Observation of swell dissipation across oceans

Abstract

Global observations of ocean swell, from satellite Synthetic Aperture Radar data, are used to estimate the dissipation of swell energy for a number of storms. Swells can be very persistent with energy e-folding scales exceeding 20,000 km. For increasing swell steepness this scale shrinks systematically, down to 2800 km for the steepest observed swells, revealing a significant loss of swell energy. This value corresponds to a normalized energy decay in time ß = 4.2 x 10-6 s -1 . Many processes may be responsible for this dissipation. Because no particular trend is found with wind magnitude and direction, the increase of dissipation rate in dissipation with swell steepness is interpreted as a laminar to turbulent transition of the boundary layer, with a threshold Reynolds number of the order of 100,000. These observations of swell evolution open the way for more accurate wave forecasting models, and provides a constraint on swell-induced air-sea fluxes of momentum and energy.

Article soumis à GRL le 12/09/2008 après rejet par Nature Geoscience (soumis en avril 2008). Modifié en Janvier 2009 et accepté en février 2009

References in corpus (1)

• Routine monitoring and analysis of ocean swell fields using a spaceborne SAR

Cited by in corpus (10)

• Semi-empirical dissipation source functions for ocean waves: Part I, definition, calibration and validation
• Observation and estimation of Lagrangian, Stokes and Eulerian currents induced by wind and waves at the sea surface
• Noise generation in the solid Earth, oceans, and atmosphere, from non-linear interacting surface gravity waves in finite depth
• Current effects on wind generated waves near an Ocean Eddy Dipole
• Space-time structure of long ocean swell fields
• Wave Climate from Spectra and its Connections with Local and Remote Wind Climate
• Ocean swell within the kinetic equation for water waves
• Ocean Wind Wave Climate Responses to Wintertime North Atlantic Atmospheric Transient Eddies and Low Frequency Flow
• Reduction of rain-induced errors for wind speed estimation on SAR observations using convolutional neural networks
• Modeling the Propagation, Breaking and Drift of Ocean Surface Wave