Impacts of cloud overlap assumptions on radiative budgets and heating fields in convective regions

Abstract Impacts of cloud overlap assumptions on radiative budgets and heating fields are explored with the aid of a cloud-resolving model (CRM), which provided cloud geometry as well as cloud micro and macro properties. Large-scale forcing data to drive the CRM are from TRMM Kwajalein Experiment and the Global Atmospheric Research Program's Atlantic Tropical Experiment field campaigns during which abundant convective systems were observed. The investigated overlap assumptions include those that were traditional and widely used in the past and the one that was recently addressed by Hogan and Illingworth (2000) , in which the vertically projected cloud fraction is expressed by a linear combination of maximum and random overlap, with the weighting coefficient depending on the so-called decorrelation length L cf . Results show that both shortwave and longwave cloud radiative forcings (SWCF/LWCF) are significantly underestimated under maximum (MO) and maximum-random (MRO) overlap assumptions, whereas remarkably overestimated under the random overlap (RO) assumption in comparison with that using CRM inherent cloud geometry. These biases can reach as high as 100 Wm − 2 for SWCF and 60 Wm − 2 for LWCF. By its very nature, the general overlap (GenO) assumption exhibits an encouraging performance on both SWCF and LWCF simulations, with the biases almost reduced by 3-fold compared with traditional overlap assumptions. The superiority of GenO assumption is also manifested in the simulation of shortwave and longwave radiative heating fields, which are either significantly overestimated or underestimated under traditional overlap assumptions. The study also pointed out the deficiency of constant assumption on L cf in GenO assumption. Further examinations indicate that the CRM diagnostic L cf varies among different cloud types and tends to be stratified in the vertical. The new parameterization that takes into account variation of L cf in the vertical well reproduces such a relationship and leads to better simulations on radiative heating fields. It is therefore desirable to specify or parameterize L cf in terms of cloud categories rather than constantly specified if to further improve the model performance.

[1]  Stephen A. Klein,et al.  The role of vertically varying cloud fraction in the parametrization of microphysical processes in the ECMWF model , 1999 .

[2]  Xianwen Jing,et al.  The features of cloud overlapping in Eastern Asia and their effect on cloud radiative forcing , 2013, Science China Earth Sciences.

[3]  A. Bott,et al.  Stochastic parameterization of cloud processes , 2014 .

[4]  D. Randall,et al.  Cloud resolving modeling of the ARM summer 1997 IOP: Model formulation, results, uncertainties, and sensitivities , 2003 .

[5]  I. Vardavas,et al.  Cloud effects on the solar and thermal radiation budgets of the Mediterranean basin , 2015 .

[6]  Vincent E. Larson,et al.  A PDF-Based Model for Boundary Layer Clouds. Part I: Method and Model Description , 2002 .

[7]  Steven J Franke,et al.  Critical level interaction of a gravity wave with background winds driven by a large-scale wave perturbation , 2009 .

[8]  Minghua Zhang,et al.  Vertical velocity in shallow convection for different plume types , 2014 .

[9]  M. Khairoutdinov,et al.  A Large Eddy Simulation Model with Explicit Microphysics: Validation against Aircraft Observations of a Stratocumulus-Topped Boundary Layer , 1999 .

[10]  U. Willén,et al.  Assessing model predicted vertical cloud structure and cloud overlap with radar and lidar ceilometer observations for the baltex bridge campaign of CLIWA-NET , 2005 .

[11]  Minghua Zhang,et al.  Tropical Cloud Heating Profiles: Analysis from KWAJEX , 2008 .

[12]  Trent M. Hare,et al.  Surface processes recorded by rocks and soils on Meridiani Planum, Mars: Microscopic Imager observations during Opportunity's first three extended missions , 2008 .

[13]  S. Bony,et al.  Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models , 2005 .

[14]  Minghua Zhang,et al.  Impact of the convection triggering function on single‐column model simulations , 2000 .

[15]  Zhang Hua.  Effect of Cloud Overlap Assumptions in Climate Models on Modeled Earth-Atmosphere Radiative Fields , 2010 .

[16]  Xin-Zhong Liang,et al.  Cloud overlap effects on general circulation model climate simulations , 1997 .

[17]  Xiaocong Wang.  Development of a toy column model and its application in testing cumulus convection parameterizations , 2015 .

[18]  Q. Fu,et al.  Multiple Scattering Parameterization in Thermal Infrared Radiative Transfer , 1997 .

[19]  Thomas P. Charlock,et al.  The Albedo Field and Cloud Radiative Forcing Produced by a General Circulation Model with Internally Generated Cloud Optics , 1985 .

[20]  Stephen A. Klein,et al.  A parametrization of the effects of cloud and precipitation overlap for use in general‐circulation models , 2000 .

[21]  Gerald G. Mace,et al.  Effect of improving representation of horizontal and vertical cloud structure on the Earth's global radiation budget. Part I: Review and parametrization , 2010 .

[22]  Minghua Zhang,et al.  An analysis of parameterization interactions and sensitivity of single‐column model simulations to convection schemes in CAM4 and CAM5 , 2013 .

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

[24]  Xianwen Jing,et al.  Application and evaluation of a new radiation code under McICA scheme in BCC_AGCM2.0.1 , 2014 .

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

[26]  J. Curry,et al.  A New Double-Moment Microphysics Parameterization for Application in Cloud and Climate Models. Part I: Description , 2005 .

[27]  Howard W. Barker,et al.  Radiative sensitivities for cloud structural properties that are unresolved by conventional GCMs , 2005 .

[28]  Bryan C. Weare.  Effects of cloud overlap on radiative feedbacks , 2001 .

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

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

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

[32]  Barbara Scherllin-Pirscher,et al.  A new dynamic approach for statistical optimization of GNSS radio occultation bending angles for optimal climate monitoring utility , 2013 .

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

[34]  Minghua Zhang,et al.  Constrained Variational Analysis of Sounding Data Based on Column-Integrated Budgets of Mass, Heat, Moisture, and Momentum: Approach and Application to ARM Measurements. , 1997 .

[35]  Shepard A. Clough,et al.  Atmospheric radiative transfer modeling: a summary of the AER codes , 2005 .

[36]  J. Comstock,et al.  Dominant role by vertical wind shear in regulating aerosol effects on deep convective clouds , 2009 .

[37]  Guoxiong Wu,et al.  Comparisons of GCM cloud cover parameterizations with cloud-resolving model explicit simulations , 2015, Science China Earth Sciences.