1    

Particulate Air Pollution from Wildfires in the Western US under Climate Change Supplementary material

Supplementary Methods 1. Details on GEOS-Chem modeling and validation

GEOS-Chem is a 3-dimensional global chemistry model that solves for the temporal and spatial evolution of gas-phase species and aerosol (http://acmg.seas.harvard.edu/geos/index.html). Input to the model consists of gridded meteorological fields at fine spatial and temporal resolution as well as both anthropogenic and wildfire emission inventories. Here we use meteorological fields from the Goddard Earth Observing System (GEOS-5) of the NASA Modeling and Assimilation Office (GMAO). We also apply projections of wildfire area burned based on our previous research (Yue et al., 2013, 2014), described below in Supplementary Methods 2. Given these input fields, GEOS-Chem calculates the chemistry, transport, and fate of atmospheric species, using equations that represent the physics and chemistry of atmospheric composition.

For this study, we used the aerosol-only version of GEOS-Chem, which includes emissions of all primary particulate matter as well as the gas-phase precursors to secondary particulate matter. Oxidation of gas-phase precursors in this version of the model is carried out through application of monthly mean fields of oxidants calculated beforehand with the full-chemistry version of GEOS-Chem. Output from GEOS-Chem consists of 3-dimensional gridded output of speciated, daily mean particulate matter in terms of mass. Total PM2.5 in the model is taken to be the sum of sulfate, nitrate, ammonium, organic carbon and black carbon. We identify wildfire-specific PM2.5 by performing a pair of simulations – one with fire emissions turned on (i.e., included in the simulation) and one without these emissions (i.e., set to zero). Wildfire-specific PM2.5 is defined as the difference in PM2.5 output from these two simulations. Our study focuses only on the climate impacts on wildfire activity, and not on the climate impacts on the transport and fate of smoke in the atmosphere. Previous research using a similar GEOS-Chem setup determined that nearly all the change in wildfire-specific PM2.5 was due to increases in area burned (Spracklen et al., 2009).

GEOS-Chem concentrations of PM2.5 over the United States, including wildfire PM2.5, have been extensively validated on a variety of timescales, including daily3-6 and seasonal7. Zhang et al. (2014)8, while focused on ozone, included validation of daily mean wildfire PM2.5 in GEOS-Chem. Simulated wildfire PM2.5 from other regions has also been evaluated on daily timescales (e.g., 9,10). We mainly use ground-based or aircraft measurements, not satellite data, to validate the GEOS-Chem surface PM2.5, including wildfire PM2.5. Satellite data of particulate matter are not useful to validate surface PM2.5 mainly because such data describe the column amount of particulate matter through the atmosphere in mg m-2, not the surface concentrations in µg m-3.

Our previous research investigating wildfire in the future used a coarse-grid version of GEOS-Chem, with a horizontal spatial resolution of 4° x 5° (latitude x longitude). The meteorological fields driving this previous version of GEOS-Chem were interpolated to the coarse grid to allow

2    

for computational ease. For this project, we drove GEOS-Chem with meteorological fields at their native (i.e., original ) resolution, 0.5°x0.667°, over the western US in order to calculate PM2.5 on a spatial scale more suitable for health analysis.

Supplementary Methods 2. Details on fire prediction method and uncertainties.

The wildfire predictions used in this work builds on our earlier research (Yue et al., 2014; Yue et al., 2013). In Yue et al. (2013), we first built a fire prediction model using observed relationships between area burned and a range of meteorological factors for the 1978-2004 timeframe over the western US. The Yue et al. (2013) model captures ~50% of the observed interannual variability in area burned in forested regions, with somewhat less success in more arid regions. In Yue et al (2014), we refined the model to better predict observed area burned in California. By taking into account effects of elevation, population, fuel load, and the Santa Ana winds, the improved model captures as much as 60% of the observed interannual variability in southwestern California.

The meteorological drivers of wildfire identified in Yue et al. (2013, 2014) are all readily calculated by climate models for the present-day and future. In Yue et al. (2013), we utilized the online archive of climate model output maintained by the Intergovernmental Panel on Climate Change (IPCC). Prediction of future area burned could thus be generated by applying our fire prediction model to the relevant meteorological fields (temperature, relative humidity, etc.), yielding future area burned.

For Yue et al. (2013, 2014), we used the output from an ensemble of 15 climate models simulating the A1B climate scenario, which put a greater emphasis on alternative energy sources, improved energy efficiency, and reduced costs of energy supply. The greenhouse gas CO2 reaches 522 ppm by 2050 in this scenario (Solomon et al., 2007), resulting in moderate warming (Meehl and Stocker, 2007). Over this relatively short time frame from the present-day to 2050, the A1B scenario is consistent with two moderate scenarios in the newer Representative Concentration Pathways, RCP4.5 and RCP6.0 (Moss et al., 2010). Differences in the climate response among these moderate scenarios are swamped by the differences among models, with each model characterized by its own sensitivity to changing greenhouse gases) (e.g. Wuebbles et al., 2014). Put another way, the uncertainty in the 2050s climate response for a single scenario is comparable in magnitude to the range of climate responses for these moderate scenarios.

We applied our fire prediction model separately to each of the 15 models in the IPCC ensemble, and calculated the resulting mean and median area burned for each ecoregion or gridcell. Results suggest that the warmer summer climate by the 2050s leads to increases in the median area burned by 24-169% across the West (Yue et al., 2013), depending on the ecoregion. Yue et al. (2014) found that area burned likely doubles in southwestern California by the 2050s and increases by ~35% in the Sierra Nevada and ~10% in central western California.

Our use of multi-model climate projections in Yue et al. (2013, 2014) allowed us to identify robust trends in area burned in the future climate. It also permitted us to quantify the uncertainty

3    

in our projections and to identify model outliers. For example, while all the models predict significant increases in surface temperature averaged over the West, the spread in model response is several degrees C. Trends in precipitation and relative humidity, two other drivers of wildfire, are not robust across the models for the region. These differences among climate models in the projections of future temperature, precipitation, and relative humidity lead to substantial variation among the projections for area burned. Despite these uncertainties, 11 out of 15 models agreed that area burned would increase across the western US by the 2050s, with the greatest increases in the Desert Southwest, southwestern California, and in forested regions (Yue et al., 2013, 2014). For more details on uncertainty analysis, see Yue et al. (2013, 2014).

To calculate the emissions, transport, and fate of wildfire-specific PM2.5, we implemented the area burned projections into GEOS-Chem, the chemical transport model described in Supplementary Methods 1. To avoid the computational expense of having to do many GEOS-Chem simulations (i.e., one simulation for each of the 15 climate model projections), we applied only the median areas burned in each model gridcell. This method has the added advantage of discounting outliers in the climate model predictions, thus increasing confidence in our results for wildfire-specific PM2.5.

Table A.1. Development of Fire Smoke Risk Index (FSRI) based on smoke wave characteristics

Length (days) Length Score Intensity (µg/m3) Intensity

Score Number of smoke

waves/Year Number

Score No smoke wave 0 No smoke wave 0 No smoke wave 0 2.0-2.5 1 6-10 1 0.167-0.5 1 2.51-3.0 2 10.01-15 2 0.51-1.0 2 3.01-3.5 3 15.01-20 3 1.01-1.5 3 3.51-4.0 4 20.01-35 4 1.51-2.0 4 >4.0 5 >35 5 >2.0 5

Note: FSRI  represents a county’s overall smoke wave severity (in either the present day or the future) and is calculated as the sum of 3 scores: 1) the score of the smoke wave’s length, 2) the score of the smoke wave’s intensity represented as wildfire-specific PM2.5, and 3) the score of the number of smoke waves/year. Higher FSRI values represent more severe smoke waves. FSRI values are 0 (no estimated smoke wave s); 1 (counties with summed scores below the median value of summed scores in all counties), 2 (counties with summed scores in the 35-50th percentile), 3 (counties with summed scores in the 20-35th percentile), 4 (counties with summed scores in the 5-20th percentile), and 5 (counties with summed scores >5th percentile, i.e. the highest 5%). FSRI percentiles were based on present-day estimates.

4    

Table A.2. Comparison of 6-year average (2004-2009) wildfire-specific PM2.5 levels in 11 states in the Western US counties by state using 1) area weighted averaging; and 2) population weighted averaging using 2010 Census population at census tract level.

State Population weighted (µg/m3)

Area weighted (µg/m3)

Arizona 0.15 0.15 California 1.15 1.16 Colorado 0.63 0.61 Idaho 1.00 1.05 Montana 1.12 1.13 Nevada 0.65 0.58 New Mexico 0.19 0.20 Oregon 1.29 1.30 Utah 0.54 0.49 Washington 0.84 0.85 Wyoming 0.61 0.62

5    

Table A.3. Summary Statistics for Daily Present-Day (2004-2009) and Future (2046-2051) County-Level Wildfire-Specific PM2.5 Levels among 561 Western US Counties During Fire Seasons (May-October) (µg/m3)

Minimum Mean Median 75th Percentile

90th Percentile

95th Percentile

99th Percentile Maximum

Present-day wildfire-specific PM2.5 levels 0.00 0.69 0.13 0.42 1.29 2.52 9.81 596.7 Future wildfire-specific PM2.5 levels 0.00 1.13 0.17 0.57 1.71 3.45 14.8 2874 Present-day PM2.5 from non-fire sources 0.01 2.92 2.42 3.57 5.12 6.49 11.1 146.8  

6    

Table A.4. Assignment of FSRI Values Based on Sum of Three Scores and Percentage of Counties with Assigned Index Value

Percentage of Counties at this FSRI Level Sum of Three

Scores* FSRI Present Day Future, Under Climate Change

0 0 (no risk) 24.4% 21.6% 1-6 1 35.1% 19.1% 7 2 11.2% 5.3%

8, 9 3 13.6% 17.8% 10, 11 4 11.8% 18.9% 12-15 5 (highest risk) 3.9% 17.3%

FSRI, Fire Smoke Risk Index. *From Supplementary Table A.1.

7    

Table A.5. Present (2005) and Future (2050) Estimated Populations in the Western US

Total population Elderly population Children population Future (2050) 82,702,651 12,342,309 18,657,015 Present (2005) 57,042,111 6,668,303 11,689,353 Difference* 25 million 5.7 million 7 million

*The difference represents the approximate increase in each population in the future.

8    

Figure A.1. Proportion of total PM2.5 contributed by wildfire against total PM2.5 levels in each county-day across the western US in the present day. The dashed green line indicates the World Health Organization (WHO) standard for daily PM2.5 (25µg/m3). The dashed red line indicates the National Ambient Air Quality Standards’ regulatory standards for daily PM2.5 (35µg/m3).

9    

Figure A.2. Frequency distribution of PM2.5 concentrations from present-day wildfire-specific (blue) and non-fire (red) sources of PM2.5 concentrations.

10    

(b)

(c) Figure A.3. Number of smoke waves based on (a) the primary smoke wave definition (cutoff= 6 µg/m3), (b) smoke wave definition with cutoff =10 µg/m3, and (c) smoke wave definition with cutoff =20 µg/m3. Maps on the left represent the present day (based on 2004-2009 data). Maps on the right represent the future under climate change (based on projected data for the years 2046-2051).

11    

Figure A.4. Difference in Fire Smoke Risk Index (FSRI) between future and present day (FSRIfuture minus FSRIpresent day). References Meehl GA, Stocker TF (2007) Global Climate Projections. Climate Change 2007: The Physical

Science Basis:747-845. Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, van Vuuren DP, Carter TR,

Emori S, Kainuma M, Kram T, Meehl GA, Mitchell JFB, Nakicenovic N, Riahi K, Smith SJ, Stouffer RJ, Thomson AM, Weyant JP, Wilbanks TJ (2010) The next generation of scenarios for climate change research and assessment. Nature 463:747-756.

Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt K, Tignor M, Miller H (Eds.)_(2007) Climate Change 2007: The Physical Science Basis. IPCC (Intergovernmental Panel on Climate Change) Fourth Assessment Report. Cambridge University Press.

Wuebbles D, Meehl G, Hayhoe K, Karl TR, Kunkel K, Santer B, Wehner M, Colle B, Fischer EM, Fu R, Goodman A, Janssen E, Kharin V, Lee H, Li WH, Long LN, Olsen SC, Pan ZT, Seth A, Sheffield J, Sun LQ (2014) CMIP5 Climate Model Analyses: Climate Extremes in the United States. B Am Meteorol Soc 95:571-583.

Yue X, Mickley LJ, Logan JA (2014) Projection of wildfire activity in southern California in the mid-twenty-first century. Clim Dynam 43:1973-1991.

Yue X, Mickley LJ, Logan JA, Kaplan JO (2013) Ensemble projections of wildfire activity and carbonaceous aerosol concentrations over the western United States in the mid-21st century. Atmos Environ 77:767-780.