Importance of dry deposition parameterization choice in global simulations of surface ozone

Abstract

Dry deposition is a major sink of tropospheric ozone. Increasing evidence has shown that ozone dry deposition actively links meteorology and hydrology with ozone air quality. However, there is little systematic investigation on the performance of different ozone dry deposition parameterizations at the global scale and how parameterization choice can impact surface ozone simulations. Here, we present the results of the first global, multidecadal modelling and evaluation of ozone dry deposition velocity (v d ) using multiple ozone dry deposition parameterizations. We model ozone dry deposition velocities over 1982-2011 using four ozone dry deposition parameterizations that are representative of current approaches in global ozone dry deposition modelling. We use consistent assimilated meteorology, land cover, and satellite-derived leaf area index (LAI) across all four, such that the differences in simulated v d are entirely due to differences in deposition model structures or assumptions about how land types are treated in each. In addition, we use the surface ozone sensitivity to v d predicted by a chemical transport model to estimate the impact of mean and variability of ozone dry deposition velocity on surface ozone. Our estimated v d values from four different parameterizations are evaluated against field observations, and while performance varies considerably by land cover types, our results suggest that none of the parameterizations are universally better than the others. Discrepancy in simulated mean v d among the parameterizations is estimated to cause 2 to 5 ppbv of discrepancy in surface ozone in the Northern Hemisphere (NH) and up to 8 ppbv in tropical rainforests in July, and up to 8 ppbv in tropical rainforests and seasonally dry tropical forests in Indochina in December. Parameterization-specific biases based on individual land cover type and hydroclimate are found to be the two main drivers of such discrepancies. We find statistically significant trends in the multiannual time series of simulated July daytime v d in all parameterizations, driven by warming and drying (southern Amazonia, southern African savannah, and Mongolia) or greening (high latitudes). The trend in July daytime v d is estimated to be 1 % yr −1 and leads to up to 3 ppbv of surface ozone changes over 1982-2011. The interannual coefficient of variation (CV) of July daytime mean v d in NH is found to be 5 %-15 %, with spatial distribution that varies with the dry deposition parameterization. Our sensitivity simulations suggest this can contribute between 0.5 to 2 ppbv to interannual variability (IAV) in surface ozone, but all models tend to underestimate interannual CV when compared to long-term ozone flux observations. We also find that IAV in some dry deposition parameterizations is more sensitive to LAI, while in others it is more sensitive to climate. Comparisons with other published estimates of the IAV of background ozone confirm that ozone dry deposition can be an important part of natural surface ozone variability. Our results...

Dry Deposition of Ozone Over Land: Processes, Measurement, and Modeling

“…Substantial v d differences also occur among different ozone dry deposition schemes (Schwede et al, 2011;A. Y. H. Wong et al, 2019;Z.…”

“…Reviews of Geophysics large differences in summer interannual variability and 30-year trends in v d across four schemes implemented in one global atmospheric chemistry model (A. Y. H. Wong et al, 2019). Studies comparing schemes in depth (Schwede et al, 2011;Z.…”

“…Lin et al, 2017M. Lin et al, , 2019Matichuk et al, 2017;Silva & Heald, 2018;Solberg et al, 2008;Tang et al, 2011;Val Martin et al, 2014;Vautard et al, 2005;Vieno et al, 2010;Walker, 2014;Wild, 2007;A. Y. H. Wong et al, 2019).…”

“…Among models, differences are twofold to threefold in estimates of ozone dry deposition for a given location (Hardacre et al, 2015;Schwede et al, 2011;Z. Wu et al, 2018;A. Y. H. Wong et al, 2019) and in estimates of the global annual tropospheric ozone loss through dry deposition (Hardacre et al, 2015;Stevenson et al, 2006;Wild, 2007;Young et al, 2013Young et al, , 2018.…”

Satellite soil moisture data assimilation impacts on modeling weather and ozone in the southeastern US – part I: an overview

“…This contrasts with some other approaches which also account for the influences of SM, vapor pressure deficit and vegetation density, or couple stomatal resistance with photosynthesis. For calculating the nonstomatal surface resistance components, prescribed seasonal-and LULC-dependent constants are used in the Wesely scheme, adjusted by environmental variables such as wetness and radiation, whereas in other existing schemes, impacts of friction velocity and vegetation density are also considered (e.g., Charusombat et al, 2010;Park et al, 2014;Val Martin et al, 2014;Wu et al, 2018;Mills et al, 2018b;Anav et al, 2018;Wong et al, 2019;Clifton et al, 2020, and the references therein). Aerodynamic resistance and quasi-laminar resistance are both sensitive to surface properties such as surface roughness.…”