1. Data assimilation for validation of climate modeling systems Pierre Gauthier Department of Earth and Atmospheric Sciences Université du Québec à Montréal

2. Validation of atmospheric models Transpose Atmospheric Model Intercomparison Experiments (AMIP) * Comparison of atmospheric models against each other under same conditions (e.g., initial conditions provided by the same analysis) * Short term forecasts as in NWP Intercomparison of data assimilation systems * More difficult to carry out due to the added complexity coming from observations, data assimilation components, and atmospheric model * Impact on both the analysis (information content) or on the forecasts

3. Schematic of the data assimilation process (from Rodwell and Palmer, 2007)

4. Validation of atmospheric models against observations Short-term forecast used by the assimilation * Common ground provided by a short term forecast, the background state  Sums up the information gained from observations * Monitoring of averages of observations minus background is used to detect biases  in new or existing observations  the model itself (detection of biases) particularly if the observation dataset has been carefully quality controlled

5. Monitoring and quality control Statistics based on innovations (y -HX b ): example from TOVS radiances

6. Using NWP to assess climate models (Rodwell and Palmer, 2007) Impact of changes to climate models usually done by comparing several long climate runs with perturbed models * Uncertainty associated with the physical processes used in the model (Stainforth et al., 2005) Assimilation produces analysis increments to correct the model forecast to bring it closer to the observations * Reduced analysis increments is an indication that the model has improved its fit to observations * Presence of spin-up can be associated with model differences with respect to what has been observed * Examination of the physical tendencies in the early stages of the forecast can provide useful information about imbalances in the model

7. Schematic of the data assimilation process (from Rodwell and Palmer, 2007)

8. Time series of precipitation rates averaged over the Northern Hemisphere (Gauthier and Thépaut, 2001)

9. RMS error w.r.t. unperturbed model vs. Simulated climate sensitivity (from Rodwell and Palmer, 2007)

10. Comparing physical tendencies for different processes in experiments with perturbed physics Total tendency Convection Dynamical cooling Rodwell and Palmer (2007)

11. Conclusions Data assimilation and reanalyses * often based on an adapted NWP suite for which the model short term forecasts have been thoroughly validated Using a climate model to do data assimilation * provide detailed information about systematic departures from observations Examination of the physical tendencies associated with the first instants of a forecast can * Indicate how imbalances in the physical processes may cause excessive model sensitivity which increase the uncertainty of climate predictions Observation datasets used for reanalyses could be valuable for studies on climate model validation * Added value for the data prepared for reanalyses

12. Conclusion (cont’d) For coupled systems, the complexity is increasing and this approach is certainly to be encouraged Parameter estimation with coupled models (Sugiura et al., 2008) to adjust parameter related to surface fluxes Found it was necessary to adjust also other parameterizations (1) the wind sensitivity parameter in the ocean (2) the isopycnal diffusion coefficient in the OGCM, (3) the mixing length in an atmospheric boundary layer in the AGCM, (4) the relaxation time for large-scale condensation in the AGCM, (5) the range of relative humidity change in the AGCM, (6) the standard height for precipitation efficiency in the AGCM, and (7) the adjustment time for cumulus convection in the AGCM