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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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Loretta j mickley co is amos p k a tai and daniel j jacob

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


Loretta j mickley co is amos p k a tai and daniel j jacob

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


Loretta j mickley co is amos p k a tai and daniel j jacob

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


Loretta j mickley co is amos p k a tai and daniel j jacob

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


Loretta j mickley co is amos p k a tai and daniel j jacob

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


Loretta j mickley co is amos p k a tai and daniel j jacob

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


Loretta j mickley co is amos p k a tai and daniel j jacob

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


Loretta j mickley co is amos p k a tai and daniel j jacob

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


Loretta j mickley co is amos p k a tai and daniel j jacob

  • 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


Loretta j mickley co is amos p k a tai and daniel j jacob

  • 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.


Loretta j mickley co is amos p k a tai and daniel j jacob

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]


Loretta j mickley co is amos p k a tai and daniel j jacob

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


Loretta j mickley co is amos p k a tai and daniel j jacob

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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