1. Use of a high-resolution cloud climate data set for validation of Rossby Centre climate simulations Presentation at the EUMETSAT Meteorological Satellite Conference, Prague, Czech Republic, 31 May - 4 June 2004 Karl-Göran Karlsson Colin Jones Ulrika Willén Klaus Wyser SMHI, Norrköping Sweden

2. Contents: The cloud datasets from SCANDIA, the Rossby Centre RCA climate model and the ERA-40 project Results from direct comparisons - area means and mean 2-D distribution of cloudiness Introduction of a filtering concept based on RTM simulations Ambiguities due to contributions from optically very thin clouds Conclusions and future plans Use of a high-resolution cloud climate data set for validation of Rossby Centre climate simulations

3. The SCANDIA cloud climatology from NOAA AVHRR Example for afternoon cloud frequencies in July 1991-2000 Reference: Karlsson, K.-G., 2003: A 10-year cloud climatology over Scandinavia derived from NOAA AVHRR imagery, Int. J. Climatology, 23, 1023-1044. Climatology also available on two CD-ROMs 100 80 60 40 20 0 (%)

4. The Rossby Centre RCA2 climate model Convective parameterisation by Kain and Fritsch (1990) A high resolution (44 km grid, 24 layers) climate simulation model for studies of regional climate change effects (downscaling experiments) Simulation of present climate using ECMWF analyses as forcing and for lower and lateral boundary conditions Large-scale condensation scheme by Rasch and Kristjánsson (1998) and Slingo (1987) Reference: http://www.smhi.se/en/index.html (choose R&D, regional climate modelling) 17-year climate simulation 1985-2001 Assuming Maximum-Random cloud overlap

5. The ERA-40 cloud data set Maximum-Random Cloud Overlap ECMWF Re-Analysis data set (second version) ECMWF T159L60 model version (~130 km horizontal resolution, 40 vertical layers) Forecasted cloud data set (four six-hour forecasts per day) Reference: http://www.ecmwf.int/research/era/

6. Results for the entire Nordic area Time series 1991-2000 of mean monthly total cloud amount (%) for SCANDIA, RCA2 and ERA-40.

7. Seasonal 2-D distribution for the entire SCANDIA area SCANDIA RCA2 ERA-40 WINTER SPRING SUMMER AUTUMN

8. We conclude: Simulated cloud amounts agree quite well with observed amounts concerning the 2-D distribution but amounts are generally higher, especially in winter and over land surfaces. But, is this what we should expect? DEFINITELY NOT!!

9. Cloud representation in climate models - Finite vertical resolution in models creates ambiguities in interpretation of 2D cloud datasets Two real cloud situations with two different resulting horizontal cloud coverages (black bar) One and the same model representation of the two real cloud cases Conclusion: We should expect modelled cloud amounts to be understimated!!!

10. Indications of limited cloud detection ability from satellite- retrievals and SYNOP observations Results from the SHEBA year

11. Question: Are there significant contributions to the RCA-simulated fractional cloud cover from optically very thin clouds? YES!! Conclusion: These clouds must be filtered out from the RCA data set if they are not detected by SCANDIA!

12. Method for filtering optically very thin clouds Simulate cloudy radiances for AVHRR channels with RTM model Determine the lower detectability limit for SCANDIA Chosen RTM: Signal Simulator for Cloudy Retrieval (SSCR) - Discrete-Ordinate (DISORT) method described by Nakajima and King (1992) and Nakajima (1995). - Simulation of clouds by inserting them as homogeneous sub-layers with certain specified characteristics (water phase, volume size distribution and particle optical thickness) Filter out clouds with optical thicknesses below the SCANDIA detectability limit from the RCA data set

13. Example of SSCR simulations over land surfaces Indicating detectability limit close to optical thickness = 1.0 *) *) Not normalised for sun elevation and scaled with a factor 2.55

14. Example of SSCR simulations over ocean surfaces Indicating detectability limit close to optical thickness = 1.0 *) Not normalised for sun elevation and scaled with a factor 2.55

15. Conclusions - Lessons learnt: ERA-40 cloudiness show better agreement with SCANDIA but shares the RCA2 problem over land surfaces RCA2 cloudiness resembles SCANDIA in the horizontal distribution and in the seasonal cycle. Significant overestimation of cloudiness over land surfaces is seen for the winter half of the year indicating both dynamical and physical problems RCA2 excessive cloudiness in winter is not accompanied with a positive bias in surface temperatures Complex compensating error structure involving radiation scheme and surface flux parameterisation

16. Conclusions - Lessons learnt: (cont’d) However, filtering of optically very thin clouds from model data sets appears necessary for final confirmation of results The cloud detectability limit for SCANDIA, as indicated by SSCR simulations, appears to lie close to optical thicknesses of 1.0 Filtering work started based on the filtering of clouds where the average vertically averaged optical depth in the cloud covered fraction of a grid column is below 1.0

17. Future plans Further filtering attempts exploring the possibility to filter out potentially vertically resolved clouds in RCA - Enables studies of the performance of simulated effects from resolved and non-resolved clouds (e.g., parameterisation of convection) Development of Climate Monitoring SAF tools to adapt cloud climatological data sets for enabling comparisons with climate models - CM-SAF data simulation tool Filter model data set for optically very thin clouds (thinner than optical thickness 1.0) and repeat the comparison with the SCANDIA climatology