• DocumentCode
    2144494
  • Title

    Assimilation of remote sensing data to monitor the terrestrial carbon cycle: The carbon observatory of geoland

  • Author

    Calvet, J.C. ; Viterbo, P. ; Ciais, P. ; Van den Hurk, B. ; Moors, E. ; Kaptein, A. ; Leroy, M. ; Sabater, J. Munoz

  • Author_Institution
    Centre Nat. de Recherches Meteorol., Toulouse
  • Volume
    7
  • fYear
    2004
  • fDate
    20-24 Sept. 2004
  • Firstpage
    4647
  • Abstract
    The existing land data assimilation projects (NLDAS, GLDAS, ELDAS) do not include interactive vegetation land surface models, which limits the use of remote sensing data. The analysed variable in LDAS is soil moisture, only, and there is a need to account for vegetation biomass to monitor the biosphere vegetation-atmosphere CO2 exchange. The geoland Integrated Project (2004-2007) co-funded by the European Commission, aims at addressing European and global environment issues, based on the use of remote sensing data. The carbon observatory of geoland, hereinafter referred to as "geoland/Carbon", provides a pre-operational global carbon accounting system, dealing with the impact of weather and climate variability on ecosystems fluxes and carbon stocks, on daily to seasonal and inter-annual time scales. The solution chosen in geoland/Carbon is to merge the LDAS approach and the interactive vegetation models, by making two communities work together: the meteorologists involved in ELDAS and the carbon modellers. In particular, we investigate the relationship between weather and climate variability and terrestrial CO2 fluxes. The integration of in situ meteorological measurements and different satellite remote sensing sources of information are made by using assimilation techniques. In order to integrate the existing approaches and to deliver an assessment based on independent modelling results, two land surface models are used: 1) an operational scheme (ECMWF) used in numerical weather forecast models, modified to describe an interactive vegetation (based on ISBA-A-gs, Meteo-France); 2) a carbon-water-energy land surface scheme, fitted with carbon dynamics in biomass and soil pools, and with ecosystem dynamics (LSCE). The assimilation system can be run at the global scale with both carbon models. The assimilated output fields are checked against global observations of different nature, such as eddy covariance networks, long term ecological time serie- s (e.g. IGBP transects), forest and soil carbon inventories, or satellite products that were not used at first in the assimilation procedure. At the end of this project, ECMWF is able to propose a single near-operational system based on components of the two approaches
  • Keywords
    atmospheric composition; atmospheric techniques; carbon; carbon compounds; climatology; data assimilation; ecology; environmental factors; forestry; geophysics computing; moisture; soil; terrain mapping; vegetation mapping; weather forecasting; AD 2004 to 2007; C; CO2; ECMWF; ELDAS; European Center for Medium Range Weather Forecast; European Commission; European environment issues; GLDAS; Global Land Data Assimilation System; IGBP transects; ISBA-A-gs; LSCE; Meteo-France; NLDAS; North America Land Data Assimilation System; biosphere vegetation-atmosphere CO2 exchange; carbon dynamics; carbon stocks; carbon-water-energy land surface scheme; climate variability; ecosystem dynamics; eddy covariance networks; forest carbon inventory; geoland Integrated Project; geoland carbon observatory; geoland/Carbon; global environment issues; global observations; in situ meteorological measurements; inter annual time scale; interactive vegetation land surface models; land data assimilation projects; long term ecological time series; numerical weather forecast models; preoperational global carbon accounting system; remote sensing data; satellite products; satellite remote sensing sources; seasonal time scale; single near operational system; soil carbon inventory; soil moisture; soil pools; terrestrial carbon cycle; vegetation biomass; Biomass; Ecosystems; Land surface; Linear discriminant analysis; Meteorology; Observatories; Predictive models; Remote monitoring; Satellites; Vegetation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 IEEE International
  • Conference_Location
    Anchorage, AK
  • Print_ISBN
    0-7803-8742-2
  • Type

    conf

  • DOI
    10.1109/IGARSS.2004.1370193
  • Filename
    1370193