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

November 19, 2002

• an infrared, high resolution Fourier spectrometer

• covers the spectra range650 - 3050 cm-1 (3.3 - 15.4 m)

• has 2 viewing modes: a nadir view and a limb view

Tropospheric Emission Spectrometer (TES)

• will be launched in early 2004 • makes 14.56 orbits per day• repeats its orbit every 16 days (or

233 orbits)

Products Nadir Limb

Temperature X X

Surface temp. X

Land surf. emissivity X

O3 X X

CO X X

H2O X X

CH4 X X

NO X

HNO3 X

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

Use GEOS-CHEM 3-D model to simulate “true” CO concentration field

Apply averaging kernels for TES to simulate nadir retrievals of CO

Make a prioriestimate of CO sources

by applying errorsto 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:

Source DistributionFossil Fuels Biofuels

Biomass Burning

• Other Sources: oxidation of methane and other VOCs• Main sink: reaction with atmospheric OH

Inversion Analysis State Vector

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

Modeled COLevel 13 (6 km)15 Mar. 2001, 0 GMT

North Americanfossil fuel CO tracer

• 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

Simulating the Data

CO at 510.9 hPa for March 15, 2001

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

• Minimize a maximum a posteriori cost function

xa = a priori estimate of the CO sources (state vector)

xi = estimate of the state vector for the ith iteration

yret = retrieved profileymod = forward model simulation of xi

K = Jacobian (from tagged CO method)Ŝ = a posteriori error covarianceSx = error covariance of sources Sy = error covariance of observations

= instrument error (Si) + model error (Sm) + representativeness error (Sr)

ret mod T 1 ret mod T 1y a x a(x) (y y ) S (y y ) (x x ) S (x x )J

T -1 -1 -1 T -1 ret mod -1i+1 i i y i x i y x i ax = x +(K S K +S ) [K S (y -y )-S (x -x )]

T -1 -1 -1i y i xS = (K S K +S )

• Gauss-Newton iterative method

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

ret ta a iy y A(F(x ) y ) G nε

moda ay y A(F(x) n y )

n = model noise based on variance of (Sm + Sr)

Gin = instrument noise

(Sm + Sr) = 2I

G = gain matrix associated with Ai = NESR of instrument Si = G(in)(in)TGT

n = Gaussian white noise with unity variance

} The retrieved profile andthe forward model mustbe consistent

The Forward Model

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

t0+24hr

forecast 1

forecast 2

t0+48hr

assume that the dominant error structures for the 24-hr period are representative of the errors in the assimilation

• 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

Forecast Error

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)

Model Level 8 (1.5 km)

Relative error

Relative error

r = 5%

GEOS-CHEM

2x2.5o cell

TRACE-P

Representativeness Error (Sr)

• Estimate 1 about 2x2.5o mean value of TRACE-P observations

• Use TRACE-P aircraft observations to estimate the subgrid-scale variability of CO

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

Sensitivityof a posteriorisolution totrue 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