• Argo, 2000: Argo float data and metadata from Global Data Assembly Centre (Argo GDAC). SEANOE, accessed 22 September 2020, http://doi.org/10.17882/42182.

    • Crossref
    • Export Citation
  • Balaguru, K., P. Chang, R. Saravanan, L. R. Leung, Z. Xu, M. Li, and J.-S. Hsieh, 2012: Ocean barrier layers’ effect on tropical cyclone intensification. Proc. Natl. Acad. Sci. USA, 109, 14 34314 347, https://doi.org/10.1073/pnas.1201364109.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Balaguru, K., G. R. Foltz, L. R. Leung, E. D. Asaro, K. A. Emanuel, H. Liu, and S. E. Zedler, 2015: Dynamic potential intensity: An improved representation of the ocean’s impact on tropical cyclones. Geophys. Res. Lett., 42, 67396746, https://doi.org/10.1002/2015GL064822.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bosart, L. F., W. E. Bracken, J. Molinari, C. S. Velden, and P. G. Black, 2000: Environmental influences on the rapid intensification of Hurricane Opal (1995) over the Gulf of Mexico. Mon. Wea. Rev., 128, 322352, https://doi.org/10.1175/1520-0493(2000)128<0322:EIOTRI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Byers, H. R., 1959: General Meteorology. McGraw-Hill, 540 pp.

  • Chen, S., J. A. Cummings, J. M. Schmidt, E. R. Sanabia, and S. R. Jayne, 2017: Targeted ocean sampling guidance for tropical cyclones. J. Geophys. Res. Oceans, 122, 35053518, https://doi.org/10.1002/2017JC012727.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Curcic, M., and B. K. Haus, 2020: Revised estimates of ocean surface drag in strong winds. Geophys. Res. Lett., e2020GL087647, https://doi.org/10.1029/2020GL087647.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • D’Asaro, E. A., T. B. Sanford, P. P. Niiler, and E. J. Terrill, 2007: Cold wake of hurricane Frances. Geophys. Res. Lett., 34, L15609, https://doi.org/10.1029/2007GL030160.

    • Search Google Scholar
    • Export Citation
  • D’Asaro, E. A., and et al. , 2014: Impact of typhoons on the ocean in the Pacific. Bull. Amer. Meteor. Soc., 95, 14051418, https://doi.org/10.1175/BAMS-D-12-00104.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeMaria, M., C. R. Sampson, J. A. Knaff, and K. D. Musgrave, 2014: Is tropical cyclone intensity guidance improving? Bull. Amer. Meteor. Soc., 95, 387398, https://doi.org/10.1175/BAMS-D-12-00240.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Domingues, R., and et al. , 2015: Upper ocean response to Hurricane Gonzalo (2014): Salinity effects revealed by targeted and sustained underwater glider observations. Geophys. Res. Lett., 42, 71317138, https://doi.org/10.1002/2015GL065378.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Domingues, R., and et al. , 2019: Ocean observations in support of studies and forecasts of tropical and extratropical cyclones. Front. Mar. Sci., 6, 446, https://doi.org/10.3389/fmars.2019.00446.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Donelan, M. A., and et al. , 2004: On the limiting aerodynamic roughness of the ocean in very strong winds. Geophys. Res. Lett., 31, L18306, https://doi.org/10.1029/2004GL019460.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dong, J., and et al. , 2017: Impact of assimilating underwater glider data on Hurricane Gonzalo (2014) forecasts. Wea. Forecasting, 32, 11431159, https://doi.org/10.1175/WAF-D-16-0182.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 1986: An air–sea interaction theory for tropical cyclones. Part I: Steady-state maintenance. J. Atmos. Sci., 43, 585605, https://doi.org/10.1175/1520-0469(1986)043<0585:AASITF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Goni, G., and et al. , 2009: Applications of satellite-derived ocean measurements to tropical cyclone intensity forecasting. Oceanography, 22, 190197, https://doi.org/10.5670/oceanog.2009.78.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haakman, K., J. M. Sayol, C. G. van der Boog, and C. A. Katsman, 2019: Statistical characterization of the observed cold wake induced by North Atlantic hurricanes. Remote Sens., 11, 2368, https://doi.org/10.3390/rs11202368.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Halliwell, G., L. K. Shay, J. K. Brewster, and W. J. Teague, 2011: Evaluation and sensitivity analysis to an ocean model to hurricane Ivan. Mon. Wea. Rev., 139, 921945, https://doi.org/10.1175/2010MWR3104.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jaimes, B., and L. K. Shay, 2009: Mixed layer cooling in mesoscale oceanic eddies during Hurricanes Katrina and Rita. Mon. Wea. Rev., 137, 41884207, https://doi.org/10.1175/2009MWR2849.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jaimes, B., and L. K. Shay, 2010: Near-inertial wave wake of Hurricanes Katrina and Rita over mesoscale oceanic eddies. J. Phys. Oceanogr., 40, 13201337, https://doi.org/10.1175/2010JPO4309.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jaimes, B., L. K. Shay, and E. W. Uhlhorn, 2015: Enthalpy and momentum fluxes during Hurricane Earl relative to underlying ocean features. Mon. Wea. Rev., 143, 111131, https://doi.org/10.1175/MWR-D-13-00277.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jaimes, B., L. K. Shay, and J. K. Brewster, 2016: Observed air-sea interactions in tropical cyclone Isaac over Loop Current mesoscale eddy features. Dyn. Atmos. Oceans, 76, 306324, https://doi.org/10.1016/j.dynatmoce.2016.03.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Judt, F., S. S. Chen, and M. Curcic, 2016: Atmospheric forcing of the upper ocean transport in the Gulf of Mexico: From seasonal to diurnal scales. J. Geophys. Res. Oceans, 121, 44164433, https://doi.org/10.1002/2015JC011555.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kossin, J. P., and M. DeMaria, 2016: Reducing operational hurricane intensity forecast errors during eyewall replacement cycles. Wea. Forecasting, 31, 601608, https://doi.org/10.1175/WAF-D-15-0123.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leben, R. R., 2005: Altimeter-derived loop current metrics. Circulation in the Gulf of Mexico: Observations and Models, W. Sturges and A. Lugo-Fernandez, Eds., Geophys. Monogr., Vol. 161, Amer. Geophys. Union, 181–201.

    • Crossref
    • Export Citation
  • Leipper, D. F., and D. Volgenau, 1972: Hurricane heat potential of the Gulf of Mexico. J. Phys. Oceanogr., 2, 218224, https://doi.org/10.1175/1520-0485(1972)002<0218:HHPOTG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Levitus, S., 1982: Climatological Atlas of the World Ocean. NOAA Prof. Paper 13, 173 pp.

  • Lin, I.-I., and et al. , 2003: New evidence for enhanced ocean primary production triggered by tropical cyclone. Geophys. Res. Lett., 30, 1718, https://doi.org/10.1029/2003GL017141.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, I.-I., C.-C. Wu, K. A. Emanuel, I.-H. Lee, C.-R. Wu, and I.-F. Pun, 2005: The interaction of Supertyphoon Maemi (2003) with a warm ocean eddy. Mon. Wea. Rev., 133, 26352649, https://doi.org/10.1175/MWR3005.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, I.-I., C.-H. Chen, I.-F. Pun, W. T. Liu, and C.-C. Wu, 2009: Warm ocean anomaly, air sea fluxes, and the rapid intensification of Tropical Cyclone Nargis (2008). Geophys. Res. Lett., 36, L03817, https://doi.org/10.1029/2008GL035815.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mainelli, M., M. DeMaria, L. K. Shay, and G. Goni, 2008: Application of oceanic heat content estimation to operational forecasting of recent Atlantic category 5 hurricanes. Wea. Forecasting, 23, 316, https://doi.org/10.1175/2007WAF2006111.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Malkus, J. S., 1962: Large-scale interactions. Physical Oceanography, M. N. Hill, Ed., The Sea—Ideas and Observations on Progress in the Study of the Seas, Vol. 1. John Wiley and Sons, 86–322.

  • McCaskill, C., L. K. Shay, J. K. Brewster, and P. C. Meyers, 2016: Development and assessment of the Systematically Merged Pacific Ocean Regional Temperature and Salinity (SPORTS) climatology for ocean heat content estimations. J. Atmos. Oceanic Technol., 33, 22592272, https://doi.org/10.1175/JTECH-D-15-0168.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meyers, P. C., L. K. Shay, and J. K. Brewster, 2014: Development and analysis of the systematically merged Atlantic regional temperature and salinity climatology for oceanic heat content estimates. J. Atmos. Oceanic Technol., 31, 131149, https://doi.org/10.1175/JTECH-D-13-00100.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meyers, P. C., L. K. Shay, J. K. Brewster, and B. Jaimes, 2016: Observed ocean thermal response to Hurricanes Gustav and Ike. J. Geophys. Res. Oceans, 121, 162179, https://doi.org/10.1002/2015JC010912.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Minnett, P. J., and et al. , 2019: Half a century of satellite remote sensing of sea-surface temperature. Remote Sens. Environ., 233, 111366, https://doi.org/10.1016/j.rse.2019.111366.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mrvaljevic, R. K., and et al. , 2013: Observations of the cold wake of Typhoon Fanapi (2010). Geophys. Res. Lett., 40, 316321, https://doi.org/10.1029/2012GL054282.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Potter, H., 2014: A study of turbulent processes at the air-sea interface in high wind speeds. Ph.D. dissertation, University of Miami, 233 pp.

  • Potter, H., 2018: The cold wake of Typhoon Chaba (2010). Deep-Sea Res. I, 140, 136141, https://doi.org/10.1016/j.dsr.2018.09.001.

  • Potter, H., H. C. Graber, N. J. Williams, C. O. Collins III, R. J. Ramos, and W. M. Drennan, 2015: In situ measurements of momentum fluxes in typhoons. J. Atmos. Sci., 72, 104118, https://doi.org/10.1175/JAS-D-14-0025.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Potter, H., W. M. Drennan, and H. C. Graber, 2017: Upper ocean cooling and air-sea fluxes under typhoons: A case study. J. Geophys. Res. Oceans, 122, 72377252, https://doi.org/10.1002/2017JC012954.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Potter, H., S. F. DiMarco, and A. H. Knap, 2019: Tropical cyclone heat potential and the rapid intensification of Hurricane Harvey in the Texas Bight. J. Geophys. Res. Oceans, 124, 24402451, https://doi.org/10.1029/2018JC014776.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Preisendorfer, R., and C. Mobley, 1988: Principal Component Analysis in Meteorology and Oceanography. Elsevier, 425 pp.

  • Price, J. F., 1981: Upper ocean response to a hurricane. J. Phys. Oceanogr., 11, 153175, https://doi.org/10.1175/1520-0485(1981)011<0153:UORTAH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Price, J. F., R. A. Weller, and R. Pinkel, 1986: Diurnal cycling: Observations and models of the upper ocean response to diurnal heating, cooling, and wind mixing. J. Geophys. Res., 91, 84118427, https://doi.org/10.1029/JC091iC07p08411.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rudzin, J. E., L. K. Shay, B. Jaimes, and J. K. Brewster, 2017: Upper ocean observations in eastern Caribbean Sea reveal barrier layer within a warm core eddy. J. Geophys. Res. Oceans, 122, 10571071, https://doi.org/10.1002/2016JC012339.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rudzin, J. E., L. K. Shay, and W. E. Johns, 2018: The influence of the barrier layer on SST response during tropical cyclone wind forcing using idealized experiments. J. Phys. Oceanogr., 48, 14711478, https://doi.org/10.1175/JPO-D-17-0279.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rudzin, J. E., L. K. Shay, and B. Jaimes de la Cruz, 2019: The impact of the Amazon–Orinoco River plume on enthalpy flux and air–sea interaction within Caribbean Sea tropical cyclones. Mon. Wea. Rev., 147, 931950, https://doi.org/10.1175/MWR-D-18-0295.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rudzin, J. E., S. Chen, E. R. Sanabia, and S. R. Jayne, 2020: The air–sea response during Hurricane Irma’s (2017) rapid intensification over the Amazon–Orinoco River plume as measured by atmospheric and oceanic observations. J. Geophys. Res. Atmos., 125, e2019JD032368, https://doi.org/10.1029/2019JD032368.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schade, L. R., and K. A. Emanuel, 1999: The ocean’s effect on the intensity of tropical cyclones: Results from a simple coupled atmosphere–ocean model. J. Atmos. Sci., 56, 642651, https://doi.org/10.1175/1520-0469(1999)056<0642:TOSEOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scharroo, R., W. H. F. Smith, and J. L. Lillibridge, 2005: Satellite altimetry and the intensification of Hurricane Katrina. Eos, Trans. Amer. Geophys. Union, 86, 366, https://doi.org/10.1029/2005EO400004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shay, L. K., 2006: Positive feedback regimes during tropical cyclone passage. 14th Conf. on the Interaction of the Sea and the Air, Atlanta, GA, Amer. Meteor. Soc., 10.7, https://ams.confex.com/ams/Annual2006/techprogram/paper_99462.htm.

  • Shay, L. K., and E. W. Uhlhorn, 2008: Loop current response to Hurricanes Isidore and Lili. Mon. Wea. Rev., 136, 32483274, https://doi.org/10.1175/2007MWR2169.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shay, L. K., G. J. Goni, and P. G. Black, 2000: Effects of a warm oceanic feature on Hurricane Opal. Mon. Wea. Rev., 128, 13661383, https://doi.org/10.1175/1520-0493(2000)128<1366:EOAWOF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thomson, R. E., and I. V. Fine, 2003: Estimating mixed layer depth from oceanic profile data. J. Atmos. Oceanic Technol., 20, 319329, https://doi.org/10.1175/1520-0426(2003)020<0319:EMLDFO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vargas, M. A., T. Miles, S. Glenn, P. Hogan, R. Watlington, and B. Lacour, 2019: Impact of glider data assimilation on the global ocean forecasting system during the 2018 Hurricane season. OCEANS 2019 MTS/IEEE SEATTLE, Seattle, WA, Institute of Electrical and Electronics Engineers, 5 pp., https://doi.org/10.23919/OCEANS40490.2019.8962824.

    • Crossref
    • Export Citation
  • Whitaker, W. D., 1967: Quantitative determination of heat transfer from sea to air during passage of Hurricane Betsy. Ph.D. dissertation, Texas A&M University, 18 pp.

    • Crossref
    • Export Citation
  • Wijesekera, H. W., and M. C. Gregg, 1996: Surface layer response to weak winds, westerly bursts, and rain squalls in the western Pacific Warm Pool. J. Geophys. Res. Oceans, 101, 977997, https://doi.org/10.1029/95JC02553.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wong, A., R. Keeley, and T. Carval, 2020: Argo quality control manual for CTD and trajectory data. Argo Rep., 73 pp., https://doi.org/10.13155/33951.

    • Crossref
    • Export Citation
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Upper-Ocean Temperature Variability in the Gulf of Mexico with Implications for Hurricane Intensity

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  • 1 a Department of Oceanography, Texas A&M University, College Station, Texas
  • | 2 b Marine Meteorology Division, Naval Research Laboratory, National Research Council, Monterey, California
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Abstract

Strong winds in tropical cyclones (TCs) mix the ocean, causing cooler water from below the thermocline to be drawn upward, reducing sea surface temperature (SST). This decreases the air–sea temperature difference, limits available heat energy, and impacts TC intensity. Part of TC forecast accuracy therefore depends upon the ability to predict sea surface cooling; however, it is not well understood how underlying ocean conditions contribute to this cooling. Here, ~4400 Argo profiles in the Gulf of Mexico were used in a principal component analysis to identify the modes of variability in upper-ocean temperature, and a 1D mixed layer model was used to determine how the modes respond to surface forcing. It was found that the first two modes explain 75% of the variance in the data, with high mode-1 scores being broadly characterized as having warm SST and deep mixed layer and mode-2 scores being characterized as having high SST and a shallow mixed layer. Both modes have distinct seasonal and spatial variability. When subjected to the same model forcing, mode-1- and mode-2-characteristic waters with equal tropical cyclone heat potential (TCHP) respond very differently. Mode-2 SST cools faster than mode 1, with the difference being most pronounced at lower wind speeds and when comparing early-season storms with late-season storms. The results show that using TCHP as a marker for SST response during TC forcing is insufficient because it does not fully capture subsurface ocean thermal structure. This result underscores the need for continual subsurface monitoring so as to accurately initialize the upper ocean in coupled TC models.

Potter’s ORCID: 0000-0003-0142-107X.

Rudzin’s ORCID: 0000-0002-9802-8663.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Henry Potter, hpotter@tamu.edu

Abstract

Strong winds in tropical cyclones (TCs) mix the ocean, causing cooler water from below the thermocline to be drawn upward, reducing sea surface temperature (SST). This decreases the air–sea temperature difference, limits available heat energy, and impacts TC intensity. Part of TC forecast accuracy therefore depends upon the ability to predict sea surface cooling; however, it is not well understood how underlying ocean conditions contribute to this cooling. Here, ~4400 Argo profiles in the Gulf of Mexico were used in a principal component analysis to identify the modes of variability in upper-ocean temperature, and a 1D mixed layer model was used to determine how the modes respond to surface forcing. It was found that the first two modes explain 75% of the variance in the data, with high mode-1 scores being broadly characterized as having warm SST and deep mixed layer and mode-2 scores being characterized as having high SST and a shallow mixed layer. Both modes have distinct seasonal and spatial variability. When subjected to the same model forcing, mode-1- and mode-2-characteristic waters with equal tropical cyclone heat potential (TCHP) respond very differently. Mode-2 SST cools faster than mode 1, with the difference being most pronounced at lower wind speeds and when comparing early-season storms with late-season storms. The results show that using TCHP as a marker for SST response during TC forcing is insufficient because it does not fully capture subsurface ocean thermal structure. This result underscores the need for continual subsurface monitoring so as to accurately initialize the upper ocean in coupled TC models.

Potter’s ORCID: 0000-0003-0142-107X.

Rudzin’s ORCID: 0000-0002-9802-8663.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Henry Potter, hpotter@tamu.edu
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