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P.S. Monks H. Boesch G. Cappelluti

Preparatory work on the use of remote sensing techniques for the detection and monitoring of GHG emissions from the Scottish land use sector. P.S. Monks H. Boesch G. Cappelluti. Edinburgh, 01/09/2008.

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P.S. Monks H. Boesch G. Cappelluti

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  1. Preparatory work on the use of remote sensing techniques for the detection and monitoring of GHG emissions from the Scottish land use sector P.S. Monks H. Boesch G. Cappelluti Edinburgh, 01/09/2008

  2. 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. Main objective

  3. Specific objectives • To combine ECOSSE soil model, JULES vegetation model and other CO2 flux datasets with the Lagrangian transport model NAME to generate CO2 distributions over UK/Scotland. • To model CO2 fields over the UK/Scotland region for several weeks during summer/autumn for the years 2006 and 2007. • To retrieve atmospheric CO2 for the 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.

  4. Workpackages • WP1: Integration of soil 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

  5. WP1 - Integration of soil and vegetation models into atmospheric transport model

  6. WP1 (soil + vegetation + transport) • Output: Model-generated CO2 distributions over Scotland. • Fulfilments: Specific objectives 1 and 2. NAME transport model + Met Office meteorological data + CO2 flux datasets = CO2 distribution over Scotland

  7. NAME dispersion model • A number of particles is released from a start point. • The individual particles are followed for a set time period or until they leave a target region • The new location at each time step is based on the wind fields. • Can be run forward or backward with time. • Can be coupled with inversion scheme for surface fluxes.

  8. NAME setup • Backward runs from Scotland/UK to retrieve the residence time per grid box. • Spatial resolution: currently 1º × 1º grid for several altitudes. • Temporal resolution: currently 15 min time steps. • Computational requirements: 0.5 hours per case. • Data requirements: Met Office meteorological data.

  9. Residence times

  10. Three hourly residence times

  11. 17% 33% 50% NAME new version Meeting with Met Office in Leicester on 30 July 2008: • Hourly residence times → to extend the footprint sequence of CO2 exchange. • Trajectory origins and their statistical importance → to work out a weighted average of the CO2 concentration over the grid.

  12. Flux models Model predictions of atmospheric CO2 mole fractions Atmospheric CO2 mole fractions of air samples from surface, towers, aircraft Set of sources and sinks that minimises the difference ECOSSE Predicts the impacts of changes in land use and climate change on greenhouse gas emissions from organic soils. CO2 flux from organic soils in different land use scenarios. Carbon Tracker Modules representing atmospheric transport, air-sea exchange, photosynthesis and respiration by the terrestrial biosphere, CO2 release to the atmosphere by fires and combustion of fossil fuels. JULES Represents the land surface in meteorological and climate models. CO2 flux from any mixture of 9 surface types: broadleaf trees, needleleaf trees, temperate grass, tropical grass, shrubs, urban, inland water, bare soil and ice.

  13. Flux datasets • Carbon Tracker: biosphere, sea, fossil fuel, fire and overall • JULES: vegetation (Joerg Kaduk, Geography, Leicester) • ECOSSE: soil (Jo Smith, Aberdeen) • NAEI: anthropogenic emissions JULES Gross Primary Production (GPP): rate at which an ecosystem produces, captures and stores chemical energy as biomass. Average over 5 years: 1999-2003. Units: kgC/m2/s.

  14. Global flux distribution

  15. Flux distribution over transport domain

  16. CO2 distribution + Flux distribution over transport domain Residence times from transport model ΔCO2over target area Trajectory origins & statistical importance from transport model Concentration distribution over transport domain + initial CO2over target area

  17. CO2 moles transported into the release area • Residence times every 3h. • Each time slot: residence time × flux = CO2 moles ending up into the release domain. • Currently only 5 time slots: 0-3h, 3-6h, 6-9h, 9-12h, 12h-10d. • Last time slot: average overall flux in 12h-10d.

  18. ΔCO2 over target area CO2 mole fractions ending up into the release domain. These concentrations describe the overall CO2 collected by the air from the Earth's surface through its way to the release areas.

  19. CO2 over target area absolute CO2 concentration of the release area + exchanged CO2 concentration = data comparable with those from satellite

  20. Work plan of WP1 • Integration of NAME new version into Leicester algorithm. • ECOSSE CO2 fluxes about current land use scenarios (2003-2005, 3 hourly, 1 km × 1 km, 30W-20E / 25N-85N). Data ready at the end of September 2008. • JULES CO2 fluxes about current land use scenarios (1999-2006, 3 hourly, 30W-20E / 25N-85N). Data ready at the end of September 2008.Meeting with Joerg Kaduk at the end of September 2008. • Integration of JULES into ECOSSE by the end of the Project? • NAEI CO2 fluxes. • Model validation through air samples taken across Scotland, possibly at different altitudes. • Possibility of using HadCM3 instead of NAME. • Possibility of analysing methane too, especially from paddy fields. • Also N2O may be considered, but not enough variability can be seen. • The role of permafrost might be considered.

  21. WP2 - Space-based CO2 data

  22. WP2 (Space-based CO2 data) • Output: Retrieval of atmospheric CO2 distributions for the UK/Scotland region from SCIAMACHY/ENVISAT with assessment of the uncertainties and SCIAMACHY capabilities for observing carbon fluxes released from the soil. • Fulfilments: Specific objectives 3 and 4.

  23. Work plan of WP2 • CO2 distributions from SCIAMACHY will be compared with patterns and magnitude of the CO2 distribution retrieved by the algorithm. • Evaluation of whether current patterns of soil emissions can be picked up by satellites. • NDVI maps from NOAA AVHRR can be used to evaluate patterns and magnitude of the CO2 fluxes provided by ECOSSE and JULES. • Possibility of modifying SCIAMACHY cloud filtering algorithm by making its cloud threshold more flexible, in order to increase the number of soundings over the region of interest. • Possibility of modifying SCIAMACHY CO2 retrieval algorithm by integrating the O2 normalisation, which is used by OCO, in order to better assess the potentialities of OCO.

  24. Space-based data

  25. WP3 - Generation of spatio-temporal patterns of CO2 for different land-use scenarios

  26. WP3 (future land-use scenarios) • Output: Simulations of CO2 fields for future land-use change scenarios of the UK/Scotland region and assessment of the OCO/GOSAT capabilities to observe and monitor carbon fluxes due to land-use change. • Fulfilments: Specific objectives 5 and 6.

  27. Work plan of WP3 • ECOSSE CO2 fluxes about future land use scenarios. Initial land use change scenarios: • moorland to forestry, • forestry to moorland. Ultimate scenarios: IPCC A1, A2, B1, B2. Data ready at the end of September 2008. • These scenarios will be compared with the abilities of the future satellite missions to assess • how large is a flux needed to be quantified? • how soon can we expect to detect the predicted changes? • the potential of satellites to monitor CO2 fluxes for land use and climate change.

  28. Project work plan Up-to-date GANTT chart Initial GANTT chart

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