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© Crown copyright 2006Page 1 The Cloud Feedback Model Intercomparison Project (CFMIP) Progress and future plans Mark Webb, Keith Williams, Mark Ringer, Alejandro Bodas Salcedo, Cath Senior (Hadley Centre) Tomoo Ogura, Yoko Tsushima, (NIES, JAMSTEC Japan) Sandrine Bony, Majolaine Chiriaco (IPSL/LMD) Joao Teixeira (NRL) and CFMIP contributors (BMRC,GFDL,UIUC,MPI,NCAR)
© Crown copyright 2006Page 2 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 +/-2K atmosphere only and 2xCO2 slab experiments Aim to identify key uncertainties Link climate feedbacks to cloud observations ISCCP simulator required (Klein & Jakob, Webb et al) Now have data for 13 GCM versions from 8 groups Website shows data available, publications, plans, etc.
© Crown copyright 2006Page 3 Modelling and Prediction of Climate variability and change CFMIP: Website
© Crown copyright 2006Page 4 Comparison of +/- 2K and slab model experiments Ringer 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 -2 K -1 )
© Crown copyright 2006Page 5 Changes in ISCCP cloud types slab vs +/-2K Ringer et al, 2006 Values are global mean change in each cloud type per degree of warming (% / K) High top Mid-level top Low top Thin Medium Thick Ringer et al, 2006
© Crown copyright 2006Page 6 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 7 CFMIP cloud feedback classes Webb et al 2006 Models with positive low cloud feedback over larger areas have higher sensitivity
© Crown copyright 2006Page 8 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 9 Tsushima et al.,2006 Cloud liquid(2xCO2 -1xCO2 kg/kg)Cloud ice (1xCO2 kg/kg) [hPa] -2e-5 0 2e-50 2e-5 Climate sensitivity [hPa] MIROC-HS MIROC-LS HadSM4 GFDL UIUC
© Crown copyright 2006Page 10 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 11 Williams and Tselioudis (Climate Dyn in revision) ISCCP observational cloud cluster regimes (20N-20S)
© Crown copyright 2006Page 12 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
© Crown copyright 2006Page 13 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 Will become part of standard IPCC diagnostic protocol by time of AR5 (agreed by WGCM Sep 06)
© Crown copyright 2006Page 14 CFMIP Phase II – looking further ahead Understanding of modelled cloud-climate feedback mechanisms Evaluation of model clouds using observations Assessment of cloud-climate feedbacks Co-ordinators: Mark Webb, Sandrine Bony, Rob Colman Project advisor: Bryant McAvaney Main objective : A better assessment of modelled cloud-climate feedbacks for IPCC AR5
© Crown copyright 2006Page 15 Modelling and Prediction of Climate variability and change CFMIP Phase II – planned approach Develop improved cloud diagnostic techniques in climate models : - CFMIP CloudSat/CALIPSO simulator - Cloud water budget / tendency terms - 3 hourly data at key locations (ARM sites, GPCI ) Explore the sensitivities of cloud feedbacks to differing model assumptions using idealised climate change experiments Demonstrate the application of these techniques to the understanding and evaluation of cloud climate feedbacks via pilot studies Organise a systematic cloud feedback model comparison with the next generation of climate models (ideally by embedding suitable cloud diagnostics in the AR5 experimental protocol.)
© Crown copyright 2006Page 16 Modelling and Prediction of Climate variability and change CFMIP CloudSat/CALIPSO simulator (C3S) This is a modular cloud simulator framework which will allow a number of cloud simulator modules to be plugged into climate models via a standard interface. This is currently under development in collaboration with various groups: - Hadley Centre (Alejandro Bodas-Salcedo, Mark Webb, Mark Ringer) - LLNL (Steve Klein, Yuying Zhang) - LMD/IPSL (Marjolaine Chiriaco, Sandrine Bony) - CSU (Johnny Lyo, John Haynes, Graeme Stephens) - PNNL ( Roger Marchand )
© Crown copyright 2006Page 17 C3S/CloudSat comparison with UK NWP model (Alejandro 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 18 ACTSIM 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 19 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 20 [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 21 GCSS/WGNE Pacific Cross-section Intercomparison (GPCI) ISCCP total cloud cover Sea Surface Temperature GPCI is a working group of the GEWEX Cloud System Study (GCSS) Models and data are analyzed along a Pacific Cross section from Stratocumulus, to Cumulus and to deep convection Period: June-July-August 1998 and 2003 Time resolution: 0, 3, 6, 9, 12, 15, 18, 21 UTC Joao Teixeira
© Crown copyright 2006Page 22 g/kg Mean GPCI liquid water cross section - JJA98 from three climate models liquid water Too shallow -> fog Is this too much liquid water? How deep should the PBL be..? Joao Teixeira
© Crown copyright 2006Page 23 Mean diurnal cycle: ISCCP cloud cover peak values of Sc cloud cover around N peak values of mid/high clouds close to ITCZ Diurnal cycle: max in (early) morning local time Joao Teixeira
© Crown copyright 2006Page 24 JJA98 mean diurnal cycle: model low cloud cover peak values too far to the south (around 26 N) realistic diurnal cycle: morning max Joao Teixeira
© Crown copyright 2006Page 25 Modelling and Prediction of Climate variability and change Proposed point diagnostics for CFMIP Phase II - 3 hourly instantaneous model data years of data to give stable statistics - on a grid of locations covering the GPCI and TWP - additional key locations (e.g. ARM sites, high latitudes) - in AMIP and idealised climate change experiments The aim is to align climate model diagnostics with those in use in GCSS/ARM to encourage a wider group of people to examine and criticise cloud feedbacks in climate models For example this would give insight into the impact of diurnal cycle errors on cloud-climate feedbacks
© Crown copyright 2006Page 26 Modelling and Prediction of Climate variability and change CFMIP Phase II physics sensitivity tests Sensitivity tests are proposed using the idealised climate change experiments developed by Brian Soden: i.e. AMIP and AMIP + composite CMIP SST anomaly The aim is to quantify the impact of certain differences in model formulation on cloud-climate feedbacks by implementing consistent treatments across models Possible examples include: - fixed liquid cloud water content and radiative properties - consistent precipitation & mixed phase partitioning - consistent boundary layer resolution - consistent simple shallow convection scheme? - 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 CFMIP Phase II timescales Concrete project proposal by Jan 2007 Aim for endorsement by WGCM and GEWEX SSG in early 2007 Joint CFMIP/ENSEMBLES meeting Paris April 2007 Development of diagnostics / pilot studies Systematic model inter-comparison with new model versions (preferably as part of AR5 models)
© Crown copyright 2006Page 28 Modelling and Prediction of Climate variability and change Use of CERES/GERB products in CFMIP PII CFMIP Phase I – mostly ISCCP/ERBE/ISCCP_FD All of the following will benefit CFMIP Phase II: CERES SRBAVG GEO, SYN/AVG/ZAVG products CERES/MODIS cloud retrievals CloudSat/CALIPSO/CERES merged products Surface + ATM + TOA products (e.g. SARB) Diurnal cycle GEO/GERB data The barriers are often in bringing the sampling and statistical summaries from satellite products and GCM diagnostics into line – e.g. providing ISCCP D1-like tau-Pc histograms
© Crown copyright 2006Page 29 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 1 The Cloud Feedback Model Intercomparison Project (CFMIP) Progress and future plans Mark Webb (Hadley Centre) and CFMIP contributors.
© 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 Met Office The Cloud Feedback Model Intercomparison Project Plans for CFMIP-2 Mark Webb (Met Office Hadley Centre) Hadley Centre Seminar,
© 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 Towards understanding the mechanisms responsible for different cloud-climate responses in GCMs. Mark Webb, Adrian Lock (Met.
Numerical Weather Prediction Parametrization of diabatic processes Clouds (4) Cloud Scheme Validation Richard Forbes and Adrian Tompkins
Robin Hogan (with input from Anthony Illingworth, Keith Shine, Tony Slingo and Richard Allan) Clouds and climate.
Page 1© Crown copyright 2007 Constraining the range of climate sensitivity through the diagnosis of cloud regimes Keith Williams 1 and George Tselioudis.
Page 1© Crown copyright Simulation of radar reflectivities in the UK Met Office model: comparison with CloudSat Data Alejandro Bodas-Salcedo, M.E. Brooks.
Cloud Resolving Models: Their development and their use in parametrization development Richard Forbes, Adrian Tompkins.
ESA Climate Change Initiative Climate Modelling User Group CMUG
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.
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.
Robin Hogan Anthony Illingworth, Ewan OConnor, Jon Shonk, Julien Delanoe, Andrew Barratt University of Reading, UK And the Cloudnet team: D. Bouniol, M.
Clouds and their turbulent environment Robin Hogan, Andrew Barrett, Natalie Harvey Helen Dacre, Richard Forbes (ECMWF) Department of Meteorology, University.
Research and Development Project Improving the prediction of heavy precipitating systems over La Plata Basin LPB-ReD Presented by Alice M. Grimm Based.
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,
Report from DAOS-WG Roger Saunders and Tom Hamill with input from WG members.
© University of Reading Richard Allan Department of Meteorology, University of Reading Thanks to: Jim Haywood.
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.
Robin Hogan Julien Delanoe, Ewan OConnor, Anthony Illingworth, Jonathan Wilkinson University of Reading, UK Quantifying the skill of cloud forecasts from.
Page 1© Crown copyright Operational Use of ECMWF products at the Met Office: Current practice, Verification and Ideas for the future Tim Hewson 17 th June.
International efforts in Climate Modeling Projections, Predictions and Downscaling Coordinated by the World Climate Research Program (WCRP) CMIP5: The.
Robin Hogan Ewan OConnor, Anthony Illingworth University of Reading, UK Clouds radar collaboration meeting 17 Nov 09 Ground based evaluation of cloud forecasts.
Data Assimilation Training Course, Reading, 5-14 May 2010 Observation Operators in Variational Data Assimilation David Tan, Room 1001
Reading, UK Parametrizations and data assimilation © ECMWF 2012 Marta JANISKOVÁ ECMWF Parametrizations and data assimilation.
IRS2004, Busan, August 2004 Using Satellite Observations and Reanalyses to Evaluate Climate and Weather Models Richard Allan Environmental Systems Science.
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