Retrieval of CO2 Column Abundances from Near-Infrared Spectroscopic Measurements

1. Retrieval of CO2 Column Abundances from Near-Infrared Spectroscopic Measurements Vijay Natraj

2. Outline • Introduction • Retrieval Strategy • Case Study: Retrieval of Aircraft Measurements • Future Work • Conclusion

3. Introduction Since 1860, global mean surface temperature has risen ~1.0 °C with a very abrupt increase since 1980. Atmospheric levels of CO2 have risen from ~ 270 ppm in 1860 to ~370 ppm today. Does increasing atmospheric CO2 drive increases in global temperature? Do increasing temperatures increase atmospheric CO2 levels?

4. Where are the Missing Carbon Sinks? • Only half of the CO2 released into the atmosphere since 1970 has remained there. The rest has been absorbed by land ecosystems and oceans • What are the relative roles of the oceans and land ecosystems in absorbing CO2? • Is there a northern hemisphere land sink? • What are the relative roles of North America and Eurasia • What controls carbon sinks? • Why does the atmospheric buildup vary with uniform emission rates? • How will sinks respond to climate change? • Reliable climate predictions require an improved understanding of CO2 sinks • Future atmospheric CO2 increases • Their contributions to global change

5. Why Measure CO2 from Space?Improved CO2 Flux Inversion Capabilities • Current State of Knowledge • Global maps of carbon flux errors for 26 continent/ocean-basin-sized zones retrieved from inversion studies • Studies using data from the 56 GV-CO2 stations • Flux residuals exceed 1 GtC/yr in some zones • Network is too sparse • Inversion tests • global XCO2 pseudo-data with 1 ppm accuracy • flux errors reduced to <0.5 GtC/yr/zone for all zones • Global flux error reduced by a factor of ~3. Flux Retrieval Error GtC/yr/zone Rayner & O’Brien, Geophys. Res. Lett. 28, 175 (2001)

6. OCO Mission • First global, space-based observations of atmospheric CO2with accuracy, resolution and coverage needed to characterise the geographic distribution of CO2 sources and sinks and quantify their variability • High resolution spectroscopic measurements of reflected sunlight in near IR CO2 and O2 bands • Remote sensing retrieval algorithms will process these data to yield estimates of column-averaged CO2 dry air mole fraction (XCO2) with accuracies near 0.3% • Chemical transport models will use OCO XCO2 data and other measurements to retrieve the spatial distribution of CO2 sources and sinks on regional scales over two annual cycles

7. Problem Description • Aim: to develop and test algorithms for the retrieval of XCO2 from spectrometric measurements in three NIR bands. • Any retrieval problem can be broadly divided into two main components, viz., a forward model and an inverse method. • Forward model: describes the radiative transfer in the atmosphere • computation of absorption coefficients • key parameters to compute radiances: scattering and absorption optical depths, single scattering albedo and surface reflectance • convolution function to simulate instrument response • Inverse method: compare the measured spectrum with the computed spectrum, and iteratively improve the computed spectrum to best match the observed spectrum • Optimal Estimation Theory [Rodgers, 2000]

8. Fundamentals of Atmospheric RT Fundamental Equation of RT μ: cosine of zenith angle I: specific intensity J: source function (multiple scattering) τ: optical depth

9. Key Parameters Influencing RT • optical depth: amount of extinction a beam of light experiences travelling between two points • surface reflectance: ratio of intensity reflected from surface to that incident on it • function of wavelength • can depend on the zenith and azimuthal angles (BRDF) • single scattering albedo: fraction of energy scattered to that removed from radiance stream • conservative scattering: ω0 = 1 • pure absorption: ω0 = 0 • function of optical depth • phase function: describes amount of light scattered from incident direction into scattered direction • function of scattering angle

10. Spectroscopy • Column-integrated CO2 abundance => Maximum contribution from surface • High resolution spectroscopic measurements of reflected sunlight in near IR CO2 and O2 bands O2 A-band Clouds/Aerosols, Surface Pressure “weak” CO2 band Column CO2 “strong” CO2 band Clouds/Aerosols, H2O, Temperature

11. Retrieval Strategy

12. Inverse Method xa: a priori state vector Sε: measurement error covariance Sa: a priori error covariance Measurement Description y: measurement vector x: state vector f(x): forward model ε: measurement error Cost Function

13. Inverse Method … Continued • A priori constraints obtained from • Climatological data • Measurements • Markov descriptions • Levenberg-Marquardt method dx: state vector update K: weighting function (Jacobian) γ: Levenberg-Marquardt parameter

14. Case Study: Retrieval of Aircraft Measurements • High precision, high resolution O2 A-band spectra of sunlight reflected from ocean surface (O’Brien et al.,J. Atmos. Oceanic Tech., Feb. 1997 and Dec. 1998) • Retrieve column O2 with precisions required for OCO • Retrieval Strategy • Forward Model: multistream, multiple scattering RT model with BRDF at the surface • ILS provided by O'Brien • Inverse method based on optimal estimation theory

15. Retrieval: First Cut rms residual = 8.8%

16. Retrieval: Second CutWavelength Scaling rms residual = 2.3%

17. Retrieval: Third CutContinuum Level, Tilt, Zero Offset, ILS Width Fits rms residual = 1.4%

18. Retrieval: Fourth CutLine Mixing, Solar Feature Removal rms residual = 1.1%

19. Future Work • Polarisation • Surface Types • Other RT Models • Analytic Weighting Functions • Sensitivity Tests • Speed Improvements

20. Conclusion • Algorithm developed to retrieve XCO2 from spectroscopic measurements of absorption in NIR bands • Retrieved column O2 with precision ~ 1% • Demonstrates potential to retrieve column O2 with precisions around 0.1% by averaging sufficient soundings • Indicates feasibility of retrieving XCO2with precisions better than 0.3%

21. Acknowledgements • California Institute of Technology • Yuk Yung, Run-Lie Shia, Xun Jiang, Zhonghua Yang • Jet Propulsion Laboratory • Hartmut Boesch, Geoff Toon, Bhaswar Sen, David Crisp, Charles Miller • Commonwealth Scientific and Industrial Research Organisation • Denis O’Brien • University of Washington • Zhiming Kuang