Preprints
https://doi.org/10.5194/acp-2022-763
https://doi.org/10.5194/acp-2022-763
 
04 Jan 2023
04 Jan 2023
Status: this preprint is currently under review for the journal ACP.

Technical Note: Constraining the hydroxyl (OH) radical in the tropics with satellite observations of its drivers: First steps toward assessing the feasibility of a global observation strategy

Daniel C. Anderson1,2, Bryan N. Duncan2, Julie M. Nicely2,3, Junhua Liu2,4, Sarah A. Strode2,4, and Melanie B. Follette-Cook5 Daniel C. Anderson et al.
  • 1GESTAR II, University of Maryland Baltimore County, Baltimore, MD, USA
  • 2Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 3Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
  • 4GESTAR II, Morgan State University, Baltimore, MD, USA
  • 5Mesoscale Atmospheric Processes Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA

Abstract. Despite its importance in controlling the abundance of methane (CH4) and a myriad of other tropospheric species, the hydroxyl radical (OH) is poorly constrained due to its large spatial heterogeneity and the inability to measure tropospheric OH with satellites. Here, we present a methodology to infer tropospheric column OH (TCOH) in the tropics over the open oceans using a combination of a machine learning model, output from a simulation of the GEOS model, and satellite observations. Our overall goals are to assess the feasibility of our methodology, to identify potential limitations, and to suggest areas of improvement in the current observational network. The methodology reproduces the variability of TCOH from independent 3D model output and of observations from the Atmospheric Tomography mission (ATom). While the methodology also reproduces the magnitude of the 3D model validation set, the accuracy of the magnitude when applied to observations is uncertain because current observations are insufficient to fully evaluate the machine learning model. Despite large uncertainties in some of the satellite retrievals necessary to infer OH, particularly for NO2 and HCHO, current satellite observations are of sufficient quality to apply the machine learning methodology, resulting in an error comparable to that of in situ OH observations. Finally, the methodology is not limited to a specific suite of satellite retrievals. Comparison of TCOH determined from two sets of retrievals does show, however, that systematic biases in NO2, resulting both from retrieval algorithm and instrumental differences, lead to relative biases in the calculated TCOH. Further evaluation of NO2 retrievals in the remote atmosphere is needed to determine their accuracy. With slight modifications, a similar methodology could likely be expanded to the extra-tropics and over land, with the benefits of increasing our understanding of the atmospheric oxidation capacity and, for instance, informing understanding of recent CH4 trends.

Daniel C. Anderson et al.

Status: open (until 15 Feb 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Insufficient credit to other efforts', Anonymous Referee #1, 15 Jan 2023 reply

Daniel C. Anderson et al.

Daniel C. Anderson et al.

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Short summary
We describe a methodology that combines machine learning, satellite observations, and 3D chemical model output to infer the abundance of the hydroxyl radical (OH), a chemical that removes many trace gases from the atmosphere. The methodology successfully captures the variability of observed OH, although further observations are needed to evaluate absolute accuracy. Current satellite observations are of sufficient quality to infer OH, but retrieval validation in the remote tropics is needed.
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