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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.

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Presentation on theme: "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."— Presentation transcript:

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 chang e.

3 Measurements of Carbon Flux

4 Objectives II The specific objective and deliverables are 1.To integrate the ECOSSE soil model together with the Jules vegetation model into the Lagrangian transport model NAME 2.To model CO 2 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 3.To generate atmospheric CO 2 for UK/Scotland region the from SCIAMACHY/ENVISAT for the same time period including detailed assessment of the uncertainties 4.To assess the capabilities of SCIAMACHY for observing carbon fluxes released from the soil 5.To simulate CO 2 fields for the UK/Scotland region using future land-use change scenarios 6.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 CO 2 data WP3: Generation of spatio-temporal patterns of CO 2 for different land-use Scenarios

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

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

8  CO 2

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

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13 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 CO 2 fields: Requires fluxes for each grid box (e.g. as ASCII table)

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

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

16 JULES

17 JULES – MAP – Fisher (Sheffield)

18 Jo Smith Aberdeen

19 ECOSSE

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

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

22 Detailed plan of action a)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. b)LU - Investigate cloud filters. Output = method development. c)LU - Investigate oxygen normalisation. Output = improved CO 2 product for the UK area. d)AU - Use ECOSSE runs (without vegetation coupled through JULES) to produce ascii files of CO 2 across UK – timeframe (Jo – complete changes; get data together); do run for UK – within 1 month?). Output = ascii files to be used by LU. e)LU - Use ECOSSE files together with NAME files to distribute CO 2 across Scotland. Output = evaluation of whether current patterns of soil emissions can be picked up by satellites. f)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 CO 2 from model (TM3?) or other source. ­Distribute CO 2 using NAME. Output = cross evaluation of use of satellite data and of models. g)AU - Run ECOSSE with future scenarios. Run in same way as before. Can HadCM3 be used instead of NAME? (LU investigate). Output = 3D CO 2 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 ). 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 CO 2 fluxes from land use and climate change.

23 Land-use change Land use change implies loss or gain of the carbon stored in the soil and consequently determines emission or absorption of CO 2. 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.

24 WP2: Space-based CO 2 data

25 WP2 Work: – CO 2 retrievals for the UK/Scotland region for summer/autumn of 2006 and – Detailed off-line linear error analysis studies Output: error quantified space-based CO 2 retrievals Fulfills: Objective 3

26 Measuring CO 2 from Space Approach: Collect spectra of CO 2 and O 2 and absorption in sunlight reflected from the surface and scattered from the atmosphere Normalize CO 2 absorption with O 2 absorption to remove effects of varying surface pressure & topography and minimize aerosol/cloud effects Measurements yields total CO 2 column with high sensitivity to CO 2 near surface Sensitivity to CO 2

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

28 Making Precise CO 2 Measurements from Space Scattering from Clouds/Aerosols, H 2 O, Temperature O 2 A-band CO  m CO  m High resolution spectra of reflected sunlight in near-IR CO 2 and O 2 bands used to retrieve X CO2 –1.61  m CO 2 band: Column CO 2 –2.06  m CO 2 band: Column CO 2, clouds/aerosols –0.76  m O 2 A-band: Surface pressure, clouds/aerosols  Self-consistent retrieval, no additional information needed High spectral resolution enhances sensitivity and minimizes biases Scattering from Clouds/Aerosols, Surface Pressure, Temperature Column CO 2 Sensitivity to CO 2

29 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 km 2 ) 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 Nadir Local Nadir Glint Spot Ground Track Glint

30 4  5  OCO Will Provide Dramatically Improved Spatial and Temporal CO 2 Sounding ~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

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

32 January July Global Single Sounding X CO2 Retrieval Error Surface climatology + AOD histogram (CCM3 model over ice) No systematic errors included here (i.e. perfect Forward Model) X CO2 Error (ppm) Single Sounding X CO2 Error (ppm) Nadir Glint Glint effect for snow not taken into account

33 January July Number of Cloud-free OCO Soundings MODIS 1 km 2 cloud mask accumulated to effective OCO pixel size Nadir (SZA < 85 o ) Glint (SZA < 75 o ) Number of Soundings per 16 day Repeatcycle and 1 0 x1 0 Bin

34 Ensemble X CO2 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) January July Nadir (SZA < 85 o ) Glint (SZA < 75 o ) Ensemble X CO2 Error per 16 day Repeatcycle and 1 0 x1 0 Bin (ppm) Glint (SZA < 75 o )

35 OCO Summary OCO has the potential to provide accurate retrieval of CO 2 columns that will enable computation of CO 2 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

36 OCOSCIAMACHY/ENVISAT LaunchScheduled for 12/2008March 2002 ObjectiveCO 2 solelyMany atmospheric trace gases ModesNadir, glint, targetNadir (limb, occultation) Range3 narrow NIR bands8 Channels from UV to NIR ResolutionHigh: 0.05 nm – 0.1 nmLow/medium: 0.2 – 1.5 nm Ground Pixel3 km 2 60 x 30 km 2 RetrievalOptimal EstimationFSI-WFM (linear least-square fit) ENVISAT/SCIAMACHY OCO Comparison of SCIAMACHY and OCO

37 SCIAMACY – CO 2 How do we measure atmospheric CO 2 ? – WFM-DOAS retrieval technique (Buchwitz et al., JGR, 2000) designed to retrieve the total columns of CH 4,CO, CO2, H 2 O and N 2 O 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 CO 2 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 CO 2 using Full Spectral Initiation (FSI) WFM-DOAS”, Barkley et al., ACP, 6, ,2006 – Computationally expensive  SCIAMACHY, on ENVISAT, is a passive hyper-spectral grating spectrometer covering in 8 channels the spectral range nm at a resolution of nm Typical pixel size = 60 x 30 km 2 SCIAMACHY

38 Carbon Fusion Oct07 CO 2 Time Series for N-America from SCIAMACHY

39 CO 2 Time Series for Siberia from SCIAMACHY

40 Comparison of SCIAMACHY to Aircraft Observations at Surgut Better agreement at km

41 Comparison of SCIAMACHY to Surface CO 2 : USA (±5°lon/lat of site) SCIAMACHY = Red Surface = Blue Difference to Mean ValueDirect Comparison

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44 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 CO 2 (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 CO 2 (although SCIAMACHY is a non-ideal instrument for CO 2 ) Comparison of vegetation indices vs. CO 2 indicate SCIAMACHY can track biological signal at regional level (though at limit of sensitivity)

45 SCIAMACHY Monthly Mean CO 2 for UK

46 SCIAMACHY CO 2 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 km 2 ) of SCIAMACHY result in frequent cloud perturbations OCO-type retrieval could increase the number of useful soundings (normalization with O 2 column) and decrease the sensitivity to small cloud perturbations

47 WP3: Generation of spatio- temporal patterns of CO 2 for different land-use Scenarios

48 WP3 Work: – The simulated CO 2 fields and the spatio-temporal patterns will be compared to the observed CO 2 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 CO 2 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

49 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 CO 2 fluxes for land use and climate change.

50 1.To integrate the ECOSSE soil model together with the JULES (Joint UK Land Environment Simulator) vegetation model into the Lagrangian transport model NAME 2.To model CO 2 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 3.To analyse atmospheric CO 2 for UK/Scotland region from SCIAMACHY/ENVISAT for the same time period including detailed assessment of the uncertainties 4.To assess the capabilities of SCIAMACHY for observing carbon fluxes released from the soil 5.To simulate CO 2 fields for the UK/Scotland region using future land-use change scenarios 6.To assess the capabilities of future satellite instruments (OCO, GOSAT) to observe and monitor carbon fluxes due to land-use change Preparatory work on the use of remote-sensing techniques for the detecting and monitoring of GHG emissions from the Scottish land use sector


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