A novel approach for introducing cloudspatial structure into cloud radiative transferparameterizations

Dong Huang1,2 and Yangang Liu1

1Brookhaven National Laboratory, Upton, NY 11973, USA2Environmental & Climate Sciences Department Brookhaven National Laboratory 75 Rutherford Dr.,Upton, NY 11973, USA

E-mail: dhuang@bnl.gov

Received 16 June 2014, revised 6 November 2014Accepted for publication 24 November 2014Published 18 December 2014

AbstractSubgrid-scale variability is one of the main reasons why parameterizations are needed in large-scale models. Although some parameterizations started to address the issue of subgrid variabilityby introducing a subgrid probability distribution function for relevant quantities, the spatialstructure has been typically ignored and thus the subgrid-scale interactions cannot be accountedfor physically. Here we present a new statistical-physics-like approach whereby the spatialautocorrelation function can be used to physically capture the net effects of subgrid cloudinteraction with radiation. The new approach is able to faithfully reproduce the Monte Carlo 3Dsimulation results with several orders less computational cost, allowing for more realisticrepresentation of cloud radiation interactions in large-scale models.

S Online supplementary data available from stacks.iop.org/ERL/9/124022/mmedia

Keywords: cloud structure, parameterization, radiative transfer

1. Introduction

Parameterizations in a global climate model (GCM) aredesigned to describe the ‘collective effects’ of processes thatoccur at scales smaller than GCM grid sizes (Randallet al 2003). Parameterizations of many processes such asradiation transfer and autoconversion employ the assumption ofindependent column approximation (ICA), i.e., there is nointeraction between subcolumns and the grid-average effectsdepend only on the probability distribution function (PDF) ofrelevant variables (Pincus et al 2003). ICA approaches use one-point statistical information (e.g., PDF), called subgrid varia-bility in this letter and structural information (e.g., spatialorganization and arrangement) that can be characterized bymulti-point statistics is generally ignored. However, coherentstructures have been found at scales ranging from droplet

clusters to organized cloud, and have complex interactions withradiation, dynamical processes, and mesoscale environmentsystems (Kostinski and Shaw 2001, Marshak et al 2005,Feingold et al 2010). Failure to include subgrid cloud andconvection structures can lead to inadequate simulations oflarge-scale features (Lin et al 2011). It has been found thatignoring cloud spatial organization tends to underestimate oroverestimate the domain-average radiation fluxes dependent onmany factors, e.g., solar angle and cloud geometry (Zuidemaand Evans 1998, Barker et al 1999, Scheirer and Macke 2003,Davis and Mineev-Weinstein 2011, Hogan and Shonk 2013).

Given the detailed cloud field, the radiation field can befound by numerically solving the 3D transport equation(Evans 1998). In many applications, the knowledge of 3Dcloud field is unavailable or expensive to obtain. It is oftendifficult to draw any theoretical conclusion based on the 3Dapproach: there could be numerous configurations of 3Dcloud field that will give statistically similar radiation char-acteristics (Anisimov and Fukshansky 1992). Besides these,numerically solving the 3D problem is too expensive to use in

Environmental Research Letters

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practical applications. In climate models, it is a standardpractice to employ the ICA assumption, i.e., divide thedomain into two (clear and cloudy) or more subcolumns(Pincus et al 2003, Shonk and Hogan 2008) and indepen-dently calculate the radiation flux within each subcolumn.

Previous efforts on parameterization of 3D cloud-radia-tion interaction in large-scale models have focused on binarymedium or oversimplified closure assumptions (Anisimovand Fukshansky 1992, Vainikko 1973, Titov 1990, Pomran-ing 1996, Tompkins and Di Giuseppe 2007, Stephens 1988,Kassianov and Veron 2011, Hogan and Shonk 2013). Herewe present a new statistical physics-like simulation approachthat makes a direct connection between the statistical char-acterization of cloud structure and the statistical moments ofthe radiation field by properly averaging the 3D equation. Theunknowns of the resultant statistical radiative transport (SRT)equations are actually the statistical moments of the radiationfield, and the model inputs are some statistical moments of the3D medium structure. In this letter, we show that a spatialcorrelation function can serve as the key to statisticallydescribing cloud–radiation interactions.

2. Basic theory and method

To examine what structural information is needed for thetransport problem, let us consider radiation transfer in acloudy atmosphere vertically confined within [0, 1] where thetop is at z= 0. The monochromatic radiance at r= (x, y, z) indirection Ω μ φ= ( , ) is denoted by I(r, Ω), where μ is thecosine of the zenith angle and φ is the azimuth angle. Thesolar radiance field satisfies the 3D radiative transfer equationof integral form (Chandrasekar 1950):

∫

∫∫

Ω Ω

Ω Ω Ω Ω Ω

μσ

μω σ

+ ′ ′ ′

= ′ ′ ′

× ′ ′ ′ ′ ′ +π

( )

I I z

z

p I I

r r r

r r

r r r

( , )1

( ) ( , )d

1( ) ( )d

( , , ) ( , )d , , (1)

E

E0

4b

where Ω μ′ = + ′ −z zr r ( )/ , I(rb, Ω) is the incoming solarradiance at the upper boundary for downward directions(μ< 0) and is the surface reflection for upward directions(μ> 0); σ(r) and ω0(r) are respectively the cloud extinctioncoefficient and single scattering albedo; and p(r, Ω′, Ω) is thescattering phase function. The second term on the left-handside is the path extinction and the first term on the right-handside is the source due to scattering. Note that the extinctioncoefficient is normalized with regard to the depth of theatmosphere layer and its integral over [0, 1] corresponds tothe optical depth of the cloudy atmosphere. The interval ofintegral is given by E= [0, z] for downward directions andE= [z, 1] for upward directions. Here we trintroduce theergodic hypothesis, which implies that the ansport processesshould possess certain translational invariance in spatialcoordinate and thus lead to the equality of ensemble andspatial averages (Titov 1990, Rybicki 1965). It is feasible to

assume that the deterministic transfer equation is valid foreach member of the ensemble system. Therefore, statistics isintroduced only to account for the lack of knowledge aboutthe detailed structure of the cloud, not about the equationsgoverning the transport processes.

We further assume that the scattering phase function p andsingle scattering albedo ω0 depend only on height z. Let <···>denote the horizontal or ensemble average, the vertical profileof horizontally-averaged radiance can be obtained afterapplying the notations

Ω Ω Ω Ωσ= ′ = ′ ′I z I U z Ir r r¯ ( , ) ( , ) and ¯ ( , ) ( ) ( , ) ,

∫

∫ ∫

Ω Ω

Ω Ω Ω Ω

Ω

μ

μω

+ ′ ′

= ′ ′ ′ ′ ′ ′ ′

+π

( )

I z U z z

z z U z p z

I z

¯ ( , )1 ¯ ( , )d

1( )d ¯ ( , ) ( , , )d

¯ , . (2)

E

E0

4

b

This averaging process is conceptually similar to theReynolds averaging widely used in fluid dynamics (Rey-nolds 1895). The domain-average radiance I is now explicitlypresented in equation (2) but the equation is not closed since anew variable U still needs to be determined. The variable U isthe mean product of radiance and extinction coefficient and,with the assumption of horizontally-invariant single scatteralbedo, radiation absorption A at any level can be readilyfound by: ∫ Ω Ωω= − π

⎡⎣ ⎤⎦A z z U z( ) 1 ( ) ¯ ( , )d0 4. Multiplying

equation (1) with σ(r) and performing the horizontal averageagain, we obtain an equation for U :

∫

∫∫

Ω Ω

Ω Ω Ω Ω

Ω

μσ σ

μω

σ σ

+ ′ ′ ′

= ′ ′

× ′ ′ ′ ′ ′ ′

+π

( )

U z I z

z z

p z I

U z

r r r

r r r

¯ ( , )1

( ) ( ) ( , ) d

1( )d

( , , ) ( ) ( ) ( , ) d

¯ , . (3)

E

E0

4

b

To solve this equation, we can either truncate the higherorder terms at certain point, or introduce independenthypotheses of closure to determine the higher order terms interms of the lower order ones. Here the second approach isused. We modified the standard Intercomparison of 3DRadiation Codes (I3RC) community Monte Carlo model(Cahalan et al 2005, Pincus and Evans 2010) to compute andrecord 3D radiance field. Based on the analysis of MonteCarlo simulations with a variety of cloud cases (the supple-mentary material, available at stacks.iop.org/ERL/9/124022/mmedia provides some details on how the closure is derived),the higher order term Ωσ σ ′ ′Ir r r( ) ( ) ( , ) can be approxi-mated in two steps:

Ω Ω

Ω Ω

σ σ

σ

σ σ

σ

′ ′ ≈ ′ ′

+ ′ ′ − ′ ′

×′ − ′

′ − ′

⎡⎣ ⎤⎦I f z I z

I f z I z

f z

f za

r r r

r r

r r

r

( ) ( ) ( , ) ( ) ¯ ( , )

( ) ( , ) ( ) ¯ ( , )

( ) ( ) ( )

( ) ( ), (4 )

1

21

1

21

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Environ. Res. Lett. 9 (2014) 124022 D Huang and Y Liu

Ω Ω Ωσ ′ ′ ≈ ′ ′ + ′ ′I f z U z f z I z br r( ) ( , ) ( ) ¯ ( , ) ( ) ¯ ( , ). (4 )22 3

σ σ ′r r( ) ( ) is the spatial covariance function of cloudextinction coefficients at levels z and z′ along the direction Ωand, with an appropriate normalization, it becomes the spatialautocorrelation function. For the rest of this letter, theterms covariance and autocorrelation are interchangeable.The correlation function provides a measure of the spatialstructure of cloud extinction coefficient. ′ =f z( )1

σ σ′ ′ ′ ′ + Δ ′ + Δ ′x y z x x y y zinf { ( , , ) ( , , ) : Δ ∈ ∞x [0, ],Δ ∈ ∞y [0, ]}, i.e., the minimum autocorrelation at level z'.The coefficients f2 and f3 depend only on the horizontalaverage (σ) and variance (V) of cloud extinction coefficient inthe corresponding layer: σ′ = ′f z z( ) 2 ¯ ( ),2 and ′ =f z( )3

σ σ σ′ − ′ = ′ − ′V z z z( ) ¯ ( ) (r ) 2 ¯ ( )2 2 2 . It can be verified that,for binary media, equations (4a) and (4b) will converge to theclosure scheme of Titov (1990) that was specifically designedfor binary media. To accurately calculate the direct (unscat-tered) radiation, knowledge about the PDF of extinctioncoefficient may also be needed.

Given the closure scheme of equation (4), the weightedmean radiation field ΩU z¯ ( , ) can be determined by analyti-cally or numerically solving the 1D Volterra integralequation (3) and then the mean radiation field ΩI z¯ ( , ) can bereadily obtained through equation (2).

It can be seen from equations (3) and (4) that the termΩσ σ ′ ′Ir r r( ) ( ) ( , ) is the key to describing the interaction

between the 3D radiation and cloud fields; the functionσ σ ′r r( ) ( ) serves as a direct linkage between the meanradiation field and 3D cloud structure and encapsulates infor-mation of the 3D cloud structure across various spatial scales.Figure 1 shows an example of spatial correlation function for a3D cloud field simulated by a large eddy simulation model(Pincus and Evans 2010). When the separation distance is zero,the cloud field at r′= r perfectly correlates with itself, i.e.,σ σ σ′ = +V zr r( ) ( ) ( ) ¯ (z)2 . The correlation functionapproaches the asymptote σ σ ′z¯ (z) ¯ ( ), i.e., statistically inde-pendent, once the separation distance becomes large enough.The decorrelation length is related to mean cloud size in the x-dimension in this example. It should be noted that the corre-lation function can also be smaller than σ σ ′z¯ (z) ¯ ( ) if theproperties are negatively correlated.

3. Numerical results

To evaluate the new approach, we perform a suite of numericalsimulations using a discrete ordinate method for angular dis-cretization (Bass et al 1986). The angular discretization uses anequal-area Carlson quadrature scheme and therefore both thelatitudinal and longitudinal variations are represented by thediscrete ordinates. The spatial (vertical) discretization is chosento make the mean optical depth of each layer not exceeding 1.Equation (3) is now converted to a system of linear equationswith the vertical and angular discretizations. To use efficientsolvers like DISORT (Stamnes et al 1988), further simplifica-tions of equation (3) will be required. Since the purpose of this

letter is to demonstrate the viability of the SRT approach, furthersimplification and numerical optimization will be the topic of afuture research. A successive order approximation is used tosimulate multiple scattering where the lower order scatteringserves as the source for the higher order calculation (Shabanovet al 2000). For the zero-order radiation calculation, the sourcefunction is set to be unit which corresponds to a collimateincident beam without any diffuse component. The first-orderscattering is then determined with the source function based onthe zero-order solution. This process is repeated until the solu-tion converges with a prescribed criteria. For the medium with avery large optical thickness, a large number of vertical layersmay be needed and the successive order approximation methodcan be tedious. For all simulations performed in this research, weassume perfect knowledge about the cloud spatial correlationfunctions so that the accuracy of the second order closurescheme (equation (4)) can be evaluated against the full 3DMonte Carlo results (Barker et al 1999).

To examine the impacts of ignoring cloud structure,called 3D effects here, we also compare the SRT results withtwo widely-used approximation methods: Plane-parallelapproximation (PPA) and ICA (Barker et al 1999, Oreo-poulos and Barker 1999, Barker and Davis 2005). The PPAapproach assumes plane parallel geometry with each layertaking horizontal mean optical properties and ignores thehorizontal fluctuations. The ICA approach assumes no nethorizontal transport of particles or photons between columnsso that the mean radiation characteristics can be obtained bysolving a1D deterministic transport equation for each columnand averaging the resultant solutions.

The first group of simulations are based on an idealizedcheckerboard-like medium as shown in figure 2(a); this case isnotoriously challenging for 1D transport models since itsexaggeration of 3D transport effects (Wiscombe 2005). Theoptical thickness of the black and white cells is 18 and 0. Theaspect ratio of each individual cell, defined as the ratio of thehorizontal dimension to the vertical dimension, varies from 0.01to 100 for different simulations. The medium is illuminatedfrom above by collimated light at 0° or 30° zenith angles. Thelateral boundary condition is assumed to be periodic and thelower boundary is ideally black. The single scattering albedo ofthe medium is 1. We adopt Henyey–Greenstein scattering hereto represent the scattering phase function (Henyey and Green-stein 1941) and the asymmetry parameter is set to 0.85.

An aspect ratio value of 1.0 is used to obtain the twoexamples of spatial correlation function shown in figure 2(b).Unlike the stratocumulus cloud shown in figure 1, the spatialcorrelation function of the checkerboard medium is periodicand does not vanish with increasing distance. The correlationfunction is also capable of describing the anisotropic struc-tures of the medium since it indicates quite different patternsalong different directions.

For the 0° illumination, the scene albedo simulated by the3D Monte Carlo method approaches 0.210 at the small aspectratio limit and 0.292 at the large aspect ratio limit (figure 2(c)).For the 30° illumination, the scene albedo ranges from 0.310 to0.449 for various aspect ratios (figure 2(d)). As expected,neither ICA nor PPA solutions show any dependence on the

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Environ. Res. Lett. 9 (2014) 124022 D Huang and Y Liu

aspect ratio. The scene albedos by the PPA and ICA approa-ches are 0.383 and 0.296 for the 0° illumination, and 0.445 and0.310 for the 30° illumination. The PPA albedos are alwayshigher than others, owing to the Jensen’s inequality for convexfunctions (Jensen 1906). The resulting albedo bias from PPAand ICA varies with aspect ratio and is up to 70% of the truealbedo of the checkerboard medium.

In contrast, the SRT albedos follow closely with the 3DMonte Carlo curves over the entire range of aspect ratio,suggesting that the SRT faithfully represents the dependenceof horizontal transport effects on aspect ratio. For the 0°illumination, the SRT solutions successfully reproduce thereduction of albedo that is due to radiative channeling andoften found at small illumination angles, i.e., horizontaltransport enhances the domain-average transmission (andsuppresses reflection) relative to the ICA solutions (Davis andMarshak 2010). For a higher illumination angle, the net effectof horizontal transport is to reduce transmission (and enhancereflection) and as a result both the 3D and SRT solutionsasymptote the PPA solutions at small aspect ratio limit. Thehorizontal transport effects are most evident when the aspectratios are small and the discrepancy between SRT and ICAresults decreases with increasing aspect ratio. When the hor-izontal dimension of each cell is much larger than its verticaldimension, the net effect of horizontal transport becomenegligible and the SRT solutions asymptote those of the ICA

regardless of illumination angle. We find that whether hor-izontal transport enhances or suppress the scene albedodepends on many factors, including vertical and horizontalarrangement, horizontal fluctuation of optical properties of themedium, scattering phase function, and illumination angle.

The second group of simulations are for a cumulus cloudsystem simulated by the DARMA model (Ackermanet al 1995) shown in figure 3(a). Figure 3(b) shows the spatialautocorrelation functions of the cloud extinction at levelz= 0.5 for two horizontal directions. It can be seen that thecumulus cloud appears to be statistically isotropic despite itslarge spatial variability. Using the more realistic cloud fieldmay provide a better estimate of 3D effects in real worldclouds. The single scattering albedo of cloud droplet is 1.0and the scattering phase function is the same as the first case.Boundary conditions are the same as in the first case. Toillustrate the dependence of horizontal transport on cloudstructure, we vary the cloud aspect ratio from 0.01 to 100 torepresent different levels of horizontal transport effects andkeep other cloud properties fixed. Apparently, the PPA andICA results do not depend on cloud aspect ratio. For the 0°illumination, the SRT solutions successfully reproduce thereduction of albedo with increasing horizontal transport butthe SRT approach seems to slightly overestimate the scenealbedo compared to the 3D results (figure 3(c)). For the 30°illumination, horizontal transport tends to enhance the scene

Figure 1. Spatial autocorrelation function of a stratocumulus cloud. (a) 3D rendering of the isosurface at extinction coefficient value of 20; (b)a horizontal cross section of the 3D cloud extinction coefficient field at z= 0.6; and (c) the spatial autocorrelation function of this cross sectionalong the x direction.

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Environ. Res. Lett. 9 (2014) 124022 D Huang and Y Liu

albedo, as suggested by figure 3(d). As expected, both theSRT and 3D results converge to the ICA results when cloudaspect ratio becomes very large.

The third group of simulations are for the stratocumuluscloud system shown in figure 1. The single scattering albedo ofcloud droplet is 0.98 and the scattering phase function is thesame as the first case. The incidental radiation is collimatedlight of 0° zenith angle. Boundary conditions are the same as inthe first case. The scene albedos calculated using the SRT andthe reference 3D Monte Carlo approaches are respectively0.230 and 0.229, while the cloud absorptance from the SRTand 3D approach is 0.223 and 0.225. The accuracy of the SRTcalculation is within 1% of the reference value, while the ICAand PPA result in −5% and 10% errors for this stratocumuluscase. It can be seen from these three examples that the mag-nitude of 3D transport effects varies with many factors,including illumination angle, horizontal fluctuation, and shapeof the spatial correlation function (Barker et al 1999).

To evaluate if SRT is able to accurately simulate thedependence of radiative fluxes on illumination angle, wecomputer the reflectance (normalized upward fluxes at theupper boundary) as a function of solar zenith angle. The resultfor transmittance (normalized downward fluxes at the lowerboundary) is not shown here since it complements withreflectance for a non-absorptive medium. It can be seen from

figure 4 that, for the checkboard case, the SRT agrees extre-mely well with the full 3D calculations. The differencebetween 3D and ICA indicates the magnitude of 3D effects. Atsmall solar angles (<18°), the 3D effects reduce the domain-average reflectance by up to 20%, primarily due to photonleaking from clouds to the clear region. For large solar angles,the 3D effects actually enhance the domain-average reflectanceby up to 80%. The enhancement of reflectance increases withsolar angle and is mainly due to cloud side illumination effects(Hogan and Shonk 2013). The SRT approach very accuratelyreproduces the 3D effects for all the examined solar angles.

Lastly, the computational cost for the new approach isevaluated and compared with the conventional 1D approa-ches. To assure fair comparisons, the PPA and ICA approa-ches also use the same solver as the SRT, in other words,

ΩU z¯ ( , ) is set to be Ωσ z I z¯ ( ) ¯ ( , ) in equation (2) for the PPAand ICA approaches. The computational cost of the ICAapproach is linearly proportional to the number of sub-columns used to represent the horizontal heterogeneity. Forthe tested cloud cases, the SRT approach is 2–3 times moreexpensive than the PPA approach while the ICA approach with100 subcloumns is 30–50 times more expensive than the SRTapproach. Depended on the number of photon used in theMonte Carlo simulation, the computational cost of the full 3Dapproach can be several orders more than the PPA approach.

Figure 2. Visualization of the checkerboard medium (a), its autocorrelation functions along the x, diagonal, and z directions (b). Scenealbedos are calculated with various aspect ratios using the 3D Monte Carlo, SRT, PPA, and ICA approaches for 0° (c) and 30° (d)illumination angles.

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Environ. Res. Lett. 9 (2014) 124022 D Huang and Y Liu

4. Summary

In this letter a new approach is presented to represent unre-solved cloud structure in the radiation parameterization. Byusing a statistical-physics-like concept, we develop a simple

1D statistical transport theory that naturally utilizes a two-point spatial correlation function to describe subgrid-scaleinteractions that are traditionally only captured by computa-tionally expensive 3D models. The proposed spatial correla-tion function encodes the most important information aboutthe spatial arrangement and morphology of clouds andtherefore introduces the dependence of radiation field on the3D structure. Comparison studies of three types of transportmedia representing checker board, cumulus clouds, andstratocumulus clouds show that the statistical theory is cap-able of quantitatively capturing the properties of 3D transportmodels with several orders less computational costs, e.g.,enhancement or suppression of reflection by allowing hor-izontal transport. In practice the 1D stochastic transporttransfer approach are expected to lead to a reduction of thecomputational burden compared to the brute-force MonteCarlo approach, and a significant increase of accuracy com-pared to the widely used approximation methods. Alsonoteworthy is that the spatial correlation function appears tobe much smoother than the cloud field, indicating that thecorrelation function should be readily parameterized usingpoint process models or stochastic geometry (Stoyanet al 1995).

It is also important to account for other sources of errorsuch as unresolved temporal variability and spectral resolu-tion in order to develop an accurate cloud radiative transfer

Figure 3. Visualization of a cumulus cloud (a), its horizontal autocorrelation functions along the x and diagonal directions (b). Scene albedosare calculated with various aspect ratios using the 3D Monte Carlo, SRT, PPA, and ICA approaches for 0° (c) and 30° (d) illumination angles.

Figure 4. The scene albedo calculated using different approaches as afunction of solar zenith angle. The checkboard cloud case with anaspect ratio of 1 is used to for the simulations.

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Environ. Res. Lett. 9 (2014) 124022 D Huang and Y Liu

parameterization (Pincus and Stevens 2013). Further simpli-fication and evaluation of this approach at other spectralregions and broadband calculations will be the topic of ourfuture work. It should be noted that the new approachdeveloped in this study holds great promise to account forcloud structure in other cloud-related parameterizations.

Acknowledgments

This work is supported by the US Department of Energy’sEarth System Modeling (ESM) and Atmospheric ScienceResearch (ASR) programs, and the Laboratory DirectedResearch & Development program of Brookhaven NationalLaboratory. We are grateful to Drs Yuri Knyazikhin, AnthonyDavis, Warren Wiscombe, and Robert McGraw for theirencouragement and insightful advice.

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Environ. Res. Lett. 9 (2014) 124022 D Huang and Y Liu