The Cloud Feedback Model Inter-comparison Project (CFMIP) From CFMIP-1 to CFMIP-2

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The Cloud Feedback Model Inter-comparison Project (CFMIP)

From CFMIP-1 to CFMIP-2Mark Webb (Hadley Centre Met Office)

Sandrine Bony (IPSL)

Rob Colman (BMRC)

(with help from many others)

CFMIP/ENSEMBLES Workshop, Paris, April 2007


Modelling and Prediction of Climate variability and change

CFMIP-1 (2003-date)

WCRP Working Group on Coupled Modelling (WGCM) endorsed

Set up by Bryant McAvaney (BMRC) and Herve Le Treut (LMD)

Systematic intercomparison of cloud feedbacks in climate models

+/-2K perpetual July and 2xCO2 ‘slab’ experiments

Aims were to identify key uncertainties and to link cloud-climate feedbacks to cloud observations

ISCCP simulator required (Klein & Jakob 1999, Webb et al 2001)

Experiments were mostly run in parallel with AR4/CMIP3

See www.cfmip.net for details


Comparison of +/- 2K and slab model experimentsRinger et al, GRL 2006

Cess experiments capture the spread in cloud feedback from slab experiments.

Offset due to suppressed clear-sky feedbacks in slab vs Cess

Values are global mean changes in NET, SW and LW CRF per degree of warming (Wm-2K-1)


Webb et al Climate Dynamics 2006 – CFMIP models

LW cloud feedback (W/m2/K)SW cloud feedback (W/m2//K)Net cloud feedback (W/m2/K)

LW cloud feedback (W/m2/K)SW cloud feedback (W/m2//K)Net cloud feedback (W/m2/K) Areas with

small LW cloud feedbackexplain 60% ofspread in NETcloud feedback

Cloud feedbackin these areasdominated byreductions inlow level cloudamount


Williams et al Climate Dynamics 2006

RMS-differences of present-day variability composites against observations for 10 CFMIP/CMIP model versions. The five models with smallest RMS errors tend to have higher climate sensitivities. (Consistent with Bony & Dufresne 2005)

1.01.01.91.51.1

0.91.11.01.11.2

ERBE/NCEPLW

2.12.41.72.23.4

1.21.31.51.81.7

ERBE/ERASW

1.11.01.61.41.3

1.01.21.01.21.1

ERBE/ERALW

2.33.02.22.83.7

1.61.31.92.12.2

ERBE/NCEPSW

1.61.91.92.02.4

1.21.21.41.51.5

AvgRMS

3.0K

2.3-3.5K

3.8K

2.9-4.4K

AvgClimate Sens.&Range


Tsushima et al 2006: Importance of the mixed-phase cloud distribution in the control climate for assessing the response of clouds to carbon dioxide increase: a multi-model study. (Clim Dyn)

Williams and Tselioudis 2007: GCM intercomparison of global cloud regimes: Present-day evaluation and climate change response. (Clim. Dyn)

Taylor et al 2007: Estimating shortwave radiative forcing and response in climate models (J Climate in press)

Ogura at al 2007: Climate sensitivity of a general circulation model with different modelling assumptions (submitted to J Met Soc Japan)

Gregory and Webb 2007: Tropospheric adjustment induces a cloud component in CO2 forcing. (submitted to J Climate)

Murphy et al 2007: A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles (submitted to Phil. Trans. Roy. Soc. London)

Other CFMIP related studies


Modelling and Prediction of Climate variability and change

CFMIP-I continues…

Environment Canada and NCAR data recently received, and a re-run of GFDL AM2 with daily diagnostics in progress

CFMIP I slab data are now freely available via PCMDI Archive now comprises 12 model versions from 9 modelling groups and includes daily data from 8 model versions This resource will allow many ISCCP data studies to be applied to a representative selection of climate models

Details of how to access the data from PCMDI are available at www.cfmip.net - please make use of our data!


CFMIP Phase II – looking further ahead

Understandingof modelled cloud-climate

feedback mechanisms

Evaluation of model clouds

using observations

Assessment ofcloud-climate

feedbacks

Co-ordinators: Mark Webb, Sandrine Bony, Rob ColmanProject advisor: Bryant McAvaney

Main objective : A better assessment of modelled cloud-climate feedbacks for IPCC AR5


Understanding physical cloud feedback mechanisms

Our current understanding of the physical cloud feedback mechanisms that operate across climate models is limited.

Why do many models have positive sub-tropical low cloud feedbacks while others’ are negative?

What model assumptions affect the climate sensitivity of models? - cloud parametrization/radiative properties - deep convection - precipitation efficiency - BL mixing (shallow convection and stratocumulus topped PBL) Mixing schemes are as important as cloud schemes as clouds are just one aspect of the larger hydrological cycle.


Understanding physical cloud feedback mechanisms

CFMIP-2 will put more emphasis on: - understanding the physical mechanisms controlling cloud feedbacks in models - assessing the credibility of those mechanisms

Part of this is gaining a better understanding of the impact that decisions made during the model development cycle have on cloud climate feedbacks – e.g: - temperature dependencies - RH vs specific humidity dependencies - stability dependencies

We think that this is necessary if we are to: - improve the representation of cloud feedback processes - reduce spread in model cloud feedback in the longer term


Barriers to understanding cloud feedback mechanisms

People analysing cloud feedbacks are often model users and/or from the satellite/model evaluation community, and so climate model cloud diagnostics have so far been designed more for quantitative evaluation than understanding of physical mechanisms.

While more detailed diagnostics are used by model developers and the SCM/CRM community, these are mainly used in the context of present day climate, and are not currently available across climate change experiments.

Currently there is no widely accepted and cost effective experimental framework for investigating the impact of different modelling assumptions on cloud feedbacks or testing hypotheses about cloud feedback mechanisms.


Proposed strategy for CFMIP II

1/ Continue to develop cloud feedback evaluation techniques and tools for evaluating models with cloud satellite products: - ISCCP simulator - CloudSat/CALIPSO simulator

2/ Develop and use process level diagnostics to understand cloud feedback mechanisms. - cloud tendency terms (condensation, precipitation,..) - diabatic heating increments - high frequency / diurnal cycle diagnostics at key locations - GCSS Pacific Cross Section - ARM/BSRN/CloudNet sites



3/ Work with modelling groups to submit detailed cloud diagnostics to systematic climate model inter-comparisons (AR5/CMIP4/CFMIPII)

4/ Develop a ‘lightweight’ idealised experimental framework for studying cloud feedbacks in climate models – e.g. - Hansen style CO2 doubling ‘forcing’ experiments - Brian Soden’s patterned SST perturbations - both relative to 10 year AMIP integrations (1985-1995?)



5/ Run co-ordinated sensitivity experiments to understand the impact of different model assumptions on cloud climate feedbacks

6/ Work with GCSS/parametrization community to understand and assess the credibility of the cloud feedback mechanisms in different models


How do we implement this strategy?

There will be various talks this week which will describe our plans in more detail….

GEWEX/GCSS - CFMIP collaboration is central to the strategy for CFMIP II

We hope that there will be many ideas from people here this week who are not yet involved in CFMIP

We hope to formalise the plans for CFMIP II by the end of the summer based on the recommendations from this workshop


Changes in ISCCP cloud types slab vs +/-2KRinger et al, 2006

Note systematic increases in optical depth of low clouds – indicating increase in liquid water path?

High top

Mid-level

top

Low top

Thin Medium Thick

Ringer et al, 2006

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The Cloud Feedback Model Inter-comparison Project (CFMIP) From CFMIP-1 to CFMIP-2