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

Orbiting Carbon Observatory (OCO)

The The OOrbiting rbiting CCarbon arbon OObservatory (bservatory (OCOOCO) ) MissionMission

Vijay Natraj

Ge152Ge152Wednesday, 1 March 2006


Atmospheric CO2: the Primary Anthropogenic Driver of Climate Change

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?

Since 1860, global mean surface temperature has risen ~1.0 °C with a very abrupt increase since 1980.

“Keeling Plot”


• 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

An Uncertain Future:Where are the Missing Carbon Sinks?


• 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., Science 290, 1342 (2000).

The Global Carbon Cycle: Many Questions


Why Measure CO2 from Space?Improved CO2 Flux Inversion Capabilities

Rayner & O’Brien, Geophys. Res. Lett. 28, 175 (2001)

• 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.

1.2

0.6

0.0Fig. 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

Flu

x R

etrie

val E

rror

sG

tC/y

ear/

Zon

e

OCO


45

Why Measure CO2 from Space? Dramatically Improved Spatiotemporal Coverage

The O=C=O orbit pattern (16-day repeat cycle)

45


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


XCO2 Retrieved from Bore-Sited CO2 and O2 Spectra Taken Simultaneously

Clouds/Aerosols, Surface Pressure Clouds/Aerosols, H2O, TemperatureColumn CO2

• 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


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

45

810

Ground tracksover the tip of South America

Spatial samplingalong ground track


Operational Strategy Maximizes Information Content and Measurement

Validation Opportunities

Nadir Mode

TargetMode

Glint Mode

• 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


Q20Sampling 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

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

XC

O2 (

pp

m)

XC

O2 (

pp

m)

MAY


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 (0.02c 0.05), light to moderate aerosol loadings (0.05a 0.15), over ocean and land surfaces. INSET: Distribution of XCO2 errors (ppm) for each case


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 36 (LatLon) bins based on AVHRR cloud data (O’Brien, 2001).


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).


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


The Pushbroom Spectrometer Concept

Crosstrack

Wav

elen

gth

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 45 region every 16 days.


OCO Data Product Pipeline

AIRS: T, P, H2O

Data Assimilation

Models

OCT

JUL

APR

JAN

InversionModels

Calibration &Validation

Data

Temporally Varying CO2 Source/Sink

Maps

Global 1 ppm XCO2

Maps

Spectral Radiances

Space-borne Data

Acquisition

Level 3

Level 2

Level 4

Ancillary DataFTIR: XCO2

GVCO2: [CO2]MODIS: Aerosol

NCEP Fields

• 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).


Retrieval Algorithm

Forward Model

Instrument Simulator

Global CO2 Maps

O2 A Band

CO2

CO2

XCO2

Retrieval Process

Radiative Transfer Model

Calculate Input Parameter

Monochromatic RT Calculation

Frequency Loop

Adjustment To The Atmospheric /Surface State x

Inversion Model

Calculated Spectrum f(x) and Jacobiansdf/dx

Convergence ?

Incoming Spectra

yes

no


Summary

XC

O2 (

pp

m)

• 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

• O2 A 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

Climate Forcing/Response

•T/H2O/O3 AIRS/TES/MLS

•Clouds CloudSat•Aerosols CALIPSO

•CO2 OCO

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

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