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Data assimilation of atmospheric CO 2 at ECMWF in the context of the GEMS project

Data assimilation of atmospheric CO 2 at ECMWF in the context of the GEMS project. Richard Engelen ECMWF. Thanks to Soumia Serrar and Fr é d é ric Chevallier. Global and regional Earth-System (atmosphere) Monitoring using Satellite and in-situ data (GEMS).

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Data assimilation of atmospheric CO 2 at ECMWF in the context of the GEMS project

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  1. Data assimilation of atmospheric CO2 at ECMWF in the context of the GEMS project Richard Engelen ECMWF Thanks to Soumia Serrar and Frédéric Chevallier

  2. Global and regional Earth-System (atmosphere) Monitoring using Satellite and in-situ data (GEMS) T. Hollingsworth1, O. Boucher2, H. Eskes3, C. Granier4, V.H. Peuch5, P. Rayner6, M. Schultz7, A. Simmons11 ECMWF; 2 UK Met Office; 3 KNMI, NL; 4 Service d'Aéronomie CNRS, 5 Meteo-France; 6 Laboratoire des Sciences du Climat et de l‘Environnement; 7 Forschungszentrum Jülich, Germany

  3. Prime Objectives • Creation of an pre-operational global monitoring system for greenhouse gases, reactive gases, and aerosols in the troposphere and in the stratosphere by early 2009. • This system will be capable of producing near-real-time (depending on data availability) and retrospective global analyses for monitoring atmospheric composition, and short-range forecasts to drive regional air-quality models. • It will provideinformation relevant to the Kyoto and Montreal protocols, and to the UN Convention on Long-Range Trans-boundary Air Pollution.

  4. GEMS themes GHG AER GRG RAQ Greenhouse Gases (GHG) Global Reactive Gases (GRG) Aerosols (AER) Regional Air Quality (RAQ) Production (PRO) & Validation (VAL)

  5. Greenhouse Gases (GHG) GHG • Theme coordinator: Peter Rayner, LSCE, France • Development of an operational system to use satellite and in-situ data to monitor the concentrations of greenhouse gases, and their associated surface sources and sinks, and to attribute these sources and sinks to controlling processes. [g-C/m2/day] Carbon fluxes from the terrestrial biosphere on August 10, 2003 (heat wave in Europe and drought in the USA) as simulated by the model ORCHIDEE

  6. 2-step process GOSAT IASI AIRS OCO 4D-Var atmospheric data assimilation (12 hour assimilation window) Flasks Prior fluxes Tall towers 4D-Var flux inversion (1 year inversion window) Updated fluxes

  7. Terminology • Free running CO2 model: CO2 is transported using constrained meteorology from operations every 12 hours. • Reanalysis/assimilation: CO2 is constrained by AIRS, while meteorology is constrained by all other available operational observations at the same time. Fluxes used in the model • CASA diurnal natural biosphere • Takahashi ocean • Andres anthropogenic • GFED2 fire emissions

  8. CO2 reanalysis with AIRS Monthly mean CO2 column-mean mixing ratios, after 8 months of assimilating AIRS radiances, show small but significant changes to a simulation with free-running CO2. After validation these differences will be used to estimate modifications in the prior surface flux fields. Analysed CO2

  9. The AIRS radiances are also able to adjust the shape and amplitude of a CO2 plume resulting from extensive biomass burning over Africa. The GEMS monitoring system will have the capability to follow these plumes in near-real time in terms of CO2, CO, and aerosol concentrations, constrained by various satellite observations.

  10. Early Validation Comparisons with flight data over Hawaii (courtesy of Pieter Tans, NOAA/ESRL, shows a clear improvent of the analysis over the free-running model. The top plot shows a best-case individual comparison (11 May 2003). The bottom plot shows an average over all available flight profiles between 1 January and 30 October 2003.

  11. Molokai, Island, Hawaii Blue: free-running model Red: reanalysis Black: observations

  12. Molokai, Island, Hawaii Blue: free-running model Red: reanalysis

  13. Surgut, Siberiacourtesy of Dr Machida, NIES

  14. Surgut, Siberia

  15. Molokai, Island, Hawaii

  16. Individual Profiles 11 May 2003 10 ppmv 9 October 2003

  17. Issues: • Missing sink is not modelled and therefore cannot take out excess CO2 from the atmosphere • The background constraint is too strong • The AIRS bias correction might not work properly • AIRS might not provide a strong enough constraint to adjust the concentrations There is a summer trend in atmospheric CO2 that is not corrected by the AIRS observations at the moment. • Possible solutions: • Increase background errors to allow bigger adjustments from the AIRS observations • Apply a model bias correction to keep the problem as un-biased as possible. This has to be accounted for in subsequent flux inversions. • Use optimized fluxes instead of CASA

  18. Conclusions • GEMS is well on its way to build an atmospheric composition monitoring system • First 1-year CO2 reanalysis has been run using AIRS observations • Potential problems are being identified and where possible corrected before a new run will be started • Results will be provided to inverse flux model at LSCE

  19. Molokai, Island, Hawaii Blue: without fire emissions Red: with fire emissions

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