Effects of the short-term temporal variability of meteorological variables on
soil temperature in northern high-latitude regions have been investigated.
For this, a process-oriented land surface model has been driven using an
artificially manipulated climate dataset. Short-term climate variability
mainly impacts snow depth, and the thermal diffusivity of lichens and
bryophytes. These impacts of climate variability on insulating surface layers
together substantially alter the heat exchange between atmosphere and soil.
As a result, soil temperature is 0.1 to 0.8 ∘C higher when
climate variability is reduced. Earth system models project warming of the
Arctic region but also increasing variability of meteorological variables and
more often extreme meteorological events. Therefore, our results show that
projected future increases in permafrost temperature and active-layer
thickness in response to climate change will be lower (i) when taking into
account future changes in short-term variability of meteorological variables
and (ii) when representing dynamic snow and lichen and bryophyte functions in
land surface models.
Introduction
Soil temperature is an important physical variable of a terrestrial ecosystem
since it controls many functions of microbes and plants. In permafrost
regions, soil temperature also defines the biologically active part of the
soil that is thawing in summer (active layer). Therefore, impacts of future
warming on soil temperature have been investigated in numerous experimental
and modelling studies during the past decades. Large-scale soil temperature
is mainly determined by vertical heat conduction. Therefore, soil temperature
usually follows an annual sinusoidal cycle of air temperature with a damped
oscillation . That is why the projected large increase in
air temperature in the Arctic region over the next 100 years
is raising large concerns about the response of soil
temperature and hence permafrost thawing in the Arctic. Indeed, measurements
during the last decades already show an increasing permafrost temperature
and active-layer thickness in
response to global warming. Also, initial modelling results confirm such simple
response of increasing future soil temperature and active-layer thickness
. As a result of
increasing soil temperature and active-layer thickness, heterotrophic
respiration is suggested to increase because of the temperature response of
biochemical functions and the
additional availability of decomposable substrate
potentially leading to a positive
climate–carbon cycle feedback .
Meteorological variables, such as air temperature and precipitation, will not
only change gradually into the future, but also their short-term variability
and frequency of extreme events is projected to change
. For instance, for
northern high-latitude regions, climate models project an increase of the
annual maximum of the daily maximum temperature by 4 ∘C by
2100 while annual maximal daily precipitation is
projected to increase by 20 % in these areas by 2100. At the same
time, many ecosystem functions respond non-linearly to environmental factors;
cf. for instance the temperature-dependence of biochemical functions
. Therefore, effects of the short-term (daily to weekly)
variability of meteorological variables on the long-term (decadal) mean
ecosystem functions can enhance or dampen the effect of a general gradual
warming . That is why there is a strong
need to understand such effects of climate variability on ecosystem states
and functions in addition to gradual changes in order to reliably project
future ecosystem state dynamics and climate. In this context, effects of
climate variability on soil temperature in northern high-latitude
environments have not been studied so far: In addition to a gradual warming
of Arctic air and soil temperature, what are the specific effects of changing
short-term variability of meteorological variables on the long-term mean
annual or seasonal soil temperature? Will a short-term variability change
have the capability to enhance or dampen the anticipated soil warming?
Due to the well-known dampening effects of snow, near-surface vegetation and
the organic layer pp. 361–369, one would expect no to
little additional effects of changing air temperature fluctuations on soil
temperature, in particular on subsoil and permafrost temperature.
However, air temperature variability will have an impact on snow height
indirectly through snow density and also directly when
temperature periodically rises above the melting point. In addition, the
dependence of soil and near-surface vegetation conductivity on water and ice
content complicates the picture because water and ice
contents themselves are also temperature-dependent. Snow manipulation
experiments have proven the large spatial heterogeneity of soil
temperature in cold regions due to snow height heterogeneity
. The temporal variability of insulating layers and their
properties should be of similar importance for soil temperature.
At high latitudes, near-surface vegetation consists to a large extent of
lichens and bryophytes, which often form a continuous layer on the ground.
Lichens are symbiotic organisms consisting of a fungus and at least one green
alga or cyanobacterium, while bryophytes are non-vascular plants which have
no specialized tissue such as roots or stems. Both groups cannot actively
control their water uptake or loss, but they tolerate drying and are able to
reactivate their metabolism on rewetting. Typical species of upland regions
at high latitudes are feather bryophytes such as Hylocomium splendens and Pleurozium schreberi or the lichen
Cladonia stellaris. This near-surface vegetation grows on top
of any organic horizon and is hence important for heat fluxes between land and
atmosphere. In particular also for this layer, thermal and hydrological
properties depend highly on water and ice content. Hence, lichens and
bryophytes dynamically influence the vertical heat conduction
.
This study investigates the effects of temporal variability of
meteorological variables on snow and lichen/bryophyte insulating properties
and hence soil temperature in permafrost regions. For this, a recently
advanced land surface model (LSM) has been used that also represents
permafrost-specific processes, and in particular a dynamic snow
representation and a dynamic near-surface vegetation model
. While the model has been evaluated against several
types of observations in other studies
, here mean annual
ground temperature (MAGT) is evaluated again against different observations
or other modelling studies. Then, the model is run with two distinct climate
forcing datasets, one control dataset and one that has identical long-term
averages but reduced day-to-day variability of meteorological variables, such
as air temperature and precipitation. The differences in long-term average
results from these two model runs will therefore demonstrate the exclusive
effects of temporal variability of climate variables and extreme
meteorological events on MAGT in high-latitude permafrost regions.
MethodsThe land surface model JSBACH
The Jena Scheme for Biosphere–Atmosphere Coupling in Hamburg (JSBACH) is the
land surface scheme for the Max Planck Institute Earth System Model (MPI-ESM)
. It runs coupled to the atmosphere inside the
ESM or offline forced by observation-based or projected climate input data.
This model has recently been advanced by several processes which are
particularly important in cold regions : coupling of soil
hydrology and heat conduction via latent heat of fusion and the effects of
soil ice and water content on thermal properties, and a snow model for soil
insulation. The model simulates heat conduction and soil hydrology in a 1-D
vertical scheme using several layers . The version used
in this study has been updated from the one used in by two
additional deep soil layers for thermal and hydrological processes of 13 and
30 m, respectively, which lead to a total potential soil profile of
53 m. However, soil hydrological processes are constrained by the
depth to the bedrock. Another constraint on soil hydrological processes is
the potentially available pore volume, which is reduced by ice content.
In contrast to the model version described in , here we use
a further advanced snow module that includes dynamic snow density
and snow thermal properties . In this approach, the
snow density (ρsnow) follows a similar representation to that in
. It is initialized with a minimum value of
ρmin=50kgm-3. Then the compaction effect is
included as a function of time and a maximum density (ρmax=300kgm-3) value (Eq. ),
ρsnowt+1=ρsnowt-ρmaxexp-0.002⋅Δt3600+ρmax,
where Δt is the time step length of model simulation. Additionally,
when there is new snowfall, snow density is updated by taking a weighted
average of fresh-snow density (ρmin) and the calculated snow
density value of the previous time step.
Snow density controls snow heat conduction parameters.
Equations () and () show the
relationships of volumetric snow heat capacity (csnow) and snow
heat conductivity (λsnow) to snow density following the
approach of and . With no previous snow
layers, csnow is initialized with an average value of
0.52MJm-3K-1 and λsnow with
0.1Wm-1K-1,
csnow=cice⋅ρsnow,
where cice is the specific heat capacity of ice (2106Jkg-1K-1), and
λsnow=2.9×10-6⋅ρsnow2.
Another important advancement of the JSBACH model version used in this study
is the inclusion of a dynamic lichen and bryophyte model
. This model is designed to predict lichen
and bryophyte net primary productivity (NPP) in a process-based way from
available light, surface temperature, atmospheric carbon dioxide
concentration, and water content of lichens and bryophytes. Furthermore, it
is applicable when estimating various impacts of lichens and bryophytes on
biogeochemical cycles . The
model includes a dynamic representation of the surface cover which depends on
the balance of growth due to NPP and reduction by disturbance, such as fire
. The coverage of the layer determines its influence on
heat exchange between atmosphere and soil. The layer thickness and porosity
are set to 4.5 cm and 80 %, respectively.
The lichen and bryophyte water balance is integrated into the scheme of
hydrological fluxes in JSBACH. In addition, the lichen and bryophyte layer is
fully integrated into the heat conduction scheme and hence also functions as
a soil insulating layer . Soil insulation depends on the
fractional grid cell coverage of the lichen and bryophyte layer as well as on
its hydrological status. Thereby, thermal diffusivity of this layer is
computed as a function of water, ice and air content in the lichen and
bryophyte layer . The simulated relations between
thermal properties of the lichen and bryophyte layer and water content agree
well with field observations. provide a complete
description of the dynamic lichen and bryophyte model in JSBACH. The model
version used here differs from only with respect to the
parametrization of the snow layer, which has a slightly longer compression
time, and a few bug fixes. This updated version is also used in
, where it shows good agreement with site-level soil
temperature observations.
Forcing data
The JSBACH model driver estimates half-hourly climate forcing data using
daily data of maximum and minimum air temperature, precipitation, shortwave
and longwave radiation, specific humidity and surface pressure. We are using
global data at 0.5∘ spatial resolution, which has been produced
following the description in . The historical data from
1901 to 1978 came from WATCH Forcing Data , and for
the period 1979–2010 European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis data
have been bias-corrected against the WATCH forcing data following
as described in .
For a specific additional projection into the future (REDVARfut,
Sect. ), meteorological data during 2011–2100 have been
obtained from the Coupled Model Intercomparison Project Phase 5 (CMIP5) output of the Max Planck Institute Earth System Model
following the representative concentration pathway (RCP)
8.5. Meteorological data of the two grid cells representing the Canadian and
Russian sites were cut out and then also bias-corrected to the
observation-based period following as described in
.
Grid cells are divided into four tiles according to the four most dominant
vascular plant functional types of this grid cell . This
vascular vegetation coverage is assumed to stay constant over the time of
simulation. In the model simulations used in this study, we apply new soil
parameters. Hydrological parameters have been assigned to each soil texture
class following according to the percentage of sand,
silt and clay at 1 km spatial resolution as indicated by the
Harmonized World Soil Database . Thermal parameters have been
estimated as in at a 1 km spatial resolution.
Then, averages of 0.5∘ grid cells have been calculated. Soil depth
until bedrock follows the map used in based on
.
Meteorological forcing data with manipulated variability
Based on the climate data described above (subsequently called CNTL dataset),
an additional climate dataset has been developed. This dataset shows reduced
day-to-day variability but conserved long-term mean values as compared to
CNTL, as described in detail in . The dataset with reduced
variability is called REDVAR. In that dataset, the variability of daily
values is reduced by a variance factor of k=0.25 (see Beer et al., 2014,
for details), but the mean seasonal cycle is conserved. The seasonal
variability is represented by an 11-year running average across the same dates.
Differently from , seasonal means in the REDVAR dataset were
exactly preserved by normalization with respect to the CNTL dataset for the
annual quarters December–January–February, March–April–May, June–July–August
and September–October–November for each year individually.
For the specific additional projection until 2100 at site-level scale,
bias-corrected future climate data have been manipulated such that the
short-term variability of meteorological variables is dynamically
reducing during 2011–2100, in contrast to the REDVAR dataset for which
a constant reduction factor has been applied. This additional artificial
dataset is called REDVARfut in the following. For REDVARfut, the variance
factor k is set to change linearly from 1 to 0.1 over these 90 years
following Eq. ():
k=1-2.7-5⋅d,
where d is the day relative to 1 January 2011. This has been done for two
grid cells representing one location in Canada (medium recent MAGT) and one
location in eastern Siberia (cold recent MAGT) (cf. Sect. ). The
CNTL and REDVARfut datasets are identical for the time period before 2011.
Model experiments
For addressing the research question about effects of climate variability on
mean annual ground temperature in permafrost regions (cf. Sect. ),
artificial model experiments are conducted in this study. In addition to the
control model run (CNTL), in one model experiment called REDVAR the land
surface model has been driven by an artificial climate dataset that
represents a reduced short-term (day-to-day) climate variability while the
decadal averages are conserved (Sect. ). Then,
differences in decadal averages of simulated snow and lichen and bryophyte
properties and ultimately soil temperature can be interpreted exclusively due
to a difference in variability of meteorological variables.
Two different kinds of such experiments are presented in this study. The main
experiments are conducted at the pan-Arctic scale over historical to recent
time periods (1901–2010). Here, CNTL and REDVAR model runs are done exactly
the same way including the spin-up approach for bringing state variables,
such as soil temperature, in equilibrium with pre-industrial climate. At the
end, results are compared from “two different worlds” with the same average
climate, one with a constantly lower variability of meteorological variables
than the other.
The second kind of experiments has been performed at site-level scale. Here,
JSBACH has been run over the period 1901–2100 (CNTL), and a second model run
with constantly increasing reduction of climate variability
(REDVARfut, see Sect. ) has been performed for the period
2011–2100. This experiment additionally clarifies the effects of changing
future climate variability on permafrost temperature. The REDVARfut
experiment additionally contributes to the question on how climate data
should be prepared in order to perform so-called offline model experiments
in the future. Of particular concern are potential biases in future
projections of ecosystems states using LSMs because in these projections
anomalies of raw ESM output is usually added to recent short-term variability
of meteorological variables. Even if that is the most reliable approach of
conducting such future projections at the moment, still we need to address
the question of how high the bias could be just because a change in short-term
variability has been neglected. The REDVARfut experiment has been conducted
for two grid cells representing two sites, one Canadian site at about
62.2∘ N, -75.6∘ E with MAGT of about -5 ∘C, and one eastern
Siberian site at about 72.2∘ N, 147∘ E with MAGT of about
-10 ∘C. At these sites, JSBACH results differed by only 0.7
and 0.2 ∘C from the borehole measurements.
State variables have been brought into equilibrium using a spin-up approach
prior to the transient model runs (1901–2010 or 1901–2100). We assume the
time period 1901–1930 to be a representative for pre-industrial climatology
following and . Therefore, randomly selected years
from that period have been used. For a proper spin-up of soil physical state
variables in permafrost regions, we suggest a two-step procedure. First,
a 50-year model run with the above-described randomly selected climate from
the period 1901–1930 has been done without considering any freezing and
thawing. This first spin-up will bring the soil temperature and water pools
in an initial equilibrium with pre-industrial climate. In a second step, another
100-year spin-up with the same climate data is performed, but now freezing
and thawing are switched in order to have all pools including soil ice and
water content, and soil temperature in equilibrium with climate.
Mean annual ground temperature evaluation
The permafrost-enhanced JSBACH model has been intensively evaluated elsewhere
. The model version used here has recently
also been extensively evaluated against site-level observations
. In this paper, the simulated MAGT is again evaluated against various other datasets at
different spatial scales. First, JSBACH model results are compared to model
results from the Geophysical Institute Permafrost Lab (GIPL) 1.3 model over Alaska for the
period 1980–1989. For this we downloaded GIPL model results at
2 km× 2 km grid cell size from
http://arcticlcc.org/products/spatial-data/show/simulated-mean-annual-ground-temperature.
Then, the map was reprojected to geographic lat–lon using a bilinear method
and further aggregated to 0.5∘ grid cell size in order to be
comparable with JSBACH outputs. For this comparison we used JSBACH mean soil
temperature results from layer 7 (38 m depth) and during 1980–1989.
Then, spatial details of MAGT are compared to the information from the
Geocryological Map of Yakutia using also model results from
layer 7 but a mean value during 1960–1989. The depth of 38 m ensures
that temperature variation is negligible and hence comparable to the
information in the observation-based map. The time period 1960–1989
represents observations used to create this map . Last,
JSBACH subsoil temperature is compared to pan-Arctic borehole measurements
collected by the Global Terrestrial Network for Permafrost (GTN-P) initiative
using model results from
the layer corresponding to the measurement depth and from the year 2008. The
respective GTN-P Thermal State of Permafrost (TSP) snapshot data
was downloaded from the National Snow and Ice Data
Center (NSIDC) at http://nsidc.org/data/G02190\#.
Analysis
In order to analyse effects of variability of meteorological variables on
snow and near-surface vegetation properties and hence soil temperature, model
results from the period 1980–2009 have been averaged. As the averages of
climate forcing data is similar between both experiments, REDVAR and CNTL,
(relative) differences in long-term average model results, such as snow depth
or soil temperature, show the effects of short-term variability of climate
forcing data on ecosystem states and functions. Usually, differences are
calculated as REDVAR minus CNTL, and relative differences accordingly as
(REDVAR-CNTL) / CNTL. Therefore, relative differences are displayed as
a fraction (no unit). In Figs. to the grey area
represents all land outside the (sporadic) permafrost zone which is masked by
applying a long-term mean air temperature threshold of -3 ∘C.
In order to evaluate the short-term variability of the REDVARfut and CNTL
time series in Sect. , the mean absolute difference
(MAD) of both daily time series is computed for each year as
MADx,y=1n∑i=1nxi-yi.
Here, i denotes the day of the year, and n=365 or n=366.
ResultsMean annual ground temperature evaluation
When comparing against a global dataset of MAGT at depth ranging usually from 1 to 20 m (GTN-P initiative),
JSBACH shows almost no bias (-0.4 ∘C) and a root mean square
error of 3 ∘C Fig. . JSBACH represents the spatial
variation in MAGT reasonably well with
a coefficient of determination of 0.5. Figure shows that, for
a number of measurements between 0 and -1 ∘C, JSBACH
simulates a larger variation ranging from 2 to -8 ∘C. In
addition, JSBACH clearly underestimates MAGT at three borehole sites in the
Canadian High Arctic (data about -10 ∘C, model about
-22 ∘C), which requires further evaluation, for example about the
representativeness of these data points or about the validity of snowfall
input data to the model.
Evaluation of mean annual ground temperature against GTN-P borehole
measurements. Model results are taken from the depth of observation for each
point.
Difference in subsoil temperature (∘C) between the models
JSBACH and GIPL1.3 from the University of Alaska Fairbanks (1980–1989
average). JSBACH results from 38 m depth.
Difference in subsoil temperature (∘C) between the JSBACH model
(1960–1990 average) and the Geocryological Map of Yakutia .
JSBACH results from 38 m depth. The right-hand-side figure shows the
difference to MAGT mean minus standard deviation (spatial uncertainty) from
the Geocryological Map of Yakutia.
When looking at alternative estimates of spatial details of MAGT, JSBACH
underestimates or overestimates MAGT by about 2 to 4 ∘C depending on the location (Figs. and
). The JSBACH results for Alaska are compared
to another model output. JSBACH overestimates MAGT in many areas in Alaska by
several ∘C, while also underestimating MAGT at the southern end
of the North Slope (Fig. ). In eastern Siberia (Yakutia), the model
usually underestimates MAGT by 2 to 6 ∘C (Fig. )
as compared to an observation-based map . However, the
cold bias is largely reduced when taking the uncertainty (standard deviation)
in the original geocryological map into account (Fig. ). Then,
the difference is negligible in many regions. Still, there is a very strong
cold bias in the mountainous regions of eastern Siberia. When taking the map
uncertainty into account (Fig. ), the model still underestimates
MAGT by about 6 to 8 ∘C here. This bias also cannot be
explained by the general warm bias of very low MAGT in the geocryological map
when comparing to GTN-P observations . In fact, very low snow
depth model results in these areas of about 15 cm on average (data
not shown) seem to be the reason for a too-low insulation of soil during
a very cold winter.
Comparison of 1980–2009 averages of meteorological variables (REDVAR-CNTL)
or (REDVAR-CNTL) / CNTL. Air temperature colour scale adjusted to
Fig. .
Comparison of 1980–2009 standard deviations of meteorological variables
(REDVAR-CNTL) or (REDVAR-CNTL) / CNTL.
Climate forcing data comparison
The long-term (1980–2010) averages of air temperature differ by only
0.015 ∘C at maximum or 0.004 % between CNTL and
REDVAR in permafrost regions (Fig. a). Long-term
precipitation averages are also similar between the datasets, with differences of
-0.2 to 0.1 % (Fig. b).
In contrast, the difference in short-term variability of meteorological
variables at daily resolution between both datasets is remarkable. Although
the statistical transformation of variables has been performed at residuals
to the mean seasonal cycle (Sect. ), still the standard
deviation of air temperature at daily resolution is usually 0.2 to
1 ∘C lower in the REDVAR dataset than in CNTL, or 2 to
10 % (Fig. a). That means that temperature of warmer
days have been reduced, while air temperature of colder days have been
increased such that the overall mean air temperature is similar.
Interestingly, the amount of variability difference between the two datasets
also depends on the location. For example, smaller differences in standard deviation are visible
in colder regions, such as eastern Siberia and the
Canadian High Arctic. One explanation for this pattern is the higher mean
seasonal cycle in continental climate, which has not been manipulated
(Sect. ) and which therefore dominates stronger the
overall variability, which is analysed in Fig. a. REDVAR
precipitation standard deviation is also usually 2 to 6 % lower than
precipitation standard deviation of the CNTL dataset
(Fig. b). Hence, in this artificial climate dataset,
extremely heavy rainfall or snowfall is reduced, while small precipitation
amounts are increased.
Comparison of mean winter (DJF) season snow properties during 1980–2009.
Shown is the relative difference (REDVAR-CNTL) / CNTL expressed as a fraction
(–).
Autumn (SON) 1980–2009 average snowmelt relative difference. Relative
difference (REDVAR-CNTL) / CNTL expressed as a fraction (–).
Comparison of lichen and bryophyte 1980–2009 average properties. Relative
difference (REDVAR-CNTL) / CNTL expressed as a fraction (–).
Comparison of 1980–2009 average soil temperature (REDVAR minus CNTL). Shown
are absolute differences (∘C). Topsoil and subsoil refer to
depths of 3 cm and 38 m, respectively.
REDVARfut experiment results at a Canadian site
(62.2∘ N, 75.6∘ E) during 2011–2100 showing the
effects of changing climate variability on future soil temperature. Ten-year
moving means are shown.
REDVARfut experiment results at a Siberian site
(72.2∘ N, 147∘ E) during 2011–2100 showing the effects of
changing climate variability on future soil temperature. Ten-year moving means
are shown.
Climate variability effects on snow properties
Importantly, snow depth is up to 20 % higher under conditions of reduced climate
variability (Fig. a). In fact, the snow depth
difference can be explained by differences in snow water equivalents of the same
magnitude (Fig. b). In contrast, the slightly higher snow
density under reduced climate variability (Fig. c) is not able
to explain the difference in snow depth. Snowmelt flux differences in autumn
between both model experiments of 10 to 40 % (Fig. )
demonstrate clearly that, under reduced air temperature variability during the
beginning of the snow season, individual snowmelt events and hence the total
snowmelt flux are reduced. Besides snow depth, the thermal diffusivity of
snow controls the overall heat conduction. Figure d shows that,
under conditions of reduced climate variability, thermal diffusivity of snow is
0.5 to 2.5 % higher in high-latitude regions.
Climate variability effects on thermal diffusivity of lichens and bryophytes
Thermal diffusivity of lichens and bryophytes differs only marginally between
the REDVAR and CNTL model experiments over most of the northern high-latitude
permafrost regions (Fig. a). In western Siberia and Quebec,
winter thermal diffusivity of bryophytes and lichens is up to 12 %
lower under conditions of reduced climate variability (Fig. a). In
contrast, summer diffusivity of bryophytes and lichens is usually higher
under reduced variability of meteorological variables (Fig. b).
Under these climate conditions, it rains a little bit more often and air
temperature is not extreme, resulting in more moist conditions for lichens and
bryophytes, and hence higher thermal diffusivity. In tundra the difference is
about 2 %, while in the boreal forest it can be up to 6 %
(Fig. b).
Ultimate climate variability effects on soil temperature
The estimated long-term average of both topsoil and subsoil temperature
differs between REDVAR and CNTL experiments (Fig. a and b).
Soil is 0.1 to 0.8 ∘C warmer when climate variability is reduced
(Fig. a and b). These results and also the spatial pattern are
similar between topsoil and subsoil values (Fig. a and b), with
a slightly larger effect on topsoil temperature. Soil temperature differences are
larger in winter, with values up to 1.5 ∘C, than in
summer, when differences are typically 0.2–0.5 ∘C (Fig. c and d).
Effects of future changes of climate variability on soil temperature
In order to analyse effects of changing variability of
meteorological variables into the future, the results of the
respective additional future projections at two sites are displayed as time
series in Figs. and . In contrast to the
continental model experiments, in these additional point simulations the
variability of meteorological variables is increasingly reduced
during 2011–2100 in the REDVARfut input dataset, while the historical climate
until 2010 is identical (Sect. ).
The bias-corrected MPI-ESM CMIP5 model output following RCP8.5 shows
increasing air temperature at both locations (solid blue line in
Figs. a and a). Precipitation is also
increasing but not constantly (solid blue line in Figs. b
and b). Meteorological forcing data of the REDVARfut dataset
(red lines) show similar long-term averages to the CNTL dataset
(Figs. a, b, and a, b). Hence, REDVARfut
meteorological variables follow the general positive trend. However, the two
time series increasingly differ in their day-to-day and week-to-week
variability by design. This is shown by the mean absolute difference of daily
data (cf. Eq. ) in the insets of
Fig. a and b as well as Fig. a and b.
These CNTL and REDVARfut climate datasets have been used as forcing data for
JSBACH in the additional point-scale model runs. The respective soil
temperature results are compared to each other in Fig. c and
d as well as Fig. c and d. The increasing differences in the
variability of meteorological variables under conserved long-term averages
lead to an increasing difference in topsoil temperature
(Figs. c and c); i.e. the overall
increasing topsoil temperature due to increasing air temperature is a bit
higher in the case of reduced climate variability. This effect is also visible in
38 m depth (Figs. d and d) even
though short-term atmospheric data fluctuations in general should be most
filtered at this soil depth.
Discussion
Climate model projections show increasing variability of meteorological
variables and hence increasing frequency of extreme meteorological events
along with a gradually changing climate (change of
long-term mean values) . Because of the non-linearity
of ecosystem response functions, changing extreme-event frequency and
changing variability of meteorological variables can have a higher impact on
ecosystem state and function than a gradual change of mean meteorological
variables . This study contributes to this
overall question from a theoretical point of view with LSM experiments for
which artificially manipulated climate forcing datasets have been employed.
These climate datasets practically do not differ in their decadal averages
(Sect. ), whereas they do show a substantial difference
in the short-term (daily) variability (Sect. ).
Therefore, differences in simulated state variables and fluxes over 30-year
periods (soil temperature in this case) will be only due to differences in
temporal variability of meteorological variables. This study addresses
particularly the question about the effect of climate variability on soil
temperature in northern high-latitude regions. The CNTL experiment shows
higher climate variability than the artificial experimental REDVAR
dataset (Sects. and ), and
respective model result differences between experiments using the manipulated
climate REDVAR and the CNTL dataset are shown in Sect. .
Methodologically, it is important to artificially design a climate dataset
with reduced temporal variability because otherwise there is a high
risk for producing a physically unrealistic climate conditions. However, for
interpreting the results in terms of future ecosystem responses to
increasing climate variability , the results
of the CNTL model run are compared against the results of the REDVAR model
run in this discussion section (CNTL–REDVAR).
In contrast to the climate forcing data, the long-term average of both
topsoil and subsoil temperature differs between REDVAR and CNTL experiments
(Fig. a and b). The same is true for respective future
projections (Figs. and ). In fact, under
conditions of higher variability of meteorological variables and higher frequency of
extreme events (CNTL vs. REDVAR experiments), soil will be cooler
(Figs. c, d; ; and ) if
all other environmental factors are similar. That means that the projected
increase in future variability of meteorological variables
has the potential to dampen soil warming occurring as
a function of increasing mean air temperature. To further understand the
underlying processes, individual effects of climate variability on snow and
near-surface vegetation properties are discussed in the following paragraphs.
For land–atmosphere heat conduction the thermal properties of snow,
near-surface vegetation (e.g. bryophytes and lichens), the soil organic
layer, and their spatial extent and heights are of major importance
. Snow generally
insulates the soil from changing atmospheric temperature. However, effects
are smaller during the melting period in spring because the snow is wet and
conductivity therefore higher, and more importantly, the soil-to-air gradient
in temperature is small. The insulation effect of near-surface vegetation
also differs among the seasons because of the high dependence of thermal
properties on water and ice contents of lichens and bryophytes. Usually, dry
lichens and bryophytes during a continental summer should insulate much more
than during wet spring or autumn, or during the ice-rich wintertime.
This theoretical study shows that one major effect of higher climate
variability on cold-region environments is a lower snow water equivalent
(Sect. ), which directly translates into lower snow depth
values. The potential alternative explanation for a lower snow depth would be
a higher snow density. However, the results show exactly the opposite
(Fig. c). In addition to snow depth, snow thermal properties are
also an important factor for heat conduction. However, winter snow thermal
diffusivity is some percent lower under conditions of higher climate variability
(CNTL–REDVAR). Therefore, the net snow-related effect of higher
climate variability on soil temperature – that is, a cooler soil
(Sect. ) – is explained by snow depth differences alone,
i.e. a lower snow depth under conditions of higher climate variability.
The reason for these snow water equivalent differences are more often
circumstances of melting snow during the beginning of the snow season when
day-to-day variability of air temperature is higher
(Sect. ). These results also point to an interesting
combination of impacts of both changing variability and gradually
changing mean values on ecosystem states because both changes can lead to
a threshold value (melting point in this case) being passed. These impacts can be
seen in Sect. when combining temporal climate variability
effects on snow water equivalent results (Fig. ) and snowmelt
flux results (Fig. ) with longitudinal pattern of these results
towards a continental climate, which can be interpreted in terms of gradual
climate change when substituting space for time. Overall, these findings show
that projected higher climate variability in future can lead to lower snow
depth, which will reduce a soil warming in response to air warming. Future
studies should clarify if these temporal variability effects of
meteorological variables on snow depth are lower or higher when taking into
account lateral heterogeneity of soil properties or snow,
for instance due to snow intercept by topography or vegetation.
In addition to the insulating effect of snow, lichens and bryophytes growing
on the ground influence heat conduction . It is
interesting to note that, when climate variability is higher (CNTL
conditions), bryophyte and lichen thermal diffusivity can be substantially
higher in winter and lower in summer in the same region
(Sect. ). This fact points to an important role of
near-surface vegetation: it will insulate less from air temperature during
winter and insulate more during summer with increasing climate variability in
future. These effects of climate variability on thermal diffusivity of
lichens and bryophytes and hence soil temperature are in the same direction
as snow effects (Sect. ), again reducing the soil warming
effect of future climate change.
Effects of climate variability on both snow and bryophyte and lichen
properties are in the same direction (Sects. and
). As a result, soil will be cooler under conditions of higher climate
variability (Sect. ). Recent modelling studies suggest
a soil temperature increase of 0.02 ∘C per year since 1960
, which translates into 2 ∘C in 100 years.
Such soil temperature increase has also been projected using the JSBACH model
under the RCP4.5 scenario , while under the
strong-warming scenario RCP8.5, the soil temperature increase might be up to 6 to
8 ∘C. Lower soil temperature under
conditions of higher climate variability in the range 0.1 to 0.8 ∘C (Sect. ) demonstrates that, under increasing variability
of meteorological variables and increasing extreme events in the Arctic
, the effect of gradual air temperature increase on
soil temperature and hence active-layer thickness will be dampened.
Such dampening of future soil warming will also reduce the otherwise positive
biogeochemical feedback to climate .
Our results are conservative here because the 99 percentiles of air
temperature and precipitation from the artificial dataset (REDVAR) differ by
only 1–4 ∘C (temperature) and 1–10 %
(precipitation). These values are at the lower end of the range of climate
model projections for the Arctic region until 2100 .
The presented effects of short-term variability of meteorological variables
on ecosystem states and functions, such as soil temperature, are also
important from a methodological point of view. To study the effects of
environmental change on ecosystems, LSMs are usually forced by historical and
reanalysis climate data for the past and present periods, and by future
climate results from Earth system models. Since ESM results usually show
biases, the ESM outputs cannot be used directly to drive the LSM offline
model runs but first need to be bias-corrected . The
results of the presented REDVAR and REDVARfut experiments demonstrate that
such bias-correction methods should account for the projected change in
short-term (daily) variability in addition to general trends.
Soil temperature is projected to arrive at values around the freezing point
in 38 cm depth over the major part of the current permafrost area
. Therefore, differences of soil temperature of 0.1 to
0.8 ∘C due to changing climate variability would have an
effect on active-layer thickness and permafrost extent, too. It would be
interesting to generate an additional artificial REDVARfut dataset with
pan-Arctic cover and investigate in detail the impacts of climate variability
on active-layer thickness and permafrost extent at the end of the century in
a future project
Our findings have three major implications for future permafrost science:
New, highly controlled laboratory and field experiments are required in order to confirm modelling
results about climate variability effects on permafrost soil temperature.
Future developments of land surface models should include dynamic models of snow, and lichens and bryophytes.
Statistical methods need to be developed such that future forcing data for climate change impact studies
can be prepared in a way that a potential change in short-term variability and frequency of extreme events is preserved.
Conclusions
Artificial model experiments have been used in order to quantify the impact
of the variability of meteorological variables on the long-term mean of mean
annual ground temperature in permafrost-affected terrestrial ecosystems. In
future, the soil temperature response to increasing climate variability and
extreme-event frequency (soil cooling) will be opposite to the response of
soil temperature to gradually increasing air temperature (soil warming). Is
has been shown that snow and near-surface vegetation dynamics are the
underlying mechanisms for this. Therefore, dynamics of snow and lichen and
bryophyte functions need to be represented in Earth system models for validly
projecting future permafrost soil states and land–atmosphere interactions,
and hence future climate. Our findings also point to the need to represent
changes in short-term variability of meteorological variables in
bias-corrected climate data of future periods.
The land surface model JSBACH used in this study is
intellectual property of the Max Planck Society for the Advancement of
Science, Germany. The JSBACH source code is distributed under the Software
License Agreement of the Max Planck Institute for Meteorology, and it can be
accessed on personal request. The steps to gain access are explained under
the following link: http://www.mpimet.mpg.de/en/science/models/license/ (MPI, 2018a).
The CNTL climatic fields used in this study as forcing data for the JSBACH
model are available upon registration under the following link (the tag
“Geocarbon” has to be selected):
https://www.bgc-jena.mpg.de/geodb/projects/Home.php (MPI, 2018b).
The new REDVAR climatic fields used in this study as forcing data of the JSBACH
model are available as netCDF files from the authors upon request.
The map of soil temperature and active-layer thickness for the region of
Yakutia which is used as a part of our model evaluation is available under
the following link: 10.1594/PANGAEA.808240 (Beer at al., 2013).
GTN-P Thermal State of Permafrost (TSP) snapshot data used in this study for
model evaluation are available from the National Snow and Ice Data Center
(NSIDC, 2018) at 10.7265/N57D2S25.
GIPL model results at 2 km × 2 km grid cell size for Alaska used in this
study for model evaluation are available from
http://arcticlcc.org/products/spatial-data/show/simulated-mean-annual-ground-temperature (ALCC, 2018).
JSBACH output data which are presented as maps in this study are available as
netCDF files from the authors on request.
The authors declare that they have no conflict of
interest.
This article is part of the special issue “Changing Permafrost
in the Arctic and its Global Effects in the 21st Century (PAGE21)
(BG/ESSD/GMD/TC inter-journal SI)”. It is not affiliated with a conference.
Acknowledgements
Financial support came from the
European Union FP7-ENV project PAGE21 under contract number GA282700. Model
simulations were performed on resources provided by the Swedish National
Infrastructure for Computing (SNIC) at Linköping University. We
acknowledge the Land Department, Max Planck Institute for Meteorology,
Hamburg, Germany, for JSBACH code maintenance. Special thanks to Ulrich Weber
at the Max Planck Institute for Biogeochemistry, Jena, Germany, for climate
data processing. We thank Charles Koven, two anonymous reviewers, and the
editor Julia Boike for constructive reviews that helped to improve a previous
version of the paper. We further acknowledge the borehole temperature dataset
“IPA-IPY Thermal State of Permafrost (TSP) Snapshot Borehole Inventory,
Version 1.0”, downloaded from NSIDC. Edited
by: Julia Boike Reviewed by: two anonymous referees
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