CO 2 Data Assimilation at ECMWF

1. CO2 Data Assimilation at ECMWF Richard Engelen European Centre for Medium-Range Weather Forecasts Reading, United Kingdom Many thanks to Phil Watts, Frédéric Chevallier, Tony McNally, and Erik Andersson.

2. 4D-Var data assimilation Minimize the following cost function: where : state vector increments : observation operator : observation departures Depending on the specification of their respective error covariance matrices B and R, the background term Jb and the observation term Jo will determine the new trajectory of the model.

3. 4D-Var data assimilation

4. 4D-Var data assimilation Set up of current research mode assimilation (CY25R4): • High resolution trajectory run : First Guess Resolution: TL511  40 km, 60 levels up to 0.1 hPa  Compare OBS - FG : Data quality control • Minimisation using tangent linear + adjoint model Resolution: up to TL159  125 km, 60 levelsSuccessive minimisations at TL95 and TL159 with simplified physics. Update of the trajectory at high resolution in between. • Observation time window : 12 hours  Adjust initial state iteratively to fit Observations

5. CO2 in the 4D-Var 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. It is retrieved at the observation location using the 4D-Var fields of temperature, specific humidity and ozone. The CO2 variable itself is limited to a column-averaged mixing ratio with a fixed profile shape. There are plans to include CO2 as a tracer variable in the model, but this is not available yet.

6. AIRS Data Stream ECMWF NASA NESDIS ORA 2378 ch., all pixels 324 ch., 1 out of 18 pixels when operational Cloud Detection Bias correction Black List 4D-Var system ECFS MARS ~ 100 channels Assimilated Fields T, q, etc. CO2 MARS ECFS

7. Channel flagging 324 input channels Cloud Detection Blacklisting Quality Control +/- 100 channels remaining

8. Cloud detection A new cloud detection algorithm has been developed by Tony McNally and Phil Watts (Revised version submitted to Q.J. R. Meteor. Soc.). Instead of detecting clear Field of Views, it detects clear channels. It makes use of the fact that channels with weighting functions peaking above the cloud are not affected by the cloud. To determine if a channel is affected by clouds, clear radiances calculated from the model are compared to the observations. After sorting the channels based on the peak of their weighting function, differences between simulated and observed radiances approach zero within the error margin of both model and observations. This is used to determine if a channel is clear or not. The method does not rely on the cloud estimation of the forecast model and maximises the amount of extracted information, which are significant advantages.

9. Cloud detection: example Surface 600 mb 100 mb

10. AIRS bias relative to ECMWF background

11. Bias correction • Bias correction is necessary to properly assimilate AIRS observations in the 4D-Var system. • The bias correction is done for each individual channel. • When an air-mass dependent correction is used for AIRS, it is possible to remove part of the CO2 signal and create air-mass dependent biases in the CO2 analysis values. This effect is the strongest when atmospheric CO2 is correlated with any of the predictors. • At the moment a global mean bias correction is used for each individual AIRS channel. This bias correction is calculated with fixed CO2 values in the radiative transfer.

12. Blacklist A so-called blacklist is used in the data assimilation system to remove all channels that are not fit to use. Examples are the blacklisting of the short-wave band of AIRS to avoid problems with solar radiation in the radiative transfer modelling, and the blacklisting of defunct channels. The blacklist can easily be adapted to include or exclude extra channels. Also, different data filters can be put in to use e.g. only data over ocean or only data with a scan angle less than a certain value.

13. Global assimilation with real AIRS data • Assimilation experiments were done for the period 24 – 27 January 2003 and for 24 – 27 April 2003. • 324 channels were available before the blacklisting process. • Cloud detection algorithm is used to screen for cloud-free channels. • Different CO2 assimilation set-ups: • Single column value with the same mixing ratio in the troposphere and the stratosphere using all available channels • Single column value with the same mixing ratio in the troposphere and the stratosphere using 10 stratospheric channels • Single column value with the same mixing ratio in the troposphere and the stratosphere using 10 tropospheric channels • Double column experiment with all available channels

14. Assimilation using all channels First results with the sink variable analysis using all available channels show an unexpected spatial distribution. Siberia in wintertime should have increased CO2 levels in the troposphere, not decreased. Note that the absolute values of the model results are not verified against flask observations. Results are presented here for spatial distribution only! Colour scales are also slightly different.

15. Assimilation using 10 stratospheric channels When only 10 stratospheric channels are used, the spatial distribution looks more like what we would expect. Highest concentrations in the tropics with decreasing values to higher latitudes. Note that the absolute values of the model results are not verified against flask observations. Results are presented here for spatial distribution only! Colour scales are also slightly different.

16. Assimilation using 10 tropospheric channels When only 10 tropospheric channels are used, we lose quite a bit of our coverage due to clouds. However, Siberia now indeed shows increased CO2 levels in the troposphere, not decreased. Note that the absolute values of the model results are not verified against flask observations. Results are presented here for spatial distribution only! Colour scales are also slightly different.

17. Assimilation using 10 tropospheric channels Tropospheric CO2 (left) and number of observations per grid box as used in the average (right). Northern hemisphere in winter has a lot of cloud cover in the lower and middle troposphere. Therefore, to get a decent time average, we need a relative long time series of data.

18. Double column analysis The introduction of a double sink provides a bit more flexibility in the estimation of CO2. The tropopause is estimated from the background temperature profile and independent values for stratospheric and tropospheric CO2 are then estimated. Errors (sa) are estimated by calculating the CO2 Jacobians within the analysis and using the 20 ppmv background error (sb) and 1K observation errors (Sy) : The analysed values are then gridded, weighted by their individual analysis errors. This assumes that the errors are uncorrelated in time and space, which is a bit optimistic.

19. Double column analysis

20. Double column analysis The fully correlated mean errors on the left show the pessimistic error estimates. The number of channels used in the analysis (shown below) provides an indication of how deep in the troposphere we look.

21. Effect of background bias The effect of the background bias is shown by filtering out any CO2 analysis values that have analysis errors too close to the background error.

22. Conclusion and Questions • The ECMWF column CO2 product is getting into shape. • Still some work to do: use of best available background, bias correction, quality control, validation (?). • What do we know about the correlation between the stratosphere and troposphere? • This is important, because we have so many AIRS channels that are (partly) sensitive to the stratosphere. The specified background covariance matrix has therefore an important effect on how information from these channels is distributed through the vertical. • Are we able to specify horizontal correlations for gridded surface fluxes? • This will be important if we want to go one big step further and start CO2 model inversions within the ECMWF 4D-var framework.