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Deterministic prediction of stratospheric sudden warming events in the Global/Regional Integrated Model system (GRIMs)

Song, K., Son, S.-W. and Charlton-Perez, A. (2020) Deterministic prediction of stratospheric sudden warming events in the Global/Regional Integrated Model system (GRIMs). Climate Dynamics. ISSN 0930-7575

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To link to this item DOI: 10.1007/s00382-020-05320-4

Abstract/Summary

The boreal-winter stratospheric sudden warming (SSW) events and their prediction skills by an operational numerical weather prediction model are examined by applying the Global/Regional Integrated Model system (GRIMs) for 18 SSW events from 1980–2012. Based on the mean squared skill score of the 10-hPa geopotential height field, which considers the SSW spatial structure, most SSW events are predicted with a maximum forecast lead time of approximately 15 days. The vortex-displacement SSW events are slightly better predicted than the vortex-split SSW events, and the predictions are improved during El Niño or easterly quasi-biennial oscillation winters. However, the skill difference in vortex morphology and background state is statistically insignificant. The decomposition of model errors into zonal-mean and eddy errors reveals that the model errors mostly result from eddy components. In particular, eddy-amplitude errors, which originate from a misrepresentation of the planetary-scale wave amplitude (i.e., polar vortex strength), play an important role in determining the SSW prediction skill with a non-negligible contribution from eddy-phase errors. These errors are mostly caused by the misdetection of short-term wave activities immediately before the SSW onset. Furthermore, an improved SSW prediction through well-represented planetary-scale wave activities is related to an improved prediction in the troposphere on time scales of 10 days and longer. This result confirms that better representation of the stratosphere could lead to improved subseasonal- to-seasonal predictions in the northern extratropics.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:91320
Publisher:Springer

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