Download Interannual variability of plant phenology in tussock tundra

Global Change Biology (2003) 9, 743±758
Interannual variability of plant phenology in tussock
tundra: modelling interactions of plant productivity,
plant phenology, snowmelt and soil thaw
M . T . V A N W I J K * {, M . W I L L I A M S *, J . A . L A U N D R E { and G . R . S H A V E R {
*IERM, University of Edinburgh, Edinburgh EH9 3JU, UK, {The Ecosystems Center, Marine Biological Laboratory, Woods Hole,
MA 02543, USA
Abstract
We present a linked model of plant productivity, plant phenology, snowmelt and soil
thaw in order to estimate interannual variability of arctic plant phenology and its effects
on plant productivity. The model is tested using 8 years of soil temperature data, and
three years of bud break data of Betula nana. Because the factors that trigger the end of
the growing season of arctic vegetation are less well known than those of the start of the
growing season, three hypotheses were formulated and tested for their effects on productivity and its sensitivity to climate change; the hypothesised factors determining the
end of the growing season were frost, photoperiod and periodic constraints.
The performance of the soil thermal model was good; both the onset of soil thaw in
spring and the initiation of freezing in autumn were predicted correctly in most cases.
The phenology model predicted the bud break date of Betula nana closely for the three
different years. The soil thaw model predicted similar growing season start dates under
current climate as the models based on sum of temperatures, but it made significantly
different predictions under climate change scenarios, probably because of the non-linear
interactions between snowmelt and soil thaw. The uncertainty about the driving factors
for the end of the growing season, in turn, resulted in uncertainty in the interannual
variability of the simulated annual gross primary productivity (GPP). The interannual
variability ranged from 2 25 to + 26% of the mean annual GPP for the frost hypothesis,
from 2 20 to + 20% for the photoperiod hypothesis and only from 2 7 to + 7% for the
periodic hypothesis. The different hypotheses also resulted in different sensitivity to
climate change, with the frost hypothesis resulting in 30% higher annual GPP values
than the periodic hypothesis when air temperatures were increased by 3 ÊC.
Keywords: LAI, modelling, phenology, primary production, The Arctic, tundra
Received 5 June 2002; revised version received 5 September 2002 and accepted 11 December 2002
Introduction
Plant phenology is an important variable in the study of
the possible effects of climate change on the productivity
and the distribution of terrestrial vegetation types
(Heimann et al., 1998; Walther et al., 2002). Accurate
phenology models are the important tools for predicting
vegetation responses to climatic variability, as the
Correspondence: M. T. Van Wijk, Wageningen University,
Plant Production Systems, Postbus 430, 6700 AK, Wageningen,
The Netherlands, tel. ‡31-(0)317486102, fax ‡31-(0)317484892,
e-mail: [email protected]
ß 2003 Blackwell Publishing Ltd
presence or absence of a photosynthetically active canopy
has dramatic effects on ecosystem processes and on biosphere/atmosphere exchanges (Running & Nemani,
1991; Goetz & Prince, 1996; Sellers et al., 1996).
One of the consequences of global warming is an increase in arctic surface temperatures. In the Alaskan
Arctic, a warming trend has already been detected
(Overpeck et al., 1997). Temperature increases will affect
the timing of snowmelt in the spring, lead to an earlier
soil thaw and thereby alter the start of the growing
season. Snow-free periods in the Arctic may increase
1 month or more in the next century (Maxwell, 1992).
Already indications exist that the start of the growing
743
744 M . T . V A N W I J K et al.
season in arctic ecosystems is shifting to earlier dates in
the spring (Myneni et al., 1997).
Springtime plant activity in the Arctic is largely initiated by snowmelt. The end of the growing season can be
triggered by temperature, photoperiod, genetic constraints and/or internal plant cycles of nutrient use
(Shaver & Kummerow, 1992; Oberbauer et al., 1998).
Global warming can influence the phenological processes
both directly and indirectly: increased temperatures will
lead directly to earlier snowmelt and thereby to potentially earlier plant activity. Higher temperatures can also
lead to a longer period of soil thaw in the year,
and thereby to an extension of the growing season
(Oberbauer et al., 1998). Indirect effects can include effects
of soil thaw on mineralisation rates (Goulden et al., 1998)
and nutrient availability, which, in its turn, can
influence both the start and the end of the growing
season (Larigauderie & Kummerow, 1991; Shaver &
Kummerow, 1992).
For winter-dormant species, bud break, or dormancy
release, is a critical phenological event that determines
plant growth and development during the growing
season (Pop et al., 2000). After the start of dormancy at
the end of the growing season, buds enter a rest phase,
during which they stay dormant, regardless of the environmental conditions. Following sufficient chilling during
this rest phase, buds enter a quiescent phase during
which they are responsive to the environmental conditions. In the Arctic, the timing of snowmelt and soil thaw
are the key environmental conditions determining dates
of leaf flush for non-evergreen species (Chapin & Shaver,
1985; Shaver & Kummerow, 1992). Moreover for evergreen species snowmelt and soil thaw initiate plant
growth and bud break, and the start of photosynthetic
activity of so-called `wintergreen' species (Shevtsova et al.,
1997; Welker et al., 1997).
In most current phenology models, the initiation of the
growing season is modelled using cumulative thermal
summation (White et al., 1997). The mean air temperatures or soil temperatures are summed above an arbitrary
threshold (usually 0 or 5 8C), in most cases by using some
sort of scaling function in order to include non-linear
temperature effects, until a critical value is exceeded, at
which point a certain phenological event is predicted to
occur (White et al., 1997; Pop et al., 2000). The models
have been applied successfully in order to simulate the
initiation of the growing season in trees (see for example,
HaÈnninen (1990, 1995) and the overview in White et al.
(1997)) and they are now also being applied in order to
predict changes in arctic vegetation (Pop et al., 2000).
However, with the on-going global change, the empirically based parameter values of these types of models are
likely to change as a result of alterations in the onset of
snowmelt, soil thaw and the interactions between these
processes. We, therefore, need robust models that are
based on hypothesised mechanisms of interaction and
the underlying physical processes, which can be tested
quantitatively against measurements.
Besides being an important factor in directly influencing plant phenology, the correct prediction of the timing
of snowmelt and soil thaw is also essential for many other
key processes in the Arctic including the flow of energy,
development of permafrost, hydrological processes, decomposition, and nutrient and water availability
(Lachenbruch & Marshall, 1986; Manabe & Wetherald,
1986; Kane et al., 1992; Goulden et al., 1998). As these
processes in their turn can also affect plant phenology
(Larigauderie & Kummerow, 1991; Shaver &
Kummerow, 1992) the development of process-based
models of soil water/soil energy balance will give an
opportunity to study both direct and indirect relationships between the environment and plant phenological
patterns.
We propose, here, a linked plant productivity model
for the Arctic containing snow pack, soil thermal and
phenological submodels. In this model, we hypothesise
that the onset of snowmelt and soil thaw initiates plant
activity. Without soil thaw, which is strongly influenced
by the occurrence of snowmelt, neither leaf development
nor photosynthesis of higher plants can take place, because transport from the roots is inhibited and the loss of
water during leaf activity cannot be compensated for by
soil water uptake. In this model, no modelling for chilling
is included. We assume that the arctic winters are so long
and cold, that enough so-called `chilling units' are accumulated during the winter and this will in no case delay
the occurrence of bud break in late spring. Pop et al.
(2000) showed that under current climate conditions,
and also with changing climate conditions predicted for
the next 50 years, plants are accumulating more than
enough chilling units needed for bud break. The submodels are built into the soil±plant-atmosphere (SPA) model
that has been applied extensively in order to study the
productivity of arctic ecosystems (Williams et al., 2000,
2001a).
In this study, we first tested the combined SPA-snowthermal model on a long time series of soil temperature.
After this test, the plant phenology model was parameterised for acidic tussock tundra by using data from the
long-term experimental plots (e.g. Shaver et al., 2001) and
phenological data published by Oberbauer et al. (1998).
We compared the predictions of this phenology model to
the results of traditional temperature sum models. Although the factors influencing the onset of the growing
season are reasonably well known ± that is, snowmelt
and soil thaw ± this is much less the case for the end of
the growing season (White et al., 1997; Oberbauer et al.,
1998). We investigated three hypotheses that the end of
ß 2003 Blackwell Publishing Ltd, Global Change Biology, 9, 743±758
M O D E L L I N G P L A N T P H E N O L O G Y I N T H E A R C T I C 745
the growing season of arctic vegetation is triggered by
(i) temperature, (ii) photoperiod or (iii) periodic constraints in which genetic factors together with internal
plant cycles result in a finite growing period (Oberbauer
et al., 1998; Shaver & Kummerow, 1992). We developed
three phenological submodels in order to represent each
of these and quantified the effects of the hypotheses on
the simulated annual estimates of gross primary production (GPP) for a tussock tundra site, which has been the
subject of a long-term study (e.g. Shaver et al., 2001). We
also applied the model in order to quantify the effects of
increased air temperature on growing season length and
annual GPP of the vegetation. As the parameters of the
phenology submodel can be difficult to determine accurately, we performed a sensitivity analysis in order to
quantify the effects of uncertainty in the parameter
values of the phenology submodel on estimates of plant
productivity.
Materials and methods
Site
Toolik Lake is located in the northern foothills of the
Brooks Range, Alaska (68838'N, 149834'W, elevation
760 m). The area around the lake has been studied intensively and is part of the US network of Long-Term Ecological Research sites (Hobbie et al., 1994; Shaver, 1996).
The vegetation type chosen for this study is moist acidic
tundra, one of the most common arctic vegetation types
in both North America and Eurasia (Bliss & Matveyeva,
1992). The experimental layout consists of four large replicate blocks of moist tussock tundra, each block containing four plots of 5 20 m2 arranged parallel to each other
with buffer strips of 1 m wide between them. The site has
been studied for more than 20 years (e.g. Shaver et al.,
2001) and both plant physiological and soil characteristics
are known.
Baseline models
The basic model is the SPA model including a soil thermal submodel as described by Williams et al. (2001b) onto
which the snow pack and phenology submodels are
linked. The SPA model (see Williams et al., 1996 for a
full description) is a multilayer simulator of C3 vascular
plant processes. The modelled ecosystem structure is
described by vertical variations among canopy layers in
light absorbing area, photosynthetic capacity (related to
foliar N) and plant hydraulic properties. The model has
originally been developed for temperate ecosystems, but
has been applied lately to diverse arctic ecosystems, and
the specific arctic adjustments made to the model are
described extensively in Williams et al. (2000, 2001a).
Included in the model is a soil water balance model, in
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which the transpiration calculated by the vegetation part
of SPA is extracted form the soil and possible effects of
soil water stress on stomatal conductance are included
(Williams et al., 1996).
In order to account for the effects of freezing and
thawing, and the presence of permafrost, on the availability of liquid soil water, a soil temperature model was
linked to SPA in Williams et al. (2001b). The thermal
model is based on the model presented by Hinzman
et al. (1998). The surface soil temperature is calculated
by solving the energy balance equation (Hinzman et al.,
1998) and the subsurface thermal model is based on the
solution of the one-dimensional conduction equation:
]T
]
]T
Capp
ˆ
K
…1†
]t ]z
]z
where Capp is the apparent volumetric soil heat capacity
in J m 3 8C; T is the soil temperature in 8C; t is the time
in s; z is the spatial coordinate in vertical direction in m.
We used a one-dimensional finite element formulation in
order to discretise and solve Eqn (1). The important process of freezing and thawing of the soil was treated
similarly to that of Hinzman et al. (1998) and the release
of latent heat during the process of freezing was spread
out over the temperature range between 0 and and 2 8C.
The snow model described by Lynch-Stieglitz (1994)
was linked to this thermal model. The snow pack is
modelled with three snow layers. Heat and mass
(water) flow within the pack are physically modelled.
Radiation conditions determine the surface energy fluxes
and heat flow within the pack is governed by linear
diffusion. Each layer is characterised by a volumetric
water-holding capacity. As such, melt-water generated
in a layer will remain in the layer if the liquid water
content of the layer is less than the holding capacity of
the layer. Otherwise, it will flow down to a lower layer
where it will either refreeze in the layer, remain in the
layer in the liquid state, or drain through the layer.
Finally, two independent processes govern the increase
in density of the pack. A simple parameterisation is used
in order to describe mechanical compaction, or compaction because of the weight of the overburden, and a
separate densification is accomplished via the melting±
refreezing process. All the equation and parameters of
the model physics are given in Lynch-Stieglitz (1994). The
snow pack model is important because in winter the snow
pack acts like a giant insulating blanket, preventing the
escape of heat from the warm soil to the cold atmosphere,
or conversely, for reducing the cold wintertime temperature signal well before it reaches the ground. The low
thermal conductivity of snow, about an order of magnitude lower than that of the soil, makes snow an especially
good insulator. As such, the snow cover results in much
warmer wintertime ground temperatures.
746 M . T . V A N W I J K et al.
Parameterisation of baseline models: SPA, thermal model
and snow model
The parameterisation of the SPA model together with the
thermal and snow model is based on previous published
studies: the SPA parameterisation is based on Williams
et al. (2000) using the parameter values for the tussock
tundra site; the parameterisation of the snow model is
based on Lynch-Stieglitz (1994) and Stieglitz et al. (1999),
whereas the parameterisation of the thermal model is
based on Hinzman et al. (1998). The parameterisation of
the phenological part of the model will be discussed
below.
Phenology model: description and parameterisation
The phenology model is based on a standard model of
development through the year (Fig. 1). The model consists of six parameters: (i) the baseline leaf area index
(LAI) in the winter period, (ii) the maximum LAI in
summer, (iii) the start of leaf development, (iv) the end
of leaf development at the moment of maximum LAI,
(v) the start of leaf browning and (vi) the end of the
growing season with only the evergreen leaves present.
In our phenology model, the start date of the leaf development is determined by the occurrence of soil thaw at
10-cm depth. This is the trigger for leaves to expand and
generate a clear increase in LAI compared to the winter
baseline LAI value. The threshold of soil thaw at 10-cm
depth was determined using the phenological and soil
temperature measurements presented by Oberbauer et al.
(1998) for a similar ecosystem located close to the experimental site of Shaver et al. (2001). This 10-cm threshold
LAI (m2 m−2)
1.6
a2
also resulted in the model not being sensitive to freezethaw cycles in the surface soil layers and the growing
season only starting when there is a substantial thawperiod. We used LAI measurements (using detailed
measurements obtained in an above-ground harvest
(see Shaver et al., 2001) and with an LAI-2000 canopy
analyser (LI-COR, Inc., Lincoln, NB, USA) during the
growing season in 2000 in order to determine the baseline
and maximum LAI. The value of the baseline LAI was
0.5 m2 m 2 and of the maximum LAI was 1.5 m2 m 2. For
both the period between the start of the leaf area development to the level of maximum leaf area and for the
period between the latter and the end of the growing
season, we took a period of 25 days, both based on the
results of Oberbauer et al. (1998).
We tested three hypotheses for determining the end of
the growing season: (i) the end of the growing season is
determined by light conditions, and therefore is finished
at about the same day each year (denoted as the `photoperiod' hypothesis); (ii) the growing season is about the
same length in days each year (denoted as `periodic'
hypothesis; Sùrenson (1941) defined species in Greenland
that have a finite growing period controlled by genetic
constraints as `periodic' species); (iii) the growing season
is ended by the first severe frost period, which we defined as a frost period in which the soil is frozen to a
depth of 5 cm (denoted as the `frost end' hypothesis). We
took as threshold the depth of 5 cm in order to make sure
that the frosts represented substantial events, and not just
one night of temperatures below 0 8C. We parameterised
the models based on the three different concepts by using
data published by Oberbauer et al. (1998) for 1995 and
1996. The overall parameterisation of the phenology
model with the three hypotheses is presented in Table 1.
a3
1.4
Model test and analysis of results
1.2
The combined SPA model ± including the thermal model
and the snow model ± was driven by hourly meteorological measurements, including measurements of
radiation, temperature, wind speed, humidity and precipitation. Continuous measurements were available
for the years 1993±2000. Only during two periods were
precipitation data missing: from October to December
1993 and from October 1994 to May 1995. These meteorological measurements were collected at the main meteorological station at Toolik Lake. Soil temperature was also
measured at the station, using Omega Engineering
thermocouples with copper-constantan wire; the thermocouples were calibrated at least two times a year in order to
prevent instrumental drift. We used soil temperature
measurements at depths of 5, 10 (both from 1998 onwards),
20, 50 and 100 cm (all three from January 1993 onwards) in
1.0
0.8
0.6
Baseline LAI
Baseline LAI
a1
0.4
a4
0.2
0.0
0
25
50
75 100 125 150 175 200 225 250 275 300 325 350
Day of year
Fig. 1 A graphical representation of the model used for describing the leaf area development throughout the year (LAI is leaf
area index; a1, a2, a3 and a4 are parameters representing the days
of year after which, respectively, the growing season starts, the
period with maximum LAI starts, the period with LAI decline
starts and the growing season ends).
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M O D E L L I N G P L A N T P H E N O L O G Y I N T H E A R C T I C 747
Table 1
Parameter values of the phenology model (for explanation see text)
Parameter
Photoperiod
Periodic
Frost
Baseline LAI
Maximum LAI
Start date (a1)
Start date maximum LAI (a2)
End-date maximum LAI (a3)
End-date growing season (a4)
0.5
1.5
thaw determined
a1 ‡ 25 days
day of year 220
a3 ‡ 25 days
0.5
1.5
thaw determined
a1 ‡ 25 days
a2 ‡ 60 days
a3 ‡ 25 days
0.5
1.5
thaw determined
a1 ‡ 25 days
frost determined
a3 ‡ 25 days
order to test the combined SPA-thermal-snow model. The
measurements have a resolution of 1 8C.
After this thorough test of the thermal submodel, we
applied the combined SPA model to the tussock tundra
site and calculated the starting days of the growing
season ± that is, the bud break dates ± and compared
these to two empirical temperature sum models. In the
most simple model, mean daily temperatures above zero
are summed. The other model is the Forcing Unit (FU)
model presented by HaÈnninen (1995) and Pop et al.
(2000):
FU day
FU day
1
1
ˆ 10=f1 ‡ e‰
ˆ0
if Tair 0 C
0:08…Tair 18:0†Š
…2†
g if Tair > 0 C …3†
Both models predict the bud break date as the first day
when a certain threshold of the temperature sum or the
total of FUs is passed. We determined the thresholds of
the two models by empirically fitting that threshold on
the 1995 bud break data of Betula nana presented by Pop
et al. (2000) of a tussock tundra site close to the acidic
tussock tundra site that is studied in this paper. With the
three models parameterised on the same ecosystem, we
compared the predictions of the bud break dates if an
increased temperature scenario (air temperature plus
2 8C) is applied. Betula nana is a dominant deciduous
species in this type of ecosystem and is the species that
shows the highest responsiveness to experimental treatments (Shaver et al., 2001).
The SPA model was applied in order to estimate the
interannual variation of GPP for the vegetation type present in the experimental site, using submodels based on
the three different concepts to quantify the end of the
growing season.
Sensitivity analysis and climate change effects
In order to test the effects of a temperature-induced interannual variability of the 25-day leaf growth period in
spring, we also let it vary between 22.5 and 27.5 days
depending on the temperatures during the 25-day period
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after the start date of the growing season. With the phenology model parameterisation we also calculated the
annual GPP values for 1993±2000 and compared those
to the output from the baseline model ± that is, with the
original parameterisation presented in Table 1.
Effects of possible changes in climate on snowmelt, soil
thaw, phenology and GPP were estimated by running the
model with four different temperature scenarios: air temperatures measured for 1993±2000 are increased with 1, 2
and 3 8C, respectively, throughout the year. Also one
scenario is used in which winter temperature is increased
with 4 8C and summer temperature by 2 8C, as global
circulation models currently predict a stronger increase
in warming in winter than in summer (Cattle & Crossley,
1995; Rowntree, 1997; IPCC, 1998). Precipitation falls as
snow if the air temperature is below 0 8C (Lynch-Stieglitz,
1994). In the climate scenarios, therefore, with increasing
temperature, less snow is falling and the precipitation
that is falling with an air temperature that is higher
than 0 8C is treated as rainfall.
We also performed a sensitivity analysis in which we
quantified the effect of small changes in the start and end
dates of the growing season on the simulated values of
annual GPP. The dates determined with the three different hypotheses concerning the end of the growing season
and the start dates are shifted with values between 5
and ‡ 5 days and the corresponding values of annual
GPP for the years 1993±2000 are calculated.
Results
Test of model vs soil temperature data
The 8 years of temporal development of measured and
modelled soil temperature data at a depth of 20 cm
agreed closely (Fig. 2). We used the depth of 20 cm because this was the depth for which 8 years of continuous
data were available. In order to illustrate the effect of
including a snow pack model, we also plotted the results
of the thermal model if the snow pack model was turned
off. The effect of including the snow pack model is
very clear, especially in the winter; because of the
748 M . T . V A N W I J K et al.
1993
20
0
−10
−20
0
100
200
Day of year
−20
−30
300
0
100
200
300
Day of year
1996
Model with snow
Model without snow
Measurements
Model with snow
Model without snow
Measurements
Soil temperature (⬚C)
Soil temperature (⬚C)
−10
10
10
0
−10
−20
−30
0
100
200
Day of year
0
−10
−20
−30
300
0
100
1997
20
200
Day of year
300
1998
20
Model with snow
Model without snow
Measurements
0
−10
−20
−30
0
100
Model with snow
Model without snow
Measurements
10
Soil temperature (⬚C)
10
Soil temperature (⬚C)
0
1995
20
200
Day of year
0
−10
−20
−30
300
0
100
Model with snow
Model without snow
Measurements
Soil temperature (⬚C)
−10
−20
−30
100
Model with snow
Model without snow
Measurements
10
0
0
200
Day of year
300
2000
20
10
200
Day of year
1999
20
Soil temperature (⬚C)
Model with snow
Model without snow
Measurements
10
Soil temperature (⬚C)
Soil temperature (⬚C)
10
−30
1994
20
Model with snow
Model without snow
Measurements
300
0
−10
−20
−30
0
100
200
300
Day of year
Fig. 2 Measured and simulated soil temperatures at depth 20 cm for 1993±2000; model results are shown with and without a snow pack
model.
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M O D E L L I N G P L A N T P H E N O L O G Y I N T H E A R C T I C 749
insulating properties of the snow, the soil temperatures
in winter are much higher when the snow pack model is
included.
In some cases, predictions and measurements do deviate. Missing snow data cause the major underestimation
of the soil temperatures at the end of 1993 (notice that the
model with and without the snow SUB model does not
deviate during that period). There are smaller underestimations of the soil temperatures visible during the winter
periods of 1995±1996 and 1996±1997. A clear underestimation of soil temperatures is visible at the end of 1998.
According to the precipitation data almost no snow had
fallen during the second part of 1998 (notice again the
small difference between the model with and without
the presence of a snow pack). The measured soil temperatures, however, seem to indicate that there was a significant amount of snow present; possibly spatial
redistribution of snow by the wind can cause this discrepancy in the model. Other deviations in the model are
the late increase in soil temperatures in the springs of
1996 and 2000, although the start of soil temperatures
above 0 8C is captured correctly.
Of the two depths that are used as the main triggers in
the phenology model for leaf expansion and leaf senescence ± that is, 5 and 10 cm ± we only had 3 years of data
available (Fig. 3 and Table 2). Again, the model performed well, although model deviations are present, not
surprisingly during those periods discussed earlier
(Fig. 2). The deviations between simulated and measured
triggers for the beginning (thaw at a depth of 10 cm) and
the end of the growing season (freezing at a depth of
5 cm) are not large, and do not seem to be systematic,
although the amount of data for these specific depths was
limited. The model was 3 days late in 1999 and correct in
2000 for the 10-cm-depth thaw trigger, whereas the
model was 3 days early in 1998 and 1999, and 2 days
late in 2000 for the 5-cm freezing trigger. At a depth of
20 cm, the model performed well during the whole 8-year
period and it is unlikely that the model deviations at the
more shallow depths of 10 and 5 cm will be systematically different from those at 20-cm depth. For the 3 years
presented here the model performance for all depths is
similar; the explained variance for all 3 years is 0.9 or
higher.
The variability in soil temperatures is also captured
reasonably well by outputs of the model for the deeper
locations. We show the results of 1999, as an example, for
depths 50 and 100 cm (Fig. 4). At depth 100 cm, the seasonal variation of the soil temperature seems to be underestimated. So despite the absence of spatial interactions
(snow redistribution by wind or horizontal energy flow
through soil) the model gives an accurate estimation of
the interannual and depth variations of the period when
soil thaw in the different soil layers is present.
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Simulation of bud break data
Comparison with data of bud break indicates that the
simulation of the start dates of the growing season of
Betula nana is satisfactory (Table 2). These results suggest
that the linkage of the start of the growing season (the
flush of deciduous species) to the combined snow pack±
soil thermal model is reliable.
The simulated start dates of the growing season for the
combined SPA model, the temperature sum model and
the FU model show a very similar pattern for the period
from 1993 to 2000 (Fig. 5a). However, this similarity disappears when climate change scenarios are applied to the
models (Fig. 5b). If the air temperatures are increased by
2 8C, the changes predicted by the three models for the
years 1993±2000 are often different. For example in 1999
the shift in start date of both the temperature sum and the
forcing unit models was 6 days less than for the combined SPA model, whereas in 1997 it was 4±6 days more
than for the SPA model.
Effects of end of growing season hypotheses on simulated
annual GPPs
The three different hypotheses for the end of the growing
season give similar annual estimates of GPP for the control plot for the years 1993±2000 (Fig. 6), although the
frost-determined end of the growing season gives values
slightly higher than those based on the other two hypotheses. Also clearly shown is the fact that the output based
on the `periodic' hypothesis has the lowest interannual
variability.
There is a negative relationship between the start date
and the annual GPP, so that the later the start of the
growing season, the lower the annual GPP (Fig. 7). The
overall decline in GPP is about 4 g C m 2 yr 1 per day
that the start of the growing season is delayed. This
relationship exists for simulations based on all three hypotheses, but is clearer for the results from the photoperiod and periodic hypotheses. Output based on the
periodic hypothesis shows the smallest negative slope ±
a decline in GPP of about 1.5 g C m 2 yr 1 for each day
that the start of the growing season is delayed. In this
hypothesis, a later start in the season is compensated
for by a later end of the growing season, whereas for
the photoperiod hypothesis a later start of the growing
season means automatically a decrease in the length of
the growing season.
During harvests in the plots in 1982, 1983, 1984, 1989
and 1995, the annual net primary production (ANPP) of
the vegetation varied between 120 and 180 g C m 2 yr 1
(Shaver et al., 2001). If one uses the relatively conservative
relation found in diverse ecosystems in which net primary production (NPP) is between 0.45 and 0.50 of the
750 M . T . V A N W I J K et al.
1998, 10 cm depth
1998, 5 cm depth
20
20
Model
Measurements
15
10
Soil temperature (⬚C)
Soil temperature (⬚C)
Model
Measurements
0
−10
−20
10
5
0
−5
−10
−15
−30
0
100
−20
300
0
300
1999, 10 cm depth
Model
Measurements
15
Soil temperature (⬚C)
10
5
0
−5
−10
−15
10
5
0
−5
−10
−15
0
100
200
Day of year
−20
300
0
100
200
Day of year
300
2000, 10 cm depth
2000, 5 cm depth
20
20
Model
Measurements
Model
Measurements
15
10
Soil temperature (⬚C)
Soil temperature (⬚C)
200
Day of year
20
Model
Measurements
15
−20
100
1999, 5 cm depth
20
Soil temperature (⬚C)
200
Day of year
0
−10
10
5
0
−5
−10
−15
−20
0
100
200
Day of year
300
−20
0
100
200
Day of year
300
Fig. 3 Measured and simulated soil temperatures at depths 5 and 10 cm for 1998±2000 (only for 1995 at depth of 5 cm were the data based
on temperature intervals of 0.1 8C; for all the other years and depths the precision was 1 8C).
Table 2 Measured data of bud break of Betula nana (in parentheses the standard deviation) as reported by Pop et al. (2000) and the
simulated start dates by the SPA model
Year
Start date based on soil temperature simulations
Bud break date Betula nana
1995
1996
1997
159
166
157
159 (4.6)
168 (2.3)
160 (4.9)
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M O D E L L I N G P L A N T P H E N O L O G Y I N T H E A R C T I C 751
1999, 50 cm depth
(a)
180
10
Model
Measurements
Start date (Julian days)
Soil temperature (⬚C)
5
0
−5
−10
−15
−20
Soil thaw model (SPA)
Forcing unit model
Temperature sum model
175
170
165
160
155
150
0
100
200
145
300
Day of year
1993
1994
1995
1996
1997
1998
1999
2000
Year
(b)
0
−2
1999, 100 cm depth
Shift in start date (days)
10
Model
Measurements
Soil temperature (⬚C)
5
0
−5
−4
−6
−8
−10
−12
Soil thaw model (SPA)
Forcing unit model
Temperature sum model
−14
−10
−16
1994
1996
1998
2000
Year
−15
0
100
200
300
Day of year
Fig. 4 Measured and simulated soil temperatures at depths 50
and 100 cm for 1999.
GPP (Waring et al., 1998), our GPP estimates ± ranging
between 260 and 430 g C m 2 yr 1 (Fig. 6) ± compare very
well with these values.
Sensitivity analysis and climate change scenarios
Increasing the length of the growth period of leaf area
from the baseline value to the maximum value leads to
a small decrease in the annual simulated values of GPP.
A growing period of 22.5 days leads in all end-ofgrowing-season hypotheses to an increase of the mean
annual GPP of 4% compared to a growing period of
27.5 days. The effect on the annual GPP is, therefore,
relatively small, especially when compared to the interannual variability that is present in the simulated GPP ±
between 7 and 25% of the mean annual value depending
on which model of the three end-of-growing-season
hypotheses is used.
ß 2003 Blackwell Publishing Ltd, Global Change Biology, 9, 743±758
Fig. 5 (a) Simulated start dates for 1993±2000 using the SPA
model with the snowpack ± soil thaw submodels, the temperature sum model and the forcing units models, all calibrated on
the 1995 bud break date, day of year 159; (b) change in start dates
as a result of an air temperature increase of 2 8C.
If in the different climate scenarios the air temperature
is increased, the annual GPP estimates show a strong
increase (Fig. 8) and the period of soil thaw in summer
is extended (Fig. 9). The model output based on the frost
hypothesis reacts with an especially strong increase in
estimated annual GPP. If the air temperature is increased
by 3 8C, the annual GPP simulated by the model using
this hypothesis is about 30% higher than that based on
the periodic hypothesis. This difference is caused by the
fact that in the frost hypothesis both the start and the end
date of the growing season will change, resulting in
longer growing seasons (upto 20 days longer), whereas
for the periodic hypothesis the start date will be earlier,
but the lengths of the growing season will be the same.
An increase in air temperature during the year does
not automatically lead to a similar increase in soil temperature (Fig. 10). For example, during winter, if the
snow pack thickness is less as a result of increased
752 M . T . V A N W I J K et al.
Periodic
450
400
400
GPP (g C m−2 yr−1)
GPP (g C m−2 yr−1)
Photoperiod
450
350
300
250
350
300
250
200
200
1993
1994
1995
1996
1997
1998
1999
1993
2000
1994
1995
1996
1997
1998
1999
2000
Year
Year
Frost
450
GPP (g C m−2 yr−1)
400
350
300
250
200
1993
1994
1995
1996
1997
1998
1999
2000
Year
Fig. 6 Simulated interannual variability of annual gross primary productivity (GPP) for the three different hypotheses (frost end,
periodic and photoperiod) concerning the end of the growing season; black line is the mean value of the 8 years.
500
450
GPP (g C m−2 yr −1)
400
Annual GPP (g C m−2 yr−1)
Photoperiod
Periodic
Frost
350
300
250
450
Photoperiod
Periodic
Frost
400
350
200
250
Baseline
200
145
T+1
T+2
T + 3 T + 4 and 2
Scenario
150
155
160
165
170
175
Start date
Fig. 7 Relationship between the start date of the growing season
and the annual simulated gross primary productivity (GPP) for
the three different hypotheses concerning the end of the growing
season.
Fig. 8 Effects of increasing temperature on the simulated
annual gross primary productivity (GPP); baseline is the simulation in the current climate; in T ‡ 1 air temperature is increased
with 1.0 8C throughout the year, in T ‡ 2 with 2.0 8C, in T ‡ 3 with
3.0 8C and in T ‡ 4 and 2 with 4.0 8C in winter and 2.0 8C in
summer.
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M O D E L L I N G P L A N T P H E N O L O G Y I N T H E A R C T I C 753
1993
0.00
−0.25
Depth (m)
−0.50
−0.75
−1.00
−1.25
−1.50
100
150
200
Day of year
250
300
Fig. 9 Sensitivity of the soil thaw pattern throughout the year to
changes in air temperature (coloured areas represent the depths
during the period in which the temperature was 0 8C; black:
baseline run; dark grey: extra days in which soil was thawed at
scenario temperature plus 1 8C; light grey: extra period in which
soil was thawed at the scenario in which temperature in summer
was plus 2 8C and in winter plus 4 8C).
temperatures, the insulating properties of the snow pack
can be more limited. An increased air temperature can
actually lead to lower soil temperatures during the
winter of 1995 and during autumn/winter of 1999 (the
latter indicated by the arrow). The results of the winter of
1995 are probably influenced by the missing precipitation
data during that winter.
The results of the sensitivity analysis, in which the
effects of small changes in the start and the end dates of
the growing season on the simulated annual GPP values
are quantified (Fig. 11), clearly indicate the importance of
an accurate estimation of the onset and end of the growing season; a change in 5 days in the start of the growing
season can lead to a change in the annual GPP of about
10%. Although in the periodic hypothesis the change in
the start date is compensated by an equivalent change
in the end date, there is still an increase in GPP visible
because of higher light values in spring. Changes in the
end date of the growing season are influential; a shift in
5 days for the frost and the photoperiod hypotheses leads
to change of the simulated annual GPP of about 6%.
Discussion
In the Arctic, plant activity is determined by snowmelt
and soil thaw in spring (Chapin & Shaver, 1985; Shaver &
Kummerow, 1992). In this study, we present a model
ß 2003 Blackwell Publishing Ltd, Global Change Biology, 9, 743±758
that, from a process-based perspective, simulates Arctic
plant phenology by linking submodels of snow pack
development, soil thermal processes and plant activity.
The linked snow pack and soil thermal model performed
well in simulating measured soil temperature data. Soil
thaw at a depth of 10 cm accurately predicted for 3 years
the measured bud break data of Betula nana, without
further empirical fine-tuning of the model parameters.
The SPA model, including the different submodels for
leaf loss, simulated a high interannual variability of
gross primary productivity ± ranging from 260 to
430 g C m 2 yr 1 (Fig. 6) ± stressing the need for long-term
carbon (C) exchange datasets in order to test models. The
three phenology models, in which the end date of the
growing season was determined by light conditions, periodic genetic restraints, or by frost, resulted in different
interannual variability, with relative differences from the
average 8 years value ranging from 20 to ‡ 20% for the
photoperiod hypothesis, from 7 to ‡ 7% for the periodic hypothesis and from 25 to ‡ 26% for the frost
hypothesis (Fig. 6). There was also a clear difference in
the sensitivity of plant productivity to climate change
scenarios for the three hypotheses, with the model
based on the frost hypothesis showing the highest increase in productivity with increasing temperatures.
The comparison of the simulated bud break dates of
the combined SPA model with the temperature sum
based models showed that, for current climate situation,
the predictions of both types of models were similar
(Fig. 5a). However, when temperature increases were
incorporated, the predictions of the models strongly deviated (Fig. 5b). These deviations for the individual years
could not easily be linked to the meteorological characteristics of these years. The resulting start dates simulated
by the SPA soil thaw submodel are determined by a
complex interplay between the severity of the winter,
the amount of snow that has fallen across an area in the
winter, the speed at which temperatures increase in
spring and the actual spring temperatures. When the
deviations between the different end-of-season phenology models are linked to the results of the sensitivity
analysis (Fig. 11), the simulated differences of up to
6 days in the start of the growing season between the
different models could result in differences of more
than 10% in simulated annual GPP. This shows that it
can be dangerous to apply empirically based phenology
models to climate change scenarios. Air temperature,
snowmelt and soil thaw are interrelated in a non-linear
manner and simple temperature sum models do not incorporate these interrelationships. The temperature sum
models can work satisfactorily in the current climate, but
this does not mean that the empirically based thresholds
and the weighting parameters as used in the forcing unit
model will hold in future.
754 M . T . V A N W I J K et al.
0.6
Snow depth (m)
0.5
Baseline scenario
T + 2 8C scenario
0.4
0.3
0.2
0.1
0.0
1993
1994
1995
1996
1997
1998
1999
2000
2001
1998
1999
2000
2001
Soil temperature at 10 cm depth (8C)
Year
15
10
5
0
−5
−10
−15
−20
−25
1993
1994
1995
1996
1997
Year
Fig. 10 Effects of an increase of 2 8C on simulated snow depth and soil temperatures at 10-cm depth (line: baseline model; dotted: model
temperature plus 2 8C; note: precipitation data were missing during October to December 1993 and from October 1994 to May 1995; arrow
indicates the period in which T ‡ 2 8C scenario simulates lower soil temperatures than with the baseline scenario while precipitation data
were fully available).
The test of the soil thermal model showed that the
linked snow pack±soil thermal model performed well.
The model captured the key temporal patterns and predicted the timing of soil thaw accurately, not only at
shallow depths, but also deeper in the soil. The model
errors can in most cases be linked to missing data or the
non-spatiality of the model. Overall, the thermal model ±
in combination with SPA and the snow model ± provides
a good basis for predicting soil thaw periods in arctic
environments. It also gives insight into more complex
feedbacks. For example, an increase in air temperature
can in some years lead to lower snow depths, thereby
leading to smaller snow insulation, and thereby in effect
sometimes leading to lower soil temperatures (Fig. 10, see
arrow).
Modelling the period of soil thaw accurately is not only
important in order to determine the length of possible
plant activity in the arctic, but is also important for other
biological processes such as decomposition. Goulden et al.
(1998), for example, showed in a boreal forest that the
decomposition of organic matter increased ten-fold upon
soil thawing. This result shows that the quantification of
the thaw and therefore the biologically more active
period is essential for understanding the biogeochemistry
of boreal and arctic ecosystems.
Plant phenology has a large impact on interannual
variability of GPP, and thereby it also strongly affects
the net carbon dioxide (CO2) uptake (Tenhunen et al.,
1996). Our results also show that the type of phenology
model is important; the processes determining the end of
the growing season are much less well understood than
the processes determining the start of the growing season
and the conceptual model chosen clearly influences the
interannual variability quantified with the model. The
simulated sensitivity of plant productivity to climate
change is strongly affected by the end-of-growing season
hypothesis that is used. The frost-determined end of the
season leads to larger increases in plant productivity than
the other hypotheses under climate change. The annual
GPP of an ecosystem with plant species that can take full
advantage of the potential increase in growing season
length, both in spring and in autumn, can have upto 30%
ß 2003 Blackwell Publishing Ltd, Global Change Biology, 9, 743±758
M O D E L L I N G P L A N T P H E N O L O G Y I N T H E A R C T I C 755
(a) 400
Photoperiod
Periodic
Frost
GPP (g C m−2 yr−1)
380
360
340
320
300
280
260
−6
−4
−2
0
2
4
Earlier
6
Later
Change in start date (days)
(b) 400
Photoperiod
Periodic
Frost
GPP (g C m−2 yr−1)
380
360
340
320
300
280
−6
−4
−2
0
2
Earlier
4
6
Later
Change in end date (days)
Fig. 11 Sensitivity of annual gross primary productivity (GPP)
to small changes in the start (a) and end date (b) of the growing
season; error bars represent the standard deviation of the interannual variability in GPP for the years 1993±2000.
greater annual GPP than ecosystems with only periodic
plant species (Fig. 8). It is thus critical that we study the
phenological characteristics of key species in more detail.
Difference in plant response to climate change can be
linked to the separation in plant phenological types made
by Sùrenson (1941). Sùrenson distinguished two phenological patterns in tundra species from Greenland: (i)
periodic species, characterized by a finite growing period
controlled by genetic constraints and (ii) aperiodic
species, which are species that continue to function until
the environment becomes unfavourable. With increasing
growing season length we would expect that periodic
species with fixed growing intervals are clearly at a disadvantage relative to aperiodic species. In order to test
this hypothesis, the effects of climate changes on the
length of the growing season of different plant species
should be studied in more detail, and the consequential effects on competitiveness and productivity. For
ß 2003 Blackwell Publishing Ltd, Global Change Biology, 9, 743±758
example, Polygonum bistorta responded to snow removal
and soil warming by becoming active earlier and senescing earlier, thereby showing no change in growing
season length (Starr et al., 2000). According to the model
results presented here, this still could mean an increase in
plant productivity, although smaller than if the plant
could delay the autumn senescence.
The model presented here was applied at ecosystem
scale, where it showed that the quantification of the end
of the growing season is an important aspect in the modelling of plant productivity. Lack of data currently prevents us from ascertaining which of the hypotheses is the
best approximation to the tussock tundra ecosystem
studied in this paper. Individual species comprising the
ecosystem probably display a combination of the three
hypotheses, thereby further complicating ecosystem level
behaviour.
In the current quantification of the interannual variability of GPP, no variation of the maximum LAI is included, because the determining processes are poorly
understood. Probable determining factors are the growth
during the last season and the current year soil thaw,
both length and depth, determining, respectively, the
plants' stored nutrient pool and the current year availability of nutrients. Including these factors could increase
the interannual variability of GPP estimated in this study;
two consecutive warm years would, in the second year,
lead to a longer growing season and a higher maximum
LAI, resulting in a higher annual GPP value. On the other
hand, two consecutive cold years would, in the second
year, lead to a lower GPP estimate. Shaver et al. (1986)
showed that leaf growth of Eriophorum vaginatum
L. stopped and storage reserves began to be replenished
when rhizome nitrogen (N) concentrations reached a certain similar minimum concentration for both the control
and fertilisation treatments. This strongly suggests that
both timing and magnitude of the leaf development of
Eriophorum vaginatum L. in the current year are dependent on the amount of nutrients stored not only during the
current year but also during the previous years. A reliable
quantification of the responsiveness of this type of ecosystem to climate change will have to incorporate the
interannual coupling of resource-acquisition, phenology
and growth.
At the moment, the coupling between biogeochemical
cycles, plant resource-acquisition, phenology and growth
is not possible to model in a reliable manner, simply
because of the lack of data. The model presented here is
a step forward because it links the processes of snow
accumulation and thaw, soil energy exchanges and
plant phenology, although a simplification in the model
is the abrupt change in the seasons; in reality, freeze-thaw
cycles and patchy snow cover, especially close to obstructions, do not give these abrupt changes. Expanding the
756 M . T . V A N W I J K et al.
model by incorporating biogeochemistry will take longterm datasets of nutrient-availability, plant nutrient
status, leaf area development, soil temperature, soil
water content and meteorology.
Higher air temperatures throughout the year increase
both soil thaw depth and soil thaw period (Fig. 9),
thereby also influencing the nutrient availability for
plants. An increased nutrient availability could lead to
shifts in species composition, higher leaf areas and higher
leaf N values, and thereby to even higher GPP values
than we predict in Fig. 8. The increases of nutrient availability can, however, be balanced by increases in plant
and microbial assimilation and/or increases in nutrient
losses via denitrification or leaching.
Increased shrub density and higher leaf area values
could also lead to unexpected negative feedbacks in the
system. In the long-term fertilisation experiment of tussock tundra in Toolik Lake, the depth of thaw of the
fertilised plots is much lower than that of the control
plots, probably because of two changes in the thermal
characteristics of the ecosystem. First, the canopy of the
fertilised plots was much denser and taller, thereby reducing the amount of solar energy that penetrates to the
surface of the soil (McFadden et al., 1998). Second,
the increased thickness of the litter layer of the fertilised
plots reduces the thermal conductivity of the upper
layer of the soil system. These two changes could influence the timing of soil thaw of the deeper soil layers and,
thereby, the decomposition of the organic material,
leading to a negative feedback on the start of the growing season and the expected increase of nutrient
availability.
Important elements missing in the current model are
moss photosynthesis, spatial interactions of thermal processes, microtopography of tussock tundra and phenological characteristics of individual species and plant
types present in tussock tundra. Mosses can contribute
significantly to ecosystem C exchange, especially in
spring and autumn. In the current model, we only quantified interannual variability of vascular plants. Future
development of the SPA model will focus on the inclusion of moss photosynthetic activity, along the lines of
Tenhunen et al. (1996) and Lloyd (2001). Spatial flows of
energy and matter can strongly influence the thermal and
hydrological balance of ecosystems. Although for this
study spatial patterns of heat exchange are not critical,
given the good agreement between modelled and
measured soil temperature data, for a larger scale application of the SPA model these spatial patterns will be
important. Another essential element to incorporate in
such a spatial application will be snow redistribution.
Current developments in this field include the linking
of the SPA model or a simplified version of the SPA
model, the ACM model (Williams et al., 2001a), to a
hydrological model including topographic characteristics
of arctic tundra based on the TOPMODEL approach
(Stieglitz et al., 2000). In order to test the SPA model
more thoroughly, the model must be tested on longterm, multiyear CO2, water and energy flux data in combination with soil thermal and hydrological measurements. In order to be able to do this, besides a moss
submodel, a soil respiration submodel must also be included. On a much smaller spatial scale, microtopography is an important factor in the spatial heterogeneous
setting of tussock tundra. Inter-tussock locations have
different soil physical characteristics than tussock locations and the presence of frost boils can influence the
depth of thaw considerably (Gough et al., 2000). For the
current modelling exercise we took a model parameterisation for tussock tundra as presented by Hinzman et al.
(1998), which was able to describe the average thermal
characteristics of tussock tundra sites as compared with
other vegetation types occurring in the Arctic. Another
important point is that in the current model configuration
we described phenology at an ecosystem level, whereas
individual species and plant types within one ecosystem
can have different phenological characteristics, both at
the start and at the end of the growing season. A more
differentiated, and thereby more realistic, plant phenology model will be developed in future; in this study,
we wanted to develop different plant phenology models
and hypotheses on an ecosystem level, and thereby quantify the effects of using different hypotheses on simulating GPP of tussock tundra.
Conclusions
The SPA model with the snow pack and soil thermal
submodels effectively simulated the soil energy balance
of the Arctic tundra. The phenology submodel based on
the occurrence of soil thaw in spring predicted bud break
dates of Betula nana reliably. Empirical temperature
models predicted similar bud break dates as our phenology
submodel in the current climate, but deviated when
increased temperature scenarios were run, indicating
that these simple models cannot cope well with the
non-linear interactions between climate, snowmelt and
soil thaw. The uncertainty in factors that drive the end
of growing season resulted in differences in the variability of simulated annual GPPs ranging from 20 to ‡ 20%
for the photoperiod hypothesis, from 7 to ‡ 7% for the
periodic hypothesis and from 25 to ‡ 26% for the frost
hypothesis. The hypothesis used for the factor driving the
end of the growing season also strongly influenced the
sensitivity of simulated annual GPP to climate change.
In order to decrease the uncertainty in the model result,
more rigorous testing of the model is necessary, both
spatial and temporal.
ß 2003 Blackwell Publishing Ltd, Global Change Biology, 9, 743±758
M O D E L L I N G P L A N T P H E N O L O G Y I N T H E A R C T I C 757
Acknowledgements
This work is funded by NSF grant DEB0087046, `LTER Cross site
2000: Interactions between climate and nutrient cycling in arctic
and subarctic tundras'. We also thank Sarah Morrisseau and
Joseph Rodriguez for their help during the LAI harvest and
Steve Oberbauer for supplying his published phenology data.
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