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

• Main Authors: A. Y. H. Wong, J. A. Geddes, A. P. K. Tai, S. J. Silva
• Format: Article
• Language: English
• Published: Copernicus Publications 2019-11-01
• Series: Atmospheric Chemistry and Physics
• Online Access: https://www.atmos-chem-phys.net/19/14365/2019/acp-19-14365-2019.pdf

<p>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 (<span class="inline-formula"><i>v</i>_d</span>) 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 <span class="inline-formula"><i>v</i>_d</span> 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 <span class="inline-formula"><i>v</i>_d</span> predicted by a chemical transport model to estimate the impact of mean and variability of ozone dry deposition velocity on surface ozone. Our estimated <span class="inline-formula"><i>v</i>_d</span> 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 <span class="inline-formula"><i>v</i>_d</span> among the parameterizations is estimated to cause 2 to 5&thinsp;ppbv of discrepancy in surface ozone in the Northern Hemisphere (NH) and up to 8&thinsp;ppbv in tropical rainforests in July, and up to 8&thinsp;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 <span class="inline-formula"><i>v</i>_d</span> in all parameterizations, driven by warming and drying (southern Amazonia, southern African savannah, and Mongolia) or greening (high latitudes). The trend in July daytime <span class="inline-formula"><i>v</i>_d</span> is estimated to be 1&thinsp;%&thinsp;yr<span class="inline-formula">⁻¹</span> and leads to up to 3&thinsp;ppbv of surface ozone changes over 1982–2011. The interannual coefficient of variation (CV) of July daytime mean <span class="inline-formula"><i>v</i>_d</span> in NH is found to be 5&thinsp;%–15&thinsp;%, with spatial distribution that varies with the dry deposition parameterization. Our sensitivity simulations suggest this can contribute between 0.5 to 2&thinsp;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 demonstrate the importance of ozone dry deposition parameterization choice on surface ozone modelling and the impact of IAV of <span class="inline-formula"><i>v</i>_d</span> on surface ozone, thus making a strong case for further measurement, evaluation, and model–data integration of ozone dry deposition on different spatiotemporal scales.</p>