The O rbiting C arbon O bservatory ( OCO ) Mission Vijay Natraj Ge152 Wednesday, 1 March 2006

1. The Orbiting Carbon Observatory (OCO) Mission Vijay Natraj Ge152 Wednesday, 1 March 2006

2. Atmospheric CO2: the Primary Anthropogenic Driver of Climate Change “Keeling Plot” 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. Accumulation of atmospheric CO2 has fluctuated from 1 – 6 GtC/yr despite nearly constant anthropogenic emissions. WHY?

3. An Uncertain Future:Where are the Missing Carbon Sinks? • Only half of CO2 produced by human activities over the past 30 years has remained in the atmosphere. • 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/ Eurasia? • What controls carbon sinks? • Why does the atmospheric buildup vary with uniform emission rates? • How will the sinks respond to climate change? • Climate prediction requires an improved understanding of natural CO2 sinks. • Future atmospheric CO2 increases • Their contributions to global change

4. The Global Carbon Cycle: Many Questions • Atmospheric CO2 has been monitored systematically from a network of ~100 surface stations since 1957. The ~100 GLOBALVIEW-CO2 flask network stations and the 26 continental sized zones used for CO2 flux inversions. This network is designed to measure back-ground CO2. It cannot retrieve accurate source and sink locations or magnitudes! Bousquet et al., Science290, 1342 (2000).

5. 1.2 0.6 0.0 Why Measure CO2 from Space?Improved CO2 Flux Inversion Capabilities • 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. OCO Flux Retrieval Errors GtC/year/Zone Fig. F.1.3: Carbon flux errors from simulations including data from (A) the existing surface flask network, and (B) satellite measurements of XCO2 with accuracies of 1 ppm on regional scales on monthly time scales Rayner & O’Brien, Geophys. Res. Lett. 28, 175 (2001)

6. 45 45 Why Measure CO2 from Space? Dramatically Improved Spatiotemporal Coverage The O=C=O orbit pattern (16-day repeat cycle)

7. The Orbiting Carbon Observatory (OCO) Mission • Make the first, global, space-based observations of the column integrated dry air mole fraction, XCO2, with 1 ppm precision. • Combine satellite data with ground-based measurements to characterize CO2 sources and sinks on regional scales on monthly to interannual time scales • Fly in formation with the A-Train to facilitate coordinated observations and validation plans

8. XCO2 Retrieved from Bore-Sited CO2 and O2 Spectra Taken Simultaneously • High resolution spectroscopic measurements of reflected sunlight in near IR CO2 and O2 bands provide the data needed to retrieve XCO2 • Column-integrated CO2 abundance • Maximum contribution from surface • Other data needed (provided by OCO) • Surface pressure, albedo, atmospheric temperature, water vapor, clouds, aerosols • Why high spectral resolution? • Lines must be resolved from the continuum to minimize systematic errors Clouds/Aerosols, Surface Pressure Column CO2 Clouds/Aerosols, H2O, Temperature

9. 810 45 Spatial Sampling Strategy • OCO is designed provide an accurate description of XCO2 on regional scales • Atmospheric motions mix CO2 over large areas as it is distributed through the column • Source/Sink model resolution limited to 1o x 1o • High spatial resolution • 1 km x 1.5 km footprints • Isolates cloud-free scenes • Provides thousands of samples on regional scales • 16-day repeat cycle • Provides large numbers of samples on monthly time scales Spatial sampling along ground track Ground tracks over the tip of South America

10. Nadir Mode Glint Mode Target Mode Operational Strategy Maximizes Information Content and Measurement Validation Opportunities • 1:15 PM near polar (98.2o) orbit • 15 minutes ahead of EOS A-Train • Same ground track as AQUA • Global coverage every 16 days • Science data taken on day side • Nadir mode • Highest spatial resolution • Glint mode • Highest SNR over ocean • Target mode • Validation • Airmass dependence • Comparison with surface FTS stations • Calibration data taken on night side • Solar, limb, dark, lamp

11. XCO2 (ppm) DXCO2 (ppm) Q20 Sampling Biases • 1:15 PM local sampling time chosen because • Production of CO2 by respiration is offset by photosynthetic uptake • Instantaneous XCO2 measurement is within 0.3 ppm of the diurnal average (see figure) • Atmospheric transport desensitizes OCO measurements to the clear-sky bias • Air passes through clouds on a time-scale short compared to the time needed to affect significant changes in XCO2 • Mixing greatly reduces the influence of local events & point sources on XCO2 MAY Fig. F.2.4: a) Calculated monthly mean, 24 hour average XCO2 (ppm) during May using the NCAR Match model driven by biosphere and fossil fuel sources of CO2. b) XCO2 differences (ppm) between the monthly mean, 24 hour average and the 1:15 PM value

12. Will it Work? • Accuracies of 1 ppm needed to identify CO2 sources and sinks • Realistic, end-to-end, Observational System Simulation Experiments • Reflected radiances for a range of atmospheric/surface conditions • line-by-line multiple scattering models • Comprehensive description of • mission scenario • instrument characteristics • Results • Retrieve XCO2 from single clear sky nadir sounding to 0.3-2.5 ppm precision • Rigorous constraints on the distribution and optical properties of clouds and aerosols End-to-end retrievals of XCO2 from individual simulated nadir soundings at SZAs of 35o and 75o. The model atmospheres include sub-visual cirrus clouds (0.02c 0.05), light to moderate aerosol loadings (0.05a 0.15), over ocean and land surfaces. INSET: Distribution of XCO2 errors (ppm) for each case

13. Cloud, Aerosol and Cirrus Interference Clouds, aerosols and sub-visible cirrus (high altitude ice clouds) prevent measurement of the entire atmospheric column. Sub-visible cirrus clouds are effective at scattering near infrared light because the light wavelengths and particle sizes are both ~ 1 – 2 µm. An analysis of available global data suggests that a space-based instrument will see “cloud-free” scenes only ~ 10% of the time. Geographically persistent cloud cover will be especially problematic and will induce biases in the data. Number of cloud-free scenes per month anticipated for space-based sampling averaged into 36 (LatLon) bins based on AVHRR cloud data (O’Brien, 2001).

14. O=C=O Performance Improves with Spatial Averaging Accuracy of OCO XCO2 retrievals as a function of the number of soundings for optimal (red) and degraded performance (blue) for a typical case (37.5 solar zenith angle, albedo=0.05, and moderate aerosol optical depth, a{0.76 m} = 0.15). Results from end-to-end sensitivity tests (solid lines) are shown with shaded envelopes indicating the range expected for statistics driven by SNR (N1/2) and small-scale biases (N1/4).

15. Validation Program Ensures Accuracy and Minimizes Spatially Coherent Biases • Ground-based in-situ measurements • NOAA CMDL Flask Network + Tower Data • TAO/Taurus Buoy Array • Uplooking FTS measurements of XCO2 • 3 funded by OCO • 4 upgraded NDSC • Aircraft measurements of CO2 profile • Complemented by Laboratory and on-orbit calibration Buoy Network CMDL

16. The Pushbroom Spectrometer Concept It is possible to obtain many ground-track spectra simultaneously if the instantaneous field of view (IFOV) is imaged onto a 2D detector array. In this case, wavelength information is dispersed across one dimension and cross-track scenes are dispersed along the other dimension. The instrument acquires spectra continuously along the ground track at a rate of 4.5 Hz. This results in 70 spectra/sec and 9000 spectra per 45 region every 16 days.

17. JAN JUL APR OCT OCO Data Product Pipeline AIRS: T, P, H2O • The OCO data flow from space through an automated pipeline which yields Level 1 and 2 data products. • Level 3 and Level 4 products are produced by individual Science Team members. • Preliminary tests of the retrieval algorithm demonstrate the OCO mission concept • (Kuang et al., Geophys. Res. Lett., 29 (15) 2001GL014298, 2002). Space-borne Data Acquisition Level 2 Calibration & Validation Data Spectral Radiances Level 3 Ancillary Data FTIR: XCO2 GVCO2: [CO2] MODIS: Aerosol NCEP Fields Global 1 ppm XCO2 Maps Inversion Models Data Assimilation Models Level 4 Temporally Varying CO2 Source/Sink Maps

18. Retrieval Process Global CO2 Maps Adjustment To The Atmospheric /Surface State x Inversion Model XCO2 yes Calculated Spectrum f(x) and Jacobians df/dx O2 A Band no CO2 CO2 Incoming Spectra Convergence ? Forward Model Instrument Simulator Radiative Transfer Model Monochromatic RT Calculation Frequency Loop Calculate Input Parameter Retrieval Algorithm

19. Summary • Climate Forcing/Response • T/H2O/O3 AIRS/TES/MLS • Clouds CloudSat • Aerosols CALIPSO • CO2 OCO • OCO will provide critical data for • Understanding the carbon cycle • Essential for developing carbon management strategies • Predicting weather and climate • Understanding sources/sinks essential for predicting CO2 buildup • O2A Band will provide global surface pressure measurements • OCO validates technologies critically needed for future operational CO2 monitoring missions • Satisfies a measurement need that has been identified by NPOESS, for example XCO2 (ppm)