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Loretta J. Mickley Co-Is: Amos P.K.A. Tai and Daniel J. Jacob

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 and Applied Sciences Harvard University AQ Management Contacts:

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Loretta J. Mickley Co-Is: Amos P.K.A. Tai and Daniel J. Jacob

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  1. Impact of 2000-2050 climate change on PM2.5air quality inferred from a multi-model analysis of meteorological modes Loretta J. Mickley Co-Is: Amos P.K.A. Taiand Daniel J. Jacob School of Engineering and Applied Sciences Harvard University AQ Management Contacts: Susan Anenberg and Carey Jang, EPA/OAQPS June 13-15, 2012

  2. Climate change will likely affect PM2.5 concentrations. Models disagree on the sign and the magnitude of the impacts. Racherla and Adams, 2006 • Response of sulfate PM2.5at the surface to 2000-2050 climate change. • These model results are computationally expensive. • How well do models capture variability in present-day PM2.5? A2 mg m-3 Pye et al., 2009 We need a simple tool that will allow AQ managers to readily calculate the climate consequences for PM2.5 air quality across a range of models and scenarios. A1 mg m-3

  3. Climate change over US PM2.5 dependence on met variables The dependence of PM2.5 on meteorological variables is complex. Different components have different sensitivities. Model projections have uncertainties. Temperature ? Relative humidity ? Precipitation Stagnation ? Mixing depth AQ management tool CMIP3 archive of daily meteorology: 15 IPCC models AQ response to climate change Apply observed relationships between PM2.5 and met fields

  4. Stagnation is strongly correlated with high PM2.5. Observed correlations of PM2.5 with temperature and precipitation. 1998-2008 meteorology + EPA-AQS observations Multiple linear regression coefficients for total PM2.5 on meteorological variables. Units: μg m-3 D-1 (p-value < 0.05) Increases in total PM2.5 on a stagnant day vs. a non-stagnant day. Mean PM2.5 is 2.6 μg m-3greater on a stagnant day Tai et al. 2010

  5. Dominant meteorological modes driving PM2.5variability. Principal component analysis (PCA) of 8 meteorological variablesidentifies the dominant meteorological mode driving day-to-day PM2.5 variability by region: • Transport modes for PM2.5: • Eastern US: mid-latitude cyclone and cold front passage • Pacific coast: synoptic-scale maritime inflow Jan 30 Jan 28 Midwest, Jan 2006 2 6 Observed PM2.5 (µg m-3) 1 3 PC 0 0 -1 -3 -2 -6 R = -0.54 Tai et al., 2012

  6. Fluctuations in the period of the dominant meteorological modes can largely explain interannual variability of PM2.5. • In each region, we identify the dominant meteorological mode whose mean period T is most strongly correlated with annual mean PM2.5. • In the Midwest:sensitivity dPM2.5/dΤ= ~1 µg m-3 d-1 cyclone period T R = 0.76 PM2.5 Annual mean PM2.5(µg m-3) Period Τ (d) Anomalies of annual mean PM2.5 and period of dominant meteorological mode (cyclone passage) for US Midwest Tai et al., 2012

  7. 2000-2050 climate change leads to increases in annual mean PM2.5 across much of the Eastern US, but decreases across the West. DT period, 2000-2050 day Increased maritime inflow Increased stagnation Change in period T of dominant meteorological modes, weighted average for 15 models. D PM2.5, 2000-2050 mg m-3 We apply observed sensitivity dPM2.5/dΤ to model change in period DT in each grid box. There is large variation among model projections. Corresponding change in annual mean PM2.5 concentrations

  8. Models disagree on the sign and magnitude of projected change in annual mean PM2.5, but some patterns emerge. 2000-2050 change in annual mean PM2.5 (µg m-3) Eastern US Northwest Midwest California Northeast Southeast Great Plains Pacific NW Interior NW Interior SW South-central • Likely responses: • Increase of ~0.1 µg m-3 in eastern US due to increased stagnation • Decrease of ~0.3 µg m-3 in Northwest due to more frequent maritime inflows

  9. Overall climate effect on annual PM2.5 is likely to be less than ±0.5 µg m-3. • Effect of fires on PM2.5may be most important impact in future atmosphere, especially on a daily basis. Response of PM2.5 to 2000-2050 climate change Circulation Tai et al., this work East Northwest Temperature Heald et al, 2008; Pye et al., 2009; Tai et al., 2012a Southeast (OC) Southeast (nitrate) Vegetation Wu et al., 2012 Midwest + West (OC) Wildfires Spracklen et al., 2009; Yue et al., 2012 Northwest (OC + BC) 2000-2050 change in annual mean PM2.5 (µg m-3) Tai et al., 2012

  10. Next steps: • Investigate health impacts of trends in PM2.5air quality and compare to impacts from heatwaves. Proposal submitted to NIH; PI is Francesca Dominici, Harvard. • Develop similar tool for assessing climate impact on U.S. ozone air quality, across multiple models and scenarios. 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 (PM2.5) air quality the United States: implications for PM2.5 sensitivity, Atmos. Chem. Phys., 2012a. Tai, A. P. K., L. J. Mickley, and D. J. Jacob, Impact of 2000-2050 climate change on fine particulate matter (PM2.5) air quality inferred from a multi-model analysis of meteorological modes, submitted to Atmos. Chem. Phys.,2012b.

  11. Multi-model Projection of Synoptic Period and PM2.5 Climatological observation of dPM2.5/dΤ dPM2.5/dΤ(µg m-3 d-1) × Weighted average 2000-2050 change in T (15 IPCC AR4 GCMs) ∆Τ (d) = Resulting 2000-2050 change in PM2.5 ∆PM2.5(µg m-3) [Tai et al., in prep]

  12. Project Roadmap: Identify the main meteorological modes controlling observed PM2.5 across the United States (Tai et al., 2010; 2011) Calculate the sensitivity of PM2.5 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 (PM2.5) air quality the United States: implications for PM2.5 sensitivity to climate change, submitted to Atmos. Chem. Phys., 2011. Track the changes in these modes using the IPCC AR4 archive of climate projections. Estimate the change in surface PM2.5 concentrations due to climate penalty (or climate benefit). AQ management tool Main meteorological modes driving observed PM2.5 IPCC archive of daily meteorology AQ response to climate change

  13. Evaluation of present-day meteorological modes in AR4 climate models reveals differences among models. N42° W87.5° Observed models Frequency (d-1) Modeled (2 IPCC models) and observed (NCEP/NCAR) 1981-2000 time series of frequency of dominant meteorological mode for PM2.5 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 PM2.5

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