Download The Representation of Arctic Soils in the Land Surface Model: The

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JOURNAL OF CLIMATE
VOLUME 14
The Representation of Arctic Soils in the Land Surface Model:
The Importance of Mosses
JASON BERINGER
School of Geography and Environmental Science, Monash University, Clayton, Victoria, Australia
AMANDA H. LYNCH
Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado
F. STUART CHAPIN III
AND
MICHELLE MACK
Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, Alaska
GORDON B. BONAN
National Center for Atmospheric Research, Boulder, Colorado
(Manuscript received 19 July 2000, in final form 30 January 2001)
ABSTRACT
Mosses dominate the surface cover in high northern latitudes and have the potential to play a key role in
modifying the thermal and hydrologic regime of Arctic soils. These modifications in turn feed back to influence
surface energy exchanges and hence may affect regional climate. However, mosses are poorly represented in
models of the land surface. In this study the NCAR Land Surface Model (LSM) was modified in two ways.
First, additional soil texture types including mosses and lichens were added to more realistically represent northern
soils. Second, the LSM was also modified so that a different soil texture type could be specified for each layer.
Several experiments were performed using climate data from an Arctic tundra site in 1995. The model was run
for a homogeneous loam soil column and then also for columns that included moss, lichen, peat, and sand. The
addition of a surface layer of moss underlain by peat and loam had a substantial impact on modeled surface
processes. First, moss acted as an insulative layer producing cooler summer temperatures (6.98C lower at 0.5
m) and warmer winter temperatures (2.38C higher at 0.5 m) when compared with a homogenous loam soil
column. Second, a soil column with a moss surface had a greater surface infiltration, leading to greater storage
of soil moisture in lower layers when compared with a homogeneous loam column. Last, moss modulated the
surface energy exchanges by decreasing soil heat flux (57% in July) and increasing turbulent fluxes of heat
(67% in July) and moisture (15% in July). Mosses were also more effective contributors to total latent heating
than was a bare loam surface. These results suggest that the addition of moss and the ability to prescribe different
soil textures for different soil layers result in a more plausible distribution of heat and water within the column
and that these modifications should be incorporated into regional and global climate models.
1. Introduction
Climate is predicted to warm most dramatically at
high latitudes in response to global atmospheric change
(Houghton et al. 1996), a prediction that appears to be
consistent with observed changes over the past three
decades (Serreze et al. 2001, Chapman and Walsh 1993).
In order to assess this consistency, and ultimately to
make credible predictions of future change in northern
latitudes it is vital to realistically simulate the governing
Corresponding author address: Dr. Jason Beringer, School of Geography and Environmental Science, Monash University, P.O. Box
11A, Clayton, VIC 3800, Australia.
E-mail: [email protected]
q 2001 American Meteorological Society
processes and controls on the exchange of moisture and
energy with the surface. Processes in the surface layer,
which form the interface between the ground and the
atmosphere, play a key role in controlling surface exchanges. In northern latitudes, mosses and lichens often
dominate this surface layer where, we suggest, they are
a significant factor in modulating the surface energy
exchanges and the thermal, hydrologic, nutrient, and
carbon regimes of soils (van Cleve et al. 1986).
Mosses (Bryophytes) are plants that consist of small,
slender stalks and leaves with no vascular tissue or true
roots. Mosses all reproduce by spores and can also form
new plants from small fragments of stems and leaves
that are broken off. Mosses have structures that resemble
roots, stems, and leaves, but they lack true water- and
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BERINGER ET AL.
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FIG. 1. Moss and lichen growing in (a) a boreal white spruce forest and (b) a tussock tundra environment at Council, Alaska,
(64854.59N, 163840.59W).
food-conducting tissues. Lichens consist of fungal
threads and microscopic green alga living together and
functioning as a single organism. Lichens do not have
roots, stems, and leaves, so they must receive their nutrients from rainfall. They are considered pioneer species in some habitats. Lichens are a group of composite
organisms made up of a fungus and an alga living in
symbiotic association. The fungus provides a structure
that may protect the alga from drying under harsh conditions; the alga synthesizes and excretes a specific carbohydrate that is taken up and utilized as food by the
fungus.
Beneath the moss–lichen layer is typically a layer of
peat, whose properties also differ from the underlying
mineral soil. Currently there is no explicit incorporation
of moss, lichen, or peat layers in the National Center
for Atmospheric Research Land Surface Model (NCAR
LSM; Bonan 1996) that has been used to investigate
land–atmosphere interactions in the Arctic (Lynch et al.
1999a,b; Eugster et al. 1997).
Mosses are ubiquitous in boreal forest and tundra
ecosystems, which occupy 14% of the total global land
area. In boreal forests, feather mosses (Hylocomium and
Pleurozium spp.) dominate the groundcover and can occupy 50%–100% of forest floor area (Fig. 1a). In these
stands mosses compose only a small fraction of the total
ecosystem biomass but can contribute up to 50% of total
annual net primary production (Viereck et al. 1986;
Oechel and van Cleve 1986). In contrast, mosses in
tundra ecosystems, especially peat mosses (Sphagnum
spp.), may become the dominant vegetation in terms of
total biomass and form a continuous cover over large
areas of the landscape (Fig. 1b).
Mosses form a thick, insulating layer that alters the
partitioning of incoming radiation between turbulent
fluxes (sensible and latent heat), and ground heat flux,
a determinant of soil and permafrost temperature regimes (Bonan et al. 1990; Dyrness 1982). Mosses are
particularly important in the discontinuous permafrost
zone, where the mean annual temperature is near 08C
(Nicholas and Hinkel 1996). If mosses and the underlying peat layer are removed by fire or mechanical disturbance, the active layer depth increases because of the
increased heat conducted to the permafrost (Nicholas
and Hinkel 1996; Mackay 1995; Dyrness et al. 1986).
Ultimately, thawing of permafrost may lead to thermokarst (ground surface collapse) and inundation of
lower-lying areas with water, with potentially large ecological and economic consequences (Nisbit 1989).
It has been estimated that these moss-dominated ecosystems (boreal forest and tundra) account for approximately 35% of the world’s reactive soil carbon pool
(McGuire et al. 1995). This soil carbon has accumulated
because of low soil temperatures and/or poor drainage
or is locked up in permafrost (Hobbie et al. 2000). Higher soil temperatures resulting from the disturbance of
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the moss layer or high-latitude warming could increase
decomposition rates, potentially changing these systems
from being a sink to a source of CO 2 to the atmosphere
(Ciais et al. 1995; Grulke et al. 1990) and creating a
positive feedback to warming (Oechel et al. 1993; Tans
et al. 1990; Billings et al. 1982; Post et al. 1982). Furthermore, an increased active layer depth may lower the
water table, causing greater aeration and even warmer
temperatures, in turn increasing decomposition of soil
carbon. This could represent a significant additional
source of CO 2 to the atmosphere (Oechel and Vourlitis
1994). We do not address the direct effect of mosses
and lichens on nutrient and carbon cycling; however,
changes in the thermal and hydrological regime will
have a major influence on these cycles.
Despite the potentially strong modulating effects of
moss and lichen, they are not well represented in models
of soil thermal dynamics or in models of land surface
processes dealing with the regional effects of climate
and vegetation change. At present the physical properties are generally poorly represented in land surface
models utilized in climate models, with the exception
of the Canadian Land Surface Scheme (Tilley and Lynch
1998; Verseghy 1991). This is surprising given that
moss and lichen compose such a large proportion of the
surface area in tundra and boreal systems. Current regional climate simulations for Alaska using the Arctic
Regional Climate System Model (ARCSyM; Lynch et
al. 1995) incorporate the NCAR LSM but the soil
scheme is parameterized for bare mineral soil only. The
surface properties of moss, lichen, and peat are sufficiently different from a mineral soil surface to warrant
their inclusion in land surface models, particularly for
northern ecosystems. In addition, the NCAR LSM does
not currently allow for soil layers of differing texture
types but specifies instead only one texture type for the
whole column. In this study we investigate the effect
of incorporating moss, lichen, and peat into the NCAR
LSM and investigate the consequences for land–atmosphere exchanges. Both thermal and hydraulic properties are examined as well as the effect of mosses on the
hydrology and exchanges of heat and moisture from the
surface. Our research will provide information on how
the thermal and hydrological regime may be altered
through disturbance of the moss layer and the subsequent effect on the thermal characteristics of the surface
layer.
a. Model description
The NCAR LSM is a one-dimensional model of energy, momentum, water, and CO 2 exchange between the
land and atmosphere. The model accounts for ecological
differences among surface types within a grid cell (including tundra, lakes, and wetlands) and thermal and
hydrological differences among soil types. We modified
the soil thermal and hydrological modules of LSM in
VOLUME 14
this study, as described below. A complete detailed technical description of the model is given by Bonan (1996).
1) SOIL
THERMAL AND HYDROLOGY PROCESSES
The default soil column in LSM contains six layers
with thicknesses of 0.10, 0.20, 0.40, 0.80, 1.60, and 3.20
m. In the standard configuration all six soil layers in
the original model have identical properties that are
specified with one set of parameters indicating the percentage of sand, silt, and clay for the entire soil column.
The soil thermal and hydraulic properties are derived
based on these percentages of sand, silt, and clay. Hence,
soil textures do not vary with depth.
In the present study, the paradigm of specifying percent sand, silt, and clay values has been replaced by the
specification of a single soil texture type for each one
of the six layers in the soil column. Hence a more plausible soil profile can be defined. We added moss, lichen,
and organic peat to the 12 U.S. Department of Agriculture soil texture types that were originally specified
in the NCAR LSM (Wilson and Henderson-Sellers
1985). We specify the physical properties of each of
these different texture types through a lookup table that
provides all the thermal and hydraulic parameters for
each texture type (Table 1). This is essential for the
moss, lichen, and peat types, whose properties cannot
be expressed as a function of the fraction of sand, silt,
and clay.
2) SOIL
THERMAL REGIME
The effects of adding moss, lichen, and peat texture
types were examined with regard to the soil thermal and
hydrological properties and hence it is pertinent to briefly describe the NCAR LSM thermal and hydrological
parameterization. The soil thermal regime describes the
distribution of temperature with depth and is determined
in the NCAR LSM using the following equation that
calculates the heat flux F z (W m 22 ) at depth z:
FZ 5 2k
]T
,
]z
(1)
where T is the soil temperature (K) and k is the thermal
conductivity (W m 21 K 21 ). The equation is solved for
each layer to calculate soil temperatures for the six layers with boundary conditions defined by the heat flux
into the soil column at the surface [G (W m 22 )] and
zero heat flux at the bottom of the soil column. In calculating the soil temperatures, an apparent heat capacity
[C y (J m 23 K 21 )] is used that accounts for phase change
(Lunardini 1981) by constructing a blend of frozen and
unfrozen volumetric heat capacities. When snow is on
the ground its thermal properties are blended with the
first soil layer to create a snow–soil layer. The heat
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BERINGER ET AL.
FIG. 2. The relationships between (a) matric potential (MPa) and the fraction saturated water
content and (b) hydraulic conductivity (mm s 21 ) and fraction saturated water content, for each of
the soil texture types used in the experiments.
capacity and thermal conductivity of the soil vary with
the texture type and in our parameterization are calculated using the porosity and thermal properties of the
soil solids (Table 1).
3) HYDROLOGIC
REGIME
The hydrologic regime prescribed in the model describes the interception, throughfall, snow accumulation
and melt, infiltration, surface runoff, subsurface drainage, and distribution of soil moisture within the soil
column. Soil moisture (water content) in a given layer
is predicted from the conservation of water using the
following equation:
DuDz
5 2q i 1 q o 2 e,
Dt
(2)
where D u is the change in volumetric soil water content
over time (mm 3 mm 23 ), Dz is the soil thickness (mm),
Dt is the time step (s), e is the loss by evapotranspiration
(mm s 21 ), and q i and q o are the fluxes of water (mm
s 21 ) into and out of the soil layer. Vertical water flow
in an unsaturated porous media is described using Darcy’s law and is ultimately calculated using the Richards
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equation to derive the flow between layers (Bonan
1996):
[1
]
2
]u
]
]u ]c
5
K
11 ,
]t
]z
]z ]u
(3)
where K is the hydraulic conductivity (mm s 21 ), ]c is
the difference in soil matrix potential between layers
(mm), ]u is the difference in water content between
layers (mm 3 mm 3 ), and ]z is the depth of the layer (mm).
Hydraulic properties such as hydraulic conductivity (K)
and the soil matric potential (c) vary with water content
(u) and the soil texture type (Cosby et al. 1984; Clapp
and Hornberger 1978). The relationship for the ith layer
is given as
K i 5 Ksat s i2b13
(4)
c i 5 csat s2b
i ,
(5)
where Ksat is the saturated hydraulic conductivity (mm
s 21 ), csat is the saturated matric potential (mm), u i is
the volumetric water content in the ith layer, usat is the
saturated volumetric water content, and b is the Clapp
and Hornberger parameter (Clapp and Hornberger 1978)
with s i 5 u i /usat . Previously the saturated hydraulic conductivity was determined using the percentage of sand
(Cosby et al. 1984) but now this parameter is specified
for each specific texture type (Table 1). In addition, Ksat ,
usat , csat , and b are specified for each soil texture type
in the new treatment (Table 1). Parameterization of these
properties for moss, lichen, and peat were defined following (Morrill et al. 1999) and field observations from
the Alaskan tundra and a white spruce forest (J. Beringer, unpublished data). Small differences exist between mosses and lichens in their parameterization in
this study and these are shown in Table 1.
b. Hydraulic characteristics
The hydraulic characteristics of the additional moss,
lichen, and peat texture types is examined using the
algorithms of (Clapp and Hornberger 1978) [Eqs. (3)
and (4)] that are used in the NCAR LSM (Figs. 2a and
2b). These algorithms simulate the relationships between water content, soil water suction, and hydraulic
conductivity and are subsequently used to model the
movement and distribution of soil water through each
layer of the soil column.
The relationship between soil water content and soil
water suction, known as the water characteristic curve,
determines how much moisture a soil will hold (u)
against a force such as gravity or extraction by plants
(c) (Fig. 2a; Hinzman et al. 1991). This curve, as derived from Eq. (4) shows that, as soil water content
decreases the matric potential decreases because the
small amounts of water remain in the soil become more
difficult to extract. In organic and other porous soil layers, water within the large soil pores drains easily, giving
mosses and lichens a flat water characteristic curve with
VOLUME 14
a high matric potential (less negative) at a wide range
of water contents (Hinzman et al. 1991). In this study,
for a given water content, the matric potential was smallest in loam followed by peat, sand, moss, and then lichen, suggesting that for a given soil water content that
water is less tightly held in the moss and lichen (Fig.
2a).
For any given water content, lichen is parameterized
with the highest hydraulic conductivity followed by
moss, sand, peat, and loam. Consequently, the rate of
moisture transfer in coarse texture types, for example
mosses, for a given water content will always be greater
than the loam column (Fig. 2b). This occurs because
the soil has a larger saturated hydraulic conductivity
and the hydraulic conductivity decreases more slowly
with decreasing saturation (Fig. 2b), as also found by
Wilson et al. (1987). Although these hydraulic relationships follow expected trends, actual field or laboratory
measurements of the water characteristic curves for
mosses are still needed for a more accurate parameterization of the model.
2. Model experiments
Simulations using our modified version of the NCAR
LSM were performed for an arctic tundra site at the
Imnavait Creek watershed of northern Alaska (698259N,
1488459W). The watershed is underlain by continuous
permafrost with a summer active layer depth of 50 cm
(Hinzman et al. 1991). This site has been the focus of
numerous research studies examining the role of permafrost-dominated tundra in the regional climate system
(Lynch et al. 1999b; Eugster et al. 1997; Lynch et al.
1995; Hinzman and Kane 1992).
The land surface model was configured for a tundra
ecosystem consisting of 70% ‘‘arctic grass,’’ 25% ‘‘deciduous shrub,’’ and 5% exposed ‘‘bare ground.’’ The
ground albedo was held constant across the experiments
as previous land surface studies have shown little sensitivity to soil albedo (Wilson et al. 1987). A color class
of 3 was used following Dickinson et al. (1993), which
is equivalent to a visible albedo of 0.2–0.10 for dry and
saturated conditions, respectively, and a near-infrared
albedo of 0.40–0.20 for dry and saturated conditions,
respectively. This albedo range is similar to albedos
measured over a moss-and lichen-dominated tundra ecosystem (Fig. 1b) of 0.195 (unpublished data). Spectral
reflectances of moss and lichens have not been well
documented, however, in general shortwave and nearinfrared reflectances in mosses are less than other vascular plants (Bubier et al. 1997). Lichens have a greater
shortwave reflectance than mosses and other plants and
a near-infrared reflectance less than other vascular plants
(Bubier et al. 1997). As a result, albedos of mosses and
lichens are likely to be different from current parameterizations in tundra and boreal forest environments and
the impact of these differing albedos on the surface
energy balance needs to be addressed further. It should
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BERINGER ET AL.
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FIG. 3. The vertical profiles of the soil columns and constituent layers used for each of the five
experiments.
be noted that albedo changes will result in changes in
the absolute magnitudes of fluxes but not the partitioning of energy.
A single annual time series of atmospheric forcing
data for 1995 was constructed using a composite of
observed data, proxy data from nearby stations, and
European Centre for Medium-Range Weather Forecasts
operational analysis. Simulations used the 1995 time
series forcing data repeated over three consecutive annual cycles with the first two years allowed for spinup
and the third year for analysis. Each simulation used
identical forcing data, and hence any errors inherent to
the construction of the composite forcing data are not
of concern here.
Five experiments were performed (Fig. 3) using these
forcing data. A ‘‘loam’’ experiment was performed using a homogenous column of loam soil, which is the
standard current implementation of the NCAR LSM as
coupled to the NCAR Climate System Model (Boville
and Gent 1998) and the ARCSyM (Lynch et al. 1995).
A second experiment comprising a homogeneous sand
column (designated ‘‘sand’’) was performed to compare
the effect of varying the soil texture of a whole column
(Fig. 3). A third experiment utilized a homogeneous peat
column (designated ‘‘peat’’). Two further experiments
were performed in order to investigate the sensitivity of
the simulation of Arctic soil profiles to different surface
layer soil types using the same underlying soil profile.
In each case, the profile consisted of two layers of peat
underlying the surface followed by loam soil in the low-
est three layers. The surface layer type was either moss
or lichen, and they were designated as the ‘‘moss’’ or
‘‘lichen’’ experiments, respectively (Fig. 3). These experiments were analogous to those suggested by Morrill
et al. (1999) in preliminary experiments with the Biosphere–Atmosphere Transfer Scheme model (Dickinson
et al. 1993). All experiments were performed ‘‘offline’’
wherein the NCAR LSM responds to atmospheric forcing but does not feed back to influence the forcing. It
should be noted that while the one-dimensional model
has shown a strong impact on surface fluxes, it does not
account for dynamical feedbacks with the atmosphere,
which may have a modulating effect. However, these
effects are unlikely to be eliminated entirely (Lynch et
al. 1999a). The experiments in this study are a necessary
first step in the process of analyzing the sensitivity of
a climate model to a parameterization change, and it
would be useful to assess the impact of mosses using
regional climate simulations in the Arctic.
3. Results and discussion
Model simulations for all experiments showed all layers being frozen in winter with snow persisting until
spring, which realistically follows actual conditions
(Hinzman et al. 1991). As a result the wintertime thermal and hydraulic regimes were dominated by the properties of frozen water and snow and are not presented
here. Simulations for all experiments showed that the
surface layer was unfrozen only for the months of June,
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VOLUME 14
FIG. 4. The simulated vertical profiles of temperature through the six soil layers during Jan and Jul for the moss and loam experiments.
The dotted vertical line represents the freezing point of water (273.15 K). Error bars illustrate 61 std dev.
July, and August (data not shown). We present the July
means for the third year of the simulations to illustrate
how thermal and hydraulic properties vary with depth
during midsummer.
a. Thermal regime
In LSM, the thermal regime of the model is determined by the specified values for thermal conductivity
(Ksolids ) and heat capacity (Csolids ) of the substrate solids,
the volumetric fraction of solids in the substrate (derived
from the saturated water content, also called porosity),
and the volumetric fraction of water in the substrate
(Table 1). In July, the top two layers of the moss, peat,
and lichen experiments were unfrozen, but the top three
layers were unfrozen in the sand and loam experiments
(Fig. 4). The depth of the active, or unfrozen, layer in
the moss simulations was around 50 cm and is consistent
with field observations (Hinzman et al. 1991) but simulations in the bare loam case were too deep (;1 m).
The thermal conductivity of the top layer in the moss,
lichen, and peat experiments was more than 4 times
lower than for sand and loam experiments (Fig. 5a)
because of the high air volume and lower water content
in the surface layers of moss, lichen, and peat. The
simulated thermal conductivity of the moss surface layer
in July (0.37 W m 21 K 21 ) agrees well with field observations of mosses in arctic Alaska (0.1 W m 21 K 21
when dry to 0.6 W m 21 K 21 at saturation; Hinzman et
al. 1991) and in Sphagnum or peat varied (0.04 to 1.1
W m 21 K 21 from dry to wet respectively; Brown and
Williams 1972) but are higher than values reported by
Sharrat (1997) for feathermoss on the floor of a black
spruce forest near Fairbanks, Alaska (0.030 to 0.088 W
m 21 K 21 ). Below the surface in the lower three layers
of each experiment the thermal conductivity remained
fairly constant because of the homogeneous loam texture in these lower layers. Differences in thermal conductivity between experiments were due to the variation
in water content in the lower layers. The peat experiment
had the highest water content in the lower layers and
as a result had the highest thermal conductivity of all
experiments, followed by moss, loam, lichen, and sand
experiments (Fig. 5a).
The thermal conductivity of moss, lichen, and peat
in the surface layer was low, and they effectively insulated the underlying soil. Along with differences in
apparent heat capacity, they contributed to cooler simulated summer (July) soil temperatures in the lower layers than for a mineral soil surface (i.e., loam and sand
experiments). For example, at 0.5-m depth the temperature was 6.98C lower in the moss experiment than the
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BERINGER ET AL.
TABLE 1. The different soil texture classes and the specified parameters for each texture type including the 11 original soil texture types
in the NCAR LSM model along with their original percentage of sand, silt, and clay. Moss, lichen, and peat are additional soil texture types
used in this study. Here Ksolids is the thermal conductivity of the soil solids, Csolids is the heat capacity of the soil solids, Ksat is the saturated
hydraulic conductivity, csat is the saturated matric potential, usat is the saturated volumetric water content, and b is the Clap and Hornberger
constant.
Type
No.
Description
Sand
%
Silt
%
Clay
%
Ksolids
(W m21 K21)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Sand
Loamy sand
Sandy loam
Loam
Silty loam
Sandy clay loam
Clay loam
Silty clay loam
Sandy clay
Silty clay
Clay
Peat
Moss
Lichen
92
82
58
43
17
58
32
10
52
6
22
—
—
—
5
12
32
39
70
15
34
56
6
47
20
—
—
—
3
6
10
18
13
27
34
34
42
47
58
—
—
—
8.6143
8.3991
7.9353
7.0649
6.2520
6.9323
5.7709
4.2564
6.1728
3.5856
4.5370
0.2500
0.2500
0.2500
loam experiment (Fig. 4). Conversely, this insulative
effect produced warmer wintertime (January) soil temperatures for the moss, peat, and lichen experiments at
depth with the temperature being 2.38C higher in the
moss experiment than the loam experiment at 0.5-m
depth (Fig. 4). Land surface simulations appear to be
sensitive to changes in thermal conductivity as observed
by Bonan (1991) who found that doubling or halving
thermal conductivity of the near-surface layer resulted
in a 0.88C increase or 1.18C decrease in soil temperature,
respectively, during the growing season in Alaska. Our
study showed that the difference in thermal conductivity
between moss (0.37) and loam (1.7) experiments contributed to simulated July surface soil temperatures of
11.858 and 12.258C, respectively, a difference of 0.48C.
The other determinant of the thermal regime is the
apparent heat capacity (C y ). Simulations for July show
the surface layer was relatively dry and the apparent
heat capacity was lowest for the lichen experiment, followed by moss, sand, peat, and loam experiments (Fig.
5b). The lower heat capacity for the surface moss layer
as compared with the loam layer produced a 0.48Chigher surface temperature for an equivalent energy input for the moss experiment in summer as compared
with the loam experiment (Fig. 4). Moss surface temperatures were 0.88C cooler than loam in winter (January), when the net heat flux was from the soil to the
atmosphere. Simulated differences in the apparent surface layer heat capacity were driven by differences in
porosity that were greatest for the lichen surface layer.
In addition, July simulations showed that the third layer
was thawing for the moss, peat, and lichen experiments
and a large amount of energy was dissipated in the phase
change from ice to water in that layer (Fig. 5b). Hence
the apparent heat capacity for the third layer was very
high. Similarly, for loam and sand the fourth layer was
Csolids
(J m23 K21)
2
2
2
2
2
2
2
2
2
2
2
2
2
2
136
145
165
203
239
209
260
326
242
355
314
500
500
500
116
523
794
836
367
635
394
591
830
906
325
000
000
000
Ksat
(mm s21)
0.023
0.016
0.007
0.004
0.001
0.007
0.002
0.001
0.005
0.001
0.002
0.02
0.15
0.2
558
563
111
192
677
111
845
311
756
139
csat
(mm)
usat
porosity
b
247.29
263.94
2131.88
2207.34
2454.25
2131.88
2288.93
2561.04
2158.05
2632.99
2390.66
2120.00
2120.00
285.00
0.3731
0.3857
0.4159
0.4348
0.4676
0.4159
0.4487
0.4764
0.4235
0.4814
0.4613
0.7000
0.9000
0.9500
3.39
3.86
4.50
5.77
4.98
7.20
8.32
8.32
9.59
10.38
12.13
4.00
1.00
0.50
thawing and resulted in that layer having a very high
apparent heat capacity (Fig. 5b).
The thermal and hydrological regimes are intricately
related to each other and to the character and structure
of the active layer and hence influence permafrost dynamics (Hinzman et al. 1991). For example, observations show that wet or frozen peat is highly conductive
and promotes heat loss to the atmosphere when air temperatures are cold (Nicholas and Hinkel 1996; Stoutjesdijk and Barkman 1991). During dry conditions, the
thermal effect reverses: dry peat insulates soil and permafrost from surface heat fluctuations and soils remain
cool (Oechel and van Cleve 1986; van Cleve et al.
1983). This switching of thermal effect results in an
overall cooling of soil and permafrost under moss cover.
Although this pattern has been observed in moss-dominated ecosystems, there are few data on the thermal
and hydraulic properties of mosses needed to accurately
parameterize LSM for application in the Arctic.
b. Hydraulic characteristics
The simulated water content in each layer of the soil
in LSM is a function of the hydrological balance among
precipitation, evaporation, and runoff. The distribution
of water among the layers depends on the hydraulic
conductivity and porosity of the soil. Because of the
high hydraulic conductivity specified for mosses and
lichens, water infiltrated much more quickly into the
lower layers, resulting in higher water contents in these
lower layers, during the summer (July) simulations of
the moss and lichen experiments (Fig. 5c). Greater surface evaporation (due to warmer surface temperatures)
and high infiltration rates in the surface layer in the
lichen and moss experiments resulted in lower water
contents in the surface layer in comparison with the
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VOLUME 14
FIG. 5. The simulated Jul vertical profiles of (a) thermal conductivity, (b) apparent heat capacity, (c) fraction saturated water content, (d)
matric potential, (e) hydraulic conductivity, and (f) temperature, through the soil columns for each of the five experiments. The vertical
depth axis is illustrated using a log10 scale.
1 AUGUST 2001
BERINGER ET AL.
3333
FIG. 6. The simulated mean daily summer (Jul) sensible, latent, and ground heat fluxes for all experiments. The
ground evaporation for each experiment is also shown and is a subset of the total latent heat flux.
other experiments (Fig. 5c). Water contents tended to
be evenly distributed among all soil layers for the sand
and loam experiments because of the homogeneous nature of the texture type in these soil profiles. In summary, the new modifications to specify different properties for each soil layer alters the hydrologic regime of
the entire soil column and provides a more plausible
representation of the hydrology of the column. This can
be used to improve simulations using ARCSyM as discussed later.
The low water content and high porosity of the surface layer in the moss and lichen experiments resulted
in low matric potentials that would offset the tendency
of these layers to drain quickly (Fig. 5d). The peat layers
beneath the surface layer of the moss and lichen experiments were also drier than in other experiments,
resulting in a lower matric potential for those layers
(Fig. 5d). Below the third layer, matric potentials were
similar among all experiments except sand because
these lower layers were loam in all other experiments
(Fig. 5d). Variations in matric potential in these lower
layers arose solely from differences in water content.
The sand experiment had a much higher matric potential
throughout the profile, indicating that water was not held
as tightly.
Hydraulic conductivity of the surface layer varied
greatly among experiments, with the highest conductivities simulated for moss, followed by sand, lichen, peat,
and then loam (Fig. 5e). This generally reflected the
order of saturated hydraulic conductivities specified for
specific texture types (Table 1) but was also controlled
by the water content of the respective surface layers.
The major exceptions were the lichen experiment that
had a moderate simulated conductivity in July (Fig. 5e)
due to its low surface water content and despite its high
saturated conductivity (Table 1). Hydraulic conductivities in the second and sixth layers were lower than the
surface layer because of the lower saturated hydraulic
conductivity of the soil texture types that were specified
for those layers (Fig. 5e). There was a slight increase
in hydraulic conductivity for moss and lichen experiments in the first loam layer (fourth layer) due to an
increased water content in that layer.
c. Surface energy exchanges and hydrology
The addition of moss and lichen not only affects the
thermal and hydrologic regime in the simulations but
also modulated the exchanges of energy to and from the
surface. During winter (January), simulated mean
monthly energy fluxes in all experiments were dominated by a loss of sensible and ground heat from the
surface as it cooled. Mean monthly fluxes reached a
summer maximum during June and July, with latent heat
fluxes being dominant, followed by ground and then
sensible heat fluxes (Fig. 6).
Surface energy exchanges were similar between the
loam, sand, and peat experiments and then also between
moss and lichen experiments. Here we present only the
homogeneous loam column that is currently used in the
ARCSyM model (Lynch et al. 1995) and the moss underlain by peat column, which best approximates the
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JOURNAL OF CLIMATE
true soil profile in most tundra and boreal forest sites.
The moss experiment showed a 57% reduction in ground
heat flux in July in comparison with the loam experiment
(Fig. 6), due to the lower thermal conductivity of the
surface layer in the moss experiment. As a consequence
of the reduced ground heat flux a larger proportion of
incoming solar energy was directed into the turbulent
fluxes of heat and water in the moss in comparison with
the loam profile. For example, the sensible heat flux
during July in the moss experiment was 67% greater
than that in the loam experiment, and the latent heat
flux, which was the largest of the surface fluxes, was
15% greater in the moss than the loam profile. The
ground evaporation made up 44% of the total evapotranspiration in the moss experiment but only 37% in
the loam experiment, suggesting that mosses are more
effective contributors to total latent heat flux than a bare
loam soil surface in these simulations.
Recent simulations using ARCSyM coupled with the
existing NCAR LSM at Sagwon, Alaska, have shown
that the modeled August latent heat fluxes were around
30% lower than observed values and that ground heat
fluxes were around 3 times higher than observed values
(Lynch et al. 1999b). Our results suggest that moss,
lichen, and peat are climatically important and that simulated differences in ARCSyM fluxes could be improved by adding them to the NCAR LSM, which is
coupled to ARCSyM.
Although the spatial extent of tundra and boreal forest
ecosystems affected by moss and lichen is limited in a
global sense and is unlikely to be an area of strong focus
in a global climate model, a regional climate model such
as ACRSyM may have a substantial area of tundra or
boreal forest in the domain. In addition, the grid size
of the regional model is smaller and hence any attenuation of these effects due to the mosaicking of vegetation types is less than in the global climate model.
The spatial extent of the column in these experiments
is the same as that in ARCSyM and hence is directly
applicable. Although the experiments presented here are
for a tundra vegetation, it is recognized that the absolute
magnitude of the effect of mosses in a boreal forest
system may be attenuated through shading by an overhead canopy. However, the relative sensitivities are the
same as in the tundra experiments presented here. In
addition, many boreal forest ecosystems have a sparse
canopy, and hence the effects of mosses may still be
quite important. It is also surface cover representation
that is of increasing importance to realistic simulations
of high-latitude climate.
The addition of mosses also affected the simulated
hydrological balance, with the moss column having a
lower July surface runoff (0.702 mm day 21 ) when compared with the homogeneous loam case (1.193 mm
day 21 ). Wilson et al. (1987) also showed that modeled
runoff decreased with increasing coarseness of the soil
texture. Field measurements suggest that downslope
movement of water during significant precipitation
VOLUME 14
events occurs primarily in the surface organic layer
(Hinzman et al. 1991). The decreased runoff for the
moss column was due to the higher hydraulic conductivity of moss and the effective infiltration of precipitation into the soil profile. Simulated July infiltration
rates were 3.696 (mm day 21 ) for moss and 3.231 (mm
day 21 ) for loam. As a consequence, the simulated drainage through the profile in July was slightly higher in
the moss (0.270 mm day 21 ) than the loam experiment
(0.267 mm day 21 ).
4. Conclusions
The physical properties of mosses and lichens are
sufficiently different from those of a bare loam soil that
they warrant explicit parameterization in land surface
models used in northern latitudes. The addition of variable soil texture types with depth in conjunction with
the addition of moss, lichen, and peat layers resulted in
a more plausible representation of northern soils in the
NCAR LSM. The addition of a surface moss layer resulted in higher simulated winter soil temperatures and
cooler summer temperatures. In addition, the distinct
water characteristics of the moss surface resulted in simulated greater infiltration, lower surface runoff, and lower surface moisture contents. The insulating properties
of mosses reduced simulated soil heat fluxes, resulting
in greater energy available to be partitioned into latent
and sensible heat fluxes. Therefore sensible and latent
heat fluxes were greater in simulations with a surface
moss layer. Last, further empirically based field and
laboratory studies of the thermal and in particular hydraulic properties are still needed, and it will be important to examine the sensitivity of these parameters
in simulated land–atmosphere processes.
Acknowledgments. This research is supported through
the Arctic System Science (ARCSS) program of the
National Science Foundation (OPP-9732126 and OPP9732461). We thank Dr. Larry Hinzman for providing
meteorological observations from Imnavait Creek. The
helpful comments of Dr. Michaela Dommisse, Cath Copass, and Dr. Keith Olsson are appreciated.
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