1. The UK Universities contribution to the analysis of GOSAT L1 and L2 data: towards a better quantitative understanding of surface carbon fluxes
Paul Palmer, Michael Barkley, Peter Bernath, Hartmut Bösch, Martyn Chipperfield, Liang Feng, Manuel Gloor, Paul Monks, Marko Scholze, Parvadha Suntharalingam, and Martin Wooster

2. Project overview: an integrative approach
WP1 Calibration/validation activities:
Models of land-surface exchange and atmospheric transport
Comparison with TCCON FTS
ACE FTS CH4 and CO2 profiles
AMAZONICA aircraft and surface measurements of CO2 and CH4
WP2 Intercomparisons of space-borne CO2 & CH4 data:
CO2: OCO, SCIA, AIRS, ACE
CH4: SCIA, IASI
Obj 1: to generate long-term consistent data record
Obj 2: to generate SWIR/TIR product
WP3 Surface flux inversions:
4 models: GEOS-Chem, TOMCAT, TM3, and MOZART
3 approaches: 4DVAR, batch-mode KF, and EnKf
WP4 Improved source attribution:
Analyse CO2-CO-CH4-OCS variations.
OCS: ACE
CO: TES, AIRS, SCIA, ACE
WP5 Pyroconvection:
SWIR and TIR ls sensitive to LT/FT and FT/UT.
Link with land-surface properties (e.g., fire radiative power) and CFD fire model
advantages:
Radiance of sun gives higher S/N than emission
Limb view gives longer path length ~500 km (lower detection limits) than nadir
“Self-calibrating” so excellent long-term accuracy and precision
Disadvantages:
Modest global coverage
Samples only free troposphere

3. Project overview: an integrative approach
WP1 Calibration/validation activities:
Models of land-surface exchange and atmospheric transport
Comparison with TCCON FTS
ACE FTS CH4 and CO2 profiles
AMAZONICA aircraft and surface measurements of CO2 and CH4
WP2 Intercomparisons of space-borne CO2 & CH4 data:
CO2: OCO, SCIA, AIRS, ACE
CH4: SCIA, IASI
Obj 1: to generate long-term consistent data record
Obj 2: to generate SWIR/TIR product
WP3 Surface flux inversions:
4 models: GEOS-Chem, TOMCAT, TM3, and MOZART
3 approaches: 4DVAR, batch-mode KF, and EnKf
WP4 Improved source attribution:
Analyse CO2-CO-CH4-OCS variations.
OCS: ACE
CO: TES, AIRS, SCIA, ACE
WP5 Pyroconvection:
SWIR and TIR ls sensitive to LT/FT and FT/UT.
Link with land-surface properties (e.g., fire radiative power) and CFD fire model

4. (AMAZon Integrated Carbon Analysis)
Unique UK contributions to wider GOSAT cal/val activities
AMAZONICA
(AMAZon Integrated Carbon Analysis)
Aquatic
Carbon
Vegetation
Modelling
Greenhouse Gas
Synthesis
Biomass
Inventories
Climate Response
Troposphere
Greenhouse Gases
Earth
Observations
Ecosystem
Gas Fluxes
Synthesis of top-down and bottom-up approaches to better understand major basin-wide CO2, CO, and CH4 flux processes
Monthly CO2, CO, and CH4 profiles for 48 months
Mean ACE-FTS
Mean MIPAS
MIPAS climatology
131 matching profiles
De Mazière et al., ACP, 8, 2421 (2008)
ACE instrument
ACE CO2 progress (5-25 km): a) Use ACE temperatures (for now) and retrieve tangent heights using the N2 continuum b) Use selected temperature-insensitive CO2 lines to get CO2 profiles Model studies look promising: Foucher et al. ACPD (submitted).

5. Project overview: an integrative approach
WP1 Calibration/validation activities:
Models of land-surface exchange and atmospheric transport
Comparison with TCCON FTS
ACE FTS CH4 and CO2 profiles
AMAZONICA aircraft and surface measurements of CO2 and CH4
WP2 Intercomparisons of space-borne CO2 & CH4 data:
CO2: OCO, SCIA, AIRS, ACE
CH4: SCIA, IASI
Obj 1: to generate long-term consistent data record
Obj 2: to generate SWIR/TIR product
WP3 Surface flux inversions:
4 models: GEOS-Chem, TOMCAT, TM3, and MOZART
3 approaches: 4DVAR, batch-mode KF, and EnKf
WP4 Improved source attribution:
Analyse CO2-CO-CH4-OCS variations.
OCS: ACE
CO: TES, AIRS, SCIA, ACE
WP5 Pyroconvection:
SWIR and TIR ls sensitive to LT/FT and FT/UT.
Link with land-surface properties (e.g., fire radiative power) and CFD fire model

6. Generating a consistent space-borne record of CO2 and CH4
SCIAMACHY
AIRS
GOSAT
OCO
Objective: Generate consistent multi-sensor CO2 and CH4 datasets to obtain:
Much denser spatial and temporal sampling for source/sink estimation
Long-term data records
Specific Tasks: Intercomparison of satellite products to identify, characterize and remove biases in the data products:
Detailed comparison of CO2 retrievals from GOSAT and OCO:
- Characterization of retrieval differences with simulations, spectra from collocated soundings and comparisons to common validation site.
2) Comparison of CO2 and CH4 products to operational and U. Leicester products retrieved from to SCIAMACHY, AIRS and IASI

7. Project overview: an integrative approach
WP1 Calibration/validation activities:
Models of land-surface exchange and atmospheric transport
Comparison with TCCON FTS
ACE FTS CH4 and CO2 profiles
AMAZONICA aircraft and surface measurements of CO2 and CH4
WP2 Intercomparisons of space-borne CO2 & CH4 data:
CO2: OCO, SCIA, AIRS, ACE
CH4: SCIA, IASI
Obj 1: to generate long-term consistent data record
Obj 2: to generate SWIR/TIR product
WP3 Surface flux inversions:
4 models: GEOS-Chem, TOMCAT, TM3, and MOZART
3 approaches: 4DVAR, batch-mode KF, and EnKf
WP4 Improved source attribution:
Analyse CO2-CO-CH4-OCS variations.
OCS: ACE
CO: TES, AIRS, SCIA, ACE
WP5 Pyroconvection:
SWIR and TIR ls sensitive to LT/FT and FT/UT.
Link with land-surface properties (e.g., fire radiative power) and CFD fire model

8. Progress of flux estimation studies depends on WP1 & WP2
UKMO Leicester
Jan - Feb
Small
Large
BM KF
EnKF
4DVar
GEOS-Chem X
MOZART
TM3
TOMCAT
Regional flux estimation will use the NAME Lagrangian model
Focus on (a) wildfires and (b) metropolitan areas over Europe

9. Project overview: an integrative approach
WP1 Calibration/validation activities:
Models of land-surface exchange and atmospheric transport
Comparison with TCCON FTS
ACE FTS CH4 and CO2 profiles
AMAZONICA aircraft and surface measurements of CO2 and CH4
WP2 Intercomparisons of space-borne CO2 & CH4 data:
CO2: OCO, SCIA, AIRS, ACE
CH4: SCIA, IASI
Obj 1: to generate long-term consistent data record
Obj 2: to generate SWIR/TIR product
WP3 Surface flux inversions:
4 models: GEOS-Chem, TOMCAT, TM3, and MOZART
3 approaches: 4DVAR, batch-mode KF, and EnKf
WP4 Improved source attribution:
Analyse CO2-CO-CH4-OCS variations.
OCS: ACE
CO: TES, AIRS, SCIA, ACE
WP5 Pyroconvection:
SWIR and TIR ls sensitive to LT/FT and FT/UT.
Link with land-surface properties (e.g., fire radiative power) and CFD fire model

10. Interpreting GOSAT CO2 data using CO, COS, and CH4 data will improve CO2 source attribution
COS, VOCs
CO, CH4, VOCs, NOx, HCN, H2
BIG IDEA: Observed correlations between CO2 and other species arise from common sources, source regions and atmospheric transport
Objective: Develop multiple-species inverse analysis framework to incorporate remote sensing measurements using 3-D atmospheric model simulations

11. Project overview: an integrative approach
WP1 Calibration/validation activities:
Models of land-surface exchange and atmospheric transport
Comparison with TCCON FTS
ACE FTS CH4 and CO2 profiles
AMAZONICA aircraft and surface measurements of CO2 and CH4
WP2 Intercomparisons of space-borne CO2 & CH4 data:
CO2: OCO, SCIA, AIRS, ACE
CH4: SCIA, IASI
Obj 1: to generate long-term consistent data record
Obj 2: to generate SWIR/TIR product
WP3 Surface flux inversions:
4 models: GEOS-Chem, TOMCAT, TM3, and MOZART
3 approaches: 4DVAR, batch-mode KF, and EnKf
WP4 Improved source attribution:
Analyse CO2-CO-CH4-OCS variations.
OCS: ACE
CO: TES, AIRS, SCIA, ACE
WP5 Pyroconvection:
SWIR and TIR ls sensitive to LT/FT and FT/UT.
Link with land-surface properties (e.g., fire radiative power) and CFD fire model

12. Large uncertainties in the distribution, magnitude and vertical transport of biomass burning emissions
Active fire pixel, coloured by date of detection
Sahel
Day of Feb 2004
Meteosat Imaging Disk
(15 mins temporal resl.)
04/02/04 – 14/02/04
Deciduous woodland
Deciduous shrubland
Savanna
Cropland
BIG IDEA: use FRP with GOSAT NIR/TIR CO2 measurements to quantify the influence of biomass burning on the vertical profile of CO2.
Method: Gonzi and Palmer, submitted, 2008 (example using CO)
Every 15 mins the “fire radiative power” from all active fire pixels within the study area can be measured and summed to provide a total estimate of the fuel consumption rate (the amount required to produce the observed radiative heat). Here we see max rates of 100 tonnes/sec.
Fire radiative power [MW]
Rate of biomass combustion [kg/sec]