Sub-seasonal to Seasonal Hindcasts of Stratospheric SuddenWarming by BCC_CSM1.1(m): A Comparison with ECMWF View Full Text


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Article Info

DATE

2019-05

AUTHORS

Jian Rao, Rongcai Ren, Haishan Chen, Xiangwen Liu, Yueyue Yu, Yang Yang

ABSTRACT

This study focuses on model predictive skill with respect to stratospheric sudden warming (SSW) events by comparing the hindcast results of BCC_CSM1.1(m) with those of the ECMWF’s model under the sub-seasonal to seasonal prediction project of the World Weather Research Program and World Climate Research Program. When the hindcasts are initiated less than two weeks before SSW onset, BCC_CSM and ECMWF show comparable predictive skill in terms of the temporal evolution of the stratospheric circumpolar westerlies and polar temperature up to 30 days after SSW onset. However, with earlier hindcast initialization, the predictive skill of BCC_CSM gradually decreases, and the reproduced maximum circulation anomalies in the hindcasts initiated four weeks before SSW onset replicate only 10% of the circulation anomaly intensities in observations. The earliest successful prediction of the breakdown of the stratospheric polar vortex accompanying SSW onset for BCC_CSM (ECMWF) is the hindcast initiated two (three) weeks earlier. The predictive skills of both models during SSW winters are always higher than that during non-SSW winters, in relation to the successfully captured tropospheric precursors and the associated upward propagation of planetary waves by the model initializations. To narrow the gap in SSW predictive skill between BCC_CSM and ECMWF, ensemble forecasts and error corrections are performed with BCC_CSM. The SSW predictive skill in the ensemble hindcasts and the error corrections are improved compared with the previous control forecasts. More... »

PAGES

479-494

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00376-018-8165-8

    DOI

    http://dx.doi.org/10.1007/s00376-018-8165-8

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112965226


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    Turtle is a human-readable linked data format.

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    RDF/XML is a standard XML format for linked data.

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