A new estimate of oceanic CO2 fluxes by machine learning reveals the impact of CO2 trends in different methods

Global oceans have absorbed a substantial portion of the anthropogenic carbon dioxide (CO2) emitted into the atmosphere. Data-based machine learning (DML) estimates for the oceanic CO2 sink have become an import part of the Global Carbon Budget in recent years. Although DML models are considered objective as they impose very few subjective conditions in optimizing model parameters, they face the challenge of data scarcity problem when applied to mapping ocean CO2 concentrations, from which air-sea CO2 fluxes can be computed. Data scarcity forces DML models to pool multiple years’ 10 data for model training. When the time span extends to a few decades, the result could be largely affected by how ocean CO2 trends are obtained. This study extracted the trends using a new method and reconstructed monthly surface ocean CO2 concentrations and air-sea fluxes in 1980-2020 with a spatial resolution of 1x1 degree. Comparing with six other products, our results show a smaller oceanic sink and the sink in early and late year of the modelled period could be overestimated if ocean CO2 trends were not well processed by models. We estimated that the oceanic sink has increased from 1.790.47 PgC yr in 15 1980s to 2.580.20 PgC yr in 2010s with a mean acceleration of 0.027 PgC yr.