1. R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading ECMWF Cloud and Radiation Parametrization: Recent Activities Richard Forbes, Maike Ahlgrimm, Jean-Jacques Morcrette, Martin Köhler Evaluation of models University of Reading, 17-18 Nov 2009

2. R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading Some ECMWF Cloud/Radiation Recent Parametrization Activities 1. Development of cloud and precipitation parametrization (prognostic variables and microphysical processes…..) 2. Evaluation of cloud/precip with CloudSat/CALIPSO (Radar reflectivity) 3. Evaluation of cloud regimes (TCu - new dual-mass flux shallow convection scheme) 4.Representation of aerosol and radiative impacts (GEMS/MACC)

3. R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading 1. Cloud Scheme Developments

4. R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading ECMWF Cloud Scheme Developments WATER VAPOUR CLOUD Liquid/Ice PRECIP Rain/Snow Evaporation Autoconversion Evaporation Condensation CLOUD FRACTION Current Cloud Scheme 2 prognostic cloud variables (condensate & cloud fraction) + water vapour. Diagnostic liquid/ice split as a function of temperature between 0°C and -23°C. Diagnostic representation of precipitation. CLOUD FRACTION New Cloud Scheme 5 prognostic cloud variables (liquid, ice, snow, rain, cloud fraction). Additional sources/sinks for new processes. New explicit/implicit solver

5. R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading New 5-prognostic cloud microphysics Liquid vs Ice Fraction New prognostic schemeCurrent diagnostic scheme Temperature Liquid Water Fraction 1.0 0.0 -23ºC Temperature 0ºC0ºC

6. R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading Model Ice Water Path (IWP) (1 year climate) New 5 prognostic cloud microphysics Ice vs. Snow CloudSat 1 year climatology From Waliser et al. (2008) Current scheme (IWC) New scheme (IWC+SWC) Observed Ice Water Path (IWP) g m -2

7. R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading From Waliser et al. (2009), JGR Widely varying estimates of IWP from different satellite datasets! Verification Annual average Ice Water Path from Satellite CloudSat

8. R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading 2. Evaluation with CloudSat

9. R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading Radar Reflectivity Along-track model vs. CloudSat comparison Spatial distribution of cloud/precipitation reflectivities generally very good! However, there are some discrepancies that are highlighted by the radar reflectivity comparison

10. R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading Radar Reflectivity vs. Height Frequency of Occurrence Tropics over ocean 30S to 30N for February 2007 Radar Reflectivity Statistics Significantly higher occurrence of cloud in model – but is this due to overestimating the precipitation fraction? Lack of low reflectivity mid- level and low-level cloud ? Relatively too frequent low-level high reflectivity convective rainfall Peak reflectivities too high altitude (from convective snow)

11. R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading 3. Regime Evaluation (Maike Ahlgrimm)

12. R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading Regime evaluation Defining a regime: Use criteria like cloud top height, cloud thickness, cloud fraction. Geographical region Use model (dynamical) quantities. Different issues for ground based, satellite (vertical profile vs, 2D view). Compositing: To avoid focussing on potentially unrepresentative individual cases. To get large enough sample size without losing characteristics of cloud type. Zonal cross-section of frequency of cloud/precipitation occurrence Maike Ahlgrimm

13. R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading Example: Trade cumulus using CALIPSO DualM 65.1% 46.5% Control CALIPSO Control Criteria: Cloud top height <4km Over ocean 30S to 30N Cloud fraction <50% DualM CALIPSO Maike Ahlgrimm Compensating errors: Model cloud occurs too often, but has too little cloud fraction when it occurs.

14. R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading Example: Mid-latitude cold air outbreak Criteria from model: Surface pressure 1015 hPa Potential temperature difference 700 hPa to lowest model level 9K Over ocean Add criteria from satellite, such as cloud top height….

15. R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading 4. Radiation and aerosol J-J Morcrette

16. R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading Recent developments in aerosol representation in the ECMWF IFS (GEMS) ECMWF IFS model including prognostic aerosols has been run in two configurations: –In aerosol free-wheeling mode: aerosol advection and full (but simplified) aerosol physics using temperature, humidity, winds etc. from the analyses/forecasts every 12 hours –In analysis mode with subsequent forecasts In both configurations, what is included is –Sea salt aerosols (3 bins, 0.03–0.5–5–20 m) –Dust aerosols (3 bins, 0.03–0.55–0.9–20 m) –Organic matter (hydrophilic, hydrophobic) –Black carbon (hydrophilic, hydrophobic) –Sulphate aerosols (SO 4 from SO 2 sources) Morcrette et al. (2008) Benedetti et al. (2009) Model AOD analysis Jul 2003 MISR AOD Jul 2003

17. R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading AATSRMERISSEVERI MISR MODIS GEMS

18. R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading Comparisons AERONET, ECMWF climatology, GEMS-AER, GlobAEROSOL-SEVIRI (Azores) Azores/Cabo Verde 500nm

19. R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading To improve model parametrizations… The challenge is to determine real differences between the model and observations, identify the most important physical processes, understand their interactions and improve their representation in the model.

20. R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading Some Questions to Highlight How do we compare incompatible model and obs ? (different quantities, spatial and temporal scales, obs limitations/errors) –Forward models/simulators/emulators –Sub-columns or appropriate averaging –Understand the observation limitations/errors How do we evaluate physical processes ? –Regime-dependent evaluation (where particular processes dominate) –Model sensitivity studies…. –Combining different observations to evaluate physical relationships? How do we disentangle model compensating errors ? –Exploit synergy of different observations (to provide information on clouds, radiation, aerosol, water vapour all at the same time!) How important is variability on different spatial and temporal scales ? –Need temporal and spatial heterogeneity from observations –Cloud cover, cloud condensate, humidity, aerosols…..

21. R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading Questions ?

22. R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading A mixed uniform-delta total water distribution is assumed qtqt G(q t ) qsqs Cloud cover is integral under supersaturated part of PDF 1-C qtqt G(q t ) C qsqs ECMWF cloud parametrization In the real world ECMWF Cloud Parametrization Representing sub-grid variability

23. R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading Radar Reflectivity Cross-section through tropical convection CloudSat Radar Reflectivity Model Radar Reflectivity (Ice, Liq, Snow, Rain) Model Radar Reflectivity (Ice, Liq only)