DOI: 10.7280/d1vt4v
title: Large global variations in the carbon dioxide removal potential of seaweed farming due to biophysical constraints

description: 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.
description: 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.
description: # Data for: Biophysical potential and uncertainties of global seaweed
 farming, Arzeno-Soltero et al. (2023) Isabella Arzeno-Soltero
 () Benjamin T. Saenz ()
 Kristen Davis () # Updated: 2023-04-04 * Suggested
 Citations: * Corresponding publication: Arzeno-Soltero, I. B., B.T. Saenz,
 C. A. Frieder, M.C. Long, J. DeAngelo, S.J. Davis, K.A. Davis. Biophysical
 potential and uncertainties of global seaweed farming. Communications
 Earth and Environment.
 \============================================================================ Arzeno-Soltero et al. (2023) employs the Global MacroAlgae Cultivation MODeling System (G-MACMODS) to estimate the global potential for seaweed cultivation under two bounding nitrate scenarios, taking into consideration the deep uncertainty surrounding biophysical parameters. The datasets used to construct the figures in the manuscript and supplemental information include 1. \[seaweed_type]_[nutient_scenario]_group1.nc: Annually integrated variables such as growth and harvest (standard runs), Monte Carlo percentiles, and inter-annual variability statistics 2. \[seaweed_type]_[nutient_scenario]_group2.nc: Random forest feature importance and limitation coefficients (equation 7 in the main manuscript) 3. \[seaweed_type]_[nutient_scenario]_Copernicus.nc: Annually integrated growth and harvest for the ambient nutrient scenario (standard run) 4. \[seaweed_type]_validation.nc: Literature and G-MACMODS output data used to plot Supplementary Figs. 5-8 (a,c) The full G-MACMODS code can be found in [ ].