Towards a more integrated approach to tropospheric chemistry Paul Palmer Division of Engineering and...
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Transcript of Towards a more integrated approach to tropospheric chemistry Paul Palmer Division of Engineering and...
Towards a more integrated approach to tropospheric
chemistry
Paul Palmer Division of Engineering and Applied
Sciences, Harvard University
Acknowledgements: Dorian Abbot, Kelly Chance, Daniel Jacob, Dylan Jones, Loretta Mickley, Parvadha Suntharalingham, Glen Sachse (NASA LaRC)
Rise in Tropospheric Ozone over the 20th Century
Observations at mountain sites in Europe [Marenco et al., 1994]
Concentrations of O3 have increased dramatically due to human activity
Tropospheric O3 is an important climate forcing agent
Boundary layer (0-2km)
NOx, RH, CO
Continent 1 Continent 2Ocean
Free troposphere
(Greenhouse gas)
NO
HO2OH
NO2
O3hv
Direct intercontinental transport of pollutants
Global background O3
O3
O3
Impact of human activity on background O3
RH+OH HCHO + products
Global 3d chemistry
transport model (GEOS-CHEM)
Constructing a self-consistent representation of the
atmosphere
GOME,
MOPITT,
SCIAMACHY
TES, OMI
•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
Global Ozone Monitoring Experiment
•HCHO slant columns fitted: 337-356nm
HCHO JULY 1997
Isoprene
Biomass Burning
Isoprene dominates HCHO production over US during
summer Southern Oxidant Study 1995
North Atlantic Regional Experiment 1997
[ppb]
Surface source (mostly isoprene+OH)
Continental outflow
Alt
itu
de
[km
]
Alt
itu
de
[km
]
measurements GEOS-CHEM model
Defined background CH4 + OH
HCHO columns – July 1996HCHO columns – July 1996
r2 = 0.7 n = 756Bias = 11%
Model:Observed HCHO columns
[1016molec cm-2]
GEOS-CHEM HCHO GOME HCHO
[1012 atoms C cm-2 s-
1]
GEIA isoprene emissions (7.1 Tg C)
BIOGENIC ISOPRENE IS THE MAIN SOURCE OF HCHO IN U.S. IN SUMMER
Using HCHO Columns to Map Isoprene Emissions
isoprene
HCHOhours
OH
hours
Displacement/smearing length scale 10-100 km
h, OH
EISOP = ___________kHCHO HCHO
Yield ISOPHCHO
Isoprene emissions (July 1996)
[1012 atom C cm-2 s-
1]
50
(5.7 Tg C)
7.1 Tg C
GEIA
GOME
GOME isoprene emissions (July 1996) agree with surface measurements
r2 = 0.77
Bias -12%
ppb0 12
GOME
r2 = 0.53
Bias -3%
GEIA
10
16 m
ole
cu
les c
m-2
°C
0
2.5
-2
2
GOME T GOME
95
INTERANNUAL VARIABILITY IN GOME HCHO COLUMNS (1995-2001)
August Monthly Means & Temperature AnomalyT
97
98
01
00
99
96
1016 molecules cm-20 2.5
Abbot et al, 2003
CO inverse modeling•Product of incomplete combustion; main sink is OH
•Lifetime ~1-3 months
•Relative abundance of observations•Big discrepancy between Asian emission inventories and observations
110 E 120 E 130 E 140 E 150 E 160 E
Longitude
0 N
10 N
20 N
30 N
40 N
50 N
Lat
itu
de
DC-8 FlightsP-3B Flights
CMDL network for CO and CO2
TRACE-P (Transport And Chemical Evolution over the
Pacific) data can improve level of disaggregation of
continental emissions
Observation vector y
State vector (Emissions x)
Modeling Overview
xs = xa + (KTSy-1K + Sa
-1)-1 KTSy-1(y – Kxa)
y = Kxa +
Inverse model
x = Annual emissions from Asia (Tg C/yr)
y = TRACE-P CO (ppb)
Forward model(GEOS-CHEM)
China Japan
Southeast AsiaRest of
World
Global 3D CTM 2x2.5 deg resolution
[OH] from full-chemistry model (CH3CCl3 = 6.3 years)
Korea
Biomass burning AVHRR (Heald/Logan)
Fuel consumption (Streets)
x = emissions
from individual countries
and individual processes
(BB, BF, FF)
Observation
A priori
CO
[p
pb
]
Lat [deg]
A priori emissions have a large negative bias in the boundary layer
xs = xa + (KTSy-1K + Sa
-1)-1 KTSy-1(y –
Kxa)
SS = (KTSy-1K + Sa
-1)-1
Xs = retrieved state vector (the CO sources)Xa = a priori estimate of the CO sourcesSa = error covariance of the a priori K = forward model operatorSy = error covariance of observations = instrument error + model error + representativeness error
Inverse Model (a.k.a. Weighted linear least-
squares)
Gain matrix
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
TRACE-P
All latitudes
(measured-model) /measured
Alt
itu
de [
km
]
Mean bias
RREModel error (y*RRE)2
~38ppb (>70% of total
observation error)
Ch
ina (
BB
)
Best estimate is insensitive to inverse model assumptions
A prioriA posteriori
1-sigma uncertaint
y
CO
[p
pb
]
Lat [deg]
A posteriori emissions improve agreement with
observations
Observation
A priori
A posteriori
Kore
a +
Ja
pan
South
east
Asi
a
Chin
a (
BB)
Rest
of
Worl
d
Chin
a
(anth
ropogenic
)
[1018 molec cm-2]
MOPITT shows low CO columns over Southeast Asia during TRACE-P
GEOS-CHEM
MOPITT
MOPITT – GEOS-CHEM
[1018 molec cm-2]c/o Heald, Emmons, Gille
Large differences over NW Indian & SE Asia
Observed CO2:CO correlations are consistent with Chinese biospheric emissions of CO2 40% too high
Offshore China
Over Japan
Slope (> 840 mb) = 22
R2 = 0.45
Slope (> 840 mb) = 51
R2 = 0.76
JapanChina
Suntharalingam et al, 2003
• Problem: Modeled Chinese CO2:CO slopes are 50% too large
CO2/CO
50% CO increase from inverse model not enough
Reconciliation with observations: decrease a CO2 source with high CO2:CO
biosphere
Future satellite missions
The “A Train”
MODIS/ CERES IR Properties of Clouds
AIRS Temperature and H2O Sounding
Aqua
1:30 PM
CloudsatPARASOL
CALPSO- Aerosol and cloud heightsCloudsat - cloud dropletsPARASOL - aerosol and cloud polarizationOCO - CO2
CALIPSOAura
OMI - Cloud heights
OMI & HIRDLS – Aerosols
MLS& TES - H2O & temp profiles
MLS & HIRDLS – Cirrus clouds
1:38 PM
OCO
1:15 PM
OCO - CO2 column
C/o M. Schoeberl
• Due for launch in 2004 • IR, high res. Fourier spectrometer (3.3 - 15.4 m)• Has 2 viewing modes: nadir and limb• Spatial resolution of nadir view = 8x5 km2
Potential of TES nadir observations of CO: An Observing System Simulation Experiment
Jones et al, 2003
Objective: Determine whether nadir observations of CO from TES have enough information to reduce uncertainties in estimates of continental sources of CO
New Concept: test science objectives of satellite instruments before launch
Inverse model with realistic errors
After 8 days of observations (operating half time)
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
State estimation (e.g., large-scale t-dep. source inversions)
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 continental emissions
110 E 120 E 130 E 140 E 150 E 160 E
Longitude
0 N
10 N
20 N
30 N
40 N
50 N
Lat
itu
de
DC-8 FlightsP-3B Flights
cold front
cold air
warm air
Main transport processes:
DEEP CONVECTION
OROGRAPHIC LIFTING
FRONTAL LIFTING
100 E 130 E 160 E 190 E 220 E 250 E 280 E
Longitude
0 N
10 N
20 N
30 N
40 N
50 N
60 N
La
titu
de
DC-8 FlightsP-3B Flights
Feb – April 2001
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)
CH3CCl3 : CO relationships
= value above latitudinal
“background”
0
5
10
15
20
25
30
35
40
45
Gg
/yr
CH 3CCl 3
CCl 4
CFC-11
CFC-12
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%
Eastern Asia estimates
Large global & regional implications
Methodology has the potential to monitor magnitude and trends of emissions of a wide range of environmentally important gases
Previous workThis work
0.9
1.4
2.3
3.0
Platform multiple ERS-2 Terra ENVISAT Space station
Aura TBD TBD
Sensor TOMS GOME MOPITT MODIS/MISR
SCIAMACHY MIPAS SAGE-3 TES OMI MLS CALIPSO OCO
Launch 1979 1995 1999 1999 2002 2002 2004 2004 2004 2004 2004 2005
O3 N N/L L L N/L N L
CO N N/L L N/L
CO2 N/L N
NO L
NO2 N N/L N
HNO3 L L
CH4 N/L N
HCHO N N/L N
SO2 N N/L N
BrO N N/L N
HCN L
aerosol N N N L N N
N = NadirL = Limb
Satellite data will become integral to the study of tropospheric chemistry in the next
decade
[1018 molec cm-2]
MOPITT shows low CO columns over Southeast Asia during TRACE-P
GEOS-CHEM
MOPITT
MOPITT – GEOS-CHEM
[1018 molec cm-2]c/o Heald, Emmons, Gille
Largest difference
•Launched March 2002
•GOME + IR channels (CO, CH4, CO2)
•Nadir and limb viewing capabilities
•X-Y pixel resolution ~26x15 km (nadir)
SCIAMACHY/Envisat instrument
Initial comparisons
look promising (8/23/02)
C/o A. Maurellis
Eastern Europe through Africa
CO
vertical column = slant column /AMF
satellite
dHCHO
Earth Surface
HCHO mixing ratio C()
lnIB/
Scattering weights
Shape factorw() = - 1/AMFG lnIB/
Sig
ma c
oord
inate
(
)
S() = C() air/HCHO
AMF = AMFG w() S() d1
1
0
GEOS-CHEM
GEOS-CHEMGOME GOME GEOS-CHEM
1016 molecules cm-2
SEASONAL VARIABILITY IN GOME HCHO COLUMNS (’97)
0 2.5
r>0.75bias~20%
MAR
APR AUG
MAY
JUN
SEP
JUL
OCT
GEOS-CHEM
Isoprene “volcano”
[1016 molec cm-
2]
July 7 1996
July 20 1996
mm
c/o Y-N. Lee, Brookhaven National Lab.
Missouri Illinois
Kansas
[ppb]
Aircraft data @ 350 m during July 1999
OZARKS
SOS 1999
GOME
Surface temperature [K]
Sla
nt
colu
mn
HC
HO
[1
016 m
ol
cm
-2]
Temperature dependence of isoprene emission
Direct & indirect emissions
Correlations between different species provide additional constraints to inverse
problems, e.g.
Western Pacific
CO, CO2, halocarbons, BC, + many others…
Asian continent
2 km
Fresh emissions
EX = (X:CO) ECO