Estimates suggest that over 4 gigatons per year of carbon dioxide (Gt-CO₂/year) be removed from the atmosphere by 2050 to meet international climate goals. One strategy for carbon dioxide removal is seaweed farming, however, its global potential remains highly uncertain. Here, we apply a dynamic seaweed growth model that includes growth-limiting mechanisms, such as nitrate supply, to estimate the global potential yield of four types of seaweed. We estimate that harvesting 1 Gt/year of seaweed carbon would require farming over 1 million km² of the most productive exclusive economic zones, located in the equatorial Pacific. We estimate cultivation area would need to be tripled to attain an additional 1 Gt/year of harvested carbon, indicating dramatic reductions in carbon harvest efficiency beyond the most productive waters. Improving the accuracy of annual harvest yield estimates requires better understanding of biophysical constraints and seaweed loss rates, such as disease, grazing, and wave erosion.

Results presented in this paper are from G-MACMODS, the global MacroAlgal Cultivation MODel, which predicts spatially-resolved (1/12th deg resolution) cultivated seaweed yield with constraints from both extrinsic (environmental forcing) and intrinsic factors (biological parameters; e.g., growth rates, nitrate uptake, nitrogen exudation, and mortality, among others). To test sensitivities and evaluate uncertainties, we performed 1012–1066 simulations of macroalgal growth and harvest for each of four seaweed types (defined using biophysical characteristics from currently-farmed temperate and tropical red and brown genera). Each simulation sampled from a uniform distribution of parameter values spanning the full range of relevant values reported in the literature. Environmental forcing included water temperature, solar irradiance, current velocities, wave height, wave period, and nitrate concentrations, sourced from a combination of satellite measurements (MODIS) and global ocean model simulations (HYCOM and CESM). Although we tested the model with forcing data from different years, results reported here reflect the year 2017 (a recent year without strong El Niño/La Niña anomalies, and a representative seasonally-varying climatology of physically-mediated nitrate fluxes. Simulations that use parameter values best supported by the literature or deemed the most appropriate by the authors are termed "standard runs." Seeding and harvesting for each seaweed type were optimized based on the yields from this standard configuration. We assess the importance of different model parameters using “random forest” classification analysis.