edu.ucar.gdex::07f089eb-fcd8-4fd0-9a0d-38b45cedd9dc eng; USA utf8 dataset GDEX Curator UCAR/NCAR - GDEX datahelp@ucar.edu pointOfContact 2022-07-21T12:01:35-06:00 ISO 19115 Geographic information - Metadata ISO 19115:2003(E) https://doi.org/10.5065/cg38-b505 https://doi.org/10.1002/2014JC010227 https browser Air-sea exchange of methanol and acetone during HiWinGS: Estimation of air phase, water phase gas transfer velocities Yang, M., Blomquist, B. W., & Nightingale, P. D. (2014). Journal of Geophysical Research: Oceans, 119(10), 7308–7323. https://doi.org/10.5194/acp-14-7499-2014 https browser Air–sea fluxes of oxygenated volatile organic compounds across the Atlantic Ocean Yang, M., Beale, R., Liss, P., Johnson, M., Blomquist, B., & Nightingale, P. (2014). Atmospheric Chemistry and Physics, 14(14), 7499–7517. https://doi.org/10.3389/fmicb.2014.00243 https browser Microbial acetone oxidation in coastal seawater Dixon, J. L., Beale, R., Sargeant, S. L., Tarran, G. A., & Nightingale, P. D. (2014). Frontiers in Microbiology, 5. https://doi.org/10.1002/jgrc.20322 https browser Methanol, acetaldehyde, and acetone in the surface waters of the Atlantic Ocean Beale, R., Dixon, J., Arnold, S., Liss, P., & Nightingale, P. (2013). Journal of Geophysical Research: Oceans, 118(10), 5412–5425. https://doi.org/10.1016/j.marchem.2010.08.003 https browser High-resolution measurement of multiple volatile organic compounds dissolved in seawater using equilibrator inlet–proton transfer reaction-mass spectrometry (EI–PTR-MS) Kameyama, S., Tanimoto, H., Inomata, S., Tsunogai, U., Ooki, A., Takeda, S., et al. (2010). Marine Chemistry, 122(1), 59–73. https://doi.org/10.1007/s00216-007-1324-x https browser Determination of acetone in seawater using derivatization solid-phase microextraction Hudson, E. D., Okuda, K., & Ariya, P. A. (2007). Analytical and Bioanalytical Chemistry, 388(5), 1275–1282. https://doi.org/10.1029/2005GL023285 https browser Oceanic uptake and the global atmospheric acetone budget Marandino, C. A., Bruyn, W. J. D., Miller, S. D., Prather, M. J., & Saltzman, E. S. (2005). Geophysical Research Letters, 32(15). https://doi.org/DOI:10.1029/2019JD031288 https browser Ocean biogeochemistry control on the marine emissions of brominated very short-lived ozone-depleting substances: a machine-learning approach Wang, S., Kinnison, D, Montzka, S, Apel, E., Hornbrook, R., Hills, A., et al. (2019). Journal of Geophysical Research. Surface seawater concentration of acetone: monthly climatology 2020-07-06 publication 2020-07-01 modified https://doi.org/10.5065/cg38-b505 UCAR/NCAR - GDEX datahelp@ucar.edu publisher Wang, Siyuan UCAR/NCAR - Atmospheric Chemistry Observations and Modeling Laboratory siyuan@ucar.edu author Emmons, Louisa UCAR/NCAR - Atmospheric Chemistry Observations and Modeling Laboratory emmons@ucar.edu author Marandino, Christa Helmholtz-Zentrum für Ozeanforschung Kiel cmarandino@geomar.de author De Bruyn, Warren Chapman University debruyn@chapman.edu author Saltzman, Eric University of California Irvine eric.saltzman@uci.edu author Tanimoto, Hiroshi National Institute for Environmental Studies tanimoto@nies.go.jp author Yang, Ming-Xi Plymouth Marine Laboratory miya@pml.ac.uk author Kameyama, Sohiko Hokkaido University skameyama@ees.hokudai.ac.jp author Omori, Yuko University of Tsukuba omori.yuko.ft@u.tsukuba.ac.jp author 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. Siyuan Wang UCAR/NCAR - Atmospheric Chemistry Observations and Modeling Laboratory siyuan@ucar.edu pointOfContact Louisa Emmons UCAR/NCAR - Atmospheric Chemistry Observations and Modeling Laboratory emmons@ucar.edu pointOfContact application/x-netcdf Dataset DataCite Resource Type 2014-10-16 revision 3.1 DataCite Metadata Working Group originator resourceTypeGeneral EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC CHEMISTRY > CARBON AND HYDROCARBON COMPOUNDS > NON-METHANE HYDROCARBONS/VOLATILE ORGANIC COMPOUNDS EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > ORGANIC CARBON theme NASA/GCMD Earth Science Keywords Science and Services Keywords 2018-03-15 revision 8.6 Global Change Master Directory (GCMD). 2018. GCMD Keywords, Version 8.6. Greenbelt, MD: Global Change Data Center, Science and Exploration Directorate, Goddard Space Flight Center (GSFC) National Aeronautics and Space Administration (NASA). URL (GCMD Keyword Forum Page): https://earthdata.nasa.gov/gcmd-forum Creative Commons Attribution 4.0 International License. None 0.9 1.25 eng; USA -180.0 180.0 -90.0 90.0 application/x-netcdf UCAR/NCAR - CISL - CDP resourceProvider https://gdex.ucar.edu/dataset/id/07f089eb-fcd8-4fd0-9a0d-38b45cedd9dc.html https browser Surface seawater concentration of acetone: monthly climatology Metadata Link download