Strongly Coupled Lagrangian Data Assimilation of Surface Drifter in a Coupled Ocean-Atmosphere System

The ocean, as a vast energy reservoir, plays a critical role in global climate dynamics through the transfer of momentum, heat, and freshwater at the air-sea interface. Accurate estimation of ocean currents is essential for understanding these dynamics and their broader impact on climate variability. Despite the general sparsity of in situ oceanic data, surface drifters offer a valuable opportunity to capture detailed and frequent observations of sea surface currents, which are crucial for examining the complex processes at the air-sea interface. Previous studies (Sun and Penny et al. 2022; Dong and Sun et al. 2023) have demonstrated that assimilating surface drifter trajectories using Lagrangian Data Assimilation (LaDA) in ocean-only systems significantly improves the estimation of mesoscale ocean currents, ocean heat content, and horizontally polarized near-inertial waves during tropical cyclone events in regions such as the Gulf of Mexico and the Indian Ocean. Building on these findings, this study investigates the impact of assimilating drifter data within a coupled system of intermediate complexity using the Modular Arbitrary-Order Ocean-Atmosphere Model (MAOOAM) and Observing System Simulation Experiments (OSSEs). To determine the optimal approach for utilizing drifter trajectory data, we examined two primary strategies: the Eulerian approach, which translates Lagrangian positions into Eulerian velocities, and the fully Lagrangian method, which integrates both the original fluid states and augmented drifter state variables into the system state vector. Both Lagrangian and Eulerian drifter assimilation techniques were evaluated using the Ensemble Transform Kalman Filter (ETKF) across two coupling intensities within MAOOAM—one with strong interaction and the other with weak interaction between the atmosphere and ocean. Our findings indicate a distinct advantage of the fully Lagrangian method over the Eulerian approach, particularly in estimating ocean stream functions and temperature. With a large ensemble size and a short data assimilation window, the Lagrangian ensemble method effectively constrains the propagation of atmospheric state errors. The results underscore the potential utility of assimilating surface drifter data in coupled data assimilation systems for climate prediction and reanalysis. Given the typically under-observed state of the ocean compared to the atmosphere, these findings highlight the critical importance of integrating surface drifter observations to enhance the accuracy of coupled ocean-atmosphere models. This study lays a foundation for future research aimed at advancing forecast models and deepening our understanding of the complex interactions at the air-sea interface, contributing to more precise climate reanalysis and predictions.