Chemical Data Assimilation using CAM and DART: Tests with CO Remote-Sensed Measurements

1. Chemical Data Assimilation using CAM and DART: Tests with CO Remote-Sensed Measurements Avelino Arellano, Jr. and Peter Hess Atmospheric Chemistry Division, NCAR Kevin Raeder and Jeffrey Anderson Data Assimilation Research Section, NCAR

2. Goal: • Provide a consistent and likely representation of CO distribution • Develop a spatially and temporally robust estimates of CO emissions. (In the context of model improvement through parameter estimation) Problem: Current estimation procedure (or inversion) rely on GCTMs to map the emission (surface fluxes) to observable CO state variables. As such, errors need to be reasonably accounted for. A promising technique is a joint data assimilation and parameter estimation using Ensemble Kalman Filter (EnKF).

3. MODELS • DART • http://www.image.ucar.edu/DAReS • CAM3.1 with CO as a tracer • Used standard CAM3.1 with FV dycore (4ox5o) and a simplified CO chemistry, • mainly based on CAM’s carbon aerosol package. Emissions and sinks of CO • prescribed.

4. OBSERVATIONS For T,U,V : NCEP BUFR (includes radiosonde T,U,V; ACARS data T,U,V; SATwind U,V, etc) For CO : NASA Terra MOPITT CO Retrievals (used 500 hPa subset, dynamic avg kernels) e.g.

5. To check the validity and performance of current DART-CAM-CO implementation: Conduct Observing System Simulation Experiments (OSSEs) Given that we know the ‘true’ state and we have a best guess of the probability distribution of the initial state, can we reproduce the truth by assimilating various synthetic observations?

6. A. Generate Initial Ensembles (80 members) • For CAM variables taken from previous CAM climatological runs • (Kevin Raeder) • For CO generated by running CAM (CO) with FV dycore for 1 week using • MOZARTv2 initial field and the ensemble of CAM/CLM2 initial conditions. • (i.e. variability of CO generated from the ensemble alone).

7. B. Generate Synthetic Observations • Generating synthetic obs is easily facilitated in DART (as one of the DART tools): • 1) Took initial ens #40 and run CAM-CO with prescribed sources and sink. • Assumed that generated model states are the “true” states. • We sample the true states using the NCAR BUFR and MOPITT CO obs location and time (truth). • Perturbed the sample by adding a Gaussian noise with variance represented • by the obs instrument error variance (synthetic obs). Synthetic Obs – Truth (example: 07-Jan-2003 500 hPa)

8. C. Carry out 3 OSSEs • Using the same initial ensembles (1st 20 members) for T,U,V,CO and the same CO sources and sinks prescribed in generating synthetic observations, we carry out the following experiments for a 7-day period with 20-member ensemble: • Forecast–>Run DART-CAM-CO w/ no assimilation. • Analysis T,U,V–>Run DART-CAM-CO w/ assimilation of T,U,V only. • Analysis T,U,V,CO–>Run DART-CAM-CO w/ assimilation of T,U,V and CO.

9. Initial Results RMSEs for both assimilation approaches to the prescribed observation RMSEs.

10. Similar improvements in RMSE and RMS. • RMSEs for both assimilation approaches to the prescribed observation RMSEs.

11. Assimilation of T,U,V also provides better match of modeled atmospheric pressure with observations.

12. Bias for both assimilation approaches to the mean bias of the observations relative to the truth.

13. RMSEs for both assimilation approaches to the prescribed observation RMSEs. Assimilating T,U,V alone provides large constraints to CO.

15. Relative Comparison in Model Space Assimilation able to reasonably reproduce the true state even for surface CO (which is not currently observed) Note: Variability of CO attributed to T,U,V. Emissions are fixed.

16. Summary • The setup for DART-CAM (CO) has been implemented using synthetic • CO observations based on MOPITT retrievals. • Initial results show the potential of current setup for model evaluation and • longer assimilation studies. • Simultaneously constraining T,U,V and CO in a GCTM offers an opportunity • for model improvements (i.e. source parameter estimation). • Challenges • a) limitation in run time ( increasing overhead with additional observation) • b) what is the optimal number of ensembles to use? • c) explore sensitivity of assimilation parameters • Future research • a) Assimilation of real obs (MOPITT CO retrievals and/or radiances) • b) Joint CO data assimilation and CO source estimation

17. Acknowledgements NSF ITR Grant 115912 NCAR MOZART group NCAR MOPITT Tim Hoar (IMAGe) Francis Vitt (ACD) Louisa Emmons (ACD)

18. OSSEs model advance obs Case 1: Forecast ensemble state variables 06 12 18 24 Case 2: Assimilation (T,U,V) assimilation Posterior Prior 06 12 18 24 Case 3: Assimilation (T,U,V, CO) 06 12 18 24

19. Overarching Goal: • Provide a consistent and likely representation of CO distribution • Develop spatially and temporally robust emissions (surface fluxes) of tropospheric CO. Has important implication to biogeochemistry.

20. Overarching Goal: • Provide a consistent and likely representation of CO distribution • Develop spatially and temporally robust emissions (surface fluxes) of tropospheric CO. Has important implication to biogeochemistry. (model improvement through parameter estimation) Current estimation procedure (or inversion) rely on GCTMs to map the source parameters to observable CO state variables. As such, errors need to be reasonably accounted for.