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Discussion papers
https://doi.org/10.5194/acp-2018-1296
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2018-1296
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 29 Jan 2019

Research article | 29 Jan 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

Evaluating wildfire emissions projection methods in comparisons of simulated and observed air quality

Uma Shankar1, Donald McKenzie2, Jeffrey P. Prestemon3, Bok Haeng Baek4, Mohammed Omary4, Dongmei Yang4, Aijun Xiu4, Kevin Talgo5, and William Vizuete1 Uma Shankar et al.
  • 1Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599-7431, USA
  • 2School of Environmental and Forest Sciences, University of Washington, Seattle, WA, 98195, USA
  • 3USDA Forest Service, Southern Research Station, Research Triangle Park, NC, 27709, USA
  • 4University of North Carolina at Chapel Hill-Institute for the Environment, Chapel Hill, NC, 27517, USA
  • 5CSRA Incorporated, Research Triangle Park, NC, 27709, USA

Abstract. Climate warming has been implicated as a major driver of recent catastrophic wildfires world-wide but analyses of regional differences in U.S. wildfires show that socioeconomic factors also have a large role. We previously leveraged statistical projections of annual areas burned (AAB) over the fast-growing Southeastern U.S. that include both climate and socioeconomic changes from 2011 to 2060, and developed wildfire emissions estimates over the region at 12-km × 12-km resolution to enable air quality (AQ) impact assessments for 2010 and selected future years. These estimates employed two AAB datasets, one using statistical downscaling (statistical d-s), and another using dynamical downscaling (dynamical d-s) of climate inputs from the same climate realization. This paper evaluates these wildfire emissions estimates against the U.S. National Emissions Inventory (NEI) as a benchmark in contemporary (2010) simulations with the Community Multiscale Air Quality (CMAQ) model, and against network observations for ozone and particulate matter below 2.5 µm in diameter (PM2.5). We hypothesize that our emissions estimates will yield model results that meet acceptable performance criteria, and are comparable to those using the NEI. The three simulations, which differ only in wildfire emissions, compare closely, with differences in ozone and PM2.5 below 1 % and 8 % respectively, but have much larger maximum mean fractional biases (MFBs) against observations (25 % and 51 % respectively). The largest biases for ozone are in the fire-free winter, indicating that modeling uncertainties other than wildfire emissions are mainly responsible. Statistical d-s, with the largest AAB domain-wide, is 7 % more positively biased and 4 % less negatively biased in PM2.5 on average than the other two cases, while dynamical d-s and the NEI differ only by 2 %–3 % partly because of their equally large summertime PM2.5 underpredictions. Primary species (elemental carbon, and ammonium from ammonia) have good-to-acceptable results, especially for the downscaling cases, providing confidence in our emissions estimation methodology. Compensating biases in sulfate (positive), and in organic carbon and dust (negative) lead to acceptable PM2.5 performance. As these species are driven by secondary chemistry or non-wildfire sources, their production pathways can be fruitful avenues for CMAQ improvements. Overall, the downscaling methods match, and sometimes exceed the NEI in simulating current wildfire AQ impacts, while enabling such assessments much farther into the future.

Uma Shankar et al.
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Status: open (until 26 Mar 2019)
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Short summary
We evaluate two wildfire emissions estimates for the Southeastern U. S. based on projected annual areas burned in 2011–2060, against a benchmark wildfire inventory in air quality (AQ) simulations for 2010, and against AQ network observations. Our emissions estimates compare well with the benchmark but all three simulations have large biases compared to observations. We find our methods suitable to assess current and future wildfire AQ impacts, but also identify areas for AQ model improvements.
We evaluate two wildfire emissions estimates for the Southeastern U. S. based on projected...
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