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GLOBAL BIOGEOCHEMICAL CYCLES, VOL. 21, GB1012, doi:10.1029/2006GB002760, 2007
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Effects of soil freezing and thawing on vegetation
carbon density in Siberia: A modeling analysis with
the Lund-Potsdam-Jena Dynamic Global Vegetation
Model (LPJ-DGVM)
C. Beer,1,2 W. Lucht,3 D. Gerten,3 K. Thonicke,4 and C. Schmullius1
Received 18 May 2006; revised 8 November 2006; accepted 16 November 2006; published 20 February 2007.
[1] The current latitudinal gradient in biomass suggests a climate-driven limitation of
biomass in high latitudes. Understanding of the underlying processes, and quantification
of their relative importance, is required to assess the potential carbon uptake of the
biosphere in response to anticipated warming and related changes in tree growth and
forest extent in these regions. We analyze the hydrological effects of thawing and freezing
of soil on vegetation carbon density (VCD) in permafrost-dominated regions of
Siberia using a process-based biogeochemistry-biogeography model, the LundPotsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM). The analysis is based on
spatially explicit simulations of coupled daily thaw depth, site hydrology, vegetation
distribution, and carbon fluxes influencing VCD subject to climate, soil texture,
and atmospheric CO2 concentration. LPJ represents the observed high spring peak of
runoff of large Arctic rivers, and simulates a realistic fire return interval of 100 to
200 years in Siberia. The simulated VCD changeover from taiga to tundra is comparable
to inventory-based information. Without the consideration of freeze-thaw processes
VCD would be overestimated by a factor of 2 in southern taiga to a factor of 5 in northern
forest tundra, mainly because available soil water would be overestimated with major
effects on fire occurrence and net primary productivity. This suggests that forest growth in
high latitudes is not only limited by temperature, radiation, and nutrient availability
but also by the availability of liquid soil water.
Citation: Beer, C., W. Lucht, D. Gerten, K. Thonicke, and C. Schmullius (2007), Effects of soil freezing and thawing on vegetation
carbon density in Siberia: A modeling analysis with the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM),
Global Biogeochem. Cycles, 21, GB1012, doi:10.1029/2006GB002760.
1. Introduction
[2] High-latitude ecosystems currently experience various
changes due to warming, for example, increasing net
primary production (NPP) [Lucht et al., 2002; Nemani et
al., 2003] that is attended by northward migration of the tree
line [Serreze et al., 2000; Esper and Schweingruber, 2004],
but also increasing heterotrophic soil respiration [Oechel et
al., 1993; Zimov et al., 1993]. Owing to rising temperature,
a large fraction of the current extend of permafrost soils of
about half of the territories of Canada and the former Soviet
Union [Bonan and Shugart, 1989] is assumed to degrade
until 2100 [Anisimov and Nelson, 1997; Stendel and
Christensen, 2002]. This accompanied a risk of an additional radiative forcing of the climate system by CO2
1
Institute of Geography, Friedrich Schiller University, Jena, Germany.
Now at Max Planck Institute for Biogeochemistry, Jena, Germany.
Potsdam Institute for Climate Impact Research, Potsdam, Germany.
4
School of Geographical Sciences, University of Bristol, Bristol, UK.
2
3
Copyright 2007 by the American Geophysical Union.
0886-6236/07/2006GB002760$12.00
respired from carbon that is currently blocked in permanently frozen grounds [Goulden et al., 1998].
[3] The ability of increasing carbon uptake by expanding
forests to partly counteract these carbon emissions is unclear. To assess a potential carbon uptake by vegetation in
the high latitudes, an understanding of the environmental
factors and processes limiting the current amount of biomass as well as their relative importance is required. The
observed gradient in vegetation carbon density (VCD) from
0.6 kg carbon per m2 (kgC/m2) in the Russian tundra to
3.6 kgC/m2 in the southern taiga [Shvidenko et al., 2000]
suggests a climatic driven limitation of biomass in northern
taiga and tundra. The relationship of biomass to the depth of
the active layer [Bonan, 1989], which is the uppermost part
of the soil in permafrost areas that is thawing each summer
and therefore provides plants with liquid water and
nutrients, supports this assumption. Temperature, solar
radiation and supply of nutrient and water are reported to
limit generally the establishment and survive of seedlings,
carbon assimilation and plant growth [e.g., Bonan and
Shugart, 1989; Woodward, 1995; Körner, 2003]. However,
Briffa et al. [1998] found that tree growth was less sensitive
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to increasing temperature during the last decades, and that
this could not be explained readily from soil moisture data.
In addition, disturbances like fire and damage by insects or
wind had the capability to counteract an increasing productivity due to warming. Fire is the major disturbance agent in
the boreal zone, where between 3 and 23 million ha are
burned annually mainly as crown fires in boreal North
America, but apart from extreme fire years as surface fires
in boreal Eurasia [Kasischke et al., 2005], thereby releasing
approximately 106– 209 TgC/a.
[4] The processes controlling biomass take place in parallel or are even coupled, especially in regions with permanently frozen grounds. For instance, low temperatures
during the growing season limit not only photosynthesis
but also active-layer thickness (ALT), which impacts water
availability [Benninghoff, 1952]. Both effects of temperature impact carbon assimilation by the vegetation. Moreover, low temperatures lead to slow litter decomposition,
hence nutrient limitation [Bonan and Shugart, 1989]. The
deep litter layer also isolates the underlying soil leading to a
low thaw depth [Hinzman et al., 1991], thus low water
availability in summer again. In addition, litter moisture
determines the probability of fire. Such linkages make it
impossible to separate the even most important effects of
environmental influences on the amount of biomass by field
studies alone, in particular because biomass is the result of
different processes over decades. Experiments and observations are critical to providing the inputs and functions for
models, but it would probably be impossible to set up an
experiment to answer the questions posed here.
[5] In this paper, we investigate the effects of the alterations in the soil water balance due to freeze-thaw processes
in the soil on the amount of VCD in permafrost-dominated
regions of Siberia. The impact of soil water content which is
controlled by phase change on the productivity and fire
return interval is studied. For this purpose, we perform two
simulation experiments with the LPJ-DGVM, in which
freeze-thaw processes are excluded in the control run. Both
simulations are driven by the same observations of climatic
variables and atmospheric CO2 content. The comparison of
VCD results by these simulation experiments with forest
inventory estimates over a large transect swath in central
Siberia allows for the investigation of the working hypothesis that besides direct influences of temperature, solar
radiation and nutrient availability on the productivity, also
limitations in liquid soil water availability during the year
and the disturbance dynamics substantially contribute to
low VCD values in high-latitude permafrost regions, i.e.,
that soil thermal dynamics are crucial for the changeover of
VCD from taiga to tundra. In order to isolate better the
influence of fire disturbance and water stress, the results of
these experiments are further compared to results of two
LPJ-P model runs excluding the simulation of fire and water
stress.
2. Methods
2.1. LPJ-DGVM Overview
[6] The LPJ-DGVM is a coupled dynamic biogeographybiogeochemistry model which combines process-based rep-
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resentations of terrestrial vegetation dynamics and landatmosphere carbon and water exchanges in a modular
framework. For a detailed description and evaluation of
the model see Sitch et al. [2003]. LPJ explicitly considers
key ecosystem processes such as photosynthesis, carbon
allocation, mortality, resource competition, fire disturbance
and soil heterotrophic respiration. To account for the variety
of structure and functioning among plants, four plant
functional types (PFTs) are distinguished for the boreal
zone (see auxiliary material1). Gross primary production is
calculated on the basis of a coupled photosynthesis-water
balance scheme after Farquhar et al. [1980] with leaf-level
optimized nitrogen allocation [Haxeltine and Prentice,
1996]. The underlying model of light use efficiency
assumes no limitation of nitrogen uptake [Haxeltine and
Prentice, 1996], thus no nitrogen cycle is represented in the
model. Fire disturbance is mainly driven by litter moisture
and dead fuel load as well as PFT specific fire resistances
Thonicke et al. [2001].
[7] The LPJ-DGVM used in this study has been intensively validated against observations of key ecosystem
parameters on different scales including land-atmosphere
CO2 exchange [McGuire et al., 2001; Sitch et al., 2003;
Peylin et al., 2005], dominant vegetation distribution
[Cramer et al., 2001; Sitch et al., 2003; Beer, 2005], fluxes
of water [Gerten et al., 2004, 2005], global patterns of soil
moisture [Wagner et al., 2003], NPP enhancement due to
carbon fertilization [Gerten et al., 2005], and satelliteobserved northern latitude leaf area index anomalies [Lucht
et al., 2002].
[8] In this study specific leaf area (SLA, in m2/kg) is
related to leaf longevity (aleaf, in years) after Reich et al.
[1997] as follows:
SLA ¼ 0:22 exp 5:6 0:46 ln aleaf 12 :
ð1Þ
Moreover, parameters of boreal vegetation are slightly
adjusted in this study (see auxiliary material). With these
adjustments, a more valid distribution of dominant woody
vegetation in Siberia is achieved [Beer, 2005].
2.2. Input Data and Modeling Protocol
[9] LPJ was driven by gridded monthly fields of air
temperature, precipitation, number of rainy days, and cloud
cover at a 0.5° pixel size. This station-based data set was
provided by the Climatic Research Unit (CRU) of the
University of East Anglia, UK [Mitchell and Jones, 2005]
but revised and extended by the Potsdam Institute for
Climate Impact Research (PIK), Germany [Oesterle et al.,
2003]. As a nongridded global input, annual CO2 concentrations were used that were derived from ice-core measurements and atmospheric observations, provided by the
Carbon Cycle Model Linkage Project [McGuire et al.,
2001]. In addition, eight soil texture classes (see auxiliary
material) were used which are also available globally on a
0.5° grid [Food and Agriculture Organization, 1991]. LPJ
was run from 1901 to 2003 with a pseudo-daily time step,
preceded by a 1000-year spinup period that brought the
1
Auxiliary material data sets are available at ftp://ftp.agu.org/apend/gb/
2006gb002760. Other auxiliary material files are in the HTML.
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carbon pools and vegetation cover in equilibrium with
climate.
[10] Two model experiments were performed for the land
area of Northern Eurasia. In both, the same model version is
used but (1) with (LPJ-P) and (2) without permafrost (LPJnoP). In order to isolate the influence of fire disturbance and
water stress, the results of these experiments are further
compared to results of LPJ-P model runs excluding fire
(LPJ-noFire), and excluding water stress (LPJ-noStress).
2.3. Soil Water Balance
[11] Soil hydrology is modeled as described by Sitch et al.
[2003] and Gerten et al. [2004] following a semi-empirical
approach. There are two soil layers of constant depth
(0.5 and 1 m) and spatially varying soil texture. Plant
available soil moisture w is expressed as the fraction of
available water holding capacity Wplant, which is defined as
the difference between volumetric soil water content at field
capacity and permanent wilting point Wpwp. For calculating
soil thermal properties, the whole water content is required,
hence Wpwp is added to Wplant (see auxiliary material).
[12] The water content of each layer is updated daily by
taking into account rainfall, snowmelt, canopy interception
of precipitation, percolation, soil evaporation, transpiration
and runoff. Monthly values of precipitation, P are distributed stochastically to provide pseudo-daily values. Below a
threshold value of 0°C precipitation is defined as snow.
Snowmelt (M, mm/d) is calculated using an index equation
[Choudhury and DiGirolamo, 1998]:
M ¼ ð1:5 þ 0:007 PÞ Ta ;
ð2Þ
where Ta is the air temperature above 0°C. Percolation rate
( p, mm/d) through the soil layers depends on soil texture
and layer thickness,
p ¼ kperc w1 ;
ð3Þ
where w1 is the plant available soil moisture of the upper
layer and kperc the texture-dependent conductivity, varying
from 5 mm/d for sandy soils to 0.2 mm/d for vertisols.
Surface runoff and drainage are calculated as the excess
water above field capacity in both soil layers [Gerten et al.,
2004]; that is, all water above field capacity that does not
percolate to the respective underlying layer will run off.
[13] Under conditions of permafrost, the water balance is
altered as follows.
[14] 1. Thaw depth within the active layer changes
dynamically for each simulation day. Wplant is adjusted
daily in accordance, which directly influences runoff generation and water availability. For instance, if only a very
thin part of the soil is already thawed in spring, the storage
capacity of the soil will be small hence most of snow melt
or rainfall will run off. Lateral flow of water, which may
play an important role in permafrost regions, is not considered in the present model. This may be a shortcoming for
small-scale investigations, but implies that lateral flows are
canceled out at the large scale considered here (0.5°), which
is not an unrealistic assumption. Lateral flow among grid
cells, however, will be enabled in a forthcoming version of
the LPJ model (Stefanie Jachner, unpublished data, 2006).
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[15] 2. Water is only allowed to percolate through a soil
layer if it is completely thawed. This will increase soil
moisture of the upper layer after precipitation if the transition to the underlying layer remains frozen.
[16] 3. In addition to w a new vector stores frozen water
for both layers also as a fraction of plant available soil
water. Thus precipitation in autumn is conserved until the
next spring.
[17] Water supply to plants is the product of the plant
root-weighted soil moisture availability and a maximum
transpiration rate [Sitch et al., 2003]. The fractions of fine
roots in the upper and lower soil layer, z1 and z2 are
optimized daily for boreal needleleaved summergreen vegetation (BoNS) as a function of mm thawed water (w1,
upper layer; w2, lower layer) to give BoNS the opportunity
to access as much water from both layers as possible,
z1 ¼ w1 ðw1 þ w2 Þ1
z2 ¼ 1 z1 ;
while z1 and z2 are set to 0 if w1 + w2 = 0. This mechanism
gives BoNS an advantage over other woody PFTs in
permafrost regions by allowing them to better manage
droughts (e.g., in East Siberia). Larix gmelinii uses soil
water of the upper 15 cm under wet conditions, while roots
observed at 30– 70 cm depth are possibly essential for the
uptake of thawed water during drought [Sugimoto et al.,
2002].
[18] Links between site hydrology and the carbon cycle
are simulated by the LPJ-DGVM on several timescales. The
fast processes, photosynthesis and transpiration are coupled
by the dynamics of canopy conductance after Farquhar et
al. [1980], which strongly depend on soil moisture [Gerten
et al., 2005]. On a longer timescale, moisture-related adjustments in the allocation of carbon to different plant components [Bongarten and Teskey, 1987; Mencuccini and Grace,
1995] lead to moisture-dependent leaf area [Grier and
Running, 1977; Gholz, 1982]. The ratio between atmospheric demand and supply of water (water stress, w) is
modeled to influence the carbon allocation between leaves
and fine roots [Sitch et al., 2003],
cleaf ¼ w croot :
ð4Þ
2.4. Fire
[19] A full description of the LPJ fire module Glob-FIRM
is given by Thonicke et al. [2001]. The probability p of a
fire increases with decreasing litter moisture content as
described by
pðwÞ ¼ exp p w w1
;
e
ð5Þ
w is the PFT-weighted moisture of extinction, above which
litter cannot ignite, because all the ignition energy is
consumed to vaporize the water it contains [see Sitch et al.,
2003, Table 3]. The fire season s is calculated as the annual
arithmetic mean of the daily fire probability. A constant
nonlinear relationship between s and annual area burned A,
assuming that the fractional area burned increases the longer
the burning conditions persist, is applied [Thonicke et al.,
2001]. Fire effects are described by fire resistances, which
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combine fire intensity and fire severity in a PFT-specific
parameter (see auxiliary material).
2.5. Freeze-Thaw Processes in the Active Layer Over
Permanently Frozen Ground
[20] In a first step we determine whether a given geographic location is characterized by permanently frozen
ground. Permafrost status is applied to the whole of a grid
cell, neglecting discontinuous permafrost, since DGVMs do
not resolve subgrid localizations of spatial heterogeneity
(this is a tradeoff with their continental-scale applicability in
the absence of detailed spatial data). Surface temperature
below snow and litter (including near-surface carbon, e.g.,
of grass) is calculated by treating snow and vegetation
layers separately. If a particular grid cell is determined to
be located in a permafrost region, thaw depth is computed
by using this altered surface temperature. The simulation of
hydrological processes follows as described in section 2.3.
2.5.1. Permafrost Distribution
[21] Spatial permafrost distribution is modeled at annual
time steps using the normalized frost index F [Nelson and
Outcalt, 1987],
F¼
pffiffiffiffiffiffipffiffiffiffiffi pffiffiffiffiffiffi1
Dt þ Df
Df
:
ð6Þ
Its reliability can be assumed to also hold in a changing
climate [Anisimov and Nelson, 1997; Christensen and
Kuhry, 2000; Stendel and Christensen, 2002]. Df and Dt
represent the annual freezing and thawing degree-days (sum
of pseudo-daily temperatures that are derived from interpolated monthly data). In case of snow coverage the
temperature below snow is used for the calculation of Dt
and Df (see section 2.5.2). Thus vegetation has an impact on
the calculation of the frost index since canopy interception
of snow is taken into account in LPJ [Gerten et al., 2004].
Values of F that represent the threshold boundaries of
continuous, discontinuous and sporadic permafrost are 0.67,
0.6 and 0.5, respectively [Nelson and Outcalt, 1987;
Christensen and Kuhry, 2000]. In this study F = 0.6 is
applied to distinguish between permafrost and nonpermafrost situations.
2.5.2. Daily Thaw Depth
[22] In this work ‘thaw depth’ represents the soil depth z
of the 0°C isotherm. We assume only one isotherm instead
of two phase interfaces in autumn since the effects of the
precise discrimination of the location of the interfaces
between the two soil layers on the carbon fluxes are small
while air temperature is below the freezing point. In other
words, in autumn our variable ‘thaw depth’ corresponds to
the thawed water column length. During the growing season
when air temperature is above 0°C simulated thaw depth
matches the definition used for the temperature-measured
variable in the field.
[23] Soil temperature in °C is assumed to follow an
annual sinusoidal cycle of air temperature with a damped
oscillation [Campbell and Norman, 2000],
T ð z; t Þ ¼ Tave
þ A exp z d 1 sin W Dt z d 1 ; ð7Þ
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where Tave is the mean annual temperature above the
respective layer and
A is the
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ffi amplitude of this temperature
fluctuation. d = 2k W1 represents the damping depth
with thermal diffusivity k = l c1, where c is volumetric
heat capacity and l thermal conductivity. W = 2p t 1
represents the angular frequency of oscillation with
oscillation period t. The method of solving equation (7)
for each simulation day is described in the appendix of
[Sitch et al., 2003]. The concept of a running mean is
applied for calculating Tave; that is, the model is not required
to have knowledge of future climate. Snow cover and a
near-surface carbon layers (aboveground litter and grass
carbon density) are considered to be above the soil in this
study. The temperature above snow corresponds to the air
temperature. Equation 7 is applied to calculate the following
temperatures for each simulation day step by step:
temperature below snow, temperature below near-surface
carbon, and temperature in 0.25 m soil depth. Each of these
results is taken as input for calculating the subsequent value.
Mean depth of snow and near-surface carbon layers of the
last 31 days as well as their mean thermal diffusivity are
updated daily (see auxiliary material).
[24] Once temperature below snow or near-surface carbon
layers is estimated, soil temperature in 0.25 m depth and
thaw depth can be calculated in parallel. In doing so, soil
volumetric heat capacity c and thermal conductivity l are
required for each day, which are derived from mean
volumetric water content of the thawed fraction of the soil
of the last 31 days, w, after Campbell and Norman [2000].
The nonlinear relationship l = l(w) is approximated linearly for w 2 [0..0.1), w 2 [0.1..0.25) and w 2 [0.25..1),
respectively. While a phase change the latent heat Q is
considered as apparent heat capacity [Jumikis, 1966] in the
exponential damping term of equation (7),
Q ¼ k wthaw Qw :
ð8Þ
wthaw represents the amount of thawed soil water content
averaged over the last 31 days and Qw = 334.94 kJ/m3 is the
heat of fusion of water. We approximate k to 0.5 by
assuming a nearly linear change of the 0°C isotherm during
the short interval of a 1-day time step.
[25] Figure 1 summarizes the scheme of the thaw depth
module and visualizes feedbacks of vegetation on thaw
depth through soil moisture and insulating snow and nearsurface carbon layers. Thaw depth is finally computed by
finding the null of equation (7) numerically via Newton’s
method for each simulation day,
1
znþ1 ¼ zn þ T ðzn ; tÞðT 0 ðzn ; t ÞÞ :
ð9Þ
If the calculated thaw depth indicates a thawing or freezing
event, the associated soil water will be shifted between the
separate pools for water and ice for each layer, and a new
value for soil moisture is derived by calculating the site
hydrology. In doing so, soil thermal properties of the next
simulation day are influenced.
2.6. Freeze-Thaw Processes Outside the Permafrost
Zone
[26] Outside of the permafrost zone (determined as
described in section 2.5.1) the soil freezes during winter
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Figure 1. Principal steps in calculating daily thaw depth in
LPJ.
to a certain depth and thaws in spring at two phase interfaces
until the whole soil column is thawed. In contrast to the thaw
depth over permafrost soils during summer, the winter frost
depth has less of an importance for the vegetation functioning in the boreal zone. Trees are hardly able to make use of
water from deeper soils while the xylem is frozen in winter
[Woodward, 1995]. In addition, photosynthesis can be
neglected while temperature is clearly below the freezing
point. Thus we do not attempt to estimate frost depth during
winter but consider the whole of the soil column to be frozen
when the temperature (as calculated by equation (7)) falls
below 0°C in 10 cm depth, following Zhuang et al. [2003].
In doing so, we make sure that snow melt water in spring
does not infiltrate immediately into the soil, that is, the water
pool available to vegetation.
2.7. Study Region for Evaluation of Vegetation
Carbon Density
[27] Simulated VCD values are compared to a recently
updated inventory-based VCD data from an ecosystem
information system on a 1:1 million polygon basis, that
was made available by the International Institute for Applied
Systems Analysis (IIASA), Laxenburg, Austria. It covers a
3 million km2 broad transect swath through central Siberia
reaching from the Arctic Ocean to the southern steppe and
the Yenisey river in the West to Lake Baikal in the east
(approx. 79°E–119°E, 51°N–78°N). This ecosystem data
base [Nilsson et al., 2005] was created as part of the
European Commission’s SIBERIA-II project [Schmullius
and Hese, 2002].
3. Results
3.1. Permafrost Model Evaluation
[28] For site-specific evaluation, LPJ was run with the soil
textures of the observation points. Simulated daily thaw
depth is compared to seasonal thaw depth at four stations in
Alaska from 1995 until 1997 [Nelson, 1996] and one site in
the Siberian Far East from 1981 until 1985 (S. Zimov, Thaw
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depth measurements near River Kolyma 1981 –1985, personal communication, 2005). Thaw depth simulated by LPJ
follows the seasonal cycle of measured thaw depth values
fairly well (Figure 2). Discrepancies between modeled and
observed values are within the range of standard deviations
of observations. In many cases the model slightly underestimates the maximum thaw depth. However, this is
obviously the case only for the selection of locations shown
here for which seasonal thaw depth are available. Other
locations show as many cases of overestimation as underestimation of maximal thaw depth, as can be seen from
Figure 3. It compares LPJ-simulated ALT with 178 observations collected by the Circumpolar Active Layer Monitoring (CALM) project [Brown et al., 2003] from 29 stations
in Alaska, Canada and Russia between 1992 and 2002.
Most simulation results range between 50 and 200% discrepancy to the observations, the coefficient of determination and root mean square error are 0.3 and 21 cm,
respectively. These discrepancies are of the same magnitude
as the natural variability of ALT over only tens of meters
[Nelson et al., 1997]. Thus results by LPJ that were driven
by coarse climate and soil data are considered reasonable,
particularly as this study is focused not on the details of
permafrost dynamics but on general effects of permafrost on
the vegetation growing on permafrost soils.
[29] In addition to the seasonal cycle of thaw depth and its
maximum, Figure 4 provides an evaluation of the interannual variability of ALT at two stations in Alaska and two in
Russia. As can be seen, changes in simulated ALT from
year to year correspond well to observations. This demonstrates the ability of the algorithm to simulate the impacts of
air temperature, soil moisture and insulations on soil temperature, which act on a daily basis or even finer temporal
resolution but are here recovered from a coarser resolution
model.
[30] Spatial details of simulated ALT within permafrost
areas are shown in the auxiliary material. Continental-scale
observations of ALT are not available, hence spatial pattern
of ALT are compared to results from a state-of-the-art
freeze-thaw model, the Goodrich model [Oelke et al.,
2003]. Although, in contrast to LPJ, this model is driven
by NCEP/NCAR reanalysis climate data with a topographybased correction of surface air temperature and a radarbased information on snow water equivalent, both model
results match in their general pattern (see auxiliary material).
They agree in the increase of ALT from north to south as
well as the decrease from west to east in Northern Eurasia
that is a consequence of a more continental climate in
Eastern Siberia. LPJ, however, shows a stronger spatial
gradient in the northern taiga. ALT mostly ranges from
25 cm in the north or east up to 100 cm in the south or
west. In a few regions in Western Siberia ALT was computed
to reach 250 cm. A band of 75–250 cm values as computed
by LPJ is situated more to the north than is the case for the
Goodrich model, especially in Western Eurasia. In addition,
ALT as computed by LPJ is lower in the Far East, with
values between 25 and 50 cm. The strong impact of soil
parameters on ALT can be seen on the border between
different soil texture classes, for example, around 75°N,
95°E.
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Figure 2. Mean seasonal thaw depth (crosses) and their standard deviation (bars) at five stations in
Alaska between 1995 and 1997 [Nelson, 1996] and one station in the Russian Far East between 1981 and
1985 (S. Zimov, personal communication, 2005) in comparison to LPJ simulation results of daily thaw
depth (line).
[31] By comparing the simulated permafrost distribution
against the permafrost map by Brown et al. [1998] the
general applicability of the frost index by Nelson and
Outcalt [1987] can be confirmed (see auxiliary material).
However, by setting F in equation (6) to 0.6, which was
found to represent the border between discontinuous and
sporadic permafrost [Nelson and Outcalt, 1987; Christensen
and Kuhry, 2000], it seems that rather the zonation of
continuous permafrost was delineated, as can be seen from
discrepancies south of the Ob Bay (see auxiliary material).
Reasons for this could also be the usage of different climate
input data or different formulations for snow properties like
density and thermal conductivity.
[32] Given that the discrepancies noted are discrepancies
between models, that the comparison with observations is
on average satisfactory though with large deviations in
particular cases, and that the spatial patterns between the
models are mostly similar, we conclude that the model is
sufficiently accurate for the present study, which is focused
on the changes caused in vegetation by changes permafrost-
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induced changes in site hydrology. In the absence of more
detailed data and considerably more expensive computational effort, both of which are currently unrealistic for the
continental scale, we argue that the present model is
sufficient for use in a DGVM.
Figure 3. Comparison of simulated maximum thaw depth
with observations at 29 stations from Alaska, Canada, and
Russia from 1992 until 2002 [Brown et al., 2003].
Observations represent mean values of measurements
within 100 m2 to 1 km2 large areas with standard deviations
in the range of 5 – 20 cm [Brown et al., 2003].
3.2. Impacts of Freeze-Thaw Processes on the Water
Balance
[33] Effects of permafrost on the water balance are
quantified by analyzing runoff simulated by the LPJ-noP
and LPJ-P model runs for a number of large river watersheds (more than 200,000 km2) that are located (partially)
within the permafrost zone, and for which discharge measurements over a period of more than 10 years were available
[Vörösmarty et al., 1996]. The model results shown in
Figure 5 demonstrate the importance of freeze-thaw processes on a continental scale for the water balance in the
boreal zone. Without the consideration of these processes
(LPJ-noP model) runoff is clearly underestimated in spring
by 30 – 60% because the assumed high available water
holding capacity of the soil does not reflect the reality where
partly frozen grounds additionally constrain the holding
capacity (see section 2.3). By contrast, the enhanced model
version (LPJ-P model) leads to runoff results over basins of
large Arctic rivers that compares well with the observations
in spring. Snow melt and precipitation over a still frozen
ground during spring cause the observed runoff peak. This
process is represented by the model now.
Figure 4. Time series of active-layer thickness anomalies at four CALM [Brown et al., 2003] stations in
comparison to LPJ-P model results.
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Figure 5. Comparison of simulated monthly long-term means of fresh water runoff in Siberian Arctic
river basins with observations of river discharge [Vörösmarty et al., 1996] between 1936 and 1984.
Simulated runoff values are shifted by 1 month for the two large rivers Yenisey and Lena and by
0.5 months for the mesoscale river basins Yana and Indirgirka since no river routing scheme is embedded
in the LPJ-DGVM. LPJ-PIK denotes to results by the original LPJ model [Sitch et al., 2003].
[34] However, simulated runoff remains too low in late
summer and autumn. Additional analysis (see auxiliary
material) revealed that this is due to an overestimation of
the amount of water transpired or intercepted by vegetation
in the summer and in autumn, which is a consequence of the
DGVM simulating dense forest cover (foliage cover 95%)
throughout the region when the actual foliage cover is less
dense (20 – 75% following Hansen et al. [2003]) and
spatially heterogeneous. Model runs with an improved
lower vegetation density showed little change in the spring
runoff but correctly increased runoff in autumn, in accordance with observations [Beer et al., 2006].
[35] Despite the analysis of simulated river runoff, which
allows for conclusions about impacts of freeze-thaw processes on the water balance at continental scale, an evaluation of simulated soil moisture dynamics at stations located
in various ecosystems within the permafrost zone (Figure 6)
is required to assess the reliability of the hydrological
scheme. During the first days of soil thawing modeled
relative water content oscillate highly in contrast to observations. This is due to a very thin layer of thawed soil
simulated by LPJ during this period while observations are
considered after some centimeter of soil has already thawed.
Except for this fact, modeled soil moisture is within the
range of standard deviation of different observations at the
same site (Figure 6b) and its dynamics is represented by
model results (most notably Figure 6a and Figure 6d). As
for the thaw depth calculation, highest uncertainty in
modeled soil moisture is due to assumed homogenous soil
properties with depth. In comparison to mean observations
in the first meter of soil, LPJ seems to overestimate soil
water content in spring at Bayelva, Svalbard (Figure 6d).
The comparison to the observation at 6 cm soil depth,
however, clarifies this fact (Figure 6d). During this time
period, LPJ results represent only the water content of the
upper thin layer that is thawed.
3.3. Consequences for the Fire Return Interval
[36] As can be seen in Figure 7, simulation results of the
fire return interval (FRI) notably differ between the both
model versions LPJ-P and LPJ-noP. FRI values simulated
by LPJ-noP are considerably too long. With the consideration of freeze-thaw processes (LPJ-P) FRI is mostly
reduced to values of 100 to 200 years in Northern Eurasia,
in accordance with observations [Bonan and Shugart, 1989;
Kharuk et al., 2005].
[37] Shorter FRI values are associated with higher carbon
emissions. The LPJ-P model estimates twofold emissions in
central Siberia compared to LPJ-noP results, which are up
to fourfold in Western Siberia. Values ranges from 25 to
45 gC/m2/a on average between 1993 and 2003.
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Figure 6. Dynamics of volumetric soil water content for the year (a– c) 2003 or (d) 1999. The LPJ-P
model is driven by local soil texture information and precipitation (except for Figure 6c) for this
comparison. Asterisks denote daily precipitation (mm). In Figure 6b, means and standard deviations of
four plots are shown. Data were provided by Overduin [2005] (a), A. Rodionov, personal communication,
2006 (Figure 6b), S. Zimov, personal communication, 2006 (Figure 6c), and Roth and Boike [2001]
(Figure 6d).
[38] The reason for this increase in fire activity is as
follows. Increased high runoff of water from snow melt and
precipitation on the surface of predominantly frozen
grounds during spring leads to dry soils. Vegetation contributes to this drying by rapidly taking up water after spring
greenup from that thin fraction of the active layer that is
already thawed. As a consequence, fire probability and thus
fire occurrence in Siberia is highest in spring [Korovin,
1996]. The global fire module embedded in the LPJ-DGVM
[Thonicke et al., 2001] uses a statistical approach that does
not resolve the interannual variability of fire occurrence, but
the demonstrated combined effect of permafrost on runoff
(see section 3.2) and FRI emphasizes the strong link
between permafrost, water balance and fire probability, a
link represented by the model LPJ-P with consequences for
the carbon density of standing vegetation.
3.4. Permafrost Effects on Growth
[39] In permafrost regions, a large amount of water is lost
as runoff during spring (section 3.2) and is hence not
available for vegetation later in the growing season. In
addition, soil water content is constrained by thaw depth
during early summer [Benninghoff, 1952]. Besides the
effects of soil moisture on fire, water availability limits
NPP in a twofold manner. As a fast process, GPP is
constrained by stomatal conductance [Farquhar et al.,
1980]. On the other hand, water availability influences the
Figure 7. Mean fire return interval in Northern Eurasia
1993– 2003 as simulated by (middle) LPJ-noP and (bottom)
LPJ-P. (top) The result by the original LPJ model [Sitch et
al., 2003] is shown for comparison.
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Figure 8. Ecosystem variables in the region around 68.25°N, 120.25°E (larch forest, northern taiga) for
the first 300 simulation years. Simulation results are with (LPJ-P) or without (LPJ-noP) the consideration
of freeze-thaw processes, or with it but without the consideration of fire (LPJ-P-noFire) or water stress
(LPJ-P-noStress). Independent estimates are shown in grey and are from (a) Schulze et al. [1999],
(b) Hansen et al. [2003], and (c, d) Stolbovoi and McCallum [2002]. The width of grey bars indicates the
range of observations (Figure 8a), the standard deviation (Figure 8b), and the reported uncertainty range
[see Shvidenko et al., 2001] of forest inventory estimates (Figures 8c and 8d).
allocation of carbon to the components of plants [Bongarten
and Teskey, 1987; Mencuccini and Grace, 1995] leading to
lower leaf area under drier conditions [Grier and Running,
1977; Gholz, 1982].
[40] To differentiate between these effects, two additional
simulations excluding fire, LPJ-noFire, or excluding waterstress impacts on carbon allocation (equation (4)),
LPJ-noStress, are compared to LPJ-noP and LPJ-P for a
larch-dominated permafrost area around 68.25°N and
120.25°E in the first 300 simulation years (Figure 8).
LPJ-P results of leaf area index (LAI), foliage projected
cover (FPC), NPP and finally VCD are lowest and best
comparable to independent estimates. Without the consideration of permafrost, these parameters acquire considerably
higher (unrealistic) values. LPJ-noFire results in only slightly
higher values than LPJ-P during the first 100 simulation
years, revealing that the direct impacts of fires are not the
dominant effect.
[41] By contrast, LPJ-noStress shows higher values than
LPJ-P already after about 50 years since no extra investment
in roots is assumed (equation (4)). This illustrates the impact
of higher carbon allocation to roots on LAI and NPP, thus
VCD in permafrost regions. However, LPJ-noStress still
produces lower LAI, NPP and VCD than LPJ-noP (Figure 8).
This is due to the fact that lower water availability also
directly impacts productivity [e.g., Gerten et al., 2005].
[42] During the simulation period, fire also has a direct
impact on VCD, as can be seen by the comparison of LPJ-P
and LPJ-noFire results in Figure 8d. However, the results
shown in Figure 8 also demonstrate the impact of water
availability on growth through canopy conductance and
carbon allocation to roots. For the same location, Figure 9
clarifies the impact of plant available soil water content
(SWC) on NPP that is mediated by thaw depth. It shows
LPJ-noP and LPJ-P simulation results of NPP, thaw depth
and SWC on a daily time step for the simulation year 50.
Without the representation of freeze-thaw processes, a very
deep and saturated soil would be assumed with SWC of
about 200 mm throughout the whole year. By contrast,
SWC is constrained by thaw depth in the LPJ-P experiment
leading to reduced NPP. In addition, the timing of the first
day with photosynthesis is altered forward in time.
3.5. Vegetation Carbon Density
[43] Simulated VCD approximates inventory estimates
much better in the LPJ-P simulation as compared to LPJnoP (Figure 10a), where the deviation is considerable. VCD
is estimated by LPJ-P to range between 6 and 7 kgC/m2
south of 63°N decreasing to 1 –2 kgC/m2 north of 69°N.
Freeze-thaw processes account for a 1.5 to 2.5-fold reduction in VCD in the South (Figure 10b) which increased with
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Figure 9. Daily estimates of woody NPP simulated by the
LPJ-noP and LPJ-P models, thaw depth simulated by LPJ-P,
and soil water content (Wplant) simulated by LPJ-noP and
LPJ-P around 68.25°N, 120.25°E (larch forest, northern
taiga) in 2003.
latitude to a fivefold reduction in high latitudes. This
demonstrates the impact of freeze-thaw processes on biomass through disturbances and water availability. Soil
thermal dynamics are primary processes of boreal ecosystems that cannot be neglected, as has commonly been the
case, in DGVM simulations. Besides the fact that VCD is
lower in magnitude, its changeover between 60 and 70°E is
smoother in LPJ-P (Figure 10a) than in LPJ-noP owing to
the effect of a decreasing ALT on biomass [Bonan, 1989].
4. Discussion
[44] Freeze-thaw processes strongly codetermine the seasonality of soil moisture in the boreal zone, particularly in
the permafrost zone, where the thermal dynamics in the
active layer are significantly altered. Owing to the intrinsic
physiological coupling of the carbon and water cycles in
vegetation, with an additional important soil moisture
feedback on vegetation mediated through fire, the most
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important disturbance in the boreal forest, freeze-thaw
processes cannot be neglected in process-based analyzes
of the continental-scale carbon and water balance at boreal
latitudes.
[45] The permafrost module we therefore implemented
into the LPJ-DGVM reflects the general seasonal cycle of
thawing depth and the large-scale spatial patterns of its
maximum. We are aware of the limited significance of
comparing simulated thaw depth based on 0.5° 0.5°
averages of climate and soil data with observations at much
smaller scales (1 ha or 1 km2) because of the spatially
highly heterogeneous nature of thaw depth processes, which
are influenced by variations in soil properties, slope and
slope aspect [Washburn, 1979; Nelson et al., 1997]. Nonetheless, such a comparison allows to demonstrate the
reliability of computed first-order patterns. Our evaluation
of simulated thaw depth seasonality and spatial patterns of
ALT with observations and the results of a recent permafrost
model, the Goodrich model, shows the capability of our
method to generically represent freeze-thaw processes.
Discrepancies to observations are within the range of
variability of thaw depth that occurs naturally on a much
smaller spatial scale than applied in this study. A limitation
of the method applied is the assumption of homogenous soil
properties with depth, a limitation that equally applies to all
other large-scale permafrost models. The comparison of
results obtained from different approaches is a problem that
can currently not be resolved satisfactorily and places the
strongest constraints on our current ability to demonstrate
and validate results.
[46] It should be noted that the current snow module
embedded into the LPJ model does not take into account
vegetation effects on solar radiation extinction and atmospheric turbulence which are a far greater influence on snow
cover than interception. This could lead to an underestimation of snow depth in forest areas with effects on the soil
thermal regime. In addition to this, DGVMs do not resolve
subgrid localizations of spatial heterogeneity hence are not
able to represent discontinuous permafrost. We acknowledge that the greatest change in permafrost is likely to
Figure 10. (a) Vegetation carbon density as a function of latitude in the SIBERIA-II study transect
swath in 2003 as derived by the land-ecosystem inventory approach of the International Institute for
Applied Systems Analysis, Austria [Nilsson et al., 2005], and as simulated by LPJ-noP and LPJ-P. LPJPIK denotes to the result by the original LPJ model [Sitch et al., 2003]. (b) Ratio between LPJ-noP and
LPJ-P results shown in Figure 10a.
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happen in this zone. Further, regional-scale studies are thus
desirable to explore these effects in more detail.
[47] Given the permafrost module, our evaluation of
runoff, fire return interval and VCD as computed in response to its dynamics demonstrates the importance of
freeze-thaw processes for the functioning of boreal ecosystems. High runoff of snow melt and precipitation on a still
frozen ground in spring subsequently leads to dry soil
conditions. In permafrost regions, this effect is amplified
by the fact that vegetation is able to draw water only from
the shallow fraction of the active layer which had been
already thawed in spring and early summer. The ensuing
low soil water content, which rapidly translates into low
litter moisture, strongly increases fire probability reducing
the fire return interval. Our model results show that freezethaw processes and the related site hydrology are essential
for realistic fire disturbance modeling on a continental scale
in the boreal zone. The actual area burned, and its spatial
and temporal variability, however, depend on additional
factors such as ignition, tree allometry and structure, and
litter quality. In addition to disturbances, NPP is the main
determinant of biomass as it quantifies vegetation growth
and background mortality. The relatively low soil water
availability during the boreal summer causes low values of
NPP, hence plant growth depressed owing to low LAI and
canopy conductance in permafrost regions. This remains the
case despite various adaptations of boreal vegetation to
permafrost conditions, represented in our model, such as a
rapid greenup and shallow rooting systems. Even if the
resulting low foliage cover would be partly compensated on
the ecosystem level by more seedlings during the time, stem
density increased, also contributing to lower VCD values.
Our results support the fact that plant growth in the northern
taiga is limited by water availability in addition to other
factors, such as nitrogen uptake.
[48] The dynamic ecosystem model still simulates too
high VCD north of 60°N, where values may be up to twice
as large as independent estimates produced by IIASA from
forest inventory data (Figure 10). A number of limitations to
tree establishment and growth in high latitudes that are not
represented in the simulation model are a likely explanation
for this overestimation, for example temperature dependence of cell division [Körner, 2003] or of seed quality
and quantity [Black and Bliss, 1980; Sveinbjörnsson et al.,
2002], limitations by nutrient availability [Bonan and
Shugart, 1989] or limitation of tree establishment by standing water on the permafrost [Sveinbjörnsson et al., 2002]. In
the boreal zone, a considerable amount of heterotrophic
respiration was detected during winter [Monson et al., 2006]
with consequences on nitrogen mineralization, hence nitrogen availability during the growing season. This positive
effect of winter soil respiration on productivity is not
considered in this study but LPJ even assumes no limitation
of nitrogen uptake. Future modeling studies which aim at
clarifying the nutrient limitation of VCD will have to
consider this positive effect of winter soil respiration on
nutrient availability.
[49] However, our results clearly point out that freezethaw processes are of first order and should not be neglected
in a process-based modeling framework. The increase of
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ALT and the disappearance of permafrost under warming
conditions [Anisimov and Nelson, 1997; Stendel and
Christensen, 2002] will lead to a substantial alteration of
the site hydrology [Kane et al., 1992]. The consideration of
the most important consequences of freeze-thaw processes
on the hydrological regime in high latitudes by the LPJ-P
model give us an opportunity to arrive at more reliable
estimates of the future dynamics of the global carbon and
water balances, which are both strongly influenced by the
high latitudes. Uncertainties exist with respect to the
dynamics, for example, of peat, cryoturbation, methanogenesis and methanotropy, of which large-scale observations
are not currently available. In addition, the impact of fire on
nutrient availability should be considered for projections of
the future carbon balance in the boreal zone. Furthermore,
feedbacks between changing vegetation coverage and climate through albedo and carbon and water fluxes can be
large [e.g., Betts, 2000; Chapin et al., 2005]. Nonetheless,
the inclusion of freeze-thaw processes is a significant step
toward a more comprehensive understanding of ecosystem
processes in the boreal zone and Arctic tundra. Our freezethaw algorithm of medium complexity also is suitable for
application in the land surface scheme of a General Circulation Model since additional computation cost is minimal.
5. Conclusion
[50] The combined evaluation of river runoff, fire return
interval and vegetation carbon density, as simulated by the
permafrost-enhanced LPJ-DGVM, demonstrates that both
limitation in water availability during the growing season
and the disturbance dynamics of boreal vegetation substantially contribute to the low vegetation carbon density observed
in permafrost regions of the boreal zone. This supports the
conclusion that the availability of liquid water and soil
moisture seasonality are also major limitations of biomass
within the permafrost zone, in addition to temperature and
nitrogen availability. Since moisture dynamics are strongly
determined by the thermal dynamics in these regions, freezethaw processes are found to be ecosystem processes of first
order. This conclusion demonstrates the potential of vegetation in high latitudes to sequester more carbon than it
currently does owing to warming that leads to increasing
ALT or even the complete disappearance of permafrost.
[51] Acknowledgments. The authors thank B. Smith, S. Schaphoff,
and S. Sitch for support regarding the LPJ-DGVM code, and M. Fink,
D. Knorr, W. Cramer, V. Romanovsky, D. Riseborough, O. Anisimov, and
P. Ciais for valuable notes. A. Shvidenko and I. McCallum from the
International Institute for Applied Systems Analysis, Austria made the forest
inventory data available. We are grateful to G. and S. Zimov who provided
seasonal thaw depth and soil moisture measurements, to P. Overduin,
A. Rodionov, and J. Boike, who provided seasonal soil moisture observations, and to all members of the SIBERIA-II consortium for discussions.
Financial support was provided by the European Commission under the
SIBERIA-II project, contract EVG2-2001-00008. W. L. and D. G. were
funded by the German Ministry of Education and Research under the
DEKLIM project CVECA. We thank Nigel Roulet and an anonymous
reviewer for helpful comments on an earlier manuscript of this paper.
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C. Beer, Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10,
D-07745 Jena, Germany. ([email protected])
D. Gerten and W. Lucht, Potsdam Institute for Climate Impact Research,
P.O. Box 601203, D-14412 Potsdam, Germany.
C. Schmullius, Friedrich Schiller University, Institute of Geography,
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K. Thonicke, School of Geographical Sciences, University of Bristol,
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