Exploring the convective grey zone with regional simulations of a cold air outbreak

Cold air outbreaks can bring snow to populated areas and can affect aviation safety. Shortcomings in the representation of these phenomena in global and regional models are thought to be associated with large systematic cloud related radiative flux errors across many models. In this study, nine regional models have been used to simulate a cold air outbreak case at a range of grid spacings (1 km to 16 km) with convection represented explicitly or by a parametrization. Overall, there is more spread between model results for the simulations in which convection is parametrized when compared to simulations in which convection is represented explicitly. The quality of the simulations of both the stratocumulus and the convective regions of the domain are assessed with observational comparisons 24 hours into the simulation. The stratocumulus region is not well reproduced by the models, which tend to predict open cell convection with increasing resolution rather than stratocumulus. For the convective region the model spread reduces with increased resolution and there is some improvement in comparison to observations. Comparing models that have the same physical parametrizations or dynamical core suggest that both are important for accurately reproducing this case.

[1]  B. Barkstrom,et al.  Clouds and the Earth's Radiant Energy System (CERES): An Earth Observing System Experiment , 1996 .

[2]  George A. Isaac,et al.  ENVIRONMENT CANADA'S EXPERIMENTAL NUMERICAL WEATHER PREDICTION SYSTEMS FOR THE VANCOUVER 2010 WINTER OLYMPIC AND PARALYMPIC GAMES , 2010 .

[3]  Damian R. Wilson,et al.  A microphysically based precipitation scheme for the UK meteorological office unified model , 1999 .

[4]  Peter Bechtold,et al.  A Simple Cloud Parameterization Derived from Cloud Resolving Model Data: Diagnostic and Prognostic Applications , 2002 .

[5]  G. Martin,et al.  A New Boundary Layer Mixing Scheme. Part I: Scheme Description and Single-Column Model Tests , 2000 .

[6]  R. Hogan,et al.  The representation of the West African monsoon vertical cloud structure in the Met Office Unified Model: an evaluation with CloudSat , 2015 .

[7]  J. Geleyn,et al.  Cloud and Precipitation Parameterization in a Meso-Gamma-Scale Operational Weather Prediction Model , 2009 .

[8]  P. Field,et al.  Improving a convection‐permitting model simulation of a cold air outbreak , 2014 .

[9]  M. Yau,et al.  A Multimoment Bulk Microphysics Parameterization. Part I: Analysis of the Role of the Spectral Shape Parameter , 2005 .

[10]  Pierre Bénard,et al.  Dynamical kernel of the Aladin–NH spectral limited‐area model: Revised formulation and sensitivity experiments , 2010 .

[11]  Anurag Dipankar,et al.  A stochastic scale‐aware parameterization of shallow cumulus convection across the convective gray zone , 2016 .

[12]  J. Wyngaard,et al.  Resolution Requirements for the Simulation of Deep Moist Convection , 2003 .

[13]  Véronique Ducrocq,et al.  The Meso-NH Atmospheric Simulation System. Part I: adiabatic formulation and control simulations , 1997 .

[14]  D. Stensrud,et al.  Snowbands during the Cold-Air Outbreak of 23 January 2003 , 2004 .

[15]  T. Segawa,et al.  Nonhydrostatic Atmospheric Models and Operational Development at JMA , 2007 .

[16]  J. Walter Strapp,et al.  An Examination of Local versus Nonlocal Aspects of a TKE-Based Boundary Layer Scheme in Clear Convective Conditions , 1999 .

[17]  Jean Côté,et al.  Staggered Vertical Discretization of the Canadian Environmental Multiscale (GEM) Model Using a Coordinate of the Log-Hydrostatic-Pressure Type , 2014 .

[18]  Steven J. Woolnough,et al.  Precipitation distributions for explicit versus parametrized convection in a large‐domain high‐resolution tropical case study , 2012 .

[19]  G. Moore.  A climatology of vessel icing for the subpolar North Atlantic Ocean , 2013 .

[20]  Yen-Ting Hwang,et al.  Link between the double-Intertropical Convergence Zone problem and cloud biases over the Southern Ocean , 2013, Proceedings of the National Academy of Sciences.

[21]  P. Lacarrére,et al.  Parameterization of Orography-Induced Turbulence in a Mesobeta--Scale Model , 1989 .

[22]  Richard C. J. Somerville,et al.  On the use of a coordinate transformation for the solution of the Navier-Stokes equations , 1975 .

[23]  J. Geleyn,et al.  A statistical approach for sedimentation inside a microphysical precipitation scheme , 2008 .

[24]  A. Simmons,et al.  An Energy and Angular-Momentum Conserving Vertical Finite-Difference Scheme and Hybrid Vertical Coordinates , 1981 .

[25]  Roy W. Spencer,et al.  SSM/I Rain Retrievals within a Unified All-Weather Ocean Algorithm , 1998 .

[26]  D. Randall,et al.  A Semiempirical Cloudiness Parameterization for Use in Climate Models , 1996 .

[27]  J. Redelsperger,et al.  A turbulence scheme allowing for mesoscale and large‐eddy simulations , 2000 .

[28]  T. Davies,et al.  A mass restoration scheme for limited‐area models with semi‐Lagrangian advection , 2015 .

[29]  R. Hogan,et al.  Modelling the diurnal cycle of tropical convection across the ‘grey zone’ , 2014 .

[30]  Erik W. Kolstad,et al.  Marine cold-air outbreaks in the North Atlantic: temporal distribution and associations with large-scale atmospheric circulation , 2009 .

[31]  Paul Agnew,et al.  Investigation and prediction of helicopter‐triggered lightning over the North Sea , 2013 .

[32]  H. D. Orville,et al.  Bulk Parameterization of the Snow Field in a Cloud Model , 1983 .

[33]  D. Ricard,et al.  Improvement of the forecast of convective activity from the AROME‐France system , 2016 .

[34]  Tsuyoshi Koshiro,et al.  Origins of the Solar Radiation Biases over the Southern Ocean in CFMIP2 Models , 2014 .

[35]  J. Kain,et al.  A One-Dimensional Entraining/Detraining Plume Model and Its Application in Convective Parameterization , 1990 .

[36]  Kazuo Saito,et al.  The Operational JMA Nonhydrostatic Mesoscale Model , 2006 .

[37]  G. Thompson,et al.  Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part II: Implementation of a New Snow Parameterization , 2008 .

[38]  E. Bazile,et al.  A mass‐flux convection scheme for regional and global models , 2001 .

[39]  P. Field,et al.  The “Grey Zone” cold air outbreak global model intercomparison: A cross evaluation using large‐eddy simulations , 2017 .

[40]  H. Niino,et al.  Development of an Improved Turbulence Closure Model for the Atmospheric Boundary Layer , 2009 .

[41]  Stéphane Bélair,et al.  Boundary Layer and Shallow Cumulus Clouds in a Medium-Range Forecast of a Large-Scale Weather System , 2005 .

[42]  R. Smith.  A scheme for predicting layer clouds and their water content in a general circulation model , 1990 .

[43]  D. Ricard,et al.  Kinetic energy spectra characteristics of two convection‐permitting limited‐area models AROME and Meso‐NH , 2013 .

[44]  G. Grell,et al.  A North American Hourly Assimilation and Model Forecast Cycle: The Rapid Refresh , 2016 .

[45]  P. Woodward,et al.  The Piecewise Parabolic Method (PPM) for Gas Dynamical Simulations , 1984 .

[46]  Pierre Bénard,et al.  Semi‐Lagrangian advection scheme with controlled damping: An alternative to nonlinear horizontal diffusion in a numerical weather prediction model , 2008 .

[47]  V. Masson,et al.  The AROME-France Convective-Scale Operational Model , 2011 .

[48]  Sylvie Malardel,et al.  A Parameterization of Dry Thermals and Shallow Cumuli for Mesoscale Numerical Weather Prediction , 2009 .

[49]  M. Yau,et al.  A Multimoment Bulk Microphysics Parameterization. Part II: A Proposed Three-Moment Closure and Scheme Description , 2005 .

[50]  L. Ruby Leung,et al.  Sensitivity of U.S. summer precipitation to model resolution and convective parameterizations across gray zone resolutions , 2017 .

[51]  Peter Clark,et al.  Convection‐permitting models: a step‐change in rainfall forecasting , 2016 .

[52]  M. Diamantakis,et al.  An inherently mass‐conserving semi‐implicit semi‐Lagrangian discretization of the deep‐atmosphere global non‐hydrostatic equations , 2014 .