Wave Climate from Spectra and Its Connections with Local and Remote Wind Climate

Haoyu Jiang College of Marine Science and Technology, China University of Geosciences, Wuhan, and Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao, and Shenzhen Research Institute, China University of Geosciences, Shenzhen, China

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Lin Mu College of Marine Science and Technology, China University of Geosciences, Wuhan, and Shenzhen Research Institute, China University of Geosciences, Shenzhen, China

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Abstract

Wind-generated waves can propagate over large distances. Therefore, wave spectra from a fixed point can record information about air–sea interactions in distant areas. In this study, the spectral wave climate for a point in the tropical eastern Pacific Ocean is computed. Several well-defined wave climate systems are observed in the mean wave spectrum. Significant seasonal cycling, long-term trends, and correlations with the Southern Oscillation, the Arctic Oscillation, and the Antarctic Oscillation are observed in the local wave spectra, showing abundant climatic information. Projections of wind vectors on the directions pointing to the target location are used to connect the spectral wave climate and basin-scale wind climate, because significant correlations are observed between the wave spectra and the wind projections of both local and remote wind systems. The origins of all the identified wave climate systems, including the westerlies and the trade winds in both hemispheres, are clearly shown in wind projection maps. Some of these origins are thousands of kilometers away from the target point, demonstrating the validity of this connection. Comparisons are made between wave spectra and the corresponding local and remote wind fields with respect to seasonal and interannual variability and long-term trends. The results show that each frequency and direction of ocean wave spectra at a certain location can be approximately linked to the wind field for a geographical area, implying that it is feasible to reconstruct spectral wave climates from observational wind field data and monitor wind climates from observational wave spectra geographically far away.

© 2019 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: Lin Mu, moulin1977@hotmail.com

Abstract

Wind-generated waves can propagate over large distances. Therefore, wave spectra from a fixed point can record information about air–sea interactions in distant areas. In this study, the spectral wave climate for a point in the tropical eastern Pacific Ocean is computed. Several well-defined wave climate systems are observed in the mean wave spectrum. Significant seasonal cycling, long-term trends, and correlations with the Southern Oscillation, the Arctic Oscillation, and the Antarctic Oscillation are observed in the local wave spectra, showing abundant climatic information. Projections of wind vectors on the directions pointing to the target location are used to connect the spectral wave climate and basin-scale wind climate, because significant correlations are observed between the wave spectra and the wind projections of both local and remote wind systems. The origins of all the identified wave climate systems, including the westerlies and the trade winds in both hemispheres, are clearly shown in wind projection maps. Some of these origins are thousands of kilometers away from the target point, demonstrating the validity of this connection. Comparisons are made between wave spectra and the corresponding local and remote wind fields with respect to seasonal and interannual variability and long-term trends. The results show that each frequency and direction of ocean wave spectra at a certain location can be approximately linked to the wind field for a geographical area, implying that it is feasible to reconstruct spectral wave climates from observational wind field data and monitor wind climates from observational wave spectra geographically far away.

© 2019 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: Lin Mu, moulin1977@hotmail.com
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  • Aarnes, O. J., S. Abdalla, J. R. Bidlot, and O. Breivik, 2015: Marine wind and wave height trends at different ERA-Interim forecast ranges. J. Climate, 28, 819837, https://doi.org/10.1175/JCLI-D-14-00470.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Alves, J. H. G. M., 2006: Numerical modeling of ocean swell contributions to the global wind-wave climate. Ocean Modell., 11, 98122, https://doi.org/10.1016/j.ocemod.2004.11.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anoop, T. R., V. S. Kumar, P. R. Shanas, and G. Johnson, 2015: Surface wave climatology and its variability in the north Indian Ocean based on ERA-Interim reanalysis. J. Atmos. Oceanic Technol., 32, 13721385, https://doi.org/10.1175/JTECH-D-14-00212.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ardhuin, F., B. Chapron, and F. Collard, 2009: Observation of swell dissipation across oceans. Geophys. Res. Lett., 36, L06607, https://doi.org/10.1029/2008GL037030.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bromirski, P. D., D. R. Cayan, and R. E. Flick, 2005: Wave spectral energy variability in the northeast Pacific. J. Geophys. Res., 110, C03005, https://doi.org/10.1029/2004JC002398.

    • Search Google Scholar
    • Export Citation
  • Burk, S. D., and W. T. Thompson, 1996: The summertime low-level jet and marine boundary layer structure along the California coast. Mon. Wea. Rev., 124, 668686, https://doi.org/10.1175/1520-0493(1996)124<0668:TSLLJA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, G., B. Chapron, R. Ezraty, and D. Vandemark, 2002: A global view of swell and wind sea climate in the ocean by satellite altimeter and scatterometer. J. Atmos. Oceanic Technol., 19, 18491859, https://doi.org/10.1175/1520-0426(2002)019<1849:AGVOSA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Collard, F., F. Ardhuin, and B. Chapron, 2009: Monitoring and analysis of ocean swell fields from space: New methods for routine observations. J. Geophys. Res., 114, C07023, https://doi.org/10.1029/2008JC005215.

    • Search Google Scholar
    • Export Citation
  • Dee, D., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Earle, M. D., K. E. Steele, and D. W. C. Wang, 1999: Use of advanced directional wave spectra analysis methods. Ocean Eng., 26, 14211434, https://doi.org/10.1016/S0029-8018(99)00010-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Espejo, A., P. Camus, I. J. Losada, and F. J. Méndez, 2014: Spectral ocean wave climate variability based on atmospheric circulation patterns. J. Phys. Oceanogr., 44, 21392152, https://doi.org/10.1175/JPO-D-13-0276.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fan, Y., S. Lin, I. M. Held, Z. Yu, and H. L. Tolman, 2012: Global ocean surface wave simulation using a coupled atmosphere–wave model. J. Climate, 25, 62336252, https://doi.org/10.1175/JCLI-D-11-00621.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fan, Y., I. M. Held, S. Lin, and X. Wang, 2013: Ocean warming effect on surface gravity wave climate change for the end of the twenty-first century. J. Climate, 26, 60466066, https://doi.org/10.1175/JCLI-D-12-00410.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fan, Y., S. Lin, S. M. Griffies, and M. A. Hemer, 2014: Simulated global swell and wind-sea climate and their responses to anthropogenic climate change at the end of the twenty-first century. J. Climate, 27, 35163536, https://doi.org/10.1175/JCLI-D-13-00198.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gemmrich, J., B. Thomas, and R. Bouchard, 2011: Observational changes and trends in the northeast Pacific wave records. Geophys. Res. Lett., 38, L22601, https://doi.org/10.1029/2011GL049518.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gerling, T. W., 1992: Partitioning sequences and arrays of directional ocean wave spectra into component wave systems. J. Atmos. Oceanic Technol., 9, 444458, https://doi.org/10.1175/1520-0426(1992)009<0444:PSAAOD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gillett, N. P., T. D. Kell, and P. D. Jones, 2006: Regional climate impacts of the southern annular mode. Geophys. Res. Lett., 33, L23704, https://doi.org/10.1029/2006GL027721.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gulev, S. K., and L. Hasse, 1998: North Atlantic wind waves and wind stress fields from voluntary observing ship data. J. Phys. Oceanogr., 28, 11071130, https://doi.org/10.1175/1520-0485(1998)028<1107:NAWWAW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gulev, S. K., and V. Grigorieva, 2006: Variability of the winter wind waves and swell in the North Atlantic and North Pacific as revealed by the voluntary observing ship data. J. Climate, 19, 56675685, https://doi.org/10.1175/JCLI3936.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hanson, J. L., and O. M. Phillips, 2001: Automated analysis of ocean surface directional wave spectra. J. Atmos. Oceanic Technol., 18, 277293, https://doi.org/10.1175/1520-0426(2001)018<0277:AAOOSD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hemer, M. A., J. A. Church, and J. R. Hunter, 2010: Variability and trends in the directional wave climate of the Southern Hemisphere. Int. J. Climatol., 30, 475491, https://doi.org/10.1002/joc.1900.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hemer, M. A., Y. Fan, N. Mori, A. Semedo, and X. Wang, 2013: Projected changes in wave climate from a multi-model ensemble. Nat. Climate Change, 3, 471476, https://doi.org/10.1038/nclimate1791.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hwang, P. A., F. J. Ocampo-Torres, and H. García-Nava, 2012: Wind sea and swell separation of 1D wave spectrum by a spectrum integration method. J. Atmos. Oceanic Technol., 29, 116128, https://doi.org/10.1175/JTECH-D-11-00075.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, H., J. E. Stopa, H. Wang, R. Husson, A. Mouche, B. Chapron, and G. Chen, 2016: Tracking the attenuation and nonbreaking dissipation of swells using altimeters. J. Geophys. Res. Oceans, 121, 14461458, https://doi.org/10.1002/2015JC011536.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, H., A. Mouche, H. Wang, A. Babanin, B. Chapron, and G. Chen, 2017a: Limitation of SAR quasi-linear inversion data on swell climate: An example of global crossing swells. Remote Sens., 9, 107, https://doi.org/10.3390/rs9020107.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, H., A. V. Babanin, Q. Liu, J. E. Stopa, B. Chapron, and G. Chen, 2017b: Can contemporary satellites really estimate swell dissipation rate? Remote Sens. Environ., 201, 2433, https://doi.org/10.1016/j.rse.2017.08.037.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Q., A. V. Babanin, S. Zieger, I. R. Young, and C. Guan, 2016: Wind and wave climate in the Arctic Ocean as observed by altimeters. J. Climate, 29, 79577975, https://doi.org/10.1175/JCLI-D-16-0219.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pérez, J., F. J. Méndez, M. Menéndez, and I. J. Losada, 2014: ESTELA: A method for evaluating the source and travel time of the wave energy reaching a local area. Ocean Dyn., 64, 11811191, https://doi.org/10.1007/s10236-014-0740-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Portilla, J., 2018: The global signature of ocean wave spectra. Geophys. Res. Lett., 45, 267276, https://doi.org/10.1002/2017GL076431.

  • Portilla, J., and L. Cavaleri, 2016: On the specification of background errors for wave data assimilation systems. J. Geophys. Res. Oceans, 121, 209223, https://doi.org/10.1002/2015JC011309.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Portilla, J., F. J. Ocampo-Torres, and J. Monbaliu, 2009: Spectral partitioning and identification of wind sea and swell. J. Atmos. Oceanic Technol., 26, 107122, https://doi.org/10.1175/2008JTECHO609.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Portilla, J., J. Sosa, and L. Cavaleri, 2013: Wave energy resources: Wave climate and exploitation. Renewable Energy, 57, 594605, https://doi.org/10.1016/j.renene.2013.02.032.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Portilla, J., A. L. Caicedo, R. Padilla, and L. Cavaleri, 2015a: Spectral wave conditions in the Colombian Pacific Ocean. Ocean Modell., 92, 149168, https://doi.org/10.1016/j.ocemod.2015.06.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Portilla, J., L. Cavaleri, and G. P. Van Vledder, 2015b: Wave spectra partitioning and long term statistical distribution. Ocean Modell., 96, 148160, https://doi.org/10.1016/j.ocemod.2015.06.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Portilla, J., A. Salazar, and L. Cavaleri, 2016: Climate patterns derived from ocean wave spectra. Geophys. Res. Lett., 43, 11 73611 743, https://doi.org/10.1002/2016GL071419.

    • Search Google Scholar
    • Export Citation
  • Rueda, A., and Coauthors, 2017: Multiscale climate emulator of multimodal wave spectra: MUSCLE-spectra. J. Geophys. Res. Oceans, 122, 14001415, https://doi.org/10.1002/2016JC011957.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Semedo, A., K. Sušelj, A. Rutgersson, and A. Sterl, 2011: A global view on the wind sea and swell climate and variability from ERA-40. J. Climate, 24, 14611479, https://doi.org/10.1175/2010JCLI3718.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Semedo, A., R. Weisse, A. Behrens, A. Sterl, L. Bengtsson, and H. Günther, 2013: Projection of global wave climate change toward the end of the twenty-first century. J. Climate, 26, 82698288, https://doi.org/10.1175/JCLI-D-12-00658.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Semedo, A., R. Vettor, Ø. Breivik, A. Sterl, M. Reistad, C. G. Soares, and D. Lima, 2015: The wind sea and swell waves climate in the Nordic seas. Ocean Dyn., 65, 223240, https://doi.org/10.1007/s10236-014-0788-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Snodgrass, F. E., K. Hasselmann, G. R. Miller, W. H. Munk, W. H. Powers, and G. E. R. Deacon, 1966: Propagation of ocean swell across the Pacific. Philos. Trans. Roy. Soc. London, 259A, 431497, https://doi.org/10.1098/rsta.1966.0022.

    • Search Google Scholar
    • Export Citation
  • Stopa, J. E., and K. F. Cheung, 2014: Periodicity and patterns of ocean wind and wave climate. J. Geophys. Res. Oceans, 119, 55635584, https://doi.org/10.1002/2013JC009729.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stopa, J. E., F. Ardhuin, A. V. Babanin, and S. Zieger, 2016: Comparison and validation of physical wave parameterizations in spectral wave models. Ocean Modell., 103, 217, https://doi.org/10.1016/j.ocemod.2015.09.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, D. W. J., and J. M. Wallace, 1998: The Arctic oscillation signature in the wintertime geopotential height and temperature fields. Geophys. Res. Lett., 25, 12971300, https://doi.org/10.1029/98GL00950.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, D. W., and P. A. Hwang, 2001: An operational method for separating wind sea and swell from ocean wave spectra. J. Atmos. Oceanic Technol., 18, 20522062, https://doi.org/10.1175/1520-0426(2001)018<2052:AOMFSW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weisse, R., and H. Günther, 2007: Wave climate and long-term changes for the southern North Sea obtained from a high-resolution hindcast 1958–2002. Ocean Dyn., 57, 161172, https://doi.org/10.1007/s10236-006-0094-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, I. R., 1999: Seasonal variability of the global ocean wind and wave climate. Int. J. Climatol., 19, 931950, https://doi.org/10.1002/(SICI)1097-0088(199907)19:9<931::AID-JOC412>3.0.CO;2-O.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, I. R., S. Zieger, and A. V. Babanin, 2011: Global trends in wind speed and wave height. Science, 332, 451455, https://doi.org/10.1126/science.1197219.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, I. R., J. Vinoth, S. Zieger, and A. V. Babanin, 2012: Investigation of trends in extreme value wave height and wind speed. J. Geophys. Res., 117, C00J06, https://doi.org/10.1029/2011JC007753.

    • Search Google Scholar
    • Export Citation
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