Trends in Sea‐Air CO₂ Fluxes and Sensitivities to Atmospheric Forcing Using an Extremely Randomized Trees Machine Learning Approach

Abstract Monthly global sea‐air CO 2 flux maps are created on a 1° by 1° grid from surface water fugacity of CO 2 (fCO 2w ) observations using an extremely randomized trees (ET) machine learning technique (AOML‐ET) over the period 1998–2020. Global patterns and magnitudes of fCO 2w from AOML‐ET are consistent with other machine learning methods and with the updated climatology of Takahashi et al. (2009, https://doi.org/10.1016/j.dsr2.2008.12.009 ). However, the magnitude and trends of sea‐air CO 2 fluxes are sensitive to the treatment of atmospheric forcing. In the default configuration of AOML‐ET, the average global sea‐air CO 2 flux is −1.70 PgC yr −1 with a negative trend of −0.89 ± 0.19 PgC yr −1 decade −1 . The large negative trend is driven by a small uptake at the beginning of the record. This leads to increasing sea‐air fCO 2 gradients over time, particularly at high latitudes. However, changing the target variable in AOML‐ET from fCO 2w to sea‐air CO 2 fugacity difference, ∆fCO 2 , results in a lower negative trend of −0.51 PgC yr −1 decade −1 , though the average flux remains similar at −1.65 PgC yr −1 . This trend is close to the consensus trend of ocean uptake from machine learning and models in the Global Carbon Budget of −0.46 ± 0.11 PgC yr −1 decade −1 switching to a gas transfer parameterization with weaker wind speed dependence reduces uptake by 60% but does not affect the trend. Substituting a spatially resolved marine air CO 2 mole fraction product for the zonally invariant marine boundary layer CO 2 product yields greater influx by up to 20% in the industrialized continental outflow regions.