The Effect of Atmosphere-Ocean Coupling on the Structure and Intensity of Tropical Cyclone Bejisa in the Southwest Indian Ocean

Abstract

A set of numerical simulations is relied upon to evaluate the impact of air-sea interactions on the behaviour of tropical cyclone (TC) Bejisa (2014), using various configurations of the coupled ocean-atmosphere numerical system Meso-NH-NEMO. Uncoupled (SST constant) as well as 1D (use of a 1D ocean mixed layer) and 3D (full 3D ocean) coupled experiments are conducted to evaluate the impact of the oceanic response and dynamic processes, with emphasis on the simulated structure and intensity of TC Bejisa. Although the three experiments are shown to properly capture the track of the tropical cyclone, the intensity and the spatial distribution of the sea surface cooling show strong differences from one coupled experiment to another. In the 1D experiment, sea surface cooling (∼1 ∘C) is reduced by a factor 2 with respect to observations and appears restricted to the depth of the ocean mixed layer. Cooling is maximized along the right-hand side of the TC track, in apparent disagreement with satellite-derived sea surface temperature observations. In the 3D experiment, surface cooling of up to 2.5 ∘C is simulated along the left hand side of the TC track, which shows more consistency with observations both in terms of intensity and spatial structure. In-depth cooling is also shown to extend to a much deeper depth, with a secondary maximum of nearly 1.5 ∘C simulated near 250 m. With respect to the uncoupled experiment, heat fluxes are reduced from about 20% in both 1D and 3D coupling configurations. The tropical cyclone intensity in terms of occurrence of 10-m TC wind is globally reduced in both cases by about 10%. 3D-coupling tends to asymmetrize winds aloft with little impact on intensity but rather a modification of the secondary circulation, resulting in a slight change in structure.

Evaluation of a Mesoscale Coupled Ocean‐Atmosphere Configuration for Tropical Cyclone Forecasting in the South West Indian Ocean Basin

“…As expected in the South hemisphere, the upwelling in the CPL runs is located on the left side of the TC track in the direction of propagation (Bielli et al, 2021;Pianezze et al, 2018;Price, 1981), so on the East side of Gelena (Figures 10b-10d). In this zone, the thermocline, which corresponds to the thermal transition zone between the deep cold waters and the warmer well mixed surface waters initially at about 50 m depth, is pushed toward the surface by the upwelling.…”

“…The numerical simulation of such a process driven by the explicit advection of the oceanic currents needs a 3D ocean model (Mogensen et al., 2017; Yablonsky & Ginis, 2009). Numerous studies have indeed shown the benefit of modeling the upwelling effect in a 3D ocean model for TC forecasting (Bender et al., 2007; Bielli et al., 2021; Ginis, 2002; Jullien et al., 2014; Mogensen et al., 2017; Yablonsky & Ginis, 2009).…”

Observational evidence of overlooked downwelling induced by tropical cyclones in the open ocean

“…This presents a broad view of the possible changes in the thermal structure of the upper ocean in the aftermath of a TC, which could in turn significantly impact oceanic preconditioning and subsequently affect the intensity of upcoming TCs 57 , 58 . Coupled ocean–atmosphere models improve TC forecasts significantly by incorporating the ocean impact into the model, which is particularly notable in slow-moving TC cases 59 , 60 . Reevaluating the downwelling process in the ocean could also benefit the parameterization of numerical models for TC forecasting.…”

Sensitivity of Real‐Time Forecast for Typhoons Around Korea to Cumulus and Cloud Microphysics Schemes

“…However, only a small number of cumulus parameterization and cloud microphysics schemes were tested in this study, so there is a need to conduct more sensitivity experiments using diverse kinds of physics parametrization schemes in the future for a more sophisticated forecast performance verification. Besides, various methods to achieve better performances in typhoon forecast have been introduced by lots of studies; increasing the model horizontal resolutions (Buizza, 2010; Davis et al., 2010), improving initial condition (Cha & Wang, 2013; Y. Choi, Cha, & Kim, 2017; Kurihara et al., 1993; Liu & Tan, 2016), using alternative boundary condition (Moon et al., 2018), and reflecting the realistic atmosphere and ocean interactions by using atmospheric‐ocean coupled models (Bielli et al., 2021; Kim et al., 2014; Mogensen et al., 2017; Yesubabu et al., 2020). Therefore, other factors also should be considered and included in future studies with optimal physics combinations to further improve the overall typhoon forecast.…”