CALM: Carbon-Aware Learning for Marine Chlorophyll Reconstruction in Three Dimensions

Reconstructing three-dimensional ocean chlorophyll from sparse observations is essential for understanding marine ecosystems and carbon cycling. Direct tracer reconstruction overlooks that chlorophyll is a regulated pigment whose abundance depends on both phytoplankton carbon biomass and pigment allocation. We introduce CALM, a deep learning framework that reconstructs chlorophyll through coupled organic and inorganic carbon states. Conditioned on sparse chlorophyll, dense inorganic carbon, and environmental drivers, separate effective transport operators preserve each carbon pool’s global inventory, learned interconversion preserves their local sum, and surface and lower-boundary corrections account for external exchange. A constrained neural decoder uses a variable chlorophyll-to-carbon ratio to ensure that pigment carbon does not exceed available organic carbon. These properties follow from the architecture without conservation penalties. Experiments across three observation densities show improved reconstruction accuracy over the evaluated baselines, supported by carbon-budget audits and component ablations. At 10% sampling, CALM reduces NRMSE and RMSE by 11.6% and 19.5% over the strongest baseline.