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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)
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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]
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Tropospheric O3 is an important climate forcing agent
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Impact of human activity on background O3
hv O3 (Greenhouse gas) NO2 NO Global background O3 Free troposphere OH HO2 Boundary layer (0-2km) RH+OH HCHO + products Direct intercontinental transport of pollutants O3 O3 NOx, RH, CO Continent 1 Ocean Continent 2
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Constructing a self-consistent representation of the atmosphere
Global 3d chemistry transport model (GEOS-CHEM) GOME, MOPITT, SCIAMACHY TES, OMI
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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: nm Isoprene Biomass Burning HCHO JULY 1997
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Isoprene dominates HCHO production over US during summer
North Atlantic Regional Experiment 1997 Southern Oxidant Study 1995 measurements GEOS-CHEM model Altitude [km] Altitude [km] Defined background CH4 + OH [ppb] Continental outflow Surface source (mostly isoprene+OH)
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r2 = 0.7 n = 756 Bias = 11% Model:Observed HCHO columns
HCHO columns – July 1996 BIOGENIC ISOPRENE IS THE MAIN SOURCE OF HCHO IN U.S. IN SUMMER [1012 atoms C cm-2 s-1] GEIA isoprene emissions (7.1 Tg C) r2 = 0.7 n = 756 Bias = 11% Model:Observed HCHO columns [1016molec cm-2] GEOS-CHEM HCHO GOME HCHO
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Using HCHO Columns to Map Isoprene Emissions
kHCHO HCHO EISOP = _______________ kISOPYieldISOPHCHO Displacement/smearing length scale km hours hours HCHO OH h, OH isoprene
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Isoprene emissions (July 1996)
[1012 atom C cm-2 s-1] 5 (5.7 Tg C) Isoprene emissions (July 1996) GOME 7.1 Tg C GEIA
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GOME isoprene emissions (July 1996) agree with surface measurements
ppb 12 r2 = 0.53 Bias -3% GEIA r2 = 0.77 Bias -12% GOME
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INTERANNUAL VARIABILITY IN August Monthly Means & Temperature Anomaly
GOME HCHO COLUMNS ( ) August Monthly Means & Temperature Anomaly GOME T GOME T 2.5 2 95 99 96 1016 molecules cm-2 °C 00 -2 97 01 98 2.5 1016 molecules cm-2 Abbot et al, 2003
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CMDL network for CO and CO2
CO inverse modeling Product of incomplete combustion; main sink is OH Lifetime ~1-3 months Relative abundance of observations CMDL network for CO and CO2 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
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Modeling Overview y = Kxa + Forward model (GEOS-CHEM) Inverse model
State vector (Emissions x) 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)
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A priori Observation Global 3D CTM 2x2.5 deg resolution
Biomass burning AVHRR (Heald/Logan) Fuel consumption (Streets) Observation A priori CO [ppb] Lat [deg] A priori emissions have a large negative bias in the boundary layer China Japan Southeast Asia Rest of World Global 3D CTM 2x2.5 deg resolution [OH] from full-chemistry model (CH3CCl3 = 6.3 years) Korea x = emissions from individual countries and individual processes (BB, BF, FF)
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Inverse Model (a.k.a. Weighted linear least-squares)
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 sources Sa = error covariance of the a priori K = forward model operator Sy = error covariance of observations = instrument error + model error + representativeness error Gain matrix Choice of x… Aggregate anthropogenic emissions (colocated sources) Aggregate Korea/Japan (coarse model grid resolution)
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Error specification is crucial
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 Altitude [km] Model error (y*RRE)2 ~38ppb (>70% of total observation error) Mean bias RRE
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Best estimate is insensitive to inverse model assumptions
1-sigma uncertainty A priori A posteriori A posteriori emissions improve agreement with observations CO [ppb] Lat [deg] China (BB) China (BB) Southeast Asia Rest of World Korea + Japan China (anthropogenic) Observation A priori A posteriori
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MOPITT shows low CO columns over Southeast Asia during TRACE-P
GEOS-CHEM [1018 molec cm-2] MOPITT – GEOS-CHEM Large differences over NW Indian & SE Asia [1018 molec cm-2] c/o Heald, Emmons, Gille
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Problem: Modeled Chinese CO2:CO slopes are 50% too large
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 Japan China 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 Suntharalingam et al, 2003
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Future satellite missions
The “A Train” 1:38 PM 1:30 PM 1:15 PM Aura Cloudsat CALIPSO Aqua OCO PARASOL OCO - CO2 column OMI - Cloud heights OMI & HIRLDS – Aerosols MLS& TES - H2O & temp profiles MLS & HIRDLS – Cirrus clouds CALPSO- Aerosol and cloud heights Cloudsat - cloud droplets PARASOL - aerosol and cloud polarization OCO - CO2 MODIS/ CERES IR Properties of Clouds AIRS Temperature and H2O Sounding Due for launch in 2004 IR, high res. Fourier spectrometer ( mm) Has 2 viewing modes: nadir and limb Spatial resolution of nadir view = 8x5 km2 C/o M. Schoeberl
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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
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Spare slides
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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
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Main transport processes:
TRACE-P data can improve level of disaggregation of continental emissions Feb – April 2001 Main transport processes: DEEP CONVECTION OROGRAPHIC LIFTING FRONTAL LIFTING warm air cold air cold front
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Only a strong local source
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)
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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
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CH3CCl3 : CO relationships = value above latitudinal “background”
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Large global & regional implications
Eastern Asia estimates 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% Previous work 3.0 This work 2.3 Gg/yr 1.4 0.9 CCl4 CH3CCl3 CFC-11 CFC-12 Methodology has the potential to monitor magnitude and trends of emissions of a wide range of environmentally important gases
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Satellite data will become integral to the study of tropospheric chemistry in the next decade
Platform multiple ERS-2 Terra ENVISAT Space station Aura TBD Sensor TOMS GOME MOPITT MODIS/MISR SCIAMACHY MIPAS SAGE-3 TES OMI MLS CALIPSO OCO Launch 1979 1995 1999 2002 2004 2005 O3 N N/L L CO CO2 NO NO2 HNO3 CH4 HCHO SO2 BrO HCN aerosol N = Nadir L = Limb
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MOPITT shows low CO columns over Southeast Asia during TRACE-P
GEOS-CHEM [1018 molec cm-2] MOPITT – GEOS-CHEM Largest difference c/o Heald, Emmons, Gille [1018 molec cm-2]
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SCIAMACHY/Envisat instrument
Launched March 2002 GOME + IR channels (CO, CH4, CO2) Nadir and limb viewing capabilities X-Y pixel resolution ~26x15 km (nadir) CO Initial comparisons look promising (8/23/02) Eastern Europe through Africa C/o A. Maurellis
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vertical column = slant column /AMF
GEOS-CHEM satellite lnIB/ Sigma coordinate () dHCHO 1 Earth Surface HCHO mixing ratio C() Scattering weights Shape factor S() = C() air/HCHO w() = - 1/AMFG lnIB/ 1 AMF = AMFG w() S() d
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SEASONAL VARIABILITY IN GOME HCHO COLUMNS (’97)
GEOS-CHEM GOME GEOS-CHEM MAR JUL APR AUG MAY SEP r>0.75 bias~20% JUN OCT 1016 molecules cm-2 2.5
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Isoprene “volcano” GOME GEOS-CHEM SOS 1999 [1016 molec cm-2]
Surface temperature [K] Slant column HCHO [1016 mol cm-2] Temperature dependence of isoprene emission c/o Y-N. Lee, Brookhaven National Lab. Missouri Illinois Kansas [ppb] Aircraft 350 m during July 1999 OZARKS SOS 1999 July July [1016 molec cm-2] mm
![Isoprene volcano GOME GEOS-CHEM SOS 1999 [1016 molec cm-2]](http://slideplayer.com./slide/15099691/91/images/35/Isoprene+volcano+GOME+GEOS-CHEM+SOS+1999+%5B1016+molec+cm-2%5D.jpg "Surface temperature [K] Slant column HCHO [1016 mol cm-2] Temperature dependence of isoprene emission. c/o Y-N. Lee, Brookhaven National Lab. Missouri. Illinois. Kansas. [ppb] Aircraft 350 m during July OZARKS. SOS July July [1016 molec cm-2] mm.")
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CO, CO2, halocarbons, BC, + many others…
Correlations between different species provide additional constraints to inverse problems, e.g. EX = (X:CO) ECO 2 km Fresh emissions Direct & indirect emissions CO, CO2, halocarbons, BC, + many others… Asian continent Western Pacific
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Concluding remarks revolutionize validation interpretation Correlation
assimilation:
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