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Estimating regional C fluxes by exploiting observed correlations between CO and CO 2
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Estimating regional C fluxes by exploiting observed correlations between CO and CO 2

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  1. Estimating regional C fluxes by exploiting observed correlations between CO and CO2 Paul Palmer Division of Engineering and Applied Sciences Harvard University http://www.people.fas.harvard.edu/~ppalmer

  2. + ballpark flux estimates for fast exchange processes (109 tonnes C) 61 60 1.6 5.5 IPCC

  3. Large uncertainty China GDP (Billion 1995 yuan constant) Year China Energy Databook v6, 2004 IPCC Chinese Government Statistics Shown Downward Trend in Chinese CO2 Emissions (Streets et al., Science, 294, 1835-1837, 2001)

  4. Bottom-up Emission Inventories are Very Uncertain Emission Factor (TgC / Tg fuel) Emissions (Tg C yr-1) E = AF Coal-burning cook stoves in Xian, China Activity Rate (Tg fuel yr-1) (amount of fuel burned)

  5. Remote data have limitations in estimating regional C budgets RH + OH … CO  CO2 Direct & indirect emissions Many 100s km 10s km CMDL site 1000s km Increasing model transport error

  6. Aircraft data can improve level of disaggregation of continental emissions Feb – April 2001 NASA TRACE-P Main transport processes: • DEEP CONVECTION • OROGRAPHIC LIFTING • FRONTAL LIFTING warm air cold air cold front

  7. Sources of CO from Asia Product of incomplete combustion FF BB BF FF BB BF + Oxidation of hydrocarbons Main sink is the hydroxyl radical (OH) Lifetime ~1-3 months

  8. Offshore China Over Japan Slope (> 840 mb) = 22 R2 = 0.45 Slope (> 840 mb) = 51 R2 = 0.76 ATMOSPHERIC CO2:CO CORRELATIONS PROVIDE UNIQUE INFORMATION ON SOURCE REGION AND TYPE A priori bottom-up CO2 CO2 CO CO Top-down - CO2:CO emission ratios vary with combustion efficiency - Range in regional emission ratios reflect mix of sources and variation in fossil fuel combustion ratio Suntharalingam et al, 2004

  9. Modeling Overview Forward model (GEOS-CHEM) Inverse model ^ x = xa + (KTSy-1K + Sa-1)-1 KTSy-1(y – Kxa) Jacobian describes CTM State vector (Emissions x) y = Kxa +  Observation vector y x = Fluxes of CO and CO2 from Asia (Tg C/yr) y = TRACE-P CO and CO2 concentration data

  10. GEOS-CHEM global 3D chemical transport model • Driven by NASA GMAO met data (3/6 hr) • 2x2.5o resolution/30 vertical levels • O3-NOx-VOC-aerosol coupled chemistry • Evaluated using ground-based, aircraft, and satellite observations http://www-as.harvard.edu/chemistry/trop/geos/index.html

  11. Consistent CO and CO2 Emissions Inventories Biomass burning: Variability from observed daily firecount data (AVHRR) Heald et al, 2003 Anthropogenic emissions for 2001: domestic ff, biofuel, transport, industrial ff Streets et al, 2003

  12. Seasonal Cycle of Chinese CO and CO2 Emissions during TRACE-P TERRESTIAL BIOSPHERE: CASA (Randerson, et al, 1997) OCEAN BIOSPHERE: Takahashi et al, 1999 TOTAL TOTAL CO BIOFUEL FOSSIL Gt C yr-1 Fraction of annual emissions BIOBURN BIOSPHERE Annual Mean TRACE-P Streets et al, 2003

  13. Estimating the Jacobian [CO]/COemission Linear calculation is straightforward: JCHBB= [CO]CHBB COCHBB/emissions Global 3D CTM 2x2.5 deg resolution State vector x = emissions from individual countries and individual processes Boreal Asia (BA) China (CH) Japan (JP) Rest of World (ROW) Korea (KR) Southeast Asia (SEA) [OH] from full-chemistry model (CH3CCl3 = 6.3 years)

  14. TRACE-P Observations Remove CO2 bias using 10th percentile of [CO2]: 4-4.5 ppm GEOS-CHEM 4-6 km 2-4 km CO [ppb] CO2 [ppm] 0-2 km Latitude [deg]

  15. ^ x = xa + (KTSy-1K + Sa-1)-1 KTSy-1(y – Kxa) S = (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 ^ Linear Inverse Model

  16. GEOS-CHEM All latitudes RRE Mean bias TRACE-P GEOS-CHEM 2x2.5 cell Altitude [km] (measured-model) /measured Error specification for CO and CO2 SaAnthropogenic (c/o Streets): China (78%), Japan (17%), Southeast Asia (100%), Korea (42%) – uniform 25%Biomass burning: 50% 30%Chemistry (~CH4): 25% Biosphere: 75% SyMeasurement accuracyRepresentation Model error (most important) RRE = total observation error (y*RRE)2 ~38ppb (CO) ~1.87ppm (CO2) CO

  17. NUMBER OF EIGENVALUES OF PREWHITENED JACOBIAN  1 = DOF ~ -1/2 1/2 K = S KS  a Rodgers, 2000 CO: CH ANTH*, KRJP&, SEA, CH BB, BA BB@, ROW CO2: CH ANTH*, KRJP&, CH BB$, BA BB@, BS, ROW (inc SEA$) *Collocated sources; &coarse resolution forces merging; $observed gradients too weak to resolve source; @not well resolved

  18. Independent Inversion of CO and CO2 emissions A priori A posteriori Anthropogenic CO2 CO emissions [Tg yr-1] • Estimated Chinese anthropogenicCO(CO2) sources are currently too low (high). • Chinese biospheric CO2 fluxes are estimated too high. Biospheric CO2 K ~ CO2 emissions [Tg March 2001]   1 Results consistent with [CO2]:[CO] analysis

  19. ^ C A posteriori correlation matrix illustrates the ambiguity between anthropogenic and biospheric CO2 emissions Chinese anthropogenic CO2 Chinese biospheric CO2 CO2 state vector

  20. N N Monte Carlo approach to modeling correlations between CO and CO2 ECO =  (A + AA) (FCO + COFCO) ECO2 =  (A + AA) (FCO2 + CO2FCO2) Carbon Conservation (CO+CO2 ~ 0.9-1.0) Perturbed F 0.9   1 Unperturbed F

  21. A >> F A << F Interpretation of correlations F F A CO2 Emissions CO2 Emissions A CO Emissions CO Emissions r > 0 r < 1

  22. VALUES OF UNCERTAINTY FROM STREETS’ INCONSISTENT WITH DATA ANALYSIS AND LEAD TO SMALL CO2:CO CORRELATIONS E = AF A: CO 5-25%; CO2 5-20% F: CO 50 - 200%; CO2 5-10% Correlations: China ~0 Korea/Japan -0.2 Southeast Asia ~0 Correlations within sectors > lumped sectors

  23. Alternative Correlations Tested… Streets’ Min(A 25%) Min(A 50%) CO2:CO Correlation Korea + Japan Southeast Asia Chinese anthropogenic Also r = 0.5,…,1.0

  24. A correlation of > 0.7 is needed to start decoupling biospheric and anthropogenic CO2 Anthropogenic CO2 Anthropogenic CO A posteriori Uncertainty [unit] Biospheric CO2 Lowest correlations correspond to those calculated using Monte Carlo method

  25. Future satellite missions The “A Train” 1:38 PM 1:30 PM 1:15 PM Aura Cloudsat OCO CALIPSO Aqua 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 C/o M. Schoeberl

  26. New Concept: Testing science objectives of satellite instruments before launch Orbiting Carbon Observatory (OCO) Tropospheric Emission Spectrometer (TES) • Launch date in 2007. • Will provide column CO2 measurements • 3 spectrometers that measure CO2 at 1.61m and 2.05m and O2 at 0.76m • Field of view of spectrometers is 1x1.5 km2 • Sun-synchronous orbit with 16-day repeat cycle and 1:15 pm equator crossing time • Launched in July 2004 • An IR, high resolution Fourier spectrometer • Measures spectral range 3.3 - 15.4m • Limb and nadir view (footprint is 8x5 km2) • Sun-synchronous orbit with 16-day repeat cycle Will measurements of CO and CO2 from TES and OCO provide accurate constraints on carbon fluxes from different regions in Asia? Jones et al, 2004

  27. Simulation: Constraining Asian Carbon Fluxes from Space Generate pseudo-data from the satellites for March 1-31, 2001 CO (825 mb) along TES orbit (1 day) CO2 column along OCO orbit (1 day) ppm ppb Inverse model with realistic instrument and model errors, and which accounts for data loss due to cloud cover and the vertical sensitivity of the instruments Jones et al, 2004

  28. BorealAsia BB BorealAsia BB BorealAsia BB BorealAsia BB BorealAsia BB BorealAsia BB BorealAsia BB BorealAsia BB BorealAsia BB IndiaFuel IndiaFuel IndiaFuel IndiaFuel IndiaFuel IndiaFuel IndiaFuel IndiaFuel IndiaFuel JP/KRFuel JP/KRFuel JP/KRFuel JP/KRFuel JP/KRFuel JP/KRFuel JP/KRFuel JP/KRFuel JP/KRFuel SE AsiaFuel SE AsiaFuel SE AsiaFuel SE AsiaFuel SE AsiaFuel SE AsiaFuel SE AsiaFuel SE AsiaFuel SE AsiaFuel India BB India BB India BB India BB India BB India BB India BB India BB India BB ChinaBB ChinaBB ChinaBB ChinaBB ChinaBB ChinaBB ChinaBB ChinaBB ChinaBB SE Asia BB SE Asia BB SE Asia BB SE Asia BB SE Asia BB SE Asia BB SE Asia BB SE Asia BB SE Asia BB ChinaFuel ChinaFuel ChinaFuel ChinaFuel ChinaFuel ChinaFuel ChinaFuel ChinaFuel ChinaFuel Japan Korea Japan Korea Japan Korea Japan Korea Japan Korea Japan Korea Japan Korea SE Asia SE Asia SE Asia SE Asia SE Asia SE Asia SE Asia Rest of world Rest of world Rest of world Rest of world Rest of world Rest of world Rest of world India India India India India India India China China China China China China China Boreal Asia Boreal Asia Boreal Asia Boreal Asia Boreal Asia Boreal Asia Boreal Asia Significant reduction in uncertainty in estimates of the dominant Asian biospheric fluxes (China and Boreal Asia) A Posteriori Error Estimates [%] CO Sources A priori A priori A priori A priori A priori A priori A priori A posteriori A posteriori A posteriori A posteriori A posteriori A posteriori A posteriori CO2 Sources Biospheric CO2 Chinese biospheric fluxes weakly coupled to anthropogenic emissions Jones et al, 2004

  29. Closing Remarks • Estimated Chinese anthropogenicCO(CO2) sources are currently too low (high). • Chinese biospheric CO2 fluxes are estimated too high but they are coupled to anthropogenic CO2. Correlations between CO2 and CO can decouple these signals. • Emission correlations summed over sectors are too weak – need r > 0.7, impossible with current inverse model configuration. • Work in progress – much still to explore.