Sea surface current response patterns to tropical cyclones

Abstract

Abstract Tropical cyclones (TCs) are strong synoptic systems which induce strong sea surface currents. This paper first cross-checks a detailed surface current response to specific TCs Rammasun (2014), Kalmaegi (2014) and Sarika (2016) based on buoy/mooring observations as well as a three-dimensional numerical model (three-dimensional version of the Price-Weller-Pinkel model, 3DPWP) and a one-dimensional semi-analytical model which simplifies the driving forcing into wind stress and Coriolis force, then estimates the impact of all possible tropical cyclones using the semi-analytical model. The results show that the sea surface current response to the kinetic energy input of TCs, which is dependent on the TC configurations (translation speed, size and intensity) and the environmental configurations (Coriolis frequency and upper ocean stratification), can be represented by two simple parameters, namely the TC nondimensional translation speed (S) and the TC wind force parameter ( V ~ c ) or TC wind energy parameter ( E ~ ). S represents a combined effect of TC translation speed, size and Coriolis frequency, determines the structure of surface current response. V ~ c or E ~ represents a combined effect of TC intensity, mixed layer depth and Coriolis frequency, determines the intensity of surface current response or wind energy input into surface currents. Ekman-like divergence dominates the sea surface current response when S is small (0 5). The response pattern with S > 5 was rarely studied before. The three values range of S take up ~30.36%, ~69.36% and ~0.28% of all recorded TCs during 2001–2017, which explains why the second response pattern with 0.4 5 has been rarely studied before. Besides, the surface rightward bias is greatest at S = 2.5 (S = 1.45) for current speed (kinetic energy input rate). V ~ c ( E ~ ) determine the amplitude of current speed (wind energy input into currents). This work provides a simple and easy-to-use method to estimate the surface current response pattern to TCs when TCs and associated environmental configurations are given, which may help to improve the parameterization of TCs in regional and climate modeling. It also suggests that S is a better index than TC translation speed to classify TCs when studying the oceanic response.