Transcript Toward an integrated approach to tropospheric chemistry

Towards a more integrated
approach to tropospheric
chemistry
Paul Palmer
Division of Engineering and Applied
Sciences, Harvard University
Acknowledgements: Dorian Abbot, Kelly Chance, Colette Heald, Daniel Jacob, Dylan
Jones, Loretta Mickley, Parvadha Suntharalingham, Glen Sachse (NASA LaRC)
Rise in Tropospheric Ozone over the 20th Century
Concentrations of O3 have increased dramatically due to human activity
Observations at mountain
sites in Europe
[Marenco et al., 1994]
Tropospheric O3 is an important climate forcing agent
Impact of human activity on background O3
hv
O3
NO2
(Greenhouse gas)
NO
Global background O3
Free troposphere
OH
Boundary layer (0-2km)
HO2
RH+OH HCHO + products
NOx, RH, CO
Continent 1
O3
Direct intercontinental transport
of pollutants
Ocean
Continent 2
O3
Constructing a self-consistent
representation of the atmosphere
GOME,
MOPITT,
SCIAMACHY
TES, OMI
Global 3d
chemistry
transport model
(GEOS-CHEM)
Global Ozone Monitoring Experiment
•Nadir-viewing SBUV instrument
•Pixel 320 x 40 km2
•10.30 am cross-equator time (globe in 3 days)
•O3, NO2, BrO, OClO, SO2, HCHO, H2O, cloud
•HCHO slant
columns fitted:
337-356nm
Isoprene
Biomass Burning
HCHO JULY 1997
Isoprene dominates HCHO
production over US during summer
Defined background
[ppb]
CH4 + OH
Continental outflow
Altitude [km]
Altitude [km]
North Atlantic Regional
Southern Oxidant Study 1995
Experiment 1997
measurements
GEOS-CHEM model
Surface source (mostly
isoprene+OH)
HCHO columns – July 1996
BIOGENIC ISOPRENE IS THE MAIN SOURCE OF HCHO IN U.S. IN SUMMER
GEIA isoprene
emissions (7.1 Tg C)
[1012 atoms C cm-2 s-1]
GEOS-CHEM HCHO
Model:Observed
HCHO columns
r2 = 0.7
n = 756
Bias = 11%
GOME HCHO
[1016molec cm-2]
Using HCHO Columns
to Map Isoprene Emissions
kHCHO HCHO
EISOP = _______________
kISOPYieldISOPHCHO
Displacement/smearing length scale 10-100 km
hours
hours
HCHO
OH
isoprene
h, OH
(5.7 Tg C)
Isoprene
emissions
(July 1996)
GOME
0
5
[1012 atom C cm-2 s-1]
7.1 Tg C
GEIA
GOME isoprene emissions (July 1996)
agree with surface measurements
0
12
ppb
r2 = 0.53
r2 = 0.77
Bias -3%
Bias -12%
GEIA
GOME
INTERANNUAL VARIABILITY IN
GOME HCHO COLUMNS (1995-2001)
August Monthly Means & Temperature Anomaly
T
GOME
GOME
T
2.5
95
99
°C
96
00
97
01
-2
98
0
1016 molecules cm-2
2
0
1016 molecules cm-2 2.5
Abbot et al, 2003
CO inverse modeling
•Product of incomplete combustion; main sink is OH
•Lifetime ~1-3 months
CMDL network for CO and CO2
•Relative abundance of observations
•Big discrepancy between Asian
emission inventories and observations
TRACE-P (Transport And
Chemical Evolution over the
Pacific) data can improve level
of disaggregation of
continental emissions
Modeling Overview
State vector
(Emissions x)
y = Kxa + 
Forward model
(GEOS-CHEM)
Observation
vector y
Inverse model
xs = xa + (KTSy-1K + Sa-1)-1 KTSy-1(y – Kxa)
x = Annual emissions from Asia (Tg C/yr)
y = TRACE-P CO (ppb)
Fuel consumption (Streets)
Biomass burning AVHRR (Heald/Logan)
A priori emissions have a large negative bias in
the boundary layer
A priori
x = emissions
from individual
countries and
individual
processes (BB,
BF, FF)
CO [ppb]
Observation
Global 3D CTM 2x2.5 deg
resolution
China
Japan
Korea
Rest of
World
Southeast
Asia
[OH] from full-chemistry model (CH3CCl3 
= 6.3 years)
Lat [deg]
Inverse Model
(a.k.a. Weighted linear least-squares)
xs = xa + (KTSy-1K + Sa-1)-1 KTSy-1(y – Kxa)
Gain matrix
SS = (KTSy-1K + Sa-1)-1
Xs = retrieved state vector (the CO sources)
Xa = a priori estimate of the CO sources
Sa = error covariance of the a priori
K = forward model operator
Sy = error covariance of observations
= instrument error + model error + representativeness error
Choice of x…
-Aggregate anthropogenic emissions (colocated sources)
-Aggregate Korea/Japan (coarse model grid resolution)
GEOS-CHEM
Error specification is crucial
Sa
Anthropogenic (c/o Streets):
China (78%), Japan (17%),
Southeast Asia (100%),
Korea (42%)
Biomass burning: 50%
Chemistry (~CH4): 25%
Sy
Measurement accuracy (2%)
Representation (14ppb or 25%)
GEOS-CHEM
2x2.5 cell
Model error
~38ppb
(>70% of total
observation error)
All latitudes
Altitude [km]
(y*RRE)2
TRACE-P
RRE
Mean bias
(measured-model) /measured
Best estimate is insensitive to
inverse model assumptions
1-sigma
uncertainty
A priori
A posteriori
China (BB)
CO [ppb]
A posteriori emissions improve
agreement with observations
Observation
A priori
A posteriori
Lat [deg]
MOPITT shows low CO columns over Southeast
Asia during TRACE-P
MOPITT
MOPITT –
GEOS-CHEM
GEOS-CHEM
[1018 molec cm-2]
Large differences
over NW Indian &
SE Asia
[1018 molec cm-2]
c/o Heald, Emmons, Gille
Observed CO2:CO correlations are consistent with
Chinese biospheric emissions of CO2 40% too high
Japan
China
Slope (> 840 mb) = 22
R2 = 0.45
•
Offshore
China
Slope (> 840 mb) = 51
R2 = 0.76
Over
Japan
Problem: Modeled Chinese CO2:CO slopes are 50% too large
Reconciliation with
observations: decrease
a CO2 source with high
CO2:CO
50% CO increase from
inverse model not enough
CO2/CO
biosphere
Suntharalingam et al, 2003
Future satellite missions
The “A Train”
1:38 PM
Aura
1:30 PM
Cloudsat
CALIPSO
PARASOL
MI - Cloud heights
MI & HIRLDS – Aerosols
MLS& TES - H2O & temp profiles
MLS & HIRDLS – Cirrus clouds
1:15 PM
Aqua
OCO
OCO - CO2 column
CALPSO- Aerosol and cloud heights
MODIS/ CERES
Cloudsat - cloud droplets
IR Properties of Clouds
PARASOL - aerosol and cloud
polarization
AIRS Temperature and
OCO - CO2
H2O Sounding
• Due for launch in 2004
• IR, high res. Fourier spectrometer (3.3 - 15.4 mm)
• Has 2 viewing modes: nadir and limb
• Spatial resolution of nadir view = 8x5 km2
C/o M. Schoeberl
Concluding remarks
•Satellite observations are starting to revolutionize our
understanding of chemistry in the lower atmosphere
•Proper validation of these data with in situ measurements is
critical for their interpretation – need to integrate
•Correlations between multiple species provide untapped source
of information on source inversions
•Future will be fully-coupled chemical data assimilation:
Optimized, comprehensive 4-d view of the atmosphere
Spare slides
GEOS-CHEM global 3D
model: 101
•Driven by DAO GEOS met data
•2x2.5o resolution/26 vertical levels
•O3-NOx-VOC chemistry
•GEIA isoprene emissions
•Aerosol scattering: AOD:O3
TRACE-P data can improve
level of disaggregation of Feb – April 2001
continental emissions
Main transport
processes:
 DEEP CONVECTION
 OROGRAPHIC
LIFTING
 FRONTAL LIFTING
warm air
cold front
cold
air
Back-trajectories of top 5% of observed values
indicate local sources (removed from analysis)
Proxy for OH
Only a strong local source
Selected halocarbons measured during TRACE-P:
CH3CCl3, CCl4, Halon 1211, CFCs 11, 12 (Blake, UCI)
Potential of TES nadir observations of CO: An
Observing System Simulation Experiment
New Concept: test science objectives
of satellite instruments before launch
Objective: Determine whether
nadir observations of CO from TES
have enough information to reduce
uncertainties in estimates of
continental sources of CO
Inverse model with
realistic errors
After 8 days of
observations (operating
half time)
Jones et al, 2003
 CH3CCl3 : CO relationships
 = value above
latitudinal
“background”
Large global & regional implications
Gg/yr
45
40
35
30
25
20
15
10
5
0
Eastern Asia estimates
Previous work
This work
1.4
0.9
3.0
2.3
CH3CCl3,CCl4,CFCs 11 & 12):
-represents >80% of East Asia
ODP (70% of total global ODP)
-103.1 ODP Gg/yr (East Asia)
- East Asia ODP = 70%
- Global ODP = 20%
Methodology has the potential to monitor
magnitude and trends of emissions of a wide range
of environmentally important gases
Satellite data will become integral to the study of
tropospheric chemistry in the next decade
Platform multiple
ERS-2
Sensor
TOMS
GOME
Launch
1979
1995
O3
Terra
ENVISAT
Aura
MOPITT MODIS/ SCIAMACHY MIPAS SAGE-3 TES
MISR
1999
1999
N
CO
Space
station
N
CO2
2002
2002
2004
N/L
L
L
N/L
L
OMI
MLS
2004 2004 2004
N/L
N
TBD
TBD
CALIPSO
OCO
2004
2005
L
N/L
N/L
N
NO
L
NO2
N
N/L
HNO3
N
L
CH4
L
N/L
N
HCHO
N
N/L
N
SO2
N
N/L
N
BrO
N
N/L
N
HCN
aerosol
L
N
N = Nadir
L = Limb
N
N
L
N
N
MOPITT shows low CO columns over Southeast
Asia during TRACE-P
MOPITT
MOPITT –
GEOS-CHEM
GEOS-CHEM
[1018 molec cm-2]
Largest difference
c/o Heald, Emmons, Gille
[1018 molec cm-2]
SCIAMACHY/Envisat instrument
•Launched March 2002
•GOME + IR channels (CO, CH4, CO2)
•Nadir and limb viewing capabilities
CO
•X-Y pixel resolution ~26x15 km (nadir)
Initial comparisons
look promising
(8/23/02)
Eastern Europe through Africa
C/o A. Maurellis
vertical column = slant column /AMF
GEOS-CHEM
Sigma coordinate ()
satellite
lnIB/
dHCHO
1
HCHO mixing ratio C()
Earth Surface
Scattering weights
w() = - 1/AMFG lnIB/
Shape factor
S() = C() air/HCHO
1
AMF = AMFG  w() S() d
0
SEASONAL VARIABILITY IN GOME HCHO COLUMNS (’97)
GOME
0
GEOS-CHEM
GOME
GEOS-CHEM
MAR
JUL
APR
AUG
MAY
SEP
JUN
OCT
1016 molecules cm-2
r>0.75
bias~20%
2.5
Isoprene “volcano” GEOS-CHEM
Slant column HCHO [1016 mol cm-2]
GOME
SOS 1999
Aircraft data @ 350 m during July 1999
Temperature dependence of
isoprene emission
Kansas
July 7 1996
Missouri
Illinois
OZARKS
[ppb]
Surface temperature [K]
July 20 1996
c/o Y-N. Lee, Brookhaven National Lab.
[1016 molec cm-2]
mm
Correlations between different species provide
additional constraints to inverse problems, e.g.
EX = (X:CO) ECO
2 km
Direct &
indirect
emissions
Asian
continent
CO, CO2,
halocarbons, BC, +
many others…
Fresh
emissions
Western Pacific
Concluding remarks
•revolutionize
•validation
•interpretation
•Correlation
•assimilation: