Statistical Downscaling Prediction of Sea Surface Winds over the Global Ocean

Cangjie Sun School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia, Canada

Search for other papers by Cangjie Sun in
Current site
Google Scholar
PubMed
Close
and
Adam H. Monahan School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia, Canada

Search for other papers by Adam H. Monahan in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

The statistical prediction of local sea surface winds from large-scale, free-tropospheric fields is investigated at a number of locations over the global ocean using a statistical downscaling model based on multiple linear regression. The predictands (the mean and standard deviation of both vector wind components and wind speed) calculated from ocean buoy observations on daily, weekly, and monthly scales are regressed on upper-level predictor fields from reanalysis products. It is found that in general the mean vector wind components are more predictable than mean wind speed in the North Pacific and Atlantic, while in the tropical Pacific and Atlantic the difference in predictive skill between mean vector wind components and wind speed is not substantial. The predictability of wind speed relative to vector wind components is interpreted by an idealized model of the wind speed probability density function, which indicates that in the midlatitudes the mean wind speed is more sensitive to the vector wind standard deviations (which generally are not well predicted) than to the mean vector winds. In the tropics, the mean wind speed is found to be more sensitive to the mean vector winds. While the idealized probability model does a good job of characterizing month-to-month variations in the mean wind speed in terms of the vector wind statistics, month-to-month variations in the standard deviation of speed are not well modeled. A series of Monte Carlo experiments demonstrates that the inconsistency in the characterization of wind speed standard deviation is the result of differences of sampling variability between the vector wind and wind speed statistics.

Corresponding author address: Adam Monahan, School of Earth and Ocean Sciences, University of Victoria, P.O. Box 3065 STN CSC, Victoria BC V8W 3V6, Canada. E-mail: monahana@uvic.ca

Abstract

The statistical prediction of local sea surface winds from large-scale, free-tropospheric fields is investigated at a number of locations over the global ocean using a statistical downscaling model based on multiple linear regression. The predictands (the mean and standard deviation of both vector wind components and wind speed) calculated from ocean buoy observations on daily, weekly, and monthly scales are regressed on upper-level predictor fields from reanalysis products. It is found that in general the mean vector wind components are more predictable than mean wind speed in the North Pacific and Atlantic, while in the tropical Pacific and Atlantic the difference in predictive skill between mean vector wind components and wind speed is not substantial. The predictability of wind speed relative to vector wind components is interpreted by an idealized model of the wind speed probability density function, which indicates that in the midlatitudes the mean wind speed is more sensitive to the vector wind standard deviations (which generally are not well predicted) than to the mean vector winds. In the tropics, the mean wind speed is found to be more sensitive to the mean vector winds. While the idealized probability model does a good job of characterizing month-to-month variations in the mean wind speed in terms of the vector wind statistics, month-to-month variations in the standard deviation of speed are not well modeled. A series of Monte Carlo experiments demonstrates that the inconsistency in the characterization of wind speed standard deviation is the result of differences of sampling variability between the vector wind and wind speed statistics.

Corresponding author address: Adam Monahan, School of Earth and Ocean Sciences, University of Victoria, P.O. Box 3065 STN CSC, Victoria BC V8W 3V6, Canada. E-mail: monahana@uvic.ca
Save
  • Bates, N. R., and L. Merlivat, 2001: The influence of short-term wind variability on air–sea CO2 exchange. Geophys. Res. Lett., 28, 32813284.

    • Search Google Scholar
    • Export Citation
  • Capps, S. B., and C. S. Zender, 2009: Global ocean wind power sensitivity to surface layer stability. Geophys. Res. Lett., 36, L09801, doi:10.1029/2008GL037063.

    • Search Google Scholar
    • Export Citation
  • Culver, A. M. R., and A. H. Monahan, 2013: The statistical predictability of surface winds over western and central Canada. J. Climate,in press.

  • Donelan, M., W. Drennan, E. Saltzman, and R. Wanninkhof, Eds., 2002: Gas Transfer at Water Surfaces. Geophys. Mongr., Vol. 127, Amer. Geophys. Union, 383 pp.

  • Garratt, J., 1992: The Atmospheric Boundary Layer. Cambridge University Press, 316 pp.

  • Johnson, N., S. Kotz, and N. Balakrishnan, 1994: Continuous Univariate Distributions. Vol. 1. Wiley, 756 pp.

  • Jones, I. S., and Y. Toba, Eds., 2001: Wind Stress over the Ocean. Cambridge University Press, 307 pp.

  • Liu, W. T., W. Tang, and X. Xie, 2008: Wind power distribution over the ocean. Geophys. Res. Lett., 35, L13808, doi:10.1029/2008GL034172.

    • Search Google Scholar
    • Export Citation
  • Monahan, A. H., 2006: The probability distribution of sea surface wind speeds. Part I: Theory and SeaWinds observations. J. Climate, 19, 497520.

    • Search Google Scholar
    • Export Citation
  • Monahan, A. H., 2007: Empirical models of the probability distribution of sea surface wind speeds. J. Climate, 20, 57985814.

  • Monahan, A. H., 2012a: Can we see the wind? Statistical downscaling of historical sea surface winds in the subarctic northeast Pacific. J. Climate, 25, 15111528.

    • Search Google Scholar
    • Export Citation
  • Monahan, A. H., 2012b: The temporal autocorrelation structure of sea surface winds. J. Climate,25, 6684–6700.

  • Rice, S.O., 1945: Mathematical analysis of random noise (part 2). Bell Syst. Tech. J., 24, 46156.

  • Salameh, T., P. Drobinski, M. Vrac, and P. Naveau, 2009: Statistical downscaling of near-surface wind over complex terrain in southern France. Meteor. Atmos. Phys., 103, 253265, doi:10.1007/s00703-008-0330-7.

    • Search Google Scholar
    • Export Citation
  • Sampe, T., and S.-P. Xie, 2007: Mapping high sea winds from space: A global climatology. Bull. Amer. Meteor. Soc., 88, 1965–1978.

  • Sun, C., 2012: Statistical downscaling of sea surface winds over the global oceans. M.S. thesis, School of Earth and Ocean Sciences, University of Victoria, 111 pp.

  • van der Kamp, D., C. Curry, and A. Monahan, 2012: Statistical downscaling of historical monthly mean winds over a coastal region of complex terrain. II: Predicting wind components. Climate Dyn., 38, 13011311.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 287 65 3
PDF Downloads 127 43 2