The Averaging Kernel of CO2 Column Measurements by the Orbiting Carbon Observatory (OCO), Its Use in Inverse Modeling, and Comparisons to AIRS, SCIAMACHY, and Ground-based FTIR Brian Connor 1, Zhiming Kuang 2, Geoff Toon 3, David Crisp 3, Stephen Wood 1, Chris Barnet 4, and Michael Buchwitz 5 1 National Institute of Water and Atmospheric Research, Lauder, New Zealand 2 University of Washington 3 Jet Propulsion Laboratory, Pasadena, CA 4 5 University of Bremen, Bremen, Germany The Orbiting Carbon Observatory (OCO) is planned as the first satellite instrument dedicated exclusively to measurement of CO 2 for studies of the carbon cycle. It will measure the column weighted mixing ratio of CO 2, X CO2, at high spatial resolution (3 km 2 ) with a global repeat cycle of 16 days. If such measurements can be made at very high precision, they can be used by models of global emission and transport to infer CO 2 sources and sinks on a global scale. For this reason, OCO will target a precision of 1 ppm (~0.3%) for monthly regional averages, throughout the globe. Such high precision places unprecedented demands on both instrument and analysis. Our purpose here is to assess the uncertainties introduced by non-uniform sensitivity of the measurement as a function of height (i.e. imperfect averaging kernels), and to show how these uncertainties can be mitigated in use of the data. The sensitivity of a space-based CO 2 measurement varies with height due to the physics of spectroscopy and radiative transfer interacting with atmospheric properties, and to instrument characteristics such as the spectral bandpass, resolution, and noise. Thus it is necessary to take the profile shape and variability with height into account in retrieving the column and in comparing it to models and to other measurements for validation. The sensitivity as a function of height may be described by the averaging kernel a c : a c =  X CO2 /  x(z) = F(K,S ,S a ) where x(z) is the true CO 2 profile, S  is the covariance matrix of the measurements, S a is the covariance of the a priori CO 2 profile, and K =  y/  x for spectral measurements y. K is the Jacobian or weighting function matrix. The variance of smoothing error results in a column measurement is σ s 2 = (a c -1) T S e (a c -1) where S e is the covariance of the real atmosphere. Note that this is a source of error which is inevitable given a variable atmosphere in the presence of an imperfect averaging kernel. Our best current estimate of a global covariance for CO 2 was derived from a multi-year series of NOAA aircraft data acquired at Carr, CO (P. Tans, private communication), which has then been scaled to provide a standard deviation in X CO2 of 12 ppm (~3%; Dufour & Breon, 2003). The OCO averaging kernel depends on the solar zenith angle, surface albedo, and aerosol optical depth, as illustrated below. Variations in the kernel are much larger at high zenith angles, where the signal-to-noise ratio is poorer. Zenith angle: 35° 85° Albedo: Aerosol τ : Error (ppm) Noise Smoothing Total Albedo: Aerosol τ : Error (ppm) Noise Smoothing Total Projected CO 2 Error (ppm) Estimated uncertainties in XCO2 are shown to the right for representative zenith angles, albedo, and optical depth. Noise and smoothing error will have significantly different effects. While noise errors are truly random, smoothing error, due to the imperfect averaging kernels shown above, is likely to be geographically coherent, and thus result in systematic biases. Uncompensated, these could have serious effects on source/sink determination.