GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey AM2 cloud sensitivity to details of convection and cloud.

Slides:



Advertisements
Similar presentations
© Crown copyright 2006Page 1 CFMIP II sensitivity experiments Mark Webb (Met Office Hadley Centre) Johannes Quaas (MPI) Tomoo Ogura (NIES) With thanks.
Advertisements

ECMWF Training Course Peter Bechtold and Christian Jakob
1 Numerical Weather Prediction Parameterization of diabatic processes Convection III The ECMWF convection scheme Christian Jakob and Peter Bechtold.
Institut für Meteorologie und Klimatologie Universität Hannover
© Crown copyright Met Office Cloudier Evaluating a new GCM prognostic cloud scheme using CRM data Cyril Morcrette, Reading University, 19 February 2008.
Hirlam Physics Developments Sander Tijm Hirlam Project leader for physics.
Pedro M. M. Soares* Pedro M. A. Miranda* João Teixeira^ *University of Lisbon, CGUL, IDL, Lisbon, Portugal ^Jet Propulsion Laboratory – California Institute.
Influence of the Subcloud Layer on the Development of a Deep Convective Ensemble Boing et al., 2012.
The Problem of Parameterization in Numerical Models METEO 6030 Xuanli Li University of Utah Department of Meteorology Spring 2005.
WRF Physics Options Jimy Dudhia. diff_opt=1 2 nd order diffusion on model levels Constant coefficients (khdif and kvdif) km_opt ignored.
Geophysical Fluid Dynamics Laboratory Princeton, New Jersey Convective Vertical Velocities and Microphysics: Contrasts between 2D and 3D CSRMs Leo Donner.
Preliminary Experiments with a Dynamics-Based PDF Parameterization for Boundary Layers and Associated Clouds in GCMs Leo Donner, Huan Guo, and Chris Golaz.
Simulation of Cloud Droplets in Parameterized Shallow Cumulus During RICO and ICARTT Knut von Salzen 1, Richard Leaitch 2, Nicole Shantz 3, Jonathan Abbatt.
Isolating the Impact of Height Dependence on Cumulus Entrainment Walter Hannah May 25 th, 2011.
Atmospheric Analysis Lecture 3.
Observed Updraft & Mass Flux in Shallow Cumulus at ARM Southern Great Plains site Preliminary results Yunyan Zhang, Steve Klein & Pavlos Kollias CFMIP/GCSS.
A direct measure of entrainment David M. Romps Workshop on Large-Scale Circulations in Moist Convecting Atmospheres October 17, 2009.
The role of the mean relative humidity on the dynamics of shallow cumuli - LES Sensitivity experiments Stephan de Roode (KNMI/IMAU) Simon Axelsen (IMAU)
The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology The Effect of Turbulence on Cloud Microstructure,
Relationships between wind speed, humidity and precipitating shallow cumulus convection Louise Nuijens and Bjorn Stevens* UCLA - Department of Atmospheric.
GFS Deep and Shallow Cumulus Convection Schemes
Current issues in GFS moist physics Hua-Lu Pan, Stephen Lord, and Bill Lapenta.
Geophysical Fluid Dynamics Laboratory Review June 30 - July 2, 2009 Geophysical Fluid Dynamics Laboratory Review June 30 - July 2, 2009.
Large-Eddy Simulation of a stratocumulus to cumulus transition as observed during the First Lagrangian of ASTEX Stephan de Roode and Johan van der Dussen.
The parameterization of moist convection
GardeGarde Designing unified convection parameterizations: two proposals related to equation sets and entrainment. Jean-Marcel Piriou, Météo-France. GCSS.
1 Numerical Weather Prediction Parameterization of diabatic processes Convection III The ECMWF convection scheme Christian Jakob and Peter Bechtold.
The representation of stratocumulus with eddy diffusivity closure models Stephan de Roode KNMI.
A dual mass flux framework for boundary layer convection Explicit representation of cloud base coupling mechanisms Roel Neggers, Martin Köhler, Anton Beljaars.
Vertical Structure of the Tropical Troposphere (including the TTL) Ian Folkins Department of Physics and Atmospheric Science Dalhousie University.
Convective Parameterization Options
Budgets of second order moments for cloudy boundary layers 1 Systematische Untersuchung höherer statistischer Momente und ihrer Bilanzen 1 LES der atmosphärischen.
Aktionsprogramm 2003 Useful Analogies Between the Mass-Flux and the Reynolds-Averaged Second-Moment Modelling Frameworks Dmitrii Mironov German Weather.
Yanjun Jiao and Colin Jones University of Quebec at Montreal September 20, 2006 The Performance of the Canadian Regional Climate Model in the Pacific Ocean.
Forecast simulations of Southeast Pacific Stratocumulus with CAM3 and CAM3-UW. Cécile Hannay (1), Jeffrey Kiehl (1), Dave Williamson (1), Jerry Olson (1),
RICO Modeling Studies Group interests RICO data in support of studies.
Parameterization of the effects of Moist Convection in GCMs Mass flux schemes –Basic concepts and quantities –Quasi-steady Entraining/detraining plumes.
Boundary Layer Clouds.
Sensitivity to the PBL and convective schemes in forecasts with CAM along the Pacific Cross-section Cécile Hannay, Jeff Kiehl, Dave Williamson, Jerry Olson,
Georg A. Grell (NOAA / ESRL/GSD) and Saulo R. Freitas (INPE/CPTEC) A scale and aerosol aware stochastic convective parameterization for weather and air.
1 Making upgrades to an operational model : An example Jongil Han and Hua-Lu Pan NCEP/EMC GRAPES-WRF Joint Workshop.
Continuous treatment of convection: from dry thermals to deep precipitating convection J.F. Guérémy CNRM/GMGEC.
UTCS PP Status Report Dmitrii Mironov German Weather Service, Offenbach am Main, Germany COSMO General Meeting, Krakow, Poland
A Thermal Plume Model for the Boundary Layer Convection: Representation of Cumulus Clouds C. RIO, F. HOURDIN Laboratoire de Météorologie Dynamique, CNRS,
Development and testing of the moist second-order turbulence-convection model including transport equations for the scalar variances Ekaterina Machulskaya.
11 Implementing a New Shallow Convection Scheme into WRF Aijun Deng and Brian Gaudet Penn State University Jimy Dudhia National Center for Atmospheric.
Stratocumulus-topped Boundary Layer
Aerosol Indirect Effects in CAM and MIRAGE Steve Ghan Pacific Northwest National Laboratory Jean-Francois Lamarque, Peter Hess, and Francis Vitt, NCAR.
A Case Study of Decoupling in Stratocumulus Xue Zheng MPO, RSMAS 03/26/2008.
Shallow Moist Convection Basic Moist Thermodynamics Remarkable Features of Moist Convection Shallow Cumulus (Stratocumulus) Courtesy: Dave Stevens.
THE INFLUENCE OF WIND SPEED ON SHALLOW CUMULUS CONVECTION from LES and bulk theory Louise Nuijens and Bjorn Stevens University of California, Los Angeles.
A simple parameterization for detrainment in shallow cumulus Hirlam results for RICO Wim de Rooy & Pier Siebesma Royal Netherlands Meteorological Institute.
Work Status: The project implementation is somewhat delayed due to the uncertainty about the future of some project participants A review about analogies.
Convective Parameterization Jack Kainand Mike Baldwin OAR/NSSL/CIMMS.
Radiative-Convective Model. Overview of Model: Convection The convection scheme of Emanuel and Živkovic-Rothman (1999) uses a buoyancy sorting algorithm.
Key ingredients in global hydrological response to external forcing Response to warming => Increased horizontal moisture fluxes => Poleward expansion of.
Forecasts of Southeast Pacific Stratocumulus with the NCAR, GFDL and ECMWF models. Cécile Hannay (1), Dave Williamson (1), Jim Hack (1), Jeff Kiehl (1),
ECMWF Training Course Peter Bechtold
Shifting the diurnal cycle of parameterized deep convection over land
Application Of KF-Convection Scheme In 3-D Chemical Transport Model
Turbulence closure problem
Theories of Mixing in Cumulus Convection
Group interests RICO data required
Analysis of Parameterization in Single-Column Model
Han, J. , W. Wang, Y. C. Kwon, S. -Y. Hong, V. Tallapragada, and F
Convection scheme Yanqing Su, Ye Cheng.
Short Term forecasts along the GCSS Pacific Cross-section: Evaluating new Parameterizations in the Community Atmospheric Model Cécile Hannay, Dave Williamson,
Kurowski, M .J., K. Suselj, W. W. Grabowski, and J. Teixeira, 2018
Group interests RICO data in support of studies
transport equations for the scalar variances
Presentation transcript:

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey AM2 cloud sensitivity to details of convection and cloud paramerization – the GPCI case Ming Zhao GFDL / Princeton University September 18-21, 2006 Joint GCSS-GPCI / BLCL - RICO Workshop

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey Outline AM2 cloud and convection parameterization Experiments Results Summary

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey Tiedtke prognostic cloud scheme AM2 cloud and convection parameterization

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey Relaxed Arakawa-Schubert (RAS) convection scheme Cloud model:  Ensemble entraining plumes with each entrainment rate calculated so that it reaches a model level with neutral buoyancy  Non-entraining plumes  Tokioka entrainment rate limiter  Applying Tokioka limiter only to deep plumes above 500 hPa  Local modification of critical cloud work function Closure:  Relax plume cloud work function to specified critical values with specified time-scale.

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey UW shallow cumulus scheme (Bretherton et. al 2004) Cloud model:  Bulk entrainment-detrainment plume  Buoyancy-sorting determination of entrainment/detrainment rate  Explicit vertical momentum equation  Cumulus cloud-top penetrative mixing Closure:  Cloud-base mass flux is determined by boundary layer TKE and convective inhibition (CIN).

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey Experiments (GPCI 1998) CNTRL : AM2 default with FV dynamic core NOSHA: Same as CNTRL except disallowing RAS plumes below 500hPa NONON: Same as CNTRL except applying Tokioka to all RAS plumes (eliminating non-entraining plume) UWSCU: Same as NONON except applying UW-ShCu before RAS, and disallowing RAS plumes below UW-ShCu calculated convective depth H

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey Results

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey Liquid water path

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey SW absorption at TOA

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey Cloud fraction

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey Cloud liquid (g/kg)

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey Cloud liquid water tendencies from convection (g/kg/day)

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey Cloud liquid water tendencies from large-scale condensation (g/kg/day)

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey Large-scale/stratiform precipitation

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey Specific humidity tendencies from convection (g/kg/day)

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey Relative humidity

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey Convective mass flux (kg/m2/s)

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey NOGMC case

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey CNTRL vs. NOGMC

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey CNTRL vs NOGMC

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey Summary  Tropical low cloud fraction and condensate are susceptible to the detailed treatment of shallow convection. Weaker shallow convection leads to large increase of low clouds.  Budget analysis show that the increase of low clouds is due to increased large-scale condensation instead of convective detrainment.  Sensitivity study show that wetter lower troposphere and reduced compensating subsidence resulting from weaker shallow convection are two primary causes.

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey End

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey Total precipitation

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey Cloud fraction tendencies from convection (1/day)

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey Cloud fraction tendencies from large-scale formation (1/day)

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey Cloud fraction tendencies from turbulent erosion (1/day)

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey Cloud liquid tendencies from large-scale evaporation (g/kg/day)

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey Cloud liquid tendencies from turbulent erosion (g/kg/day)

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey Cloud liquid tendencies from microphysics (g/kg/day)

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey Vertical pressure velocity (hPa/day)

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey Summary  Tropical low cloud fraction and condensate are susceptible to the detailed treatment of shallow convection. Weaker shallow convection leads to large increase of low clouds.  Budget analysis show that the increase of low clouds is due to large-scale condensation instead of convective detrainment.  Budget analysis and sensitivity study show that wetter lower troposphere and reduced compensating subsidence resulting from weaker shallow convection are two primary causes.

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey Sub-cloud layer TKE and cloud detrainment Sub-cloud TKE Cloud detrainment 01 fraction of environmental air virtual potential temperature

GFDL Geophysical Fluid Dynamics GFDL Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey Steady state solution for the dominant balance depend on entrainment rate cloud liquid cloud fraction steady state solution depend on both entrainment rate and cloud-base mass flux / cloud work function