Journal
JOURNAL OF CLIMATE
Volume 36, Issue 6, Pages 1863-1879Publisher
AMER METEOROLOGICAL SOC
DOI: 10.1175/JCLI-D-22-0452.1
Keywords
Teleconnections; Jets; Intraseasonal variability; Clustering; Machine learning; Neural networks
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Previous research on intraseasonal variability of the Wintertime Pacific jet has mainly used EOF/PC analysis to characterize two leading modes of variability: zonal extension or retraction and a -20 degrees meridional shift of the jet's exit region. However, the variability within the jet and the relative importance of tropical and extratropical processes in driving such variability are not well understood. In this study, SOM analysis is applied to 73 Northern Hemisphere cold seasons of 250-hPa zonal winds to identify 12 characteristic physical jet states, shedding light on the underlying physical processes and downstream impacts of Pacific jet variability.
Previous research regarding the intraseasonal variability of the wintertime Pacific jet has employed empiri-cal orthogonal function (EOF)/principal component (PC) analysis to characterize two leading modes of variability: a zonal extension or retraction and a -20 degrees meridional shift of the jet exit region. These leading modes are intimately tied to the large-scale structure, sensible weather phenomena, and forecast skill in and around the vast North Pacific basin. However, variability within the wintertime Pacific jet and the relative importance of tropical and extratropical processes in driving such variability, is poorly understood. Here, a self-organizing maps (SOM) analysis is applied to 73 Northern Hemisphere cold seasons of 250-hPa zonal winds from the NCEP-NCAR reanalysis data to identify 12 characteristic physical jet states, some of which resemble the leading EOF Pacific jet patterns and combinations of them. Examination of teleconnection patterns such as El Nin similar to o-Southern Oscillation (ENSO) and the Madden-Julian oscillation (MJO) provide insight into the varying nature of the 12 SOM nodes at inter-and intraseasonal time scales. These relationships suggest that the hitherto more common EOF/PC analysis of jet variability obscures important subtleties of jet structure, revealed by the SOM analy-sis, which bear on the underlying physical processes associated with Pacific jet variability as well as the nature of its down-stream impacts.
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