Grantee Research Project Results
2018 Progress Report: Planning for an Unknown Future: Incorporating Meteorological Uncertainty into Predictions of the Impact of Fires and Dust on US Particulate Matter
EPA Grant Number: R835884Title: Planning for an Unknown Future: Incorporating Meteorological Uncertainty into Predictions of the Impact of Fires and Dust on US Particulate Matter
Investigators: Fischer, Emily , Barnes, Elizabeth , Pierce, Jeffrey
Current Investigators: Fischer, Emily , Pierce, Jeffrey , Barnes, Elizabeth
Institution: Colorado State University
EPA Project Officer: Chung, Serena
Project Period: January 1, 2016 through December 31, 2018 (Extended to December 31, 2020)
Project Period Covered by this Report: January 1, 2018 through December 31,2018
Project Amount: $349,969
RFA: Particulate Matter and Related Pollutants in a Changing World (2014) RFA Text | Recipients Lists
Research Category: Air , Climate Change , Early Career Awards
Objective:
The objective of this research is to determine how model uncertainty in future meteorology translates into uncertainty in the contributions of smoke and dust to future particulate matter (PM) episodes.
Progress Summary:
Work in Year 3 has built on the present understanding of past and future wildfire occurrence in the western US with the following approach.
- We use Lasso regression to objectively identify the environmental conditions that best explain observed variance in interannual summertime (June, July, August) large wildfire burn area for ecoregions in the Western US. From here, we contrast the historical variance explained by plant- and atmospheric-centric predictors.
- We use CMIP5 model output to quantify the change and future spread in the predictors that explain historical variance for each fire-prone ecoregion. We contrast the trends and spread in atmospheric-centric and plant-centric predictors, and discuss what the changes may imply for future burn area.
- Finally, we combine historical relationships between burn area and predictors with CMIP5 output to examine the spread in future burn area forecast from plant-centric and atmospheric- centric variables, and compare this to the spread between RCP 4.5 and 8.5 scenarios.
Based on this approach we have found that:
- Regardless of what predictors are selected by either the plant- or atmospheric-centric regressions, the ensemble mean of individual CMIP5 output driven burn area show an increase in burn area. However, changes in the historical relationships between features and burn area, the legacy of burn scars on the landscape, or shifts in fuel availability, changes in wildland fire management, are not accounted for by our methods and could change that result.
- The predicted change in burn area is extremely sensitive to what environmental predictors are chosen to drive burn area. For these regions, CMIP5 models show the largest change (increase) in Temperature and VPD (Figure 5), so predicting future wildfire burn area with these variables will provide an upper limit to future burn area estimated by statistical models. For regressions that rely on variables with smaller longterm trends, like precipitation, long-term changes are less dramatic. This is the case in the Marine Regime Mountains, where the regression coefficients for the atmospheric-centric regression is dominated by precipitation.
- As discussed previously, the regressions tend to underestimate the largest and overestimate the smallest summer burn area values. This is most pronounced in the Mediterranean Regime Mountains. The failure of the regularized regressions to reproduce the historical amplitude of year-to-year burn area variability shows that the spread in summer burn area estimated for any regression driven by CMIP5 model output is an underestimate. This is easiest to see with the boxplots showing the distribution of summer burn area for MTBS and CMIP5 driven regression estimates for the years 1984-2016, where the width of the regression distributions are sometimes narrower than the MTBS distribution. In other words, the spread for CMIP5 driven summer burn area for the decades 2040-2060 and 2080-2100 shown in figure 6 are overconfident (the distributions should be wider). Though the magnitude is an underestimate, for a given regression and RCP scenario, we can see how the spread changes over time to ascertain how the spread may change.
- In reality, the division between plant-centric and atmospheric-centric environmental predictors shown is primarily for demonstration purposes, as these variables are related, can influence each other, and can all be used as indicators of wildfire potential across ecoregions. One of the points demonstrated by this work is that estimates in future burn area are extremely sensitive to what predictors are chosen. Here, predictive features are chosen objectively based on their ability to explain historical variance in year-to-year burn area, and we find that the variables that can explain the historical variance are not necessarily the ones that have the largest changes predicted by CMIP5 models.
- However, this work shows that increases in burn area are extremely likely, as both regression types for both RCPs show an increase. The uncertainty moving forward should focus on what fuel will remain and how that will change the relationships between wildfire occurrence and environmental conditions shown in this and other work.
Future Activities:
Our two goals for the no-cost extension year are to 1) finish and submit a manuscript on the spread in environmental conditions that may drive future western US summertime wildfire burn area, and 2) prepare and submit a manuscript on the spread in environmental conditions that may drive past and future southwestern US springtime fine dust concentrations.
References:
- Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., et al. (2011).The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656), 553–597.
- Hand, J. L., White, W. H., Gebhart, K. A., Hyslop, N. P., Gill, T. E., and Schichtel, B. A. ( 2016), Earlier onset of the spring fine dust season in the southwestern United States, Geophys. Res. Lett., 43, 4001– 4009, doi:10.1002/2016GL068519.
- Jolly, W. M., and Johnson, D. M. (2018). Pyro-Ecophysiology: Shifting the Paradigm of Live Wildland Fuel Research. Fire, 1(1), 8.
- Rothermel, R. C. (1983). How to predict the spread and intensity of forest and range fires.
- Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and RangeExperiment Station. https://doi.org/10.2737/INT-GTR-143
- Simms, D. L., and Law, M. (1967). The ignition of wet and dry wood by radiation. Combustion and Flame, 11(5), 377–388.
- Swann, A. L. S., Hoffman, F. M., Koven, C. D., and Randerson, J. T. (2016). Plant responses to increasing CO2 reduce estimates of climate impacts on drought severity. Proceedings of the National Academy of Sciences of the United States of America, 113(36), 10019–10024.
- Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society. Series B, Statistical Methodology, 58(1), 267–288.
- Yue, X., Mickley, L. J., Logan, J. A., and Kaplan, J. O. (2013). Ensemble projections of wildfire activity and carbonaceous aerosol concentrations over the western United States in the mid-21st century. Atmospheric Environment, 77, 767–780.
Journal Articles on this Report : 2 Displayed | Download in RIS Format
| Other project views: | All 18 publications | 9 publications in selected types | All 9 journal articles |
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Brey SJ, Ruminski M, Atwood SA, Fischer EV. Connecting smoke plumes to sources using Hazard Mapping System (HMS) smoke and fire location data over North America. Atmospheric Chemistry and Physics 2018;18(3):1745-1761. |
R835884 (2017) R835884 (2018) R835884 (Final) |
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Brey SJ, Barnes EA, Pierce JR, Wiedinmyer C, Fischer EV. Environmental conditions, ignition type, and air quality impacts of wildfires in the southeastern and western US. Earth's Future 2018;6(10):1442-1456. |
R835884 (2018) R835884 (Final) |
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Supplemental Keywords:
particulate matter, fires, dust, climate, synoptic meteorologyProgress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.