Evaluation and Sensitivity Analysis of an Ocean Model Response to Hurricane Ivan (PREPRINT)

An ocean model response to Hurricane Ivan \2012004\202 in the northwest Caribbean Sea and Gulf of Mexico is evaluated to determine what aspects of ocean model performance need to be improved in coupled tropical cyclone forecast models. A control experiment is performed using quasi-optimal choices of initial ocean fields, atmospheric forcing fields, air-sea flux parameterizations, vertical mixing parameterization, and both horizontal and vertical resolution. Alternate experiments are conducted with one single attribute altered to quantify model sensitivity to that attribute and identify where model improvement efforts should be most effectively directed. All experiments are driven by wind stress and wind speed fields that resolve the eye and eyewall structure of the storm. Forcing that does not resolve this inner-core structure substantially degrades the ocean model response. Representation of the ocean by one-dimensional mixed layer models fails to capture important physical aspects of the ocean response that affect SST cooling and heat flux from ocean to atmosphere. Ocean model initialization with respect to the correct locations of ocean currents and eddies, and also to providing accurate upper-ocean temperature and salinity is the most important factor for insuring good ocean model performance. Next in importance are surface momentum and heat flux parameterizations. Wind stress drag coefficient parameterizations that yield values at high wind speed of 2.3-2.5 x 10-3 produce the most realistic wind-driven current profiles. Turbulent heat flux drag coefficient parameterizations impact heat flux from ocean to atmosphere but have little impact on SST cooling because this is dominated by entrainment at the mixed layer base. The choice of vertical mixing parameterization has a significant impact on surface heat flux from ocean to atmosphere.

Prepared in collaboration with University of Miami, Miami, FL. Submitted for publication in Monthly Weather Review.