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Estimating Atmospheric CO 2 using AIRS Observations in the ECMWF Data Assimilation System

Estimating Atmospheric CO 2 using AIRS Observations in the ECMWF Data Assimilation System. Richard Engelen European Centre for Medium-Range Weather Forecasts. Thanks to Yogesh Tiwari and Fr é d é ric Chevallier for model comparison plots. Outline. Why estimate CO 2 at a NWP centre?

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Estimating Atmospheric CO 2 using AIRS Observations in the ECMWF Data Assimilation System

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  1. Estimating Atmospheric CO2 using AIRS Observations in the ECMWF Data Assimilation System Richard Engelen European Centre for Medium-Range Weather Forecasts Thanks to Yogesh Tiwari and Frédéric Chevallier for model comparison plots

  2. Outline Why estimate CO2 at a NWP centre? Current setup of CO2 data assimilation system Error estimation Monthly mean results Comparisons with independent observations Comparisons with CO2 models Outlook Radon experiments

  3. Why at a NWP centre? Advantages: • Strong constraint on temperature and water vapour from all sorts of conventional and satellite observations, which allows focus on extraction of CO2 information from AIRS • Experience with handling, processing, and assimilation of large amounts of data • Good observation monitoring capability Disadvantage: • Time scale conflicts between medium-range weather forecast and environment monitoring (e.g., bias correction, tracer transport modelling)

  4. Description of current CO2 assimilation system CO2 is currently treated as a so-called ‘column’ variable within the 4D-Var data assimilation system. This means that CO2 is not a model variable and is therefore not moved around by the model transport. For each AIRS observation location a CO2 variable is added to the control (minimisation) vector. The CO2 estimates therefore make full use of the 4D-Var fields of temperature, specific humidity and ozone. The CO2 variable itself is limited to a column-averaged tropospheric mixing ratio with fixed profile shape, but a variable tropopause. A background of 376 ppmv is used with a background error of 30 ppmv. 18 channels in the long-wave CO2 band are used

  5. Channel selection

  6. Error estimates

  7. Assimilation Error

  8. Results

  9. Comparison with JAL Flight data kindly provided by H. Matsueda, MRI/JMA

  10. Comparison with JAL St.dev. = 1.3 ppmv and RMS = 1.4 ppmv for 5-day mean on a 6˚ x 6˚ grid box St.dev. = 1.5 ppmv and RMS = 1.7 ppmv for 5-day mean on a 6˚ x 6˚ grid box St.dev. = 1.0 ppmv and RMS = 1.1 ppmv for 5-day mean on three 6˚ x 6˚ grid boxes Flight data kindly provided by H. Matsueda, MRI/JMA

  11. Comparison with CMDL Molokai Island, Hawaii Dots: CMDL flight observation; Black line: ECMWF estimate Dotted line: Background value Flight data kindly provided by Pieter Tans, NOAA/CMDL

  12. St.dev.=0.7; RMS=1.1 St.dev.=1.6; RMS=1.6 St.dev.=0.6; RMS=0.6 St.dev.=1.0; RMS=1.6 Comparison with CMDL Scatter diagrams between mean flight profile concentrations and analysis estimates for various stations show good results. Flight data kindly provided by Pieter Tans, NOAA/CMDL

  13. LMDz TM3 Jan - Feb Mar - Apr May - Jun Jul - Aug Sep - Oct Nov - Dec AIRS compared with models for 2003 2 ppmv Solid = AIRS Dashed = Model

  14. Comparison with LMDz LSCE CO2 simulation ECMWF estimates

  15. Outlook • Experimental work on CO2 data assimilation will evolve into a full greenhouse gas data assimilation system within GEMS project • Other satellite observations will be assimilated: • IASI • CrIS • OCO • GOSAT Main issue will be the definition of our background error covariance matrix. This represents the error in the model transport and the prescribed fluxes.

  16. Radon simulation Radon Radon 12 hour Forecast 12 hour Forecast Analysis Analysis

  17. Radon experiments

  18. Radon experiments

  19. Radon experiments

  20. Radon experiments

  21. Radon experiments

  22. Radon experiments

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