Seasonal response of surface wind to SST perturbation in the Northern Hemisphere
- 4 Downloads
The seasonal response of surface wind speed to sea surface temperature (SST) change in the Northern Hemisphere was investigated using 10 years (2002–2011) high–resolution satellite observations and reanalysis data. The results showed that correlation between surface wind speed perturbations and SST perturbations exhibits remarkable seasonal variation, with more positive correlation is stronger in the cold seasons than in the warm seasons. This seasonality in a positive correlation between SST and surface wind speed is attributable primarily to seasonal changes of oceanic and atmospheric background conditions in frontal regions. The mean SST gradient and the prevailing surface winds are strong in winter and weak in summer. Additionally, the eddy–induced response of surface wind speed is stronger in winter than in summer, although the locations and numbers of mesoscale eddies do not show obvious seasonal features. The response of surface wind speed is apparently due to stability and mixing within the marine atmospheric boundary layer (MABL), modulated by SST perturbations. In the cold seasons, the stronger positive (negative) SST perturbations are easier to increase (decrease) the MABL height and trigger (suppress) momentum vertical mixing, contributing to the positive correlation between SST and surface wind speed. In comparison, SST perturbations are relatively weak in the warm seasons, resulting in a weak response of surface wind speed to SST changes. This result holds for each individual region with energetic eddy activity in the Northern Hemisphere.
Keywordseasonality positive correlation sea surface temperature (SST) gradient marine atmospheric boundary layer (MABL) height mesoscale eddy
Unable to display preview. Download preview PDF.
We thank Liwen Bianji, Edanz Group China (www.liwenbianji.cn), for editing the English text of a draft of this manuscript.
- Dee D P, Uppala S M, Simmons A J, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda M A, Balsamo G, Bauer P, Bechtold P, Beljaars A C M, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer A J, Haimberger L, Healy S B, Hersbach H, Hólm E V, Isaksen L, Kållberg P, Köhler M, Matricardi M, McNally A P, Monge–Sanz B M, Morcrette J J, Park B K, Peubey C, de Rosnay P, Tavolato C, Thépaut J–N, Vitart F. 2011. The ERA–Interim reanalysis: configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137(656): 553–597.CrossRefGoogle Scholar
- Ma X H, Jing Z, Chang P, Liu X, Montuoro R, Small R J, Bryan F O, Greatbatch R J, Brandt P, Wu D X, Lin X P, Wu L X. 2016b. Weatern boundary currents regulated by interaction between ocean eddies and the atmosphere. Nature, 535(7613): 533–537, https://doi.org/10.1038/nature18640.CrossRefGoogle Scholar
- Nakamura H, Sampe T, Tanimoto Y, Shimpo A. 2004. Observed associations among storm tracks, jet streams and midlatitude oceanic fronts. In: Earth’s Climate: The Ocean–Atmosphere Interaction. AGU, Washington, p.329–345.Google Scholar
- Small R J, Bacmeister J, Bailey D, Baker A, Bishop S, Bryan F, Caron J, Dennis J, Gent P, Hsu H M, Jochum M, Lawrence D, Muñoz E, diNezio P, Scheitlin T, Tomas R, Tribbia J, Tseng Y H, Vertenstein M. 2014. A new synoptic scale resolving global climate simulation using the Community Earth System Model. J. Adv. Model. Rarth Syst., 6(4): 1 065–1 094, https://doi.org/10.1002/2014MS000363.CrossRefGoogle Scholar