Prediction of atmospheric rivers over the North Pacific and its connection to ENSO in the North American multi-model ensemble (NMME)

Yang Zhou, Hye Mi Kim

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


Prediction skills of the wintertime atmospheric rivers (ARs) and moisture flux over the Northeast Pacific in response to El Niño Southern Oscillation (ENSO) forcing are evaluated from the North American Multi-Model Ensemble (NMME) hindcasts (CFSv2, CCSM4, CanCM3, CanCM4, GEOS5, and GFDL CM2.1). The skill is estimated for the active AR season, December–February (DJF) with initial conditions around early November. Models underestimate the climatological moisture flux to different extents corresponding with various climatological biases in predictions of sea surface temperature (SST) and large-scale atmospheric circulation fields. The anomalous moisture flux and AR frequency over the Northeast Pacific are predicted in the models but in weaker amplitude than the reanalysis. Significant regional biases are shown in the anomalous landfalling AR frequency corresponding with ENSO, underlining the challenge in regional precipitation forecasts.

Original languageEnglish
Pages (from-to)1623-1637
Number of pages15
JournalClimate Dynamics
Issue number5-6
StatePublished - 1 Sep 2018

Bibliographical note

Funding Information:
Fig. 10 Zonally averaged climatological AR frequency over a the Northeast Pacific (180°E–120°W) and b the west coast of North America (blue contour in Fig. 8a) along 20°N–60°N. 5-point smoothing is applied with equal weighting in b Fig. 11 Same as Fig. 10b, except for anomalies during a El Niño and b La Niña winters Acknowledgements The authors thank the NMME program partners and acknowledge the help of NCEP, IRI and NCAR personnel in creating, updating and maintaining the NMME archive, with the support of NOAA, NSF, NASA and DOE. The authors thank Adam Herrington and Bonnie Stephens for proofreading. This study was supported by NOAA Grant NA15OAR4310078 and KMA Research and Development Program under Grant KMIPA 2016-6010.

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© 2017, Springer-Verlag GmbH Germany.


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