Global Atmospheric Budget of Acetone: Air‐Sea Exchange and the Contribution to Hydroxyl Radicals. Issue 15 (7th August 2020)

• Record Type: Journal Article
• Authors: Wang, Siyuan; Apel, Eric C.; Schwantes, Rebecca H.; Bates, Kelvin H.; Jacob, Daniel J.; Fischer, Emily V.; Hornbrook, Rebecca S.; Hills, Alan J.; Emmons, Louisa K.; Pan, Laura L.; Honomichl, Shawn; Tilmes, Simone; Lamarque, Jean‐François; Yang, Mingxi; Marandino, Christa A.; Saltzman, Eric S.; de Bruyn, Warren; Kameyama, Sohiko; Tanimoto, Hiroshi; Omori, Yuko; Hall, Samuel R.; Ullmann, Kirk; Ryerson, Thomas B.; Thompson, Chelsea R.; Peischl, Jeff; Daube, Bruce C.; Commane, Róisín; McKain, Kathryn; Sweeney, Colm; Thames, Alexander B.; Miller, David O.; Brune, William H.; Diskin, Glenn S.; DiGangi, Joshua P.; Wofsy, Steven C.;
• Is Part Of: Journal of geophysical research. Volume 125:Issue 15(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-08-07
• Subjects: acetone -- air‐sea exchange -- machine learning -- HOx radicals; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2020JD032553
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: 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. In this work, the global budget of acetone is evaluated using two global models: CAM‐chem and GEOS‐Chem. CAM‐chem uses an online air‐sea exchange framework to calculate the bidirectional oceanic acetone fluxes, which is coupled to a data‐oriented machine‐learning approach. The machine‐learning algorithm is trained using a global suite of seawater acetone measurements. GEOS‐Chem uses a fixed surface seawater concentration of acetone to calculate the oceanic fluxes. Both model simulations are compared to airborne observations from a recent global‐scale, multiseasonal campaign, the NASA Atmospheric Tomography Mission (ATom). We find that both CAM‐chem and GEOS‐Chem capture the measured acetone vertical distributions in the remote atmosphere reasonably well. The combined observational and modeling analysis suggests that (i) the ocean strongly regulates the atmospheric budget of acetone. The tropical and subtropical oceans are mostly a net source of acetone, while the high‐latitude oceans are a net sink. (ii) CMIP6 anthropogenic emission inventory may underestimate acetone and/or its precursors in the Northern Hemisphere. (iii) The MEGAN biogenic emissions model may overestimate acetone and/or its precursors, and/or the biogenic oxidation mechanisms may overestimate the acetone yields. (iv) The models consistently overestimate acetone in the upper troposphere‐lower stratosphere over the Southern Ocean in austral winter. (v) Acetone contributes up to 30–40% of hydroxyl radical production in the tropical upper troposphere/lower stratosphere. Plain Language Summary: Acetone is widely observed in the Earth's atmosphere, with mixing ratios ranging from parts‐per‐trillion levels in the stratosphere to parts‐per‐billion levels in polluted regions. Acetone is directly emitted from a wide variety of natural and anthropogenic sources and is also produced from the photochemical oxidation of a number of precursors. The role of the ocean is complicated; acetone is produced in the ocean from the photolysis of colored dissolved organic materials or from biological processes but is also removed via microbial uptake. Previous studies have found that the direction and magnitude of oceanic acetone fluxes vary dramatically with seasons and locations. In this work, we use a data‐oriented machine‐learning approach to predict the surface seawater concentration of acetone, leveraging in situ acetone measurements in the surface seawater around the globe. This machine learning‐based approach shows promising potential and can be expanded to the bottom‐up oceanic emissions of other climate‐relevant compounds. Key Points: We develop an online air‐sea exchange module for acetone, with ocean biogeochemistry represented using data‐oriented machine learning Two separate global acetone simulations are compared to global‐scale multiseasonal airborne observations Global models consistently overestimate acetone in the upper troposphere over the Southern Ocean in austral winter