Evaluation of multi-source forcing datasets for drift trajectory prediction using Lagrangian models in the South China Sea

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The performance of different forcing datasets for trajectory prediction in the South China Sea and the sensitivity of prediction accuracy from selected datasets and model-based methods have been studied using Lagrangian models. Seven global ocean forecast or reanalysis forcing datasets including three current forcing datasets (CMEMS, CMEMS-R, and GlobCurrent), three wind forcing datasets (NCEP, ERA5, and IFREMER) and one waves forcing dataset (MFWAM) were selected to advect the virtual drifters for modeling the drift trajectories of dummies and SVP drifters in four experimental regions. We use an integrated analysis framework to conduct several holistic prediction experiments, evaluate quantitatively the accuracy of trajectory prediction using statistical indicators and FSLE analysis, and to further estimate the uncertainty factors from data and model levels. The experimental results show that the CMEMS-R can meet the needs of trajectory prediction of dummies within 48 hours in Region 1, but neither CMEMS nor CMEMS-R can better follow the complex ocean current pattern in Region 2, which leads to the poor results of the 24-hour trajectory prediction driven by CMEMS or CMEMS-R. In the deep water regions (Region 3-4), the performance of the GlobCurrent derived from satellite-observations for modeling the trajectories of SVP drifters is comparable with the CMEMS in general, and shows obvious advantages in Region 3, even though CMEMS has a finer spatial and temporal resolution. The two wind forcing datasets, ERA5 and IFREMER, show roughly similar performance in the trajectory prediction in Region 1. Compared with the significant difference between current forcing datasets, the three wind forcing datasets have relatively consistent temporal and spatial variations in the other three experimental regions. In four experimental regions, the separation distance between the simulated and actual trajectories increases in a uniform linear mode with mean separation velocity. In general, the selection of current forcing fields has more influence on the trajectory prediction of the three types of floating objects than other factors. The ocean current pattern represented by related current forcing datasets is the key to this essential influence.