Effect of improving representation of horizontal and vertical cloud structure on the Earth's global radiation budget. Part I: Review and parametrization

A poor representation of cloud structure in a general circulation model (GCM) is widely recognised as a potential source of error in the radiation budget. Here, we develop a new way of representing both horizontal and vertical cloud structure in a radiation scheme. This combines the ‘Tripleclouds’ parametrization, which introduces inhomogeneity by using two cloudy regions in each layer as opposed to one, each with different water content values, with ‘exponential-random’ overlap, in which clouds in adjacent layers are not overlapped maximally, but according to a vertical decorrelation scale. This paper, Part I of two, aims to parametrize the two effects such that they can be used in a GCM. To achieve this, we first review a number of studies for a globally applicable value of fractional standard deviation of water content for use in Tripleclouds. We obtain a value of 0.75 ± 0.18 from a variety of different types of observations, with no apparent dependence on cloud type or gridbox size. Then, through a second short review, we create a parametrization of decorrelation scale for use in exponential-random overlap, which varies the scale linearly with latitude from 2.9 km at the Equator to 0.4 km at the poles. When applied to radar data, both components are found to have radiative impacts capable of offsetting biases caused by cloud misrepresentation. Part II of this paper implements Tripleclouds and exponential-random overlap into a radiation code and examines both their individual and combined impacts on the global radiation budget using re-analysis data. Copyright c

[1]  R. Colman,et al.  A comparison of climate feedbacks in general circulation models , 2003 .

[2]  Robert Pincus,et al.  Overlap assumptions for assumed probability distribution function cloud schemes in large‐scale models , 2005 .

[3]  D. Randall,et al.  Climate models and their evaluation , 2007 .

[4]  I. Musat,et al.  On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles , 2006 .

[5]  Xiaoqing Wu,et al.  Long-Term Behavior of Cloud Systems in TOGA COARE and Their Interactions with Radiative and Surface Processes. Part I: Two-Dimensional Modeling Study , 1998 .

[6]  H. Barker,et al.  Accounting for subgrid‐scale cloud variability in a multi‐layer 1d solar radiative transfer algorithm , 1999 .

[7]  Xiaoqing Wu,et al.  Long-Term Behavior of Cloud Systems in TOGA COARE and Their Interactions with Radiative and Surface Processes. Part III: Effects on the Energy Budget and SST. , 2001 .

[8]  Philip J. Rasch,et al.  Parameterizing Vertically Coherent Cloud Distributions , 2002 .

[9]  Anthony J. Illingworth,et al.  Ice cloud inhomogeneity: Quantifying bias in emissivity from radar observations , 2000 .

[10]  D. Schertzer,et al.  Discrete angle radiative transfer: 3. Numerical results and meteorological applications , 1990 .

[11]  Y. Gu,et al.  Cirrus cloud horizontal and vertical inhomogeneity effects in a GCM , 2006 .

[12]  T. Wilbanks,et al.  Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change , 2007 .

[13]  J. Morcrette,et al.  Impact of a New Radiation Package, McRad, in the ECMWF Integrated Forecasting System , 2008 .

[14]  Robin J. Hogan,et al.  Deriving cloud overlap statistics from radar , 2000 .

[15]  Pavlos Kollias,et al.  Impact of Dynamics and Atmospheric State on Cloud Vertical Overlap , 2008 .

[16]  Qiang Fu,et al.  The sensitivity of domain‐averaged solar fluxes to assumptions about cloud geometry , 1999 .

[17]  D. Randall,et al.  A cloud resolving model as a cloud parameterization in the NCAR Community Climate System Model: Preliminary results , 2001 .

[18]  Solar radiative fluxes for stochastic, scale-invariant broken cloud fields , 1992 .

[19]  Qiang Fu,et al.  High-Cloud Horizontal Inhomogeneity and Solar Albedo Bias , 2002 .

[20]  Wei Yu,et al.  Evaluation of model clouds and radiation at 100 km scale using GOES data , 1997 .

[21]  R. Hogan,et al.  Parameterizing Ice Cloud Inhomogeneity and the Overlap of Inhomogeneities Using Cloud Radar Data , 2003 .

[22]  Brian Cairns,et al.  Absorption within inhomogeneous clouds and its parameterization in general circulation models , 2000 .

[23]  R. Davies,et al.  Plane Parallel Albedo Biases from Satellite Observations. Part II: Parameterizations for Bias Removal. , 1998 .

[24]  Howard W. Barker,et al.  Representing cloud overlap with an effective decorrelation length: An assessment using CloudSat and CALIPSO data , 2008 .

[25]  Gerald G. Mace,et al.  Cloud-Layer Overlap Characteristics Derived from Long-Term Cloud Radar Data , 2002 .

[26]  M. Giorgetta,et al.  Tests of Monte Carlo Independent Column Approximation in the ECHAM5 Atmospheric GCM , 2007 .

[27]  Jean-Jacques Morcrette,et al.  The Overlapping of Cloud Layers in Shortwave Radiation Parameterizations , 1986 .

[28]  Howard W. Barker,et al.  Overlap of fractional cloud for radiation calculations in GCMs: A global analysis using CloudSat and CALIPSO data , 2008 .

[29]  Samantha A. Smith,et al.  Analysis of Aircraft, Radiosonde, and Radar Observations in Cirrus Clouds Observed during FIRE II: The Interactions between Environmental Structure, Turbulence, and Cloud Microphysical Properties , 2001 .

[30]  Xin-Zhong Liang,et al.  Radiative Effects of Cloud Horizontal Inhomogeneity and Vertical Overlap Identified from a Monthlong Cloud-Resolving Model Simulation , 2005 .

[31]  Robin J. Hogan,et al.  Tripleclouds: An Efficient Method for Representing Horizontal Cloud Inhomogeneity in 1D Radiation Schemes by Using Three Regions at Each Height , 2008 .

[32]  Sally A. McFarlane,et al.  Albedo bias and the horizontal variability of clouds in subtropical marine boundary layers: Observations from ships and satellites , 1999 .

[33]  H. Barker A parameterization for computing grid-averaged solar fluxes for inhomogeneous marine boundary layer , 1996 .

[34]  G. Stephens,et al.  An Assessment of the Parameterization of Subgrid-Scale Cloud Effects on Radiative Transfer. Part I: Vertical Overlap. , 2004 .

[35]  D. Randall,et al.  Stochastic generation of subgrid‐scale cloudy columns for large‐scale models , 2004 .

[36]  Oleg A. Krasnov,et al.  Continuous Evaluation of Cloud Profiles in Seven Operational Models Using Ground-Based Observations , 2007 .

[37]  The relationship between α and the cross‐correlation of cloud fraction , 2006 .

[38]  A. Slingo,et al.  Sensitivity of the Earth's radiation budget to changes in low clouds , 1990, Nature.

[39]  J. Curry,et al.  Cloud overlap statistics , 1989 .

[40]  A. Slingo,et al.  Studies with a flexible new radiation code. I: Choosing a configuration for a large-scale model , 1996 .

[41]  J. Morcrette,et al.  A fast, flexible, approximate technique for computing radiative transfer in inhomogeneous cloud fields , 2003 .

[42]  Robin J. Hogan,et al.  Effect of improving representation of horizontal and vertical cloud structure on the Earth's global radiation budget. Part II: The global effects , 2010 .

[43]  Robert F. Cahalan,et al.  The albedo of fractal stratocumulus clouds , 1994 .

[44]  E. Clothiaux,et al.  Assessing 1D atmospheric solar radiative transfer models: Interpretation and handling of unresolved clouds , 2003 .

[45]  R. Marchand,et al.  A description of hydrometeor layer occurrence statistics derived from the first year of merged Cloudsat and CALIPSO data , 2009 .

[46]  H. Barker,et al.  A Parameterization for Computing Grid-Averaged Solar Fluxes for Inhomogeneous Marine Boundary Layer Clouds. Part II: Validation Using Satellite Data , 1996 .