© crown copyright met office cfmip-2 techniques for understanding cloud feedbacks in climate...

© 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


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


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


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


Assessment of

cloud-climate

responses


Understanding

GCM

process/sensitivity

studies

CRMs/LES/SCMs

via GCSS

A-Train/ISCCP

& simulators

Evaluation

CFMIP-GCSS case study: Wednesday AM


Assessment of

cloud-climate

responses


Understanding

GCM

process/sensitivity

studies

CRMs/LES/SCMs

via GCSS

A-Train/ISCCP

& simulators

Evaluation


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


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


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


South East Tropical Pacific Section


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)


6. Cumulus capped5. Decoupled Sc over Cu4. Decoupled Sc not over Cu3. Well mixed Sc

SW cloud response and transition between boundary layer regimes


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)


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


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


Control and +2K Control and +2K Control and +2K

+2K – control

RH q e

RH response q response e response


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)


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


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


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

© crown copyright met office cfmip-2 techniques for understanding cloud feedbacks in climate...

Documents

cloud area

shallow cloud feedbacks

better understanding

little high cloud condensate

water vapour

s control

gcm feedbacks

sst perturbation