Preprints
https://doi.org/10.5194/acp-2021-1068
https://doi.org/10.5194/acp-2021-1068
 
20 Jan 2022
20 Jan 2022
Status: a revised version of this preprint was accepted for the journal ACP and is expected to appear here in due course.

Satellite soil moisture data assimilation impacts on modeling weather variables and ozone in the southeastern US – Part 2: Sensitivity to dry deposition parameterizations

Min Huang1,a, James H. Crawford2, Gregory R. Carmichael3, Kevin W. Bowman4, Sujay V. Kumar5, and Colm Sweeney6 Min Huang et al.
  • 1College of Science, George Mason University, Fairfax, VA, USA
  • 2NASA Langley Research Center, Hampton, VA, USA
  • 3College of Engineering, The University of Iowa, Iowa City, IA, USA
  • 4Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
  • 5NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 6NOAA Earth System Research Laboratory Global Monitoring Division, Boulder, CO, USA
  • anow also visiting: National Centers for Environmental Prediction, College Park, MD, USA

Abstract. Ozone (O3) dry deposition is a major O3 sink. Realistically representing this process in models is important for accurately simulating O3 concentrations and exceedances, as well as assessing the O3 impacts on human and ecosystem health. As a follow-up study of Huang et al. (2021), soil moisture (SM) data from NASA’s Soil Moisture Active Passive mission are assimilated into the Noah-Multiparameterization land surface model within the NASA Land Information System framework, semicoupled with Weather Research and Forecasting model with online Chemistry regional-scale simulations covering the southeastern US. Major changes in the used modeling system include enabling the dynamic vegetation option, adding the irrigation process, and updating the CH (i.e., surface exchange coefficient for heat) scheme. Two different dry deposition schemes are implemented, i.e., the Wesely scheme and a “dynamic” scheme, in the latter of which dry deposition parameterization is coupled with photosynthesis and vegetation dynamics. It is demonstrated that, when the “dynamic” scheme is applied, the modeled O3 dry deposition velocities as well as the total, stomatal and cuticular O3 fluxes are overall larger and 2–3 times more sensitive to the SM changes due to the data assimilation (DA). We also highlight that, the configuration of the SM factor controlling stomatal resistance (i.e., the β factor which presents dependencies on soil type and hydrological regime) can strongly affect the quantitative results. Referring to multiple observation and observation-derived evaluation datasets, which may be associated with variable extents of uncertainty, the model performance of vegetation, surface fluxes, weather, and surface O3 concentrations, shows mixed responses to the DA, some of which display land cover dependency. Finally, using model-derived concentration- and flux-based policy relevant O3 metrics as well as their matching exposure-response functions, the relative biomass/crop yield losses for several types of vegetation/crops are estimated to be within a wide range below 20 %. Their sensitivities to the model’s dry deposition scheme and the implementation of SM DA are discussed.

Min Huang et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-1068', Anonymous Referee #1, 17 Feb 2022
  • RC2: 'Comment on acp-2021-1068', Anonymous Referee #2, 17 Feb 2022
  • AC1: 'Author response to reviews', Min Huang, 14 Apr 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-1068', Anonymous Referee #1, 17 Feb 2022
  • RC2: 'Comment on acp-2021-1068', Anonymous Referee #2, 17 Feb 2022
  • AC1: 'Author response to reviews', Min Huang, 14 Apr 2022

Min Huang et al.

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Short summary
This study demonstrates that, ozone dry deposition modeling can be improved by revising the model's dry deposition parameterizations to better represent the effects of environmental conditions including the soil moisture fields. Applying satellite soil moisture data assimilation is shown to also have added value. Such advancements in coupled modeling and data assimilation can benefit the assessments of ozone impacts on human and vegetation health.
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