Transcript PPT - Harvard University

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. Jacob
School of Engineering and Applied Sciences
Harvard 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.
Racherla and Adams, 2006
Response of sulfate PM2.5 at
the surface to 2000-2050
climate change.
• These model results are
computationally expensive.
A2
mg
m-3
• How well do models capture
variability in present-day
PM2.5?
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
2
The dependence of
PM2.5 on meteorological
variables is complex.
Different components have
different sensitivities.
Model projections have
uncertainties.
Climate change
over US
PM2.5 dependence
on met variables
Temperature
?
?
Relative humidity
Precipitation
Stagnation
?
Mixing depth
AQ management tool
CMIP3 archive of
daily meteorology:
15 IPCC models
Apply observed relationships
between PM2.5 and met fields
AQ response to
climate change
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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-3
greater on a stagnant day
Tai et al. 2010
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Dominant meteorological modes driving PM2.5 variability.
Principal component analysis (PCA) of 8 meteorological variables
identifies the dominant meteorological mode driving day-to-day PM2.5
variability by region:
PC(t) = aT T(t)+ a precip precip(t)+ aSLP SLP(t)+...
Transport modes for PM2.5:
 Eastern US: mid-latitude
cyclone and cold front passage
 Pacific coast: synoptic-scale
maritime inflow
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0
0
0
-1
-3
-2
3
-6 -3
1
0
2
6
1
-2 -1
PC
PM2.5
6
PC
2
Jan 30
Jan 28
Midwest, Jan 2006
-6
R = -0.54
0
5
10
15
20
25
Observed
PM2.5
(µg m-3)
30 al., 2012
Tai et
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Fluctuations in the period of the dominant meteorological modes can
largely explain interannual variability of PM2.5.

In the Midwest: sensitivity dPM2.5/dΤ = ~1 µg m-3 d-1
1
In each region, we identify the dominant meteorological mode whose
mean period T is most strongly correlated with annual mean PM2.5.
-0.5
0
0.5
PM2.5cyclone period
2000
2002
2004
2006
2008
2010
Anomalies of annual mean PM2.5 and period of dominant
meteorological mode (cyclone passage) for US Midwest
Tai et al., 2012
Period Τ (d)
R = 0.76
0
0.5
cyclone
period T
PM2.5
-0.5
Annual mean
PM2.5 (µg m-3)

2000-2050 climate change leads to increases in annual mean PM2.5
across much of the Eastern US, but decreases across the West.
D T period, 2000-2050
day
0.6
Increased
maritime inflow
0.4
0.2
0.0
-0.2
-0.4
-0.6
Increased
stagnation
Change in period T of dominant meteorological
modes, weighted average for 15 models.
D PM2.5, 2000-2050
mg m-3
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
Corresponding change in annual mean
PM2.5 concentrations
0.7
We apply observed sensitivity
dPM2.5/dΤ to model change in
period DT in each grid box.
There is large variation among
model projections.
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-0.5
-1.0
2000-2050
change in
annual mean
PM2.5 (µg m-3)
0.0
0.5
Models disagree on the sign and magnitude of projected change
in annual mean PM2.5, but some patterns emerge.
Eastern US
Northwest
weighted average
95% confidence interval
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
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 Overall climate effect on annual PM2.5 is
likely to be less than ±0.5 µg m-3.
Response of PM2.5
to 2000-2050
climate change
 Effect of fires on PM2.5 may be most
important impact in future atmosphere,
especially on a daily basis.
Circulation
East
Tai et al., this work
Northwest
Temperature
Heald et al, 2008; Pye et al.,
2009; Tai et al., 2012a
Southeast (OC)
Southeast (nitrate)
Vegetation
Midwest +
West (OC)
Wu et al., 2012
Wildfires
Northwest
(OC + BC)
Spracklen et al., 2009;
Yue et al., 2012
-0.6
Tai et al., 2012
-0.4
-0.2
0.0
0.2
0.4
2000-2050 change in annual mean PM2.5 (µg m-3)
0.6
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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.
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.
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-0.5
Multi-model Projection of -1.0
Synoptic Period and PM2.5
4
2
0
dPM2.5/dΤ
(µg m-3 d-1)
-2
-4
0.6
Climatological
observation of
dPM2.5/dΤ
×
0.4
0.2
0.0
-0.2
∆Τ
(d)
Weighted average
2000-2050 change in T
(15 IPCC AR4 GCMs)
-0.4
=
-0.6
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
∆PM2.5
(µg m-3)
Resulting 2000-2050
change in PM2.5
[Tai et al., in prep]
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).
AQ management tool
IPCC archive of
daily
meteorology
Main meteorological
modes driving observed
PM2.5
AQ response
to climate
change
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Frequency (d-1)
Frequency of meteorological mode (d! 1)
0.14
0.16
0.18
0.20
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_echam5
1985
1990
Year
1995
2000
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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