Assessment of the Potential of Observations from TES to Constrain Emissions of Carbon Monoxide Dylan B. A. Jones, Paul I. Palmer, Daniel J. Jacob Division of Engineering and Applied Sciences, and Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA Kevin W. Bowman, John Worden California Institute of Technology, Jet Propulsion Laboratory, Pasadena, CA Ross N. Hoffman Atmospheric and Environmental Research, Inc. Lexington, MA November 19, 2002

Tropospheric Emission Spectrometer (TES) an infrared, high resolution Fourier spectrometer covers the spectra range 650 - 3050 cm-1 (3.3 - 15.4 mm) has 2 viewing modes: a nadir view and a limb view Products Nadir Limb Temperature X Surface temp. Land surf. emissivity O3 CO H2O CH4 NO HNO3 will be launched in early 2004 makes 14.56 orbits per day repeats its orbit every 16 days (or 233 orbits)

Apply averaging kernels for TES to simulate nadir retrievals of CO OBJECTIVE: Determine whether nadir observations of CO from TES will have enough information to reduce uncertainties in our estimates of regional sources of CO APPROACH: Assume that we know the true sources of CO Use GEOS-CHEM 3-D model to simulate “true” CO concentration field Apply averaging kernels for TES to 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

Source Distribution Other Sources: oxidation of methane and other VOCs Fossil Fuels Biofuels Biomass Burning Other Sources: oxidation of methane and other VOCs Main sink: reaction with atmospheric OH

Inversion Analysis State Vector EUFF NAFF ASFF ASBB AFBB CHEM SABB RWFF RWBB FF = Fossil Fuel + Biofuel All sources include contributions from oxidation of VOCs OH is specified Use a “tagged CO” method to estimate contribution from each source Use inverse method to solve for annually-averaged emissions “True” Emissions (Tg/yr) NAFF: 121.3 SABB: 96.5 EUFF: 131.1 RWBB: 98.0 ASFF: 258.3 RWFF: 149.8 ASBB: 96.0 CHEM: 1125.0 AFBB: 193.9 Total = 2270 Tg/yr

Modeled CO Level 13 (6 km) 15 Mar. 2001, 0 GMT North American fossil fuel CO tracer

Simulating the Data Sample the synthetic atmosphere along the orbit of TES Assume that the TES nadir footprint of 8 x 5 km is representative of the entire GEOS-CHEM 2º x 2.5º grid box We consider data only between the equator and 60ºN CO at 510.9 hPa for March 15, 2001

Simulating the Data The retrieved profile is: yret = ya + A(F(xt) – ya) ya = a priori profile xt = true emissions yt = F(xt) A = averaging kernels F(x) = GEOS-CHEM P > 250 mb yret ya

Methodology xa = a priori estimate of the CO sources (state vector) Minimize a maximum a posteriori cost function Gauss-Newton iterative method xa = a priori estimate of the CO sources (state vector) xi = estimate of the state vector for the ith iteration yret = retrieved profile ymod = forward model simulation of xi K = Jacobian (from tagged CO method) Ŝ = a posteriori error covariance Sx = error covariance of sources Sy = error covariance of observations = instrument error (Si) + model error (Sm) + representativeness error (Sr)

} The Forward Model Gein = instrument noise We apply the same ya and A used for the retrieved profile to GEOS-CHEM (forward model) } The retrieved profile and the forward model must be consistent Gein = instrument noise G = gain matrix associated with A ei = NESR of instrument Si = G<(ein)(ein)T>GT n = Gaussian white noise with unity variance sn = model noise based on variance of (Sm + Sr) (Sm + Sr) = s2I

Constructing the Covariance Matrix for the Model Error The NMC method (Parrish and Derber, 1992): assume that the difference between forecasts on different timescales, valid at the same time, is representative of the forecast error structure Assimilation Model t0+24hr forecast 2 t0 forecast 1 t0+48hr assume that the dominant error structures for the 24-hr period are representative of the errors in the assimilation

Forecast Error root mean square error based on the difference of 89 pairs of 48-hr and 24-hr forecasts of CO during TRACE-P, Feb-April 2001 Model Level 16 (8 km) CO Mixing Ratio (ppb)

Scaling the Forecast Errors Compare model with observations of CO from TRACE-P and estimate the relative error Assume mean bias in relative error is due to emission errors Remove mean bias and assume the residual relative error (RRE) is due to transport

Scaling the Forecast Errors Compare model with observations of CO from TRACE-P and estimate the relative error Assume mean bias in relative error is due to emission errors Remove mean bias and assume the residual relative error (RRE) is due to transport Mean forecast error RRE RRE  2-3 x mean forecast error

Model Error (15 March 2001, 0 GMT) Model Level 16 (8 km) Relative error Model Level 8 (1.5 km) Relative error

2x2.5o cell Representativeness Error (Sr) TRACE-P GEOS-CHEM Use TRACE-P aircraft observations to estimate the subgrid-scale variability of CO GEOS-CHEM 2x2.5o cell TRACE-P Estimate 1s about 2x2.5o mean value of TRACE-P observations sr = 5%

Assumptions Assume that sources are uncorrelated with uncertainty (Sx)jj = 50% (for the chemistry source we assume 25%) Assume that the transport errors are spatially uncorrelated Cloud free conditions A posteriori results are based on statistics from an ensemble of 100 realizations of the instrument noise and model noise

Inversion Results (5 days of TES data, Mar. 10-15th, 2001) a priori a posteriori true Model successfully retrieves the true emissions for all sources. Caveat: perfect Gaussian error statistics with zero bias

Resolution Matrix (KTSy-1K + Sx-1)-1KTSy-1K Sensitivity of a posteriori solution to true state

How well do we constrain the sources with only upper tropospheric (P < 500 hPa) observations? Good constraint (because of large volume of data), but errors are larger  lower tropospheric retrievals provide useful additional information

Conclusions TES retrievals of CO have the potential to constrain regional sources of CO, but proper error characterization will be crucial – particularly model error; real data will not have unbiased Gaussian error statistics which will reduce the information we can extract Upper tropospheric retrievals alone may provide considerable information to constrain the sources Since most of the CO information is in the lower troposphere, TES retrievals below 500 hPa will be useful in constraining the sources, despite the instrument’s reduced sensitivity in the lower troposphere