Content uploaded by Jason P. Evans
Author content
All content in this area was uploaded by Jason P. Evans on Jan 18, 2015
Content may be subject to copyright.
Geosci. Model Dev., 7, 2121–2140, 2014
www.geosci-model-dev.net/7/2121/2014/
doi:10.5194/gmd-7-2121-2014
© Author(s) 2014. CC Attribution 3.0 License.
Implementation of a soil albedo scheme in the CABLEv1.4b
land surface model and evaluation against MODIS
estimates over Australia
J. Kala
1
, J. P. Evans
1
, A. J. Pitman
1
, C. B. Schaaf
2
, M. Decker
1
, C. Carouge
1
, D. Mocko
3
, and Q. Sun
2
1
Australian Research Council Centre of Excellence for Climate Systems Science and Climate Change Research Centre,
University of New South Wales, Sydney, NSW, 2052, Australia
2
Science Applications International Corporation at NASA Goddard Space Flight Centre, NASA, Greenbelt, MD, USA
3
Department of Earth and the Environment, Boston University, Boston, MA, USA
Correspondence to: J. Kala (j.kala@unsw.edu.au, jatin.kala.jk@gmail.com)
Received: 23 January 2014 – Published in Geosci. Model Dev. Discuss.: 13 March 2014
Revised: 20 August 2014 – Accepted: 23 August 2014 – Published: 23 September 2014
Abstract. Land surface albedo, the fraction of incoming so-
lar radiation reflected by the land surface, is a key com-
ponent of the Earth system. This study evaluates snow-free
surface albedo simulations by the Community Atmosphere
Biosphere Land Exchange (CABLEv1.4b) model with the
Moderate Resolution Imaging Spectroradiometer (MODIS)
and the Satellite Pour L’Observation de la Terre (SPOT)
albedo. We compare results from offline simulations over the
Australian continent. The control simulation has prescribed
background snow-free and vegetation-free soil albedo de-
rived from MODIS whilst the experiments use a simple pa-
rameterisation based on soil moisture and colour, originally
from the Biosphere Atmosphere Transfer Scheme (BATS),
and adopted in the Common Land Model (CLM). The control
simulation, with prescribed soil albedo, shows that CABLE
simulates overall albedo over Australia reasonably well, with
differences compared to MODIS and SPOT albedos within
±0.1. Application of the original BATS scheme, which uses
an eight-class soil classification, resulted in large differences
of up to −0.25 for the near-infrared (NIR) albedo over large
parts of the desert regions of central Australia. The use of a
recalibrated 20-class soil colour classification from the CLM,
which includes a higher range for saturated and VIS (visi-
ble) and NIR soil albedos, reduced the underestimation of
the NIR albedo. However, this soil colour mapping is tuned
to CLM soil moisture, a quantity which is not necessarily
transferrable between land surface models. We therefore re-
calibrated the soil color map using CABLE’s climatologi-
cal soil moisture, which further reduced the underestimation
of the NIR albedo to within ±0.15 over most of the conti-
nent as compared to MODIS and SPOT albedos. Small ar-
eas of larger differences of up to −0.25 remained within
the central arid parts of the continent during summer; how-
ever, the spatial extent of these large differences is substan-
tially reduced as compared to the simulation using the default
eight-class uncalibrated soil colour map. It is now possible
to use CABLE coupled to atmospheric models to investigate
soil-moisture–albedo feedbacks, an important enhancement
of the model.
1 Introduction
The albedo of the land surface is the ratio of upwelling to
downwelling shortwave radiation and determines the frac-
tion of incoming solar radiation reflected back to the atmo-
sphere. It is one of the key drivers of the Earth’s climate
as it determines, in part, the amount of energy available to
drive processes in the atmosphere and the land surface (e.g.
Dickinson, 1983). The incorrect prescription or parameter-
isation of surface albedo can result in large model biases.
Therefore, the correct representation of albedo in land sur-
face models (LSMs), whether prescribed or parameterised, is
of critical importance to the surface energy and hydrological
cycle.
The overall albedo of the land is a function of the veg-
etation, soil, and snow albedos. The main factor which de-
termines which of these three albedos has the strongest
Published by Copernicus Publications on behalf of the European Geosciences Union.
2122 J. Kala et al.: Evaluation of surface albedo in the CABLE LSM
influence on the overall surface albedo is the fractional area
covered by each of vegetation, soil and snow. These are com-
monly parameterised as a function of leaf area index (LAI),
the total one-sided surface area of leaf per ground surface
area (Bonan, 2008). When LAI is high, most of the incom-
ing solar energy is reflected, scattered, and/or absorbed by
the vegetation canopy and only a small proportion of radia-
tion reaches the ground; the overall albedo is primarily that
of the vegetation canopy. When LAI is small, the converse is
true and the overall albedo is increasingly represented by the
albedo of the soil or snow.
Vegetation albedo is a function of the radiative properties
of the canopy. These properties include the leaf transmittance
and reflectance, leaf angle or orientation, canopy clumping,
and structure. Leaf transmittance and reflectiveproperties de-
termine how much radiation penetrates through the canopy
and are usually prescribed in LSMs for each plant functional
type (PFT) in the visible (VIS, 0.4–0.7µm) and near-infrared
(NIR, 0.7–4.0 µm) bands. This distinction is important since
green canopies absorb most of the solar radiation in the VIS
waveband for photosynthesis but reflect and transmit most
of the radiation in the NIR waveband (Bonan, 2008). Leaf
structural and physical properties can also influence within-
canopy shadowing, which allows higher exposure of the un-
derlying soil and/or snow cover, especially in low-density
forests (Davidson and Wang, 2004). Leaf orientation influ-
ences albedo since the maximum incident solar radiation on
a leaf occurs when the beam is perpendicular to the surface
(Bonan, 2008).
Soil albedo is a function of soil colour, determined partly
by organic composition and soil moisture, with saturated
soils generally having lower albedo than dry soils (Idso et al.,
1975). This is especially important in transitional climatic
regions, where significant soil moisture variability drives
strong land–atmosphere coupling (e.g. Koster et al., 2004).
Although the dependence of soil albedo on soil moisture
has been well established from field experiments (e.g. Idso
et al., 1975), not all LSMs include this feedback and re-
cent studies have shown that it plays in important role in
seasonal droughts in the central US (Zaitchik et al., 2013).
Recent studies over eastern Australia have shown that the
use of time-varying MODIS albedo (as opposed to monthly
mean climatologies from Advanced Very High Resolution
Radiometer (AVHRR)) in a regional climate model improved
mean air temperature simulations and, to a lesser extent, pre-
cipitation (Meng et al., 2013). This was particularly evident
in arid regions where the overall albedo is predominantly in-
fluenced by soil rather than vegetation.
Vegetation and soil albedo are also influenced by the solar
zenith angle, especially in desert regions (Wang et al., 2005).
This only applies under clear-sky conditions (i.e. direct-beam
radiation) when there is little or no scattering of the incom-
ing shortwave radiation. In the morning just after sunrise and
late afternoon before sunset, albedo is generally higher as
compared to midday when the sun is directly overhead. The
inclusion of soil and vegetation albedo dependence on solar
zenith angle during clear-skyconditionshasimprovedalbedo
simulations in some LSMs (Liang et al., 2005).
With recent developments in satellite remote sensing, sev-
eral surface albedo products are now available at a high spa-
tial and temporal resolution and spanning several years. This
has allowed for the careful evaluation of albedo in various
LSMs (e.g. Wei et al., 2001; Oleson et al., 2003; Zhou et al.,
2003; Wang et al., 2004) and the development of vegetation
and soil albedo parameterisations (e.g. Liang et al., 2005;
Yang et al., 2008). These remotely sensed products have
also allowed the mapping of land surface parameters, such
as the spatial and temporal distribution of PFTs, LAI, and
soil colour, for use in LSMs (Lawrence and Chase, 2007).
Clearly, the use of satellite remote sensing can be very useful
in both the evaluation and development of LSMs.
This paper focusses on the Community Atmosphere Bio-
sphere Land Exchange (CABLE) model (Wang et al., 2011),
an LSM designed to simulate fluxes of heat, moisture, and
carbon at the land surface. While several studies have used
CABLE (e.g. Cruz et al., 2010; Zhang et al., 2011; Pit-
man et al., 2011; Wang et al., 2012; Exbrayat et al., 2013),
previous studies have not explicitly examined simulations
of surface albedo. The aim of this paper is to address this
key knowledge gap by comparing CABLE albedo simula-
tions with remotely sensed albedo estimates to better quan-
tify the uncertainties in CABLE’s albedo parameterisation.
Section 2 provides an overview of CABLE with detailed de-
scription of the parameterisation of surface albedo. This is
followed by the experimental design and description of the
satellite remote-sensing products used to benchmark the CA-
BLE albedo simulations. Results are presented in Sect. 3 and
discussed in Sect. 4.
2 Methods
2.1 Model description
CABLE simulates fluxes of energy, water, and carbon at the
land surface and can be run as an offline model with pre-
scribed meteorology (e.g. Abramowitz et al., 2008; Wang
et al., 2011; Kala et al., 2014) or fully coupled to an atmo-
spheric model within a global (Mao et al., 2011; Lorenz et al.,
2014) or regional context (Hirsch et al., 2014). CABLE is a
key part of the Australian Community Climate Earth System
Simulator (ACCESS, see http://www.accessimulator.org.au),
a fully coupled Earth system science model used in the Cou-
pled Model Intercomparison Project Phase 5 (CMIP-5) . The
version used in this study is CABLEv1.4b.
In CABLEv1.4b (Wang et al., 2011), the one-layered, two-
leaf canopy radiation module of Wang and Leuning (1998) is
used for sunlit and shaded leaves and the canopy micromete-
orology module of Raupach (1994) is used for computing
surface roughness length, zero-plane displacement height,
Geosci. Model Dev., 7, 2121–2140, 2014 www.geosci-model-dev.net/7/2121/2014/
J. Kala et al.: Evaluation of surface albedo in the CABLE LSM 2123
!
Snow&free!surface!albedo!
(NIR!and!VIS)!(Eq.!A1)!
Direct!combined!effective!
canopy!and!soil!reflectance!
(Eq.!A2)!
Diffused!combined!
effective!canopy!and!soil!
reflectance!(Eq.!A3)!
Direct!canopy!
reflectance! (Eq.!A4)!
Soil!reflectance!
(prescribed)!
Direct!canopy!
extinction!
coefficient!(Eq.!A6)!
Diffuse!canopy!
reflectance! (Eq.!A5)!
Diffuse!canopy!
extinction!coefficient!
(Eq.!A6)!
LAI!
(prescribed)!
Extinction!
coefficient!for!
black!canopy!for!
direct!radiation!
(Eqs!A7,9,10,11)!
Extinction!
coefficient!for!
black!canopy!for!
diffuse!radiation!
(Eq.!A8)!
Reflectance!of!
horizontally!
homogeneous!
black!canopy!
(Eq.!A12)!
Solar!zenith!
angle!(
θ
in!Eqs.!
A5,7,9)!
Leaf!angle!
(prescribed)!
Leaf!transmittance!
and!reflectance!(NIR!
and!VIS,!prescribed)!
Fraction!of!direct& beam!shortwave!
radiation!(f
b
!term!in!Eq.!A1,!
empirically!determined!when!
offline!or!from!atmospheric!model!
when!coupled)!!
Figure 1. Schematic illustration of snow-free surface albedo parameterisation in CABLE. Boxes with dashed lines represent user-defined
input parameters to the model. The boxes with solid black lines represent the equations described in Appendix A and the boxes in solid red
lines represent terms used in the equations.
and aerodynamic resistance. The model also consists of a sur-
face flux module to compute the sensible and latent heat flux
from the canopy and soil, the ground heat flux, as well as net
photosynthesis. A soil module is used for the transfer of heat
and water within the soil and snow, and an ecosystem car-
bon module based on Dickinson et al. (1998) is used for the
terrestrial carbon cycle. A detailed description of each of the
modules can be found in Kowalczyk et al. (2006) and Wang
et al. (2011).
Land albedo in CABLE is a function of the vegetation
albedo, snow albedo, and the background-snow-free and
vegetation-free soil albedo (the fractional albedo of inland
water surfaces was not considered in the simulations). The
parameterisation of albedo is part of the canopy radiative
transfer model. The latter accounts for direct-beam and dif-
fuse radiation separately, and within each stream, albedo is
computed separately in the NIR and VIS wavebandsas plants
utilise energy differently in these two parts of the spectrum.
Appendix A provides a detailed description of the albedo pa-
rameterisation, and a schematic illustration is presented in
Fig. 1.
The overall albedo of the surface (snow-free) is a function
of the direct and diffuseeffective reflectances and the fraction
of direct-beam shortwave radiation in the NIR and VIS wave-
bands (Eq. A1 and Fig. 1). When running CABLE offline,the
fraction of direct-beam shortwave radiation is computed em-
pirically from incoming shortwave radiation (meteorological
input to the model), solar constant, Julian day of year, and
solar zenith angle, following Spitters (1986). When coupled,
it is provided by the atmospheric radiation module. The di-
rect and diffuse effective reflectances are a function of the
canopy reflectance and extinction coefficients for direct and
diffuse radiation, the soil reflectance, and LAI (see Eqs. A2
and A3 and Fig. 1). In CABLEv1.4b, LAI is prescribed as
the model does not include a dynamic vegetation model or
dynamic phenology. The soil reflectance is derived from the
prescribed background snow-free soil albedo and vegetation-
free soil albedo.
The canopy reflectance for direct radiation is a function
of the direct and diffuse extinction coefficients for a black
canopy and the reflectance of a homogenous canopy with
horizontal black leaves (Eq. A4). The canopy reflectance
for diffuse radiation is, in turn, dependent on the canopy
reflectance for direct radiation, and the solar zenith angle
(Eq. A5). The extinction coefficients for direct and diffuse
radiation are a function of the corresponding extinction co-
efficients for a black canopy and the leaf transmittance and
reflectance (Eq. A6). The direct and diffuse extinction co-
efficients for a black canopy are a function of solar zenith
angle, LAI, and leaf angle (Eqs. A7 to A11). Finally, the re-
flectance of a horizontal homogeneous canopy with horizon-
tal black leaves is also a function of the leaf radiative prop-
erties (Eq. A12). In summary, the albedo parameterisation in
CABLE is reasonably complex, as illustrated in Fig. 1. User-
defined input parameters which influence albedo are the LAI,
background-snow and vegetation-free soil albedo, leaf angle,
and the leaf transmittance and reflectance.
While it is common to prescribe LAI and leaf physi-
cal and radiative properties in most LSMs, several LSMs
include simple parameterisations for the background-snow
and vegetation-free soil albedo based on soil moisture con-
tent. Since this soil-moisture–albedo feedback has been
www.geosci-model-dev.net/7/2121/2014/ Geosci. Model Dev., 7, 2121–2140, 2014
2124 J. Kala et al.: Evaluation of surface albedo in the CABLE LSM
Figure 2. (a) Distribution of PFTs in the domain, (b) prescribed background-snow-free soil albedo from Houldcroft et al. (2009) used in the
CNTL experiment, and (c) soil colours used in the PSALB experiment. The PFTs in panel (a) are shown in Table 2.
shown to be important (e.g. Vamborg et al., 2011; Za-
itchik et al., 2013), we added a simple parameterisation
based on soil colour and moisture, originally developed for
the Biosphere-Atmosphere Transfer Scheme (BATS) LSM
(Dickinson et al., 1993) and adopted by the Common Land
Model (CLM) (Zhou et al., 2003):
α
soil
= α
sat
+ min
α
sat
,max
0.11(11− 40θ
sm
),α
dry
, (1)
where α
sat
and α
dry
are the albedo of saturated and dry soils
respectively, dependent on the soil colour (light to dark, see
Table
1), and θ
sm
is the surface volumetric soil moisture con-
tent. The saturated- and dry-soil albedos in the VIS wave-
band as shown in Table 1 are simply assumed to be twice
those in the NIR waveband. As noted by Wang et al. (2004),
this assumption is not unreasonable, although some studies
have shown that this ratio varies geographically (Tsvetsin-
skaya et al., 2002).
2.2 Simulations
CABLEv1.4b was used within the NASA Land Informa-
tion System (LIS-6.1) (Kumar et al., 2006, 2008), a flexi-
ble software platform designed as a land surface modelling
and hydrological data assimilation system. A grid resolution
of 0.25
◦
× 0.25
◦
was utilised, covering continental Australia.
Our domain is shown in Fig. 2a which also illustrates the dis-
tribution of PFTs used (Table 2). The model was forced with
the Modern-Era Retrospective Analysis for Research and
Applications (MERRA) reanalysis (Rienecker et al., 2011)
at 3-hourly intervals from 2001 to 2008 and initialised from
a previous 30-year spin-up. This year range was chosen as
it corresponded with the availability of the remotely sensed
albedo products. The forcing variables included incoming
long-wave and shortwave radiation, air temperature, specific
humidity, surface pressure, wind speed, and precipitation.
The MERRA reanalysis was bias-corrected for precipitation
using the Australian Bureau of Meteorology Australian Wa-
ter Availability gridded precipitation data set (Jones et al.,
2009), following Decker et al. (2013). A monthly mean
MODIS-derived LAI climatology from Yuan et al. (2011)
was used for the simulations as shown in Fig. 3. Although
monthly mean values are used in the simulations, we show
seasonal means in Fig. 3 to help the interpretation of seasonal
Table 1. Saturated- and dry-soil albedos for different soil colours
(Fig. 2c) in the VIS and NIR wavebands.
Soil
α
sat
α
dry
colour VIS NIR VIS NIR
1 0.12 0.24 0.24 0.48
2 0.11 0.22 0.22 0.44
3 0.10 0.20 0.20 0.40
4 0.09 0.18 0.18 0.36
5 0.08 0.16 0.16 0.32
6 0.07 0.14 0.14 0.28
7 0.06 0.12 0.12 0.24
8 0.05 0.10 0.10 0.20
differences in albedo in Sect. 3. Monthly ambient carbon
dioxide concentrations were prescribed using measurements
from Baring Head, New Zealand (Keeling et al., 2005). Out-
puts were saved every hour for the direct and combined (di-
rect and diffuse) albedos in the VIS and NIR wavebands re-
spectively.
As discussed in Sect.
2.1, in CABLEv1.4b, the
background-snow-free and vegetation-free soil albedos are
prescribed by default. We used the MODIS-derived vegeta-
tion and snow-free background soil albedo data from Hould-
croft et al. (2009) shown in Fig. 2b. In this data set, bare-
soil regions, as defined by the International Geosphere–
Biosphere Programme (IGBP) land use classification map,
which was also used in CABLE, are assigned the mean
albedo over the data period (October 2002 to Decem-
ber 2006), while partially vegetated pixels are assigned a
soil albedo derived from a linear relationship between albedo
and the Normalised Difference Vegetation Index (NDVI). A
linear regression model is then used to estimate the back-
ground soil albedo corresponding to zero LAI (Houldcroft
et al., 2009). This simulation was the control (CNTL) ex-
periment. An additional simulation was also carried out with
the background-snow and vegetation-free albedo parame-
terised using Eq. (1), hereafter referred to as experiment
PSALB (where PSALB refers to parameterised (P) soil (S)
albedo (ALB)). The spatial distribution of soil colours for
the PSALB experiment is shown in Fig. 2c. For both the
Geosci. Model Dev., 7, 2121–2140, 2014 www.geosci-model-dev.net/7/2121/2014/
J. Kala et al.: Evaluation of surface albedo in the CABLE LSM 2125
Figure 3. Seasonal mean LAI from Yuan et al. (2011) (monthly means are used in the simulations).
Table 2. Names of plant functional types (PFTs) and soil types
shown in Fig. 2a.
PFT
number PFT
1 Evergreen needleleaf
2 Evergreen broadleaf
3 Deciduous needleleaf
4 Deciduous broadleaf
5 Mixed forest
6 Closed shrublands
7 Open shrublands
8 Woody savannas
9 Savannas
10 Grasslands
11 Permanent wetlands
12 Croplands
13 Urban and built-up
14 Cropland mosaics
15 Snow and ice
16 Barren
CNTL and PSALB simulations, leaf transmittance and re-
flectance properties and leaf angles were prescribed for each
PFT following previous studies using CABLE (Pitman et al.,
2011; Avila et al., 2012). Sample model name list files for
the CNTL and PSALB experiments are available online at
https://bitbucket.org/jkala/gmd-2014-9/src.
2.3 MODIS albedo
The albedo products from MODIS have been extensively
used for the purpose of evaluating albedo from various LSMs
(Oleson et al., 2003; Zhou et al., 2003; Wang et al., 2004).
In this study, we used the MODIS MCD43GF (MCD stands
for (MODIS Combined Terra and Aqua), GF stands for:
Gap-free) 30-arc-second gap-filled snow-free albedo product
(available at http://www.umb.edu/spectralmass/terra_aqua_
modis/modis_brdf_albedo_cmg_gap_filled_snow_free_
product_mcd43gf_v005). The MCD43D product utilises di-
rectional reflectances from both the Aqua and Terra MODIS
instruments to retrieve an appropriate surface anisotropy
model and thus intrinsic measures of surface albedo (Lucht
et al., 2000; Schaaf et al., 2002; Wang et al., 2004). The
MCD43 product is validated to stage 3 signifying that the
high-quality retrievals are within 5% of field measurements.
Additionally, a very recent field evaluation of the MCD43A
product (the standard 500m MCD43 product from which the
MCD43D is derived) found root mean square errors of less
than 0.03 over agricultural and grassland sites, and less than
0.02 over forested sites, during dormant snow-free periods
(Wang et al., 2014). To avoid interpreting results that are
within observational uncertainty, we only show differences
between the MODIS and the simulated albedo which are
greater than 0.05. The MCD43D product also provides data
quality flags for each grid cell, and approximately 75%
of grid cells over the domain of interest were classified as
high-quality (flags 0 and 1), and 25% were temporally fitted
(flag 2). These temporally fitted points were mostly confined
to north of 20
◦
S, i.e. the northern tropical regions where
cloud fraction is generally high.
To enable comparison with the simulations, the MODIS
albedo products were interpolated to the grid domain used
for the simulations. Following previous studies (Oleson et al.,
2003; Zhou et al., 2003; Wang et al., 2004), we compared the
CABLE-simulated direct-beam VIS and NIR albedos at local
solar noon (obtained by combining the appropriate longitude
bands from hourly outputs) to the VIS and NIR black-sky
albedos from MODIS. The MCD43 product retrieval is at-
tempted every 8 days over 16 days of potential input. We
computed means of the local solar noon direct VIS and NIR
direct-beam albedos from CABLE over the same time inter-
val of data availability to enable more meaningful compar-
isons. The CABLE combined (direct and diffuse) VIS and
NIR albedos were compared to the MODIS blue-sky VIS and
NIR albedos. The MODIS blue-sky albedo represents both
the diffuse and direct radiation and uses MODIS aerosol op-
tical depth (the MOD04 product), where available, or 0.2 as
a mean climatology, where unavailable. The blue-sky albedo
used here is also valid at local solar noon and hence is com-
pared with the CABLE combined VIS and NIR albedos at
the same time.
2.4 SPOT albedo
Given that the prescribed soil albedo for the CNTL experi-
ment is MODIS-derived, we face the issue that the CNTL and
benchmarking data set are from the same source. Hence, we
also use an alternative remotely sensed albedo data set, the
www.geosci-model-dev.net/7/2121/2014/ Geosci. Model Dev., 7, 2121–2140, 2014
2126 J. Kala et al.: Evaluation of surface albedo in the CABLE LSM
Figure 4. Yearly and seasonal difference between MODIS and SPOT (MODIS-SPOT) VIS and NIR black-sky albedo.
Figure 5. Mean yearly and seasonal differences between (a) CNTL and MODIS albedo (CNTL-MODIS) and (b) CNTL and SPOT albedo
(CNTL-SPOT) over the period 2001–2008. December-January-February (DJF) is summer; March-April-May (MAM) is autumn; June-July-
August (JJA) is winter; September-October-November (SON) is spring.
Satellite Pour L’Observation dela Terre (SPOT) albedo prod-
uct (Lacaze et al., 2012). This data set comprises a black-
and-white-sky albedo at a 10-day frequency and a resolution
of 1/112 of a degree (approximately 0.89km). The SPOT
product has not undergone as extensive a field evaluation
as the MODIS albedo; however, it is considered to be of
comparable quality to MODIS (Lacaze et al., 2012). Disney
et al. (2004) compared MODIS and SPOT albedos to field
observations over an agricultural site in the UK and found
MODIS and SPOT albedos to correspond well. They found
Geosci. Model Dev., 7, 2121–2140, 2014 www.geosci-model-dev.net/7/2121/2014/
J. Kala et al.: Evaluation of surface albedo in the CABLE LSM 2127
Table 3. Root mean square error (RMSE) and bias (scaled by 100) between the CNTL experiment and MODIS and SPOT black-sky (Black-
S) visible (VIS) and near-infrared (NIR) albedo on the one hand and MODIS blue-sky (Blue-S) VIS and NIR albedo on the other hand on a
yearly and seasonal timescale.
YEARLY DJF MAM JJA SON
RMSE Bias RMSE Bias RMSE Bias RMSE Bias RMSE Bias
MODIS
Black-S-VIS 3.43 2.40 2.71 1.13 3.80 2.80 4.28 3.36 3.37 2.30
Black-S-NIR 7.18 −6.06 8.85 −7.86 7.11 −5.91 6.45 −4.97 6.72 −5.52
Blue-S-VIS 6.75 6.30 6.17 5.63 6.94 6.53 7.53 7.11 6.43 5.91
Blue-S-NIR 3.52 2.10 3.53 1.88 3.54 1.97 3.99 2.60 3.37 1.94
SPOT
Black-S-VIS 3.43 2.63 2.46 0.77 3.69 2.84 4.79 4.14 3.55 2.78
Black-S-NIR 6.69 −5.90 9.03 −8.29 7.00 −6.20 5.27 −4.11 5.96 −4.99
Figure 6. Same as in Fig. 5 but for the PSALB experiment. The northern and central boxes in the black-sky NIR yearly panel show the
regions for which a time series is plotted in Fig. 7.
www.geosci-model-dev.net/7/2121/2014/ Geosci. Model Dev., 7, 2121–2140, 2014
2128 J. Kala et al.: Evaluation of surface albedo in the CABLE LSM
Table 4. Same as in Table 3 but for the PSALB experiment.
YEARLY DJF MAM JJA SON
RMSE Bias RMSE Bias RMSE Bias RMSE Bias RMSE Bias
MODIS
Black-S-VIS 3.40 0.64 3.37 −0.59 3.59 0.99 3.85 1.66 3.43 0.50
Black-S-NIR 9.47 −7.65 10.96 −9.38 9.44 −7.58 8.75 −6.54 9.08 −7.12
Blue-S-VIS 4.83 3.56 4.27 2.74 5.02 3.79 5.57 4.51 4.66 3.21
Blue-S-NIR 5.48 −0.45 5.56 −0.80 5.60 −0.61 5.61 0.15 5.42 −0.54
SPOT
Black-S-VIS 3.35 0.81 3.48 −1.02 3.53 0.97 4.12 2.37 3.42 0.90
Black-S-NIR 8.99 −7.56 11.13 −9.88 9.31 −7.93 7.57 −5.75 8.37 −6.67
Figure 7. Monthly time series of difference between PSALB and
MODIS (PSALB-MODIS) spatially averaged over the northern and
central boxes shown in the black-sky NIR yearly panel in Fig. 6.
that the SPOT albedo tends to vary more smoothly than the
MODIS albedo due to the SPOT product being derived over
a longer averaging window, whereas the MODIS data is pro-
cessed over shorter time blocks. Since there is no equivalent
post-processed blue-sky SPOT albedo product comparable to
the MODIS blue-sky product, we only used the SPOT VIS
and NIR black-sky product for comparison with the CABLE-
simulated direct-beam VIS and NIR albedo at local solar
noon. A comparison between MODIS and SPOT VIS and
NIR black-sky albedo is shown in Fig. 4. The main differ-
ence between the two products is that the MODIS product
has a slightly higher NIR albedo during JJA (winter) over
central and southwest Australia and a slightly lower albedo
over the forests of eastern Australia and the northern savan-
nah during summer (DJF) and autumn (MAM).
Table 5. Saturated- and dry-soil albedos for 20-class soil colours
(Fig. 11) in the VIS and NIR wavebands.
Soil
α
sat
α
dry
colour VIS NIR VIS NIR
1 0.25 0.50 0.36 0.61
2 0.23 0.46 0.34 0.57
3 0.21 0.42 0.32 0.53
4 0.20 0.40 0.31 0.51
5 0.19 0.38 0.30 0.49
6 0.18 0.36 0.29 0.48
7 0.17 0.34 0.28 0.45
8 0.16 0.32 0.27 0.43
9 0.15 0.30 0.26 0.41
10 0.14 0.28 0.25 0.39
11 0.13 0.26 0.24 0.37
12 0.12 0.24 0.23 0.35
13 0.11 0.22 0.22 0.33
14 0.10 0.20 0.20 0.31
15 0.09 0.18 0.18 0.29
16 0.08 0.16 0.16 0.27
17 0.07 0.14 0.14 0.25
18 0.06 0.12 0.12 0.24
19 0.05 0.10 0.10 0.21
20 0.04 0.08 0.08 0.16
2.5 AMSR–E soil moisture
Given the dependance of the soil albedo parameterisation
on soil moisture, it is useful to quantify the uncertainties in
the simulated soil moisture. Given the lack of in situ soil
moisture observations, we used satellite-derived soil mois-
ture from the Advanced Microwave Scanning Radiometer –
Earth Observing System (AMSR–E), which uses brightness
temperatures to derive surface soil moisture. The version of
the AMSR–E data used in this study is described in Liu et al.
(2009) and has been available since July 2002. Hence, we
only use data from the period 2003 to 2008.
Geosci. Model Dev., 7, 2121–2140, 2014 www.geosci-model-dev.net/7/2121/2014/
J. Kala et al.: Evaluation of surface albedo in the CABLE LSM 2129
Figure 8. Yearly and seasonal soil moisture from AMSR–E and the PSALB experiment, and the difference between PSALB and AMSR–E
(PSALB-AMSR_E).
In summary, the CNTL simulation uses prescribed soil
albedo and the PSALB experiment parameterises soil albedo
based on Eq. (1). Both simulations are compared to MODIS
and SPOT albedo estimates, and we use AMSR–E soil mois-
ture as means of quantifying the uncertainties in CABLE soil
moisture. An initial analysis of the differences between CA-
BLE on the one hand and MODIS and SPOT albedos on the
other showed that most of the differences greater than ±0.05
were statistically significant at 95%. Hence, we simply show
the absolute differences. In this context, deviations of more
than 0.1–0.2 from remotely sensed estimates are considered
to be large enough to warrant further improvements to the
model.
3 Results
Figures 5 and 6 show the yearly and seasonal differences
between CABLE MODIS blue-sky and black-sky NIR and
VIS albedo and SPOT black-sky VIS and NIR albedo for the
CNTL and PSALB experiments (designated CNTL-MODIS
and PSALB-MODIS respectively). Biases and root mean
square errors (RMSEs) are shown in Tables 3 and 4 respec-
tively, with RMSE and bias values scaled by 100 such that
small differences are easier to see. The CNTL experiment
(with prescribed soil albedo) shows that CABLE simulates
albedo well (Fig. 5) when compared to both MODIS and
SPOT albedos. The model has a systematic underestimation
of the black-sky NIR albedo, especially during DJF (sum-
mer) of around 0.1 and an overestimation of between 0.05
and 0.1 of the blue-sky VIS albedo for all seasons. This over-
estimation of blue-sky VIS albedo applies to most of the in-
terior continent which has low LAI (Fig. 3). This suggests
that part of this bias may be inherited fromthe prescribed soil
albedo used (Fig. 2b). However, the bias is also present in the
Figure 9. Zero-lag correlation between the differences in monthly
mean soil moisture between CABLE and AMSR_E (CA-
BLE− AMSR_E): (a) CABLE and MODIS black-sky NIR albedo
(CABLE - MODIS) and (b) CABLE and SPOT black-sky NIR
albedo (CABLE-SPOT). Correlations are computed at 95% signif-
icance level over a monthly time series from 2003 to 2008, which
corresponds to the availability of AMSR_E soil moisture.
northern tropical areas which have an LAI of 2.0, where veg-
etation should have a larger influence. The northern tropical
regions are also the areas where the MODIS albedo used for
evaluation had higher percentages of temporally fitted data,
which might also contribute to these biases. We also note that
there is a consistent difference of 0.05 to 0.1 for the blue-sky
NIR albedo in densely vegetated areas of Tasmania and the
northern tropics. This has been documented elsewhere for
other LSMs which use a similar two-stream radiation trans-
fer scheme as is used in CABLE. For example, Pinty et al.
(2011) report that the lowering of the NIR leaf scattering co-
efficient below its true value was required to correct the ab-
sorption due to multiple scattering within a structurally het-
erogeneous canopy.
Figure 6 shows that the implementation of the soil albedo
scheme resulted in similar differences to those found in the
www.geosci-model-dev.net/7/2121/2014/ Geosci. Model Dev., 7, 2121–2140, 2014
2130 J. Kala et al.: Evaluation of surface albedo in the CABLE LSM
Table 6. Same as in Table 3 but for the PSALB_20 experiment.
YEARLY DJF MAM JJA SON
RMSE Bias RMSE Bias RMSE Bias RMSE Bias RMSE Bias
MODIS
Black-S-VIS 2.66 −0.15 2.98 −1.40 2.87 0.23 2.95 0.91 2.67 −0.36
Black-S-NIR 10.36 −9.34 11.94 −11.07 10.31 −9.21 9.42 −8.15 9.99 −8.93
Blue-S-VIS 3.32 2.42 2.79 1.54 3.57 2.67 4.11 3.42 3.10 2.04
Blue-S-NIR 4.74 −2.91 5.2 −3.35 4.93 −3.02 4.42 −2.23 4.77 −3.05
SPOT
Black-S-VIS 2.57 0.0 3.2 −1.85 2.85 0.19 3.16 1.61 2.54 0.03
Black-S-NIR 9.94 −9.28 12.19 −11.61 10.27 −9.60 8.28 −7.39 9.28 −8.51
Figure10. Seasonal differences in albedo,net radiation (Rnet), sensible heat (Qh), latentheat (Qle) flux,and screen-level-derivedtemperature
(T2) between the PSALB and CNTL experiments (PSALB-CNTL).
Geosci. Model Dev., 7, 2121–2140, 2014 www.geosci-model-dev.net/7/2121/2014/
J. Kala et al.: Evaluation of surface albedo in the CABLE LSM 2131
Table 7. Same as in Table 3 but for the PSALB_20C experiment.
YEARLY DJF MAM JJA SON
RMSE Bias RMSE Bias RMSE Bias RMSE Bias RMSE Bias
MODIS
Black-S-VIS 2.30 0.18 2.54 −1.03 2.54 0.57 2.72 1.20 2.26 −0.02
Black-S-NIR 9.52 −8.62 11.13 −10.30 9.45 −8.48 8.63 −7.51 9.12 −8.21
Blue-S-VIS 3.30 2.92 2.61 2.08 3.58 3.18 4.20 3.88 2.98 2.53
Blue-S-NIR 3.43 −1.82 3.72 −2.20 3.62 −1.91 3.42 −1.20 3.40 −1.98
SPOT
Black-S-VIS 2.20 0.34 2.73 −1.47 2.49 0.55 3.00 1.91 2.16 0.38
Black-S-NIR 9.10 −8.54 11.37 −10.81 9.41 −8.84 7.47 −6.74 8.40 −7.77
CNTL experiment for the black- and blue-sky VIS albedos
but large differences of up to −0.25 for the black- and blue-
sky NIR albedos. These large differences were confined to
central Australia (shown by the black box in the black-sky
NIR yearly panel), which is the most arid part of the conti-
nent. The larger differences for the NIR as compared to the
VIS albedos were to be expected as NIR albedo is gener-
ally larger in magnitude than VIS albedo. The fact that these
differences are confined to inland arid regions suggests that
the mechanisms leading to high albedo values in desert re-
gions are not being adequately represented. Similar to CNTL,
the PSALB experiment also showed largerdifferences during
DJF (summer) as compared to the other seasons, noticeably
in the northern tropical regions (also shown by a black box
in the black-sky NIR yearly panel). A monthly time series of
the differences between PSALB and MODIS over the cen-
tral and northern areas (Fig.
7) shows that PSALB consis-
tently underpredicts the black-sky NIR albedo during sum-
mer in the north (the monsoon season), whereas the differ-
ences in the central arid region show little monthly variation.
The CNTL experiment showed similar consistent underes-
timation of black-sky NIR albedo for the northern tropical
region, suggesting that these differences are related to the pa-
rameterisation of vegetation rather than soil albedo.
The soil albedo scheme implemented depends on soil
colour, which is prescribed (Fig. 2c), and soil moisture. To
examine the uncertainties in the simulated soil moisture, we
compared yearly and seasonal means of soil moisture from
the PSALB experiment against AMSR–E satellite-estimated
surface soil moisture. CABLE’s surface soil moisture is rep-
resentative of the first 2.2cm of the soil, and details of the nu-
merical scheme used to solve the one-dimensional Richard’s
equation can be found in Kowalczyk et al. (2006). While
comparing an LSM soil moisture to a satellite-derived prod-
uct is not strictly comparing like to like, our goal here is
to identify whether there are any spatial similarities in the
differences between CABLE albedo and soil moisture from
satellite-derived alternatives rather than to examine the abso-
lute soil moisture values. CABLE’s soil moisture is generally
Figure 11. Twenty-class soil colour maps used for the (a)
PSALB_20 and (b) PSALB_20C simulations. The corresponding
saturated and dry VIS and NIR albedos for each soil colour are
shown in Table 5.
higher compared to AMSR–E for most of the continent
(Fig. 8), especially during DJF and SON. Higher soil mois-
ture should result in lower simulated soil albedo and hence
larger differences when compared to MODIS. Hence, this
could partly explain the large deviations in the NIR albedo.
To further quantify the contribution of the uncertainties
in CABLE-simulated soil moisture on albedo, we computed
the correlation between the monthly mean differences in CA-
BLE surface soil moisture and AMSR_E soil moisture on the
one hand and CABLE black-sky NIR albedo and MODIS
and SPOT estimates on the other hand. This is shown in
Fig. 9a and b respectively. The correlations were computed
over the period 2003–2008 (we did not compute correla-
tions on yearly and seasonal timescales as the time series
was too short), and results shown are at the 95% signifi-
cance level. A negative correlation shows that an overestima-
tion of soil moisture (i.e. a positive difference between CA-
BLE and AMSR_E) is correlated with an underestimation in
albedo (i.e. a negative difference between CABLE and re-
motely sensed (MODIS and SPOT) black-sky NIR albedo).
Large parts of the centre of the continent showed a nega-
tive correlation, with SPOT albedo showing larger and more
statistically significant correlations than MODIS. Hence, at
www.geosci-model-dev.net/7/2121/2014/ Geosci. Model Dev., 7, 2121–2140, 2014
2132 J. Kala et al.: Evaluation of surface albedo in the CABLE LSM
Figure 12. Same as in Fig. 6 but for the PSALB_20 experiment.
least part of the large differences in the black-sky NIR albedo
over the centre of the continent can be attributed to CABLE
overestimating soil moisture.
Figure 10 shows the difference in overall albedo and sur-
face fluxes between the PSALB and CNTL experiments
(PSALB-CNTL). The lower albedo values in central Aus-
tralia for the PSALB experiment result in an increase in net
radiation of up to 45–50Wm
−2
, most of which results in in-
creased sensible heat because the continental interior is gen-
erally dry. The only noticeable change in latent heat is during
the summer monsoon season (DJF) over the northern tropi-
cal regions, when high precipitation leads to higher available
soil water. Also illustrated in Fig. 10 is a diagnostic screen
temperature showing the lower albedo and higher net radia-
tion and sensible heat for the PSALB experiment leading to
a temperature which is higher by up to 0.5
◦
C.
Such large deviations from MODIS albedo with this sim-
ple parameterisation have also been noted with the CLM
LSM. To reduce these deviations, Lawrence and Chase
(2007) extended the eight-soil colour class to 20 colours to
include a higher range of VIS and NIR albedosthan observed
from MODIS. They also generated a new MODIS-consistent
soil colour map by fitting VIS and NIR soil albedos which
reproduced the MODIS monthly values at local solar noon
as closely as possible, given a model climatological monthly
mean soil moisture. Hence, we implemented the 20-class soil
colour map used by Lawrence and Chase (2007) and under-
took a similar recalibration using CABLE’s soil moisture.
The CLM and CABLE calibrated soil colour maps are shown
in Fig. 11a and b respectively, and the corresponding satu-
rated and dry VIS and NIR albedos are shown in Table 5. The
new soil colour maps clearly better reflect the spatial distri-
butionof MODIS soil albedo than the default eight-class map
(Fig. 2).
Figure 12 shows the differences in albedo between CA-
BLE with the 20-class CLM calibrated soil colour map
(Fig. 11) (experiment PSALB_20) and MODIS as well as the
difference to SPOT. The domain-averaged bias and RMSEs
are shown in Table 6. The large differences in the black-sky
and blue-sky NIR albedo in central Australia (Fig. 12) are
clearly reduced. Comparisons with the SPOT product show a
larger reduction compared to MODIS for the black-sky NIR
Geosci. Model Dev., 7, 2121–2140, 2014 www.geosci-model-dev.net/7/2121/2014/
J. Kala et al.: Evaluation of surface albedo in the CABLE LSM 2133
Figure 13. Same as in Fig. 10 but for the PSALB_20 experiment.
albedo, which is related to MODIS having a slightly higher
NIR albedo than SPOT (Fig. 4). The overestimation of the
blue-sky VIS albedo is also reduced. However, we note that
although the differences in black-sky NIR albedo at the cen-
tre of the continent are reduced, differences in the black-sky
NIR albedo increase by 0.05 to 0.1 to the west and north
of the continent when compared to the PSALB experiment
(Fig. 6). Hence, the domain-averaged statistics shown in Ta-
ble 6 do not show a marked improvement when compared to
the PSALB experiment (Table 4). The differences in overall
broadband albedo (combined VIS and NIR, direct and dif-
fuse albedo, averaged at all model times), heat fluxes, and
diagnostic screen temperature between the PSALB_20 and
CNTL experiment are shown in Fig. 13. The differences at
the centre of the continent are not as large as compared to the
PSALB experiment (Fig. 10), but overall albedo is generally
underestimated as compared to the CNTL.
Using a colour map which is recalibrated to CABLE soil
moisture (Fig. 11b) (experiment PSALB_20C) reduces the
difference in NIR albedo at the centre of the continent further
(Fig. 14), but the systematic underestimation of the local-
noon black-sky NIR albedo by 0.05–0.15 as compared to
MODIS and SPOT over most of the continent remains, with
the largest differences being in summer (DJF). The differ-
ence in dry and saturated albedo between each successive soil
colour class (Table 5) is 0.01–0.02; hence, such differences
are within the expected range. Although the differences with
MODIS and SPOT are largely reduced when compared to the
PSALB experiment (Fig. 6), relatively small areas with dif-
ferences of up −0.20 at the centre of the continent remain.
This suggests an inherent limitation of this parameterisation
in soil albedo in very arid regions. The domain-averaged
statistics are illustrated in Table 7, showing an improvement
when compared to the PSALB (Table 4). The differences in
www.geosci-model-dev.net/7/2121/2014/ Geosci. Model Dev., 7, 2121–2140, 2014
2134 J. Kala et al.: Evaluation of surface albedo in the CABLE LSM
Figure 14. Same as in Fig. 12 but for the PSALB_20C experiment.
overall albedo, heat fluxes,anddiagnosticscreentemperature
as compared to the CNTL are shown in Fig. 15. These differ-
ences are now small enough to enable us to use the scheme
to explore soil-moisture–albedo feedbacks within CABLE.
4 Discussion
CABLE traditionally prescribes background soil albedo and
hence does not allow for soil-moisture–albedo feedbacks,
which the literature suggests can be important. To address
this issue, we implemented a simple soil albedo scheme,
based on soil moisture and colour, which has been used
in other LSMs. Two simulations were conducted: the con-
trol (CNTL) experiment, with prescribed soil albedo de-
rived from MODIS, and another with parameterised soil
albedo (PSALB). The CNTL simulation showed relatively
small differences in albedo when compared to MODIS and
SPOT albedos, whereas the PSALB experiment showed
much larger differences, especially in the VIS albedo. The
differences were up to −0.25 and mainly in central Australia.
The better performance of the CNTL compared to PSALB is
not surprising as the CNTL experiment uses a background
soil albedo which is itself derived from earlier versions of
MODIS albedo (Houldcroft et al., 2009). The equally small
differences when compared to an alternative remotely sensed
albedo product, SPOT, give us confidence that the small dif-
ferences are not simply due to CNTL soil albedo and the
benchmarking data set being from the same source.
The large differences in the NIR albedo in the desert
regions of Australia have been found elsewhere. Wang
et al. (2004) compared albedo simulations globally from the
CLM2 LSM to MODIS and also found similar large differ-
ences in the NIR albedo in central Australia (see Fig. 5c in
Wang et al., 2004). Other studies have also found that the
largest differences in NIR albedo from LSMs tend to be in
desert and arid regions such as the Sahara (Wei et al., 2001;
Oleson et al., 2003; Zhou et al., 2003; Wang et al., 2004).
The much larger differences for the NIR as compared to the
VIS albedo as found in this study have also been reported by
Wang et al. (2004). This is partly due to the fact that NIR
albedos over snow-free surfaces are larger in magnitude than
the VIS albedo and, hence, likely to show larger differences.
Geosci. Model Dev., 7, 2121–2140, 2014 www.geosci-model-dev.net/7/2121/2014/
J. Kala et al.: Evaluation of surface albedo in the CABLE LSM 2135
Figure 15. Same as in Fig. 13 but for the PSALB_20C experiment.
Given the large differences in albedo between MODIS
and LSMs, Lawrence and Chase (2007) developed MODIS-
consistent land surface parameters, including the mapping of
PFTs, LAI, and soil colour for use within the CLM3 LSM.
They demonstrated that the use of the modified-parameter
maps improved surface albedo simulations when compared
with MODIS albedo. In some instances, this resulted in im-
proved simulations of precipitation and near-surface temper-
ature. We therefore carried out a similar procedure and tested
the modified CLM-calibrated soil colour map of Lawrence
and Chase (2007). We also carried out a similar calibra-
tion using CABLE climatological soil moisture. The use of
these maps resulted in a reduction in the difference in the
NIR albedo in central Australia as compared to MODIS and
SPOT estimates, with the CABLE-calibrated map resulting
in smaller differences as compared to the CLM-calibrated
map. Comparisons with the CNTL overall albedo and heat
fluxes showed differences which were small enough to war-
rant use of the new scheme in CABLE to further explore soil-
moisture–albedo feedbacks.
The use of recalibrated maps, whilst reducing the differ-
ence between CABLE MODIS and SPOT estimates, did not
completely fix the issue of underestimation of the local-noon
black-sky NIR albedo, as there were still small areas in cen-
tral Australia where differences in the local-noon NIR black-
sky albedo were up to approximately −0.2. There may be
several reasons for this. Firstly, as was shown in Fig. 9, at
least part of the large differences in the NIR albedo can be at-
tributed to CABLE overestimating surface soil moisture and,
hence, simulating lower albedo. Secondly, the parameterisa-
tion and coefficients in Eq. (1) were originally developed
for the BATS LSM (Dickinson et al., 1993), subsequently
adopted in CLM and now in CABLE. Equation (1) is based
on an absolute soil moisture value and this presents issues
www.geosci-model-dev.net/7/2121/2014/ Geosci. Model Dev., 7, 2121–2140, 2014
2136 J. Kala et al.: Evaluation of surface albedo in the CABLE LSM
with regard to the universal application of the scheme ir-
respective of LSMs, as the latter vary considerably in their
treatment of soil moisture (Koster et al., 2009) as well as the
processes which influence soil moisture (Koster and Milly,
1997). Whilst we recalibrated the soil colour maps, we have
not recalibrated the coefficients used in Eq. (1), as this for-
mulation was designed such that the soil albedos range in
a nonlinear manner between their saturated and dry values
(Dickinson et al., 1993). Rather than altering the formulation,
we choose to recalibrate the soil colour maps. Additionally, it
is assumed that the ratio of the NIR to VIS albedo is exactly
a factor of 2. However, Wang et al. (2005) have shown that
this ratio from MODIS data over the arid part of central Aus-
tralia is 2.69. We could make use of a higher factor and this
would help over Australia, but it would also lead to larger
differences elsewhere in global simulations.
One cause of the large differences between LSM-
simulated and observed albedo in arid regions is the depen-
dence of soil albedo on solar zenith angle (Wang et al., 2005;
Yang et al., 2008) and the lack of an explicit physical rep-
resentation of this relationship in many LSMs. Wang et al.
(2005) devised a semi-empirical scheme to relate bare-soil
albedo at a single site in the Sahel to solar zenith angle
and show improvements in albedo and surface flux simula-
tions when the scheme was applied to the Noah land sur-
face model. However, their simulations were on the site scale
and over a very short time frame (less than 2 months) and
may not be easily applicable to regional or global simula-
tions over longer time frames.
Liang et al. (2005) developed
a “dynamic–statistical” parameterisation of snow-free albedo
using MODIS albedo and soil moisture from a land data as-
similation system over North America. While the dynami-
cal part of the model represents the physical dependencies of
surface albedo on solar zenithangle and surface soil moisture
etc., the statistical model provides parameter estimates spe-
cific to geographic location. This scheme has been shown to
significantly improve albedo simulations in CLM over North
America, but a globally applicable scheme is yet to be tri-
alled. Hence, we identify this an important future direction
for albedo parameterisation development in CABLE.
5 Conclusions
Surface albedo is a key element of the surface energy bal-
ance as it determines the amount of solar energy absorbed
at the surface and redistributed into sensible and latent heat,
which, in turn, drive the surface energy and water cycles.
In this study, we investigated how well CABLEv1.4b simu-
lates albedo compared with MODIS and SPOT estimates. We
also tested a new simple parameterisation for the soil albedo,
which is prescribed and held constant in time in the standard
version of CABLE. This is an important step for the model
as it enables the feedback between albedo and soil moisture
to be represented. Our results show that CABLEv1.4b sim-
ulates overall albedo reasonably well when the soil albedo
is prescribed, as would be expected. The new parameterisa-
tion for soil albedo, after calibration to produce a MODIS-
consistent soil colour map, which is also tuned to CABLE
soil moisture, resulted in satisfactory comparisons with both
MODIS-derived albedo and an alternative, remotely sensed
albedo product, SPOT. Hence, there is now added capacity
and value within CABLE to further explore soil-moisture–
albedo feedbacks.
Our results also highlight the issue of parameterisations
which are based on soil moisture, a quantity which is not
interchangeable between LSMs. Hence, a process of recal-
ibration is required as this can have significant impacts on
the surface energy balance. The recalibration carried out
for this study may need to be repeated if future model
developments have a significant influence on soil mois-
ture. Given the availability of MODIS and SPOT albedo
products, we therefore argue that the evaluation of LSM-
simulated albedo is an important part of any standard model
evaluation and/or benchmarking protocol. This should ide-
ally be adopted across the LSM community.
Geosci. Model Dev., 7, 2121–2140, 2014 www.geosci-model-dev.net/7/2121/2014/
J. Kala et al.: Evaluation of surface albedo in the CABLE LSM 2137
Appendix A: Parameterisation of surface
albedo in CABLEv1.4b
The overallalbedoof the land surface for shortwave radiation
(α
s
) is defined as
α
s
= 0.5
X
j=1,2
(ρ
(dir,j)
f
b
+ ρ
(dif,j)
(1− f
b
)), (A1)
where f
b
is the fraction of direct-beam shortwave radiation
and ρ
(dir,j)
and ρ
(dif,j)
are the effective combined soil and
canopy reflectance for direct and diffuse radiation in the VIS
(j = 1) and NIR (j = 2) spectral bands.
The effective combined canopy reflectances (ρ
(dir,j)
and
ρ
(dif,j)
) in each band in Eq. (A1) are defined as
ρ
(dir,j)
= ρ
(can_dir,j)
+ (ρ
(soil,j)
− ρ
(can_dir,j)
)exp(−2k
∗
(dir,j)
3), (A2)
ρ
(dif,j)
= ρ
(can_dif,j)
+ (ρ
(soil,j)
− ρ
(can_dif,j)
)exp(−2k
∗
(dif,j)
3), (A3)
where ρ
(can_dir,j)
and ρ
(can_dif,j)
are the canopy reflectance
for direct and diffuse radiation, ρ
(soil,j)
is the soil reflectance,
k
∗
(dir,j)
and k
∗
(dif,j)
are the extinction coefficients for direct
and diffuse radiation, and 3 is the LAI.
The canopy direct and diffuse reflectance (ρ
(can_dir,j)
and
ρ
(can_dif,j)
) in each band in Eqs. (A2) and (A3) are defined
as
ρ
(can_dir,j)
=
2k
dir
k
dir
+ k
dif
ρ
(can_black,j)
, (A4)
ρ
(can_dif,j)
= 2
π
2
Z
0
ρ
(can_dir,j)
sin(θ) cos(θ )dθ, (A5)
where k
dir
and k
dif
are the extinction coefficients for a
canopy with black leaves for direct and diffuse radiation,
ρ
(can_black,j)
is the reflectance of a horizontally homogeneous
canopy with horizontal black leaves, and θ is the solar zenith
angle.
The extinction coefficients for a real canopy (k
∗
dir
and k
∗
dif
)
in Eqs. (A2) and (A3) and black canopy (k
dir
and k
dif
) in
Eqs. (A4) and (A5) are related as follows (Goudriaan and
van Laar, 1994):
k
∗
(dir,j)
= k
dir
(1− ω
j
)
1
2
, k
∗
(dif,j)
= k
dif
(1− ω
j
)
1
2
, (A6)
where ω
j
is the scattering coefficient for each waveband and
is equal to the sum of the canopy reflectance and transmit-
tance.
The extinction coefficients for a black canopy (k
dir
and
k
dif
) in Eqs. (A4) and (A5) are defined as
k
dir
(θ) =
G
cos(θ)
, (A7)
k
dif
= −
1
3
ln
3
Z
0
exp(−k
dir
(θ)λ)dλ
, (A8)
where λ is the cumulative canopy LAI from the canopy top
and G is the ratio of the projected area of leaves in the direc-
tion perpendicular to the direction of incident solar radiation
and the actual leaf area:
G = φ
1
+ φ
2
cos(θ), (A9)
φ
1
= 0.5− χ(0.633+ 0.33χ ), (A10)
φ
2
= 0.877(1− 2φ
1
), (A11)
where χ is an empirical parameter related to the leaf angle
distribution applicable over the range [−0.4, 0.6].
Finally, the reflectance of a horizontally homogeneous
canopy with horizontal black leaves (ρ
(can_black,j)
) in
Eq. (A4) is defined as
ρ
(can_black,j)
=
1− (1− ω
j
)
1
2
1+ (1− ω
j
)
1
2
. (A12)
www.geosci-model-dev.net/7/2121/2014/ Geosci. Model Dev., 7, 2121–2140, 2014
2138 J. Kala et al.: Evaluation of surface albedo in the CABLE LSM
Acknowledgements. All the authors except David Mocko, Crystal
B. Schaaf, and Qingsong Sun are supported by the Australian
Research Council Centre of Excellence for Climate System
Science (CE110001028). This work was also supported by the
NSW Environment Trust (RM08603). We thank CSIRO and the
Bureau of Meteorology through the Center for Australian Weather
and Climate Research for their support in the use of the CABLE
model. We thank the National Computational Infrastructure at
the Australian National University, an initiative of the Australian
Government, for access to supercomputer resources. We thank
the NASA GSFC LIS team for support in coupling CABLE to
LIS. The MODIS-derived background soil albedo was provided
by Peter R. J. North from the Department of Geography, Swansea
University, Swansea, United Kingdom. The modified MODIS
LAI data was provided by Hua Yuan from the Land-Atmosphere
Interaction Research Group at Beijing Normal University. The
AMSR–E soil moisture data was provided by Yi Liu from the
University of New South Wales. The SPOT albedo product
was obtained from: http://www.geoland2.eu/index.jsp, and we
formally acknowledge the use of the SPOT albedo as per the data
policy: “The research leading to these results has received funding
from the European Community’s Seventh Framework Program
(PF7/2007–2013) under grant agreement no. 218795. The BioPar
SPOT/VEGETATION albedo products were originally defined in
the framework of the PF5/CYCLOPES project. They are a joint
property of CNES and VITO under copyright geoland2. They
have been generated from the SPOT VEGETATION data under
copyright CNES and distributed by VITO”. All of this assistance is
gratefully acknowledged.
Edited by: R. Marsh
References
Abramowitz,G., Leuning,R., Clark, M.,and Pitman, A.:Evaluating
the Performance of Land Surface Models, J. Climate, 21, 5468–
5481, 2008.
Avila, F. B., Pitman, A. J., Donat, M. G., Alexander, L. V., and
Abramowitz, G.: Climate model simulated changes in temper-
ature extremes due to land cover change, J. Geophys. Res., 117,
D04108, doi:10.1029/2011JD016382, 2012.
Bonan, G.: Ecological climatology, Cambridge University Press,
2nd Edn., 2008.
Cruz, F. T., Pitman, A. J., and Wang, Y.-P.: Can the stomatal re-
sponse to higher atmospheric carbon dioxide explain the unusual
temperatures during the 2002 Murray-Darling Basin drought?, J.
Geophys. Res., 115, D02101, doi:10.1029/2009JD012767, 2010.
Davidson, A. and Wang, S.: The effects of sampling resolution on
the surface albedos of dominant land cover types in the North
American boreal region, Remote Sens. Environ., 93, 211–224,
2004.
Decker, M., Pitman, A. J., and Evans, J. P.: Groundwater Con-
straints on Simulated Transpiration Variability over South-
eastern Australian Forests, J. Hydrometeorol., 14, 543–559,
doi:10.1175/JHM-D-12-058.1, 2013.
Dickinson, R. E.: Land surface processes and climate-surface albe-
dos and energy balance, Adv. Geophys., 25, 305–353, 1983.
Dickinson, R. E., Henderson-Sellers, A., and Kennedy, P. J.:
Biosphere-Atmosphere Transfer Scheme (BATS) Version 1e as
coupled to the NCAR Community Model, NCAR Tech. Note,
NCAR/TN-387+STR, 72 pp., Natl. Cent. Atmos. Res., Boulder,
Colo., 1993.
Dickinson, R. E., Shaikh, M., Bryant, R., and Graumlich, L.: Inter-
active Canopies for a Climate Model, J. Climate, 11, 2823–2836,
1998.
Disney, M., Lewis, P., Thackrah, G., Quaife, T., and Barnsley, M.:
Comparison of MODIS broadband albedo over an agricultural
site with ground measurements and values derived from Earth
observation data at a range of spatial scales, Int. J. Remote Sens.,
25, 5297–5317, 2004.
Exbrayat, J.-F., Pitman, A. J. J., Abramowitz, G., and Wang, Y.-
P.: Sensitivity of net ecosystem exchange and heterotrophic res-
piration to parameterization uncertainty, J. Geophys. Res., 118,
1640–1651, doi:10.1029/2012JD018122, 2013.
Goudriaan, J. and van Laar, H. H.: Modelling crop growth pro-
cesses, Kluwer, Amsterdam, the Netherlands, 1994.
Hirsch, A. L., Kala, J., Pitman, A. J., Carouge, C., Evans, J. P.,
Haverd, V., and Mocko, D.: Impact of Land Surface Initialization
Approach on Subseasonal Forecast Skill: A Regional Analysis in
the SouthernHemisphere, J. Hydrometeorol.,15, 300–319, 2014.
Houldcroft, C. J., Grey, W. M. F., Barnsley, M., Taylor, C. M., Los,
S. O., and North, P. R. J.: New Vegetation Albedo Parameters and
Global Fields of Soil Background Albedo Derived from MODIS
for Use in a Climate Model, J. Hydrometeorol., 10, 183–198,
2009.
Idso, S. B., Jackson, R. D., Reginato, R. J., Kimball, B. A., and
Nakayama, F. S.: The Dependence of Bare Soil Albedo on Soil
Water Content, J. Appl. Meteorol., 14, 109–113, 1975.
Jones, D., Wang, W., and Fawcett, R.: High-quality spatial climate
data-sets forAustralia, Aust. Meteorol.Mag., 58, 233–248, 2009.
Kala, J., Decker, M., Exbrayat, J.-F., Pitman, A. J., Carouge, C.,
Evans, J. P., Abramowitz, G., and Mocko, D.: Influence of Leaf
Area Index Prescriptions on Simulations of Heat, Moisture, and
Carbon Fluxes, J. Hydrometeorol., 15, 489–503, 2014.
Keeling, C. D., Piper, S. C., Bacastow, R. B., Wahlen, M., Whorf,
T. P., Heimann, M., and Meijer, H. A.: Atmospheric CO
2
and
13
CO
2
exchange with the terrestrial biosphere and oceans from
1978 to 2000: observations and carbon cycle implications, pages
83–113, in: A History of Atmospheric CO
2
and its effects on
Plants, Animals, and Ecosystems, edited by: Ehleringer, J. R.,
Cerling, T. E., Dearing, M. D., Springer Verlag, New York, 2005.
Koster, R. D. and Milly, P. C. D.: The Interplay between Transpi-
ration and Runoff Formulations in Land Surface Schemes Used
with Atmospheric Models, J. Climate, 10, 1578–1591, 1997.
Koster, R. D., Guo, Z., Dirmeyer, P. A., Bonan, G., Chan, E.,
Cox, P., Davies, H., Gordon, C. T., Kanae, S., Kowalczyk, E.,
Lawrence, D., Liu, P., Lu, C.-H., Malyshev, S., McAveney, B.,
Mitchell, K., Mocko, D., Oki, T., Oleson, K. W., Pitman, A., Sud,
Y. C., Taylor, C. M., Verseghy, D., Vasic, R., Xue, Y., and Ya-
mada, T.: Regions of strong coupling between soil moisture and
precipitation, Science, 305, 1138–1140, 2004.
Koster, R. D., Guo, Z., Yang, R., Dirmeyer, P. A., Mitchell, K., and
Puma, M. J.: On the Nature of Soil Moisture in Land Surface
Models, J. Climate, 22, 4322–4335, 2009.
Kowalczyk, E. A., Wang, Y. P., Law, R. M., Davies, H. L., McGre-
gor, J. L., and Abramowitz, G.: The CSIRO Atmosphere Bio-
sphere Land Exchange model for use in climate models and as
an offline model. Commonwealth Scientific and Industrial Re-
Geosci. Model Dev., 7, 2121–2140, 2014 www.geosci-model-dev.net/7/2121/2014/
J. Kala et al.: Evaluation of surface albedo in the CABLE LSM 2139
search Organisation Marine and Atmospheric Research Paper
013, November 2006, 37 pp., available at: www.cmar.csiro.au/
e-print/open/kowalczykea_2006a.pdf, 2006.
Kumar, S., Peters-Lidard, C., Tian, Y., Houser, P., Geiger, J., Olden,
S., Lighty, L., Eastman, J., Doty, B., Dirmeyer, P., Adams, J.,
Mitchell, K., Wood, E., and Sheffield, J.: Land information sys-
tem: An interoperable framework for highresolution land surface
modeling, Environ. Model. Softw., 21, 1402–1415, 2006.
Kumar, S. V., Peters-Lidard, C. D., Eastman, J. L., and Tao, W.-
K.: An integrated high-resolution hydrometeorological modeling
testbed using LIS and WRF, Environ. Model. Softw., 23, 169–
181, 2008.
Lacaze, R., Makhmara, H., and Smets, B.: Towards an Operational
GMES Land Monitoring Core Service BioPar Product User
Manual SPOT/VEGETATION V1 (BP-05), Tech. Rep. BP-RP-
BP053, l1.22, available at: http://web.vgt.vito.be/documents/
BioPar/g2-BP-RP-BP053-ProductUserManual-ALBV1.pdf
(last access: 18 April 2013), 2012.
Lawrence, P. J. and Chase, T. N.: Representing a new
MODIS consistent land surface in the Community Land
Model (CLM 3.0), J. Geophys. Res.-Biogeo., 112, G01023,
doi:10.1029/2006JG000168, 2007.
Liang, X.-Z., Xu, M., Gao, W., Kunkel, K., Slusser, J., Dai,
Y., Min, Q., Houser, P. R., Rodell, M., Schaaf, C. B., and
Gao, F.: Development of land surface albedo parameteriza-
tion based on Moderate Resolution Imaging Spectroradiome-
ter (MODIS) data, J. Geophys. Res.-Atmos., 110, D11107,
doi:10.1029/2004JD005579, 2005.
Liu, Y. Y., van Dijk, A. I. J. M., de Jeu, R. A. M., and Holmes,
T. R. H.: An analysis of spatiotemporal variations of soil and
vegetation moisture from a 29-year satellite-derived data set
over mainland Australia, Water Resour. Res., 45, W07405,
doi:10.1029/2008WR007187, 2009.
Lorenz, R., Pitman, A. J., Donat, M. G., Hirsch, A. L., Kala, J.,
Kowalczyk, E. A., Law, R. M., and Srbinovsky, J.: Represen-
tation of climate extreme indices in the ACCESS1.3b coupled
atmosphere-land surface model, Geosci. Model Dev., 7, 545–
567, doi:10.5194/gmd-7-545-2014, 2014.
Lucht, W., Schaaf, C., and Strahler, A.: An algorithm for the re-
trieval of albedo from space using semi-empirical BRDF models,
IEEE T. Geosci. Remote, 38, 977–998, 2000.
Mao, J., Phipps, S. J., Pitman, A. J., Wang, Y. P., Abramowitz,
G., and Pak, B.: The CSIRO Mk3L climate system model v1.0
coupled to the CABLE land surface scheme v1.4b: evaluation
of the control climatology, Geosci. Model Dev., 4, 1115–1131,
doi:10.5194/gmd-4-1115-2011, 2011.
Meng, X., Evans, J., and McCabe, M.: The influence of inter-
annually varying albedo on regional climate and drought, Clim.
Dynam., 42, 787–803, doi:10.1007/s00382-013-1790-0, 2013.
Oleson, K. W., Bonan, G. B., Schaaf, C., Gao, F., Jin, Y., and
Strahler, A.: Assessment of global climate model land sur-
face albedo using MODIS data, Geophys. Res. Lett., 30, 1443,
doi:10.1029/2002GL016749, 2003.
Pinty, B., Andredakis, I., Clerici, M., Kaminski, T., Taberner, M.,
Verstraete, M. M., Gobron, N., Plummer, S., and Widlowski,
J.-L.: Exploiting the MODIS albedos with the Two-stream In-
version Package (JRC-TIP): 1. Effective leaf area index, vegeta-
tion, and soil properties, J. Geophys. Res.-Atmos., 116, D09105,
doi:10.1029/2010JD015372, 2011.
Pitman, A. J., Avila, F. B., Abramowitz, G., Wang, Y. P., Phipps,
S. J., and de Noblet-Ducoudré, N.: Importance of background
climate in determining impact of land-cover change on regional
climate, Nature Climate Change, 9, 472–475, 2011.
Raupach, M. R.: Simplified expressions for vegetation roughness
length andzero-plane displacement asfunctions of canopyheight
and area index, Bound.-Lay. Meteorol., 71, 211–216, 1994.
Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeis-
ter, J., Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs,
L., Kim, G.-K., Bloom, S., Chen, J., Collins, D., Conaty, A.,
da Silva, A., Gu, W., Joiner, J., Koster, R. D., Lucchesi, R.,
Molod, A., Owens, T., Pawson, S., Pegion, P., Redder, C. R., Re-
ichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz, M., and
Woollen, J.: MERRA: NASA’s Modern-Era Retrospective Anal-
ysis for Research and Applications, J. Climate, 24, 3624–3648,
2011.
Schaaf, C. B., Gao, F., Strahler, A. H., Lucht, W., Li, X., Tsang,
T., Strugnell, N. C., Zhang, X., Jin, Y., Muller, J.-P., Lewis, P.,
Barnsley, M., Hobson, P., Disney, M., Roberts, G., Dunderdale,
M., Doll, C., d’Entremont, R. P., Hu, B., Liang, S., Privette, J. L.,
and Roy, D.: First operational BRDF, albedo nadir reflectance
products from MODIS, Remote Sens. Environ., 83, 135–148,
2002.
Spitters, C.: Separating the diffuse and direct component of global
radiation and its implications for modeling canopy photosynthe-
sis Part II.Calculation of canopyphotosynthesis, Agr. ForestMe-
teorol., 38, 231–242, 1986.
Tsvetsinskaya, E. A., Schaaf, C. B., Gao, F., Strahler, A. H., Dick-
inson, R. E., Zeng, X., and Lucht, W.: Relating MODIS-derived
surface albedo to soils and rock types over Northern Africa and
the Arabian peninsula, Geophys. Res. Lett., 29, 67.1–67.4, 2002.
Vamborg, F. S. E., Brovkin, V., and Claussen, M.: The effect
of a dynamic background albedo scheme on Sahel/Sahara pre-
cipitation during the mid-Holocene, Clim. Past, 7, 117–131,
doi:10.5194/cp-7-117-2011, 2011.
Wang, Y.-P. and Leuning, R.: A two-leaf model for canopy con-
ductance, photosynthesis and partitioning of available energy I:
Model description and comparison with a multi-layered model,
Agr. Forest Meteorol., 91, 89–111, 1998.
Wang, Y. P., Kowalczyk, E., Leuning, R., Abramowitz, G., Rau-
pach, M. R., Pak, B., van Gorsel, E., and Luhar, A.: Di-
agnosing errors in a land surface model (CABLE) in the
time and frequency domains, J. Geophys. Res., 116, G01034,
doi:10.1029/2010JG001385, 2011.
Wang, Y. P., Lu, X. J., Wright, I. J., Dai, Y. J., Rayner, P. J., and
Reich, P. B.: Correlations among leaf traits provide a significant
constraint on the estimate of global gross primary production,
Geophys. Res. Lett., 39, L19405, doi:10.1029/2012GL053461,
2012.
Wang, Z., Zeng, X., Barlage, M., Dickinson, R. E., Gao, F., and
Schaaf, C.B.: Using MODISBRDF and Albedo Data toEvaluate
Global Model Land Surface Albedo, J. Hydrometeorol., 5, 3–14,
2004.
Wang, Z., Barlage, M., Zeng, X., Dickinson, R. E., and Schaaf,
C. B.: The solar zenith angle dependence of desert albedo, Geo-
phys. Res. Lett., 32, L05403, doi:10.1029/2004GL021835, 2005.
Wang, Z., Schaaf, C. B., Strahler, A. H., Chopping, M. J., Roman,
M. O., Shuai, Y., Woodcock, C. E., Hollinger, D. Y., and Fitz-
jarrald, D. R.: Evaluation of MODIS albedo product (MCD43A)
www.geosci-model-dev.net/7/2121/2014/ Geosci. Model Dev., 7, 2121–2140, 2014
2140 J. Kala et al.: Evaluation of surface albedo in the CABLE LSM
over grassland, agriculture and forest surface types during dor-
mant and snow-covered periods, Remote Sens. Environ., 140,
60–77, 2014.
Wei, X., Hahmann, A. N., Dickinson, R. E., Yang, Z.-L., Zeng,
X., Schaudt, K. J., Schaaf, C. B., and Strugnell, N.: Compar-
ison of albedos computed by land surface models and evalua-
tion against remotely sensed data, J. Geophys. Res.-Atmos., 106,
20687–20702, 2001.
Yang, F., Mitchell, K., Hou, Y.-T., Dai, Y., Zeng, X., Wang, Z.,
and Liang, X.-Z.: Dependence of Land Surface Albedo on So-
lar Zenith Angle: Observations and Model Parameterization, J.
Appl. Meteorol. Clim., 47, 2963–2982, 2008.
Yuan, H.,Dai, Y., Xiao,Z., Ji, D., and Shangguan, W.: Reprocessing
the MODIS Leaf Area Index products for land surface and cli-
mate modelling, Remote Sens. Environ., 115, 1171–1187, 2011.
Zaitchik, B. F., Santanello, J. A., Kumar, S. V., and Peters-Lidard,
C. D.: Representation of soil moisture feedbacks during drought
in NASA Unified WRF (NU-WRF), J. Hydrometeorol., 14, 360–
367, doi:
10.1175/JHM-D-12-069.1, 2013.
Zhang, Q., Wang, Y. P., Pitman, A. J., and Dai, Y. J.: Limi-
tations of nitrogen and phosphorous on the terrestrial carbon
uptake in the 20th century, Geophys. Res. Lett., 38, L22701,
doi:10.1029/2011GL049244, 2011.
Zhou, L., Dickinson, R. E., Tian, Y., Zeng, X., Dai, Y., Yang, Z.-L.,
Schaaf, C. B., Gao, F., Jin, Y., Strahler, A., Myneni, R. B., Yu,
H., Wu, W., and Shaikh, M.: Comparison of seasonal and spatial
variations of albedos from Moderate-Resolution Imaging Spec-
troradiometer (MODIS) and Common Land Model, J. Geophys.
Res.-Atmos., 108, 4488, doi:10.1029/2002JD003326, 2003.
Geosci. Model Dev., 7, 2121–2140, 2014 www.geosci-model-dev.net/7/2121/2014/
