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Phase 1 Report Paul S. Monks, Hartmut Boesch, Gennaro Cappelluti

Preparatory work on the use of remote-sensing techniques for the detecting and monitoring of GHG emissions from the Scottish land use sector. Phase 1 Report Paul S. Monks, Hartmut Boesch, Gennaro Cappelluti. Objective.

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Phase 1 Report Paul S. Monks, Hartmut Boesch, Gennaro Cappelluti

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  1. Preparatory work on the use of remote-sensing techniques for the detecting and monitoring of GHG emissions from the Scottish land use sector Phase 1 Report Paul S. Monks, Hartmut Boesch, Gennaro Cappelluti

  2. Objective The main objective is to develop a method for generating atmospheric GHG fields for the UK/Scotland region from emissions of the land sector and to assess the capabilities of existing and future satellite instruments to monitor carbon fluxes owing to land-use change.

  3. Measurements of Carbon Flux

  4. Objectives II The specific objective and deliverables are • To integrate the ECOSSE soil model together with the Jules vegetation model into the Lagrangian transport model NAME • To model CO2 fields for the UK/Scotland region for several weeks during summer/autumn for the years 2006 and 2007 with and without fluxes from the ECOSSE model • To generate atmospheric CO2 for UK/Scotland region the from SCIAMACHY/ENVISAT for the same time period including detailed assessment of the uncertainties • To assess the capabilities of SCIAMACHY for observing carbon fluxes released from the soil • To simulate CO2 fields for the UK/Scotland region using future land-use change scenarios • To assess the capabilities of future satellite instruments (OCO, GOSAT) to observe and monitor carbon fluxes due to land-use change

  5. Workpackages • WP1: Integration of soils and vegetation models into atmospheric transport model • WP2: Space-based CO2 data • WP3: Generation of spatio-temporal patterns of CO2 for different land-use Scenarios

  6. WP1- Integration of soils and vegetation models into atmospheric transport model

  7. WP1 • Work: Generate CO2 field for Scotland • Atmospheric transport – NAME • Soil – ECOSSE • Vegetation – JULES • Anthropogenic - NAEI • Output: model-generated annual CO2 flux with the key source and sink terms constrained • Fulfills: Objectives 1 and 2

  8. CO2

  9. UK Met Office NAME • NAME is the UK met office dispersion model: • A number of particles are released from a start point. • The individual particles are followed and the new location at each time step is based on the wind fields. • Particles are followed for a set time period or until they leave a target region. • Model output might include surface contact time. • Model can be run with time forward or backward. • Name can be coupled with inversion scheme for surface fluxes

  10. Setup of NAME Model • Setup of Name: • Run backward from Scotland/UK and store the surface contact time per grid box • Temporal and spatial resolution: • - currently 1x1 grid for several altitudes and 15 min. timesteps • - finer resolution possible over source region, currently 15 min..) • Computational requirements: 0.5 hours per day for UK on a 1x1 grid • Data requirements: met fields from UKMET • Calculation of 3D CO2 fields: • Requires fluxes for each grid box (e.g. as ASCII table)

  11. Comparison with sources and sinks: NDVI from NOAA AVHRR average 07/2003

  12. Comparison with sources and sinks: NDVI from NOAA AVHRR average 10/2003

  13. JULES

  14. JULES – MAP – Fisher (Sheffield)

  15. Jo Smith Aberdeen

  16. ECOSSE

  17. HadGEM2 JULES UK community land surface model SUNDIAL Model of soil C and N - arable soils RothC Model of soil C MOSES Soil water • ECOSSE • Model of soil C and N • all soil types & • all land uses TRIFFID Plant model

  18. Plan for this work • We met with Aberdeen to discuss ECOSSE • Within 1 month AU will provide LU with ASCII files containing the CO2 released across the UK with spatial resolution 1 km2 and for the timeframe 2003-2004. • Once the integration of ECOSSE into JULES is tested and results robust enough, LU will use the CO2 ASCII files generated by JULES. • In the meantime NAME will be able to run 1) with the CO2 distribution from ECOSSE assuming grassland as vegetation and 2) with the CO2 from JULES without using ECOSSE as soil model.

  19. Detailed plan of action LU - Do NAME runs to assess residence time of air at the surface in the different areas, so can assess whether need to do detailed simulation of Ireland (Leicester – immediately). Output = information about spatial domaine needed for soil fluxes. LU - Investigate cloud filters. Output = method development. LU - Investigate oxygen normalisation. Output = improved CO2 product for the UK area. AU - Use ECOSSE runs (without vegetation coupled through JULES) to produce ascii files of CO2 across UK – timeframe 2003-2005 (Jo – complete changes; get data together); do run for UK – within 1 month?). Output = ascii files to be used by LU. LU - Use ECOSSE files together with NAME files to distribute CO2 across Scotland. Output = evaluation of whether current patterns of soil emissions can be picked up by satellites. LU - (with input from AU) - Investigate use of JULES/ECOSSE coupled version (UL). Repeat (d) to produce Sciamachy type maps. Add regional databases of anthropogenic emissions to ECOSSE files. Add in starting conditions of CO2 from model (TM3?) or other source. ­Distribute CO2 using NAME. Output = cross evaluation of use of satellite data and of models. AU - Run ECOSSE with future scenarios. Run in same way as before. Can HadCM3 be used instead of NAME? (LU investigate). Output = 3D CO2 fields i) IPCC SRES A1, A2, B1, B2 ii) Changing land use Uplands -> Forestry Forestry uplands -> peatland Etc (AU will supply scenarios used in RERAD UAB-015-07). h) LU / AU - Compare results of future scenarios with the abilities of future missions. How soon can we expect to detect the predicted changes? How large a flux is needed to be quantified? Output = potential of satellites to monitor CO2 fluxes from land use and climate change.

  20. Land-use change • Land use change implies loss or gain of the carbon stored in the soil and consequently determines emission or absorption of CO2. • Initially the land use change scenarios to analyse will be: 1) uplands to forestry and 2) forestry uplands to peat land. Action – Check with Geeta that these scenarios are acceptable.

  21. WP2: Space-based CO2 data

  22. WP2 • Work: • CO2 retrievals for the UK/Scotland region for summer/autumn of 2006 and 2007. • Detailed off-line linear error analysis studies • Output: error quantified space-based CO2 retrievals • Fulfills: Objective 3

  23. Measuring CO2 from Space • Approach: • Collect spectra of CO2 and O2 and absorption in sunlight reflected from the surface and scattered from the atmosphere • Normalize CO2 absorption with O2 absorption to remove effects of varying surface pressure & topography and minimize aerosol/cloud effects • Measurements yields total CO2 column with high sensitivity to CO2 near surface Sensitivity to CO2

  24. The NASA Orbiting Carbon Observatory (OCO) OCO will acquire the space-based data needed to identify CO2 sources and sinks on regional scales over the globe and quantify their variability over the seasonal cycle • Approach: • Collect spectra of CO2 and O2 absorption in reflected sunlight • Use these data to resolve variations in the column averaged CO2 dry air mole fraction,XCO2over the sunlit hemisphere • Validate measurements to ensure XCO2 accuracies of 1 - 2 ppm (0.3 - 0.5%) on regional scales at monthly intervals

  25. Making Precise CO2 Measurements from Space • High resolution spectra of reflected sunlight in near-IR CO2 and O2 bands used to retrieve XCO2 • 1.61 m CO2 band: Column CO2 • 2.06 m CO2 band: Column CO2, clouds/aerosols • 0.76 m O2 A-band: Surface pressure, clouds/aerosols • Self-consistent retrieval, no additional information needed • High spectral resolution enhances sensitivity and minimizes biases Sensitivity to CO2 O2 A-band CO2 2.06 m CO2 1.61m Column CO2 Scattering from Clouds/Aerosols, H2O, Temperature Scattering from Clouds/Aerosols, Surface Pressure, Temperature

  26. Nadir Glint Spot Ground Track Local Nadir On-orbit Measurement Strategy • Optimized to minimize bias and yield high Signal/Noise observations over the globe • Nadir Observations: tracks local nadir • + Small footprint (< 3 km2) isolates cloud-free scenes and reduces biases from spatial inhomogeneities over land •  Low Signal/Noise over dark ocean • Glint Observations: views “glint” spot • + Improves Signal/Noise over oceans • More interference from clouds Glint

  27. OCO Will Provide Dramatically Improved Spatial and Temporal CO2 Sounding 45 • ~200 samples per degree of latitude as OCO moves along its orbit track on day side of the Earth • Uniform sampling of land and ocean • 7 M soundings per 16-day repeat cycle

  28. Effects of Clouds and Aerosols Red points show cloud-free hits along each orbit track Clear-sky frequency vs. spatial resolution computed using the MODIS 1 km cloud product OCO 1-Day Bösch et al., JGR, 2006 OCO 3-Days GLAS Satellite: Clouds reduce number of usable samples, but OCO still collects thousands of samples on regional scales each month. Chevallier et al. 2006

  29. Global Single Sounding XCO2 Retrieval Error • Surface climatology + AOD histogram (CCM3 model over ice) • No systematic errors included here (i.e. perfect Forward Model) Glint Nadir January Glint effect for snow not taken into account July XCO2 Error (ppm) Single Sounding XCO2 Error (ppm)

  30. Number of Cloud-free OCO Soundings MODIS 1 km2 cloud mask accumulated to effective OCO pixel size Glint (SZA < 75o) Nadir (SZA < 85o) January July Number of Soundings per 16 day Repeatcycle and 10x10 Bin

  31. Ensemble XCO2 Retrieval Error per Repeat cycle • Surface climatology + AOD climatology (for AOD < 0.3) + number of cloud-free soundings (Remark: only subset of cloud-free soundings might be retrieved) • Again: No systematic errors included here (i.e. perfect Forward Model) Nadir (SZA < 85o) Glint (SZA < 75o) Glint (SZA < 75o) January July Ensemble XCO2 Error per 16 day Repeatcycle and 10x10 Bin(ppm)

  32. OCO Summary • OCO has the potential to provide accurate retrieval of CO2 columns that will enable computation of CO2 fluxes over regional scales on seasonal time scales • OCO has a narrow swath width of 10 km and will not provide global coverage • Japanese CO2 instrument (GOSAT) will be launched at same time as OCO and combining both datasets can largely increase sampling

  33. Comparison of SCIAMACHY and OCO ENVISAT/SCIAMACHY OCO

  34. SCIAMACY – CO2 SCIAMACHY • How do we measure atmospheric CO2? • WFM-DOAS retrieval technique (Buchwitz et al., JGR, 2000) designed to retrieve the total columns of CH4,CO, CO2, H2O and N2O from spectral measurements in NIR made by SCIAMACHY • Least squares fit of model spectrum + ‘weighting functions’ to observed sun-normalised radiance • We use WFM-DOAS to derive CO2 total columns from absorption at ~1.56 μm • Key difference to Buchwitz’s approach: • No look-up table • Calculate a reference spectrum for every single SCIAMACHY observation i.e. to obtain ‘best’ linearization point – no iterations • See “Measuring atmospheric CO2 using Full Spectral Initiation (FSI) WFM-DOAS” , Barkley et al., ACP, 6, 3517-3534,2006 • Computationally expensive  SCIAMACHY, on ENVISAT, is a passive hyper-spectral grating spectrometer covering in 8 channels the spectral range 240-2040 nm at a resolution of 0.2-1.4 nm Typical pixel size = 60 x 30 km2

  35. CO2 Time Series for N-America from SCIAMACHY Carbon Fusion Oct07

  36. CO2 Time Series for Siberia from SCIAMACHY

  37. Comparison of SCIAMACHY to Aircraft Observations at Surgut Better agreement at 1.5-2.0 km

  38. Comparison of SCIAMACHY to Surface CO2: USA Direct Comparison Difference to Mean Value SCIAMACHY = RedSurface = Blue (±5°lon/lat of site)

  39. SCIAMACHY Summary • Encouraging first results from the FSI algorithm • Good agreement with FT stations at Egbert & Park Falls (bias -2 to -4%) • Good agreement with TM3 model (more uptake in summer) • Good agreement with aircraft data over Siberia • Good agreement with AIRS CO2 (accounting for different vertical sensitivity) • Good agreement with surface network • Good correlation with vegetation spatial distribution • Key point: SCIAMACHY can track changes in near surface CO2 (although SCIAMACHY is a non-ideal instrument for CO2) • Comparison of vegetation indices vs. CO2 indicate SCIAMACHY can track biological signal at regional level (though at limit of sensitivity)

  40. SCIAMACHY Monthly Mean CO2 for UK

  41. SCIAMACHY CO2 for Overpass over UK • SCIAMACHY orbit geometry and clouds largely reduce number of available soundings per overpass: • SCIAMACHY has global coverage every 3-4 days • Large ground pixel size (30x60 km2) of SCIAMACHY result in frequent cloud perturbations • OCO-type retrieval could increase the number of useful soundings (normalization with O2 column) and decrease the sensitivity to small cloud perturbations

  42. WP3: Generation of spatio-temporal patterns of CO2 for different land-use Scenarios

  43. WP3 • Work: • The simulated CO2 fields and the spatio-temporal patterns will be compared to the observed CO2 data from SCIAMACHY taking into account measurement sensitivities and uncertainties. • Output: • an assessment of the capabilities of SCIAMACHY for observing carbon fluxes released from the soil • a simulation of CO2 fields for the UK/Scotland region using future land-use change scenarios and • an assessment of the capabilities of future satellite instruments to observe and monitor carbon fluxes owing to land-use change (Objective 6) • Fulfills: Objective 4, 5, 6

  44. Or simply • The future scenarios will be compared with the abilities of the future satellite missions to assess 1) how large a flux is needed to be quantified, 2) how soon we can expect to detect the predicted changes and thus 3) the potential of satellites to monitor CO2 fluxes for land use and climate change.

  45. Preparatory work on the use of remote-sensing techniques for the detecting and monitoring of GHG emissions from the Scottish land use sector To integrate the ECOSSE soil model together with the JULES (Joint UK Land Environment Simulator) vegetation model into the Lagrangian transport model NAME To model CO2 fields for the UK/Scotland region for several weeks during summer/autumn for the years 2006 and 2007 with and without fluxes from the ECOSSE model To analyse atmospheric CO2 for UK/Scotland region from SCIAMACHY/ENVISAT for the same time period including detailed assessment of the uncertainties To assess the capabilities of SCIAMACHY for observing carbon fluxes released from the soil To simulate CO2 fields for the UK/Scotland region using future land-use change scenarios To assess the capabilities of future satellite instruments (OCO, GOSAT) to observe and monitor carbon fluxes due to land-use change

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