Global Estimates of Marine Gross Primary Production Based on Machine Learning Upscaling of Field Observations
Yibin Huang, David Nicholson, Bangqin Huang, Nicolas Cassar
Abstract
Abstract Approximately half of global primary production occurs in the ocean. While the large‐scale variability in net primary production (NPP) has been extensively studied, ocean gross primary production (GPP) has thus far received less attention. In this study, we derived two satellite‐based GPP models by training machine learning algorithms (Random Forest) with light‐dark bottle incubations (GPP LD ) and the triple isotopes of dissolved oxygen (GPP 17Δ ). The two algorithms predict global GPPs of 9.2 ± 1.3 × 10 15 and 15.1 ± 1.05 × 10 15 mol O 2 yr −1 for GPP LD and GPP 17Δ , respectively. The projected GPP distributions agree with our understanding of the mechanisms regulating primary production. Global GPP 17Δ was higher than GPP LD by an average factor of 1.6 which varied meridionally. The discrepancy between GPP 17Δ and GPP LD simulations can be partly explained by the known biases of each methodology. After accounting for some of these biases, the GPP 17Δ and GPP LD converge to 9.5 ∼ 12.6 × 10 15 mol O 2 yr −1 , equivalent to 103 ∼ 150 Pg C yr −1 . Our results suggest that global oceanic GPP is 1.5–2.2 fold larger than oceanic NPP and comparable to GPP on land.