Temporal Filtering Enhances the Skewness of Sea Surface Winds

Adam H. Monahan School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia, Canada

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Abstract

The component of the sea surface wind in the along-mean wind direction is known to display pronounced skewness at many locations over the ocean. A recent study by Proistosescu et al. found that the skewness of daily 850-hPa air temperature measured by radiosondes is typically reduced by bandpass filtering. This behavior was also shown to be characteristic of correlated additive–multiplicative (CAM) noise, which has been proposed as a generic model for non-Gaussian variability in the atmosphere and ocean. The present study shows that if the cutoff frequency is not too low, the skewness of the along-mean wind component is enhanced by low-pass filtering, particularly in the equatorial band and in the midlatitude storm tracks. The filter time scale beyond which skewness is systematically reduced by filtering is of the daily to synoptic scale, except in a narrow equatorial band where it is of subseasonal to seasonal time scales. This behavior is reproduced in an idealized stochastic model of the near-surface winds, in which key parameters are the characteristic time scales of the nonlinear dynamics and of the noise. These results point toward more general approaches for assessing the relative importance of multiplicative noise or dynamical nonlinearities in producing non-Gaussian structure in atmospheric and oceanic fields.

© 2018 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: Adam H. Monahan, monahana@uvic.ca

Abstract

The component of the sea surface wind in the along-mean wind direction is known to display pronounced skewness at many locations over the ocean. A recent study by Proistosescu et al. found that the skewness of daily 850-hPa air temperature measured by radiosondes is typically reduced by bandpass filtering. This behavior was also shown to be characteristic of correlated additive–multiplicative (CAM) noise, which has been proposed as a generic model for non-Gaussian variability in the atmosphere and ocean. The present study shows that if the cutoff frequency is not too low, the skewness of the along-mean wind component is enhanced by low-pass filtering, particularly in the equatorial band and in the midlatitude storm tracks. The filter time scale beyond which skewness is systematically reduced by filtering is of the daily to synoptic scale, except in a narrow equatorial band where it is of subseasonal to seasonal time scales. This behavior is reproduced in an idealized stochastic model of the near-surface winds, in which key parameters are the characteristic time scales of the nonlinear dynamics and of the noise. These results point toward more general approaches for assessing the relative importance of multiplicative noise or dynamical nonlinearities in producing non-Gaussian structure in atmospheric and oceanic fields.

© 2018 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: Adam H. Monahan, monahana@uvic.ca
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  • Chen, N., D. Giannakis, R. Herbei, and A. J. Majda, 2014: An MCMC algorithm for parameter estimation in signals with hidden intermittent instability. SIAM/ASA J. Uncertainty Quantif., 2, 647669, https://doi.org/10.1137/130944977.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., 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
  • Gelaro, R., and Coauthors, 2017: The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Climate, 30, 54195454, https://doi.org/10.1175/JCLI-D-16-0758.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • GMAO, 2015: MERRA-2 inst1_2d_atm_Nx: 2d, 1-hourly, instantaneous, single-level, assimilation, single-level diagnostics V5.12.4, Goddard Space Flight Center Distributed Active Archive Center (GSFC DAAC), accessed July–August 2017, https://doi.org/10.5067/3Z173KIE2TPD.

    • Crossref
    • Export Citation
  • Kloeden, P. E., and E. Platen, 1992: Numerical Solution of Stochastic Differential Equations. Springer-Verlag, 636 pp.

    • Crossref
    • Export Citation
  • Lim, Y.-K., R. M. Kovach, S. Pawson, and G. Vernieres, 2017: The 2015/16 El Niño event in context of the MERRA-2 reanalysis: A comparison of the tropical Pacific with 1982/83 and 1997/98. J. Climate, 30, 48194842, https://doi.org/10.1175/JCLI-D-16-0800.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Monahan, A. H., 2004: A simple model for the skewness of global sea surface winds. J. Atmos. Sci., 61, 20372049, https://doi.org/10.1175/1520-0469(2004)061<2037:ASMFTS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Monahan, A. H., 2006a: The probability distribution of sea surface wind speeds. Part I: Theory and SeaWinds observations. J. Climate, 19, 497520, https://doi.org/10.1175/JCLI3640.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Monahan, A. H., 2006b: The probability distribution of sea surface wind speeds. Part II: Dataset intercomparison and seasonal variability. J. Climate, 19, 521534, https://doi.org/10.1175/JCLI3641.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Monahan, A. H., 2012: The temporal autocorrelation structure of sea surface winds. J. Climate, 25, 66846700, https://doi.org/10.1175/JCLI-D-11-00698.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Penland, C., and P. D. Sardeshmukh, 2012: Alternative interpretations of power-law distributions found in nature. Chaos, 22, 023119, https://doi.org/10.1063/1.4706504.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Proistosescu, C., A. Rhines, and P. Huybers, 2016: Identification and interpretation of nonnormality in atmospheric time series. Geophys. Res. Lett., 43, 54255434, https://doi.org/10.1002/2016GL068880.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sardeshmukh, P. D., and P. Sura, 2009: Reconciling non-Gaussian climate statistics with linear dynamics. J. Climate, 22, 11931207, https://doi.org/10.1175/2008JCLI2358.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sardeshmukh, P. D., G. P. Compo, and C. Penland, 2015: Need for caution in interpreting extreme weather statistics. J. Climate, 28, 91669187, https://doi.org/10.1175/JCLI-D-15-0020.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schneider, T., T. Bischoff, and H. Płotka, 2015: Physics of changes in synoptic midlatitude temperature variability. J. Climate, 28, 23122331, https://doi.org/10.1175/JCLI-D-14-00632.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sura, P., 2011: A general perspective of extreme events in weather and climate. Atmos. Res., 101, 121, https://doi.org/10.1016/j.atmosres.2011.01.012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sura, P., and A. Hannachi, 2015: Perspectives of non-Gaussianity in atmospheric synoptic and low-frequency variability. J. Climate, 28, 50915114, https://doi.org/10.1175/JCLI-D-14-00572.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Teng, Q., A. H. Monahan, and J. C. Fyfe, 2004: Effects of time averaging on climate regimes. Geophys. Res. Lett., 31, L22203, https://doi.org/10.1029/2004GL020840.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, W. F., A. H. Monahan, and D. Crommelin, 2014: Parametric estimation of the stochastic dynamics of sea surface winds. J. Atmos. Sci., 71, 34653483, https://doi.org/10.1175/JAS-D-13-0260.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tziperman, E., and L. Yu, 2007: Quantifying the dependence of westerly wind bursts on the large-scale tropical Pacific SST. J. Climate, 20, 27602768, https://doi.org/10.1175/JCLI4138a.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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