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© Crown copyright Met Office Convection plans Alison Stirling
© Crown copyright Met Office Priorities for convection parametrization Main Systematic Errors: Diurnal cycle MJO Monsoons AEW coupling Feedback with `large-scale environment Transient response to boundary layer
© Crown copyright Met Office Interaction with large scale Ascent generates CAPE Heating generates ascent Depth and amplitude of convective response key to improving monsoon, MJO, AEW coupling
© Crown copyright Met Office Interaction with large scale progress and further work: UM high resolution convection simulations over a large domain allows circulations resulting from convection to develop. Increased entrainment improves coupling between convection and large-scale (Klingaman et al 2013) …but it cant be high all the time… Introduce physically realistic entrainment dependence: (stability and cloud area) Explore effects of large scale on other parts of scheme
© Crown copyright Met Office Interaction with boundary layer Cold pools Important for: 1. Triggering and closure Temperature and moisture differences allow CAPE to be present locally even when the mean state is stable Additional KE to overcome CIN 2. Entrainment affects cloud area (and therefore buoyancy and vertical velocity) Enhanced lifting Two zones of temperature and moisture Requires memory of previous precipitation events Introduce energetics of boundary layer thermals Modify triggering and closure to have dependence on this MOAP secondment to work on cold pools
© Crown copyright Met Office Research areas 1. Entrainment / detrainment Dependence on cloud area Dependence on stability Adaptivity 2. Interaction with large scale What is the profile of ascent caused by convective heating? How does ascent affect closure and at what levels is it important? 3. Interaction with boundary layer Representing energetic response to CIN Representing cold pools 4. Memory Relative importance of cloud area, rh variability, BL variability Prognostic for cloud area 5. Closure How do surface processes and upper level processes combine?
© Crown copyright Met Office Higher resolutions Mass flux: Separate into component parts vertical velocity, cloud area, and cloud number Multiple plume: Quantify the need for a multi-plume approach Allows: grid-size sensitivity, inclusion of microphysics better coupling with PC2 stochasticity Applications submitted for Reading and Leeds CASE students to work on different aspects of grey-zone problem. Convection position available! See Closing date: 14 th July 2013
© Crown copyright Met Office Questions and answers
© Crown copyright Met Office Unification Give each component of the existing convection scheme an improved physical basis. Entrainment: Include dependence on stability and cloud area Detrainment: Reformulate to be adaptive all the way up the cloud, and let the level of adaptivity depend on cloud area Triggering and closure: Base on energetics of boundary layer processes and large-scale ascent
© Crown copyright Met Office Unification details Research areas include: Cloud area representation Cold pool representation Boundary layer thermal energetics Convective response to large-scale ascent
© Crown copyright Met Office UM run Stu Webster Indian Ocean 200m from 2.2km b.c.s 4000 x 2600 points 3 days
© Crown copyright Met Office What controls convective depth? Vertical extent of CAPE Entrainment Detrainment Cloud area? Boundary layer processes? (e.g. cold pools) High entrainment increases coupling to LS ascent/ descent, but it cant be high all the time!
© Crown copyright Met Office What controls heating amplitude? Closure Rate of CAPE creation? Inversion removal? Ascent?
Laura Davies, University of Reading, UK. Supervisors: Bob Plant, Steve Derbyshire (Met Office)
Clouds and their turbulent environment Robin Hogan, Andrew Barrett, Natalie Harvey Helen Dacre, Richard Forbes (ECMWF) Department of Meteorology, University.
© Crown copyright Met Office Regional climate model formulation PRECIS Workshop, Reading University, 23 rd – 27 th April 2012.
1 Numerical Weather Prediction Parametrization of diabatic processes Convection II The parametrization of convection Peter Bechtold, Christian Jakob, David.
Cloud Resolving Models: Their development and their use in parametrization development Richard Forbes, Adrian Tompkins.
Predictable Chaotic Exhibits memory Equilibrium Towards non-equilibrium Acknowledgements LD is supported by NERC CASE award NER/S/A/2004/ Conclusions.
DYMECS: Dynamical and Microphysical Evolution of Convective Storms (NERC Standard Grant) University of Reading: Robin Hogan, Bob Plant, Thorwald Stein,
1 00/XXXX © Crown copyright Carol Roadnight, Peter Clark Met Office, JCMM Halliwell Representing convection in convective scale NWP models : An idealised.
Developing a new general circulation model for planetary atmospheres - how (and why!) Claire Newman Kliegel Planetary Science Seminar March 1st 2005.
Entrainment as the main controling parameter of convective activity in the 3MT moist-physics unifying scheme. Trials for an extension towards a 'cold-pool.
1 Numerical Weather Prediction Parameterization of diabatic processes Convection III The ECMWF convection scheme Peter Bechtold and Christian Jakob.
Dynamical and Microphysical Evolution of Convective Storms (DYMECS) University: Robin Hogan, Bob Plant, Thorwald Stein, Kirsty Hanley, John Nicol Met Office:
6. Equilibrium fluctuations for time-varying forcing. Values of constant term larger than expected from RCE run correspond to fluctuations greater than.
4. First look Initial analysis of contrasting timeseries (Figure 2) shows: Shorter timescales have a smaller range of mass fluxes with lower maxima and.
Some questions on convection that could be addressed through another UK field program centered at Chilbolton Dan Kirshbaum 1.
Parametrization of PBL outer layer Martin Köhler Overview of models Bulk models local K-closure K-profile closure TKE closure.
1 PV Generation in the Boundary Layer Robert Plant 18th February 2003 (With thanks to S. Belcher)
Page 1© Crown copyright 2005 Use of EPS at the Met Office Ken Mylne and Tim Legg.
WWRP 1 October 2010 THORPEX report to the WGNE David Burridge THORPEX IPO.
Regional Models Lake Victoria Model Prepared by C. Tubbs, P. Davies, Met Office UK Revised, delivered by P. Chen, WMO Secretariat SWFDP-Eastern Africa.
© Crown copyright 2006Page 1 The Cloud Feedback Model Intercomparison Project (CFMIP) Progress and future plans Mark Webb (Hadley Centre) and CFMIP contributors.
1 Peter Bechtold and Christian Jakob Numerical Weather Prediction Parametrization of diabatic processes Convection I An overview.
Global and Planetary WRF Claire Newman (Caltech, Ashima Research) Mark Richardson (Ashima Research) Anthony Toigo (Cornell)
1 Numerical Weather Prediction Parameterization of diabatic processes Convection III The ECMWF convection scheme Christian Jakob and Peter Bechtold.
Earth System Prediction Capability ESPC Earth System Prediction Capability (ESPC) Presentation to AMS Board on Enterprise Communications September 2012.
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.
© Crown copyright 2006Page 1 The Cloud Feedback Model Intercomparison Project (CFMIP) Progress and future plans Mark Webb, Keith Williams, Mark Ringer,
Parametrizations in Data Assimilation ECMWF Training Course May 2012 Philippe Lopez Physical Aspects Section, Research Department, ECMWF (Room 113)
Arctic observing system for regional NWP Harald Schyberg (met.no), Frank Thomas Tveter (met.no) Roger Randriamampianina (met.no), Trygve Aspelien (met.no)
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