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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
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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
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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)
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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
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Transcom, Paris 13 June 2005 Channel selection
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Transcom, Paris 13 June 2005 Error estimates
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Transcom, Paris 13 June 2005 Assimilation Error
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Transcom, Paris 13 June 2005 Results
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Transcom, Paris 13 June 2005 Comparison with JAL Flight data kindly provided by H. Matsueda, MRI/JMA
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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
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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
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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
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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
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Transcom, Paris 13 June 2005 Comparison with LMDz ECMWF estimates LSCE CO 2 simulation
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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.
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Transcom, Paris 13 June 2005 Radon simulation 12 hour Forecast Analysis Radon Analysis Radon 12 hour Forecast
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Transcom, Paris 13 June 2005 Radon experiments
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Transcom, Paris 13 June 2005 Radon experiments
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Transcom, Paris 13 June 2005 Radon experiments
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Transcom, Paris 13 June 2005 Radon experiments
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Transcom, Paris 13 June 2005 Radon experiments
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Transcom, Paris 13 June 2005 Radon experiments
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