1. Transcom, Paris 13 June 2005 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 Frédéric Chevallier for model comparison plots

2. Transcom, Paris 13 June 2005 Outline Why estimate CO 2 at a NWP centre? Current setup of CO 2 data assimilation system Error estimation Monthly mean results Comparisons with independent observations Comparisons with CO 2 models Outlook Radon experiments

3. Transcom, Paris 13 June 2005 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 CO 2 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. Transcom, Paris 13 June 2005 Description of current CO 2 assimilation system CO 2 is currently treated as a so-called ‘column’ variable within the 4D-Var data assimilation system. This means that CO 2 is not a model variable and is therefore not moved around by the model transport. For each AIRS observation location a CO 2 variable is added to the control (minimisation) vector. The CO 2 estimates therefore make full use of the 4D-Var fields of temperature, specific humidity and ozone. The CO 2 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 CO 2 band are used

5. Transcom, Paris 13 June 2005 Channel selection

6. Transcom, Paris 13 June 2005 Error estimates

7. Transcom, Paris 13 June 2005 Assimilation Error

8. Transcom, Paris 13 June 2005 Results

9. Transcom, Paris 13 June 2005 Comparison with JAL Flight data kindly provided by H. Matsueda, MRI/JMA

10. Transcom, Paris 13 June 2005 Comparison with JAL Flight data kindly provided by H. Matsueda, MRI/JMA 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

11. Transcom, Paris 13 June 2005 Comparison with CMDL Flight data kindly provided by Pieter Tans, NOAA/CMDL Molokai Island, Hawaii Dots: CMDL flight observation; Black line: ECMWF estimate Dotted line: Background value

12. Transcom, Paris 13 June 2005 Comparison with CMDL Flight data kindly provided by Pieter Tans, NOAA/CMDL Scatter diagrams between mean flight profile concentrations and analysis estimates for various stations show good results. St.dev.=1.6; RMS=1.6 St.dev.=0.7; RMS=1.1 St.dev.=1.0; RMS=1.6 St.dev.=0.6; RMS=0.6

13. Transcom, Paris 13 June 2005 TM3 LMDz Jan - Feb Mar - Apr May - Jun Jul - Aug Sep - Oct Nov - Dec Solid = AIRS Dashed = Model 2 ppmv AIRS compared with models for 2003

14. Transcom, Paris 13 June 2005 Comparison with LMDz ECMWF estimates LSCE CO 2 simulation

15. Transcom, Paris 13 June 2005 Outlook Experimental work on CO 2 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. Transcom, Paris 13 June 2005 Radon simulation 12 hour Forecast Analysis Radon Analysis Radon 12 hour Forecast

17. Transcom, Paris 13 June 2005 Radon experiments

18. Transcom, Paris 13 June 2005 Radon experiments

19. Transcom, Paris 13 June 2005 Radon experiments

20. Transcom, Paris 13 June 2005 Radon experiments

21. Transcom, Paris 13 June 2005 Radon experiments

22. Transcom, Paris 13 June 2005 Radon experiments