Introduction
Albedo change radiative perturbations due to land use and land cover change
(LULCC) have long been considered some of the strongest climate forcing
mechanisms at global and regional scales (Cess, 1978; Otterman, 1977),
yet results from recent historical LULCC modeling studies reveal an order of
magnitude spread in the temperature response from albedo change forcings
(Brovkin et al., 2006; Lawrence et al., 2012; Pongratz et al., 2010).
This is likely because in regions and months with snow cover, the
interactions between vegetation and snow significantly complicate the
relationship between the change in forest cover fraction and surface albedo
(αs; de Noblet-Ducoudré et
al., 2012). Outcomes of model inter-comparison studies (LUCID; Boisier et al., 2012) employing identical LULCC
prescriptions suggest that, apart from the way individual land surface
models (LSMs) implement LULCC in their own land cover map (i.e., differences
in biogeography), model differences in the way αs is
parameterized could be a significant source of this spread (de
Noblet-Ducoudré et al., 2012; Pitman et al., 2009). Recent attributional
analysis by Boisier et al. (2012) suggests that the
contribution from the latter is indeed comparable to the former and worthy
of further investigation, particularly given the importance of albedo
radiative feedbacks when ground or canopy surfaces are covered with snow
(Crook and Forster, 2014; Hall and Qu, 2006).
Simulated αs over snow-covered forests by climate models is often
biased high (Essery, 2013; Loranty et al., 2014; Roesch, 2006). While
most climate models distinguish between snow intercepted in forest canopies
and snow on the ground, many differ in how they parameterize the fractions
of ground and canopy that are covered with snow for given masses of lying
and intercepted snow (Essery, 2013; Qu and Hall, 2007). This is likely
because, rather than trying to simulate the complex processes of canopy snow
interception and unloading as is done by many sophisticated,
physically based snow models (Essery et al., 2009, 2013), many climate models must employ simplified parameterizations to reduce
computational demands. In their assessment of αs feedbacks
simulated by 14 Coupled Model Intercomparison Project 5 (CMIP5) models, Qu and Hall (2014) found that the
largest intermodel spread in αsoccurred in northern latitude
regions and suspected it to be the reason for the differences in the large
range of local feedbacks. As with their previous inter-comparison analysis
(Qu and Hall, 2007), Qu and Hall (2014) asserted that
parameterizations of snow masking in many CMIP5 models may still require
improvement.
We hypothesize that parameterizations of snow masking by vegetation can be
refined and improved in many climate models. To this end, we evaluate albedo
parameterizations of six prominent climate models in greater detail in order
to pinpoint major sources of bias and inter-model variability. Rather than
running the full land model, we extract only the requisite equations
(parameterizations) enabling albedo prediction using observed forest
structure and daily meteorology. Climate models are typically evaluated by
looking at differences between their results and observation. In the
presence of snow, a bias in the simulated albedo may be due to deviations in
the modeled snow cover or to an inaccurate representation of forest cover
(biogeography) in the climate model. Thus, it is difficult to unravel the
single contributions to the overall error, making it challenging to
benchmark albedo schemes by this approach. By contrast, in this study the
albedo schemes are not embedded in the climate models but are isolated and
driven directly by observation, making it easier to evaluate their
performance. Predicted albedos for both forest and open areas are compared
to daily MODIS retrievals spanning three snow cover seasons in three case
regions of boreal Norway. Radiative forcings from the conversion of forests
to open lands are then computed, providing an additional metric for
benchmarking errors in the simulated albedo. We compare the performance of
the six albedo schemes to that in which albedo is predicted with a purely
empirical model developed in parallel, concluding with a discussion about
the efforts required to improve albedo prediction accuracy by climate
models.
Material and methods
MODIS albedo
We employed Version 006 (v006) MCD43A 1-day daily Albedo/bidirectional reflectance distribution function (BRDF) product with a 500 by 500 m spatial resolution (Wang and Schaaf, 2013; Wang et al.,
2012), taking the direct beam (“black-sky”) αs at local solar
noon for visible (VIS; 0.3–0.7 µm) and near-infrared (NIR; 0.7–5.0 µm) spectral bands for the time periods spanning January through May
(2007) and November through May (2008–2009). The v006 product uses multiple clear
sky views available over a 16-day period to provide daily αs
values that represent the best BRDF possible with the day of interest
emphasized. This includes as many overpasses as are available per day
(while earlier versions of the algorithm, including the Direct Broadcast
version, were limited to only four observations per day; Shuai, 2010),
enabling it to better capture the daily albedo with an algorithm that more
strongly emphasizes all contributions from the single day of interest
(Wright et al., 2014).
Forest structure and meteorology
Structural attributes like leaf area index (LAI), canopy height, and canopy
cover fraction were derived from regional aerial lidar campaigns undertaken in June of 2009 following Solberg et al. (2009). The maximum, minimum, and median values of these
attributes connected to each MODIS pixel included in the analysis are
presented in Table 1.
Minimum, maximum, and median tree height (H80), canopy
cover fraction, and LAI in the sampled evergreen needleleaf forests of each
study region (sampled June, 2009). H80 is the 80th percentile of laser
scanning first echoes, corresponding to canopy surface height in meters
above ground which is correlated to biomass and used as a proxy for tree
height.
Study
Sample area
Tree height, (H80; m)
Canopy cover fraction
LAI (m-2 m-2)
region
(km2)
(Number of )
min
max
median
min
max
median
min
max
median
MCD43A pixels
Flisa (n= 65)
14.0
3.1
15.8
11.8
25 %
77 %
63 %
0.55
2.35
1.73
Rena (n= 34)
7.3
5.7
13.0
9.8
50 %
80 %
63 %
1.31
1.82
1.52
Drevsjø (n= 36)
7.7
3.2
10.2
7.5
27 %
52 %
40 %
0.43
1.21
0.81
Regional mean
29.0*
4.0
13.0
9.7
34 %
69.7 %
55.3 %
0.76
1.79
1.35
* Value is column sum.
Daily meteorological observations of mean and maximum wind speed
(m s-1), mean and maximum near-surface air temperatures (∘C),
snow depth (cm), and precipitation (mm) were taken from measuring stations
in the municipalities of Drevsjø (675 m), Flisa (200 m), and Rena (250 m)
located in eastern Norway (Fig. 1) in the county of Hedmark
(Norwegian Meteorological Institute, 2013). Additional
meteorological information not available directly, such as snow density and
snowfall, were computed with empirical models and the available observations
as inputs. For example, precipitation was partitioned into snow and rain
following the empirical analysis of Dai (2008) in which rain occurred
more frequently than snow over land when air temperatures exceeded 1.2 ∘C. Snow density was computed with snow depth, air
temperature, and wind speed based on the empirical work of Meløysund et al. (2007).
Study regions showing the location of the open
(“Cropland” or “Bog/Wetland”) and coniferous forested sites included in
the analysis. Meteorological station locations are also indicated.
Site-specific air temperatures were adjusted using the station-measured
observations and an environmental lapse rate of -6.5 ∘C km-1.
All three sub-regions lie in the Köppen–Geiger climate zone “Dsc” (boreal)
but experience variations in snow fall amount and frequency and the temporal
extent of the snow cover season (time series of daily observed meteorology are presented as Fig. S1 in the
Supplement).
Local forest management plans were used to identify forest stands of pure
(> 95 % volume, m3 ha-1) evergreen needleleaf forest
cover within a ∼ 5 km radius and ∼ 50 m altitude
range of a weather monitoring station. Evergreen needleleaf species in the
region included Scots Pine (Pinus sylvestris L.) and Norway Spruce (Picea abies (L.) H. Karst.). Twelve
open area sites within the same 5 km proximity to a weather station were
selected in order to simulate forcings associated with regional LULCC
(forest to open), shown in Fig. 1. In total, 135 forested MODIS pixels
(approximately 2900 hectares) and 12 open area pixels (8 cropland, 4
wetland/peatland) were included in the sample.
Albedo parameterizations in climate models
The albedo parameterizations chosen for the analysis (Table 2) were selected
because they are widely employed in climate/earth system models and because
they are diverse with respect to the parameterization of ground masking by
vegetation, which can be classified according to three prevailing methods
introduced in Qu and Hall (2007; and later described in Essery, 2013). Briefly, the first method estimates radiative transfer
between the vegetation canopy and the ground surface; the second method
combines the vegetation and ground albedos with weights determined by
vegetation cover; the third method combines the snow-free and snow
albedo with weights determined by snow cover. Varying degrees of complexity
in albedo parameterizations stem from the way snow albedo metamorphosis
effects are treated and the way vegetation structure is utilized.
Albedo parameterizations included in the analysis and their
associated land and climate models.
Land model origin of
Climate model
Snow albedo
Vegetation
Forest
Technical
Other supporting
αs parameterizations
masking effectb
structure
documentation
references
CLASS
CGCM4; CanCM4
prognostic
type 2
yes
Verseghy (2009)
Verseghy et al. (1993)
procedure
CLM4.0
NCAR CCSM4;
prognostic
type 1
yes
Oleson et al. (2010)
Dickinson (1983); Flanner and
NCAR CESM; Nor-ESM
procedure
Zender (2006); Sellers (1985)
GISS II
GISS GCM II;
prognostic
type 3
no
Hansen et al. (1983)
Matthews (1984)
GISS GCM ModelE
procedure
JULESa
UKMO HadGEM2
prognostic
type 3
yes
Best (2009)
Marshall (1989); Sellers (1985);
(2-stream)
procedure
Wiscombe and Warren (1980)
JULESa
UKMO HadCM3
diagnostic
type 3
yes
Best (2009)
Essery et al. (2001)
(all-band)
procedure
JSBACH
MPI-ESM
diagnostic
type 2
yes
Reick et al. (2012)
Otto et al. (2011)
procedure
a Formerly MOSES.
b Classification based on Qu and Hall (2007).
We note that we do not run the entire land models offline; rather, we
extract only the equations (parameterizations) required to calculate the
surface albedos of both open terrain and forests. In some (albeit limited)
cases, certain parts of the albedo parameterizations have been slightly
modified for technical reasons, rendering them not fully identical to those
implemented in the full model (see Sect. S3 in the Supplement).
Direct beam (black-sky) albedos are calculated at local solar noon to be
compatible with the MODIS retrievals. The albedo parameterizations of
JSBACH (Jena Scheme for Biosphere–Atmosphere Coupling in Hamburg)
and the Goddard Institute for Space Studies (GISS) II model do not differentiate between direct and diffuse beam components
and are assumed to represent the total- or “blue-sky” albedo. The direct
beam component, however, typically dominates the total albedo under
clear-sky conditions (Ni and Woodcock, 2000; Wang, 2005; Wang and Zeng,
2009) and were thus deemed reasonable for purpose of comparison.
Regression modeling
Non-linear multiple regressions are performed using the forest structure and
meteorological observations as predictor variables. The functional form of
the models are adapted from several important physically based
parameterizations found in many current albedo schemes. Equation (1) is the best
performing model:
αs=k1+k2(1-e-LAI)+k3tanh(d/k4)e-k5(LAI)+1-11+e-k6TMax,
where LAI, d, and TMax are leaf area index, snow depth, and maximum daily
(24 h) temperature, respectively. k1 is the ground albedo (directional
hemispherical) without the forest canopy scaled by a canopy radiative
fraction term (1-e-LAI) and the parameter k2, with k2
representing the maximum albedo difference at the highest observed LAI
values. See the Supplement (Sect. S4) for a detailed overview and
description of the regression model and its theoretical underpinnings, its
parameters (Table S5), and its performance statistics (Table S5).
Radiative forcing
Top-of-atmosphere (TOA) radiative forcings for the conversion of
forest (evergreen needleleaf only) to open land (Δαs, Open–Forest) are computed using a 3-D four spectral band, eight-stream
radiative transfer model (Myhre et al., 2007) based on the
discrete ordinate method (Stamnes et al., 1988). The four
spectral bands are divided into the spectral regions 300–500, 501–850,
851–1500, and 1501–4000 nm where MODIS VIS albedos are included in the
first two bands and MODIS NIR albedos are included into the latter two
bands. The reported RF is the integration over the four spectral bands. The
radiative transfer code has been compared to detailed line-by-line
calculations for various applications with agreement of the order of 10 %
(Myhre et al., 2009; Randles et al., 2013).
The model is run with a 3 h time step with a horizontal resolution of
1∘ × 1∘ and a vertical resolution of 40 layers.
Meteorological data from the ECMWF is used in the radiative transfer
simulations and several atmospheric aerosol types are included in the model
(Myhre et al., 2007). LULCC RF is estimated by taking the
difference in the net shortwave radiative flux at TOA after setting the
monthly mean αs of the entire 1∘ × 1∘ grid
cell (centered over the domains of case study region) first to that of open
lands then to that of forests.
Results
Albedo
When looking at regional averages in predicted αs presented in
Fig. 2, no single model apart from the regression model (“REG”)
performed consistently well across all months at both Forest and Open sites
and for both spectral bands. Starting with the NIR band (Fig. 2, left
column), JSBACH showed clear positive biases at both Open and Forest sites
for most months. Positive biases in GISS II were more prevalent for Forest
although positive biases were also found at Open sites for months with
partial snow cover (November, April, May). Large positive biases for the Joint UK Land Environment Simulator (JULES)
2-stream (“JUL-2”) scheme were limited to Forest and to winter months of
January, February, and March. With the exception of February, slight negative
biases by JUL-2 at the Open sites were found in all months except February; this
was true also for the JULES All-band scheme (“JUL-AB”) with the exception
of March. The largest difference between the two JULES schemes occurred for
Forest, where JUL-AB consistently underpredicted αs in all
months except May. Large negative biases in Forest by CLASS were found in
November and January, with smaller negative biases in February.
(a–d) Remotely sensed (MCD43A, y axes) and modeled
(x axes) direct-beam albedos (monthly means, 2007–2009) in evergreen
needleleaf forests (a; b) and adjacent open areas (c; d) for both
near-infrared and visible bands averaged across all three study regions;
(e; f): November–May mean bias (regional and monthly means, 2007–2009) and
insolation-weighted mean bias. (a), (c), and (e) show the VIS band; (b), (d),
and (f) show the NIR band. High solar zenith angles precluded the number of sufficient
MODIS retrievals in December; thus December mean biases were excluded from
the November–May mean; MB=1N∑i=1N(αModel-αObs.)
Moving on to the VIS band (Fig. 2, right column), most schemes overpredicted
αs during the months January–March at the Open sites. The largest
spread (i.e., standard deviation, SD) at the Open sites occurred during
November (SD = 0.08), when the largest negative bias was found for CLM4 and
positive bias for JSBACH. Like in the NIR band, results varied more at the
Forest sites where biases across months were more evenly distributed around
zero (1:1 line). Again, here we found positive biases in JUL-2 yet
negative biases in JUL-AB during January–April. Positive biases by JSBACH were
mostly confined to November, January, and February at both Open and Forest sites.
Unlike the NIR band in which positive biases at Open sites by GISS II were
limited to November, April, and May, positive biases occurred for the VIS band
in all months; however, the positive biases in Forests seen for the NIR band
during November, February, and April were reduced. Like the NIR band, large negative
biases were found for CLASS for November, January, and February.
In general, Fig. 2 shows that the inter-model spread was smaller for the
VIS band predictions relative to NIR, and at Open sites relative to Forest
sites. Figure 2 also indicates that the inter-model spread in αs
predictions for both bands and land cover types was larger during November–February
and smaller during March–May. With the exception of JUL-2 in the NIR
band, all models overpredicted November–May mean Δαs (Fig. 2e and f, Open–Forest) in both spectral bands. Models with negative
αs biases at Forest sites and positive αsbiases at
Open sites – such as CLASS and JUL-AB – led to some of the largest
positive Δαs biases. For some schemes like GISS II and
JSBACH, positive αs biases at both Open and Forest sites offset
each other resulting in low Δαs biases, particularly in
the NIR band. Only for the NIR band (Fig. 2e) did any model underpredict
Δαs. Here, JUL-2 under- and overpredicted αs at
Forest and Open sites, respectively.
Monthly αs biases were often reduced when weighted by the
relative share of monthly insolation during November–May, as seen in Fig. 2
particularly for the JSBACH and CLASS schemes, which suggests that a large
share of the bias occurred during winter months.
Radiative forcing
November–May mean (2007–2009) TOA RF from simulated LULCC (Δα, Open–Forest) are presented in Fig. 3a for each of the three case
study regions. In Rena and Drevsjø, all models overpredicted Δαs and thus simulated LULCC RF. No clear patterns emerged
regarding relationships between RF error, model, and study region; RF errors
in REG, CLM4, and CLASS were larger in Rena (green bars) relative to
Drevsjø (red bars) – while RF errors were larger for the JULES models,
JSBACH, and GISS II for Drevsjø relative to Rena. One would expect a
larger spread in the modeled RF for Drevsjø given the larger inherent
variability in vegetation structure in the forest sample (Table 1) and given
the fundamental differences in the way each albedo scheme handles vegetation
structure (Sect. S3), yet we found the largest inter-model spread
occurring in Rena (RF SD = 0.075), where the normalized mean errors (NME)
ranged from 6 to 58 % for JSBACH and CLASS, respectively (Fig. 3b,
green right-hand y axis). For Drevsjø, the inter-model spread was smaller
(RF SD = 0.067), with RF NME ranging from 14 to 54 % for CLM4 and
JUL-AB respectively. One possible explanation is that Rena experienced more
frequent precipitation events, more fluctuating maximum daily temperature
(above and below freezing), and a snowpack that tended to melt more rapidly
in early spring than in Drevsjø (Fig. S1 in the Supplement) – all of which complicated
the prediction of ground and forest canopy αs in the presence of
snow.
(a) Radiative forcing (RF) from simulated vs. remotely
sensed (MCD43A) albedo differences (Open–Forest), 2007–2009 November–May
mean (excluding December). (b) mean absolute error (MAE), normalized mean absolute error (NME, and rank, 2007–2009 November–May mean. Rank
values in bold correspond to the regional mean, whereas individual case
region ranks are listed over each bar (colors defined in (a) legend).
Right-hand y axis (NME) colors correspond to individual bar colors.
MAE=1N∑i=1NRFModel-RFObs. ; NME=∑i=1NRFModel-RFObs.∑i=1NRFObs-1.
The inter-model spread was lowest in Flisa (RF SD = 0.05), with RF NME
ranging from 2 % for the Regression model to 22 % for CLASS,
respectively. In Flisa, JSBACH and JUL-AB underestimated the strength of the
vegetation masking effect (Δαs bias) and thus the
simulated LULCC RF. Together with CLASS, these two schemes also led to some
of the largest RF spreads across sub-regions by any single model, where RF
NME for JUL-AB ranged from 10 to 54 % for Flisa and Drevsjø,
respectively; for CLASS 22 to 58 % for Flisa and Rena, respectively;
and for JSBACH from 6 to 32 % for Flisa and Drevsjø, respectively.
For JSBACH, the result of having a positive Δαs bias in
Drevsjø (Table S6; Figs. S25 and S28) and a negative Δαs bias in Flisa (Table S6; Figs. S23 and S26) is a regional mean RF
(Fig. 3a, grey bar) that most closely resembled the MODIS-based RF. With MAE
(or NME) as a metric, however, JSBACH only ranked third of seven (Fig. 3b,
top). Although not ranked first in all sub-regions, REG led to the most
accurate regional mean RF prediction (MAE/NME, Fig. 3b, grey).
It is worth reiterating that some schemes such as that of GISS II severely
overpredicted αs at both Open and Forest sites (Fig. 2), which
was not reflected in Δαs or Δαs RF,
thereby giving the impression that the scheme ranked relatively high in
accuracy.
Discussion
A notable finding of our study is that parameterizations of open area
αs – which is governed mostly by the albedo of snow from January
through early April – contributed as much to Δαs prediction error as that of forests (Fig. 2). The bias was mostly positive
although there is some evidence that MODIS may underestimate the albedo of
cold dry snow (Jin et al., 2002; Stroeve et al., 2005; Wang and Zender,
2010), particularly in VIS bands (Wang and Zender, 2010).
Jin et al. (2002), for example, assert that there may be up to a
10 % negative bias in the MODIS pure dry snow albedo (Jin et
al., 2002), which could partially explain why most models in our study
tended to overestimate αs during the coldest months of January and
February (Fig. 2). An additional source of negative MODIS albedo bias could
stem from the spatial heterogeneity of the landscape comprising the actual
pixel signature, which could extend up to 500 m beyond the specified spatial
footprint at high latitudes (Cescatti et al., 2012; Wang et al., 2012)
and thus include the spectral signatures of built structures, other
vegetation cover (trees), vegetation shadowing (from trees), etc. We note
also that January and most of February experience solar zenith angles
> 70∘ for our case study regions; at these angles
the atmospheric correction algorithm degrades and the uncertainty in the
MODIS retrievals is increased (Lucht et al., 2000; Schaaf et al., 2002;
Stroeve et al., 2005). Factoring in any potential negative MODIS snow
αs bias would reduce some of the positive open area biases
(Fig. 2) but not all of it, particularly for CLASS and JSBACH, whose
positive open area αs biases were particularly large during
months with snow cover. Snow αs was reset to a maximum after a
fresh snowfall event (Tables S2 and S3); however, MODIS albedo retrievals
were far below the prescribed maximum snow albedo values of these two
schemes after fresh snowfall events (Figs. S23–S25 for JSBACH and Figs. S29–S31 for CLASS), particularly for the VIS band.
The two schemes with regional mean RF NMEs (Fig. 3b) above 20 % were the
CLASS and JUL-AB schemes. For CLASS, RF NME > 20 % occurred
for all three sub-regions. The Δαs RF bias of CLASS was
due to overpredictions at open area sites and underpredictions at forested
sites. The latter is due to the parameterization of canopy transmittance
that is based on an extinction coefficient that incorporates a correction
factor of 0.6 and 0.8 for NIR and VIS bands, respectively (Eqs. S10–S11).
Lowering the correction factor to 0.5 and 0.6 for NIR and VIS bands,
respectively, lowers the extinction coefficient and increases canopy
transmittance, which serves to reduce the negative albedo biases in forests, particularly at high solar zenith angles (November–February). The lower
extinction coefficient is in line with more recent observations in boreal
evergreen forests (Aubin et al., 2000; Balster and Marshall, 2000). As mentioned earlier, at the open sites the VIS albedo constant of 0.95 for fresh
snow was too high; the maximum remotely sensed VIS albedo after a fresh
snowfall event was 0.88 (all study regions), and adjusting it to 0.90 would
alleviate some of this bias (disregarding potential MODIS biases).
Although JUL-AB (formerly MOSES v. 2.2) ranked sixth of seven overall when
considering only the regional mean RF MAE and NME, in two of the three study
regions (Flisa and Rena) it performed quite well, with RF NMEs of < 11 and < 16 % for Flisa and Rena, respectively. The large RF
NME for Drevsjø was a result of a severe negative bias in the predicted
αs of forests (Fig. S10), which resulted in large positive
Δαs biases (Table S7). The explanation is due to the use of
vegetation-specific snow albedo parameters that were too low for forests in
this region – forests that were characterized as having the lowest median
tree heights, LAIs, and canopy cover fractions out of the three forested
sub-regions (Table 1).
Of the existing land model schemes included in this study, the albedo
parameterizations of JUL-2 performed best in the LULCC RF simulations (Fig. 3), although we note that it underestimated the strength of the vegetation
masking effect (Δαs) in the NIR band while overestimating
it in the VIS band (Fig. 2; consistent across all three individual study
regions; Table S6), which may have had offsetting effects in the RF
simulations. A closer inspection of the daily αs time series
(Sect. S5.2) reveals that forest albedo ( Figs. S14–16) may be too sensitive to
snow depth (Fig. S1), an important variable in the parameterization of
snow cover fraction (Eq. S2). For example, αs predictions were
biased positive at snow depths above 0.6 m (typical in Rena and Drevsjø
during the winter-spring of 2008 and 2009) while biased negative at Flisa
during 2007 and 2008 for which snow depths never exceeded 0.4 m. This same
sensitivity of forest αs to snow depth was also found for the
GISS II scheme – another type 3 scheme – resulting in positive αs biases in forests. This sensitivity to snow depth was not evident for
JUL-AB – the third type 3 scheme. This is because, unlike GISS II and
JUL-2, snow albedo is vegetation-dependent and constrained by satellite
remote sensing (MODIS).
In agreement with findings in Essery (2013), we generally find that
no single type of scheme (as described in Sect. 2.1 and in Qu and Hall, 2007) stood out as performing better or worse relative to the
others. In their latest CMIP5 simulations, Qu and Hall (2014) assert
that type 2 schemes – or those which parameterize albedo as a function of
vegetation cover rather than snow cover – generally tended to overestimate
the strength of the snow albedo masking effect (Δαs) due
to negative biases in forest αs predictions. For JSBACH – a
type 2 scheme – we did not detect this bias; rather, we found positive
biases in Forest in both bands, particularly during the snow season which is
consistent with findings of Brovkin et al. (2013) and Hagemann et al. (2013). NIR albedo predictions in Flisa and Rena during snow-free
periods were also biased high (figures in Sect. S5.4) resulting in
underestimations of NIR Δαs, which we attributed to a
snow-free vegetation albedo constant that was too high (Table S3). The
positive RF bias seen at Drevsjø (Fig. 3) stemmed from negative biases in
the springtime (March–May) VIS αs in forests (Fig. S29). This
may be attributed to the default use of 1 as the stem area index (SAI) used
in the masking parameterization (Reick et al., 2012); observational
evidence suggests this may be too high in boreal regions in spring
(Lawrence and Chase, 2007).
While the simulated Δαs RF by GISS II appeared relatively
robust (Fig. 3), αs predictions in Forest and Open were strongly
positively biased in both spectral bands. In forests, this could be
attributed to two main factors: (i) a dependence on snow-free albedo
constants that were too high, particularly when applied at the denser (i.e.,
high canopy cover fraction, Table 1) sites of Flisa and Rena; (ii) a strong
dependency on snow depth and/or lack of explicit representation of forest
structure in the masking expression which led to overpredictions in Rena and
Drevsjø (Figs. S39 and S40), regions that experienced snow depths
greater than 60 cm for much of the winter and early spring in 2008 and 2009
(March–late April). NIR biases at the open sites (Figs. S35–37) were
attributed to the use of snow-free vegetation constants that were too high
(Table S4).
Sources of RF biases in CLM4 were harder to discern, as the sign of the
predicted Δαs bias was not consistent across study sites
and months. Δαs bias was negative and mostly limited to
March and April at Flisa and Rena (Table S6). Δαs bias was
positive at Drevsjø and occurred mostly in April and May due to
overpredictions in both NIR and VIS αs in Forest and
underpredictions in both NIR and VIS αs at Open sites (Figs. S17–S22).
Not surprisingly, the purely empirical αs model presented here
(Eq. 1) calibrated with local forest structure and meteorological
observations performed best on average throughout the region (i.e., Fig. 3;
MAE, NME, and Rank). However, to our surprise, it did not rank first in all
study regions; it ranked fifth in Rena which was the region with the
fewest forest structure, meteorological, and MODIS albedo retrievals. This
highlights the high-performance dependencies of purely empirically based
models on the underlying data sets to which they are calibrated. Although it
is tempting to recommend its application over existing modeling schemes in
boreal regions, rigorous evaluation efforts are needed to assess the
degree of transportability and reliability when applied in other regions
with different forest structures and climate regimes (Bright et
al., 2015).
Conclusions
LULCC radiative forcings (RF) from changes in simulated land surface albedo
(Δαs) as predicted by the albedo parameterizations
employed by six leading climate models were evaluated using observed
meteorology and forest structure for a case region in Norway and by
comparing them with MODIS daily albedo retrievals. Compared to RF estimations based
on MODIS albedo, most of the albedo schemes overestimated the magnitude of
the simulated regional mean RF (Fig. 3) by overestimating Δαs (Fig. 2), although results varied between three sub-regions within
the broader case study region. For instance, in a sub-region characterized
as having the highest forest productivity and lowest seasonal snow cover of
the three (Flisa), albedo schemes of two land models (JSBACH and JULES
All-band) underestimated Δαs RF.
Efforts to uncover sources of systematic albedo biases proved challenging as
no clear discernible patterns could be detected across study regions or
between the different types of schemes (Sect. 2.3), although some
systematic sources of bias in forest αs were identified for the
albedo schemes of CLASS, JULES All-band, JSBACH, and GISS II. Severe
negative albedo bias in winter months in CLASS – evident across all three
study regions – was attributed to the parameterization of canopy
transmittance. For GISS II, persistent positive αs biases were
linked to snow-free vegetation albedos (both VIS and NIR bands) that were
too high and to a snow cover masking parameterization that did not
explicitly account for differences in forest structure. Biases in forests in
the JULES All-band scheme can be easily alleviated by adjusting (in our case
increasing) the vegetation-dependent snow albedo values for “Evergreen
Needleleaf” forest, which, in our study, were based on MODIS latitude band
averages (Gao et al., 2005). Similarly for JSBACH, forest
biases can be easily reduced by lowering the snow-free vegetation albedo
value in the NIR band.
Despite the albedo biases identified here in both forests and open areas,
the normalized mean absolute error (NME) of the 3-year regional mean RF
from the LULCC simulations was below 20 % for four of the six albedo
schemes, which is remarkably high accuracy for climate models considering
that they must depend on reduced complexity land surface schemes (relative
to 3-D radiative transfer models or sophisticated snow–ice physics models).
Although we have only evaluated evergreen needleleaf forests, extending this
or similar empirical analyses to other forest types or climate regimes would
give additional insight into the albedo predictive capacities of the
parameterizations employed in the current generation of climate models.