1. r.p.allan@reading.ac.uk © University of Reading 2010 1 Richard Allan Department of Meteorology, University of Reading Thanks to: Jim Haywood and Malcolm Brooks (Met Office) Using Geostationary Earth Radiation Budget data to evaluate global models and radiative processes

2. r.p.allan@reading.ac.uk All-sky Clear-sky ShortwaveLongwave Radiative biases in Met Office global NWP model

3. r.p.allan@reading.ac.uk All-sky Clear-sky ShortwaveLongwave Convective cloud Radiative biases in Met Office global NWP model Convective outflow

4. r.p.allan@reading.ac.uk All-sky Clear-sky ShortwaveLongwave Convective cloud Radiative biases in Met Office global NWP model Convective outflow Mineral dust Surface albedo

5. r.p.allan@reading.ac.uk All-sky Clear-sky ShortwaveLongwave Convective cloud Radiative biases in Met Office global NWP model Convective outflow Mineral dust Surface albedo Marine stratocumulus

6. r.p.allan@reading.ac.uk Marine stratocumulus Key in determining model uncertainty in climate sensitivity e.g. Bony and Dufresne (2005); Clemment et al. (2010) Model biases in physical properties Commonality: NWP and climate parametrizations © University of Reading 2010 6 CERES FM3 (Aqua) -SSF Albedo 13:11 Model albedo 5 June 2006 12:00 GERB albedo

7. r.p.allan@reading.ac.uk Model stratocumulus cloud: too bright, too much water - Implications for Cloud Feedback, e.g. Stephens (2010) © University of Reading 2010 7 Allan et al. (2007) QJRMS

8. r.p.allan@reading.ac.uk NWP model cloud radiative bias: overcast Sc-cover pixels only (2003-2010) © University of Reading 2010 8 LW cloud effect too small (~ -5 Wm -2 ) SW cloud effect too large yet too little stratocumulus cloud cover

9. r.p.allan@reading.ac.uk Systemic bias in climate models? Radiative bias: climate models minus ERBS

10. r.p.allan@reading.ac.uk Mineral dust and surface albedo (model-GERB) Progressive reduction in model biases through continual evaluation and improvement See also poster by Margaret Woodage for evaluation of dust scheme in HiGEM model

11. r.p.allan@reading.ac.uk © University of Reading 2009 11 GERBILS aircraft campaign Lead by Jim Haywood 18-28 June 2007

12. r.p.allan@reading.ac.uk © University of Reading 2009 12 Persistent contrail cirrus case study Combining satellite data with models to understand physical processes

13. r.p.allan@reading.ac.uk © University of Reading 2009 13 Persistent contrail cirrus case study Combining satellite data with models to understand physical processes

14. r.p.allan@reading.ac.uk © University of Reading 2009 14 Persistent contrail cirrus case study Combining satellite data with models to understand physical processes

15. r.p.allan@reading.ac.uk © University of Reading 2009 15 Persistent contrail cirrus case study Combining satellite data with models to understand physical processes

16. r.p.allan@reading.ac.uk © University of Reading 2009 16 Persistent contrail cirrus case study Combining satellite data with models to understand physical processes

17. r.p.allan@reading.ac.uk © University of Reading 2009 17 Persistent contrail cirrus case study Combining satellite data with models to understand physical processes

18. r.p.allan@reading.ac.uk © University of Reading 2009 18 Persistent contrail cirrus case study Combining satellite data with models to understand physical processes

19. r.p.allan@reading.ac.uk © University of Reading 2009 19 CERES FM3 (Aqua) FLASH fluxes 13:25 Contrail induced cirrus? Window flux Inverse greenhouse parameter SW fluxes (Wm -2 ) Using GERB/NWP model estimate radiative effect of contrail cirrus: LW ~ 40 Wm -2 SW up to 80 Wm -2 stratocumulus LW fluxes (Wm -2 )

20. r.p.allan@reading.ac.uk © University of Reading 2009 20 Cloud effective radius Cloud optical thickness Contrail region: higher optical thickness, smaller effective radius SEVIRI cloud properties (0.8 and 1.6 μm channels) Water cloud

21. r.p.allan@reading.ac.uk © University of Reading 2009 21 Haywood et al. (2009) JGR Using GERB-like/SEVIRI and 4km forecast model to quantify contrail radiative effects: example at 14:00, 20 March 2009

22. r.p.allan@reading.ac.uk Case II: 25-26/06/2010

23. r.p.allan@reading.ac.uk LW and SW cloud radiative effect Estimated using GERB/SEVIRI minus NCEP clear-sky LW and SW cloud radiative effect up to 80 Wm -2 (small net effect at midday) But would cirrus have formed anyway? © University of Reading 2009 23 12-18 UTC

24. r.p.allan@reading.ac.uk Summary Mineral dust aerosol –Systematic model LW radiative bias at TOA up to 40 Wm -2 –GERBILS field campaign Model Cloud –Subtropical stratocumulus too bright, too much liquid water –Cannot tune to TOA radiation; need to get bulk physical properties right. Issues with LWP retrievals (μ-wave/visible) –Highly sensitive to model changes (e.g. convection) Persistent contrail cirrus events –Up to 80 Wm -2 LW and SW cirrus radiative effect –Small net effect, but large dynamical and diurnal forcing –Would cirrus have formed anyway? –Any contrail effect following Iceland volcano aircraft shutdown? © University of Reading 2010 24

25. r.p.allan@reading.ac.uk Was there any contrail effect following the Iceland Volcano? © University of Reading 2010 25 Airspace closureAirspace reopens

26. r.p.allan@reading.ac.uk Was there any contrail effect following the Iceland Volcano? © University of Reading 2010 26 Airspace closureAirspace reopens...unlikely! Probably more related to advection of dry air and subsequent encroachment of mid-latitude system. GERB HR (V003) LW fluxes

27. r.p.allan@reading.ac.uk Major update to model parametrizations 15 July 2010 Stratocumulus cloud now too extensive © University of Reading 2010 27 GERBModel 12 July 19 July Continuous monitoring

28. r.p.allan@reading.ac.uk28 LW bias in climate models May-July Average clear-LW bias over whole region 3-15 Wm -2 depending upon model & satellite dataset/period

29. r.p.allan@reading.ac.uk Ongoing evaluation: diurnal cycle of convection in high resolution models © University of Reading 2009 29 Pearson et al. (2010) JGR in press (CASCADE project)