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WP5 Pyroconvection: SWIR and TIR l s sensitive to LT/FT and FT/UT.

The UK Universities contribution to the analysis of GOSAT L1 and L2 data: towards a better quantitative understanding of surface carbon fluxes.

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WP5 Pyroconvection: SWIR and TIR l s sensitive to LT/FT and FT/UT.

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  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

  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. AMAZONICA (AMAZon Integrated Carbon Analysis) Troposphere Greenhouse Gases Ecosystem Gas Fluxes Biomass Inventories Earth Observations Aquatic Carbon Vegetation Modelling ACE instrument Greenhouse Gas Synthesis Climate Response 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 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). Mean MIPAS De Mazière et al., ACP, 8, 2421 (2008) MIPAS climatology 131 matching profiles Unique UK contributions to wider GOSAT cal/val activities

  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 CO2and CH4 SCIAMACHY AIRS GOSAT OCO Objective: Generate consistent multi-sensor CO2and CH4datasets 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 NAME @ Leicester Jan - Feb Small Large • 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 CO2 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. 1 3 6 9 14 Day of Feb 2004 Large uncertainties in the distribution, magnitude and vertical transport of biomass burning emissions Active fire pixel, coloured by date of detection Sahel 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) Fire radiative power [MW] Rate of biomass combustion [kg/sec]

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