One of the primary meteorological causes of the winter precipitation deficits and droughts in California (CA) is anomalous developments and maintenance of upper-tropospheric ridges over the northeastern Pacific. In order to understand and find the key factors controlling the winter precipitation variability in CA, the present study examines two dominant atmospheric modes of the 500 hPa geopotential height in the Northern Hemisphere using an Empirical Orthogonal Function (EOF) and their associated large-scale circulation patterns for the last 41 winters (1974/75–2014/15). Explaining 17.5% of variability, the second mode (EOF2) shows strong anti-cyclonic circulations in the North Pacific and cyclonic circulations in the eastern USA and mid-latitude North Atlantic, similar to the atmospheric circulation observed in the 2013/14 drought of CA. EOF2 is tightly and significantly correlated with CA winter precipitation. EOF2 is associated with warm western-cool eastern tropical Pacific, resembling a mirror image of canonical El Niño events. In particular, it is found that, since the mid-1990s, sea surface temperatures (SSTs) in the western tropical Pacific have been more tightly correlated with EOF2 and with the variability of CA precipitation. A diagnostic regression model based on the west-east SST difference in the tropical Pacific developed for two recent decades (1994/95–2014/15) has been found to capture the slow-moving interannual variability of the CA winter precipitation (about 50%). The regression model performs well, especially for the central and northern CA precipitation, where the impacts of El Niño Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) on precipitation are indecisive. Our results emphasize the significant role of the western tropical Pacific SST forcing in the recent past, and in turn on CA droughts and potentially other precipitation extremes.
Myoung, B.; Yeh, S.-W.; Kim, J.; Kafatos, M. C. Impacts of Pacific SSTs on Atmospheric Circulations Leading to California Winter Precipitation Variability: A Diagnostic Modeling. Atmosphere 2018, 9, 455; doi:10.3390/atmos9110455
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