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25 - Estimation of atmospheric CO 2 from AIRS infrared satellite radiances in the ECMWF data assimilation system Richard Engelen European Centre for Medium-Range Weather Forecasts
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Page 1: ICDC7, Boulder 25 - 30 September 2005 Estimation of atmospheric CO 2 from AIRS infrared satellite radiances in the ECMWF data assimilation system Richard.

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

Page 2: ICDC7, Boulder 25 - 30 September 2005 Estimation of atmospheric CO 2 from AIRS infrared satellite radiances in the ECMWF data assimilation system Richard.

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!

Page 3: ICDC7, Boulder 25 - 30 September 2005 Estimation of atmospheric CO 2 from AIRS infrared satellite radiances in the ECMWF data assimilation system Richard.

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)

Page 4: ICDC7, Boulder 25 - 30 September 2005 Estimation of atmospheric CO 2 from AIRS infrared satellite radiances in the ECMWF data assimilation system Richard.

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.

Page 5: ICDC7, Boulder 25 - 30 September 2005 Estimation of atmospheric CO 2 from AIRS infrared satellite radiances in the ECMWF data assimilation system Richard.

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.

Page 6: ICDC7, Boulder 25 - 30 September 2005 Estimation of atmospheric CO 2 from AIRS infrared satellite radiances in the ECMWF data assimilation system Richard.

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

Page 7: ICDC7, Boulder 25 - 30 September 2005 Estimation of atmospheric CO 2 from AIRS infrared satellite radiances in the ECMWF data assimilation system Richard.

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

Page 8: ICDC7, Boulder 25 - 30 September 2005 Estimation of atmospheric CO 2 from AIRS infrared satellite radiances in the ECMWF data assimilation system Richard.

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

Page 9: ICDC7, Boulder 25 - 30 September 2005 Estimation of atmospheric CO 2 from AIRS infrared satellite radiances in the ECMWF data assimilation system Richard.

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

Page 10: ICDC7, Boulder 25 - 30 September 2005 Estimation of atmospheric CO 2 from AIRS infrared satellite radiances in the ECMWF data assimilation system Richard.

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

Page 11: ICDC7, Boulder 25 - 30 September 2005 Estimation of atmospheric CO 2 from AIRS infrared satellite radiances in the ECMWF data assimilation system Richard.

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

Page 12: ICDC7, Boulder 25 - 30 September 2005 Estimation of atmospheric CO 2 from AIRS infrared satellite radiances in the ECMWF data assimilation system Richard.

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.

Page 13: ICDC7, Boulder 25 - 30 September 2005 Estimation of atmospheric CO 2 from AIRS infrared satellite radiances in the ECMWF data assimilation system Richard.

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

Page 14: ICDC7, Boulder 25 - 30 September 2005 Estimation of atmospheric CO 2 from AIRS infrared satellite radiances in the ECMWF data assimilation system Richard.

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

Page 15: ICDC7, Boulder 25 - 30 September 2005 Estimation of atmospheric CO 2 from AIRS infrared satellite radiances in the ECMWF data assimilation system Richard.

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.


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