1. Estimating regional C fluxes by exploiting observed correlations between CO and CO 2 Paul Palmer Division of Engineering and Applied Sciences Harvard University http://www.people.fas.harvard.edu/~ppalmer

2. IPCC 6160 5.5 1.6 + ballpark flux estimates for fast exchange processes (10 9 tonnes C)

3. IPCC Chinese Government Statistics Shown Downward Trend in Chinese CO 2 Emissions (Streets et al., Science, 294, 1835-1837, 2001) China Energy Databook v6, 2004 China GDP (Billion 1995 yuan constant) Year Large uncertainty

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

5. RH + OH  …  CO  CO 2 1000s km Direct & indirect emissions CMDL site Many 100s km 10s km Increasing model transport error Remote data have limitations in estimating regional C budgets

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

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

8. Offshore China Over Japan Slope (> 840 mb) = 51 R 2 = 0.76 Slope (> 840 mb) = 22 R 2 = 0.45 Suntharalingam et al, 2004 ATMOSPHERIC CO 2 :CO CORRELATIONS PROVIDE UNIQUE INFORMATION ON SOURCE REGION AND TYPE - CO 2 :CO emission ratios vary with combustion efficiency - Range in regional emission ratios reflect mix of sources and variation in fossil fuel combustion ratio A priori bottom-up Top-down CO CO 2

9. Observation vector y State vector (Emissions x) Modeling Overview Inverse model x = Fluxes of CO and CO 2 from Asia (Tg C/yr) y = TRACE-P CO and CO 2 concentration data Forward model (GEOS-CHEM) x = x a + (K T S y -1 K + S a -1 ) -1 K T S y -1 (y – Kx a ) ^ y = Kx a +  Jacobian describes CTM

10. http://www-as.harvard.edu/chemistry/trop/geos/index.html GEOS-CHEM global 3D chemical transport model Driven by NASA GMAO met data (3/6 hr) 2x2.5 o resolution/30 vertical levels O 3 -NO x -VOC-aerosol coupled chemistry Evaluated using ground-based, aircraft, and satellite observations

11. Consistent CO and CO 2 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 CO 2 Emissions during TRACE-P TERRESTIAL BIOSPHERE: CASA (Randerson, et al, 1997) OCEAN BIOSPHERE: Takahashi et al, 1999 Gt C yr -1 Fraction of annual emissions CO Annual Mean Streets et al, 2003 TOTAL FOSSIL BIOSPHERE BIOBURN BIOFUEL TOTAL TRACE-P

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

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

15. Linear Inverse Model x = x a + (K T S y -1 K + S a -1 ) -1 K T S y -1 (y – Kx a ) S = (K T S y -1 K + S a -1 ) -1 X s = retrieved state vector (the CO sources) X a = a priori estimate of the CO sources S a = error covariance of the a priori K = forward model operator S y = error covariance of observations = instrument error + model error + representativeness error Gain matrix ^ ^

16. S y Measurement accuracy Representation Model error (most important) GEOS-CHEM Error specification for CO and CO 2 S a Anthropogenic (c/o Streets): China (78%), Japan (17%), Southeast Asia (100%), Korea (42%) – uniform 25% Biomass burning: 50% 30% Chemistry (~CH 4 ): 25% Biosphere: 75% GEOS-CHEM 2x2.5  cell TRACE-P All latitudes (measured-model) /measured Altitude [km] Mean bias RRE CO (y*RRE) 2 ~38ppb (CO) ~1.87ppm (CO 2 ) RRE = total observation error

17. NUMBER OF EIGENVALUES OF PREWHITENED JACOBIAN  1 = DOF K = S KS ~  -1/21/2 a CO: CH ANTH*, KRJP &, SEA, CH BB, BA BB @, ROW CO 2 : 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 Rodgers, 2000

18. Independent Inversion of CO and CO 2 emissions A priori A posteriori CO 2 emissions [Tg March 2001] CO emissions [Tg yr -1 ] Biospheric CO 2 Anthropogenic CO 2  1 ~ K Results consistent with [CO 2 ]:[CO] analysis Estimated Chinese anthropogenic CO(CO 2 ) sources are currently too low (high). Chinese biospheric CO 2 fluxes are estimated too high.

19. CO 2 state vector A posteriori correlation matrix illustrates the ambiguity between anthropogenic and biospheric CO 2 emissions Chinese anthropogenic CO 2 Chinese biospheric CO 2 ^ C

20. Monte Carlo approach to modeling correlations between CO and CO 2 E CO =  (A +  A  A) (F CO +  CO  F CO ) E CO2 =  (A +  A  A) (F CO2 +  CO2  F CO2 ) Carbon Conservation (CO+CO 2 ~ 0.9-1.0) Perturbed F N N Unperturbed F  10.9 

21. r > 0 CO Emissions CO 2 Emissions FF AA CO Emissions CO 2 Emissions FF AA  A >>  F  A <<  F r < 1 Interpretation of correlations

22. VALUES OF UNCERTAINTY FROM STREETS’ INCONSISTENT WITH DATA ANALYSIS AND LEAD TO SMALL CO 2 :CO CORRELATIONS E = A F  A: CO 5-25%; CO 2 5-20%  F: CO 50 - 200%; CO 2 5-10% Correlations: China ~0 Korea/Japan -0.2 Southeast Asia ~0 Correlations within sectors > lumped sectors

23. Alternative Correlations Tested… CO 2 :CO Correlation Chinese anthropogenic Korea + Japan Southeast Asia Streets’ Min(  A 25%) Min(  A 50%) Also r = 0.5,…,1.0

24. A correlation of > 0.7 is needed to start decoupling biospheric and anthropogenic CO 2 A posteriori Uncertainty [unit] Anthropogenic CO 2 Biospheric CO 2 Anthropogenic CO Lowest correlations correspond to those calculated using Monte Carlo method

25. Future satellite missions The “A Train” MODIS/ CERES IR Properties of Clouds AIRS Temperature and H 2 O Sounding Aqua 1:30 PM Cloudsat PARASOL CALPSO- Aerosol and cloud heights Cloudsat - cloud droplets PARASOL - aerosol and cloud polarization OCO - CO 2 CALIPSO Aura OMI - Cloud heights OMI & HIRLDS – Aerosols MLS& TES - H 2 O & temp profiles MLS & HIRDLS – Cirrus clouds 1:38 PM OCO 1:15 PM OCO - CO 2 column C/o M. Schoeberl

26. Launch date in 2007. Will provide column CO 2 measurements 3 spectrometers that measure CO 2 at 1.61  m and 2.05  m and O 2 at 0.76  m Field of view of spectrometers is 1x1.5 km 2 Sun-synchronous orbit with 16-day repeat cycle and 1:15 pm equator crossing time Orbiting Carbon Observatory (OCO) New Concept: Testing science objectives of satellite instruments before launch Tropospheric Emission Spectrometer (TES) 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 km 2 ) Sun-synchronous orbit with 16-day repeat cycle Will measurements of CO and CO 2 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 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 CO 2 column along OCO orbit (1 day) CO (825 mb) along TES orbit (1 day) ppm ppb Jones et al, 2004

28. Significant reduction in uncertainty in estimates of the dominant Asian biospheric fluxes (China and Boreal Asia) China Fuel JP/KR Fuel SE Asia Fuel India Fuel China BB SE Asia BB India BB Boreal Asia BB China Japan Korea SE Asia India Boreal Asia Rest of world A priori A posteriori China Fuel JP/KR Fuel SE Asia Fuel India Fuel China BB SE Asia BB India BB Boreal Asia BB China Japan Korea SE Asia India Boreal Asia Rest of world A priori A posteriori China Fuel JP/KR Fuel SE Asia Fuel India Fuel China BB SE Asia BB India BB Boreal Asia BB China Japan Korea SE Asia India Boreal Asia Rest of world A priori A posteriori China Fuel JP/KR Fuel SE Asia Fuel India Fuel China BB SE Asia BB India BB Boreal Asia BB China Japan Korea SE Asia India Boreal Asia Rest of world A priori A posteriori China Fuel JP/KR Fuel SE Asia Fuel India Fuel China BB SE Asia BB India BB Boreal Asia BB China Japan Korea SE Asia India Boreal Asia Rest of world A priori A posteriori China Fuel JP/KR Fuel SE Asia Fuel India Fuel China BB SE Asia BB India BB Boreal Asia BB China Japan Korea SE Asia India Boreal Asia Rest of world A priori A posteriori China Fuel JP/KR Fuel SE Asia Fuel India Fuel China BB SE Asia BB India BB Boreal Asia BB China Japan Korea SE Asia India Boreal Asia Rest of world A priori A posteriori CO Sources CO 2 Sources Biospheric CO 2 A Posteriori Error Estimates [%] China Fuel JP/KR Fuel SE Asia Fuel India Fuel China BB SE Asia BB India BB Boreal Asia BB China Fuel JP/KR Fuel SE Asia Fuel India Fuel China BB SE Asia BB India BB Boreal Asia BB Chinese biospheric fluxes weakly coupled to anthropogenic emissions Jones et al, 2004

29. Closing Remarks Estimated Chinese anthropogenic CO(CO 2 ) sources are currently too low (high). Chinese biospheric CO 2 fluxes are estimated too high but they are coupled to anthropogenic CO 2. Correlations between CO 2 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.