The Role of Air–Sea Interactions in Atmospheric Rivers: Case Studies Using the SKRIPS Regional Coupled Model
The Role of Air–Sea Interactions in Atmospheric Rivers: Case Studies Using the SKRIPS Regional Coupled Model
复制标题
海气相互作用在大气河流中的作用:使用 SKRIPS 区域耦合模型的案例研究
DOI:
10.1029/2020jd032885
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发表时间:
2021
期刊:
影响因子:
--
通讯作者:
Hoteit, Ibrahim
中科院分区:
文献类型:
--
作者:
Sun, Rui;Subramanian, Aneesh C.;Cornuelle, Bruce D.;Mazloff, Matthew R.;Miller, Arthur J.;Ralph, F. Martin;Seo, Hyodae;Hoteit, Ibrahim
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%.
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影响因子:
4.6
作者:
Yang Zhou;Hyemi Kim
通讯作者:
Hyemi Kim
影响因子:
5.1
作者:
Rui Sun;A. Subramanian;A. Miller;M. Mazloff;I. Hoteit;B. Cornuelle
通讯作者:
B. Cornuelle
影响因子:
5.2
作者:
M. Nayak;G. Villarini;D. Lavers
通讯作者:
M. Nayak;G. Villarini;D. Lavers
影响因子:
4.9
作者:
Banzon, Viva F.;Reynolds, Richard W.;Xue, Yan
通讯作者:
Xue, Yan
影响因子:
3.8
作者:
DeFlorio, Michael J.;Waliser, Duane E.;Vitart, Frederic
通讯作者:
Vitart, Frederic