Abstract Marginal seas contribute disproportionately to the ocean carbon cycle but remain poorly constrained due to strong spatial and seasonal variability. Here, we combine newly collected in situ particle imagery with machine learning to reconstruct monthly, depth‐resolved climatologies of particle biovolume and size distribution in the South China Sea, the largest tropical marginal sea in the North Pacific. Particle biovolume peaks in winter due to monsoon‐driven mixing, with secondary summer maxima in regions influenced by upwelling and river plumes. Although particle size generally covaries with biovolume, seasonal decoupling occurs in plume‐dominated zones. Applying a regionally optimized size‐based model, we estimate an annual carbon export of 111.7 ± 6.0 Tg C yr −1 , corresponding to a high export efficiency of 17.5 ± 0.9%, exceeding typical low‐latitude values. Our study integrates observational‐modeling approaches to highlight the diverse physical and biogeochemical drivers of particle heterogeneity and an elevated biological pump efficiency in complex marginal sea.
Xu et al. (Sun,) studied this question.