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Impact of 2000-2050 climate change on PM2.5 air quality inferred from a multi-model analysis of

meteorological modes

Loretta J. Mickley

Co-Is:Amos P.K.A. Tai and Daniel J. JacobSchool of Engineering and Applied SciencesHarvard University

AQ Management Contacts:Susan Anenberg and Carey Jang, EPA/OAQPS

June 13-15, 2012

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Climate change will likely affect PM2.5 concentrations. Models disagree on the sign and the magnitude of the impacts.

mg m-3

mg m-3

Racherla and Adams, 2006

Pye et al., 2009

Response of sulfate PM2.5 at the surface to 2000-2050 climate change.

• These model results are computationally expensive.

• How well do models capture variability in present-day PM2.5?

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A1

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.

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CMIP3 archive of daily meteorology: 15 IPCC models

AQ response to climate change

Apply observed relationships between PM2.5 and met fields

AQ management tool

Climate change over US

PM2.5 dependence on met variables

Temperature

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?

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Stagnation

Relative humidity

Precipitation

Mixing depth

The dependence of PM2.5 on meteorological variables is complex.

Different components have different sensitivities.

Model projections have uncertainties.

Multiple linear regression coefficients for total PM2.5 on meteorological variables. Units: μg m-3 D-1 (p-value < 0.05)

Stagnation is strongly correlated with high PM2.5.

Mean PM2.5 is 2.6 μg m-3 greater on a stagnant day

Tai et al. 2010

Observed correlations of PM2.5 with temperature and precipitation.

1998-2008 meteorology + EPA-AQS observations

Increases in total PM2.5 on a stagnant day vs. a non-stagnant day.

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Principal component analysis (PCA) of 8 meteorological variables identifies the dominant meteorological mode driving day-to-day PM2.5 variability by region:

Midwest, Jan 2006

R = -0.54

2

1

0

-1

-2

6

3

0

-3

-6

PCObserved

PM2.5

(µg m-3)

Transport modes for PM2.5: Eastern US: mid-latitude

cyclone and cold front passage

Pacific coast: synoptic-scale maritime inflow

Jan 28 Jan 30

Tai et al., 2012

Dominant meteorological modes driving PM2.5 variability.

Fluctuations in the period of the dominant meteorological modes can largely explain interannual variability of PM2.5.

Anomalies of annual mean PM2.5 and period of dominant meteorological mode (cyclone passage) for US Midwest

Ann

ual m

ean

PM

2.5 (

µg m

-3)

Per

iod

Τ (d

)

Tai et al., 2012

R = 0.76PM2.5

cyclone period T

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

2000-2050 climate change leads to increases in annual mean PM2.5 across much of the Eastern US, but decreases across the West.

Corresponding change in annual mean PM2.5 concentrations

mg m-3

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We apply observed sensitivity dPM2.5/dΤ to model change in period DT in each grid box.

There is large variation among model projections.

Change in period T of dominant meteorological modes, weighted average for 15 models.

dayD T period, 2000-2050

D PM2.5, 2000-2050

Increased stagnation

Increased maritime inflow

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2000-2050 change in

annual mean PM2.5 (µg m-3)

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

Models disagree on the sign and magnitude of projected change in annual mean PM2.5, but some patterns emerge.

Northeast

Midwest

Southeast

Great Plains

South-central

Interior NW

Interior SW

Pacific NW

California

Eastern US Northwest

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Overall climate effect on annual PM2.5 is likely to be less than ±0.5 µg m-3.

Effect of fires on PM2.5 may be most important impact in future atmosphere, especially on a daily basis.

Response of PM2.5 to 2000-2050 climate change

2000-2050 change in annual mean PM2.5 (µg m-3)

CirculationTai et al., this work

TemperatureHeald et al, 2008; Pye et al.,

2009; Tai et al., 2012a

VegetationWu et al., 2012

WildfiresSpracklen et al., 2009;

Yue et al., 2012

East

Northwest

Southeast (OC)

Southeast (nitrate)

Midwest + West (OC)

Northwest (OC + BC)

Tai et al., 2012

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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.

Next steps:

• Investigate health impacts of trends in PM2.5 air 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.

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Multi-model Projection of Synoptic Period and PM2.5

[Tai et al., in prep]

Climatological observation of dPM2.5/dΤ

dPM2.5/dΤ (µg m-3 d-1)

∆Τ (d)

∆PM2.5 (µg m-3)

Weighted average 2000-2050 change in T(15 IPCC AR4 GCMs)

Resulting 2000-2050 change in PM2.5

×

=

Project Roadmap:

1. Identify the main meteorological modes controlling observed PM2.5 across the United States (Tai et al., 2010; 2011)

2. 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.

3. Track the changes in these modes using the IPCC AR4 archive of climate projections.

4. Estimate the change in surface PM2.5 concentrations due to climate penalty (or climate benefit).

IPCC archive of daily meteorology

AQ response to climate change

Main meteorological modes driving observed PM2.5

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AQ management tool

Evaluation of present-day meteorological modes in AR4 climate models reveals differences among models.

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

Freq

uenc

y (d

-1)

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

N42° W87.5°

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Observed

models