Variability of the Surface Wind and Ocean Circulation Over the Northern California Shelf.

The spatial and temporal variability of the wind and turbulent fluxes of momentum, heat and water vapor over the coastal shelf of northern California was studied using aircraft data to relate surface forcing and wind stress curl to the variations in sea surface temperature, synoptic weather and location relative to the coastline. Wind observations from both summer and winter were used to characterize the spatial variation of wind stress and wind stress curl. Results from two field experiments--Shelf Mixed Layer Experiment (SMILE) and Coastal Ocean Dynamics Experiment (CODE)--showed local maximum values of wind stress curl to be associated with major coastal topographic features such as points and capes regardless of wind direction. For highly supercritical flows during strong winds in summer, the highest values of wind stress curl were observed near a hydraulic jump propagating from Stewarts Point. A linearized, two-layer, vertically-integrated, reduced-gravity model of coastal upwelling was developed to determine the importance of both the wind stress and its curl in the evolution of the thickness of the upper layer. Results showed enhanced upwelling or diminished downwelling over areas of positive curl. Nearshore, the effect of curl was diminished by the presence of the coastal topography as compared to its effect when applied offshore. Similar results were obtained from the analytical solution of a simplified frictionless form of the model. The fluxes of momentum, heat and moisture were parameterized by bulk aerodynamic formulas after reduction to neutral stability. Results from winter and summer were within the range of published results of previous studies. A vertically-resolving numerical model of coupled air-sea interaction was used to show that the changes in the sea surface temperature due to coastal upwelling have no significant feedback on the drag coefficient in winter. A multi-level discrete variational assimilation technique was developed to optimize the fit of a numerical model to actual observations by minimizing a cost function that measures the deviation of the model from observations, treating the model as a constraint. An assimilation technique based on a Lagrange-multiplier weighted least-squares estimation was also developed for cases with observations only at the initial and final times of the assimilation. A statistical test based on a chi^2-distribution was applied to determine the goodness of fit of the observations and the model. Results show the potential of these methods in assimilating atmospheric and oceanic data into models.