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Type Classification of Sudden Stratospheric Warming Based on Pre- and Postwarming Periods

Cited 2 time in wos
Cited 2 time in scopus
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Title
Type Classification of Sudden Stratospheric Warming Based on Pre- and Postwarming Periods
Other Titles
성층권 돌연승온 발생 전후 기간에 근거한 유형 분류
Authors
Choi, Hyesun
Kim, Baek-Min
Choi, Wookap
Subject
Meteorology & Atmospheric Sciences
Keywords
Arctic; Waves; atmospheric; Atmosphere; Stratosphere-troposphere coupling; Stratosphere
Issue Date
2019-04
Citation
Choi, Hyesun, Kim, Baek-Min, Choi, Wookap. 2019. "Type Classification of Sudden Stratospheric Warming Based on Pre- and Postwarming Periods". JOURNAL OF CLIMATE, 32(8): 2349-2367.
Abstract
In existing literature, sudden stratospheric warming (SSW) events have been typically defined as displacement or split types. Detailed reexamination of SSW evolution has revealed that an SSW event often alters its type before and after the central day of the warming event. On the basis of this observation, we objectively define three types of SSW using wave amplitude: displacement?displacement (DD) type, displacement?split (DS) type, and split?split (SS) type. The geopotential height (GPH) amplitude of zonal wavenumbers 1 and 2 averaged over 55°?65°N at 10 hPa was used as a criterion for the classification. If the amplitude of zonal wavenumber 1 is larger (smaller) than that of wavenumber 2 before and after the central day of SSW, the event is regarded as a DD (SS) type. If the amplitude of zonal wavenumber 1 is larger than that of wavenumber 2 before the central day but is smaller after that day, the event is regarded as a DS type. The above classification algorithm has been applied to both reanalysis data and model results. We observe that conventional split-type SSW events identified by previous studies can be categorized as either DS- or SS-type events, each type of which exhibits different evolution characteristics. In particular, they are distinctively different during the pre-warming period. In the SS type, the characteristics of the conventional split type are more obvious, and the features that differ from those of the DD type are the most robust. The model results generally resemble the reanalysis data, particularly in the DD cases.
URI
https://repository.kopri.re.kr/handle/201206/10893
DOI
http://dx.doi.org/10.1175/JCLI-D-18-0223.1
Appears in Collections  
2019-2019, Development and Application of the Korea Polar Prediction System (KPOPS) for Climate Change and Disasterous Weather Events (19-19) / Kim, Joo-Hong (PE19130)
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