Estimating the impact of climate change on fine particulate matter across the United States:

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Estimating the impact of climate change on fine particulate matter across the United States: Use of principal component analysis as a quick gauge across models and scenarios Loretta

1 Estimating the impact of climate change on fine particulate matter across the United States: Use of principal component analysis as a quick gauge across models and scenarios Loretta J. Mickley Co-Is: Amos P.K.A. Tai and Daniel J. Jacob School of Engineering and Applied Sciences Harvard University AQ Management Contacts: Susan Anenberg and Carey Jang, EPA/OAQPS 1

5 at the surface to 2000-2050 climate change. A2 Pye et al., 2009 g m -3 These model results are computationally expensive.

2 Coming climate change will likely affect concentrations. Models disagree on the sign and the magnitude of the impacts Racherla and Adams, 2006 Response of sulfate PM 2.5 at the surface to climate change. A2 Pye et al., 2009 g m -3 These model results are computationally expensive. How well do models capture variability in present-day PM 2.5? A1 We need a simple tool that will allow AQ managers to readily calculate the climate consequences for air quality across a range of models and scenarios. g m -3 2

3 Project Roadmap: 1. Identify the main meteorological modes controlling observed across the United States (Tai et al., 2010; 2011) 2. Calculate the sensitivity of to changes in the frequency of the dominant meteorological modes (Tai et al., 2011) 3. Track the changes in these modes using the IPCC AR4 archive of climate projections. 4. Estimate the change in surface concentrations due to climate penalty (or climate benefit). AQ management tool IPCC archive of daily meteorology Main meteorological modes driving observed AQ response to climate change 3

Stagnation is strongly correlated with high PM 2.5.

1998-2008 meteorology + EPA-AQS observations Multiple linear

Units: μg m -3 D -1 (p-value < 0.05) Increases in total PM 2.

4 Stagnation is strongly correlated with high PM 2.5. Correlations of PM 2.5 with key meteorological variables meteorology + EPA-AQS observations Multiple linear regression coefficients for total PM 2.5 on meteorological variables. Units: μg m -3 D -1 (p-value < 0.05) Increases in total PM 2.5 on a stagnant day vs. a nonstagnant day. Mean PM 2.5 is 2.6 μg m -3 greater on a stagnant day Tai et al

5 Dominant meteorological modes driving PM 2.5 in much of Midwest and East are associated with cyclone passage Principal component (PC) decomposition of eight meteorological variables (x k ) to identify dominant meteorological regimes that drive PM 2.5 variability: PC j = 8 å k=1 a kj x k - x k s xk PC Time series for dominant PC and deseasonalized PM 2.5 : Midwest in Jan PC r = Dominant PC in Midwest consists of low T, low and rising surface pressure, strong NW wind. Meteorology signals the arrival of a cold front. Dominant PC in East is cyclone passage, in West is maritime inflow. Tai et al., 2011 PC Observed PM 2.5 (µg m -3 ) Jan 28 Jan 305

6 Frequency analysis of dominant meteorological modes can quantify sensitivity of PM 2.5 to changes in the frequencies of that mode Apply frequency analysis to each year of time series of NCEP/NCAR meteorology and EPA-AQS total PM 2.5 concentrations Observed annual mean PM 2.5 anomaly (μg m -3 ) Cyclone period cyclone period r = 0.76 Cyclone period anomaly (d) Annual mean PM 2.5 and cyclone period anomalies from long-term trend in US Midwest Sensitivity of annual PM 2.5 to cyclone period = ~1 μg m -3 d -1 (0.94±0.43 μg m -3 d -1 ) For every one-day increase in time interval between cold fronts, annual mean PM 2.5 increases by ~ 1 g m -3. Tai et al

7 Frequency (d -1 ) Frequency of meteorological mode (d! 1 ) Evaluation of present-day meteorological modes in AR4 climate models reveals differences among models. N42 W87.5 Observed model s NCEP/NCAR giss_model_e_r mpi_echam Year Modeled (2 IPCC models) and observed (NCEP/NCAR) time series of frequency of dominant meteorological mode for PM 2.5 in U.S. Midwest Some models capture both the long-term mean and variability of meteorological mode frequency well. As a first step, we use only those models that capture present-day mean and variability of frequency to predict future PM 2.5 7

8 climate change leads to increases in annual mean PM 2.5 across much of the Eastern US change in period of dominant meteorological modes for PM 2.5 variability averaged over 9 IPCC models Corresponding change in annual mean PM 2.5 concentrations (unit: µg m -3 ) day g m -3 We choose the 9 models whose frequency of the dominant meteorological modes best agrees with observations. We apply sensitivity of to changing frequency of dominant meteorological mode in the A1B atmosphere. Models show increased duration of stagnation, with corresponding increases in annual mean. There is huge variation among models. 8

9 Project Roadmap: 1. Identify the main meteorological modes controlling observed across the United States (Tai et al., 2010; 2011) 2. Calculate the sensitivity of to the frequency of the dominant meteorological mode. (Tai et al., 2011) Tai, A.P.K., L.J. Mickley, D.J. Jacob, E.M. Leibensperger, L. Zhang, J.A. Fisher, and H.O.T. Pye, Meteorological modes of variability for fine particulate matter () air quality the United States: implications for sensitivity to climate change, submitted to Atmos. Chem. Phys., Track the changes in these modes using the IPCC AR4 archive of climate projections. 4. Estimate the change in surface concentrations due to climate penalty (or climate benefit). AQ management tool IPCC archive of daily meteorology Main meteorological modes driving observed AQ response to climate change 9

Impact of climate change on PM 2.5 air quality inferred from a multi-model analysis of meteorological modes

Impact of climate change on PM 2.5 air quality inferred from a multi-model analysis of meteorological modes Impact of 2000-2050 climate change on PM 2.5 air quality inferred from a multi-model analysis of meteorological modes Loretta J. Mickley Co-Is: Amos P.K.A. Tai and Daniel J. Jacob School of Engineering