Acetone is one of the most abundant oxygenated volatile organic compounds (VOCs) in the atmosphere. The oceans impose a strong control on atmospheric acetone, yet the oceanic fluxes of acetone remain poorly constrained. The air-sea exchange of acetone is largely controlled by the surface seawater concentration of acetone. This dataset consists of the surface seawater concentration of acetone, predicted by an observationally trained machine-learning algorithm (random forest). The observationally trained machine-learning algorithm is discussed in Wang et al. (2019). The training dataset includes ship-borne observations from a number of previous studies: Yang et al. (2014a); Yang et al. (2014b); Dixon et al. (2014); Beale et al. (2013); Kameyama et al. (2010); Hudson et al. (2007); Marandino et al. (2005); Marandino et al. (Knorr06). This dataset is in 0.9*1.25 (degree*degree) horizontal resolution (finite volume), and can be used to calculate the bi-directional air-sea exchange of acetone.
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