Page 1 Radiative Transfer Modeling for the Retrieval of CO 2 from Space Vijay Natraj June 11, 2007 Thesis Defense Seminar
Page 2 Outline Motivation OCO mission Column O 2 retrievals Aerosol characterization Polarization Future Work
Page 3 Introduction: Carbon Sinks? Atmospheric Carbon dioxide (CO 2 ) –Primary man-made greenhouse gas –Mixing ratios increased by ~25% since 1860 –Only half of the CO 2 from fossil fuel emissions in atmosphere Outstanding Issues –Where are the CO 2 sinks? –Why does atmospheric buildup vary with uniform emission rates? –How will CO 2 sinks respond to climate change?
Page 4 Climate Change Discussions at the G8
Page 5 Why Measure CO 2 from Space? Studies from GV-CO 2 stations –Flux residuals exceed 1 GtC/yr in some zones –Network is too sparse Inversion tests –Global X CO2 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 Courtesy: Rayner and O’Brien, Flux Residuals (Gt/yr/zone) Flux Residuals (Gt/yr/zone)
Page 6 Precise CO 2 Measurements Needed Space-based X CO2 estimates will improve constraints on CO 2 fluxes –Near global coverage on monthly intervals –Precisions of 1–2 ppm (0.3–0.5%) on regional scales –No spatially coherent biases > 1–2 ppm (0.3 to 0.5%) on regional scales CO 2 Mixing Ratio (ppm) Latitude Latitude
Page 7 Effects of Gaussian Noise on X CO2
Page 8 The Orbiting Carbon Observatory (OCO) Spectra of CO 2 and O 2 absorption in reflected sunlight used to estimate X CO2 Random errors and biases no larger than ppm ( %) on regional scales at monthly intervals OCO will make the first space-based measurements with the precision and resolutions needed to quantify CO 2 sources and sinks and monitor their variability.
Page 9 OCO Fills a Critical Measurement Gap OCO will make precise global measurements of X CO2 needed to monitor CO 2 fluxes on regional to continental scales. Spatial Scale (km) CO 2 Error (ppm) OCO Flask Site Aqua AIRS Aircraft 0 Flux Tower Globalview Network NOAA TOVS ENVISAT SCIAMACHY
Page 10 Spectroscopy Clouds/Aerosols, Surface PressureClouds/Aerosols, H 2 O, TemperatureColumn CO 2 O 2 A-band CO m CO m Column-integrated CO 2 abundance –Maximum contribution from surface Why high spectral resolution? –Enhances sensitivity, minimizes biases
Page 11 OCO Observation Strategy Nadir Mode: tracks local nadir –Small footprint (< 3 km 2 ) isolates cloud- free scenes and reduces biases from spatial inhomogeneities over land –Low Signal/Noise over dark ocean Glint Mode: views “glint” spot –Improves Signal/Noise over oceans –More interference from clouds Target Mode –Tracks a stationary surface calibration site to collect large numbers of soundings Local Nadir Glint Spot Ground Track
Page 12 OCO Retrieval Algorithm
Page 13 Retrieval of Column O 2 High precision, high resolution O 2 A band spectra of sunlight reflected from ocean surface [O’Brien et al., 1997, 1998] Can we retrieve column O 2 with precisions required for OCO?
Page 14 Retrieval: First Cut rms residual = 8.8% Green - computed Black - measured Black - residual
Page 15 Continuum Level, Tilt, Zero Offset, ILS Width Fits rms residual = 1.4% Brown - computed Black - measured Black - residual
Page 16 Conclusions Algorithm developed to retrieve X CO2 from spectroscopic measurements of absorption in NIR bands Retrieved column O 2 with precision ~ 1% Demonstrates potential to retrieve column O 2 with precisions around 0.1% by averaging sufficient soundings Indicates feasibility of retrieving X CO2 with precisions better than 0.3%
Page 17 Aerosols: Major Source of Retrieval Uncertainty Ground surface Aerosol Layer 1 Aerosol Layer 2 Light reaching the detector Photon path length is modified through multiple scattering by aerosols Incident light
Page 18 Global Aerosol Climatology Courtesy: Kahn et al., 2001
Page 19 Aerosol Optical Properties Each mixing group is a combination of 4 different aerosol components from a basic set of 7 [Kahn et al., 2001]. Sulfate (land/water), seasalt, carbonaceous, black carbon: spherical => Mie code [de Rooij and van der Stap, 1984] Mineral dust (accumulated/coarse): mixture of oblate and prolate spheroids => T-matrix code [Mishchenko and Travis, 1998] Lognormal distribution Polarization fully considered
Page 20 Scattering Matrix (755 nm)
Page 21 Forward Model Details Park Falls, Wisconsin, July (SZA = 31°) Exponential drop-off in aerosol extinction (scale height: ~ 1 km, optical depth: 0.1) Forward model: RADIANT [Christi and Stephens, 2004] + single scattering approximation for polarization Lorentzian instrument lineshape function (resolving powers: O 2 A band: 17000, CO 2 bands: 20000)
Page 22 Weighting Functions (O 2 A band)
Page 23 Weighting Functions (1.61 µm CO 2 band)
Page 24 Weighting Functions (2.06 µm CO 2 band)
Page 25 Normalized Extinction Coefficient
Page 26 Single Scattering Albedo
Page 27 Normalized Extinction Coefficient
Page 28 Single Scattering Albedo
Page 29 Sensitivity Tests I Measurement error: 0.43 ppm Smoothing error: 0.29 ppm Maximum error due to incorrect assumption of aerosol type within retrieval group –Group 1: 0.27 ppm –Group 2: 0.02 ppm –Group 3: 0.03 ppm
Page 30 Sensitivity Tests II Type 8 Type 9
Page 31 Conclusions Incorrect knowledge of aerosol type could lead to significant X CO2 errors. Retrieval of aerosol optical properties (extinction, single scattering albedo, scattering matrix) –Scattering matrix Retrieval of microphysical parameters (characteristic radius, width of distribution, refractive index –Particle shape –Aerosol components Statistical Approach
Page 32 Polarization and the Stokes Parameters Electromagnetic radiation can be described in terms of the Stokes parameters: I, Q, U and V –I - total intensity –Q & U - linear polarization –V - circular polarization Degree of Polarization (for OCO)
Page 33 Importance of Polarization Polarization is a result of scattering Atmosphere: molecules, aerosols and clouds Surfaces can also polarize, in some cases significantly (e.g., ocean) The satellite instrument could be sensitive to polarization –OCO measures the radiation polarized perpendicular to the principal plane Polarization depends on solar and viewing angles –Spatial biases in retrieved trace gas column densities
Page 34 Two Orders of Scattering (2OS) to Compute Polarization Full multiple-scattering vector RT codes too slow to meet large-scale operational processing requirements Scalar computation causes two kinds of errors –Polarization components of the Stokes vector neglected –Intensity incorrectly calculated Major contribution to polarization comes from first few orders of scattering (multiple scattering is depolarizing) Single scattering does not account for the correction to intensity due to polarization
Page 35 2OS Model Schematic
Page 36 2OS Model Outline Intensity still calculated with full multiple scattering scalar model S = I sca +I cor -Q 2 Fast correction to standard scalar code Exact through second order Analytic Jacobians
Page 37 Simulation Scenarios DarwinPark FallsNy Alesund AlgeriaSouth PacificLauder These locations are OCO validation sites
Page 38 Simulation Details –Aerosol loadings [ : 0-0.3] –Aerosol type according to Kahn et al. [2001] –Lambertian surfaces [albedos from ASTER library] –T, H 2 O: ECMWF –CO 2, P: Match/CASA model data [Olsen and Randerson, 2004] –Realistic Measurement Noise [SNR: ~450, 350, 275]
Page 39 Radiance Spectra Different colors represent different aerosol amounts
Page 40 Spectral Residuals Different colors represent different aerosol amounts
Page 41 Scalar Spectral Residuals Different colors represent different aerosol amounts
Page 42 X CO2 and Surface Pressure Errors - I Algeria (Jan) Darwin (Jan) Ny Alesund (Apr)
Page 43 X CO2 and Surface Pressure Errors - II Lauder (Jan) South Pacific (Jan) Park Falls (Jan)
Page 44 X CO2 and Surface Pressure Errors - III Algeria (Jan) Darwin (Jan) Ny Alesund (Apr)
Page 45 X CO2 and Surface Pressure Errors - IV Lauder (Jan) South Pacific (Jan) Park Falls (Jan)
Page 46 Forward Model Error vs. Measurement Noise 2OS Scalar
Page 47 Conclusions Ignoring polarization could lead to significant (~ 10 ppm) errors in X CO2 retrievals 2OS model gives X CO2 errors that are much smaller than other biases Two orders of magnitude faster than a full vector calculation Additional overhead in the range of 10% of the scalar computation
Page 48 Future Work Cirrus Surface Types Aerosol vertical distribution Spectroscopy Line Mixing Speed Improvements
Page 49 Acknowledgments Yuk Yung John Seinfeld, Rick Flagan, Paul Wennberg Hartmut Boesch, Rob Spurr David Crisp, Charles Miller, Geoff Toon, Bhaswar Sen, Hari Nair, James McDuffie, Denis O’Brien, Mick Christi Run-Lie Shia, Jack Margolis, Zhiming Kuang, Mao-Chang Liang, Xun Jiang, Dan Feldman, Xin Guo Friends Family
Page 50 To be continued …