Simulating the Sierra Nevada snowpack: The impact of snow albedo and
multi-layer snow physics

D. Waliser^1^, J. Kim^2^, Y. Xue^2^, R. Fovell^2^, A. Hall^2^, K. N.
Liou^2^, J. McWilliams^2^, Y. Chao^1^, A. Eldering^1^, Q. Li^2^, S.
Kapnick^2^, R. Vasic^2,3^, F. De Sale^2^, and Y. Yu^2^

^1^Jet Propulsion Laboratory/California Institute of Technology

^2^University of California, Los Angeles

^3^National Centers for Environmental Prediction

Abstract

Two important concerns in simulating the cold season snowpack, snow
related with anthropogenic aerosol deposition and the treatment of
detailed show physics within the framework of multi-layer snow model,
have been investigated in numerical experiments. The experiments with
snow albedo consider future changes in anthropogenic emissions and
consequently the amount of black carbon (BC) deposition on the Sierra
Nevada snowpack. It has been found that the decrease in snow albedo due
to the increase in local emissions will enhance snowmelt and runoff in
the early part of a cold season, resulting in reduced runoff in the
later part of the cold season. The increase in snow albedo associated
with reduced anthropogenic emissions, results in the opposite effects;
the decrease (increase) in snowmelt and runoff during the early cold
season and increase (decrease) in the late cold season. The timing and
relative magnitude of the sensitivity of snow water equivalence (SWE),
snowmelt, and runoff vary systematically according to terrain elevation;
as terrain elevation increases the peak response of these fields occurs
later in the cold season. The response of SWE to the alterations in snow
albedo found in this study also shows that the effects of snow albedo on
the Sierra Nevada snowpack are further enhanced via local snow-albedo
feedback. Results in this experiment suggests that reduction in local
emissions, hence the increase in snow albedo, can alleviate the early
snowmelt and reduced runoff in late winter and early spring caused by
the global climate change induced by the increase in anthropogenic
emissions, at least partially. The most serious uncertainties in this
study are; the lack of quantitative knowledge in the amount of
anthropogenic BC deposits on the Sierra Nevada snowpack and the
relationship between the amount of BC deposition and snow albedo, a
subject of future study. The comparison of the spring snowpack simulated
with a single- and multi-layer snow model shows that a more realistic
treatment of snow physics within the framework of a multi-layer snow
model can improve the simulation of snowpack, especially during spring
when snow ablation is significant.

1\. Introduction

The snowpack in the Sierra Nevada region is important to the water
resources in California. The high elevation snowpack serves as a natural
reservoir which stores fresh water during the wet cold season and
releases it gradually during the dry warm season. About 60% of the water
supply for southern California comes from melting Sierra Nevada
snowpack. Snowmelt also affects hydropower generation in California
(Vicunna et al. 2008). The impact of global warming on the snowpack in
the Sierra Nevada has become one of the leading topics in the regional
climate change studies for the California region (Leung and Ghan 1999;
Kim 2001; Kim et al. 2003). Snow budget in the Sierra Nevada is affected
by a number of factors such as insolation, air temperature, and
orography. Previous studies on the impact of climate change on the
Sierra Nevada snowpack has focused solely on the impact of low
tropospheric warming (e.g., Leung and Ghan 1999; Kim 2001; Kim et al.
2003; Cayan et al. 2008) in addition to precipitation, since low level
temperatures affect two important factors, rainfall-snowfall
partitioning and snow ablation, in determining snow budget. For a more
comprehensive understanding and projection of the Sierra Nevada snowpack
in future climate, it is necessary to investigate the role of other
factors that also affect snow budget.

Snow albedo is among the most important local parameters in shaping the
spatiotemporal evolution of snowpack. Surface insolation that determines
the portion of insolation absorbed by the snowpack is the leading energy
source in the evolution of snowpack, especially during the melting
period. Thus alterations in snow albedo can exert significant impact on
snowpack during the course of accumulation and ablation. Surface albedo
of sufficiently deep snowpack depends largely on the size of ice grain
and impurities within or at the surface ice grains (e.g., Wiscombe and
Warren 1980; Warren and Wiscombe 1980; Yang et al. 1997; Mölders et al.
2008). Previously, the impact of snow grain size has been incorporated
into snow albedo formulation in terms of snow age or surface temperature
or a combination of both, and has been examined in a number of
evaluation studies (e.g., Yang et al. 1997; Molotch and Bales 2006;
Mölders et al. 2008). The impact of snow albedo changes due to dust and
BC particles deposited on snowpack remains largely unexplored. This is
an important concern because the amount of BC deposition is closely
related with anthropogenic emissions. Thus, anthropogenic emissions that
drive the ongoing global climate change can also affect local snowpack
via the alteration in snow albedo in addition to the increase in
low-level temperature.

Importance of aerosol deposition on snow albedo in the Sierra Nevada
region can be inferred from previous studies. In a series of theoretical
studies, Wiscombe and Warren (1980) and Warren and Wiscombe (1980)
showed that impurities in snowpack such as dusts and BC can reduce snow
albedo in the spectral range shorter than 1 μm where most of solar
energy resides. For ice grain radius of 100μm, for example, their
calculations show that the average snow albedo for the wavelengths
between 0.4 and 1 μm varies from near unity, i.e., almost total
reflection of insolation, for pure snow to below 0.4 with a presence of
a small amount of soot within the snow layer. These studies also show
that the presence of dust particles also influences snow albedo in a
similar way. Recently, observational studies (Husar et al. 2001;
VanCuren et al. 2005; Painter et al. 2007) reveal that depositions of
dusts and BC of local and Asian origins can alter snow albedo and snow
ablation in the western US region. Similar impacts of dust and BC on
snow have also been observed in the polar region as well (McConnell et
al. 2007). Significant anthropogenic emissions in California, in
conjunction with prevailing westerly winds that transport fine
particulates into the Sierra Nevada region, can alter the snow albedo in
the Sierra Nevada region. Thus, the sensitivity of the Sierra snowpack
to the deposition of particulates needs investigation.

Another challenge in simulating snowpack is the complexity in the
physical processes interior of the snowpack. Snow models that have been
used in climate simulation ranges from a relatively simple single snow
layer model that considers only a limited physical processes within
snowpack to state-of-the-art multi-layer models that can resolve a
number of important physical processes within snowpack over extended
periods (e.g., Yang et al. 1997; Slater et al. 2001; Ek et al. 2003; Xue
et al. 2003). Most regional climate models use single layer
representations of snow cover. The problem with single layer
representations is that for the melting process to occur, the
temperature of the entire snow layer must rise above the freezing point
before the layer starts to melt. In reality, the near surface layer can
readily warm up relative to deeper levels and begin the melting process.
Incorporating this realism into a model would alter snowpack loss rate
significantly, not only for the spring snow ablation period but also for
the winter snow accumulation period. Xue et al. (2003) have recently
constructed a multi-layer snow model to improve the snow ablation
process on the basis of considerably complex snow schemes (Anderson
1976; Jordan 1991) with substantial simplification and improvements in
physics. The snow model has been subsequently incorporated into the
recent SSiB-3. Tests of the new snow model against in-situ data (Xue et
al. 2003) and in the Project for Intercomparison of Land Surface
Parameterization Schemes (Bowling et al. 2002; Nijssen et al. 2002;
Rutter et al. 2008) showed that the new model performs better than more
traditional simplified schemes. These tests showed that the multi-layer
treatment of snowpack results in faster snowmelt in high elevation
regions. Considering the importance of long-term snow budget in water
resources for California, the difference in snowpack simulation due to
more physically-based snow model needs close examination in order to
improve the projection of the impact of anthropogenic global climate
change on the Sierra Nevada snowpack and in turn on the water resources
in California.

This study examines the impact of snow albedo and the multi-layer
treatment of snow physics on simulating the snowpack in the Sierra
Nevada region. Experimental designs for examining the impact of snow
albedo and the multi-layer snow physics are presented in Section 2.
Sections 3 and 4 present the results obtained in the snow albedo and the
multi-layer snow physics experiments, respectively. Conclusions and
discussions based on these experiments are presented in Section 5.

2\. Experimental design

The Weather Research and Forecast (WRF) model version 2.2.1 (Skamarock
et al. 2005) has been used in these experiments. Details of the WRF
model can be found on the WRF model website http://wrf-model.org, and
will not be included here. In the snow albedo sensitivity study, the
model domain covers California with a 12km horizontal resolution and 28
atmospheric and four soil layers in the vertical (Figure 1a). The
physics options selected for this study includes the NOAH land-surface
scheme (Chang et al. 1999), the RRTM longwave radiation scheme (Mlawer
et al. 1997), Dudhia (1989) shortwave radiation, and the WSM 3-class
with simple ice cloud microphysics scheme. Note that cumulus convection
is inactive in this fine resolution simulation. The simulation period
covers the seven month period October 2050-April 2051 using the initial
and lateral boundary data obtained from the results in a 36km resolution
WRF model simulation that in turn is driven by the large-scale data from
an NCAR-CCSM3 climate scenario corresponding to the SRES-A1B emission
scenario (Kim et al. 2008). This allows us to zoom in the fields
simulated in a coarse resolution simulation over a region of special
interests without incurring excessive computational resources. This also
allows us to avoid excessive spectral gap between large-scale forcing
data and the regional simulation (Kim et al. 2008).

For the multi-layer snow model study, the model domain covers the entire
conterminous US region at an 80km resolution in the horizontal (Figure
1b). The WRF model physics selected for the experiment are the same as
those in the snow albedo sensitivity study above except that the
Kain-Fritsch scheme is activated to compute convection. In addition, the
SSiB/Monin-Obukhov scheme is used for computing surface turbulent fluxes
in conjunction with the SSiB scheme. Snow simulations in single and
multi-layer snow model are examined within the context of the SSiB
model.

![](media/image1.png){width="4.35in" height="1.8in"}

Figure 1. The model domains and the corresponding terrain
representations in (a) the snow albedo sensitivity study (12km) and (b)
multi-layer snow model sensitivity study (80km).

3\. Sensitivity of the Sierra Nevada SWE to snow albedo

The impact of aerosol deposition on snow albedo and subsequently on the
cold season Sierra Nevada snowpack is investigated in a sensitivity
study in which five cold season simulations are performed with 5
different snow albedo fields. In the control simulation, the default
snow albedo field provided in the Noah land-surface model (LSM) is used.
In the remaining four sensitivity runs, the default snow albedo values
are modified to be 75, 90, 110, and 125% of the control run. The two
smaller snow albedo values represent the cases in which BC emissions in
California will continue to increase in the future. The two simulations
with larger snow albedo values assume the cases in which anthropogenic
emissions will be reduced by successful implementation of recent
mandates by California\'s governor (Steiner et al. 2006). The
sensitivity study based on the snow albedo values prescribed in this way
can be used for preliminary qualitative examinations; this is inevitable
because the amount of aerosol deposits on the Sierra Nevada snowpack and
the quantitative relationship between aerosol deposition and snow albedo
remain poorly understood. The model data for elevations above the 1750m
level within the Sierra Nevada region (marked by the white box in Figure
1a) are analyzed according to elevation ranges defined at 250m intervals
above the 1750m level.

![](media/image2.png){width="4.6194444444444445in"
height="1.5597222222222222in"}

Figure 2. The monthly-mean snowfall and SWE in the Sierra Nevada region
above the 2000m level simulated in the 12km resolution control run.

The intraseasonal variations in SWE simulated in the control run differ
significantly according to the elevation range (Figure 2). The timing of
snowfall is similar in all elevations, but the amount of snowfall
increases with increasing elevation (Figure 2a). This is because higher
elevation regions remain above the freezing level for longer periods
during the cold season (Kim 2001). The variation of the snowfall amount
according to elevation range is significantly amplified in the SWE field
(Figure 2b). In December, the SWE in the highest elevation range (above
3000m) is about 5 times the value in the lowest elevation range
(2000-2250m) despite the fact that the accumulated snowfall in the
highest elevation range is only twice as large as in the lowest
elevation range. The large differences in SWE between elevation ranges
are due to larger snowmelt in the lower elevation ranges. Below 2500m,
snowpack is almost completely depleted in February; above the level,
snow depletion occurs one month later.

![](media/image3.png){width="3.2694444444444444in"
height="3.0069444444444446in"}

Figure 3. The ratio of the SWE in the 4 sensitivity runs to that in the
control run within 6 elevation ranges: (a) 1750-2000m, (b) 2000-2250m,
(c) 2250-2500m, (d) 2500-2750m, (e) 2750-3000m, and (f) above 3000m.
Note that the ordinate in (c) - (f) is in a logarithmic scale.

The most notable features in the sensitivity of the Sierra Nevada
snowpack to snow albedo are that the magnitude of the sensitivity is
larger in higher elevation regions than in lower elevation regions
(Figure 3). In the lowest two elevation ranges, 1750-2000m and
2000-2250m, reduction of snow albedo by 25% resulted in the reduction in
SWE by as much as 20% of the values in the control run (Figures 3a,b).
Increases in snow albedo result in similar sensitivity in SWE but with
an opposite sign and slightly larger magnitudes. Another notable result
in the experiment is that the timing of peak sensitivity varies
according to the sign of the snow albedo changes as well as terrain
elevations. In all elevation ranges, the peak percentage reduction of
SWE due to the decrease in snow albedo appears about one month earlier
than the peak percentage increase of SWE in response to increased snow
albedo. In the lowest two elevation ranges, the largest reduction in the
simulated SWE corresponding to 10 and 25% reductions in snow albedo
(blue and black lines, respectively) occurs in December; the largest
impact of the increased snow albedo by 10 and 25% (red and green lines,
respectively) in the same elevation range occurs in January. Similar
differences in the timing of the occurrence of maximum sensitivity
according to the decrease and increase in snow albedo occurs in all
elevation ranges. In the highest elevation range (Figure 3f) the
difference may become as large as two months. The timing of the peak SWE
sensitivity to the snow albedo changes also vary according to terrain
elevation. In the lowest two elevation ranges, the peak reduction in SWE
due to decreased snow albedo occurs in December (Figure 3a.b); it
appears in February in the two highest elevation ranges (Figure 3e,f).
The peak response timing of SWE to the increased snow albedo also show
similar elevation dependences; January in the lowest two ranges and
March in the highest two elevation regions. The discrepancy between the
timing of the peak response to the increase and decrease of snow albedo
reveals that the alterations in snow albedo due to BC deposits are
further amplified through local snow-albedo feedback.

![](media/image4.png){width="3.2694444444444444in"
height="3.0597222222222222in"}

Figure 4. Same as Figure 3, but snowmelt. Note the differences in the
ordinate scale.

With the decrease in snow albedo, snowmelt increases in earlier months
of the cold season. The timing of the increased snowmelt appears in
later months as terrain elevation increases as well (Figure 4). In
response to the alterations in snow albedo, the simulated snowmelt
varies most noticeably in high elevation regions; the snowmelt changes
in the lowest two elevation ranges are small because snowfall in these
low elevation regions melts quickly regardless of the snow albedo
values. In the two mid-elevation regions (Figures 4b,c), the snowmelt
increases by 10% in November, followed by a 25% decrease in January in
response to the decrease in snow albedo by 25% of the control run (black
lines). A lesser decrease in snow albedo (blue line) resulted in similar
peak snowmelt increase timing, however, the peak snowmelt reduction
occurs one month later than in the lager snow albedo decrease run. The
timing of peak snowmelt changes (early-season increase and late-season
decrease) due to smaller snow albedo values in the two high elevation
regions (Figures 4e,f) occurs later than in the mid-elevation regions
and with somewhat larger magnitudes. The increases in snow albedo (green
and red lines in Figure 4) result in the reduction in snowmelt during
the earlier part of the cold season and snowmelt increase in the later
part of the season. The timing of the early season snowmelt reduction
due to the increased snow albedo is similar to that in the early season
snowmelt increase in smaller snow albedo runs; however, the timing of
the late season snowmelt increase due to larger snow albedo values
occurs later than the snowmelt decrease in response to decreased snow
albedo. Thus, the most notable impact of the decrease in snow albedo is
enhanced (reduced) snowmelt in earlier (later) part of the cold season,
resulting in adverse impacts on warm season water resources in
California. The two experiments with larger snow albedo values (lines
red and green) shows that increase in snow albedo will suppress snowmelt
in the early part of the cold season and will enhance in the later part
of the season. This can partially alleviate the adverse impact of global
warming on California water resources which will promote earlier snow
depletion. The timing of peak impact of altered snow albedo on the
simulated snowmelt also varies with elevation in a similar way as for
SWE, i.e., the peak response appears later in higher elevation ranges
than in lower regions, especially in the cases of increased snow albedo.

The changes in snowmelt due to the alterations in snow albedo result in
notable changes in runoff during the early and late part of the cold
season. Decreases in snow albedo result in runoff increases in the early
part of the cold season and decreased runoff in the late part of the
cold season (Figure 5, black and blue lines). The response of runoff to
the decreased snow albedo is directly related with the corresponding
responses in SWE and snowmelt examined above. Increases in snow albedo
result in opposite effects; decrease (increase) in runoff during early
(late) cold season. This runoff response to increased snow albedo is
also consistent with the corresponding responses of SWE and snowmelt
discussed above. Similar to SWE and snowmelt, the timing of peak
response of the simulated runoff to snow albedo occurs later in the cold
season as terrain elevation increases.

![](media/image5.png){width="3.209722222222222in" height="3.0in"}

Figure 5. Same as Figure 4, but runoff. Note the differences in the
ordinate scale.

4\. Snowpack differences between a single- and multi-layer snow model
simulation

An additional uncertainty in snowpack and snowmelt simulation derives
from the model physical formulation, including many important snow
processes that occur in the snow pack such as snow compaction, heat
conduction, snow grain growth, and snow melting. It includes an
efficient snow cover layering system for realistically simulating the
snow process. All these important snow processes discussed above are
included in SSiB-3 (Xue et al., 2003) but are not included in SSiB-1.
There are three prognostic variables in SSiB-3: specific enthalpy, SWE,
and snow depth. This model was developed on the basis of up-to-date
comprehensive and complex snow schemes but with substantial
simplifications and improvements. Specific enthalpy is used in the
energy balance equation. Using enthalpy rather than temperature greatly
simplifies the computational procedure for calculating phase change
within the snow layer. The single layer treatment used in SSiB-1 require
consideration of solar energy distributing into the energy and water
balance for an entire snow layer during the melting process, while a
multi-layer treatment can simulate more realistic snowmelt processes;
i.e., snowmelt starts from the top of a snow layer which received most
of the insolation on the snowpack. The lack of this process can lead to
slower snow melting in a single-layer snow model used in SSiB-1. The
impact of more realistic snow physics on simulating cold season snowpack
is examined by comparing the snowpack in two simulations; one with a
single-layer snow model and another with a multi-layer snow model in the
SSiB model. The simulation starts from April 1, 1998 for the following
three month period. The NCEP reanalysis data is used for the lateral
boundary and initial conditions. The simulated SWE data are compared
with the observed data of Mote et al. (2005).

A single layer snow model used in SSiB-1 (Figure 6b) overestimates both
observed snowcover and SWE (Figure 6a). The 3-layer snow model used in
SSiB-3 produces more reasonable snow extent and SWE (Figure 6c) compared
to the results from a single-layer snow model. Both snow models do not
simulate the snowpack in the Sierra Nevada region due to a coarse
spatial resolution used in these simulations. Figure 7 shows the
difference between the simulated and observed SWE. To clearly show the
improvement in simulating SWE by the use of the multi-layer snow model,
we divide snow areas into three regions: western U.S. (W), northern
Canada (N), and northeastern Canada (NE). In W, the use of a three-layer
snow model reduces the root-mean-square error (RMSE) in the single-layer
snow model simulation by 50%. For the western part of W that includes
the Sierra Nevada region, the RMSE is reduced by as much as 80% due to
the use of the multi-layer snow model. In N and NE, the improvement in
SWE simulation by the use of the multi-layer snow model is more
substantial. The absolute bias in the single-layer snow model simulation
is reduced by almost 90%, and the spatial correlation between the
simulated and observed SWE is increased by 50% and 25% for N and NE,
respectively, by the use of a multi-layer snow model.

![](media/image6.png){width="2.25in" height="3.5097222222222224in"}

Figure 6. The monthly mean SWE (mm): (a) Observation, and simulated with
(b) a single layer snow model (SSiB-1) and (c) 3-layer snow model
(SSiB-3).

![](media/image7.png){width="2.25in" height="2.4895833333333335in"}

Figure 7. The monthly mean SWE (mm) simulation errors against
observation: (a) a single layer snow model (SSiB-1) and (b) 3-layer snow
model (SSiB-3).

5\. Conclusions and discussions

The effects the deposition of anthropogenic BC on snow albedo and the
subsequent evolution of the Sierra Nevada snowpack have been
investigated using the WRF model by varying the default snow albedo
field provided with the Noah LSM. To represent the impact of aerosols on
snow albedo for increased or decreased local emissions in a qualitative
way, the snow albedo is decreased or increased by 10 and 25% of the
default value in the Noah LSM in four sensitivity runs. The decrease and
increase of snow albedo qualitatively represent the increase and
decrease of local BC emissions, respectively. The simulations are
performed for the cold season October 2050-April 2051 using the initial
and large-scale forcing data corresponding to a NCAR-CCSM3 climate
scenario generated with the SRES-A1B emission scenario.

The control simulation in which the default snow albedo values provided
with the Noah LSM are used show that the variations in the snowfall
amount and SWE during the course of the cold season vary significantly
according to terrain elevation. The elevation dependence is caused
mainly by the large low-level temperature variations associated with
significant orography and the location of freezing level which appears
between the 2000 and 2250m levels in the region during the period of
November-February. Previous studies over complex terrain (Giorgi et al.
1997; Kim 2001) found significant difference in precipitation and snow
accumulation across the mean freezing level altitude.

Decreased snow albedo, i.e., increased anthropogenic aerosol deposition,
promotes early season snowmelt and runoff resulting in smaller SWE in
the early part of the cold season. The reduced early season SWE in turn
results in reduces snowmelt and runoff in the later part of the cold
season. Thus, reduced snow albedo that can occur by the increase in
local emissions and subsequently aerosol deposition on the Sierra Nevada
snowpack tends to enhance earlier depletion of the snowpack. This will
further reduce warm season water resources additional to the effects of
the low-level warming induced by increased greenhouse gases. Increased
snow albedo that can result from the reduction in emissions in
California and aerosol deposition on the Sierra Nevada snowpack, reduces
snowmelt and runoff, thus increasing SWE, in the early part of the cold
season. The resulting increase in early season SWE enhances late season
snowmelt and runoff. This can partially alleviate the effects of the
global warming on the Sierra Nevada snowpack and the warm season water
resources in California.

The assessment of the impact of anthropogenic emissions on the cold
season Sierra snowpack via altering snow albedo obtained in this study
is qualitative. The largest uncertainties in the current study are due
to the lack of quantitative knowledge on two most important factors; the
amount of aerosol deposition on the Sierra Nevada snowpack and the
relationship to link the amount of aerosol deposition and snow albedo.
The results of this study reveal that the modification of snow albedo
due to anthropogenic aerosol deposition can result in significant
alterations in the Sierra snowpack, hence the warm season water supply
in California. Quantifying the amount of anthropogenic aerosol
deposition on the Sierra snowpack and the associated snow albedo changes
is a topic of future research.

A comparison of the SWE fields simulated with the single-layer and
multi-layer snow models in SSiB-1 and SSiB-2, respectively, indicates
comprehensive treatment of snow compaction, heat conduction, snow grain
growth, and snowmelt in conjunction with multiple snow layers can
simulate snow processes more realistically and greatly improve the
simulated snowpack. This result shows that it is important to include a
comprehensive snow model in assessing the climate change impact on water
resources in California.

6\. Acknowledgement

This research has been supported by the JPL contracts 1312546 and
1288653 to the UCLA Joint Institute for Regional Earth System Science
and Engineering (JIFRESSE).

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