A global 3D chlorophyll-a dataset derived from multimodal deep learning reconstruction

Abstract. Chlorophyll-a (Chl-a) is a key variable for characterizing marine phytoplankton biomass and upper-ocean biogeochemical variability. Existing global products are generally limited to the ocean surface and therefore cannot adequately resolve subsurface vertical structure. In this study, a multimodal Profile-Surface Transformer (PST) framework was developed by integrating Biogeochemical-Argo (BGC-Argo) Chl-a profiles, Core-Argo temperature-salinity profiles, and satellite-derived surface Chl-a to reconstruct the three-dimensional (3D) vertical structure of Chl-a. A global monthly mean 3D Chl-a dataset at 1° spatial resolution for 2005–2025 was subsequently constructed. Evaluation of the dataset demonstrates that PST exhibits robust stability and effective profile reconstruction capability across various generalization scenarios. The coefficients of determination under random split, year-based split, and spatial cross-validation are 0.8923, 0.8739, and 0.8588 ± 0.0105, respectively. Furthermore, independent external validation using ship-based observations confirms that the model maintains strong generalization ability and high accuracy across different observing systems. The constructed dataset successfully reproduces known global patterns of Chl-a vertical structure and its subsurface chlorophyll maximum (SCM), as well as seasonal variability and spatial characteristics in the upper ocean. The long-term evolution of global SCM depth during 2005–2025 is characterized primarily by regionally heterogeneous redistribution superimposed on a stable zonal background, rather than by a pronounced monotonic change in the global mean depth. Overall, the constructed long-term 3D Chl-a dataset integrates surface observations and discrete biogeochemical profiles, delivering a new observationally constrained global product. This dataset is well-suited for studies on the vertical ecological structure of phytoplankton, long-term ocean biogeochemical variability, and the responses of upper-ocean ecosystems to climate change. The dataset is publicly available from Zenodo (Meng et al., 2026a) at: https://doi.org/10.5281/zenodo.19494734.