Intermediate frequency atmospheric disturbances: A dynamical bridge connecting western U.S. extreme precipitation with East Asian cold surges

Tianyu Jiang, Katherine J. Evans, Yi Deng, Xiquan Dong

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

In this study, an atmospheric river (AR) detection algorithm is developed to investigate the downstream modulation of the eastern North Pacific ARs by another weather extreme, known as the East Asian cold surge (EACS), in both reanalysis data and high-resolution global model simulations. It is shown that following the peak of an EACS, atmospheric disturbances of intermediate frequency (IF; 10–30 day period) are excited downstream. This leads to the formation of a persistent cyclonic circulation anomaly over the eastern North Pacific that dramatically enhances the AR occurrence probability and the surface precipitation over the western U.S. between 30°N and 50°N. A diagnosis of the local geopotential height tendency further confirms the essential role of IF disturbances in establishing the observed persistent anomaly. This downstream modulation effect is then examined in the two simulations of the National Center for Atmospheric Research Community Climate System Model version 4 with different horizontal resolutions (T85 and T341) for the same period (1979–2005). The connection between EACS and AR is much better captured by the T341 version of the model, mainly due to a better representation of the scale interaction and the characteristics of IF atmospheric disturbances in the higher-resolution model. The findings here suggest that faithful representations of scale interaction in a global model are critical for modeling and predicting the occurrences of hydrological extremes in the western U.S. and for understanding their potential future changes.

Original languageEnglish
Pages (from-to)3723-3735
Number of pages13
JournalJournal of Geophysical Research: Biogeosciences
Volume119
Issue number7
DOIs
StatePublished - Apr 16 2014

Funding

The data for this paper will soon be open access, please contact the author ([email protected]) for more detail. We thank Charles Jackson and an anonymous reviewer for their very helpful comments. The MERRA data used in this study was provided through the NASA GSFC. Jiang and Evans were funded through the DOE Office of Science, Biological and Environmental Research project 3ERKPE746, “Ultra High Resolution Global Climate Simulation to Explore and Quantify Predictive Skill for Climate Means, Variability, and Extremes.” Deng and Jiang were also supported by the NASA Energy and Water Cycle Study under grant NNX09AJ36G and by the DOE Office of Science Regional and Global Climate Modeling program under grant DE-SC0005596. This project used the resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the DOE Office of Science under contract DE-AC05-00OR22725.

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