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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 Workshop: Paris, th April 2007
Page 2© Crown copyright 2007 Introduction – consider options for SST-forced and slab model experiments Some issues to consider prior to this afternoons break-out group session on experimental design. CMIP 1% per year composite ΔSST pattern Climatological or AMIP control? Constructing the composite ΔSST pattern Other idealized SST perturbation experiments and approaches Slab model experiments These are just some ideas to get the discussion going…
Page 3© Crown copyright 2007 CMIP 1% per year increase in CO 2 : ΔTs at time of doubling (e.g. Wyant et al. 2006) Arguably more realistic and relevant to climate change over the 21 st century. However, the perturbation is relatively small ( ~ 1.4K) and there is little spatial structure in the tropics – resembles a uniform ΔTs in the zonal mean.
Page 4© Crown copyright 2007 Choosing the control experiment – seasonal climatology or varying (AMIP) SSTs? Climatology (e.g. 10 years) – reaches an equilibrium, which should hopefully reduce the noise in the response. AMIP – control is potentially more useful for evaluation studies, but the response will be conditioned by ENSO and other variability during the period.
Page 5© Crown copyright 2007 Constructing the composite ΔTs pattern One alternative to simple averaging might be to average the patterns i.e. ΔTs/ and then scale by ensemble mean. This would reduce biases due to very high or low sensitivity models, for example. ΔTs ΔTs /
Page 6© Crown copyright 2007 In addition, we could also consider… A simple globally uniform SST perturbation (e.g. the ensemble mean ΔTs) to determine the impact of the SST pattern versus the overall warming – a seasonally varying version of the Cess et al. (1989) method A zonally uniform SST perturbation to isolate the impact of changes to the meridional gradient in SST (analogous to the aquaplanet experiments in APE) Reducing the SSTs – this might also provide insights into feedbacks. E.g. Wyant et al. show that +2K/-2K responses are not necessarily symmetric in a given model Including a composite sea-ice response as well as the SST perturbation – but how?
Page 7© Crown copyright 2007 Other approaches – e.g. Barsugli et al. (2006) [Climate Dynamics, 27(5), Oct. 2006] Specified 2K warming/cooling at different locations in the tropics in order to investigate sensitivity to the pattern of ΔSST. Allowed them to separate the local and global sensitivities to the warming. Example shown is for TOA net radiation – local sensitivity is +ive everywhere but global sensitivity can be either +ive or -ive. Other techniques include introducing (or suppressing) warming at specific locations believed to be important. E.g. Schneider et al. [JAS, 54, May 1997] held SSTs in the Pacific cold tongue at control values while allowing temperatures elsewhere to vary in response to doubling CO 2.
Page 8© Crown copyright 2007 Slab models – equilibrium 2×CO 2 experiments, as in CFMIP1 Although not identical, the control states are relatively similar. Allows us to consider equilibrium climate sensitivity – if that is still thought to be important! Also provides continuity with previous studies. Will slab models be included in IPCC AR5? If not, what will they be replaced by (if anything)? Should CFMIP2 do them in any case?
Page 9© Crown copyright 2007 Slab models – continued Produce larger response – magnitude and spatial pattern – than the 1% per year simulations. Could produce a composite pattern from these for the SST-forced experiments? In essence this would mean CFMIP2 looking like CFMIP1 but with patterned SST experiments replacing the Cess et al. (1989) runs plus sensitivity experiments and any other idealized experiments we decide to do…
Page 10© Crown copyright 2007 Summary of points discussed Composite pattern derived from… 1%/year runs at time of CO 2 doubling Slab 2×CO 2 equilibrium runs Construction of composite pattern Simple averagingAverage of patterns scaled by mean Control simulationSeasonally varying climatology AMIP SSTs, i.e. include interannual variability Composite sea-ice pattern Include?Dont include? Other idealized experiments Classic, e.g. uniform ΔSST, reduced SST, etc More sophisticated, e.g. Barsugli et al. method Slab model experiments Exclude if not part of IPCC AR5 Perform anyway as part of CFMIP2
© Crown copyright 2006Page 1 CFMIP II sensitivity experiments Mark Webb (Met Office Hadley Centre) Johannes Quaas (MPI) Tomoo Ogura (NIES) With thanks.
© 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 The Cloud Feedback Model Intercomparison Project (CFMIP) Progress and future plans Mark Webb, Keith Williams, Mark Ringer,
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
© 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.
Met Office Hadley Centre, FitzRoy Road, Exeter, Devon, EX1 3PB United Kingdom Tel: +44 (0) Fax: +44 (0)
© Crown copyright Met Office Towards understanding the mechanisms responsible for different cloud-climate responses in GCMs. Mark Webb, Adrian Lock (Met.
Page 1© Crown copyright 2007 Constraining the range of climate sensitivity through the diagnosis of cloud regimes Keith Williams 1 and George Tselioudis.
Running a model's adjoint to obtain derivatives, while more efficient and accurate than other methods, such as the finite difference method, is a computationally.
Forcing and feedback in the climate-carbon system Jonathan Gregory 1,2, Mark Webb 2, Keith Williams 2, Marie Doutriaux-Boucher 2, Olivier Boucher 2, Piers.
Cloud Resolving Models: Their development and their use in parametrization development Richard Forbes, Adrian Tompkins.
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Climate change and pollution Eleanor J Highwood Department of Meteorology, University of Reading MSc Intelligent Buildings April 2002.
UK-India workshop on downscaling and linking to applications, UEA, January 2009 Uncertainties in future projections of extreme precipitation in the.
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1 PV Generation in the Boundary Layer Robert Plant 18th February 2003 (With thanks to S. Belcher)
Robin Hogan (with input from Anthony Illingworth, Keith Shine, Tony Slingo and Richard Allan) Clouds and climate.
INTERMEDIATE 1 PHYSICAL EDUCATION STRUCTURES AND STRATEGIES INFORMATION PACK Name : _____________________________________ Class : _________ Year : ______.
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Uncertain models and modelling uncertainty Marian Scott Dept of Statistics, University of Glasgow EMS workshop, Nottingham, April 2004.
International efforts in Climate Modeling Projections, Predictions and Downscaling Coordinated by the World Climate Research Program (WCRP) CMIP5: The.
EE-M /7: EF L12&13 1/23, v2.0 Lectures 12&13: Persistent Excitation for Off-line and On-line Parameter Estimation Dr Martin Brown Room: E1k
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Challenges in the Extraction of Decision Relevant Information from Multi-Decadal Ensembles of Global Circulation Models Dave Stainforth Acknowledgements:
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Climate in the near future – results from a simple probabilistic method Jouni Räisänen and Leena Ruokolainen Department of Physical Sciences, Division.
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