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© 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
© 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
© 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
© 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
© 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
© 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
© 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
© 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
© 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
© 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)
© Crown copyright Met Office
6. Cumulus capped5. Decoupled Sc over Cu4. Decoupled Sc not over Cu3. Well mixed Sc
SW cloud response and transition between boundary layer regimes
© Crown copyright Met Office
control
+2K SSTresponse
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)
© Crown copyright Met Office
control
+2K SSTresponse
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
© Crown copyright Met Office
Uniform +2K SST perturbation
Look at profiles on model levels
cloud water response (mg/kg)
© Crown copyright Met Office +2K – control
Proto-HadGEM3positive sub-tropical low cloudfeedback
South East PacificStratocumulus/Cumulus Transition region(97W,16S)
Control and +2K
LWC
LWC response response
© Crown copyright Met Office
Control and +2K Control and +2K Control and +2K
+2K – control
RH q e
RH response q response e response
© Crown copyright Met Office
Lock 2009 (QJRMS)
Cloud top entrainment instability parameter e (L/cp)qt
is a robust predictor of shallow cloud fraction in the UKMO LES
( denotes jump across capping inversion)
© Crown copyright Met Office
What are the potential implications for shallow cloud feedbacks? e ispositive
(L/cp)qt (L/cp)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
© 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
© 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
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