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© Crown copyright 2006Page 1 CFMIP II sensitivity experiments Mark Webb (Met Office Hadley Centre) Johannes Quaas (MPI) Tomoo Ogura (NIES) With thanks to Adrian Lock, Damian Wilson, Andy Jones, Alejandro Bodas Salcedo CFMIP/ENSEMBLES Workshop Paris, April 2007
© Crown copyright 2006Page 2 Modelling and Prediction of Climate variability and change Motivation and approach We propose running sensitivity experiments to investigate the impact of different modelling assumptions on cloud feedbacks across models Two types of sensitivity experiments are proposed: 1/ Where certain radiative feedbacks loops are cut 2/ Where elements of model physics are simplified
© Crown copyright 2006Page 3 Modelling and Prediction of Climate variability and change For example: Fix cloud liquid water contents and radiative properties seen by radiation Does suppressing any cloud liquid water content feedback make cloud feedback more positive? Does inter-model spread in cloud feedback reduce? If so, by how much? 1/ Radiative feedback loop cutting experiments
© Crown copyright 2006Page 4 Modelling and Prediction of Climate variability and change For example: Put a simple stability based low cloud fraction into several models Do low level cloud feedbacks become more negative/less positive? What is the effect on inter-model spread? 2/ Replacing parametrizations with simple alternatives
© Crown copyright 2006Page 5 Modelling and Prediction of Climate variability and change HadGEM2 + PC2 development version (Met Office) PC2 is a Tiedtke-like cloud scheme with prognostic equations for cloud liquid, cloud ice and cloud fraction ECHAM5 – Tiedtke scheme (Johannes Quaas) MIROC3.2 - statistical/PDF scheme (Tomoo Ogura) So far we have results for fixed liquid cloud properties for PC2 and ECHAM5 Control runs are 10 year AMIP runs Climate change: control + CMIP 1% patterned SST composite Liquid cloud droplet effective radius seen by radiation: 7 microns In cloud liquid water content seen by radiation: 0.2 g/kg Pilot study (three models)
© Crown copyright 2006Page 6 Modelling and Prediction of Climate variability and change Impact of fixed liquid cloud radiative properties Global mean net cloud radiative response is increased in both models and this effect comes mainly from the SW - this is consistent with what we expected However the effect is much larger in ECHAM5 than in PC2, making the two models diverge rather than converge
© Crown copyright 2006Page 7 Modelling and Prediction of Climate variability and change Impact on control simulations Fixing the liquid cloud radiative properties has made both of the models too bright with the biggest impact in ECHAM5 We plan to retune the models by applying a scaling factor to the liquid cloud fraction seen by the radiation. We may also consider using a larger effective radius
© Crown copyright 2006Page 8 Modelling and Prediction of Climate variability and change Use of tendency diagnostics and GPCI transect We also plan to use cloud condensate tendency diagnostics to understand the feedback mechanisms operating in the reference and sensitivity experiments The GCSS Pacific Cross Section Intercomparison ( GPCI ) transect samples stratocumulus, trade cumulus and deep convective regimes as well as the transitions between them Some examples with PC2 follow….
© Crown copyright 2006Page 9 Modelling and Prediction of Climate variability and change Low cloud response in the PC2 experiments
© Crown copyright 2006Page 10 Modelling and Prediction of Climate variability and change Low cloud response in the PC2 experiments
© Crown copyright 2006Page 11 Modelling and Prediction of Climate variability and change Low cloud fraction decreases along the GPCI when we fix liquid cloud radiative properties and when we warm the climate What are the possible explanations? Hypothesis 1 weaker circulation => reduced subsidence => weaker inversion => cloud breakup Can we rule out this hypothesis in any of the above cases? Low cloud response in the PC2 experiments
© Crown copyright 2006Page 12 Modelling and Prediction of Climate variability and change Low cloud response in the PC2 experiments Overlaid contour lines show liquid cloud fraction…
© Crown copyright 2006Page 13 Modelling and Prediction of Climate variability and change Hypothesis 2 Reduced convective mass flux (Held and Soden 2006) => less detrainment from shallow convection => less low level stratiform cloud Low cloud response in the PC2 experiments
© Crown copyright 2006Page 14 Modelling and Prediction of Climate variability and change Low cloud response in the PC2 experiments Overlaid contour lines show liquid cloud fraction
© Crown copyright 2006Page 15 Modelling and Prediction of Climate variability and change Hypothesis 3 (climate response only) Upper troposphere warms more than lower troposphere as climate models warm (e.g Santer 2005) => warmer (and possibly moister) free troposphere => less LW cooling at BL cloud top => less condensation => less cloud water / cloud fraction ( Note that the effect on cloud fraction could well be the opposite in any model where the cloud fraction is represented as an increasing as function of stability ) Low cloud response in the PC2 experiments
© Crown copyright 2006Page 16 Modelling and Prediction of Climate variability and change Low cloud response in the PC2 experiments Overlaid contour lines show liquid cloud fraction
© Crown copyright 2006Page 17 Modelling and Prediction of Climate variability and change Low cloud response in the PC2 experiments Overlaid contour lines show liquid cloud fraction
© Crown copyright 2006Page 18 Modelling and Prediction of Climate variability and change Low cloud response in the PC2 experiments Dynamical forcing may be responsible for some but not all changes Shallow convection may well drive low cloud feedbacks in the trades but doesnt explain the response closer to the coast Cloud top cooling may well play a role in driving the reductions in low level clouds, particularly closer to the coast
© Crown copyright 2006Page 19 Modelling and Prediction of Climate variability and change 1/ Replace liquid cloud fraction seen by radiation with a simple stability based relationship 2/ Make the radiation code see warmer temperatures above the BL and see if this reduces cloud top cooling and in turn reduces low level cloud 3/ Simplified mixed phase feedback experiment 4/ Simplified autoconversion formulation experiment Other suggestions? Other potential sensitivity tests:
© Crown copyright 2006Page 20 Modelling and Prediction of Climate variability and change The pilot study may demonstrate sensitivity tests to be useful, but the experiments will require retuning Cloud condensate tendency diagnostics provide extra information that can be used to test or suggest hypotheses on the roles of different physical processes in cloud feedback mechanisms Feedback patterns in 10 year AMIP + CMIP 1% patterned SST experiments are quite noisy compared to slab responses patterns Conclusions
© Crown copyright 2006Page 21 Modelling and Prediction of Climate variability and change Low cloud response in the PC2 experiments Overlaid contour lines show liquid cloud fraction…
© Crown copyright 2006Page 22 Modelling and Prediction of Climate variability and change Low cloud response in the PC2 experiments
© Crown copyright 2006Page 23 Modelling and Prediction of Climate variability and change Low cloud response in the PC2 experiments
© Crown copyright Met Office Towards understanding the mechanisms responsible for different cloud-climate responses in GCMs. Mark Webb, Adrian Lock (Met.
© Crown copyright Met Office CFMIP-2 techniques for understanding cloud feedbacks in climate models. Mark Webb (Met Office Hadley Centre) CFMIP/GCSS BLWG.
Met Office Hadley Centre, FitzRoy Road, Exeter, Devon, EX1 3PB United Kingdom Tel: +44 (0) Fax: +44 (0)
© 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.
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.
The Cloud Feedback Model Intercomparison Project Plans for CFMIP-2
© Crown copyright Met Office CFMIP-GCSS Intercomparison of SCM/LES (CGILS) Results for the HadGEM2 SCM Mark Webb and Adrian Lock (Met Office) EUCLIPSE/GCSS.
Page 1© Crown copyright 2007 Initial tendencies of cloud regimes in the Met Office Unified Model Keith Williams and Malcolm Brooks Met Office, Hadley Centre.
Page 1© Crown copyright 2007 Constraining the range of climate sensitivity through the diagnosis of cloud regimes Keith Williams 1 and George Tselioudis.
© Crown copyright Met Office Using stability composites to analyse cloud feedbacks in the CMIP3/CFMIP-1 slab models. Mark Webb (Met Office) CFMIP-GCSS.
© Crown copyright 2006Page 1 The Cloud Feedback Model Intercomparison Project (CFMIP) Progress and future plans Mark Webb (Hadley Centre) and CFMIP contributors.
1 03/0045a © Crown copyright Evaluating water vapour in HadAM3 with 20 years of satellite data Richard P. Allan Mark A. Ringer Met Office, Hadley Centre.
Robin Hogan (with input from Anthony Illingworth, Keith Shine, Tony Slingo and Richard Allan) Clouds and climate.
© Crown copyright Met Office Met Office SCM and CRM results Adrian Lock, Met Office, UK.
© Crown copyright 2006Page 1 The Cloud Feedback Model Intercomparison Project (CFMIP) Progress and future plans Mark Webb, Keith Williams, Mark Ringer,
Page 1© Crown copyright 2005 Damian Wilson, 12 th October 2005 Assessment of model performance and potential improvements using CloudNet data.
The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology The Effect of Turbulence on Cloud Microstructure,
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WEEK 1 You have 10 seconds to name…
DIVIDING INTEGERS 1. IF THE SIGNS ARE THE SAME THE ANSWER IS POSITIVE 2. IF THE SIGNS ARE DIFFERENT THE ANSWER IS NEGATIVE.
An important constraint on tropical cloud-climate feedback Dennis L. Hartmann and Kristin Larson Geophysical Res. Lett., 2002.
Addition 1’s to
Evaluating the Met Office global forecast model using GERB data Richard Allan, Tony Slingo Environmental Systems Science Centre, University of Reading.
Page 1© Crown copyright Simulation of radar reflectivities in the UK Met Office model: comparison with CloudSat Data Alejandro Bodas-Salcedo, M.E. Brooks.
R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading ECMWF Cloud and Radiation Parametrization: Recent Activities Richard Forbes, Maike.
Met Office GPCI simulations Adrian Lock. © Crown copyright UK Met Office simulations in GPCI HadGAM1 climate – for IPCC AR4 38 levels (~300m at 1km),
© Crown copyright Met Office Cloudier Evaluating a new GCM prognostic cloud scheme using CRM data Cyril Morcrette, Reading University, 19 February 2008.
1 Copyright © 2010, Elsevier Inc. All rights Reserved Fig 2.1 Chapter 2.
© Crown copyright Met Office Some thoughts on s12 stratocumulus feedback Adrian Lock EUCLIPSE WP3 meeting, Toulouse, April 2012.
Jeopardy Q 1 Q 6 Q 11 Q 16 Q 21 Q 2 Q 7 Q 12 Q 17 Q 22 Q 3 Q 8 Q 13
Boundary Layer Clouds. Intertropiccal Convergence Zone (ITCZ) Trade cumulus Transition Stratus and stratocumulus subsidence Trade wind inversion St &
SUBTRACTING INTEGERS 1. CHANGE THE SUBTRACTION SIGN TO ADDITION
© University of Reading Richard Allan Department of Meteorology, University of Reading Thanks to: Jim Haywood and Malcolm.
APR CRM simulations of the development of convection – some sensitivities Jon Petch Richard Forbes Met Office Andy Brown ECMWF October 29 th 2003.
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Morrison/Gettelman/GhanAMWG January 2007 Two-moment Stratiform Cloud Microphysics & Cloud-aerosol Interactions in CAM H. Morrison, A. Gettelman (NCAR),
Chapter 5 Test Review Sections 5-1 through 5-4. Simplify each expression. 1)2) 3)4) 5) 6)
TWO STEP EQUATIONS 1. SOLVE FOR X 2. DO THE ADDITION STEP FIRST
The ASTEX Lagrangian model intercomparison case Stephan de Roode and Johan van der Dussen TU Delft, Netherlands.
© University of Reading Radiative effects of persistent aircraft contrails: a case study Richard Allan Environmental Systems.
1 Dynamical Polar Warming Amplification and a New Climate Feedback Analysis Framework Ming Cai Florida State University Tallahassee, FL 32306
1 MET 112 Global Climate Change MET 112 Global Climate Change - Climate Feedbacks Professor Menglin Jin San Jose State University Outline Stability/instability.
Shortwave and longwave contributions to global warming under increased CO 2 Aaron Donohoe, University of Washington CLIVAR CONCEPT HEAT Meeting Exeter,
CCSM AMWG Meeting June 25, 2003 Status of CAM Bill Collins and Leo Donner National Center for Atmospheric Research and Geophysical Fluid Dynamics Laboratory.
Page 1© Crown copyright 2005 RF01/RF02: LES sensitivity studies Adrian Lock and Eoin Whelan.
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1 PV Generation in the Boundary Layer Robert Plant 18th February 2003 (With thanks to S. Belcher)
Forecast simulations of Southeast Pacific Stratocumulus with CAM3 and CAM3-UW. Cécile Hannay (1), Jeffrey Kiehl (1), Dave Williamson (1), Jerry Olson (1),
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