Optimizing TES Observations for Regional CO Emission Constraints

1. Potential of Observations from the Tropospheric Emission Spectrometer to Constrain Continental Sources of Carbon Monoxide D. B. A. Jones, P. I. Palmer, D. J. Jacob, R. M. Yantosca Division of Engineering and Applied Sciences and Department of Earth and Planetary Sciences Harvard University, Cambridge, MA K. W. Bowman, J. R. Worden California Institute of TechnologyJet Propulsion Laboratory, Pasadena, CA R. N. Hoffman Atmospheric and Environmental Research, Inc. Lexington, MA I. Bey Swiss Federal Institute of Technology (EPFL) Lausanne, Switzerland December 8, 2003

2. OBJECTIVE: Determine whether nadir observations of CO from TES will have enough information to reduce uncertainties in our estimates of regional sources of CO Assume that we know the true sources of CO APPROACH: Use GEOS-CHEM 3-D model (2º x 2.5º) to simulate “true” pseudo-atmosphere Sample pseudo-atmosphere along orbit of TES and simulate nadir retrievals of CO Obtain a posteriori sources and errors; How successful are we at finding the true source and reducing the error? Make a priori estimate of CO sources by applying errors to the “true” source Use an optimal estimation inverse method

3. Inversion Analysis State Vector • total CO emissions (fossil fuel + biofuel + biomass burning) from 23 geographical regions + photochemical production of CO from CH4 and NMVOC • Use a “tagged CO” method to estimate contribution from each source with specified monthly mean OH

4. Inversion Methodology • Minimize a maximum a posteriori cost function x = estimate of the state vector (the CO sources) xa=a priori estimate of the CO sources(generated by perturbing true state) = CO retrievals F(x) = forward model simulation of x Sa= error covariance of the a priori (assume a priori error of 50%) Se= error covariance of observations = retrieval error + model error + representativeness error A =TES averaging kernel ya= TES a priori profile H(x)=GEOS-CHEM model xt= true emissions ei= Retrieval noise (< 10%) er= representativeness error em= model error

5. Characterizing the Model Error • The “NMC method” • assume that the difference between successive forecasts (48-hr minus 24-hr), which are valid at the same time, is representative of the forecast error structure - we use 89 pairs of CO forecasts generated during Feb-April 2001 • Scale forecast error structure to account for errors not captured by forecast differences Sample forecast error field along TRACE-P flight tracks and compare with model error calculated from TRACE-P observations Model error from Palmer et al. 2003, based on TRACE-P data Mean forecast error TRACE-P model error  2-4 x mean forecast error

6. Characterizing the Model Error • Model Error (Feb-April, 2001):less than 20% in much of the troposphere, large over source regions Scaled forecast error • Representativeness error: 5% based on sub-grid variability of TRACE-P data

7. Inversion Results A Priori A Posteriori True • 8 Days of pseudo-data (March 2-16, 2001) • retrievals between 0-60ºN • 60% data loss due to cloud cover (based on GEOS-3 cloud fraction) CO Emission (Tg CO/yr) SWN Am. NEN Am. EastEU SEN Am. SCN Am. WestEU NWN Am. NCN Am. With unbiased Gaussian error statistics, TES provides powerful constraints on regional sources of CO on monthly timescales CO Emission (Tg CO/yr) NWAfrica NorthS Am. SWAfrica Cent.Am. NEAfrica SouthS Am. SEAfrica M. East • A priori uncertainty 50% • A posteriori 10-15% CO Emission (Tg CO/yr) CHEM source (Tg CO/yr) SEAsia Indo-Phil Malay. CHEM ROW Japan China Korea India

8. Dominant Source Patterns Where is the information coming from? Examine the singular values and vectors of the normalized Jacobian matrix [Rodgers, 2000] K = sensitivity of CO observations with respect to sources Normalization of K with the uncertainty of the observations and sources Source patterns with singular values > 1.0 contribute information 

9. Dominant Source Patterns Singular vector 2 (l2 = 609) Singular vector 1 (l2 = 10862) CHEM CHEM Singular vector 18 (l2 = 2.33) Most sensitive to dominant signals in the free troposphere (e.g. chemical source, Chinese emissions, African biomass burning) CHEM Smaller sources such as Japan and Korea carry little information

10. Combining TES CO and OCO CO2 Measurements • Observed correlations between CO and CO2 provide additional information [Suntharalingam et al., 2003] • TES and OCO will both fly as part of A-train: combining TES CO and OCO CO2 may help resolve Japanese and Korean regions, and decouple Chinese emissions from Korean and Japanese TES averaging kernels Pressure (hPa) Pressure (hPa) OCO averaging kernel Averaging kernel Averaging kernel [B. Connor, personal communication]

11. Inversion Results Pseudo-data (CO2 columns) for March 1-31, 2001, and with an observation error of 0.3% • A priori uncertainties are 50% • A posteriori uncertainties are less than 25% for China, India, southeast Asia, and the rest of the world A priori A posteriori True OCO measurements provide sufficient information to accurately constrain regional fluxes on monthly timescales CO2 Flux (Gg C/day) ROW Flux (Gg C/day) Next Step: couple CO and CO2 inversions China ROW Japan Korea SE Asia India

12. Conclusions • TES retrievals of CO have the potential to provide valuable information to constrain regional sources of CO • The source patterns which we can best resolve will depend on: • the relative source strengths • local meteorology • sensitivity of the instrument in the lower troposphere • Combining TES CO with OCO CO2 may provide additional constraints to better quantify the carbon budget