Westward mountain-gap wind jets of the northern Red Sea as seen by QuikSCAT

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2018-03-19
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Menezes, Viviane V.
Farrar, J. Thomas
Bower, Amy S.
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10.1016/j.rse.2018.02.075
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QuikSCAT
Air-sea interaction
Wind jets
Mountain gap
Evaporation
Heat loss
Abstract
We analyse ten years of QuikSCAT satellite surface winds to statistically characterize the spatio-temporal variability of the westward mountain-gap wind jets over the northern Red Sea. These wind jets bring relatively cold and dry air from the Arabian Desert, increasing heat loss and evaporation over the region similar to cold-air outbreaks from mid and subpolar latitudes. QuikSCAT captures the spatial structure of the wind jets and agrees well with in situ observations from a heavily instrumented mooring in the northern Red Sea. The local linear correlations between QuikSCAT and in situ winds are 0.96 (speed) and 0.85 (direction). QuikSCAT also reveals that cross-axis winds such as the mountain-gap wind jets are a major component of the regional wind variability. The cross-axis wind pattern appears as the second (or third) mode in the four vector Empirical Orthogonal Function analyses we performed, explaining between 6% to 11% of the wind variance. Westward wind jets are typical in winter, especially in December and January, but with strong interannual variability. Several jets can occur simultaneously and cover a large latitudinal range of the northern Red Sea, which we call large-scale westward events. QuikSCAT recorded 18 large-scale events over ten years, with duration between 3 to 8 days and strengths varying from 3–4 to 9–10 m/s. These events cause large changes in the wind stress curl pattern, imposing a remarkable sequence of positive and negative curl along the Red Sea main axis, which might be a wind forcing mechanism for the oceanic mesoscale circulation.
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© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Remote Sensing of Environment 209 (2018): 677-699, doi:10.1016/j.rse.2018.02.075.
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Remote Sensing of Environment 209 (2018): 677-699
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