Assessment of source contributions to seasonal vegetative exposure to ozone in the U.S.

Abstract

[1] W126 is a cumulative ozone exposure index based on sigmoidally weighted daytime ozone concentrations used to evaluate the impacts of ozone on vegetation. We quantify W126 in the U.S. in the absence of North American anthropogenic emissions (North American background or "NAB") using three regional or global chemical transport models for May-July 2010. All models overestimate W126 in the eastern U.S. due to a persistent bias in daytime ozone, while the models are relatively unbiased in California and the Intermountain West. Substantial difference in the magnitude and spatial and temporal variability of the estimates of W126 NAB between models supports the need for a multimodel approach. While the average NAB contribution to daytime ozone in the Intermountain West is 64-78%, the average W126 NAB is only 9-27% of current levels, owing to the weight given to high O 3 concentrations in W126. Based on a three-model mean, NAB explains 30% of the daily variability in the W126 daily index in the Intermountain West. Adjoint sensitivity analysis shows that nationwide W126 is influenced most by NO x emissions from anthropogenic (58% of the total sensitivity) and natural (25%) sources followed by nonmethane volatile organic compounds (10%) and CO (7%). Most of the influence of anthropogenic NO x comes from the U.S. (80%), followed by Canada (9%), Mexico (4%), and China (3%). Thus, long-range transport of pollution has a relatively small impact on W126 in the U.S., and domestic emissions control should be effective for reducing W126 levels.

How emissions, climate, and land use change will impact mid-century air quality over the United States: a focus on effects at national parks

“…The spatial distribution of W126 (not shown) is similar to the daily MDA-8 O 3 (Fig. 2a) but exhibits larger values over regions of low and high ozone with more emphasis due to the sigmoidal weighting of the W126 function as discussed in Lapina et al (2014). We find that the model captures the spatial distribution of W126 across the US (r 2 = 0.70).…”

“…For O 3 , we find that simulated surface concentrations show good agreement with the mean observations over the western US (r 2 = 0.77; NMB = 4 %) but slightly overestimate O 3 (r 2 = 0.47; NMB = 16 %) over the eastern US. This annual overestimation is due to a positive bias in summertime O 3 (about 10 ppb), which is a well-known issue and has been previously documented in CESM as well as other global and regional models (e.g., Murazaki and Hess, 2006;Fiore et al, 2009;Lapina et al, 2014). Using the optimized dry deposition scheme (Sect.…”

“…We also evaluate the secondary metric W126 established to protect ecosystems and crops. The W126 is a biologically based index that estimates a cumulative ozone exposure over a 3-month growing season and applies sigmoidal weighting to hourly ozone concentrations (e.g., Lefohn et al, 1988;Lapina et al, 2014). The spatial distribution of W126 (not shown) is similar to the daily MDA-8 O 3 (Fig.…”

Seasonal prediction of US summertime ozone using statistical analysis of large scale climate patterns

“…Such models do not depend on historical observations, but they may not be able to replicate important interannual and decadal phenomena in both the atmosphere and ocean (26). In addition, dynamical forecasts of ozone require the use of chemistry transport models, which often overestimate summertime ozone concentrations in the eastern United States (28,29) In this study, we develop a linear model based on teleconnections between surface ozone and SST/SLP anomalies to predict the summertime ozone in the previous spring.…”

Impacts of anthropogenic and natural sources on free tropospheric ozone over the Middle East

“…However, due to the limitation of the tagging approach that they employed, they were not able to obtain a detailed description of the sensitivity of Middle East O 3 to the precursor emissions. In this section, we will use the adjoint of the full-chemistry GEOS-Chem model (Henze et al, 2007) to quantify O 3 source contributions, similar to previous studies (Lapina et al, 2014;Jiang et al, 2015b). The adjoint model, which includes both chemistry and transport, is run backwards to computationally efficiently provide sensitivities with respect to each of the model's emissions from each species, sector and grid cell.…”