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© Crown copyright 2006Page 1 The Cloud Feedback Model Intercomparison Project (CFMIP) Progress and future plans Mark Webb (Hadley Centre) and CFMIP contributors (Hadley Centre,IPSL, NIES/CCSR,BMRC,GFDL, MPI, NCAR,UIUC) September 2006
© Crown copyright 2006Page 2 Outline CFMIP background Low cloud feedbacks and spread in climate sensitivity Reducing uncertainty in low cloud feedbacks Future plans for CFMIP
© Crown copyright 2006Page 3 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 2xCO 2 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.
© Crown copyright 2006Page 4 Modelling and Prediction of Climate variability and change 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)
© Crown copyright 2006Page 5 Bony and Dufresne GRL 2005 Cloud radiative forcing (CRF) climate response in vertical velocity bins over tropical oceans (30N-30S) from 15 coupled climate models 8 higher sensitivity and 7 lower sensitivity Net CRF spread largest in subsidence regions, suggesting low clouds are at the heart of cloud feedback uncertainties
© Crown copyright 2006Page 6 Webb et al Climate Dynamics 2006 – CFMIP models LW cloud feedback (W/m 2 /K) SW cloud feedback (W/m 2/ /K) Net cloud feedback (W/m 2 /K) LW cloud feedback (W/m 2 /K) SW cloud feedback (W/m 2/ /K) Net cloud feedback (W/m 2 /K) Areas with small LW cloud feedbacks explain 59% of the NET cloud feedback ensemble variance Cloud feedbacks in these areas are indeed dominated by reductions in low level cloud amount (shown with ISCCP simulator)
© Crown copyright 2006Page 7 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
© Crown copyright 2006Page 8 Williams and Tselioudis (Climate Dyn submitted) ISCCP observational cloud cluster regimes (20N-20S)
© Crown copyright 2006Page 9 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)
© Crown copyright 2006Page 10 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
© Crown copyright 2006Page 11 HadSM3 Cloud Response along GCSS Pacific Cross Section transect (Negative low cloud feedback case) Mid-level cloud categories excluded.
© Crown copyright 2006Page 12 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
© Crown copyright 2006Page 13 HadGSM1 Cloud Response along GCSS Pacific Cross Section transect
© Crown copyright 2006Page 14 Modelling and Prediction of Climate variability and change 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
© Crown copyright 2006Page 15 Modelling and Prediction of Climate variability and change 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
© Crown copyright 2006Page 16 Modelling and Prediction of Climate variability and change 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)
© Crown copyright 2006Page 17 Modelling and Prediction of Climate variability and change 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
© Crown copyright 2006Page 18 C3S/CloudSat comparison with NWP model (A. Bodas-Salcedo) B A AB Transect trough a mature extra-tropical system Strong signal from ice clouds Strong signal from precipitation Cloud and precip not present in obs
© Crown copyright 2006Page 19 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, 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
© Crown copyright 2006Page 20 Cloud water budget / process diagnostics (Tomoo Ogura NIES Japan) Condensation + Precipitation + Ice-fall-in + Ice-fall-out + Advection + + CumulusConv. (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. mixingadjustment response (1)or…(5) Qc increase related to the source term Qc decrease related to the source term
© Crown copyright 2006Page 21 [Cloud water vs ] 90S 60S 30S EQ 30N 60N 90N sigma ΔCloud water (2xco2 - 1xco2, DJF) 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
© Crown copyright 2006Page 22 Modelling and Prediction of Climate variability and change 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…
© Crown copyright 2006Page 23 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
© Crown copyright 2006Page 24 Hawaii – California transect: IPCC AR4 workshop March 2005
© Crown copyright 2006Page 25 Modelling and Prediction of Climate variability and change 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+)
© Crown copyright 2006Page 26 Modelling and Prediction of Climate variability and change 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
© Crown copyright 2006Page 27 Modelling and Prediction of Climate variability and change 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
© Crown copyright 2006Page 28 Modelling and Prediction of Climate variability and change CFMIP: Website
© Crown copyright 2006Page 29 Modelling and Prediction of Climate variability and change 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 11 th -13 th April 2007
© Crown copyright 2006Page 30 Modelling and Prediction of Climate variability and change 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 Systematic model inter-comparison with new model versions
© Crown copyright 2006Page 31 Bony and Dufresne (2005) Interannual sensitivity of CRF to SST in vertical velocity bins over tropical oceans (30N-30S) from 15 coupled climate models 8 higher sensitivity and 7 lower sensitivity and observed (ISCCP FD) Models underpredict low cloud sensitivity, but higher sensitivity models less so.
© Crown copyright 2006Page 32 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) ERBE/ NCEP LW ERBE /ERA SW ERBE /ERA LW ERBE/ NCEP SW Avg RMS 3.0K K 3.8K K Avg Climate Sens.& Range
© Crown copyright 2006Page 33 CFMIP CloudSat/CALIPSO simulator (Alejandro Bodas-Salcedo) The simulator consists of 5 steps: 1. Orbital simulation 2. Sampling 3. Preprocessing 4. Subgrid sampling of cloud overlaps 5.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
© Crown copyright 2006Page 34 ACTSIM Lidar Equation Optical properties: P(,z), Q sca (z), Q abs (z) (532 nm) Lidar calibration Multiple scattering ex. lidar profile (log) ex. particles concentration profile (/m 3 ) ex. water content profile (g/m 3 ) snow ice liquid ice ACTSIM CALIPSO simulator – Marjolaine Chiriaco (LMD/IPSL)
© Crown copyright 2006Page 35 Modelling and Prediction of Climate variability and change 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
© Crown copyright 2006Page 36 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
© Crown copyright 2006Page 1 The Cloud Feedback Model Intercomparison Project (CFMIP) Progress and future plans Mark Webb, Keith Williams, Mark Ringer,
© Crown copyright 2006Page 1 CFMIP II Plans Mark Webb (Met Office Hadley Centre) Sandrine Bony (IPSL) Rob Colman (BMRC) with help from many others… CFMIP/ENSEMBLES.
© Crown copyright 2006Page 1 CFMIP II sensitivity experiments Mark Webb (Met Office Hadley Centre) Johannes Quaas (MPI) Tomoo Ogura (NIES) With thanks.
© Crown copyright Met Office The Cloud Feedback Model Intercomparison Project Plans for CFMIP-2 Mark Webb (Met Office Hadley Centre) Hadley Centre Seminar,
Numerical Weather Prediction Parametrization of diabatic processes Clouds (4) Cloud Scheme Validation Richard Forbes and Adrian Tompkins
© Crown copyright Met Office Towards understanding the mechanisms responsible for different cloud-climate responses in GCMs. Mark Webb, Adrian Lock (Met.
Robin Hogan (with input from Anthony Illingworth, Keith Shine, Tony Slingo and Richard Allan) Clouds and climate.
Cloud Resolving Models: Their development and their use in parametrization development Richard Forbes, Adrian Tompkins.
Page 1© Crown copyright 2007 Constraining the range of climate sensitivity through the diagnosis of cloud regimes Keith Williams 1 and George Tselioudis.
1 Recent (selected) results from CloudSat and the A-Train Graeme L Stephens Co-op Institute for Res. Atmosphere (CIRA) and Dept Atmospheric Sciences Colorado.
Met Office Hadley Centre, FitzRoy Road, Exeter, Devon, EX1 3PB United Kingdom Tel: +44 (0) Fax: +44 (0)
WWRP 1 October 2010 THORPEX report to the WGNE David Burridge THORPEX IPO.
Robin Hogan Anthony Illingworth Andrew Barrett Nicky Chalmers Julien Delanoe Lee Hawkness-Smith Clouds processes and climate Ewan OConnor Kevin Pearson.
ESA Climate Change Initiative Climate Modelling User Group CMUG
1 All Rights Reserved. No part of this document may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic,
Page 1© Crown copyright Simulation of radar reflectivities in the UK Met Office model: comparison with CloudSat Data Alejandro Bodas-Salcedo, M.E. Brooks.
Peter Caldwell, Steve Klein, and Yunyan Zhang (LLNL) Xin Qu, Alex Hall (UCLA) Lawrence Livermore National Lab, CA UW Atmos Phy and Chem Seminar, 1/31/11.
Training Course 2009 – NWP-PR: The Seasonal Forecast System at ECMWF 1 The Seasonal Forecast System at ECMWF Tim Stockdale European Centre for Medium-Range.
Page 1© Crown copyright 2007 CFMIP2: Options for SST-forced and slab experiments Mark Ringer, Brian Soden Hadley Centre,UK & RSMA/MPO, US CFMIP/ENSEMBLES.
Clouds and their turbulent environment Robin Hogan, Andrew Barrett, Natalie Harvey Helen Dacre, Richard Forbes (ECMWF) Department of Meteorology, University.
Data Assimilation Training Course, Reading, 5-14 May 2010 Observation Operators in Variational Data Assimilation David Tan, Room 1001
SPARC Themes Stratospheric Processes and their Role in Climate (SPARC) A Core Project of the World Climate Research Programme Co-Chairs: Ted Shepherd and.
Robin Hogan Julien Delanoe, Ewan OConnor, Anthony Illingworth, Jonathan Wilkinson University of Reading, UK Quantifying the skill of cloud forecasts from.
Robin Hogan Anthony Illingworth, Ewan OConnor, Jon Shonk, Julien Delanoe, Andrew Barratt University of Reading, UK And the Cloudnet team: D. Bouniol, M.
International efforts in Climate Modeling Projections, Predictions and Downscaling Coordinated by the World Climate Research Program (WCRP) CMIP5: The.
J. M. Gregory,* 1,2 W. J. Ingram, 2 M. A. Palmer, 3 G. S. Jones, 2 P. A. Stott, 2 R. B. Thorpe, 2 J. A. Lowe, 2 T. C. Johns, 2 and K. D. Williams 2
1 Reanalysis: Data assimilation aspects Paul Poli ECMWF Meteorological Training Course Data Assimilation and Use of Satellite Data.
Robin Hogan, Julien Delanoe Department of Meteorology, University of Reading, UK Richard Forbes European Centre for Medium Range Weather Forecasts Alejandro.
Report from DAOS-WG Roger Saunders and Tom Hamill with input from WG members.
1 Joint Frequency Distributions for Future European Climate Change Glen Harris, Ben Booth, Kate Brown, Mat Collins, James Murphy, David Sexton, Mark Webb.
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