The climate change penalty on US air quality: New perspectives from statistical models

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1 The climate change penalty on US air quality: New perspectives from statistical models Charles River Path, Boston, July 2010 Salt Lake City, January 2013 Loretta J. Mickley, Lu Shen, Xu Yue Harvard University

2 Summertime MDA8 ozone shows large decreases since the 1990s. Eastern US mean Site in southern Illinois 2012 drought + heat EPA actions have led to much cleaner air for millions of people There is large interannual variability in ozone due to the effects of meteorology From Lu Shen s interactive online tool for tracking US summer ozone:

3 How will surface air quality change in changing atmosphere? Number of people living in U.S. counties with air quality concentrations above the NAAQS thresholds in 2014 One or more pollutants Ozone (8-hour) PM 2.5 (annual or 24-hour) PM 10 (24- hour) How will these bars change in response to: increasing temperatures? trends in stagnation? increasing wildfires in the West?..

4 We know that US air quality can deteriorate during hot summers. Correlationof JJA meansurface airtemperatures withairpollutants More clear skies More biogenic VOCs More stagnant weather Faster SO 2 oxidation More organic aerosols More stagnant weather Data: EPA-AQS

Two ways to quantify the effects future climate on air quality. 1. Chemistry-climate models or chemistry transport models Make it easy to investigate the physical processes.

5 Two ways to quantify the effects future climate on air quality. 1. Chemistry-climate models or chemistry transport models Make it easy to investigate the physical processes. Have difficulty in capturing O 3 and PM 2.5 variability. High computational expense. 2. Statistical models Based on observed relationships between meteorological variables and pollutants. Low computational expense. Can take advantage of large ensembles of climate models, leading to more robust results. Here: 2050s, A1B or RCP4.5. Need relatively long records of observations. May fail to include some important physical processes

6 The influence of meteorology on air quality can be broken down into different spatial scales. Local meteorology Local T, RH, wind speed, precipitation, etc. Global background Changing climate change e.g., changes in meridional temperature gradient or water vapor Synoptic circulation Meteorological patterns on larger spatial scales (~ 1000 km) -- e.g., polar jet wind and Bermuda High

7 1. Effects of local temperature on ozone episodes: Projection of U.S. ozone episodes in 2050s atmosphere. We build a model using extreme value theory. Probability of surface ozone above a specified threshold. shape factor location factor p(y t y t > u) = 1 [1+ ξ(y µ ) t t,t σ T σ T ] 1/ξ 1 Only meteorological variable considered is maximum daily temperature. Model also needs to take into account the phenomenon known as ozone suppression at high maximum temperatures. Shen et al., 2016a ozone (ppbv) scale factor Observed MDA8 ozone, JJA Site in Maryland mean One s Daily maximum temperature (K) p value

8 We find that ozone suppression at high temperatures is a widespread phenomenon across the United States. About 20% of sites demonstrate significant decrease in O 3 -max T slope at high temperatures. AQS Steiner et al. (2010) found similar phenomenon in California. Sensitivity studies with GEOS-Chem suggest the cause is due to the nonlinearity of meteorological effects on ozone during heatwaves. We include suppression in our statistical ozone model and apply to 19 CMIP5 models. Shen et al., 2016a

9 We find that ozone suppression at high temperatures is a widespread phenomenon across the United States. AQS Sensitivity studies with GEOS-Chem suggest the cause is due to the nonlinearity of meteorological effects on ozone during heatwaves. We include suppression in our statistical ozone model and apply to 19 CMIP5 models. Shen et al., 2016a About 20% of sites demonstrate significant decrease in O 3 -max T slope at high temperatures. Steiner et al. (2010) found similar phenomenon in California. Predicted episode days Model performance R 2 = Observed episode days

10 Statistical model predicts that number of surface ozone episodes per summer increases across the US by 2050s. D episode days vs (a) Change in ozone episode days in 2050s days Mean change in ozone episode days per summer due to climate change in by 2050s in RCP4.5 scenario. Episode = MDA8 O 3 > 75 ppb (b) Standard deviation of change Sites where inclusion of Tmax does not days improve EVT model. Ozone episodes increase by 3-9 days yr -1 3in Northeast and California. U.S. average change is ~2 days yr Shen et al., 2016a

11 2. Influence of synoptic scale meteorology on U.S. air quality. Shen et al., 2015, 2017 Correlations of PM 2.5 in one sample grid box with surrounding meteorology Correlation of PM 2.5 with meteorology (a) Temperature(a) Temperature (b) Relative humidity (b) Relative humidity (c) Precipitation (c) Precip Sample grid box in Georgia Mean May-June-July, Anticyclonic circulation affects PM 2.5 in gridbox (d) East west wind (d) East west (e) wind North south (e) wind North south wind r r

12 Including synoptic circulation factors into the regression model increases the match between the model and the observations. Cross-validated coefficients of determination (R 2 ) between observed and predicted monthly PM 2.5 in US (a) Local meteorology (b) Local meteorology + synoptic factors mean R 2 mean R 2 PM % PM % R Meteorological variables: T, RH, precip, windspeeds Shen et al., 2017

13 The position of the Bermuda High west edge influences JJA mean surface ozone in the eastern US West Regime Change in ozone per degree change in: Longitude Ozone and longitude of BH west edge Latitude Ozone and latitude of BH west edge (b) West Regime: Edge of Bermuda High extends far westward. Stagnation, 3 ppb increase 85.4W West Regime East Regime (a) (c) West Regime East Regime ppbv/deg ppb per degree shift (b) (d) East Regime: Edge of Bermuda High skims along the coast. East Regime East Regime (c) (d) ppbv/deg Shen et al., 2015

14 We make new projections of PM 2.5 concentrations in 2050s. Focus is the climate penalty on anthropogenic and biogenic PM 2.5. Tai et al. (2012) (a) local Predictors: local meteorological variables Change in annual mean PM 2.5 by 2050s μg m Note different color bars! 5 Shen et al. (2017) (b) hybrid Predictors: Local meteorology and regional scale circulation patterns New results suggest that annual mean PM 2.5 could increase μg m -3 in the eastern US, larger than previous estimates. Shen et al., 2017 μg m

15 Our statistical model predicts an especially strong response in PM 2.5 to 2050s climate in summer changes in PM 2.5 ΔPM 2.5 in JJA (a) JJA μg m ΔPM(b) 2.5 DJF in DJF μg m Faster oxidation rate, more biogenic emission, and stagnation Volatilization of ammonium nitrate Shen et al., 2017

16 Dynamical models have difficulty capturing observed sensitivity (d) GISS (e) MIROC-CHEM (f) CESM of monthly mean PM 2.5 to temperature in summer. Slopes of monthly mean JJA PM 2.5 vs. surface temperatures observations ACCMIP model another ACCMIP model (a) obs (b) CAM3.5 (c) GFDL-AM3 (g) GC-4x5 (h) GC-2x25 (i) GC-05x0667 (d) GISS (e) MIROC-CHEM (f) CESM slope > 1 μg m -3 K -1 within contour μg m -3 K -1 What are the implications for future projections of PM 2.5 from these models? (g) GC-4x5 (h) GC-2x25 (i) GC-05x0667 Shen et al., 2017

GEOS-5 meteorology overestimates the sensitivity of low cloud cover to temperature. In GEOS-Chem, ~80% SO 2 oxidation inthe easternus occurs inclouds.

17 GEOS-5 meteorology overestimates the sensitivity of low cloud cover to temperature. In GEOS-Chem, ~80% SO 2 oxidation inthe easternus occurs inclouds. As temperature rises, GEOS-5 clouds disappear too quickly, and sulfate production declines. GEOS-5 is one of meteorological products that drives GEOS-Chem. JJA daytime slopes of cloud fraction (> 680 hpa) and temperature (a) Satellite (a) GEOS K 1

3. Projections of wildfire activity and smoke concentrations over the western United States by 2050s Area burned (10 5 ha) Area burned over Southwest observed R 2 ~ 0.5 model 1.

18 3. Projections of wildfire activity and smoke concentrations over the western United States by 2050s Area burned (10 5 ha) Area burned over Southwest observed R 2 ~ 0.5 model 1. Build statistical model of area burned using meteorological variables and fire indices. 2. Apply statistical model to CMIP3 climate projections. Area burned (10 5 ha) Timeseries of area burned observed model Area burned in Southwest doubles by Apply median areas burned for present-day and future into GEOS-Chem. Yue et al., 2013, 2014, 2015

Many populous counties in the West experience 40-150% increases in smoke PM 2.5 by 2050s.

Percent increase in smoke PM 2.5 by midcentury relative to 2000s, by county.

19 Many populous counties in the West experience % increases in smoke PM 2.5 by 2050s. Much of California sees a doubling of smoke waves episodes of enhanced smoke at the 98 th % level. Percent increase in smoke PM 2.5 by midcentury relative to 2000s, by county. With collaborators from Yale, we are examining the health impacts using Medicare data. Yue et al., 2013, 2014, 2015 Liu et al., 2016a, 2016b, 2017 Large cities affected by fires

Our work underscores this sometimes overlooked health consequence of continued gr

20 Policy relevant messages. Using statistical models, we gain confidence in our predictions of a strong climate penalty on US air quality by the 2050s. Our work underscores this sometimes overlooked health consequence of continued greenhouse gas emissions (e.g., EPA 2009 Greenhouse Gas Endangerment Finding) Variability of summertime ozone can be predicted during the previous spring. (no time to show!) Statistical models have utility: o Low computational expense o Constrained by observed relationships o Robust result using ensembles of climate model output Xu Yue Lu Shen

22 3. Springtime prediction of summer ozone in the eastern US. We develop a statistical model to predict summertime ozone variability using meteorology from the previous spring. Correlation of SSTs in different seasons with JJA ozone in eastern US MAM-ΔSST Eastern ozone The model relies on: Memory of springtime SSTs in the following summer. Dependence of US temperatures on SSTs Dependence of surface ozone on US temperatures. Shen et al., 2016b

23 We can predict 45% of the variability of JJA MDA8 ozone in the eastern US using SST and SLPs in the preceding spring ozone anomaly (ppbv) Mean JJA MDA8 ozone over eastern US model, HF method observations r(ma) =0.59 r(hf) =0.67 obs (HF) model, prediction MA method (HF) prediction (MA) Ozone has been detrended via two methods: 7-year moving average (MA) 7-year Henderson filtered trend (HF) Shen et al., 2016b

Responsibilities of Harvard Atmospheric Chemistry Modeling Group

Responsibilities of Harvard Atmospheric Chemistry Modeling Group Loretta Mickley, Lu Shen, Daniel Jacob, and Rachel Silvern 2.1 Objective 1: Compile comprehensive air pollution, weather, emissions, and