Transcript Modeling of Air Quality and Regional Climate Interactions Carey Jang, Sharon Phillips, Pat Dolwick, Norm Possiel, Tyler Fox Air Quality Modeling Group, USEPA/OAQPS Yang.

Modeling of Air Quality and
Regional Climate Interactions
Carey Jang, Sharon Phillips, Pat Dolwick, Norm Possiel, Tyler Fox
Air Quality Modeling Group, USEPA/OAQPS
Yang Zhang, Kai Wang, Yaosheng Chen
North Carolina State Univ.
CMAS Conference, Chapel Hill, NC, October 7, 2008
Background
Climate is emerging as an important factor in
current integrated policy and “one-atmosphere”
multi-pollutant air quality management
perspectives
Recognize technical challenges to credibly
address climate change for policy development
Linkages between global and regional/local systems
AQ-Climate interactions across physical, chemical, met,
and econ disciplines
Objectives
Initiate EPA/OAQPS activities on climate-air quality
interactions by leveraging efforts by ORD and
scientific community to demonstrate capability for
our regulatory assessments and inform policy
relevant issues
1. Conduct a “proof of concept” assessment of the
potential impacts of climate change on regional
air quality
2. Conduct a preliminary modeling assessment of
air pollution impacts on regional climate
Interactions of Air Quality and
Regional Climate
Increased
Temperature
Changes to …
Precipitation
Changes
Cloud
Changes
Air Quality
(PM, O3, Dep., etc.)
Transport & Mixing
Changes
… and Feedbacks
OAQPS Climate-AQ Modeling
Potential Climate Impacts
on
Regional Air Quality
Current & Future Climate Scenarios
• Leveraged from EPA/ORD/NERL’s “Climate Impacts on Regional
Air Quality” (CIRAQ) Project (Gilliam and Cooter, 2007, Cooter et al.,
2007, Gustafson and Leung, 2007, Nolte et al., 2007)
• CIRAQ used global climate simulations from “GISS-II” model for
1950 – 2055, based on IPCC “A1B” SRES scenario (Mickley et al.,
GRL, 2004)
IPCC SRES Scenario
• DOE/PNNL downscaled this GCM to
Regional Climate Model (MM5) for the
two periods of 1996-2005 and 2045-2055
A1B
Modeling of Climate Impact on AQ
• Use downscaled regional meteorology for
two CMAQ 5-year simulations
– Current years: 1999-2003
– Future years: 2048-2052
Regional Downscaling of Met
• Emissions
– 2002 Current Base Case
– 2030 Future Control Scenarios
(National Control Programs)
• CMAQ Configuration
– CMAQ (version 4.6):
Continental U.S. domain with
36-km resolution & 14 vertical layers
Modeling Analysis Approach
Objectives:
– Climate Impacts on AQ
and National Control
Programs
Temporal:
Spatial:
– National
– Regions
• Northeast (NE),
Southeast (SE),
Midwest (MW),
Central (CN), West (WE)
– 5-year Ensemble
– Annual
– Seasonal
• O3 season
WE
CN
MW
(5, 6, 7, 8, 9)
• PM quarterly
– Monthly
– Daily (time series)
SE
NE
Proof of Concept Modeling:
Climate Impact on Air Quality
Ozone Results
Summer Ozone (8-hr max; 5-month avg.)
2002 Base w/ Current Climate
Summer 1999
Summer 2002
Summer 2000
Summer 2003
Summer 2001
Ensemble (1999-2003)
Summer Ozone (8-hr max; 5-month avg.)
5-yr Ensemble Meteorology
2030 Control Scenario w/ Current Climate
2002 Base w/ Current Climate
2030 Control Scenario w/ Future Climate
Climate Impact on Summer Ozone
2030 Control Scenario (Future – Current Climate)
Average Annual Temperature
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Future
Current
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Current
Future
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Temperature (K)
Temperature (summer)
Location
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Caveat: Confidence needs to be build
on predicting climate scenarios
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“Climate Penalty”? “Climate Benefit”?
Precipitation (cm)
Summer Precipitation
Precipitation
(Summer)
Climate Impact on Summer Ozone
2030 Control Scenario (Future – Current Climate)
May
July
June
August
Proof of Concept Modeling:
Climate Impact on Air Quality
PM 2.5 Results
Annual Average PM2.5
5-yr Ensemble Meteorology
2030 Control Scenario w/ Current Climate
2002 Base w/ Current Climate
2030 Control Scenario w/ Future Climate
Climate Impact on Annual PM2.5
2030 Control Scenario (Future – Current Climate)
Caveat:
The results are highly subject to underlying uncertainties
of predicted current and future climate scenarios
Climate Impact on Seasonal PM2.5
2030 Control Scenario (Future – Current Climate)
Winter
Summer
Spring
Fall
Climate Impact on PM2.5 (Summer)
D PM 2.5 (2030 future – current)
Climate penalty?
Climate benefit?
Summer Precipitation
Precipitation
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Precipitation (cm)
Confidence needs
to be built on
predicting
climate
scenarios!
OAQPS Climate-AQ Modeling
Air Pollution Impacts
on
Regional Climate
Air Pollution Impacts on Climate
Direct Effects:
Directly affects net solar radiation and photolysis (mainly PM
species, e.g., sulfate PM induced cooling-scattering, Black Carbon
induced warming-absorption, etc.)
Semi-direct Effects:
Affects PBL meteorology, such as vertical mixing, temperature
profile, atmospheric stability, winds, etc., because of changes in
radiation
Indirect Effects (evolving research):
Aerosols serve as CCN, reduce drop size and increase drop number,
reflectivity, and optical depth of low level clouds (LLC)
Aerosols act as CCN (cloud condensation nuclei) to increase lowlevel cloud cover, but spread cloud water vapor and thus decrease
the probability of rain fall
Aerosols (particularly BC) can absorb sun energy to increase cloud
evaporation and thus reduce cloud cover and probability of rain fall
OAQPS Climate-AQ Modeling
Air Pollution Impacts on Regional Climate:
Initiate applications of fully coupled meteorology/
chemistry models (e.g., WRF/CHEM, WRF/CMAQ)
Conduct a preliminary WRF/CHEM modeling to study
direct, semi-direct and indirect effects of air
pollutants on regional climate by removing
(1) All man-made emissions (Air pollution impacts)
(2) SO2 emissions
(3) NOx emissions
(4) EC/OC/VOC emissions
Modeling of AQ-Climate interactions using WRF/Chem
(2001 Jan./Jul. continental US simulations)
•
•
•
•
•
•
•
•
•
•
Period: 1-31 Jan. and 1-31 Jul. 2001
Domain: 148 × 112 grid cells
Horizontal resolution: 36 km
Vertical resolution: 34 layers
Emissions:
–
1999 NEI (v3)
–
Sea salt: online calculation
Meteorology IC and BC:
–
NCEP/NCAR Global Reanalysis •
Chemical IC and BC:
–
Gas: modified CMAQ
–
Aerosol: default (1 mg m-3)
Gas-phase chemistry:
–
CBM-Z
Aerosol module:
–
MOSAIC
Cloud chemistry module:
– Pandis, 1998
Data for model evaluation:
–
SEARCH: T, RH, WS, WD, O3, PM2.5 & comp.
–
CASTNET: T, RH, WS, WD, O3, PM comp.
–
AIRS-AQS: O3
–
IMPROVE: PM2.5 and composition
–
STN:
T, PM2.5, and composition
–
MOPITT: CO
–
GOME:
NO2
–
TOMS:
Tropospheric Ozone Residual (TOR)
–
MODIS: AOD
D PM2.5 (unit: ug/m )
3
All man-made reduction
SO2 reduction
NOx reduction
EC/OC/VOC reduction
Control Case – Base Case (July 2001 monthly avg.)
D GSW (Net shortwave flux at ground surface, unit: W/m )
2
All man-made reduction
SO2 reduction
NOx reduction
EC/OC/VOC reduction
Control Case – Base Case (July 2001 monthly avg.)
D Photolysis (NO
All man-made reduction
SO2 reduction
2 Photolysis Rate; J value unit: sec-1)
NOx reduction
EC/OC/VOC reduction
Control Case – Base Case (July 2001 monthly avg.)
D Mixing Height (PBL Height, unit: meter)
All man-made reduction
SO2 reduction
NOx reduction
EC/OC/VOC reduction
Control Case – Base Case (July 2001 monthly avg.)
D Temp (2-meter surface temperature, unit: C)
o
All man-made reduction
SO2 reduction
NOx reduction
EC/OC/VOC reduction
Control Case – Base Case (July 2001 monthly avg.)
D CNN
(Cloud Condensation Nuclei: #/cm3, supersaturation rate: S=0.1%)
All man-made reduction
NOx reduction
SO2 reduction
EC/OC/VOC reduction
Control Case – Base Case (July 2001 monthly avg.)
D Precipitation (unit: mm/day)
All
All man-made
man-made reduction
reduction
SO
SO22reduction
reduction
NO
NOxxreduction
reduction
EC/OC/VOC reduction
Control Case – Base Case (July 2001 monthly avg.)
Impacts of Air Pollution (%) (All Man-Made Reduction)
D GSW (radiation)
SO2 reduction
D Mixing
Height (PBL)
%
%
D Photolysis (NO2)
D Temp (surface
2m)
EC/OC/VOC
reduction
Control Case – Base Case (July 2001 monthly avg.)
%
%
Summary: Climate and Air Quality have signifcant effects on
each other through their complex interactions
Our "Proof-of-Concept" approach provides a useful means to
understand the impacts of potential climate change on national
control programs for O3/PM2.5 & multi-pollutant control strategies
Climate change can have significant impacts on AQ; however,
confidence needs to be built on predicting climate scenarios
Air pollution can also have significant impacts on regional climate
through its direct & indirect effects
Air pollution can aggravate air pollution itself
Key Issues and Challenges:
Linking global & regional modeling systems
Future climate/met scenarios
Future emission/economy projections
AQ-climate coupled modeling research
Thanks
The End
[email protected]
Modeling of Climate Impact on AQ
• CMAQ Configuration
– CMAQ (version 4.6), Continental U.S. domain with 36-km resolution
& 14 vertical layers
• Meteorology: downscaled from GCM
– Obtained from EPA’s CIRAQ Project: used “GISS-II” global climate
model for 1950 – 2055, based on IPCC “A1B” SRES scenario
– DOE/PNNL downscaled this GCM to Regional Climate Model (RCMMM5) for the two periods (1996-2005 and 2045-2055)
• Use downscaled regional met for
two CMAQ 5-year simulations
– Current years: 1999-2003
– Future years: 2048-2052
• Emissions
– 2002 Current Base Case
– 2030 Future Control Scenarios
Regional Downscaling of Met
Climate and Air Quality Interactions
Climate is emerging as an important factor in current
policy and multi-pollutant “one-atmosphere” air quality
management perspectives
Policy-makers rely upon collaboration with
scientific/academic research community for scientific
research and model development
Technical challenges span across regulatory assessments
Linkages between global and regional/local systems
AQ-Climate interactions across physical, chemical, met, and econ
disciplines
Objectives



Initiate climate-AQ interactions modeling activities
by leveraging efforts in ORD & scientific
communities to address policy relevant issues
Conduct a “proof-of-concept” modeling assessment
of impacts of potential climate change on regional
AQ and national control programs
Conduct a preliminary modeling assessment of air
pollution impacts on regional climate
Annual
(5-yr ensemble)
(Future – Current)
Summer (5-yr ensemble)
(Future – Current)
2-m Temp (K)
2-m Temp (K)
Precip. (% diff)
Precip. (% diff)
Source: EPA/ORD’s CIRAQ Project
Annual Averages
Total Annual Precip
Precipitation
Average Annual Temperature
Current
Current
Future
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Current
Future
Current
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Future
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Location
Location
Average Annual Cloud Cover
Cloud
Cover
Average Annual Mixing Ratio
Location
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Humidity
Mixing Ratio (g/kg)
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Cloud Cover
Future
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Precip (cm)
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Temperature (K)
Temperature
D Ozone (O3)
All man-made
man-made reduction
reduction
All
SO22reduction
reduction
SO
NO
NOxxreduction
reduction
EC/OC/VOC
EC/OC/VOC reduction
reduction
Control Case – Base Case (July 2001 monthly avg.)
D PM2.5 (unit: ug/m )
3
All man-made reduction
SO2 reduction
NOx reduction
EC/OC/VOC reduction
Control Case – Base Case (July 2001 monthly avg.)
D GSW (Net shortwave flux at ground surface, unit: W/m )
2
All man-made reduction
SO2 reduction
NOx reduction
EC/OC/VOC reduction
Control Case – Base Case (July 2001 monthly avg.)
D Photolysis (NO
All man-made reduction
SO2 reduction
2 Photolysis Rate; J value unit: sec-1)
NOx reduction
EC/OC/VOC reduction
Control Case – Base Case (July 2001 monthly avg.)
D Mixing Height (PBL Height, unit: meter)
All man-made reduction
SO2 reduction
NOx reduction
EC/OC/VOC reduction
Control Case – Base Case (July 2001 monthly avg.)
D Temp (2-meter surface temperature, unit: C)
0
All man-made reduction
SO2 reduction
NOx reduction
EC/OC/VOC reduction
Control Case – Base Case (July 2001 monthly avg.)
D CNN
(Cloud Condensation Nuclei: #/cm3, supersaturation rate: S=0.1%)
All man-made reduction
SO2 reduction
NOx reduction
EC/OC/VOC reduction
Control Case – Base Case (July 2001 monthly avg.)
D Precipitation (unit: mm/day)
All
All man-made
man-made reduction
reduction
SO
SO22reduction
reduction
NO
NOxxreduction
reduction
EC/OC/VOC
EC/OC/VOCreduction
reduction
Control Case – Base Case (July 2001 monthly avg.)
Aerosols Affect Clouds Formation (indirect effects)
Sulfate Aerosols
“Cloud Enhancer”
Sulfate gases from Volcanic
Eruption of Mt. Anatahan
(NASA, May 2003, Saipan)
Biomass Burning (BC/OC) as
“Cloud Killers”
By NASA Aqua/MODIS, September 2005
http://www.sciencedaily.com/releases/2006/07/060714082130.htm
Brazil
Annual Average PM2.5
2002 Base w/ Current Climate
Annual CM1
Annual CM4
Annual CM2
Annual CM5
Annual CM3
Ensemble (CM1-CM5)
Annual Precipitation Anomalies (Current & Future)
1999
2000
2001
2002
2003
2048
2049
2050
2051
2052
EPA/ORD’s CIRAQ Project
Ozone
Climate Impact on O3
Cloud Cover
JulyJuly
Cloud
Cover
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Current
Location
June
June
June Cloud Cover
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Location
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Cloud Cover
June Cloud Cover
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Cloud Cover
July
D Ozone (O3)
All man-made
man-made reduction
reduction
All
NO
NOxxreduction
reduction
Redo with better scale (10 ~ 20 ppb)
SO22 reduction
reduction
SO
EC/OC/VOC
EC/OC/VOCreduction
reduction
Control Case – Base Case (July 2001 monthly avg.)
Potential
Impacts
Ozone
Temperature
(insolation)
Temp. h, O3 h :
higher photochemical
oxidation rates, biogenic VOC
& mobile emissions
PM 2.5
Temp. h, PM2.5 h :
higher sulfate & organic PM
because higher atm. oxidation
Temp. h, PM2.5 i :
Lower winter nitrate PM because of
HNO3/nitrate PM partioning
Precipitation
Clouds
Precip. h, O3 i :
Precip. h, PM2.5 i :
higher scavenging of O3 and
precursors
higher scavenging of PM2.5 and
precursors
Clouds h, O3 i :
lower photochemistry/actinic
flux of O3 and precursors
Clouds h, PM2.5 h :
higher sulfate PM formation via
aqueous chemistry
Clouds h, PM2.5 i :
lower PM2.5 and precursors because
of higher scavenging
Humidity
Ventilation
(transport & Mixing)
Feedbacks
Humidity h, O3 h :
Humidity h, PM2.5 h :
higher O3 because of higher availability
of H2O & OH radical (O1D + H2O ->OH)
higher sulfate PM formation
because of higher aqueous chem
Humidity h, O3 i :
Humidity h, PM2.5 i :
lower O3 humidity because of higher
scavenging
lower PM2.5 and precursors because of
higher scavenging
Ventilation h, O3 i:
Vent. h, PM2.5 i :
lower O3 because of higher mixing
(less stagnant) & transport
downwinds
lower PM2.5 because of higher
mixing (less stagnant) & transport
downwinds
O3 h , Temp. h :
O3 is important GHG
PM 2.5 h , Temp. h :
BC/OC is important GHG
PM 2.5 h , Temp. i,
Clouds h, Precip. i:
Effects of Sulfate & nitrate aerosols
References:
Genenral (climate
impacts on AQ):
• EPA GCAQ 2007 Interim
Assessment Report (IAR)
• IPCC Climate Change
2007: WG-I to 4th IPCC
Assessment report
• Camalier & Cox, AE, 2007
• Tagaris et al., JGR, 2007
• Nolte et al., JGR, 2008
• Steiner et al., JGR, 2006
• Hogrefe et al., JGR, 2004
Temperature
• Fiore et al, JGR, 2005
• Leung & Gustafson, GRL, 2005
• Sanderson et al, GRL, 2003
• Cox & Chu, AE, 1996
Clouds & Precip.
•Langner et al, AE, 2005
•Sanderson et al, AE, 2006
• Stevenson et al., JGR, 2005
Humidity
• Liao et al, JGR, 2006
• Wise et al., AE, 2005
Ventilation
• Mickley et al., GRL, 2004
• Ellis et al, Climate Res., 2000
• Pun et al., JAWMA, 2000
Feedbacks
• GAO 2003 CC&AQ report
• Hansen, PNAS, 2001
• Jacobson, Nature, 2001
Effects of Air Pollution: July avg (WRF/Chem)
Direct Effects on
NO2 Photolysis
Semi-Direct Effects
on PBL Height
Indirect Effects on
Precipitation
Absolute Difference
Absolute Difference
Absolute Difference
% Difference
% Difference
% Difference
Courtesy of Dr. Yang Zhang from NCSU
Direct Effects of Air Pollution on NO2 Photolysis
PM2.5 Surface Mass
Jan.
July
Jan.
July
Absolute Difference
Absolute Difference
% Difference
% Difference
NO2 photolysis decreases over EUS in July,
but increases over WUS in July and over CONUS in Jan.
Semi-Direct Effects of Air Pollution on
Near-Surface Temperature (T2)
Jan.
July
Absolute
Difference
%
Difference
T2 decreases over CONUS except for Pacific N.W. in Jan./Jul.
and western TX in Jan.;
Semi-Direct Effects of Air Pollution on
PBL Height (Mixing Layer)
Jan.
July
Absolute
Difference
%
Difference
PBL Height decreases over EUS, particularly in July;
Less impact in Jan.
Indirect Effects of Air Pollution on Precipitation
Jan.
July
Absolute
Difference
%
Difference
Precipitation changes in both ways, stronger impacts in July
PM2.5 Species: WRF/CHEM (July 2001)
PM2.5
SO4
NO3
NH4
OC
BC
PM2.5 Species: WRF/CHEM (Jan. 2001)
PM2.5
SO4
NH4
OC
NO3
BC
Model Evaluation against Satellite Data:
Aerosol Optical Depth (AOD)
Jan.
July
MODIS
WRF/Chem
Statistics
Obs
Sim
N
NMB, %
NME, %
Jan.
0.168
0.042
1760
-75%
76%
Jul.
0.247
0.207
2219
-16%
71%
Evaluation of WRF Meteorological Predictions:
Jan. (Top) and Jul. (Bottom)
T2
Parameters
NMB
(%)
Precipitation
RH2
T2
RH2
WSP
WDR
Precip.
CASTNET
Jan.
Jul.
-14.3
9.7
90.6
-9.0
--
0.6
-0.9
79.9
0.9
--
SEARCH
Jan.
Jul.
-3.3
-0.2
40.4
-0.8
--
-1.5
-1.2
30.5
5.6
--
STN
Jan.
0.4
-----
Jul.
-6.1
-----
NADP
Jan.
Jul.
----27.4
----32.2
Evaluation of Chemical Predictions:
Spatial Distribution and Statistics of Surface O3 and PM2.5
Jan.
Jul.
Max 8-h O3
24-h PM2.5
Parameters
NMB
(%)
CASTNET
Jan.
Max 1-h O3
Max 8-h O3
24-h PM2.5
-18.7
-18.4
--
Jul.
9.7
14.5
--
AIRS-AQS
Jan.
-11.1
-7.9
--
Jul.
12.8
18.1
--
SEARCH
Jan.
-23.1
-9.9
24.7
Jul.
21.8
30.5
33.1
STN
Jan.
-8.6
IMPROVE
Jul.
21.5
Jan.
23.1
Jul.
8.5
Evaluation of WRF/Chem. Predictions: Column O3
Jan.
Jul.
TOMS
WRF/Chem
Statistics
Obs
Sim
N
NMB, %
NME, %
Jan.
27.81
35.38
1464
2.0
13.9
Jul.
46.36
46.23
1464
4.2
10.6
Evaluation of WRF/Chem. Predictions:
Aerosol Optical Depth (AOD)
Jan.
Jul.
MODIS
WRF/Chem
Statistics
Obs
Sim
N
NMB, %
NME, %
Jan.
0.168
0.042
1760
-75
76
Jul.
0.247
0.181
2219
-26.7
63.9
Direct Effects of PM2.5 on Shortwave Radiation and NO2 photolysis
PM2.5 Surface Mass
Shortwave Radiation
NO2 photolysis
Jan.
Jul.
PM2.5 decreases shortwave radiation over EUS in Jan/Jul, but increases it over WUS in Jan;
PM2.5 increases NO2 photolysis over WUS in Jan/Jul, but decreases it over EUS in Jul;
Strong seasonality
Indirect Effects of PM2.5 on CCN and Precipitation
PM2.5 Surface Mass
CCN (S=1%)
Precipitation
Jan.
Jul.
CCN is proportional to supersaturation and PM mass conc.;
PM2.5 can affect precipitation in both ways, with stronger impacts in Jul.
PM2.5 indirect effects are stronger in Jul. than Jan. and over EUS than WUS.
Case 2
WRF/Chem Application for 2005 July China
•
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•
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•
Period: 1-31 Jul. 2005
Domain: 164 × 97 grid cells
Horizontal resolution: 36 km
Vertical resolution: 30 layers
Emissions:
– US EPA SED-JES
– Sea salt: online calculation
Meteorology IC and BC:
– NCEP/NCAR Global Reanalysis
Chemical IC and BC:
– CMAQ
Gas-phase chemistry:
– CBM-Z
• Data for model evaluation:
Aerosol module:
–
China/NCDC:
T, RH, WS, Precip, PM, API
– MOSAIC
–
Japan (2078 sites): T, RH, WS, SO2, NO2, CO, O3, PM
Cloud chemistry module:
–
MOPITT: CO
– Pandis, 1998
–
OMI:
NO2
–
TOMS:
Tropospheric Ozone Residual (TOR)
Scenarios:
– Met; Met+Gas; Met+Gas+PM+Cld. Aq. – MODIS: AOD
Spatial Distributions of WRF Meteorological Predictions:
2-m Temperature (degree C)
Obs.
vs.
Sim.
NMB
2-m Specific Humidity (kg/kg)
Spatial Distributions of WRF Meteorological Predictions:
10-m Wind Speed (m/s)
Obs.
vs.
Sim.
NMB
Daily Total Precipitation (mm/day)
Temporal Variations of MM5/WRF Meteorological Predictions:
2-m Temperature
2-m Specific Humidity
Beijing - Beijing, China
Beijing - Beijing, China
0.03
45
Beijing
MM5
Obs
WRF/Chem
0.025
35
0.02
Q2 (kg/kg)
T2 (Degree C)
Obs
40
30
25
0.01
0.005
20
0
7/1
7/3
7/5
7/7
7/1
7/9 7/11 7/13 7/15 7/17 7/19 7/21 7/23 7/25 7/27 7/29 7/31
7/3
7/5
7/7
Shanghai - Shanghai, China
Shanghai - Shanghai, China
0.03
45
Obs
MM5
Obs
WRF/Chem
MM5
WRF/Chem
0.025
Q2 (kg/kg)
T2 (Degree C)
40
35
30
0.02
0.015
25
0.01
20
15
0.005
7/1
7/3
7/5
7/7
7/9
7/11 7/13 7/15 7/17 7/19 7/21 7/23 7/25 7/27 7/29 7/31
7/1
7/3
7/5
7/7
Local Time (LST)
7/9 7/11 7/13 7/15 7/17 7/19 7/21 7/23 7/25 7/27 7/29 7/31
Local Time (LST)
Guangzhou - Guangdong, China
Guangzhou - Guangdong, China
45
0.03
Obs
MM5
Obs
WRF/Chem
40
MM5
WRF/Chem
0.025
35
0.02
Q2(kg/kg)
T2 (Degree C)
7/9 7/11 7/13 7/15 7/17 7/19 7/21 7/23 7/25 7/27 7/29 7/31
Local Time (LST)
Local Time (LST)
Guangzhou
WRF/Chem
0.015
15
Shanghai
MM5
30
25
0.015
0.01
20
0.005
15
7/1
7/3
7/5
7/7
7/9
7/11 7/13 7/15 7/17 7/19 7/21 7/23 7/25 7/27 7/29 7/31
Local Time (LST)
7/1
7/3
7/5
7/7
7/9 7/11 7/13 7/15 7/17 7/19 7/21 7/23 7/25 7/27 7/29 7/31
Local Time (LST)
Spatial Distributions of CMAQ and WRF/Chem Predictions at Surface
Max 1-hr O3
CMAQ
WRF/
Chem
24-hr average PM2.5
Evaluation of CMAQ and WRF/Chem Predictions: Column CO
MOPITT
CMAQ
Statistics
WRF/Chem
# of data
Corr.
RMSE
NMB,
%
MM5/CMAQ
15908
0.62
21.6
-58.3
WRF/Chem
15908
0.52
19.4
-50.4
Evaluation of CMAQ and WRF/Chem Predictions: Column NO2
CMAQ
OMI
WRF/Chem
Statistics
# of
data
Corr.
RMSE
NMB,
%
MM5/CMAQ
15908
0.61
1.68
-22.0
WRF/Chem
15908
0.64
1.89
16.3
Evaluation of CMAQ and WRF/Chem Predictions: Column O3
TOMS Tropospheric O3 Residual
Statistics
CMAQ
WRF/Chem
# of
data
Corr.
RMSE
NMB,
%
MM5/CMAQ
15908
0.71
17.1
-35.2
WRF/Chem
15908
0.28
11.2
-18.2
Aerosol Direct Effects on Radiation and NO2 Photolysis
Direct Effects on
Shortwave Radiation
Direct Effects on
NO2 Photolysis
Absolute Difference
PM2.5 Mass
Percent Difference
PM2.5 decreases shortwave radiation over China;
PM2.5 decreases NO2 photolysis over China except for NW
Aerosol Semi-Direct Effects on Temperature and PBL Height
Semi-Direct Effects
on 2-m Temperature
Semi-Direct Effects on
PBL Height
Absolute Difference
PM2.5 Mass
Percent Difference
PM2.5 slightly decreases 2-m temperature over China;
PM2.5 affects 2-m specific humidity in both ways
Aerosol Indirect Effects on Precipitation and CCN
PM2.5 Mass
CCN (S = 1%)
Changes in Precipitation
China
US
Higher CCN concentrations over larger areas in China
Dominancy of suppression of precipitation over China, either ways over US
Examples and Evidences of
Important Feedbacks
• Effects of Meteorology and Climate on Gases and Aerosols
–
–
Changes in tropospheric vertical temperature structure affect transport of species
Changes in temperature, humidity, and precipitation directly affect species conc.
–
Changes in vegetation alter dry deposition and emission rates of biogenic species
• Effects of Gases and Aerosols on Meteorology and Climate
–
Decrease net downward solar radiation and photolysis (direct effect)
–
Affect PBL meteorology (e.g., near-surface air temperature, RH, wind speed, PBL height, and
atmospheric stability) (semi-direct effect)
–
Aerosols serve as CCN, reduce drop size and increase drop number, reflectivity, and optical
depth of low level clouds (LLC) (the Twomey or first indirect effect)
–
Aerosols increase liquid water content, fractional cloudiness, and lifetime of LLC;
suppress/increase precipitation (the second indirect effect)
• Evidence of Feedbacks
–
–
Satellite data have shown smoke from rain forest fires in tropical areas and burning of
agricultural vegetations can inhibit rainfall by shutting off warm rain-forming processes
Enhanced rainfall was also found downwind of urban areas or large sources and over major
urban areas
Air Pollution Impact on Regional Climate
Katrina
Hurricane,
8/29/2005
Smog in
Beijing,
China
(9/4/2004)
NASA
Beijing
MODIS
(3/2007)
Pollutants
Clouds
Zhejiang, China (Aug. 11, 2006)
aftermath of Saomei Super-typhoon
Air Pollution Impact on Radiative Forcing (direct effect),
Clouds/Rainfalls & Weather (indirect effect)
Particles as “Cloud Killers” (indirect effects)
Large plumes of smoke
can act as "cloud killers"
because of aerosols’
indirect effects. NASA's
Aqua satellite caught
this cloud-suppression
Brazil
process in action over
western Brazil and
Bolivia.
By NASA Aqua/MODIS, September 2005
http://www.sciencedaily.com/releases/2006/07/060714082130.htm
Particles as
“Cloud Killers”:
it may be
happening in the
U.S. too!
Texas
Mexico
Brazil
May 9, 2003
By NASA MODIS
fires
(Courtesy of Engel-Cox and Jill,
Battelle Memorial Institute)
By NASA Aqua/MODIS, September 2005
Comparison of Daily Max Temperature Distributions
(Downscaled vs. Retrospective Met at 85 representative sites)
Downscaled
Retrospective – Downscaled
Retrospective – Downscaled
(2 years v. 5 years)
(2 years v. 2 years)
Retrospective
•
Downscaled meteorology generates more “extreme”
conditions
–
•
•
more cool highs (< 285K) and more warm highs (>305K)
Downscaled meteorology generates 5x more days with
max temperatures > 310K (~ 100F)
Future climate modeling tends to show that max
temperatures will increase in the future
–
90th percentile > 310K