Effects of Precipitation on the Upper-Ocean Response to a Hurricane

Abstract

The effect of precipitation on the upper-ocean response during a tropical cyclone passage is investigated using a numerical model in this paper. For realistic wind forcing and empirical rain rates based on satellite climatology, numerical simulations are performed with and without precipitation forcing to delineate the effects of freshwater forcing on the upper-ocean heat and salt budgets. Additionally, the performance of five mixing parameterizations is also examined for the two forcing conditions to understand the sensitivity of simulated ocean response. Overall, results from 15 numerical experiments are analyzed to quantify the precipitation effects on the oceanic mixed layer and the upper ocean. Simulated fields for the same mixing scheme with and without precipitation indicate a decrease in the upper-ocean cooling of about 0.2°?0.5°C. This is mainly due to reduced mixing of colder water from below induced by the increased stability of the added freshwater. The cooler rainwater contributes a maximum of approximately 10% to the total surface heat loss from the ocean. The rate of freshening due to precipitation exceeds the rate of mixing of the more saline water from below, leading to a change in sign of the mixed layer salinity response. As seen in earlier studies, large uncertainty exists in the simulated upper-ocean response due to the choice of mixing parameterization. Although the nature of simulated response remains similar for all the mixing schemes, the magnitude of freshening and cooling varies by as much as 0.5 psu and 1°C between the schemes to the right of the storm track. While changes in the mixed layer and in the top 100 m of heat and salt budgets are strongly influenced by the choice of mixing scheme, integrated budgets in the top 200 m are seen to be affected more by advection and surface fluxes. However, since the estimated surface fluxes depend upon the simulated sea surface temperature, the choice of mixing scheme is crucial for realistic coupled predictive models.