Towards multi-tracer constraints on photosynthesis: unifying solar-induced chlorophyll fluorescence, carbonyl sulfide flux and carbon isotope discrimination in BESS framework

Photosynthesis governs terrestrial carbon uptake and tightly couples carbon, energy and water exchange. However, any single observation has limited spatiotemporal coverage. Eddy-covariance CO2 exchange measurements, for instance, are still underrepresented in the tropics. At the global scale, model-based estimates of photosynthesis, often quantified as gross primary productivity (GPP), remain highly uncertain. Solar-induced chlorophyll fluorescence (SIF), carbonyl sulfide (COS or OCS), and carbon isotope discrimination (Δ¹³C) provide complementary windows into photosynthesis. They offer partially independent constraints on energy partitioning, conductance limitations, and diffusion–carboxylation controls. Breathing Earth System Simulator (BESS) is a remote-sensing-driven, process-based model that couples canopy carbon assimilation, evapotranspiration, and surface energy balance. Building on BESS, we (1) incorporate the Johnson–Berry model to provide a mechanistic yet parsimonious description of energy conversion within the electron transport system, enabling SIF simulation while accounting for photosynthetic control, cyclic electron flow, and non-photochemical quenching; (2) couple OCS exchange to BESS through shared conductance pathways (stomatal and boundary-layer) and biochemical capacity (Vcmax25℃), and implement an explicit mesophyll conductance scheme so that net CO₂ assimilation is computed from chloroplastic CO₂ concentration (Cc); (3) integrate a ¹³C discrimination module that mechanistically estimates Δ¹³C along the explicitly simulated CO₂ diffusion pathway from the atmosphere to the chloroplast, accounting for fractionation during boundary layer, stomatal, and mesophyll diffusion, as well as Rubisco carboxylation. By coupling SIF, OCS exchange, and Δ¹³C within a shared canopy gas-exchange and energy-balance framework, BESS is extended into a multi-tracer forward framework that generates internally consistent predictions of these tracers together with carbon-water fluxes. Based on this framework, we aim to: (1) evaluate whether multi-tracer integration improves simulations of carbon-water fluxes; (2) explore multi-constraint parameter optimization or data assimilation using independent observations to reduce uncertainty in photosynthesis estimates; and (3) quantify relationships between tracer signals and fluxes (e.g., GPP–SIF, GPP–OCS, SIF–OCS, Δ¹³C–GPP) and their responses to environmental variability.