the cloud feedback model intercomparison project (cfmip) progress and future plans

© Crown copyright 2006

The Cloud Feedback Model Intercomparison Project (CFMIP)

Progress and future plansMark Webb (Hadley Centre)

and CFMIP contributors

(Hadley Centre,IPSL, NIES/CCSR,BMRC,GFDL, MPI, NCAR,UIUC)

September 2006


Outline

CFMIP background

Low cloud feedbacks and spread in climate sensitivity

Reducing uncertainty in low cloud feedbacks

Future plans for CFMIP


Modelling and Prediction of Climate variability and change

CFMIP: Cloud Feedback Model Inter-comparison Project

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

• WCRP Working Group on Coupled Modelling (WGCM)

• Systematic intercomparison of cloud feedbacks in GCMs

• Aim to identify key uncertainties

• Link climate feedbacks to cloud observations

•+/-2K atmosphere only and 2xCO2 ‘slab’ experiments

• ISCCP simulator (Klein & Jakob, Webb et al) mandatory

• Now data for 13 GCM versions from 8 modelling groups

• Website shows data available, publications, plans, etc.

• http://www.cfmip.net



Low cloud feedbacks and spread in climate sensitivity

Recent studies have attributed much of the spread in climate sensitivity in climate models to differences in low cloud feedbacks:

Bony & Dufresne 2005 (15 coupled AR4 models) Wyant et al 2006 (3 CPT* models) Webb et al 2006 (9 CFMIP mixed layer models)

* Low Latitude Cloud Feedback Climate Process Team (CPT)


Bony and Dufresne GRL 2005

Cloud radiative forcing (CRF) climate response in vertical velocity bins over tropical oceans (30N-30S) from 15 coupled climate models8 higher sensitivity and 7 lower sensitivity

Net CRF spread largest in subsidence regions, suggesting low clouds are ‘at the heart’ of cloud feedback uncertainties


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 feedbacksexplain 59% ofthe NET cloudfeedbackensemblevariance

Cloud feedbacksin these areasare indeeddominated byreductions inlow level cloudamount (shownwith ISCCPsimulator)


Reducing uncertainty in low cloud feedbacks

Two basic strategies:

1/ find relationships between climate feedbacks and observables and use these as a basis for constraining climate feedbacks

2/ understand the physics of feedback mechanisms in models and fix aspects which are not credible, hoping that model differences will reduce in the long term

These strategies can be pursued in parallel


Williams and Tselioudis (Climate Dyn submitted)

ISCCP observational cloud cluster regimes (20N-20S)


nclusters

i

ii

nclusters

i

ii

nclusters

i

ii CRFRFOCRFRFORFOCRFCRF111

In the cloud regime framework, the mean change in cloud radiative forcing can be thought of as having contributions from:

•A change in the RFO (Relative Frequency of Occurrence) of the regime

•A change in the CRF (Cloud Radiative Forcing) within the regime (i.e. a change in the tau-CTP space occupied by the cluster/development of different clusters).

Williams and Tselioudis (CFMIP daily data)


HadSM3 Cloud Response along GCSS Pacific Cross Section transect

Mid-level cloud categories excluded.

SW CRF response along Pacific transect in CFMIP and AR4 slab models. Cloud feedbacks are typically smaller than model biases. No clear relationship between bias and feedback


HadSM3 Cloud Response along GCSS Pacific Cross Section transect(Negative low cloud feedback case)

Mid-level cloud categories excluded.


Low cloud response in HadSM3 transition regions

Deep and shallow convection weaken in the warmer climate (consistent with Held and Soden 2006 )

Shallow convection typically detrains into two model layers in present day, but one level in the warmer climate

If a certain amount of water vapour is detrained into a single layer it will moisten that layer more than would be the case if it was spread over two layers

May explain why HadSM3 stratiform cloud fraction increases with weakening shallow convection

Hence the negative low cloud feedback in HadSM3 may be due to poor vertical resolution and so not credible


HadGSM1 Cloud Response along GCSS Pacific Cross Section transect



CFMIP Phase I continues…

Daily cloud diagnostics to be hosted by PCMDI

To be made available as a community resource

UKMO, MIROC, MPI, NCAR data currently in transit

IPSL, Env Canada promised later this year

Will allow many ISCCP cloud studies to be applied to a representative selection of climate models

Plans to build cloud clustering into ISCCP simulator to support its routine use in model evaluation



CFMIP Phase II – looking further ahead

The CFMIP Phase I experimental protocol will ideally become part of the IPCC requirements for AR5

CFMIP Phase II will aim to improve our understanding of cloud feedback mechanisms over the next 5 years by:

- using idealised climate experiments (e.g. patterned SST forcing)

- exploring sensitivities to model physics - developing more detailed model diagnostics



Proposed new diagnostics for CFMIP Phase II

CFMIP Cloudsat/CALIPSO Simulator (C3S)

Sub-timestep information- Cloud condensate budget terms- Physics increments for temperature, humidity, etc

Detailed diagnostics at key locations as used in GCSS/ARM studies (e.g GPCI)



CFMIP CloudSat/CALIPSO simulator (C3S)

Under development by Alejandro Bodas-Salcedo and Marjolaine Chiriaco (Hadley Centre, LMD/IPSL)

A package to simulate radar and lidar reflectivities from within models to support model validation using CloudSat/CALIPSO data


C3S/CloudSat comparison with NWP model(A. Bodas-Salcedo)

B

A

AB

Transect trough a mature extra-tropical systemStrong signal from ice clouds

Strong signal from precipitation

Cloud and precip not present in obs


C3S LIDAR comparison with GLAS / ICESat data

(M. Chiriaco LMD/IPSL)

Lidar signal simulated from LMDZ outputs

Lidar signal observed from GLAS spatial lidar

latitude

z, k

mz,

km

signal

Evaluation of the vertical structure of the atmosphere in models,

at global scale

Indicates excessive reflectivities from cloud ice in this climate model


Cloud water budget / process diagnostics (Tomoo Ogura NIES Japan)

t

ice)Qc(liqCondensation + Precipitation + Ice-fall-in + Ice-fall-out

+ Advection + +Cumulus Conv.

(1) (2)

(3) (4) (5)

Source term

Qc response

- Transient response of terms (1)…(5) to CO2 doubling is monitored every year.

- Terms showing positive correlation with cloud water response contribute to the cloud water variation.

mixing adjustment

response(1)or…(5)

Qc increase related to thesource term

Qc decrease related to thesource term


[Cloud water vs ]

90S 60S 30S EQ 30N 60N 90N90S 60S 30S EQ 30N 60N 90N1.0

0.6

0.2sigma

1.0

0.6

0.2

ΔCloud water (2xco2 - 1xco2, DJF)

-1 0 1

cloud decrease increase

(5)Dry conv. adj.(4)Cumulus(3) Advection(2) Ice fall in+out (1)Condens-Precip

Correlation coefficient (when positive)

contour=3e-6 [kg/kg]

Cloud water budget diagnosis in MIROC (Tomoo Ogura NIES Japan)

Decrease in mid-low level sub-tropical cloud water correlates with decrease in large-scale condensation



Detailed diagnostics at key locations

CFMIP Phase II is an opportunity to align diagnostics in a range of climate models with those in use in other areas such as GCSS, ARM and the low cloud CPT

We could provide data from AMIP and idealised climate warming simulations – for example:

3 hourly or timestep data at selected locations- GPCI points

- ARM sites - could include forcing data for SCMs/CRMs - other suggestions welcome…


Summary

A number of studies now point to low cloud feedbacks being a key uncertainty in climate models

Daily cloud/radiation/ISCCP simulator diagnostics from CFMIP Phase I will be available from PCMDI by the end of the year

Reductions in uncertainty due to cloud feedbacks will require links to observations and also understanding and criticism of feedback mechanisms in models

CFMIP Phase II is an opportunity to align diagnostics in a range of models with those in use in GCSS/ARM/CPT

Doing so should allow a wider range of expertise to be applied to analysis of multi-model cloud climate feedbacks


Hawaii – California transect: IPCC AR4 workshop March 2005

[email protected]



Alternative experimental setups

Cess +/-2K fixed season experiments are not a quantitative guide to coupled model feedbacks - no seasonal cycle - no high latitude amplification of warming

Alternative options include: - continuing use of mixed layer experiments

- re-running sections of AR4 coupled experiments with extra diagnostics - ‘patterned SST perturbation’ experiments as developed by Brian Soden (possibly AMIP+)



Proposed climate physics sensitivity tests

Consistent implementation of simplified physics in climate models to identify which contribute most

to spread in cloud feedbacks – e.g.

- suppression of interactive cloud radiative properties- simplified mixed-phase feedback processes- simplified clouds and precipitation - consistent treatment of shallow convection

Also possible tests of the effects of differing deep convective entrainment/detrainment on climate sensitivity

New process diagnostics will aid the interpretation of these experiments



Publications using CFMIP data

Williams and Tselioudis 2006 GCM intercomparison of global cloud regimes - Part I: Evaluation of present-day cloud regimes. Clim. Dyn. Submitted.

Williams and Tselioudis 2006 GCM intercomparison of global cloud regimes - Part II: Climate change response and evaluation of the change in the radiative properties of cloud regimes. Clim. Dyn. Submitted.

Ogura et al 2006 Climate sensitivity of a general circulation model with different cloud modelling assumptions. J. Meteor. Soc. Japan Submitted.

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. 27

Webb et al 2006 On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles. Clim. Dyn. 27

Ringer et al 2006 Global mean cloud feedbacks in idealized climate change experiments. Geophys. Res. Lett. 33

Williams et al 2006 Evaluation of a component of the cloud response to climate change in an intercomparison of climate models. Clim. Dyn. 26



CFMIP: Website http://www.cfmip.net



Further links between feedbacks and observables

New diagnostics will provide opportunities for new links

e.g. CloudSat/CALIPSO data may constrain models with strong mixed-phase cloud feedbacks due to excessive amounts of cloud ice

This will be an ongoing area of research, and the focus of a joint ENSEMBLES/CFMIP meeting in Paris 11th-13th April 2007



CFMIP Phase II timescales

Concrete project proposal by Jan 2007

Aim for endorsement by WGCM Mar 07

Joint CFMIP/ENSEMBLES meeting Paris April 2007

Development of diagnostics / pilot studies 2007-2008

Systematic model inter-comparison with new model versions 2008-2009


Bony and Dufresne (2005)

Interannual sensitivity of CRF to SST in vertical velocity bins over tropical oceans (30N-30S) from 15 coupled climate models8 higher sensitivity and 7 lower sensitivity andobserved (ISCCP FD)

Models underpredict low cloud sensitivity, but higher sensitivity models less so.


Williams et al Climate Dynamics 2006 (CFMIP)

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


CFMIP CloudSat/CALIPSO simulator (Alejandro Bodas-Salcedo)

The simulator consists of 5 steps:

1. Orbital simulation2. Sampling3. Preprocessing4. Subgrid sampling of cloud

overlaps5. Radar reflectivity calculated

using code provided by Matt Rogers (CSU)

Simulated CloudSat reflectivities from UKMO forecast model

Outputs:

- Reflectivity from clouds and precipitation (without attenuation)

- Total reflectivity, accounting for attenuation by gases, clouds and precipitation

- Products obtained from inputs at gridbox and subgrid scales


ACTSIM

Lidar Equation

•Optical properties: P(,z), Qsca(z), Qabs(z) (532 nm)

•Lidar calibration

•Multiple scattering

ex. lidar profile (log)

ex. particles concentration profile

(/m3)

ex. water content profile (g/m3)

snow

ice

liqui

d

ice

ACTSIM CALIPSO simulator

– Marjolaine Chiriaco (LMD/IPSL)



CFMIP Phase II C3S inter-comparison

Initially we will provide an A-train orbital simulator to allow climate modellers to save model cloud variables co-located with CloudSat/CALIPSO overpasses

Initially sampled data would be submitted to CFMIP and both simulators run centrally

As the approach matures we plan to integrate this package with the ISCCP simulator so that it can be run in-line as part of the model development cycle


Reducing uncertainty by understanding feedbacks

1/ Try to understand how physical cloud climate feedback processes are operating in models

2/ Ask ‘Is this behaviour credible?’

3/ Develop new schemes with more credible cloud-climate feedback behaviour in mind

4/ Differences between model feedbacks may reduce in the longer term

This approach has much in common with that of the low latitude cloud feedback CPT

the cloud feedback model intercomparison project (cfmip) progress and future plans

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