Monthly global sea-air CO2 flux maps are created on a 1 degrees by 1 degrees grid from surface water fugacity of CO2 (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, ). However, the magnitude and trends of sea-air CO2 fluxes are sensitive to the treatment of atmospheric forcing. In the default configuration of AOML-ET, the average global sea-air CO2 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 CO2 fugacity difference, triangle 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 CO2 mole fraction product for the zonally invariant marine boundary layer CO2 product yields greater influx by up to 20% in the industrialized continental outflow regions.