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Global satellite observations of greenhouse gas emissions

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Global satellite observations of greenhouse gas emissions. EC 5th ... MPI-BGC (DE), BIRA-IASB (BE), UPMC-SA (FR), RWE-Rheinbraun (DE), Univ. Li ge (BE) ... – PowerPoint PPT presentation

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Title: Global satellite observations of greenhouse gas emissions


1
Global satellite observations of greenhouse gas
emissions
EVERGREEN EnVisat for Environmental
Regulationof GREENhouse gasesInternational
Workshop 19-20 January 2006
  • EC 5th Framework Programme
  • EVG1-CT2002-00079
  • Coordinated by Albert P H Goede, KNMI
  • Website http//www.knmi.nl/evergreen

2
EVERGREEN Objectives
  • Retrieve emissions (sources, sinks) of CH4, CO,
    and possibly CO2 by inverse modelling of
    ENVISAT/SCIAMACHY measurements and from EOS
    Terra/MOPITT measurements
  • Radiation budget/forcing calculations employing
    ENVISAT MIPAS and MSG/GERB data
  • End user participation
  • EU policy Contribution to Kyoto Protocol and Rio
    Convention

3
EVERGREEN Partners
  • KNMI (NL, coordinator),
  • Univ. Bremen (DE),
  • Univ. Leicester (GB),
  • Univ. Heidelberg (DE),
  • NILU (NO),
  • SRON (NL),
  • MPI-BGC (DE),
  • BIRA-IASB (BE),
  • UPMC-SA (FR),
  • RWE-Rheinbraun (DE),
  • Univ. Liège (BE),
  • EC-JRC-IES (IT)

4
EVERGREEN History
  • Proposal submitted October 2001
  • Score 80 points out of 100
  • Excellent team, important though challenging
    objective, synergistic use of satellite
    instruments, links to industry
  • Did not like radiative budget/forcing part
  • Rescheduling of retrieval and validation task
  • Overall budget cut of 25
  • Project Start 1 February 2003
  • Project End 31 January 2006

5
EVERGREEN Tasks/Leaders
  • Retrieval and validation CH4, CO, CO2,
  • plus O2 and clouds.
  • M Buchwitz, Uni Bremen
  • Radiation budget modelling use of measured CH4
    distributions in radiative budget and radiative
    forcing calculations.
  • P Monks, Uni Leicester
  • (Inverse) modelling CH4, CO, CO2 emissions
    derived from concentration measurements.
  • J-F Meirink, KNMI

6
EVERGREEN Task 1 retrieval and validation
  • SCIAMACHY nadir/limb
  • ESA ENVISAT (March 2002)
  • UV-vis-NIR 2-stage
  • spectrometer measuring
  • absorbed and scattered sun
  • light (230 nm 2380 nm)
  • from the Earth atmosphere
  • (day only)
  • GERB/EUMETSAT MSG-1 (Aug 2002)
  • Measurement Total
  • Radiation Emitted from Top
  • of Atmosphere
  • MIPAS limb ESA ENVISAT
  • Infrared Fourier Transform spectrometer
    measuring the thermal emission (4.1 µm-14.6 µm)
    from the Earth atmosphere (day and night)
  • MOPITT nadir NASA EOS Terra (Dec 1999)
  • Gas correlation radiometer
  • measuring solar 2.4µm and thermal
  • 4.7 µm radiation

7
Status SATELITE data
  • ENVISAT Instrument Status
  • SCIAMACHY functions well (98 operational) Main
    source of data
  • Channels 7 (CO2) and 8 (CO, CH4) suffer from Ice
    formation
  • Reduced performance.
  • MIPAS operates in campaign mode since January
    2005 (3 days on, 4 days off). Reduced spectral
    resolution. Only test data for Jun 04-Dec 04.
  • ENVISAT on Ground Data System
  • ESA operational data processor and data
    distribution system does not function properly.
  • EVERGREEN relies on scientific data products from
    partners.
  • MOPITT Only 4.7 µm CO channel operational (out of
    8 channels)
  • GERB operational but problems with geo-location
    of observations

8
SCIAMACHY Operations Statistics
9
SCIAMACHY very reliable operation (98 duty
cycle)Could go on until 2010-2012 depending on
ENVISAT operations (primarily funding)
10
SCIAMACHY spectral range
CO2
CO
CH4
11
Ice layer on SCIAMACHY IR channels
12
SCIAMACHY IR channels 6 has no ice layer
13
Retrieval methane by Double DOAS
  • Along its path through the atmosphere a solar
    photon
  • is scattered by aerosol and cloud and reflected
    back by Earth surface into the atmosphere, to be
    eventually collected by the satellite instrument.
  • DOAS (Differential Optical Absorption
    Spectroscopy) relies on absorption of solar
    photon by target molecule. This is compared with
    adjacent spectral photon that is not absorbed by
    target molecule
  • Advantage relative measurement retrieving unknown
    atmosphere. However, relation to total columns
    needs to be established (air mass factor)
  • Double DOAS Total column amount retrieved by
    rationing to well known gas concentration
    distribution, i.e. Oxygen, taking into account
    scattering aerosol and cloud.
  • Improved CH4 rationed by CO2, both Channel 6.

14
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15
EVERGREEN Task 2 radiation modelling
  • Radiation budget at Top of Atmosphere. Impact of
    methane measurements assessed by model
    calculations
  • Radiative forcing Importance CH4 vs. CO2, N2O,
    CFC, based on measured CH4.
  • MIPAS CH4 stratospheric profile
  • SCIA CH4 total column variability
  • GERB/MSG total radiation fluxes

16
EVERGREEN Task 3 inverse modelling
  • Model Inter-comparison
  • assess and understand differences in
    chemistry-transport models, i.e. vertical
    transport boundary layer
  • Models
  • TM3/TM5 (Methane and Carbon dioxide)
  • IMAGES (Carbon monoxide)
  • Tests
  • Boundary-layer and convective transport ? 222Rn
  • General circulation ? SF6
  • Chemistry ? OH
  • CH4 forward runs

17
EVERGREEN Task 3 inverse modelling
  • Inverse modelling
  • derive emissions from measured atmospheric
    concentrations CH4 and CO
  • Starting point
  • bottom-up emission inventories ? contain large
    uncertainties on regional scale
  • partly clouded pixels included to improve
    inversion
  • Tools
  • Synthesis inversion
  • 4D-var data assimilation

18
EVERGREEN Workshop Agenda
  • Day 1 CO and CO2 measurement and model
  • Future satellites
  • Forum discussion Future prospects
  • Day 2 CH4 measurement and model
  • Radiation modelling
  • Related EC projects

19
EVERGREEN Summary
  • Retrieval validation SCIAMACHY total column
    CH4, CO and CO2.
  • Column ratios O2/CO2.
  • Retrieval validation MIPAS stratospheric CH4.
  • Radiation modelling compared with measured CH4
    data
  • Inverse modelling CH4 and CO emissions
  • Improvement CH4 emission inventory
  • Input to GHG Policy
  • Input to Energy generating Industry
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