Space-Based CO 2 Retrievals: The O rbiting C arbon O bservatory ( OCO ) Mission

1. Space-Based CO2 Retrievals: The Orbiting Carbon Observatory (OCO) Mission Vijay Natraj California Institute of Technology

2. Outline • Motivation • CO2 from Space: the OCO Mission • Retrieval Strategy • Algorithmic Challenges • Solving the Polarization Problem • Experience Gained

3. Motivation • CO2 is the primary anthropogenic driver of climate change • CO2 concentration has increased by ~35% from ~280 to 380 ppm since ~1860. • Only half of the CO2 produced by human activities over the past 30 years has remained in atmosphere • Where are the sinks? • Outstanding Issues • Why does atmospheric buildup vary substantially with uniform emission rates? • What are relative roles of the oceans and land ecosystems in absorbing CO2? • What are relative roles of North American and Eurasian sinks? • How will carbon sinks respond to climate change and further increasing CO2 levels?

4. Current CO2 Source/Sink Measurements Current understanding of atmospheric CO2 is based primarily on: • Accurate measurements from ~120 in-situ surface stations • Most stations in remote areas to avoid impact of local sources • Using a chemical transport models allows to invert CO2 sources and sinks from spatial and temporal gradients in CO2 abundance • Results limited by uncertainties in transport (horizontal winds, vertical mixing) and sparseness of measurements

5. Space-based Measurements CO2 Flux Uncertainty gC/m2/yr Current Network CO2 Sources and Sinks from Space • Global Inversion Modeling [Rayner & O’Brien, 2001] indicates that space-based measurements of column-averaged CO2 dry air mole fraction, XCO2, could dramatically improve understanding of geographic distribution of CO2 sources and sinks if they: • are acquired globally (land/ocean) • have random errors not larger than 1-2 ppm on regional scales (8o × 10o) on monthly timescales • have no significant systematic biases on regional to continental scales

6. The OCO Mission OCO is the first mission solely dedicated to space-based CO2 measurements with the precision and resolution needed to quantify CO2 sources and sinks and monitor their variability. • Spectra of reflected sunlight in NIR CO2 and O2 bands used to estimate XCO2 • Division by O2 column eliminates many uncertainties due to e.g. photon path length • A-train orbit (1:15 PM polar sun sync) • Virtual Platform: Same ground track is observer at virtually the same time • Provides many complementary measurements • 16 day repeat cycle samples seasonal cycle on semi-monthly intervals • Scheduled Launch date: Sep 2008 • Mission duration: 2 years PARASOL - aerosols, polarization data AIRS – T, P, H2O, CO2, CH4 MODIS – cloud, aerosols, albedo CloudSat – cloud climatology CALIPSO – vertical profiles of cloud & aerosol; particle size OCO – XCO2, P(surface), T, H2O, cloud, aerosol TES – T, P, H2O, O3, CH4, CO MLS – O3, H2O, CO HIRDLS – T, O3, H2O, CO2, CH4 OMI – O3, aerosol climatology

7. Observation Modes Nadir • Nadir mode • Small footprint (<3 km2) • Maximizes number of cloud-free samples in partly cloudy regions • Minimizes errors due to spatial inhomogeneities within footprint • Glint Mode • Improved Signal/Noise over oceans • Target Mode • Increases number of observations per pre-selected validation sites Glint Target

8. Mission Success Criteria ‘Space-based XCO2 soundings in clear skies with random errors and systematic biases no larger than 1 - 2 ppm (~0.3 – 0.5 %) on regional scales and monthly time scales’ • Imposes challenging requirements on • Instrument • Calibration • Validation • XCO2 Retrieval Algorithm • Highly accurate/complex retrieval algorithmhas to be developed • Sufficiently fast • Proper treatment of many physical processes that are often neglected

9. Retrieval Strategy • Simultaneous spectral retrieval of 3 bands • 1.61 m CO2 band: maximum sensitivity to CO2 near surface • O2A band and 2.06 m CO2 band provide: Surface pressure, atmospheric temperature profile, water vapor profile, aerosols/clouds profile • Albedo: from continuum • Instrument Parameters • Self-consistent retrieval, no additional information needed CO2 2.06 m O2A band CO2 1.61m Clouds/Aerosols, Surface Pressure, Temperature Column CO2 Clouds/Aerosols, H2O, Temperature

10. Retrieval Process Incoming Spectra Global CO2 Maps Adjustment To The Atmospheric /Surface State Inverse Method Final Atmospheric/Surface State yes Convergence ? Calculated Spectrum and Weighting Functions Mapping & Averaging O2 A Band no Forward Model Solar and Instrument Models Radiative Transfer Model CO2 Monochromatic RT Calculation Frequency Loop Calculate Input Parameter CO2 Retrieval Algorithm

11. Major Algorithmic Challenges I • Surface Reflectance • Real surfaces exhibit complicated, anisotropic bi-directional reflectance distribution functions (BRDFs) • Systematic errors introduced if surface reflectance behavior not known • Need models for large number of surface types Surface Type Climatology [Masson et al.]

12. Aerosol Large spatial and temporal variability of aerosol types and amounts Optical properties vary significantly Using one specific aerosol type for all scenarios can causes large systematic errors Vertical distribution also significant from a retrieval standpoint Major Algorithmic Challenges II Aerosol Climatology [Kahn et al.]

13. Major Algorithmic Challenges III Lambertian surface (a = 0.2) and SZA = 60o Polarization • Atmosphere (air molecules, aerosols, clouds) and surface are in general polarizing • Polarization requires vector RT (very slow) • OCO measures only light polarized perpendicular to principal plane • Polarization depends on solar and viewing angles and will therefore introduce spatial biases in XCO2 if not accounted for. • Sufficiently accurate and fast correction scheme required

14. Correction for Polarization • Possible approach: look-up tables • Disadvantage: multidimensional (solar and viewing angles, azimuth angle, aerosol amount and type, surface reflectance, gas absorption optical depth, etc.) • Physics-based approach desirable • Multiple scattering is depolarizing • Major contribution to polarization comes from first few scattering events • Compute intensity using scalar approach; correct for polarization assuming two scattering events (2OS) • Two orders of magnitude faster than vector calculation

15. Test Scenarios Lauder (45 S, 170 E) Jan: SZA = 27, grass Jul: SZA = 70, frost Park Falls (46 N, 90.3 W) Jan: SZA = 75, snow Jul: SZA = 35, conifer Ny Alesund (79 N, 12E) Apr: SZA = 81, snow Jul: SZA = 64, grass Surface CO2, July 1, 12 UT South Pacific (30 S, 210 E) Jan: SZA = 14, ocean Jul: SZA = 55, ocean Darwin (12 S, 130 E) Jan: SZA = 13, deciduous Jul: SZA = 36, deciduous

16. Results [Courtesy of R. Washenfelder]

17. RMS Residuals – Scalar Approximation

18. RMS Residuals – 2OS Approximation

19. XCO2 Errors – Scalar Approximation

20. XCO2 Errors – 2OS Approximation

21. Conclusions • Great uncertainty in CO2 sources and sinks • Space-based measurements can help reduce the uncertainty • OCO algorithm retrieves CO2 from simultaneous measurements of NIR gas absorption spectral regions • Major algorithmic challenges include quantifying surface reflectance, aerosol and accounting for polarization • 2OS approximation for polarization gives excellent results while achieving two orders of magnitude improvement in speed

22. Experience Gained • Challenging scientific problem • Engineering approach: optimization of speed AND accuracy • Flight project: deadline driven • Focus on problem to be solved • Creative research • Opportunities for presentations and publications

23. Acknowledgments • Yuk Yung, Caltech • Hartmut Bösch, JPL • Robert Spurr, RT Solutions • Geoff Toon, JPL • Dave Crisp, JPL • Charles Miller, JPL • Bhaswar Sen, JPL