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Representing surface wind stress response to mesoscale SST perturbations in western coast of South America using Tikhonov regularization method

  • Chaoran Cui
  • Rong-Hua ZhangEmail author
  • Hongna Wang
  • Yanzhou Wei
Article
  • 4 Downloads

Abstract

Interaction between mesoscale perturbations of sea surface temperature (SSTmeso) and wind stress (WSmeso) has great influences on the ocean upwelling system and turbulent mixing in the atmospheric boundary layer. Using daily Quik-SCAT wind speed data and AMSR-E SST data, SSTmeso and WSmeso fields in the western coast of South America are extracted by using a locally weighted regression method (LOESS). The spatial patterns of SSTmeso and WSmeso indicate strong mesoscale SST-wind stress coupling in the region. The coupling coefficient between SSTmeso and WSmeso is about 0.009 5 N/(m2.°C) in winter and 0.008 2 N/(m2.°C) in summer. Based on mesoscale coupling relationships, the mesoscale perturbations of wind stress divergence (Div(WSmeso)) and curl (Curl (WSmeso)) can be obtained from the SST gradient perturbations, which can be further used to derive wind stress vector perturbations using the Tikhonov regularization method. The computational examples are presented in the western coast of South America and the patterns of the reconstructed WSmeso are highly consistent with SSTmeso, but the amplitude can be underestimated significantly. By matching the spatially averaged maximum standard deviations of reconstructed WSmeso magnitude and observations, a reasonable magnitude of WSmeso can be obtained when a rescaling factor of 2.2 is used. As current ocean models forced by prescribed wind cannot adequately capture the mesoscale wind stress response, the empirical wind stress perturbation model developed in this study can be used to take into account the feedback effects of the mesoscale wind stress-SST coupling in ocean modeling. Further applications are discussed for taking into account the feedback effects of the mesoscale coupling in large-scale climate models and the uncoupled ocean models.

Keyword

mesoscale air-sea coupling Tikhonov’s regularization method western coast of South America 

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Notes

Acknowledgment

The authors wish to thank the anonymous reviewers for their numerous comments that helped to improve the original manuscript.

References

  1. Albert A, Echevin V, Lévy M, Aumont O. 2010. Impact of nearshore wind stress curl on coastal circulation and primary productivity in the Peru upwelling system. J. Geophys. Res., 115(C12): C12033,  https://doi.org/10.1029/2010JC006569.CrossRefGoogle Scholar
  2. Bakun A. 1990. Global climate change and intensification of coastal ocean upwelling. Science, 247(4939): 198–201,  https://doi.org/10.1126/science.247.4939.198.CrossRefGoogle Scholar
  3. Bourras D, Reverdin G, Giordani H, Caniaux G. 2004. Response of the atmospheric boundary layer to a mesoscale oceanic eddy in the northeast Atlantic. J. Geophys. Res., 109(D18): D18114,  https://doi.org/10.1029/2004JD004799.CrossRefGoogle Scholar
  4. Bryan F O, Tomas R, Dennis J M, Chelton D B, Loeb N G, McClean J L. 2010. Frontal scale air-sea interaction in high-resolution coupled climate models. J. Climate, 23(23): 6 277–6 291,  https://doi.org/10.1175/2010JCLI3665.1.CrossRefGoogle Scholar
  5. Businger J A, Shaw W J. 1984. The response of the marine boundary layer to mesoscale variations in sea-surface temperature. Dyn. Atmos. Oceans, 8(3-4): 267–281,  https://doi.org/10.1016/0377-0265(84)90012-5.CrossRefGoogle Scholar
  6. Capet X, Colas F, McWilliams J C, Penven P, Marchesiello P. 2008. Eddies in eastern boundary subtropical upwelling systems. In: Hecht M W, Hasumi H eds. Ocean Modeling in an Eddying Regime, Volume 177. American Geophysical Union, Washington. 350p,  https://doi.org/10.1029/177GM10.CrossRefGoogle Scholar
  7. Castelao R M. 2012. Sea surface temperature and wind stress curl variability near a cape. J. Phys. Oceanogr., 42(11): 2 073–2 087,  https://doi.org/10.1175/JPO-D-11-0224.1.CrossRefGoogle Scholar
  8. Chelton D B, Esbensen S K, Schlax M G, Thum N, Freilich M H, Wentz F J, Gentemann C L, McPhaden M J, Schopf P S. 2001. Observations of coupling between surface wind stress and sea surface temperature in the eastern tropical pacific. J. Climate, 14(7): 1 479–1 498,  https://doi.org/10.1175/1520-0442(2001)014<1479:OOCBSW>2.0.CO;2.CrossRefGoogle Scholar
  9. Chelton D B, Schlax M G, Freilich M H, Milliff R F. 2004. Satellite measurements reveal persistent small-scale features in ocean winds. Science, 303(5660): 978–983,  https://doi.org/10.1126/science.1091901.CrossRefGoogle Scholar
  10. Chelton D B, Schlax M G, Samelson R M. 2007. Summertime coupling between sea surface temperature and wind stress in the California Current System. J. Phys. Oceanogr., 37(3): 495–517,  https://doi.org/10.1175/JPO3025.1.CrossRefGoogle Scholar
  11. Chelton D B, Xie S P. 2010. Coupled ocean-atmosphere interaction at oceanic mesoscales. Oceanography, 23(4): 52–69,  https://doi.org/10.5670/oceanog.2010.05.CrossRefGoogle Scholar
  12. Cleveland W S, Devlin S J. 1988. Locally weighted regression: an approach to regression analysis by local fitting. J. Am. Stat. Assoc, 83(403): 596–610,  https://doi.org/10.2307/2289282.CrossRefGoogle Scholar
  13. Colas F, McWilliams J C, Capet X, Kurian J. 2012. Heat balance and eddies in the Peru-Chile current system. Climate Dyn., 39(1-2): 509–529,  https://doi.org/10.1007/S00382-011-1170-6.CrossRefGoogle Scholar
  14. Davey M, Huddleston M, Sperber K, Braconnot P, Bryan F, Chen D, Colman R, Cooper C, Cubasch U, Delecluse P, DeWitt D, Fairhead L, Flato G, Gordon C, Hogan T, Ji M, Kimoto M, Kitoh A, Knutson T, Latif M, Treut Le H, Li T, Manabe S, Mechoso C, Power S, Roeckner E, Terray L, Vintzileos A, Voss R, Wang B, Washington W, Yoshikawa I, Yu J, Yukimoto S, Zebiak S, Meehl G. 2002. STOIC: a study of coupled model climatology and variability in tropical ocean regions. Climate Dyn., 18(5): 403–420,  https://doi.org/10.1007/s00382-001-0188-6.CrossRefGoogle Scholar
  15. Frenger I, Gruber N, Knutti R, Münnich M. 2013. Imprint of Southern Ocean eddies on winds, clouds and rainfall. Nat. Geosci., 6(8): 608–612.  https://doi.org/10.1038/ngeo1863.CrossRefGoogle Scholar
  16. Gao J X, Zhang R H, Wang H N. 2019. Mesoscale SST perturbation-induced impacts on climatological precipitation in the Kuroshio-Oyashio extension region, as revealed by the WRF simulations. J. Oceanol. Limnol., 37(2): 385–397,  https://doi.org/10.1007/s00343-019-8065-5.CrossRefGoogle Scholar
  17. Gaube P, Chelton D B, Samelson R M, Schlax M G, O’Neill L W. 2015. Satellite observations of mesoscale eddy-induced Ekman pumping. J. Phys. Oceanogr., 45(1): 104–132,  https://doi.org/10.1175/JPO-D-14-0032.1.CrossRefGoogle Scholar
  18. Giordani H, Planton S, Benech B, Kwon B H. 1998. Atmospheric boundary layer response to sea surface temperatures during the SEMAPHORE experiment. J. Geophys. Res., 103(C11): 25 047–25 060,  https://doi.org/10.1029/98JC00892.CrossRefGoogle Scholar
  19. Gruber N, Lachkar Z, Frenzel H, Marchesiello P, Münnich M, McWilliams J C, Nagai T, Plattner G K. 2011. Eddy-induced reduction of biological production in eastern boundary upwelling systems. Nat. Geosci., 4(11): 787–792,  https://doi.org/10.1038/ngeo1273.CrossRefGoogle Scholar
  20. Hoffman R N, Leidner S M. 2005. An introduction to the near-real-time QuikSCAT Data. Wea. Forecasting, 20(4): 476–493,  https://doi.org/10.1175/WAF84L1.CrossRefGoogle Scholar
  21. Jin X, Dong C M, Kurian J, McWilliams J C, Chelton D B, Li Z J. 2009. SST-wind interaction in coastal upwelling: oceanic simulation with empirical coupling. J. Phys. Oceanogr., 39(11): 2 957–2 970,  https://doi.org/10.1175/2009JPO4205.1.CrossRefGoogle Scholar
  22. Li Z J, Chao Y, McWilliams J C. 2006. Computation of the streamfunction and velocity potential for limited and irregular domains. Mon. Wea. Rev., 134(11): 3 384–3 394,  https://doi.org/10.1175/MWR3249.1.CrossRefGoogle Scholar
  23. Lynch P. 1989. Partitioning the wind in a limited domain. Mon. Wea.Rev., 117(7): 1 492–1 500,  https://doi.org/10.1175/1520-0493(1989)117<1492PTWIAL>2.0.CO;2.CrossRefGoogle Scholar
  24. 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. 2016. Western boundary currents regulated by interaction between ocean eddies and the atmosphere. Nature, 535(7613): 533–537,  https://doi.org/10.1038/nature18640.CrossRefGoogle Scholar
  25. Meehl G A, Covey C, McAvaney B, Latif M, Stouffer R J. 2005. Overview of the coupled model intercomparison project. Bull. Amer. Meteor. Soc., 86: 89–93.CrossRefGoogle Scholar
  26. Minobe S, Kuwano-Yoshida A, Komori N, Xie S P, Small R J. 2008. Influence of the Gulf Stream on the troposphere. Nature, 452(7184): 206–209,  https://doi.org/10.1038/nature06690.CrossRefGoogle Scholar
  27. O’Neill L W, Chelton D B, Esbensen S K, Wentz F J. 2005. High-resolution satellite measurements of the atmospheric boundary layer response to SST variations along the Agulhas return current. J. Climate, 18(14): 2 706–2 723,  https://doi.org/10.1175/JCLI3415.1.CrossRefGoogle Scholar
  28. O’Neill L W, Chelton D B, Esbensen S K. 2010a. The effects of SST-induced surface wind speed and direction gradients on midlatitude surface vorticity and divergence. J. Climate, 23(2): 255–281,  https://doi.org/10.1175/2009JCLI2613.1.CrossRefGoogle Scholar
  29. O’Neill L W, Chelton D B, Esbensen S K. 2012. Covariability of surface wind and stress responses to sea surface temperature fronts. J. Climate, 25(17): 5 916–5 942,  https://doi.org/10.1175/JCLI-D-11-00230.1.CrossRefGoogle Scholar
  30. O’Neill L W, Esbensen S K, Thum N, Samelson R M, Chelton D B. 2010b. Dynamical analysis of the boundary layer and surface wind responses to mesoscale SST perturbations. J. Climate, 23(3): 559–581,  https://doi.org/10.1175/2009JCLI2662.1.CrossRefGoogle Scholar
  31. O’Neill L W. 2012. Wind speed and stability effects on coupling between surface wind stress and SST observed from buoys and satellite. J. Climate, 25(5): 1 544–1 569,  https://doi.org/10.1175/JCLI-D-11-00121.1.CrossRefGoogle Scholar
  32. Oerder V, Colas F, Echevin V, Masson S, Hourdin C, Jullien S, Madec G, Lemarié F. 2016. Mesoscale SST-wind stress coupling in the Peru-Chile current system: which mechanisms drive its seasonal variability? Climate Dyn., 47(7-8): 2 309–2 330,  https://doi.org/10.1007/s00382-015-2965-7.CrossRefGoogle Scholar
  33. Penven P, Echevin V, Pasapera J, Colas F, Tarn J. 2005. Average circulation, seasonal cycle, and mesoscale dynamics of the Peru Current System: a modeling approach. J. Geophys. Res., 110(C10): C10021,  https://doi.org/10.1029/2005JC002945.CrossRefGoogle Scholar
  34. Piazza M, Terray L, Boé J, Maisonnave E, Sanchez-Gomez E. 2016. Influence of small-scale North Atlantic sea surface temperature patterns on the marine boundary layer and free troposphere: a study using the atmospheric ARPEGE model. Climate Dyn., 46(5-6): 1 699–1 717,  https://doi.org/10.1007/s00382-015-2669-z.CrossRefGoogle Scholar
  35. Renault L, Molemaker M J, McWilliams J C, Shchepetkin A F, Lemarié F, Chelton D B, Illig S, Hall A. 2016. Modulation of wind work by oceanic current interaction with the atmosphere. J. Phys. Oceanogr., 46(6): 1 685–1 704,  https://doi.org/10.1175/JPO-D-15-0232.1.CrossRefGoogle Scholar
  36. Seo H, Miller A J, Norris J R. 2016. Eddy-wind interaction in the California Current System: dynamics and impacts. J. Phys. Oceanogr., 46(2): 439–459,  https://doi.org/10.1175/JPO-D-15-0086.1.CrossRefGoogle Scholar
  37. Seo H. 2017. Distinct influence of air-sea interactions mediated by mesoscale sea surface temperature and surface current in the Arabian Sea. J. Climate, 30(20): 8 061–8 080,  https://doi.org/10.1175/JCLI-D-16-0834.1.CrossRefGoogle Scholar
  38. Small R J, DeSzoeke S P, Xie S P, O’Neill L, Seo H, Song Q, Cornillon P, Spall M, Minobe S. 2008. Air-sea interaction over ocean fronts and eddies. Dyn.Atmos. Oceans, 45(3-4): 274–319,  https://doi.org/10.1016/j.dynatmoce.2008.01.001.CrossRefGoogle Scholar
  39. Spall M A. 2007. Effect of sea surface temperature-wind stress coupling on baroclinic instability in the ocean. J. Phys. Oceanogr.37(4): 1 092–1 097,  https://doi.org/10.1175/JPO3045.1.CrossRefGoogle Scholar
  40. Strub P T, Mesias J M, Montecino V, Rutilant J, Salinas S. 1998. Coastal ocean circulation off western South America. In: Robinson A, Brink K eds. The Sea. Wiley, New York. p.29–67.Google Scholar
  41. Sweet W, Fett R, Kerling J, La Violette P. 1981. Air-sea interaction effects in the lower troposphere across the north wall of the Gulf-stream. Mon. Wea. Rev., 109(5): 1 042–1 052,  https://doi.org/10.1175/1520-0493(1981)109<1042ASIEIT>2.0.CO;2.CrossRefGoogle Scholar
  42. Tikhonov A N, Arsenin V Y. 1977. Solution of Ill-Posed Problems. Winston and Sons, Washington.Google Scholar
  43. Wei Y Z, Wang H N, Zhang R H. 2019. Mesoscale wind stress-SST coupled perturbations in the Kuroshio Extension. Prog. Oceanogr., 172: 108–123,  https://doi.org/10.1016/j.pocean.2019.01.012.CrossRefGoogle Scholar
  44. Wei Y Z, Zhang R H, Wang H N. 2017. Mesoscale wind stress- SST coupling in the Kuroshio extension and its effect on the ocean. J. Oceanogr., 73(6): 785–798,  https://doi.org/10.1007/s10872-017-0432-2.CrossRefGoogle Scholar
  45. Xie S P, Philander S G H. 1994. A coupled ocean-atmosphere model of relevance to the ITCZ in the eastern Pacific. Tellus A: Dyn. Meteor. Oceanogr., 46(4): 340–350,  https://doi.org/10.3402/tellusa.v46i4.15484.CrossRefGoogle Scholar
  46. Zhang R H, Busalacchi A J. 2008. Rectified effects of tropical instability wave (TIW)-induced atmospheric wind feedback in the tropical Pacific. Geophys. Res. Lett., 35(5): L05608,  https://doi.org/10.1029/2007GL033028.CrossRefGoogle Scholar
  47. Zhang R H, Busalacchi A J. 2009. An empirical model for surface wind stress response to SST forcing induced by tropical instability waves (TIWs) in the eastern equatorial pacific. Mon. Wea. Rev., 137(6): 2 021–2 046,  https://doi.org/10.1175/2008MWR2712.1.CrossRefGoogle Scholar
  48. Zhang R H, Gao C. 2016. The IOCAS intermediate coupled model (IOCAS ICM) and its real-time predictions of the 2015-16 El Niño event. Sci. Bull., 66(13): 1 061–1 070,  https://doi.org/10.1007/s11434-016-1064-4.CrossRefGoogle Scholar
  49. Zhang R H, Gao C. 2017. Processes involved in the second-year warming of the 2015 El Niño event as derived from an intermediate ocean model. Sci. China Earth Sci., 60(9): 1 601–1 613,  https://doi.org/10.1007/s11430-016-0201-9.CrossRefGoogle Scholar
  50. Zhang R H. 2016. A modulating effect of Tropical Instability Wave (TIW)-induced surface wind feedback in a hybrid coupled model of the tropical Pacific. J. Geophys. Res., 121(10): 7326–7353,  https://doi.org/10.1002/2015JC011567.CrossRefGoogle Scholar
  51. Zhu Y C, Zhang R H. 2018. An Argo-derived background diffusivity parameterization for improved ocean simulations in the tropical Pacific. Geophys. Res. Lett., 45(3): 1 509–1 517,  https://doi.org/10.1002/2017GL076269.CrossRefGoogle Scholar
  52. Zhu Y C, Zhang R H. 2019. A modified vertical mixing parameterization for its improved ocean and coupled simulations in the tropical Pacific. J. Phys. Oceanogr., 49(1): 21–37,  https://doi.org/10.1175/JPO-D-18-0100.1.CrossRefGoogle Scholar
  53. Zuidema P, Chang P, Medeiros B, Kirtman B P, Mechoso R, Schneider E K, Toniazzo T, Richter I, Small R J, Bellomo K, Brandt P, de Szoeke S, Farrar J T, Jung E, Kato S, Li M K, Patricola C, Wang Z Y, Wood R, Xu Z. 2016. Challenges and prospects for reducing coupled climate model SST biases in the eastern tropical Atlantic and Pacific oceans. Bull. Amer. Meteor. Soc., 97(12): 2 305–2 327,  https://doi.org/10.1175/BAMS-D-15-00274.1.CrossRefGoogle Scholar

Copyright information

© Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Chaoran Cui
    • 1
    • 2
    • 3
  • Rong-Hua Zhang
    • 1
    • 2
    • 3
    • 4
    Email author
  • Hongna Wang
    • 1
    • 2
    • 4
  • Yanzhou Wei
    • 5
  1. 1.Key Laboratory of Ocean Circulation and Waves, Institute of OceanologyChinese Academy of SciencesQingdaoChina
  2. 2.Center for Ocean Mega-ScienceChinese Academy of SciencesQingdaoChina
  3. 3.University of Chinese Academy of SciencesBeijingChina
  4. 4.Qingdao National Laboratory for Marine Science and TechnologyQingdaoChina
  5. 5.State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of OceanographyMinistry of Natural ResourcesHangzhouChina

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