PM 25 air quality inferred from a multimodel analysis of meteorological modes Loretta J Mickley CoIs Amos PKA Tai and Daniel J Jacob School of Engineering and Applied Sciences Harvard University ID: 552849
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Slide1
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 UniversityAQ Management Contacts:Susan Anenberg and Carey Jang, EPA/OAQPS
1
June 13-15, 2012Slide2
Climate change will likely affect PM2.5 concentrations. Models disagree on the sign and the magnitude of the impacts.
m
g m
-3
m
g m
-3
Racherla
and Adams, 2006
Pye
et al., 2009
Response of sulfate PM
2.5
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?
A2
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.
2Slide3
CMIP3 archive of daily meteorology: 15 IPCC models
AQ response to climate change
Apply observed relationships between PM
2.5 and met fields
3
AQ management tool
Climate change over US
PM
2.5
dependence on met variables
Temperature
?
?
?
Stagnation
Relative humidity
Precipitation
Mixing depth
The dependence of PM
2.5
on
meteorological variables
is complex.
Different components have different sensitivities.
Model projections have uncertainties.Slide4
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 PM
2.5
.
Mean PM
2.5
is 2.6
μg m
-3
greater
on a stagnant day
Tai
et al.
2010
Observed correlations of PM
2.5 with temperature and precipitation.
1998-2008 meteorology + EPA-AQS observations
Increases
in total PM
2.5
on a stagnant day vs. a non-stagnant day.
4Slide5
5
P
rincipal component analysis (PCA) of 8 meteorological variables
identifies the dominant meteorological mode driving day-to-day PM
2.5
variability by region:
Midwest, Jan 2006
R
= -0.54
2
1
0
-1
-2
6
3
0
-3
-6
PC
Observed
PM
2.5
(µ
g
m
-3
)
Transport modes for PM
2.5
:
Eastern US:
mid-latitude cyclone a
nd cold front passage
Pacific coast:
synoptic-scale maritime inflow
Jan 28
Jan 30
Tai et al., 2012
Dominant
meteorological modes
driving
PM
2.5
variability.Slide6
Fluctuations in the period of the dominant meteorological modes can largely explain interannual variability of PM
2.5
.
Anomalies of annual mean PM2.5
and period of dominant meteorological mode (cyclone passage) for US Midwest
Annual mean PM
2.5
(µg m
-
3
)
Period
Τ
(d)
Tai et al., 2012
R = 0.76
PM
2.5
c
yclone period
T
In each region, we identify the dominant meteorological mode whose mean period
T
is most strongly correlated with annual mean PM
2.5
.
In the Midwest
:
sensitivity
d
PM
2.5
/
d
Τ
=
~1
µg m
-3
d-1 Slide7
2000-2050 climate change leads to increases in
annual mean PM
2.5 across much of the Eastern US, but decreases across the West.
Corresponding change in annual mean PM
2.5
concentrations
m
g m
-3
7
We
apply observed
sensitivity
d
PM
2.5
/
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.
day
D
T
period, 2000-2050
D
PM2.5, 2000-2050
Increased stagnation
Increased maritime inflowSlide8
8
2000-2050 change in annual mean PM
2.5 (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
NorthwestSlide9
9
Overall climate effect on annual PM
2.5 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 PM
2.5
to 2000-2050 climate change
2000-2050 change
in annual mean PM
2.5
(
µg m
-3
)
Circulation
Tai et al., this work
Temperature
Heald
et al, 2008;
Pye
et al., 2009; Tai et al., 2012a
Vegetation
Wu et al., 2012
Wildfires
Spracklen
et al., 2009;
Yue
et al., 2012
East
Northwest
Southeast (OC)
Southeast (nitrate)
Midwest + West (OC)
Northwest (OC + BC)
Tai et al., 2012Slide10
10
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 (PM
2.5) 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 PM
2.5
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. Slide11
11Slide12
Multi-model Projection of
Synoptic Period and PM
2.5
[Tai et al., in prep]
Climatological observation of
d
PM
2.5
/
d
Τ
d
PM
2.5
/
dΤ
(µg m-3
d-1)
∆
Τ
(d)
∆PM
2.5
(µg m
-3
)
Weighted average 2000-2050 change in
T
(15 IPCC AR4 GCMs)
Resulting 2000-2050 change in PM
2.5
×
=Slide13
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).
IPCC archive of daily meteorology
AQ response to climate change
Main meteorological modes driving observed
PM2.5
13
AQ management toolSlide14
Evaluation of present-day meteorological
m
odes 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
Frequency (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 PM
2.5
N42° W87.5°
14
Observed
models