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High Priority Science • Passive Instrument • Heritage Spacecraft • Cost Resiliency • All NASA Flight System
Orbiting Carbon Observatory (OCO)
Science -1
Why CO2?
O OCISSUES: Carbon dioxide (CO2) is the
• Principal atmospheric component of the global carbon cycle
• Primary anthropogenic driver of climate change
• Only half of CO2 produced by human activities over the past 30 years has remained in the atmosphere.
• Where are the sinks?
• Will this continue?
Atmosphere
Human Activity
LandOcean
? ?
High Priority Science • Passive Instrument • Heritage Spacecraft • Cost Resiliency • All NASA Flight System
Orbiting Carbon Observatory (OCO)
Science -3
The Global Carbon CycleNatural carbon fluxes account for 300 GtC/yr and exist in near equilibrium.
The ~6 GtC/yr produced by human activity represents only 2% of the carbon flux, but it may tip the balance
6 G
tC/y
r
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”
Does increasing atmospheric CO2 drive increases in global temperature?Do increasing temperatures increase atmospheric CO2 levels?
Atmospheric CO2: the Primary Anthropogenic Driver of Climate Change
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• What are the relative roles of the oceans and land ecosystems in absorbing CO2?
• Is there a Northern hemisphere land sink?– Relative roles of North America/ Eurasia
• What controls carbon sinks?– Why does the atmospheric buildup vary
substantially with uniform emission rates?– How will 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 FutureWhere are the Missing Carbon Sinks?
Atmospheric CO2
200
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1850 1900 1950 2000 2050 2100
pp
m
Global Mean Temperature
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OCO will dramatically reduce these uncertainties
High Priority Science • Passive Instrument • Heritage Spacecraft • Cost Resiliency • All NASA Flight System
Orbiting Carbon Observatory (OCO)
Science -6
An Uncertain Future:Where are the Missing Carbon Sinks?
• Only half of the CO2 released into the atmosphere since 1970 years 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
• Atmospheric CO2 has been monitored systematically from a network of ~100 surface stations since 1957
• Over the past 20 years– only ~1/2 of the CO2 associated with fossil
and biomass fuel combustion has remained in the atmosphere
– the remainder has been absorbed by the ocean and land ecosystems
• Carbon sinks are not well understood– Is there a Northern hemisphere land sink?
• Relative roles of North America/ Eurasia– What controls sources and sinks?
• Why does the atmospheric buildup vary from 1 - 6 GtC/year in the presence of roughly constant emission rates?
• How will the efficiency of these sinks evolve as the climate changes?
• An Integrated, global strategy needed to answer these questions.
– The US Carbon Cycle Science Program• USGCRP, NSF, DoE, USDA, NOAA,
NASA, USGS
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 can not retrieve accurate source and sink locations or magnitudes!
Bousquet et al., Science 290, 1342 (2000).
The Global Carbon Cycle: Many Questions
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Measurements Needed to Revolutionize Our Understanding of the Global Carbon Cycle
• Accurate, spatially resolved global measurements of XCO2 will revolutionize our understanding of the carbon cycle if measurement can be acquired
– With accuracies of 1 ppm
– On regional scales (8o X 10o)
– On monthly time scales
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 1ppm on regional scales on monthly time scales
Flu
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OCO
FLASK SATELLITE
Flux Errors
Fig. F.1.2 Flux Errors vs Measurement Accuracy
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Why Measure CO2 from Space?Improved CO2 Flux Inversion Capabilities
Rayner & O’Brien, Geophys. Res. Lett. 28, 175 (2001)
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.
Flu
x R
etri
eval
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or G
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r/zo
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45
Why Measure CO2 from Space?Dramatically Improved Spatiotemporal Coverage
The O=C=O orbit pattern (16-day repeat cycle)
O=C=O Measurement Objectives
Objective:
Characterize the geographic distribution of CO2 sources and sinks on regional to continental scales over seasonal to interannual time scales
Approach:• Space-based atmospheric carbon monitoring
system– Global coverage (land and ocean)
• high spatial resolution (4o x 5o)• weekly to monthly time scales
– High measurement precision
• Column CO2 measurement precision
– ~1ppm (0.3% of 370 ppm)• Resolve East-West gradients as well as
interhemispheric gradients in CO2
• Advanced Modeling tools used to retrieve
– CO2 column amounts from observations
– Sources and sinks from global CO2 maps
• Correlative Measurement Program– Validation, bias removal, diurnal cycles– Laboratory Measurements Coverage in Each 16-Day Repeat Cycle
45
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Orbiting Carbon Observatory (OCO)
Science -12
Proposed Sampling Strategy Addresses All Science Objectives
B) Satellite
• OCO will 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 – 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
810Ground tracksover the tip of South America
Spatial samplingalong ground track
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Measurement Strategy Maximizes Information Content and Measurement Validation Opportunities
Nadir Mode
TargetMode
Glint Mode
• 1:15 PM near polar orbit – 15 minutes ahead of the 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• Same path as FTIR
• Calibration data taken on night side
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OCO 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 1ox1o
• OCO flies in the A-train, 15 minutes ahead of the Aqua platform
• 1:15 PM equator crossing time yields same ground track as AQUA
• Global coverage every 16 days
• OCO samples at high spatial resolution • Nadir mode: 1 km x 1.5 km footprints
• Isolates cloud-free scenes• Provides thousands of samples on regional
scales• Glint Mode: High SNR over oceans• Target modes: Calibration
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Orbiting Carbon Observatory (OCO)
Science -15
Will it Work?
• Accuracies of 1ppm 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: The OCO payload will• meet or exceed the requirements for
measuring CO2
• provide 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
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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 FTIR measurements of XCO2
– 3 OCO
– 4 NDSC
• Aircraft measurements of CO2 profile
• Complemented by Laboratory and on-orbit calibration
Buoy Network CMDL
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Science -17
Rigorous Physics Based Retrieval Algorithms
Level 1
Level 2
Level 3
XCO2 Retrieval
Calibration
Source/SinkRetrieval
• Inverse Models• Assimilation Models Level 4
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 Hz.
This results in 24 spectra/sec and 3000 spectra per 45 region every 16 days.
2D 1024 1024 arrays are available in Si (visible) and HgCdTe (NIR) from Rockwell Sciences.
Cloud and Aerosol Interference
Clouds, aerosols and sub-visible cirrus (high altitude ice clouds) prevent measurement of the entire atmospheric column.
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.D. O’Brien (2001).
Sub-visible Cirrus Clouds
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 m.
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.
VISIBLE
1.38 m
MODIS data
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).
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Q4Science Impacts of X-band Failure
• OCO will meet its 1 ppm relative accuracy requirement under both scenarios
Wor
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The Baseline Science Mission will be achieved
Based on Fig. F.1.10
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Response:
• OCO requires no ancillary data to measure XCO2
• XCO2 measurements are relatively insensitive to the details of the underlying terrain and surface characteristics
– Observations from the high resolution O2 A-band spectrometer will be used to characterize the topographic variability within each spatial footprint
– Effects of surface albedo are discussed in Question #18
Q15OCO Geolocation Requirements (cont.)
The claimed need for 5 km geolocation (F-18) is deemed inadequate for mapping CO2 retrievals onto terrain and surface characteristics
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Question #17Q17
Question 17: Due to the critical effect of changing surface albedo, it is essential that the entire spectra are collected simultaneously. Please clarify.
Response• OCO uses grating spectrometers
– All wavelength information for a given spatial sample is recorded simultaneously on array detectors
– Each spatial sample is read out almost simultaneously • 1.4 msec per spectrum
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Response:
The wavelength-dependent albedo is retrieved as an independent variable in each spectral channel.
Question18Q18
Question 18: Please quantify the error in the CO2 column measurement resulting from surface albedo variations and uncertainties. A formal, detailed error analysis is not required here. However, you do need to demonstrate that this error source, which is not explicitly considered in the proposal, is not important relative to the error budget and specified 1 ppm accuracy level.
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Albedo Is Retrieved Explicitly
• The wavelength dependent albedo is determined from the continuum level within each spectral band as part of the simultaneous retrieval– Surface albedo changes much more slowly with wavelength than
gas vibration-rotation features– The OCO spectral resolution has been chosen to resolve the
spectral lines from the continuum in each band• Because the entire spectrum is collected almost
simultaneously in each channel, the XCO2 retrieval depends only on the spatial average of the albedo within each footprint
Q18
2.06
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Albedo Is Retrieved Explicitly
• The end-to-end retrieval simulations included wavelength-dependent albedos, which were retrieved as part of the XCO2 retrieval process. – Albedo types considered: dark ocean, desert, snow,
conifer forests and snow • Errors associated with uncertainties in the albedo
retrieval are a small part of the total error budget– The XCO2 retrieval algorithm is only weakly dependent
on the absolute value of the surface albedo (through its effects on the SNR)
– Atmospheric O2 and CO2 columns depend on differences between the line core and continuum
Q18
Demonstrate that this error source, which is not explicitly considered in the proposal, is not important relative to the error budget and specified 1 ppm accuracy level.
Fig. F.1.9: End to end test of the OCO retrieval algorithm
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Q20OCO Sampling Biases: Level 3 ProductsGlobal XCO2 Maps (16-day average)
• 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)
– Airborne measurements of CO2 profiles from COBRA and ABLE-2B substantiate this view
• 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 (no cloud bias evident in figure)
– 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
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Q20
• Level 4a: Inverted Sources and Sinks
• OCO measurements of XCO2 will not be evenly distributed in time and space.
• The inversion approaches incorporated into the OCO Mission Science strategy account for spatial and temporal inhomogeneity in observations.
– The power of OCO in constraining sources and sinks comes from the ~108 new observations over a two-year period and the relatively high density of XCO2 observations in the tropics (where constraints from contemporary surface networks are weak).
– Inversions of OCO data in combination with FTIR, aircraft, and flask observations, will revolutionize our understanding of the global carbon cycle.
OCO Sampling Bias: Level 4a ProductsGeographic Distribution of CO2 Sources & Sinks
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Q20
• Level 4b: Carbon Cycle Data Assimilation
• Data assimilation will calculate the 3-D CO2 field by combining OCO XCO2 data with
– in situ CO2 observations
– Uplooking FTIR XCO2
– Atmospheric transport model
• Analogous to modern weather forecasting
– Spatially and temporally biased observations assimilated into a physical model to produce maps with continuous spatial and temporal information.
OCO Sampling Bias: Level 4b ProductsCarbon Cycle Data Assimilation
XCO2 Assimilation Strategy
Assimilation step
Transportmodel
CO2Forecast
(F)
4-D CO2Analyses (A)for science
System statsA - FO – F
for monitoring
CO2Assimilation
cycle
OCO XCO2obs. (O)
Meteorology Source/Sinkestimates
In-situ CO2 obs.
INPUT OUTPUT
MODELS
Assimilation step
Transportmodel
CO2Forecast
(F)
4-D CO2Analyses (A)for science
4-D CO2Analyses (A)for science
System statsA - FO – F
for monitoring
CO2Assimilation
cycle
OCO XCO2obs. (O)
Meteorology Source/Sinkestimates
In-situ CO2 obs.
INPUT OUTPUT
MODELS
INPUT OUTPUT
MODELS
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Q21Question # 21
Question 21:
Level 2 data consists of a constellation of point measurements obtained in clear sky conditions. What are the spatial and temporal statistics of these measurements as a function of latitude? In particular, how will the geographical sampling of the instrument be affected by the relatively larger amount of cloudiness which tends to occur in the tropics relative to other latitudes? How will this degrade the quality of the Level 3 and Level 4 data, and how will this affect your science?
Response:– OCO acquires 740 soundings per degree of latitude along the orbit track– With 14.65 orbits/day, and a 16 day repeat cycle,
• Ground tracks are separated by ~1.5o of longitude
• Over a 16 day period, each 4o x 5o sub-region is traversed 3.3 times, yielding ~10,000 XCO2 soundings (assuming no clouds)
– Only a small fraction of these samples are needed to meet the baseline science requirements
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• Response: – Observational System Simulation
Experiments (OSSE’s) using the OCO orbit and 8 km x 8 km footprint (based on ISCCP data)
– There is no strong tropical bias in the number of CO2 soundings
– High cirrus produce a sampling bias in the tropics, but their effects are compensated by the low solar zenith angle and high SNR
Effects of Clouds on Sampling
How will the geographical sampling of the instrument be affected by the relatively larger amount of cloudiness which tends to occur in the tropics relative to other latitudes?
Average number of cloud-free hits each month in each 4 x 5 degreelatitude bin, averaged over the year.
Q21
Rayner et al. (2002)
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Probability of Viewing Cloud-Free Scenes Increases with Spatial Resolution
July
8 km x 8 km
0.0 0.5 1.0
Probability of clear-sky scene
24 km x 24 km
40 km x 40 km
Rayner, Law and O’Brien III (2001)
• OSSE’s confirm that the probability of viewing cloud-free scenes increases as the sample footprint size decreases
• The high spatial resolution (1 km x 1.5 km) provided by OCO will yield more cloud free scenes
Q21
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Q22Airborne Demonstration of +0.1% Retrieval Precision for Column O2
• Retrieval of tropospheric column O2 to + 0.1% demonstrated using reflected near infrared sunlight with an airborne O2 A-band instrument
D. M. O’Brien et al., J. Atmos. Oceanic Tech. 15, 1272 (1998).
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Q22Flight Instrument Validation
• Prior to launch the OCO flight instrument will
• Measure the CO2 column looking up towards sun
• Be compared to the OCO FTIR spectrometers.
1.571.59 1.58
Wavelength (m)
CO2 1.58 m Band
Ground-based FTIR solar spectrum in the OCO 1.58 m CO2 band recorded at Table Mountain Facility, Wrightwood, CA
(May 2002, S. Sander)
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Question 8a
Question 8a:
Quantify the science impacts of the descope options
Response:The OCO Team has identified 5 descope options
1. Relax geolocation requirement from 5 km to ~10 km2. Leave A-Train3. Reduce sampling rate from 4.5 to 3.0 Hz4. Limit science observation to NADIR viewing5. Delete 2.06 m channel
Q8a
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Q8aThe Execution of All Descope Options Defines the Minimum Science Mission
Baseline Science Mission Minimum Science MissionProvide global XCO2 measurements from space with
• 1 ppm relative accuracy• 2.5 x 105 km2 scale (4 x 5)• 16 day interval• 2 year mission
Provide global XCO2 measurements from space with
• 1 ppm relative accuracy• 1.0 x 106 km2 scale (8 x 10)• 1 month interval• 2 year mission
Combine XCO2 measurements with ground-based data to retrieve the geographic distribution of CO2 sources & sinks on seasonal to interannual timescales.
Combine XCO2 measurements with ground-based data to retrieve the geographic distribution of CO2 sources & sinks on seasonal to interannual timescales.
Use NADIR, GLINT and TARGET modes to provide independent data validation approaches
Deleted
Formation fly with the A-Train to allow coordinated observations and enhance data value to the ESE community
Deleted
Section F.2.6, pp. F-17-18
Q8a
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Q8aOCO XCO2 Retrieval Performance Quantified for Baseline & Minimum Missions
Minimum Mission
Baseline Mission
After Fig. F.1.10, p. F-8
• OCO delivers global XCO2 measurements with 1 ppm relative accuracy in the Baseline Science Mission & Minimum Science Mission.
Simulations conducted with the end-to-end OCO retrieval algorithm
Q8a
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Response:
• Relaxing the geolocation requirement from 5 km to ~10 km complicates the validation of the OCO data but does not affect the data accuracy.– Detailed response provided in Question 15
• A relaxed geolocation requirement increases difficulty in collocating OCO validation measurements using TARGET mode.– FTIR’s located in spatially uniform areas
• The relaxed geolocation requirement will not affect the ability to correlate the OCO measurements with those from the A-Train– OCO swath is substantially smaller than that of A-Train
instruments.
Descope 1Relaxed Geolocation Requirements
Q8a
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Descope 3Reduced Sampling Rate
Response:• Reducing sampling rate from 4.5 Hz to 3.0 Hz will
– Reduce the number of samples in each 4 x 5 region by 33%– Increases footprint area by 50%
• Slightly increased cloud contamination• Results quantified in the response to Question 21
Q8a
33%
Red
uct
ion
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Descope 4Delete TARGET & GLINT Modes
Response:• Deleting GLINT mode will
reduce sensitivity over oceans and at high latitudes
– OCO still meets its 1 ppm XCO2 relative accuracy goal operating only in NADIR mode
• Deleting TARGET mode decreases the number of independent validation methods
Fig. F.1.9, p. F-7
OCO retrieval performance for several NADIR viewing scenarios
Q8a
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Descope 5Delete 2.06 m Channel
Response:• Deleting the 2.06 m channel requires significantly more soundings
to be averaged to meet the 1 ppm XCO2 relative accuracy goal
Fig. F.1.8 quantifies the RSS errors for retrievals with all three spectrometers (c) and without the 2.06 m channel (b).
Q8a
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Q8aDescope 5Delete 2.06 m Channel (con’t)
• The Baseline configuration reaches the 1 ppm accuracy goal in 10 – 50 soundings while it requires 50 – 10,000 soundings to achieve this goal without the 2.06 m channel.
• To achieve 1 ppm requires–Increasing sampling grid from
4 x 5 to 8 x 10 – Increasing interval from 16
days to 1 month
Fig. F.1.10 quantifies the simulated observatory performance as a function of the number of soundings for the baseline configuration (red) and the instrument with the 2.06 m channel deleted (blue).
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OCO Addresses the High Science Priorities
XC
O2 (
pp
m)
• OCO provides 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
Climate Forcing/Response
•T/H2O/O3 AIRS/TES/MLS
•Clouds CloudSat•Aerosols CALIPSO
•CO2 OCO