Assessment of the Potential of Observations from TESto Constrain Emissions of Carbon Monoxide

Dylan B. A. Jones, Paul I. Palmer, Daniel J. JacobDivision of Engineering and Applied Sciences,

and Department of Earth and Planetary Sciences,Harvard University, Cambridge, MA

Kevin W. Bowman, John WordenCalifornia Institute of Technology,

Jet Propulsion Laboratory, Pasadena, CA

Ross N. HoffmanAtmospheric and Environmental Research, Inc.

Lexington, MA

September 19, 2002

OBJECTIVE: Determine whether nadir observations of CO from TES (Tropospheric Emission Spectrometer) will have enough information to reduce uncertainties in our estimates of regional sources of CO

APPROACH: Use the GEOS-CHEM 3-D model together withcalculated averaging kernels for TES to simulate TES nadir retrievals of CO

Use an optimal estimation inverse method to infer the sources of CO from the TES retrievals

Source Distribution

CHEM

NAFF

RWFF RWBB

AFBB

EUFF

SABB

ASFFASBB

NAFF: 121.3 SABB: 96.5EUFF: 131.1 RWBB: 98.0ASFF: 258.3 RWFF: 149.8ASBB: 96.0 CHEM: 1125.0AFBB: 193.9

“True” Emissions (Tg/yr)

Total = 2270 Tg/yr

• 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

• Sample the synthetic atmosphere along the orbit of TES

• The retrieved profile is: yret = ya + A(F(xt) – ya)

A = averaging kernels

ya = a priori profileyt = F(xt)

ya

yret

xt = true emissions

F(x) = GEOS-CHEM

P > 250 mb

Simulating the Data

Methodology

Use a sequential non-linear least squares solver to minimize a maximum a posteriori cost function

x = CO sources (state vector) xa = a priori estimate of the sourcesyret = retrieved profileF(x) = forward modelA = Averaging kernelsSx = error covariance of sources Sy = error covariance of observations = instrument error (Si) + model error (Sm) +

representativeness error (Sr)

)x(xS)x(xAF(x))(ySAF(x))(y(x) a1x

Ta

ret1y

Tret J

We apply the same ya and A used for the retrieved profileto GEOS-CHEM (forward model)

nG)y)A(F(xyy iat

aret ε

)yεA(F(x)yy aamod

= model noise based on error patterns of (Sm + Sr)

Gin = instrument noise

j

j1/2j neλε j and ej are j th eigenvalue and eigenvector

of (Sm + Sr)

G = gain matrix associated with Ai = r.m.s of instrument noiseSi = G(in)(in)TGT

n = Gaussian white noise with unity variance

(Sr)jj = 5%, based on TRACE-P aircraft data

} The retrieved profile andthe forward model mustbe consistent

The Forward Model

Constructing the Covariance Matrix for the Model Error• 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

6 – 8 km

• Small RRE in background air (low CO) • Large RRE if model incorrectly predicts

plume : (COmod small and COobs large or vice versa)

Fit the absolute values of log(COmod/COobs) vs. Max. (COmod or COobs)

|log(

CO

mo

d/C

Oo

bs)

|

CO (ppb)

Model Error

Model Level 16 (8 km)

Model Level 8 (1.5 km)

March 152001

0 GMT

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

• We use the same averaging kernels for all profile retrievals

• We consider data only between the equator and 60ºN

• We use only 5 days of simulated data, March10-15th, 2001

Inversion Results

a priori a posteriori true

Model successfully retrieves the true emissions for all sourcescaveat: perfect Gaussian error statistics with zero bias

How well do we constrain the sources with only upper tropospheric (P < 500 hPa) observations?

Good constraint, but errors are larger ( ~ x 2, for most sources) 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; 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

Future work• Inversion analysis with CO retrievals from SCIAMACHY