Chapter 34
Global CO Emission Estimates Inferred
from Assimilation of MOPITT CO,
Together with Observations of O 3 , NO 2 ,
HNO 3 , and HCHO
Xuesong Zhang, Dylan Jones, Martin Keller, Zhe Jiang, Adam E. Bourassa,
D. A. Degenstein and Cathy Clerbaux
Abstract Atmospheric carbon monoxide (CO) emissions estimated from inverse
modeling analyses exhibit large uncertainties, due, in part, to discrepancies in the
tropospheric chemistry in atmospheric models. We attempt to reduce the uncertainties
in CO emission estimates by constraining the modeled abundance of ozone (O 3 ),
nitrogen dioxide (NO 2 ), nitric acid (HNO 3 ), and formaldehyde (HCHO), which are
constituents that play a key role in tropospheric chemistry. Using the GEOS-Chem
four-dimensional variational (4D-Var) data assimilation system, we estimate CO
emissions by assimilating observations of CO from the Measurement of Pollution In
the Troposphere (MOPITT) and the Infrared Atmospheric Sounding Interferometer
(IASI), together with observations of O 3 from the Optical Spectrograph and InfraRed
Imager System (OSIRIS) and IASI, NO 2 and HCHO from the Ozone Monitoring
Instrument (OMI), and HNO 3 from the Microwave Limb Sounder (MLS). Although
our focus is on quantifying CO emission estimates, we also infer surface emissions
of nitrogen oxides (NO x = NO + NO 2 ) and isoprene. Our results reveal that this
multiple species chemical data assimilation produces a chemical consistent state that
effectively adjusts the CO–O 3 –OH coupling in the model. The O 3 -induced changes
in OH are particularly large in the tropics. We show that the analysis results in a
X. Zhang (B) · D. Jones · M. Keller
Department of Physics, University of Toronto, Toronto, ON, Canada
e-mail: xuesong.zhang@mail.utoronto.ca
Z. Jiang
School of Earth and Space Sciences, University of Science and Technology of China, Hefei,
Anhui, China
A. E. Bourassa · D. A. Degenstein
Department of Physics and Engineering Physics, University of Saskatchewan, Saskatoon, SK,
Canada
C. Clerbaux
UPMC Université Paris 6, Université Versailles St-Quentin, LATMOS-IPSL, CNRS/INSU, Paris,
France
Spectroscopie de l’Atmosphére, Service de Chimie Quantique et Photophysique, Université Libre
de Bruxelles, Brussels, Belgium
© Springer Nature Switzerland AG 2020
C. Mensink et al. (eds.), Air Pollution Modeling and its Application XXVI,
Springer Proceedings in Complexity,
https://doi.org/10.1007/978-3-030-22055-6_34
219
Global CO Emission Estimates Inferred
from Assimilation of MOPITT CO,
Together with Observations of O 3 , NO 2 ,
HNO 3 , and HCHO
Xuesong Zhang, Dylan Jones, Martin Keller, Zhe Jiang, Adam E. Bourassa,
D. A. Degenstein and Cathy Clerbaux
Abstract Atmospheric carbon monoxide (CO) emissions estimated from inverse
modeling analyses exhibit large uncertainties, due, in part, to discrepancies in the
tropospheric chemistry in atmospheric models. We attempt to reduce the uncertainties
in CO emission estimates by constraining the modeled abundance of ozone (O 3 ),
nitrogen dioxide (NO 2 ), nitric acid (HNO 3 ), and formaldehyde (HCHO), which are
constituents that play a key role in tropospheric chemistry. Using the GEOS-Chem
four-dimensional variational (4D-Var) data assimilation system, we estimate CO
emissions by assimilating observations of CO from the Measurement of Pollution In
the Troposphere (MOPITT) and the Infrared Atmospheric Sounding Interferometer
(IASI), together with observations of O 3 from the Optical Spectrograph and InfraRed
Imager System (OSIRIS) and IASI, NO 2 and HCHO from the Ozone Monitoring
Instrument (OMI), and HNO 3 from the Microwave Limb Sounder (MLS). Although
our focus is on quantifying CO emission estimates, we also infer surface emissions
of nitrogen oxides (NO x = NO + NO 2 ) and isoprene. Our results reveal that this
multiple species chemical data assimilation produces a chemical consistent state that
effectively adjusts the CO–O 3 –OH coupling in the model. The O 3 -induced changes
in OH are particularly large in the tropics. We show that the analysis results in a
X. Zhang (B) · D. Jones · M. Keller
Department of Physics, University of Toronto, Toronto, ON, Canada
e-mail: xuesong.zhang@mail.utoronto.ca
Z. Jiang
School of Earth and Space Sciences, University of Science and Technology of China, Hefei,
Anhui, China
A. E. Bourassa · D. A. Degenstein
Department of Physics and Engineering Physics, University of Saskatchewan, Saskatoon, SK,
Canada
C. Clerbaux
UPMC Université Paris 6, Université Versailles St-Quentin, LATMOS-IPSL, CNRS/INSU, Paris,
France
Spectroscopie de l’Atmosphére, Service de Chimie Quantique et Photophysique, Université Libre
de Bruxelles, Brussels, Belgium
© Springer Nature Switzerland AG 2020
C. Mensink et al. (eds.), Air Pollution Modeling and its Application XXVI,
Springer Proceedings in Complexity,
https://doi.org/10.1007/978-3-030-22055-6_34
219