We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!
Presentation is loading. Please wait.
Published byRobert Johnston
Modified over 2 years ago
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 for Climate Change Submitted to J. Climate ENSEMBLES/CFMIP workshop, Paris, 12/04/07
Page 2© Crown copyright 2007 Why look at cloud regimes in short range forecasts? Differences in the simulation of present-day cloud regimes amongst GCMs has been shown to contribute to a significant proportion of the spread in climate sensitivity (Williams and Tselioudis 2007). However, it may not be easy to identify the cause of errors in a particular regime from the model climatology. The Met Office has the unique asset of using the same physical model for its operational data assimilation, NWP forecasts and climate change projection (HadGEM1). Evaluation of cloud regimes in short range forecasts provides a framework in which the initial meteorological conditions are constrained by observations. Thus the evolution of the errors may provide information on the cause of systematic model bias.
Page 3© Crown copyright 2007 Principle questions to be addressed Are the properties of the simulated cloud regimes (e.g. frequency of occurrence; radiative effect) similar in a short simulation (a few days) as in the model climatology? Does the increased resolution in the NWP model improve the simulation of the cloud regimes? Are the cloud regime properties any closer to observations immediately after the model is initialised from operational analyses? Can initial tendencies in the state variables be associated with particular cloud regimes?
Page 4© Crown copyright 2007 Principal tropical cloud regimes
Page 5© Crown copyright 2007 Principal extra-tropical cloud regimes
Page 6© Crown copyright 2007 Initial tendencies in cloud regime properties
Page 7© Crown copyright 2007 Initial temperature tendency in cloud regimes
Page 8© Crown copyright 2007 Conclusions The simulated cloud regimes are essentially the same in a short run as for the model climatology, hence improvements (which will be relevant to both NWP and climate) can be tested in short runs (although it would be good to address a few initialisation issues). Increased resolution generally has little effect on the cloud regimes, although the simulation of tropical shallow cumulus is improved, whereas tropical deep convection is too infrequent when compared with ISCCP. The errors in the simulated cloud regimes are generally no smaller at T+0, which suggests weaknesses in the local processes (boundary layer/cloud/convection). Some of the initial tendencies in the state variables appear to be associated with particular regimes, which may help with identifying a cause.
Page 9© Crown copyright 2007
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 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 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, Keith Williams, Mark Ringer,
Robin Hogan (with input from Anthony Illingworth, Keith Shine, Tony Slingo and Richard Allan) Clouds and climate.
© 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 Scientific background and content of new gridded products Bob Lunnon, Aviation Outcomes Manager, Met Office WAFS Workshop.
Numerical Weather Prediction (Met DA) The Analysis of Satellite Data lecture 2 Tony McNally ECMWF.
IRS2004, Busan, August 2004 Using Satellite Observations and Reanalyses to Evaluate Climate and Weather Models Richard Allan Environmental Systems Science.
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.
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
Regional Models Lake Victoria Model Prepared by C. Tubbs, P. Davies, Met Office UK Revised, delivered by P. Chen, WMO Secretariat SWFDP-Eastern Africa.
Slide 1 ECMWF Data Assimilation Training Course – May 2010 Ensemble Data Assimilation Massimo Bonavita ECMWF Acknowledgments: Lars Isaksen, Elias Holm,
ESA Climate Change Initiative Climate Modelling User Group CMUG
The Robert Gordon University School of Engineering Dr. Mohamed Amish INTRODUCTION TO RESEARCH & RESEARCH METHODS.
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.
Robin Hogan Julien Delanoe, Ewan OConnor, Anthony Illingworth, Jonathan Wilkinson University of Reading, UK Quantifying the skill of cloud forecasts from.
© Crown copyright Met Office Met Office Experiences with Convection Permitting Models Humphrey Lean Reading, UK Nowcasting Workshop,
Predictable Chaotic Exhibits memory Equilibrium Towards non-equilibrium Acknowledgements LD is supported by NERC CASE award NER/S/A/2004/ Conclusions.
Sub-seasonal to seasonal prediction David Anderson.
Robin Hogan Ewan OConnor Damian Wilson Malcolm Brooks Evaluation statistics of cloud fraction and water content.
Numerical Weather Prediction (Met DA) The Analysis of Satellite Data (lecture 1:Basic Concepts) Tony McNally ECMWF.
Clouds and their turbulent environment Robin Hogan, Andrew Barrett, Natalie Harvey Helen Dacre, Richard Forbes (ECMWF) Department of Meteorology, University.
Page 1© Crown copyright 2005 Use of EPS at the Met Office Ken Mylne and Tim Legg.
Use of ocean colour (GlobColour) data for operational oceanography Rosa Barciela, NCOF, Met Office Thanks to Matt Martin (Met Office) and.
Robin Hogan Last Minute Productions Inc. How to distinguish rain from hail using radar: A cunning, variational method.
Robin Hogan Department of Meteorology University of Reading Cloud and Climate Studies using the Chilbolton Observatory.
HB 1 Forecast Products Users'Meeting, June 2005 Users meeting Summary Performance of the Forecasting System (1) Main (deterministic) model -Outstanding.
1 What constrains spread growth in forecasts initialized from ensemble filters? Tom Hamill (& Jeff Whitaker) NOAA Earth System Research Lab Boulder, Colorado,
© 2016 SlidePlayer.com Inc. All rights reserved.