1. Evaluation of cloudy convective boundary layer forecast by ARPEGE and IFS Comparisons with observations from Cabauw, Chilbolton, and Palaiseau  Comparisons (April - August 2003) Model predictions Diagnostic using model outputs  Impact of temperature and humidity biases on the surface fields  Turbulent and radiative fluxes at the surface  Perspectives Anne Mathieu, Institut Pierre Simon Laplace

2. Selection of dry or cloudy convective boundary layer Selection of days between April and August 2003 Cabauw 95 days Chilbolton 81days Palaiseau 75 days Models : ARPEGE IFS Met-Office model : turbulent fluxes are not available RACMO : results are strange – more test are needed Comparisons between models and observations done on an hourly basis

3. Cloud base height comparisons Frequency distribution of the difference between observed cloud base height predicted cloud base height  Large proportion of disagreement  ARPEGE under-estimates the cloud base height (better at Chilbolton)

4. Possible sources of error Are the ground-based observations representative of what is happening on a larger spatial scale (model grid point) ? Wrong prediction of the large scale meteorological situation ? Boundary layer scheme ? Cloud scheme ?  diagnostic computation Physical parameters retrieval using model outputs

5. Convective boundary layer : Are the clouds created via surface driven processes ? The condensation level of a parcel of air coming from the surface ~ cloud base height Frequency distribution of the difference between observed cloud base height condensation level of a parcel of air observed at 20m high  Observed clouds are predominantly created via surface driven processes

6. Diagnostics of boundary layer height and condensation level BLH LCL Advantage of working with diagnostics : Comparisons between models are independent of the parameterizations Convective boundary layer Air parcel coming from the surface Equilibrium level of the parcel = BLH Condensation level of the parcel = LCL plume

7. Frequency distributions of observed CLBH and diagnosed LCL  Slightly better overall agreement than with the model predicted CLBH  Essentially same flaws than the predicted CLBH

8. Thermodynamic characteristics of the surface Observed clouds are essentially created via surface driven processes Surface driven processes diagnosed using model outputs do not create right clouds  Model bias near the surface at the origin of the wrong cloud base height prevision.

9. Model bias near the surface - Temperature  ARPEGE over-estimates T  ECMWF slightly under-estimates T

10. Model bias near the surface – Relative Humidity  strong variability in RH  ARPEGE over-estimates RH

11. Correlation between surface biases and CLBH biases CABAUW SIRTA

12. Assimilation ? Soil scheme ? Boundary layer scheme ? Radiative and Cloud scheme ? What could be the error sources to explain surface biases ?

13. Downward radiative fluxes compared to observations ARPEGE : new radiative and cloud schemes (during 2 month)  ARPEGE : LW fluxes are under-estimated ?  IFS : no clear tendencies CABAUW SIRTA

14. Turbulent fluxes compared to observations  ARPEGE : under-estimates the latent and sensible fluxes which does not explain the temperature and relative humidity biases CABAUW

15. Conclusions For selected days of cloudy convective boundary layer on the CLOUDNET stations Predicted boundary layer cloud base height further than 300m from observations 40% of the hours for IFS 55% of the hours for ARPEGE. Same behavior in the different stations. ARPEGE : Under-estimation of the CLBH due to warm and humid biases at the surface Essential condition to have a good prediction of dry and cloudy boundary layer diurnal cycle : right surface field prediction. Soil scheme Surface layer scheme Precipitations (convection)