1. © Crown copyright Met Office CFMIP-2 techniques for understanding cloud feedbacks in climate models. Mark Webb (Met Office Hadley Centre) CFMIP/GCSS BLWG workshop, Vancouver, June 2009

2. © Crown copyright Met Office Acknowledgements Sandrine Bony, Chris Bretherton, William Ingram Adrian Lock, Hugo Lambert, Brian Mapes, Tomoo Ogura, Johannes Quaas, Mark Ringer, Pier Siebesma, Bjorn Stevens, Joao Teixeira, Keith Williams, Minghua Zhang

3. © Crown copyright Met Office Cloud Feedback Model Inter-comparison Project Phase 2 (CFMIP-2) www.cfmip.net Understanding GCM process/sensitivity studies CRMs/LES/SCMs via GCSS A-Train/ISCCP & simulators Assessment of cloud-climate responses Coordination committee: Mark Webb, Sandrine Bony, George Tselioudis, Chris Bretherton, Steve Klein Evaluation

4. © Crown copyright Met Office Assessment of cloud-climate responses Cloud Feedback Model Inter-comparison Project Phase 2 (CFMIP-2) Understanding GCM process/sensitivity studies CRMs/LES/SCMs via GCSS A-Train/ISCCP & simulators Evaluation Observational evaluation/simulators: Tuesday

5. © Crown copyright Met Office Assessment of cloud-climate responses Cloud Feedback Model Inter-comparison Project Phase 2 (CFMIP-2) Understanding GCM process/sensitivity studies CRMs/LES/SCMs via GCSS A-Train/ISCCP & simulators Evaluation CFMIP-GCSS case study: Wednesday AM

6. © Crown copyright Met Office Assessment of cloud-climate responses Cloud Feedback Model Inter-comparison Project Phase 2 (CFMIP-2) Understanding GCM process/sensitivity studies CRMs/LES/SCMs via GCSS A-Train/ISCCP & simulators Evaluation

7. © Crown copyright Met Office CFMIP-GCSS activities for better understanding of cloud-climate feedback processes Cloud process studies using: High-frequency model data at point locations (GPCI, ARM,…) Temperature, water vapour and cloud condensate budget terms Sensitivity tests to isolate key processes and test physical hypotheses

8. © Crown copyright Met Office Outputs at 115 points every 20-30 minutes GPCI / Tropical West & South East Pacific / AMMA sections ARM sites/GCSS field studies/locations with feedback spread

9. © Crown copyright Met Office Use of time step time series outputs to understand cloud feedbacks Assess impact of changes in high frequency phenomena on cloud feedbacks – e.g.: - diurnal cycle - frequency boundary layer regimes Look at relationships between instantaneous variables Identify causal links – e.g. event a precedes event b Assess ability of idealised SCM forcings to reproduce GCM feedbacks at detailed level Other ideas ? Session Tuesday afternoon

10. © Crown copyright Met Office South East Tropical Pacific Section

11. © Crown copyright Met Office Proto-HadGEM3 PC2 L38 SST forced +2K SST Stratocumulus layer which deepens away from coast and makes transition to trade cumulus Very little high cloud condensate along the SETP section Significant reduction in low cloud, most in transition region Control Uniform +2K SST cloud water (mg/kg) cloud water response (mg/kg)

12. © Crown copyright Met Office 6. Cumulus capped 5. Decoupled Sc over Cu 4. Decoupled Sc not over Cu 3. Well mixed Sc SW cloud response and transition between boundary layer regimes

13. © Crown copyright Met Office control +2K SST response Cloud water convective detrainment (mg/kg/s) cloud water (mg/kg) condensation from LW cooling (mg/kg/s) Cloud condensate tendency analysis following Ogura et al 2008a,b (JMSJ,SOLA)

14. © Crown copyright Met Office control +2K SST response Cloud water no convective detrainment (mg/kg) cloud water (mg/kg) no condensation from LW cooling (mg/kg) Sensitivity to removal of convective detrainment and cloud top cooling source terms

15. © Crown copyright Met Office Uniform +2K SST perturbation Look at profiles on model levels cloud water response (mg/kg)

16. © Crown copyright Met Office +2K – control Proto-HadGEM3 positive sub- tropical low cloud feedback South East Pacific Stratocumulus/ Cumulus Transition region (97W,16S) Control and +2K LWC  LWC response  response

17. © Crown copyright Met Office Control and +2K +2K – control RHq  e RH responseq response  e response

18. © Crown copyright Met Office Lock 2009 (QJRMS) Cloud top entrainment instability parameter  e (L/c p )  q t is a robust predictor of shallow cloud fraction in the UKMO LES (  denotes jump across capping inversion)

19. © Crown copyright Met Office What are the potential implications for shallow cloud feedbacks?  e  is  positive (L/c p )  q t (L/c p )  q  q is negative Warmer climate => increased static stability (  more positive ) =>  smaller  0.31 -> 0.30 => cloud area increases But if RH stays roughly constant then q increases at about 7%/K => stronger q jump (  q more negative ) =>  larger  : 0.30 -> 0.39 => cloud area decreases In this case, the change in the q-jump has a much larger impact on  than the static stability Positive subtropical low cloud feedback mechanism – q-jump hypothesis

20. © Crown copyright Met Office Lock 2009 argues that GCMs need to represent the buoyancy reversal process to accurately simulate cloud area and its sensitivity to  q at the capping inversion If this process is implicated in the positive feedback in this GCM, a sensitivity experiment in which the process is suppressed should make the feedback less positive or even negative The GCSS-CFMIP case study SCM experiments will be a good place to pilot such sensitivity experiments before trying in a full GCM q-jump hypothesis – a test

21. © Crown copyright Met Office Time series outputs will allow the impacts of various high frequency phenomena on cloud feedback to be examined – e.g. transitions between boundary layer regimes Tendency diagnostics will allow dominant processes (e.g. detrainment from shallow convection) to be identified Having temperature, humidity and other variables on model levels will allow more accurate diagnosis of capping inversions, allowing the development of more sophisticated dry/moist stability measures Sensitivity tests will allow: - quantification of impacts of processes/assumptions on feedback - testing of hypotheses for physical cloud feedback mechanisms Summary