Publication Date:
2022-05-27
Description:
© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Sun, R., Subramanian, A. C., Cornuelle, B. D., Mazloff, M. R., Miller, A. J., Ralph, F. M., Seo, H., & Hoteit, I. The role of air-sea interactions in atmospheric rivers: Case studies using the SKRIPS regional coupled model. Journal of Geophysical Research: Atmospheres, 126(6), (2021): e2020JD032885, https://doi.org/10.1029/2020JD032885.
Description:
Atmospheric rivers (ARs) play a key role in California's water supply and are responsible for most of the extreme precipitation and major flooding along the west coast of North America. Given the high societal impact, it is critical to improve our understanding and prediction of ARs. This study uses a regional coupled ocean–atmosphere modeling system to make hindcasts of ARs up to 14 days. Two groups of coupled runs are highlighted in the comparison: (1) ARs occurring during times with strong sea surface temperature (SST) cooling and (2) ARs occurring during times with weak SST cooling. During the events with strong SST cooling, the coupled model simulates strong upward air–sea heat fluxes associated with ARs; on the other hand, when the SST cooling is weak, the coupled model simulates downward air–sea heat fluxes in the AR region. Validation data shows that the coupled model skillfully reproduces the evolving SST, as well as the surface turbulent heat transfers between the ocean and atmosphere. The roles of air–sea interactions in AR events are investigated by comparing coupled model hindcasts to hindcasts made using persistent SST. To evaluate the influence of the ocean on ARs we analyze two representative variables of AR intensity, the vertically integrated water vapor (IWV) and integrated vapor transport (IVT). During strong SST cooling AR events the simulated IWV is improved by about 12% in the coupled run at lead times greater than one week. For IVT, which is about twice more variable, the improvement in the coupled run is about 5%.
Description:
The authors gratefully acknowledge the research funding (grant number: OSR-2-16-RPP-3268.02) from KAUST (King Abdullah University of Science and Technology). The authors also appreciate the computational resources on supercomputer Shaheen II and the assistance provided by KAUST Supercomputer Laboratory. Additional funding from the NSF (OCE2022846, and OCE2022868) and the National Oceanic and Atmospheric Administration (MAPP NA17OAR4310106 and NA17OAR4310255) is also greatly appreciated. This study is also supported by the U.S. Army Corps of Engineers (USACE)-Cooperative Ecosystem Studies Unit (CESU) as part of Forecast Informed Reservoir Operations (FIRO) under grant W912HZ-15-2-0019. The authors thank Caroline Papadopoulos for important technical support when installing software and using the Shaheen II cluster.
Repository Name:
Woods Hole Open Access Server
Type:
Article
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