOAA, College Park, MD, 20740, USA

    Wei Li, Xiaqiong Zhou, Eric Sinsky, Christopher Melhauser & Richard Wobus

  3. Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT, 06269, USA

    Malaquias Peña

  4. SRG at EMC, NCEP, NWS, NOAA, College Park, MD, 20740, USA

    Hong Guan

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  1. Wei Li
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Correspondence to Wei Li.

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Li, W., Zhu, Y., Zhou, X. et al. Evaluating the MJO prediction skill from different configurations of NCEP GEFS extended forecast. Clim Dyn 52, 4923–4936 (2019). https://doi.org/10.1007/s00382-018-4423-9

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