EGU23-13364, updated on 26 Feb 2023
https://doi.org/10.5194/egusphere-egu23-13364
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Estimation of dichloromethane emissions in East Asia using recent high-altitude aircraft observations and synthesis inversion

Zihao Wang1,2, Chris Wilson1,3, Wuhu Feng1,4, Ying Li2,5, Ryan Hossaini6,7, Elliot Atlas8, Eric Apel9, David Oram10,11, Karina Adcock10, Stephen Donnelly8, Roger Hendershot9, Alan Hills9, Rebecca Hornbrook9, Johannes Laube10,12, Richard Lueb8,9, Thomas Röckmann13, Sue Schauffler8,9, Katie Smith8, Victoria Treadaway8,14, and Martyn Chipperfield1,3
Zihao Wang et al.
  • 1School of Earth and Environment, University of Leeds, Leeds, UK. (eezw@leeds.ac.uk)
  • 2Centre for the Oceanic and Atmospheric Science at SUSTech (COAST), Southern University of Science and Technology, Shenzhen, China.
  • 3National Centre for Earth Observation (NCEO), University of Leeds, Leeds, UK.
  • 4National Centre for Atmospheric Science (NCAS), University of Leeds, Leeds, UK.
  • 5Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, China.
  • 6Lancaster Environment Centre, Lancaster University, Lancaster, UK.
  • 7Centre of Excellence in Environmental Data Science, Lancaster University, Lancaster, UK.
  • 8Rosenstiel School of Marine, Atmospheric, and Earth Science, University of Miami, Miami, USA.
  • 9Atmospheric Chemistry Observations & Modeling Laboratory (ACOM), National Center for Atmospheric Research (NCAR), Boulder, USA
  • 10Centre for Ocean and Atmospheric Sciences, School of Environmental Sciences, University of East Anglia, Norwich, UK.
  • 11National Centre for Atmospheric Science (NCAS), School of Environmental Sciences, University of East Anglia, Norwich, UK.
  • 12Forschungszentrum Jülich GmbH, IEK-7: Stratosphere, Jülich, Germany
  • 13Institute for Marine and Atmospheric Research Utrecht, Utrecht University, Utrecht, the Netherlands
  • 14now at: Chemical Sciences Laboratory (CSL), National Oceanic and Atmospheric Administration (NOAA), Boulder, USA

Although the Montreal Protocol has been successful in reducing the emissions of long-lived ozone-depleting substances, certain unregulated, chlorinated very short-lived substances (VSLS, lifetimes < 6 months) are believed to be having an increasing impact on stratospheric ozone depletion. The major sources of the chlorinated VSLS are anthropogenic. Emissions of chlorinated VSLS have been reported to be increasing from both bottom-up estimates and observations in recent years, among which dichloromethane (DCM) is the most abundant. Emissions from East Asia have been identified as contributing significantly to this increase (Oram et al., 2017; Claxton et al., 2020; Adcock et al., 2021; An et al., 2021).

Here we use synthesis inversion to derive an estimation of DCM emissions with a focus on East Asia, with input from the TOMCAT/SLIMCAT 3-D offline chemical transport model (CTM), a gridded annual global emission estimate, and aircraft observations from three recent campaigns - POSIDON (2016, https://csl.noaa.gov/projects/posidon/), StratoClim (2017, http://www.stratoclim.org/), and ACCLIP (2021, https://www2.acom.ucar.edu/acclip). The CTM contains the production and loss of DCM and is driven by reanalysed meteorology (Chipperfield, 2006), with gridded emission field of DCM (Claxton et al., 2020). In the model we set up more source regions than previous studies based on prior information, transport pathways into stratosphere, and the distribution of major city clusters. The inversion is performed by finding the minimum of the cost function (Tarantola and Valette, 1982): xa = xb+ [GTR-1G + B-1]-1GTR-1[y - Gxb], where y has the observations, xb is the prior estimate, B and R are the error covariance matrix of the prior estimates and the observations respectively, and G is the sensitivity matrix, as an operator mapping the emissions onto the observations by the CTM. Then xa can be calculated as known as the posterior estimate. Coupling the model and observations, xa is considered the best estimate and reduces the errors in the prior estimate.

We will present our analysis of DCM emissions up to the present day and compare them with previously published values and longer-term trends.

References:

Adcock et al., 2021, JGR Atmos., https://doi.org/10.1029/2020JD033137.

An et al., 2021, Nat. Commun., https://doi.org/10.1038/s41467-021-27592-y.

Chipperfield, 2006, QJR Meteorol. Soc., https://doi.org/10.1256/qj.05.51.

Claxton et al., 2020, JGR Atmos., https://doi.org/10.1029/2019JD031818.

Oram et al., 2017, ACP, https://doi.org/10.5194/acp-17-11929-2017.

Tarantola and Valette,1982, Rev. Geophys., https://doi.org/10.1029/RG020i002p00219.

How to cite: Wang, Z., Wilson, C., Feng, W., Li, Y., Hossaini, R., Atlas, E., Apel, E., Oram, D., Adcock, K., Donnelly, S., Hendershot, R., Hills, A., Hornbrook, R., Laube, J., Lueb, R., Röckmann, T., Schauffler, S., Smith, K., Treadaway, V., and Chipperfield, M.: Estimation of dichloromethane emissions in East Asia using recent high-altitude aircraft observations and synthesis inversion, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13364, https://doi.org/10.5194/egusphere-egu23-13364, 2023.