icdc7, boulder 25 - 30 september 2005 estimation of atmospheric co 2 from airs infrared satellite...
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ICDC7, Boulder 25 - 30 September 2005
Estimation of atmospheric CO2 from AIRS infrared satellite radiances in
the ECMWF data assimilation system
Richard Engelen European Centre for Medium-Range Weather Forecasts
ICDC7, Boulder 25 - 30 September 2005
Why do we need satellite data?• Red ‘x’ indicates mean flux across 15 models
• Blue circles indicate mean a posteriori uncertainty (‘within’ model error)
• Red error bars indicate model spread (‘between’ model error)
• ‘Within’ model uncertainty larger than ‘between’ model uncertainty for most regions
From Gurney et al. (2002)
• Current inversion system is data limited!
ICDC7, Boulder 25 - 30 September 2005
What observations do we have?
Radiance = F( T, CO2, H2O, O3, CO, CH4, N2O )
Radiance = F( T, CO2, H2O, O3, CO, CH4, N2O )
Atmospheric Infrared Sounder (AIRS)
ICDC7, Boulder 25 - 30 September 2005
Why do we do this at a weather forecast centre
Data Assimilation
Various sources of atmospheric observations are used to estimate atmospheric state in consistent way.
Attribution of random and systematic errors is complicated.
Spatial and temporal interpolation of information is done with atmospheric transport model.
Data Assimilation
Various sources of atmospheric observations are used to estimate atmospheric state in consistent way.
Attribution of random and systematic errors is complicated.
Spatial and temporal interpolation of information is done with atmospheric transport model.
Stand-alone Retrieval
Only observations from single satellite platform are used to estimate atmospheric state.
Attribution of random and systematic error less complicated.
Individual retrievals need to be gridded and averaged to produce 3-dimensional fields.
Stand-alone Retrieval
Only observations from single satellite platform are used to estimate atmospheric state.
Attribution of random and systematic error less complicated.
Individual retrievals need to be gridded and averaged to produce 3-dimensional fields.
ICDC7, Boulder 25 - 30 September 2005
4D-Var Data Assimilation
4-dimensional variational data assimilation is in principle a least-squares fit in 4 dimensions between the predicted state of the atmosphere and the observations.
The adjustment to the predicted state is made at time To, which ensures that the analysis state (4-dimensional) is a model trajectory.
ICDC7, Boulder 25 - 30 September 2005
4D-Var Data Assimilation
4-dimensional variational data assimilation is in principle a least-squares fit in 4 dimensions between the predicted state of the atmosphere and the observations.
The adjustment to the predicted state is made at time To, which ensures that the analysis state (4-dimensional) is a model trajectory.
X0
ICDC7, Boulder 25 - 30 September 2005
4D-Var Data Assimilation
4-dimensional variational data assimilation is in principle a least-squares fit in 4 dimensions between the predicted state of the atmosphere and the observations.
The adjustment to the predicted state is made at time To, which ensures that the analysis state (4-dimensional) is a model trajectory.
CO2 is added to the state vector as a tropospheric column amount for each AIRS observation.
X0
ICDC7, Boulder 25 - 30 September 2005
CO2 column estimates
370 380 1.0 6.0
Mar 200
3
Mar 200
4
Sep 200
3
Mar 200
3
ICDC7, Boulder 25 - 30 September 2005
Comparison with in-situ observations
Japanese flight data kindly provided by H. Matsueda, MRI/JMAJapanese flight data kindly provided by H. Matsueda, MRI/JMA
370
380
ICDC7, Boulder 25 - 30 September 2005
Can we learn from current satellite observations?
TM3 Simulations LMDz Simulations
AIRS Estimates
Tiwari et al., submitted to JGR, 2005; see also his poster
ICDC7, Boulder 25 - 30 September 2005
Can we learn from current satellite observations?
TM3 Simulations LMDz Simulations
AIRS Estimates
Tiwari et al., submitted to JGR, 2005; see also his poster
ICDC7, Boulder 25 - 30 September 2005
Next steps
• CO2, CH4, CO, and N2O are being implemented in the full 4D-Var assimilation system.
• Data from AIRS, IASI, CrIS, Sciamachy, OCO, and GOSAT can then be used in a consistent way to estimate the atmospheric concentrations of these gases.
• In-situ data will initially be used for validation, but could also be used in reanalysis mode.
• Aim is to provide consistent atmospheric distributions that are accurate enough to improve models and inverse flux estimates.
• No plans (yet) to directly estimate fluxes within the 4D-Var, because of assimilation time window restrictions.
ICDC7, Boulder 25 - 30 September 2005
Using climatological fluxes (CASA, Takahashi, and Andres) we have made a 2 year run to test the system at resolution T159 (~ 1.125˚).
CO2 in ECMWF forecast model
ICDC7, Boulder 25 - 30 September 2005
Using climatological fluxes (CASA, Takahashi, and Andres) we have made a 2 year run to test the system.
CO2 in ECMWF forecast model
ICDC7, Boulder 25 - 30 September 2005
Conclusions
• First relatively simple implementation of CO2 variable in operational data assimilation system proved successful
• Work in progress to build a full 4D-Var greenhouse gas data assimilation system that can combine observations from various satellite sensors to estimate atmospheric CO2
• These 4D atmospheric fields will then hopefully contribute to a better quantification and understanding of the carbon surface fluxes.