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Abstract We test the hypothesis that internal waves observed in flow over forest canopies are generated by Kelvin–Helmholtz instability. The waves were observed at night, under stably stratified and weak wind conditions, with a horizontally scanning aerosol lidar and an instrumented tower. The lidar images are used to determine the salient wavelength and phase propagation velocity of each episode. Time series data measured at the tower are then used to form vertical profiles of background velocity and buoyancy just before each observed wave event. The profiles are input to the Taylor–Goldstein equation to predict the phase velocity, wavelength and period of the fastest-growing linear instability, and the results compared with the lidar observations. The observed wavelengths tend to be longer than predicted by the Taylor–Goldstein theory, typically by a factor of two. That discrepancy is removed when the theory is extended to account for the effects of ambient, small-scale turbulence. 

Abstract Factors thought to influence deep cycle turbulence in the equatorial Pacific are examined statistically for their predictive capacity using a 13-year moored record that includes microstructure measurements of the turbulent kinetic energy dissipation rate. Wind stress and mean current shear are found to be most predictive of the dissipation rate. Those variables, together with the solar buoyancy flux and the diurnal mixed layer thickness, are combined to make a pair of useful parameterizations. The uncertainty in these predictions is typically 50% greater than the uncertainty in present-day in situ measurements. To illustrate the use of these parameterizations, the record of deep cycle turbulence, measured directly since 2005, is extended back to 1990 based on historical mooring data. The extended record is used to refine our understanding of the seasonal variation of deep cycle turbulence.