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1

Institute for Terrestrial and Planetary Atmospheres, School of Marine and Atmospheric Sciences, State University of New York at Stony Brook, Stony Brook, NY 11794, USA

2

Division of Polar Ocean Environment, Korea Polar Research Institute, Incheon 406-840, South Korea

3

Atmospheric Chemistry Division, National Center for Atmospheric Research, Boulder, CO 80301, USA





*

Author to whom correspondence should be addressed.



Academic Editor: Robert W. Talbot

Abstract Most previous top-down global carbon monoxide CO budget estimates have used only concentration information and shown large differences in individual source estimates. Since CO from certain sources has a specific isotopic signature, coupling the concentration and isotope fraction information can provide a better constraint on CO source strength estimates. We simulate both CO concentration and its oxygen isotopologue C18O in the 3-D global chemical transport model MOZART-4 and compare the results with observations. We then used a Bayesian inversion to calculate the most probable global CO budget. In the analysis, δ18O information is jointly applied with concentration. The joint inversion results should provide more accurate and precise inversion results in comparison with CO-only inversion. Various methods combining the concentration and isotope ratios were tested to maximize the benefit of including isotope information. The joint inversion of CO and δ18O estimated total global CO production at 2951 Tg-CO-yr in 1997, 3084 Tg-CO-yr in 1998, and 2583 Tg-CO-yr in 2004. The updated CO budget improved both the modeled CO and δ18O. The clear improvement shown in the δ18O implies that more accurate source strengths are estimated. Thus, we confirmed that the observation of CO isotopes provide further substantial information for estimating a global CO budget. View Full-Text

Keywords: carbon monoxide; inverse modeling; isotope; global CO budget carbon monoxide; inverse modeling; isotope; global CO budget





Autor: Keyhong Park 1,2,* , Louisa K. Emmons 3, Zhihui Wang 1 and John E. Mak 1

Fuente: http://mdpi.com/



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