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Page 1© Crown copyright 2007 Constraining the range of climate sensitivity through the diagnosis of cloud regimes Keith Williams 1 and George Tselioudis 2 1 Met Office, Hadley Centre for Climate Change 2 NASA GISS/Columbia University Williams, K.D. and G. Tselioudis (2007) GCM intercomparison of global cloud regimes: Present day evaluation and climate change response. Clim. Dyn. doi:10.1007/s00382-007-0232-2 ENSEMBLES/CFMIP workshop, Paris, 12/04/07
Page 2© Crown copyright 2007 The challenge! Can aspects of the present-day climate be used to provide an evaluation of GCMs which will constrain the range of climate sensitivity? Many types of evaluation have been proposed for GCMs, however very few have been demonstrated to significantly/tightly constrain the range of climate sensitivity.
Page 3© Crown copyright 2007 Identification of cloud regimes The method uses a daily mean ISCCP cloud amount histograms from each grid-point for 5- years worth of data (from observations and present-day and 2xCO 2 simulations from GCMs). A clustering algorithm is applied to each experiment to effectively group together spatio- temporal grid points with similar cloud top pressure, cloud optical depth and fractional total cloud coverage of the grid-box (following Jakob and Tselioudis, 2003). Several of the resulting clusters are subjectively combined to provide a small set of common cloud regimes from the model and observations. The tropics (20N-20S) and the snow/ice-free extra-tropics (polewards of 20N/S) are considered separately.
Page 4© Crown copyright 2007 ISCCP observational cloud regimes (Tropics)
Page 5© Crown copyright 2007 ISCCP cluster location
Page 6© Crown copyright 2007 GCM simulated tropical stratocumulus regime
Page 7© Crown copyright 2007 Climate change response 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 regime/development of different clusters).
Page 8© Crown copyright 2007 Response to doubling CO 2 (Tropics) Much of the variation in the tropical cloud response is due to differences in the radiative response of stratocumulus
Page 9© Crown copyright 2007 Global cloud response to 2xCO 2
Page 10© Crown copyright 2007 Potential to constrain the range of climate sensitivity Suggests that if the models were improved to simulate the present-day cloud regimes more realistically, the range of climate sensitivity is likely to be reduced. The method provides a metric which: Is demonstrated to be relevant to the climate sensitivity Implicitly up-weights those regimes which the GCMs suggest are most important for the global cloud radiative response. When decomposed, provides information to model developers regarding which regimes require attention.
Page 11© Crown copyright 2007 Conclusions Cloud regimes offer a useful framework in which to evaluate a GCM and analyse its climate change response. A significant contribution to the variation in the global cloud radiative response amongst the GCMs analysed here can be associated with differences in the present-day simulation (particularly the frequency of tropical stratocumulus and extra-tropical frontal cloud). (Data from more models are required to check how robust this is.) There appears to be potential to reduce the range of climate sensitivity between GCMs if the present-day cloud regimes were simulated more consistently. The method provides a metric which is demonstrated to be relevant to the climate change response, so might be considered a useful addition to a basket of measures of GCM performance.
Page 12© Crown copyright 2007 Plans for the future Currently developing a method (with Mark Webb) for projecting onto the observed regimes rather than clustering each model independently. The method uses grid-box mean CTP, albedo and cloud cover. Has the advantage of removing subjective elements of the analysis, permits comparison with the full set of ISCCP clusters, and is lighter on the amount of data required. (Although, it doesnt tell you about the natural regimes in your model). The method meets the following criteria: 1) When the ISCCP data is projected onto itself, very similar mean histograms are produced, 2) The climate change analysis leads to the same conclusions and a similar constraint on climate sensitivity as presented here. The proposed metric will be slightly modified so that you cant score highly through a compensation of errors. It is proposed that these lighter diagnostics will be in the AR5 request and that the revised metric will be calculated for all models submitting them.
Page 13© Crown copyright 2007
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.
© Crown copyright Met Office Evaluation of cloud regimes in climate models Keith Williams and Mark Webb (A quantitative performance assessment of cloud.
© Crown copyright 2006Page 1 CFMIP II sensitivity experiments Mark Webb (Met Office Hadley Centre) Johannes Quaas (MPI) Tomoo Ogura (NIES) With thanks.
Yuying Zhang, Jim Boyle, and Steve Klein Program for Climate Model Diagnosis and Intercomparison Lawrence Livermore National Laboratory Jay Mace University.
Cloud, radiation, and precipitation changes with dynamic regime: An observational analysis and model evaluation study PI: George Tselioudis Co-PI: Chris.
© Crown copyright Met Office Towards understanding the mechanisms responsible for different cloud-climate responses in GCMs. Mark Webb, Adrian Lock (Met.
1 Evaluating climate model using observations of tropical radiation and water budgets Richard P. Allan, Mark A. Ringer Met Office, Hadley Centre for Climate.
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.
Future Climate Projections. Lewis Richardson ( ) In the 1920s, he proposed solving the weather prediction equations using numerical methods. Worked.
© Crown copyright Met Office The Cloud Feedback Model Intercomparison Project Plans for CFMIP-2 Mark Webb (Met Office Hadley Centre) Hadley Centre Seminar,
Understanding Feedback Processes Outline Definitions Magnitudes and uncertainties Geographic distributions and priorities Observational requirements.
© Crown copyright 2006Page 1 The Cloud Feedback Model Intercomparison Project (CFMIP) Progress and future plans Mark Webb (Hadley Centre) and CFMIP contributors.
© Crown copyright Met Office Using stability composites to analyse cloud feedbacks in the CMIP3/CFMIP-1 slab models. Mark Webb (Met Office) CFMIP-GCSS.
LLNL-PRES-XXXXXX This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344.
© Crown copyright Met Office CFMIP-2 techniques for understanding cloud feedbacks in climate models. Mark Webb (Met Office Hadley Centre) CFMIP/GCSS BLWG.
Clouds in the Southern midlatitude oceans Catherine Naud and Yonghua Chen (Columbia Univ) Anthony Del Genio (NASA-GISS)
Page 1© Crown copyright Simulation of radar reflectivities in the UK Met Office model: comparison with CloudSat Data Alejandro Bodas-Salcedo, M.E. Brooks.
© Crown copyright Met Office CCI Cloud Assessment Mark Ringer, Met Office Hadley Centre CMUG 4 th Integration meeting, Exeter – June 2 nd – 4 th, 2014.
WP4.1: Feedbacks and climate surprises ( IPSL, HC, LGGE, CNRM, UCL, NERSC) WP4.1 has two main objectives (a) to quantify the role of different feedbacks.
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.
HAPPY 25 TH !!!! Cloud Feedback George Tselioudis NASA/GISS.
Understanding and constraining snow albedo feedback Alex Hall and Xin Qu UCLA Department of Atmospheric and Oceanic Sciences 2nd International Conference.
Towards stability metrics for cloud cover variation under climate change Rob Wood, Chris Bretherton, Matt Wyant, Peter Blossey University of Washington.
TEMPLATE DESIGN © Total Amount Altitude Optical Depth Longwave High Clouds Shortwave High Clouds Shortwave Low Clouds.
Thomas Ackerman Roger Marchand University of Washington.
Sea Ice, Solar Radiation, and SH High-latitude Climate Sensitivity Alex Hall UCLA Department of Atmospheric and Oceanic Sciences SOWG meeting January 13-14,
WGCM CFMIP/IPCC Climate Sensitivity Meeting, Exeter, April 2004 Decadal Variability in Water Vapour Richard Allan, Tony Slingo Environmental Systems Science.
© Crown copyright Met Office Climate Projections for West Africa Andrew Hartley, Met Office: PARCC national workshop on climate information and species.
1 Hadley Centre Using Earth radiation budget data for climate model evaluation Mark Ringer Hadley Centre, Met Office, UK GIST 19: Aug 27-29, 2003.
Anthony Illingworth, + Robin Hogan, Ewan OConnor, U of Reading, UK and the CloudNET team (F, D, NL, S, Su). Reading: 19 Feb 08 – Meeting with Met office.
The Solar Radiation Budget, and High-latitude Climate Sensitivity Alex Hall UCLA Department of Atmospheric and Oceanic Sciences University of Arizona October.
Robin Hogan Ewan OConnor Cloudnet level 3 products.
C LOUD R ADIATIVE K ERNELS : C LOUDS IN W/m 2 with T. Andrews, J. Boyle, A. Dessler, P. Forster, P. Gleckler, J. Gregory, D. Hartmann, S. Klein, R. Pincus,
Shortwave and longwave contributions to global warming under increased CO 2 Aaron Donohoe, University of Washington CLIVAR CONCEPT HEAT Meeting Exeter,
What new have we learned about cloud radiative effect and feedback in the last decade? Lazaros Oreopoulos (NASA-GSFC)
Jim Boyle, Peter Gleckler, Stephen Klein, Mark Zelinka, Yuying Zhang Program for Climate Model Diagnosis and Intercomparison / LLNL Robert Pincus University.
How Much Will the Climate Warm? Alex Hall and Xin Qu UCLA Department of Atmospheric and Oceanic Sciences UCLA Institute of the Environment Environmental.
1 Changes in low-latitude radiative energy budget - a missing mode of variability in climate models? Richard P. Allan, Tony Slingo Hadley Centre for Climate.
A Cloud Resolving Model with an Adaptive Vertical Grid Roger Marchand and Thomas Ackerman - University of Washington, Joint Institute for the Study of.
Deutsches Zentrum für Luft- und Raumfahrt e.V. in der Helmholtz-Gemeinschaft Institut für Physik der Atmosphäre Institut für Physik der Atmosphäre
© Crown copyright Met Office Cloudier Evaluating a new GCM prognostic cloud scheme using CRM data Cyril Morcrette, Reading University, 19 February 2008.
Theme D: Model Processes, Errors and Inadequacies Mat Collins, College of Engineering, Mathematics and Physical Sciences, University of Exeter and Met.
© Crown copyright Met Office An Introduction to PRECIS PRECIS Workshop, University of Reading, 13 th -17 th May, 2013.
© Crown copyright 2006Page 1 The Cloud Feedback Model Intercomparison Project (CFMIP) Progress and future plans Mark Webb, Keith Williams, Mark Ringer,
Robin Hogan Anthony Illingworth, Sarah Kew, Jean- Jacques Morcrette, Itumeleng Kgololo, Joe Daron, Anna Townsend Quantifying sub-grid cloud structure and.
Important data of cloud properties for assessing the response of GCM clouds in climate change simulations Yoko Tsushima JAMSTEC/Frontier Research Center.
CAUSES (Clouds Above the United States and Errors at the Surface) "A new project with an observationally-based focus, which evaluates the role of clouds,
Enhancement of Satellite-based Precipitation Estimates using the Information from the Proposed Advanced Baseline Imager (ABI) Part II: Drizzle Detection.
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