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 Technology Jet Propulsion Laboratory, Pasadena, CA R. N. Hoffman Atmospheric and Environmental Research, Inc. Lexington, MA December 8, 2003 I. Bey Swiss Federal Institute of Technology (EPFL) Lausanne, Switzerland

Assume that we know the true sources of CO 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 Make a priori estimate of CO sources by applying errors to the “true” source Use an optimal estimation inverse method Obtain a posteriori sources and errors; How successful are we at finding the true source and reducing the error? APPROACH: OBJECTIVE: Determine whether nadir observations of CO from TES will have enough information to reduce uncertainties in our estimates of regional sources of CO

Inversion Analysis State Vector total CO emissions (fossil fuel + biofuel + biomass burning) from 23 geographical regions + photochemical production of CO from CH 4 and NMVOC Use a “tagged CO” method to estimate contribution from each source with specified monthly mean OH

x = estimate of the state vector (the CO sources) x a = a priori estimate of the CO sources (generated by perturbing true state) = CO retrievals F(x) = forward model simulation of x S a = error covariance of the a priori (assume a priori error of 50%) S  = error covariance of observations = retrieval error + model error + representativeness error Minimize a maximum a posteriori cost function Inversion Methodology A = TES averaging kernel y a = TES a priori profile H(x) = GEOS-CHEM model x t = true emissions  i = Retrieval noise (< 10%)  r = representativeness error  m = model error

Characterizing the Model Error Mean forecast 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 TRACE-P model error  2-4 x mean forecast error

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

With unbiased Gaussian error statistics, TES provides powerful constraints on regional sources of CO on monthly timescales 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) NE N Am. Inversion Results SE N Am. CO Emission (Tg CO/yr) CHEM source (Tg CO/yr) West EU East EU NE Africa M. East Cent. Am. China India Japan Korea SE Asia Indo-Phil Malay. ROW CHEM SE Africa SW Africa NW Africa North S Am. South S Am. NC N Am. SC N Am. NW N Am. SW N Am. A Priori True A Posteriori A priori uncertainty 50% A posteriori 10-15%

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

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

Combining TES CO and OCO CO 2 Measurements Observed correlations between CO and CO 2 provide additional information [Suntharalingam et al., 2003] TES and OCO will both fly as part of A-train: combining TES CO and OCO CO 2 may help resolve Japanese and Korean regions, and decouple Chinese emissions from Korean and Japanese TES averaging kernels OCO averaging kernel [B. Connor, personal communication] Averaging kernel Pressure (hPa) Averaging kernel

Inversion Results Pseudo-data (CO 2 columns) for March 1-31, 2001, and with an observation error of 0.3% OCO measurements provide sufficient information to accurately constrain regional fluxes on monthly timescales A priori uncertainties are 50% A posteriori uncertainties are less than 25% for China, India, southeast Asia, and the rest of the world China CO 2 Flux (Gg C/day) ROW Flux (Gg C/day) Japan Korea SE Asia India ROW A priori A posteriori True Next Step: couple CO and CO 2 inversions

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 CO 2 may provide additional constraints to better quantify the carbon budget