Improved Quantification of Ocean Carbon Uptake by Using Machine Learning to Merge Global Models and pCO2 Data

Abstract

Abstract The ocean plays a critical role in modulating climate change by sequestering CO2 from the atmosphere. Quantifying the CO2 flux across the air‐sea interface requires time‐dependent maps of surface ocean partial pressure of CO2 (pCO2), which can be estimated using global ocean biogeochemical models (GOBMs) and observational‐based data products. GOBMs are internally consistent, mechanistic representations of the ocean circulation and carbon cycle, and have long been the standard for making spatio‐temporally resolved estimates of air‐sea CO2 fluxes. However, there are concerns about the fidelity of GOBM flux estimates. Observation‐based products have the strength of being data‐based, but the underlying data are sparse and require significant extrapolation to create global full‐coverage flux estimates. The Lamont Doherty Earth Observatory‐Hybrid Physics Data (LDEO‐HPD) pCO2 product is a new approach to estimating the temporal evolution of surface ocean pCO2 and air‐sea CO2 exchange. LDEO‐HPD uses machine learning to merge high‐quality observations with state‐of‐the‐art GOBMs. We train an eXtreme Gradient Boosting (XGB) algorithm to learn a non‐linear relationship between model‐data mismatch and observed predictors. GOBM fields are then corrected with the predicted model‐data misfit to estimate real‐world pCO2 for 1982–2018. The resulting reconstruction by LDEO‐HPD is in better agreement with independent pCO2 observations than other currently available observation‐based products. Within uncertainties, LDEO‐HPD global ocean uptake of CO2 agrees with other products and the Global Carbon Budget 2020.

Keywords

• ocean
• pCO2
• machine learning
• xgboost
• carbon flux
• global carbon budget