• Adams, P. N., D. L. Inman, and N. E. Graham, 2008: Southern California deep-water wave climate: Characterization and application to coastal processes. J. Coastal Res., 24, 10221035, doi:10.2112/07-0831.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Amante, C., and B. Eakins, 2009: ETOPO1 1 arc-minute global relief model: Procedures, data sources and analysis. NOAA Tech. Memo. NESDIS NGDC-24, 25 pp. [Available online at https://www.ngdc.noaa.gov/mgg/global/relief/ETOPO1/docs/ETOPO1.pdf.]

  • Antolinez, J. A. A., F. J. Méndez, P. Camus, S. Vitousek, E. M. Gonzalez, P. Ruggiero, and P. Barnard, 2016: A multiscale climate emulator for long-term morphodynamics (MUSCLE-morpho). J. Geophys. Res. Oceans, 121, 775791, doi:10.1002/2015JC011107.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Camus, P., F. J. Méndez, I. J. Losada, M. Menéndez, A. Espejo, J. Perez, A. Rueda, and Y. Guanche, 2014a: A method for finding the optimal predictor indices for local wave climate conditions. Ocean Dyn., 64, 10251038, doi:10.1007/s10236-014-0737-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Camus, P., and et al. , 2014b: A weather-type statistical downscaling framework for ocean wave climate. J. Geophys. Res. Oceans, 119, 73897405, doi:10.1002/2014JC010141.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Crosby, S. C., W. C. O’Reilly, and R. T. Guza, 2016: Modeling long period swell in southern California: Practical boundary conditions from buoy observations and global wave model predictions. J. Atmos. Oceanic Technol., 33, 16731690, doi:10.1175/JTECH-D-16-0038.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Erikson, L. H., C. A. Hegermiller, P. L. Barnard, P. Ruggiero, and M. van Ormondt, 2015: Projected wave conditions in the eastern North Pacific under the influence of two CMIP5 climate scenarios. Ocean Modell., 96, 171185, doi:10.1016/j.ocemod.2015.07.004.

    • 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, doi:10.1175/JPO-D-13-0276.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Graham, N. E., D. R. Cayan, P. D. Bromirski, and R. E. Flick, 2013: Multi-model projections of twenty-first century North Pacific winter wave climate under the IPCC A2 scenario. Climate Dyn., 40, 13351360, doi:10.1007/s00382-012-1435-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hanson, J. L., and R. E. Jensen, 2004: Wave system diagnostics for numerical wave models. Eighth Int. Workshop on Wave Hindcasting and Forecasting, JCOMM Tech. Rep. 29, WMO/TD 1319, North Shore, Oahu, Hawaii, U.S. Army Engineer Research and Development Center’s Coastal and Hydraulics Laboratory, E3. [Available online at http://www.waveworkshop.org/8thWaves/Papers/E3.pdf.]

  • Laugel, A., M. Menéndez, M. Benoit, G. Mattarolo, and F. Méndez, 2014: Wave climate projections along the French coastline: Dynamical versus statistical downscaling methods. Ocean Modell., 84, 3550, doi:10.1016/j.ocemod.2014.09.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Perez, J., M. Menéndez, F. J. Méndez, 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, doi:10.1007/s10236-014-0740-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Perez, J., M. Menéndez, P. Camus, F. J. Méndez, and I. J. Losada, 2015: Statistical multi-model climate projections of surface ocean waves in Europe. Ocean Modell., 96, 161170, doi:10.1016/j.ocemod.2015.06.001.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rueda, A., P. Camus, F. J. Méndez, A. Tomás, and A. Luceño, 2016a: An extreme value model for maximum wave heights based on weather types. J. Geophys. Res. Oceans, 121, 12621273, doi:10.1002/2015JC010952.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rueda, A., P. Camus, A. Tomás, S. Vitousek, and F. J. Méndez, 2016b: A multivariate extreme wave and storm surge climate emulator based on weather patterns. Ocean Modell., 104, 242251, doi:10.1016/j.ocemod.2016.06.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rueda, A., and et al. , 2017: Multiscale climate emulator of multimodal wave spectra: MUSCLE-spectra. J. Geophys. Res. Oceans, doi:10.1002/2016JC011957, in press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saha, S., S. Moorthi, H.-L. Pan, X. Wu, J. Wang, and S. Nadiga, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 10151057, doi:10.1175/2010BAMS3001.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Semedo, A., K. Suselj, 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, doi:10.1175/2010JCLI3718.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Snodgrass, F. E., G. W. Groves, K. F. Hasselmann, G. R. Miller, W. H. Munk, and W. H. Powers, 1966: Propagation of ocean swell across the Pacific. Philos. Trans. Roy. Soc. London, A259, 431497, doi:10.1098/rsta.1966.0022.

    • Search Google Scholar
    • Export Citation
  • Tolman, H. L., 2009: User manual and system documentation of WAVEWATCH III, version 3.14. U.S. Dept. of Commerce, NOAA, NWS, NCEP Tech. Note, 220 pp. [Available online at http://polar.ncep.noaa.gov/mmab/papers/tn276/MMAB_276.pdf.]

  • Trenberth, K. E., and D. A. Paolino Jr., 1981: Characteristic patterns of variability in sea level pressure in the Northern Hemisphere. Mon. Wea. Rev., 109, 11691189, doi:10.1175/1520-0493(1981)109<1169:CPOVOS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, X. L., F. W. Zwiers, and V. R. Swail, 2004: North Atlantic Ocean wave climate change scenarios for the twenty-first century. J. Climate, 17, 23682383, doi:10.1175/1520-0442(2004)017<2368:NAOWCC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, X. L., V. R. Swail, and A. Cox, 2010: Dynamical versus statistical downscaling methods for ocean wave heights. Int. J. Climatol., 30, 317332, doi:10.1002/joc.1899.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, X. L., Y. Feng, and V. R. Swail, 2012: North Atlantic wave height trends as reconstructed from the 20th century reanalysis. Geophys. Res. Lett., 39, L18705, doi:10.1029/2012GL053381.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wessel, P., and W. H. F. Smith, 1996: A global, self-consistent, hierarchical, high-resolution shoreline database. J. Geophys. Res., 101, 87418743, doi:10.1029/96JB00104.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wingfield, D. K., and C. D. Storlazzi, 2007: Spatial and temporal variability in oceanographic and meteorological forcing along central California and its implications on nearshore processes. J. Mar. Syst., 68, 457472, doi:10.1016/j.jmarsys.2007.02.008.

    • 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, doi:10.1126/science.1197219.

    • Crossref
    • Search Google Scholar
    • Export Citation
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A Multimodal Wave Spectrum–Based Approach for Statistical Downscaling of Local Wave Climate

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  • 1 Department of Ocean Sciences, University of California, and Pacific Coastal and Marine Science Center, United States Geological Survey, Santa Cruz, California
  • | 2 Departamento Ciencias y Tecnicas del Agua y del Medio Ambiente, Universidad de Cantabria, Santander, Spain
  • | 3 Environmental Hydraulics Institute, Universidad de Cantabria, Santander, Spain
  • | 4 Pacific Coastal and Marine Science Center, United States Geological Survey, Santa Cruz, California
  • | 5 Departamento Ciencias y Tecnicas del Agua y del Medio Ambiente, Universidad de Cantabria, Santander, Spain
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Abstract

Characterization of wave climate by bulk wave parameters is insufficient for many coastal studies, including those focused on assessing coastal hazards and long-term wave climate influences on coastal evolution. This issue is particularly relevant for studies using statistical downscaling of atmospheric fields to local wave conditions, which are often multimodal in large ocean basins (e.g., Pacific Ocean). Swell may be generated in vastly different wave generation regions, yielding complex wave spectra that are inadequately represented by a single set of bulk wave parameters. Furthermore, the relationship between atmospheric systems and local wave conditions is complicated by variations in arrival time of wave groups from different parts of the basin. Here, this study addresses these two challenges by improving upon the spatiotemporal definition of the atmospheric predictor used in the statistical downscaling of local wave climate. The improved methodology separates the local wave spectrum into “wave families,” defined by spectral peaks and discrete generation regions, and relates atmospheric conditions in distant regions of the ocean basin to local wave conditions by incorporating travel times computed from effective energy flux across the ocean basin. When applied to locations with multimodal wave spectra, including Southern California and Trujillo, Peru, the new methodology improves the ability of the statistical model to project significant wave height, peak period, and direction for each wave family, retaining more information from the full wave spectrum. This work is the base of statistical downscaling by weather types, which has recently been applied to coastal flooding and morphodynamic applications.

© 2017 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 e-mail: C. A. Hegermiller, chegermiller@usgs.gov

Abstract

Characterization of wave climate by bulk wave parameters is insufficient for many coastal studies, including those focused on assessing coastal hazards and long-term wave climate influences on coastal evolution. This issue is particularly relevant for studies using statistical downscaling of atmospheric fields to local wave conditions, which are often multimodal in large ocean basins (e.g., Pacific Ocean). Swell may be generated in vastly different wave generation regions, yielding complex wave spectra that are inadequately represented by a single set of bulk wave parameters. Furthermore, the relationship between atmospheric systems and local wave conditions is complicated by variations in arrival time of wave groups from different parts of the basin. Here, this study addresses these two challenges by improving upon the spatiotemporal definition of the atmospheric predictor used in the statistical downscaling of local wave climate. The improved methodology separates the local wave spectrum into “wave families,” defined by spectral peaks and discrete generation regions, and relates atmospheric conditions in distant regions of the ocean basin to local wave conditions by incorporating travel times computed from effective energy flux across the ocean basin. When applied to locations with multimodal wave spectra, including Southern California and Trujillo, Peru, the new methodology improves the ability of the statistical model to project significant wave height, peak period, and direction for each wave family, retaining more information from the full wave spectrum. This work is the base of statistical downscaling by weather types, which has recently been applied to coastal flooding and morphodynamic applications.

© 2017 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 e-mail: C. A. Hegermiller, chegermiller@usgs.gov
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