The surface energy and mass balance of ice sheets strongly depends on the
amount of solar radiation absorbed at the surface, which is mainly controlled
by the albedo of snow and ice. Here, using an Earth system model of
intermediate complexity, we explore the role played by surface albedo for the
simulation of glacial cycles. We show that the evolution of the Northern
Hemisphere ice sheets over the last glacial cycle is very sensitive to the
representation of snow albedo in the model. It is well known that the albedo
of snow depends strongly on snow grain size and the content of
light-absorbing impurities. Excluding either the snow aging effect or the
dust darkening effect on snow albedo leads to an excessive ice build-up
during glacial times and consequently to a failure in simulating
deglaciation. While the effect of snow grain growth on snow albedo is well
constrained, the albedo reduction due to the presence of dust in snow is much
more uncertain because the light-absorbing properties of dust vary widely as
a function of dust mineral composition. We also show that assuming slightly
different optical properties of dust leads to very different ice sheet and
climate evolutions in the model. Conversely, ice sheet evolution is less
sensitive to the choice of ice albedo in the model. We conclude that a proper
representation of snow albedo is a fundamental prerequisite for a successful
simulation of glacial cycles.
Introduction
The net surface mass balance of ice sheets is equal to the difference
between accumulation, which is controlled by the hydrological cycle, and of
ablation, which is determined by the surface energy balance. The surface
energy balance strongly depends on the amount of solar radiation absorbed at
the surface. While the amount of radiation reaching the surface is mainly
determined by the insolation at the top of the atmosphere and cloud cover,
the fraction of radiation absorbed at the surface is controlled by its
albedo. Since ice sheets are mostly covered by snow, the albedo of snow
plays a crucial role for the surface energy and mass balance of ice sheets.
The importance of snow and ice albedo parameterizations for ice sheet
modelling has been known for long time. By contrast, the role of snow
albedo parameterization on modelling of glacial cycles is much less
understood. Most previous simulations of glacial cycle(s), e.g. Bonelli et
al. (2009), Tarasov and Richard Peltier (2002), Zweck and Huybrechts (2005),
Charbit et al. (2007), Abe-Ouchi et al. (2007), Lunt et al. (2008), Gregoire
et al. (2012), Liakka et al. (2016), and many others, employed the so-called
positive degree day (PDD) scheme, which does not account explicitly for snow
and ice albedos.
Clear-sky and diffuse snow albedo dependence for a number of
different parameterizations (Dang et al., 2015; Gardner and Sharp, 2010;
Warren and Wiscombe, 1980) as indicated in the legend. (a) Pure snow
albedo dependence on effective snow grain radius. (b) Fresh snow
albedo (re=100µm) as a function of dust concentration.
(c) Dirty snow albedo (dust concentration of 1000 ppmw) as a
function of snow grain size. (d) Old snow albedo (re=1000µm) as a function of dust concentration. The clear-sky albedo
is for a solar zenith angle of 50∘. In CLIMBER-2 the snow albedo for
diffuse and direct radiation is identical for solar zenith angles below
60∘.
Simulated (a) global temperature anomaly, (b) sea
level, (c) ice volume, (d) surface mass balance,
(e) ablation, (f) accumulation and (g) global dust
emissions for the reference experiment. The red and blue lines in
(c)–(f) represent the Laurentide and Fennoscandian ice
sheet, respectively. The shading in (b) is the sea level range from
Spratt and Lisiecki (2016).
Modelled annual dust deposition compared to observations for the
present day (a–c) and reconstructions for the Last Glacial Maximum
(d–f). The model results (a, d) are compared to
data from Lambert et al. (2015) (b, e) and Mahowald et al. (1999) (c, f). The dataset of Mahowald et al. (1999) does not
include glaciogenic dust. For the LGM, the white lines indicate the modelled
(d) and reconstructed (e, f; Tarasov et al., 2012)
extent of the ice sheets.
The albedo of snow is a complex function of snow grain size and concentration
of light-absorbing impurities (Warren, 1982; Warren and Wiscombe, 1980).
After snowfall, snow crystals undergo rapid transformations in size and
shape, with a tendency for snow grains to grow larger with time (Colbeck,
1982). The rate of change is controlled by snow temperature and the
temperature gradient inside the snow, with melt–freeze cycles being
additionally very efficient in accelerating grain growth during snowmelt
(Brun et al., 1992; Flanner and Zender, 2006). The change in snow grain size
affects the interaction of the snow surface with the incoming solar
radiation, with larger grains increasing the path that photons are travelling
in the snow and therefore decreasing its albedo. The decrease in albedo due
to a growth of the optically equivalent snow grain size from
100 µm, typical for fresh snow, to 1000 µm, typical for
melting snow, is ∼ 10 % (Fig. 1).
Results from the offline simulations. (a) Mean surface
albedo over the area covered by NH ice sheets, (b) total surface
mass balance and (c) total ablation for the experiments indicated in
the legend.
Both radiative transfer models (Aoki et al., 2011; Hadley and Kirchstetter,
2012; Warren and Wiscombe, 1980) and direct measurements (Bryant et al.,
2013; Doherty et al., 2013; Gautam et al., 2013; Painter et al., 2010, 2012;
Skiles et al., 2012; Skiles and Painter, 2017) demonstrate that even small
amounts of light absorbing impurities (LAIs) affect the surface albedo of snow
significantly. Black carbon and desert dust are the main sources of LAI in
snow, but algal blooms and organic carbon could also play a role. Black
carbon concentrations of less than 1 ppmw (parts per million in weight) in
fresh snow can already cause decreases in albedo by several percent (Warren
and Wiscombe, 1980). The effect of mineral dust on snow albedo is much lower,
with 1 ppmw of black carbon being equivalent to roughly 100–200 ppmw of
mineral dust (Dang et al., 2015; Warren and Wiscombe, 1980). Algal blooms
over Greenland have been shown to substantially reduce surface albedo (Lutz
et al., 2016; Musilova et al., 2016; Stibal et al., 2017). Simulations show
that changes in albedo due to LAI might significantly affect the surface mass
balance of ice sheets during glacial times (Ganopolski et al., 2010; Krinner
et al., 2006), at present (Dumont et al., 2014; Tedesco et al., 2016) and in
future climate change scenarios (Goelles et al., 2015).
In this study we explore the sensitivity of ice sheet and climate evolution
over the last glacial cycle to the representation of snow albedo in the
CLIMBER-2 Earth system model of intermediate complexity. We limit the scope
of our study to the effect of snow aging and mineral dust concentration in
snow. Several lines of evidence suggest that dust deposition was
substantially larger during glacial times (Kohfeld and Harrison, 2001;
Lambert et al., 2015; Mahowald et al., 2006), particularly also at the
southern margins of the NH ice sheets, the areas most affected by ablation.
Dust is therefore likely to be an important player for the ice sheet ablation
through its effect on snow albedo. The effect of black carbon is neglected in
this study. Although it has been suggested that the effect of black carbon on
surface albedo might play an important role in the present-day climate
(Flanner et al., 2007; Hansen and Nazarenko, 2004; Yasunari et al., 2015),
most of the black carbon which is deposited over the boreal region comes from
sources related to industrial activities (Bauer et al., 2013; Lamarque et
al., 2010). We therefore assume that black carbon deposition over regions
potentially covered by ice sheets was negligible in pre-industrial times. The
effect of other LAI, like algae, is still far from being properly understood
and is therefore not considered in the present study.
(a) Summer (June–July–August) ice sheets surface albedo
at 15 ka for the reference simulation. (b–d) Surface albedo
anomalies relative to the reference for the three different offline
simulations specified in the subplots.
It is now becoming possible to use complex Earth system models based on
general circulation models to simulate the last glacial cycle (e.g. Latif et
al., 2016). In these models, over-simplistic obsolete schemes like PDD will
be substituted by a physically based energy balance approach similar to that
used in CLIMBER-2. An exploration of the impact of albedo parameterization on
glacial cycles simulations with a computationally efficient model like
CLIMBER-2 can provide useful insights into which processes are important and
should therefore be accounted for.
MethodsThe CLIMBER-2 model
CLIMBER-2 (Ganopolski et al., 2001; Petoukhov et al., 2000) includes a coarse
resolution statistical-dynamical atmosphere model, a three-basin zonally averaged
ocean model, a land surface and vegetation model (Brovkin et al., 1997) and
the 3-D polythermal ice sheet model SICOPOLIS (Greve, 1997). SICOPOLIS is
applied only to the Northern Hemisphere, with a resolution of
1.5∘× 0.75∘. Antarctica is prescribed from present-day observations. The climate component and SICOPOLIS are coupled once per 10
years through a surface energy and mass balance interface module (SEMI)
(Calov et al., 2005). SEMI performs a physically based three-dimensional
downscaling of climatological fields from the coarse atmospheric grid to the
ice sheet model grid and computes the surface mass balance and the surface
temperature using a physically based surface energy balance approach.
Importantly, precipitation is downscaled accounting for the slope effect and
the desert-elevation effect. Radiation and atmospheric temperature and
humidity are first interpolated bilinearly and then corrected for the surface
elevation of the ice sheet. Refreezing is accounted for as a constant
fraction, 0.3, of surface melt. Computed annual fields of surface ice sheet
mass balance and of surface temperature are used in SICOPOLIS as surface
boundary conditions. In turn, SICOPOLIS feeds back the average ice sheet
elevation, the fraction of land area covered by ice sheets, the sea level and
the freshwater flux into the ocean from the ablation of ice sheets and from
ice calving to the climate component.
The model has been used to explore the hysteresis in the climate–cryosphere
system (Calov and Ganopolski, 2005) and has successfully simulated the last
eight glacial cycles (Ganopolski et al., 2010; Ganopolski and Calov, 2011).
It has been used to explore the effect of dust radiative forcing on
glacial–interglacial cycles (Bauer and Ganopolski, 2014), the impact of
permafrost on simulation of glacial cycles (Willeit and Ganopolski, 2015) and
the initiation of Northern Hemisphere glaciation (Willeit et al., 2015).
Seasonal evolution of several modelled variables at 15 ka at two
locations at the southern margin of the Laurentide (left) and Fennoscandian
(right) ice sheets for the reference simulation (black lines) and from
offline simulations with no dust deposition (blue), no snow aging (red), and
no dust deposition and no snow aging (green). The variables shown are
(a) snow water equivalent, (b) surface albedo,
(c) snow grain size, (d) dust concentration in snow,
(e) ablation and (f) ice melt. The location of the two
sites is indicated by the red boxes in Fig. 5a.
The model version used in this study includes a fully interactive dust cycle
as described in Bauer and Ganopolski (2010, 2014). The direct radiative
forcing of dust loading in the atmosphere is explicitly accounted for and
dust deposition at the surface affects snow albedo both in the land surface
module and in SEMI. Compared to Bauer and Ganopolski (2010), we replaced the
precipitation dependence of dust emissions with a relative soil moisture
(θ) dependence, so that their Eq. (12) for the threshold value for
the climatological wind speed for dust emissions becomes
ut=u01+tanhcθθ-θt,
where u0=3 m s-1 is the reference threshold wind speed,
θt=0.3 is the soil moisture of
transition from semi-arid to humid conditions, and
cθ=10 is a normalization constant.
We use the parameters corresponding to the solution L1 in Bauer and
Ganopolski (2014), which assumes that the fraction of precipitation-driven
wet dust deposition is 70 % of the total, and an imaginary refractive
index of airborne dust of 0.003.
The dust deposition on ice sheets further includes dust from simulated
sediments produced by glacial erosion. This dust source is not included in
the global dust cycle model due to its very local origin, which can not be
represented on the coarse atmospheric grid. Dust deposition produced from
glaciogenic sources is parameterized based on the assumption that the
emission of glaciogenic dust is proportional to the delivery of glacial
sediments to the edge of an ice sheet (see Ganopolski et al., 2010, see Appendix A
for details). Most of the glaciogenic dust originates from the southern
flanks of the ice sheets, and this source is significant only for mature ice
sheets, which reach well into areas covered by thick terrestrial sediments.
Effect of snow aging and dust deposition on modelled sea level over
the last glacial cycle for different online experiments as indicated in the
legend.
Snow albedo parameterization
Three components in the parameterization of snow albedo used in CLIMBER-2 are
critically important, namely, the aging of pure snow, the concentration of
light-absorbing impurities in snow from dust deposition, and the synergy
between aging of snow and impurities (Warren, 1982; Warren and Wiscombe,
1980). Under “synergy” we understand here the fact that the effect of
impurities on snow albedo is much higher for “old” snow than for fresh
snow. The parameterizations described below are applied both to the surface
scheme on the atmospheric grid and to SEMI on the ice sheet grid. The surface
albedo over ice sheets is computed as
α=fsnowαsnow+1-fsnowαice,
where fsnow is the grid cell fraction which is considered to be
snow covered, αsnow is the albedo of snow and
αice is the albedo of bare ice. αice is set
to 0.4. fsnow is 1 if the snow water equivalent in the grid cell
is larger than 30 kg m-2 and linearly related to the ratio between
snowfall and ablation if the snow water equivalent is below 30 kg m-2.
Snow albedo is computed for two spectral bands (visible and near-infrared
radiation) and separately for direct beam and diffuse radiation. The diffuse
albedo values are a function of snow grain size and dust concentration at the
surface following Warren and Wiscombe (1980) (their Fig. 5 for dust radius of
1 µm and imaginary refractive index of 0.01). It is shown in
Fig. 1.
Ice sheet extent at the Last Glacial Maximum (21 ka) for
(a) the reference simulation, (b) the simulation without
snow aging and dust effect on snow albedo, (c) the simulation
without snow aging, and (d) the simulation without dust on snow.
A snow aging factor is used to represent the grain size evolution and its
effect on albedo, similarly to Dickinson et al. (1986). The snow age factor,
fage, is parameterized as a function of snow temperature
Ts and snowfall rate S on each atmospheric time step (1 day) as
fage=1-ln1+fageTScSfageTScS,
with
Sc=2×10-5kgm-2s-1
and
fageT=e0.05TS-T0+eTs-T0T0=273.15K.
The snow grain size, re in µm, and snow age factor are related
by
re=50+200⋅10fage⋅log101+1000-50200-1.
The dust mass concentration in snow is simply computed as the ratio of dust
deposition rate and precipitation rate. During snowmelt dust is assumed to
concentrate near the snow surface and dust concentration is allowed to
increase by up to a factor of 5, consistent with observations for the top
4 cm presented in Doherty et al. (2013).
Evolution of modelled aeolian (grey) and glaciogenic (magenta) dust
deposition at two locations at the southern margin of the LGM Laurentide
(a) and Fennoscandian (b) ice sheets. The location of the
two sites is indicated by the blue boxes in Fig. 8a.
The direct beam snow albedo values depend on the solar zenith angle and the
standard deviation of orography, σz as
αsnowvis,dir=αsnowvis,dif+forofμ1-αsnowvis,dif,
where
8foro=0.4⋅1-tanhσz1000,9fμ=0.5⋅31+2μ-1,
and μ is the cosine of the solar zenith angle.
To test the sensitivity of our results to the representation of snow albedo,
we have additionally introduced two alternative parameterizations of snow
albedo. The first one is from Dang et al. (2015) and the second from Gardner
and Sharp (2010). Both include the effect of snow grain size and black carbon
content. The effect of dust is computed through a black carbon equivalent
following Dang et al. (2015). The two alternative parameterizations are
compared to the standard one in Fig. 1. The different models agree on a
∼ 10 % albedo reduction caused by the aging of snow, for a snow
grain growth from ∼ 100 to ∼ 1000 µm (Fig. 1a). The
impact of dust concentration on fresh snow albedo is generally larger but
much more uncertain, ranging between 15 and 25 % albedo reduction for a dust
concentration of 1000 ppmw relative to pure snow (Fig. 1c). The differences
can be largely explained by the choice of the imaginary refractive index of
dust. The imaginary refractive index of dust varies by over 1 order of
magnitude as a function of dust composition (e.g. Fig. 7 in Dang et al.,
2015), and this range of possible values is reflected in the differences in
albedo seen in Fig. 1. The combination of aged snow with high dust
concentrations reduces snow albedo to values below 0.4 (Fig. 1b, d).
Experiments
We used CLIMBER-2 to simulate the last glacial cycle, from 130 ka (1000
years ago) to the present day. The transient model simulations are driven by
orbital forcing (Laskar et al., 2004), and the time-varying concentration of
greenhouse gases is expressed as equivalent CO2 concentration
(Ganopolski et al., 2010). The initial condition is the equilibrium climate
state computed with greenhouse gas concentration and orbital forcing of the
pre-industrial period.
First we performed a reference model simulation using the standard CLIMBER-2
surface albedo parameterization. Then we ran a set of offline simulations in
which, similarly to Bauer and Ganopolski (2017), the climate and ice sheets
are prescribed from the reference simulation and the surface mass balance is
diagnosed for experiments with different surface albedo set-ups. To separate
the importance of snow aging and dust on snow albedo, we ran offline
experiments with and without snow aging and with and without the effect of
aeolian and glaciogenic dust sources on snow albedo.
Uncertainties in modelled sea level evolution over the last glacial
cycles resulting from different parameterizations of (a) snow
albedo, (b) different values of bare ice albedo and
(c) scaling of dust emissions by factors 1/4, 1/2, 2 and 4. The
dashed black line represents the sea level reconstruction from Spratt and
Lisiecki (2016).
Finally we performed a set of online simulations using the different surface
albedo set-ups but this time allowing bidirectional coupling between the
climate and ice sheet models. Additional experiments with the alternative
albedo parameterizations described in Sect. 2.2 are used to explore the
sensitivity to different snow albedo schemes. We also tested the model
sensitivity to different values of ice albedo, ranging from 0.3 to 0.5, and
to different global dust emissions scaling factors (from 1/4× to
4×). All online experiments are listed in Table 1.
List of online model simulations; std stands for “standard”.
Snow agingDust depositionSnow albedoIce albedoREFOnOnStd0.4A1D0OnOffStd0.4A0D1OffOnStd0.4A0D0OffOffStd0.4A1D1gOnGlaciogenic onlyStd0.4A1D1p1OnOnGardner & Sharp (2010)0.4A1D1p2OnOnDang et al. (2015)0.4A1D1i03OnOnStd0.3A1D1i05OnOnStd0.5A1D1d1/4OnOn, 1/4×Std0.4A1D1d1/2OnOn, 1/2×Std0.4A1D1d2OnOn, 2×Std0.4A1D1d4OnOn, 4×Std0.4Results and discussion
Figure 2 shows the evolution of several modelled variables over the last
glacial cycle in the reference simulation. The global temperature decreases
by ∼ 6 ∘C from the Eemian interglacial (126 ka) to the Last Glacial Maximum (LGM, 21 ka) (Fig. 2a). The modelled sea level variations
agree reasonably well with available reconstructions (Spratt and Lisiecki,
2016), with a minimum sea level ∼ 120 m below the present day during
the LGM (Fig. 2b). The largest contribution to sea level comes from the
Laurentide ice sheet (Fig. 2c). The surface mass balance of the NH ice sheets
is positive through most of the last glacial cycle, except for the
deglaciation period between 20 and 10 ka (Fig. 2d), when the ablation rate
exceeds the accumulation rate (Fig. 2e, f).
The reference simulation in this study differs from previous CLIMBER-2 last
glacial cycle simulations presented in Bauer and Ganopolski (2014) and
Ganopolski et al. (2010) in that it includes a fully interactive dust cycle,
as described in Sect. 2.1. The modelled dust deposition for the present day
and the LGM is compared to available observation-based estimates in Fig. 3. A
detailed model evaluation based on these data is challenging because of the
large variability among different observation-based reconstructions (Fig. 3b,
c, e, f) (Lambert et al., 2015; Mahowald et al., 1999, 2006). Even at present
the estimated global value of dust deposition and atmospheric load varies
largely between different studies (e.g. Table 1 in Bauer and Ganopolski,
2014). Ganopolski et al. (2010) used the Mahowald et al. (1999) data for
present day and LGM, with their relative contributions scaled with sea level,
as prescribed forcing in the surface energy and mass balance interface
module. The climate–ice-sheet model has therefore been tuned for dust amounts
similar to Mahowald et al. (1999). Hence, to avoid the need to retune the
model, we scaled the model dust emissions by adjusting the dimensionless
global calibration constant cq in Eq. (8) of Bauer and Ganopolski (2010)
to get a present-day global dust deposition of ∼ 3000 Tg yr-1
(Fig. 2g), comparable to Mahowald et al. (1999).
At LGM the global dust deposition is roughly doubled in our simulations
(Fig. 2g). What is more important for the impact on surface albedo is the
spatial distribution of dust deposition. In general, at present, the dust
deposition pattern seen in observations is reasonably well captured by the
model, although it tends to slightly overestimate the annual dust deposition
at high northern latitudes (Fig. 3a–c). At the LGM, the modelled geographic
distribution of dust deposition resembles in many aspects the reconstructions
from Mahowald et al. (1999), with increased dust deposition over Siberia and
at the southern boundary of the Laurentide ice sheet over North America
(Fig. 3d, f). Although the LGM dust deposition pattern is similar also in the
reconstructions of Lambert et al. (2015), the absolute values are
substantially larger in the latter compared to the model or Mahowald et
al. (1999).
Due to the highly non-linear nature of the climate–cryosphere system,
relatively small changes in the model can have a very large impact on the
simulated coupled system response to orbital forcing. The offline
simulations with prescribed climate and ice sheets from the reference
simulation provide a mean to understand the impact of the different factors
affecting snow albedo, avoiding the model drift into unrealistic states.
In the reference simulation, the mean snow albedo over ice-covered areas
varies between 0.65 and 0.8 (Fig. 4a). This represents a substantial
reduction compared to simulations assuming fresh and pure snow. In this case
the mean albedo is ∼ 0.85, with only tiny variations due to the
dependence of snow albedo on the solar zenith angle (Fig. 4a). Most of the
time, the reduction of surface albedo by the snow aging effect is larger than
the reduction due to dust. Only around the LGM is the dust-induced effect larger than the pure snow aging effect (Fig. 4a). Geographically explicit
surface albedo differences between the different offline experiments are
shown in Fig. 5 for a time slice at 15 ka, when ablation reaches its maximum
during deglaciation. The reference simulation shows summer albedos as low as
0.4 at the ice sheet margins, where ablation occurs and the snow is old and
dust accumulates at the surface while snow is melting (Fig. 5a).
Additionally, in localized regions along the margin all snow is melted during
summer and bare ice is exposed, which additionally reduces surface albedo, as
snow albedo is larger than bare ice albedo over most of the ice sheet because
of dust concentrations below ∼ 300 ppmw. Ignoring the effect of snow
aging or dust, or both, results in increased surface albedo, mostly along the
ice sheet margins (Fig. 5b–d).
The differences in albedo are reflected in the ablation and consequently in
the surface mass balance (Fig. 4b, c). When either the snow aging or the dust
effect are ignored, the ablation integrated over the NH ice sheets is only
∼ 25 % of the value in the reference simulation (Fig. 4c). This
strong reduction in ablation results in a net surface mass balance that is
positive throughout the whole last glacial cycle (Fig. 4b).
The albedo of snow is so important for the ice sheet surface energy and mass
balance because it strongly controls the length of the snow season and
consequently ice melt (Fig. 6). Variations in the albedo of snow during the
melt season induced by snow aging and dust content can lead to variations in
the length of the snow season of several months (Fig. 6a, b) and consequently
to substantial variations in ablation and ice melt (Fig. 6e, f).
When the same experiments with and without the effect of snow aging and dust
deposition on snow albedo are repeated in the online set-up, with the
different parameterizations affecting the actual surface energy and mass
balance of the ice sheets, the modelled sea level is very different from the
reference simulation. In the most extreme case, both the snow aging and the
dust darkening effect on snow albedo are ignored. This is equivalent to
assuming that snow is always fresh and pure. In this case rapid ice build-up
occurs in the model, with sea level dropping below 400 m relative to the
present day and with the model subsequently responding only weakly to changes
in orbital forcing and greenhouse gas radiative forcing (Fig. 7). Under
these conditions, at the LGM, ice sheets cover most of North America and
Eurasia (Fig. 8b). In the experiments where the snow aging or the dust impact
on snow albedo are considered separately, excessive ice is grown over North
America and a large ice sheet develops over Eurasia (Fig. 8c, d). Also in
these experiments, sea level drops well below the estimated LGM value of
∼ 120 m (Fig. 7). Therefore, from the experiments presented and for
this model formulation, the effects of dust deposition or snow grain growth
acting separately do not allow us to simulate a last glacial cycle that is in
agreement with climate and sea level reconstructions because each factor
alone is insufficient to prevent glacial inception over Siberia. Sensitivity
experiments that separately ignore the role of dust deposition and aging with
a slightly different snow albedo reference value were not performed but
could provide additional insight.
If the snow albedo reduction by deposition of dust produced by glacial
sediment transport is ignored, the simulated sea level is very similar to the
reference experiment until the LGM. Afterwards, this additional source of
dust becomes important to reproduce a full deglaciation in the model
(Fig. 7). Dust deposition from glaciogenic origin is negligible compared to
aeolian dust over most of the glacial cycle, except during deglaciation, when
it becomes comparable or even the dominant source of dust (Fig. 9).
The choice of the snow albedo scheme also has a considerable impact on the
simulated ice volume evolution over the last glacial cycle (Fig. 10a). Using
the alternative albedo schemes of Dang et al. (2015) and Gardner and
Sharp (2010), which in general show a weaker darkening effect of dust on snow
albedo than the standard scheme used in CLIMBER-2 (Fig. 1), results again in
excessive ice growth with an LGM ice volume too large by a factor of 2 (Fig. 10a). However, it is possible that retuning of the model could allow us to
successfully simulate the last glacial cycle also with these alternative
albedo schemes. Conversely, the value used for the albedo of bare ice has a
rather limited impact on the simulated glacial cycle (Fig. 10b) because in
the model ice ablation is controlled to a large extent by the length of the
snow-free season, which is mainly controlled by snow albedo (Fig. 6a).
Scaling the dust emissions in the model up or down by a factor of up to 4
leads to a large spread in modelled sea level (Fig. 10c), with simulations
with enhanced dust emissions failing to build up enough ice at LGM and
simulations with reduced dust emissions leading to excessive ice build-up and
consequent incomplete deglaciation.
Conclusions
In this study we used an Earth system model of intermediate complexity to
show that a proper parameterization of snow albedo over ice sheets is a
crucial ingredient for a successful simulation of the last glacial cycle.
Both the snow aging effect and the effect of dust deposition on snow albedo
play a fundamental role in reducing surface albedo, particularly in the
ablation areas. While the snow aging effect on snow albedo is well
constrained by observations and theoretical modelling studies, the effect of
dust strongly depends on the assumptions about the optical properties of
dust. A realistic estimate of the effect of dust on snow albedo does
therefore probably have to account for the origin and composition of the
dust deposited over ice sheets. Additionally, substantial uncertainties in
global and regional dust fluxes during glacial times hinder a quantification
of the role of dust darkening of snow for simulating glacial cycles.
Data availability
The CLIMBER-2 model output that was employed for this study
is available on request from the authors.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
The contribution of Eva Bauer to the offline model set-up and her help with
the implementation of the fully coupled dust cycle is acknowledged. Matteo
Willeit acknowledges support by the German Climate Modeling Initiative grant
PalMod and by the German Science Foundation DFG grant GA
1202/2-1. The article processing charges for
this open-access publication were covered by the Potsdam
Institute for Climate Impact Research (PIK). Edited by: David Thornalley
Reviewed by: Jorge Alvarez-Solas and one anonymous referee
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