Why are we here, at the UCLA Conf. Center in June 2005? What is the best way of representing the physics of the atmospheric PBL in weather and climate prediction models? Simple models (parameterizations) versus a complex nature

Shallow convection over land: ARM case 1) Oklahoma ARM site 2) 21 June ) Several single-column models (SCM) versus LES start-10 am (local time) end - 3 am (next day) 4) Shallow convection develops at around 3 pm (in LES and in reality) No SCM is able to capture the diurnal cycle of convection Lenderink et al., 2004

g/kg GFDLNCARUKMO Mean crossections (JJA98): relative humidity and liquid water GCSS Pacific Crossection Intercomparison: GPCI

GPCI: Histograms of total-cloud-cover.vs.latitude (JJA 98) NCAR MeteoFrance UKMO Number of events (%)

Low cloud climate sensitivity Courtesy of Chris Bretherton – Climate Process modelling Team (CPT) Cloud feedbacks on climate sensitivity are a large source of uncertainty in climate change predictions e.g.: GFDL and NCAR have (or used to have) opposite low cloud cover sensitivity to CO2 doubling

General questions in PBL parameterization How to represent the sub-grid fluxes? –Eddy-diffusivity –Mass-flux –Eddy-diffusivity/mass-flux combination –Higher-order closures –Mixed-layer approaches How to represent cloud fraction and mean cloud water? –Pdf-based methods –Prognostic cloud fraction How to represent the interaction with surface boundary? –Ocean – Land – sub-grid orography How to integrate the equations efficiently? –Non-linear numerical stability –Accuracy and vertical resolution

Current models: do we need an integrated approach? Climate and weather prediction models use modular parameterizations: Radiation Turbulence Moist convection Clouds/precipitation Land and Ocean DYNAMICS Stable PBL Convective PBL Surface layer Shallow cumulus Deep cumulus Nature is not modular ( underlying physics is the same) Coupling between modules is often artificial Need for an integrated/unified approach

PBL parameterizations should also take into account : i) Aerosols and chemical constituents Take into account interactive aerosols and chemical components in a natural way (e.g. numerical issues in source/sink terms). ii) Non-linear numerical stability issues System of non-linear advection-reaction-diffusion equations that leads to numerical stability problems. iii) Stochastic physics and model error Fundamental for predictability and data-assimilation aspects (still fairly unexplored). iv) Scale dependent parameterizations Explicit connections to horizontal resolution, leading to a scale-independent model (physics +dynamics).

Working groups Boundary Layer Clouds Bruce A., Chin-Hoh M., Joao T., Adrian L, Martin K., Chris G., Paquita Z., SungSu P., Yunyan W., Margret V. PBL/deep-convection interaction Brian Mapes, Pier S., Chris B., Rich H., Steve K., Gabriel C., Verica S., Pedro S, Steve Klein, Roel N., Vince L. PBL/surface interaction Andy B., Alan B., Chris F., Anton B., Michael E., Ned P., Carol C., James D., Louise N., Jordi V. Stable boundary layers Branko K., Gunilla S., Bert H., Julie L., Thorsten M., Brian Medeiros., Wayne A., Robert B., Bjorn S.

1)What has happened during the last ten years? Progress, problems – practical and conceptual (e.g. Stratocumulus, surface, dry convective) 2)What are the main unsolved issues right now? Are these conceptual problems, technical, social,…? 3)Are there solutions to these current issues? Or to the older problems? What are these solutions? Working group discussions