Introduction
The impacts of climate change in the Arctic have been much
studied in recent years. Dramatic reduction in sea-ice area has been observed
over the past few decades . The observed
impacts of warming at the land surface include glacier retreat and permafrost
thaw . Both in models and
observations, warming is amplified in the polar region – surface air
temperature warming of up to 1.35 ∘Cdecade-1 is observed
in recent years, with large potential impacts
.
Permafrost is of interest not only because of the physical effects of
permafrost thaw, but because it contains large quantities of stored organic
carbon, approximately 1300–1370 Pg , which
may be released to the atmosphere in a warming climate. This could have
a significant climate feedback effect, which needs to be included in global
Earth system models (ESMs) in order to account for the full carbon budget in
the future
.
In order to simulate permafrost carbon feedbacks, the land-surface components
of ESMs should include both an appropriate carbon cycle and a representation
of the physical dynamics. The amount of carbon released from the soil is
strongly dependent on the physical state of the ground – the temperature of
the permafrost and the rate at which it thaws
. It is therefore important that the
physical dynamics of permafrost are addressed and thoroughly evaluated in
models before carbon cycle processes are considered.
There were major problems with the permafrost representation in the majority
of the CMIP5 global climate model simulations . In many
cases, permafrost processes were not represented, and the frozen land area in
many of the climate model simulations differed drastically from the
observations. show that there is little difference in
the 0∘ air temperature isotherm between models, suggesting that the
differences are mainly caused by the land-surface dynamics rather than by the
driving climate. Several land-surface schemes have since been modified to
better represent processes that are important for permafrost, for example by
including soil freezing, soil organic matter and improving the
representation of snow
.
This paper demonstrates the impact of adding new permafrost-related processes
into JULES Joint UK Land Environment
Simulator;, the land-surface scheme used in the
UK Earth system model (UKESM). Although the scarcity and uncertainty of
global data on permafrost limit the detail with which it can be represented
in a large-scale model like JULES, it is possible to capture the broad
spatial patterns of permafrost and active layer thickness (ALT) and to
realistically simulate present-day conditions.
describe in detail the relevant developments within JULES. These include the
effects of organic matter, moss, a deeper soil column and a modification to
the snow scheme. also show how these developments
impact model simulations at a high Arctic tundra site. This paper now applies
them to large-scale simulations, showing that they improve the model
performance on a large scale, and significantly impact the simulation of
permafrost under future climate scenarios. These developments result in
a more appropriate representation of the physical state of the permafrost –
a necessary precursor to considering the permafrost carbon feedback.
Methods
Standard model description
JULES is the stand-alone version of the land-surface scheme in the Hadley
Centre climate models and was originally
based on the Met Office Surface Exchange Scheme (MOSES)
. It combines a complex energy and water
balance model with a dynamic vegetation model. JULES is a community model and
is publically available from http://www.jchmr.org/jules. The work
discussed here uses a JULES version 3.4.1 augmented with improved physical
processes.
JULES represents the physical, biophysical and biochemical processes that
control the exchange of radiation, heat, water and carbon between the land
surface and the atmosphere. It can be applied at a point or over a grid and
requires temporally continuous atmospheric forcing data at frequencies of
3 h or greater. Each grid box can contain several different land covers or
“tiles”, including a number of different plant functional types as
well as non-vegetated tiles (urban, water, ice and bare soil). Each tile has
its own surface energy balance, but the soil underneath is treated as
a single column and receives aggregated fluxes from the surface tiles.
Recently a multilayer snow scheme has been adopted in JULES (described in
) in which the number of snow layers varies according
to the depth of the snow pack. Each snow layer has a prognostic temperature,
density, grain size and solid and liquid water content. This scheme
significantly improves simulations of winter soil temperatures in the
northern high latitudes . In the old zero-layer snow
scheme, the insulation from snow was incorporated into the top layer of the
soil. This scheme is currently still used when the snow depth is below
10 cm. Snow in the zero-layer scheme has a constant thermal
conductivity that is added in series to the conductivity of the top layer of
soil. In the multilayer snow model, thermal conductivity is parametrised as
a function of snow density. Snow albedo is parametrised as a function of snow
grain size .
The subsurface temperatures are modelled via a discretisation of both heat
diffusion and heat advection by moisture fluxes. The soil thermal
characteristics depend on the moisture content, as does the latent heat of
freezing and thawing. A zero-heat-flux condition is applied at the lower
boundary. The soil hydrology is based on a finite difference approximation to
the Richards' equation , using the same vertical
discretisation as the soil thermodynamics . JULES uses the
relations to describe the soil water retention curve
and calculate hydraulic conductivity and soil water suction. The soil
hydraulic parameters are calculated according to . The
default vertical discretisation is a 3 m column modelled as four layers, with
thicknesses of 0.1, 0.25, 0.65 and 2 m.
The land-surface hydrology (LSH) scheme simulates a deep water store at the
base of the soil column and allows subsurface flow from this layer and any
other layers below the water table. Topographic index data are used to
generate the wetland fraction and saturation excess runoff
.
Recent model developments
Recent developments of permafrost-related processes in JULES are described
fully in . This development work builds on previous
studies of these processes in land-surface models for example. The
implementation of these processes within JULES is briefly highlighted below.
Extended soil depth and resolution
Firstly, the number and resolution of the soil layers were increased,
a functionality already available in JULES. The soil column was extended from
3 to 10 m, with 14 layers in the top 3 m and a further 14
layers in the lower 7 m, giving 28 layers in total. This is a high
number compared with other models, since it was our intention to simulate
a well-resolved soil (for example the maximum for the CMIP5 models is 23
layers for a 10 m soil column in GFDL-ESM2M). To make sure that all of the freeze-thaw dynamics would be captured,
10 m was chosen as a large value.
Secondly, a subroutine was added to represent bedrock. When this
process is switched on in JULES, the bottom boundary of the ordinary soil
column is joined on to a further column in which only thermal diffusion
occurs. The heat flux across the bottom boundary of the ordinary soil column
is now no longer 0, and the bedrock temperatures are modelled via
a discretised heat diffusion equation. The purpose of this is partly to make
a deeper soil column more computationally tractable, as hydrology and
freeze–thaw dynamics form a large part of the computational load and these
processes do not take place in the bedrock layers.
The number and thickness of bedrock layers are set by the user when running
the model. In this study, the bedrock column was run with 100 layers of
0.5 m each, making a 50 m column, thus bringing the total soil column up to
60 m. There is a zero-heat-flux condition at the base of the bedrock column,
which in future could be changed to a geothermal heat flux.
In fact, soil is often shallower than 10 m and the bedrock should start at
varying depths depending on the spatial location, but initial tests showed
that starting the bedrock at 3 m instead of 10 m made a very small
difference compared with the impact of including a deep heat sink or not, and
the same has been shown in other models, such as in .
Organic soil parametrisation
The model uses an improved implementation of the organic soil properties that
were first introduced by . A vertical profile of soil
carbon is prescribed for each grid cell (see Eq. ) and the
soil properties are calculated accordingly for each model level.
For some of the properties the organic fraction was used to provide a linear
weighting of organic and mineral characteristics as
in. However, the saturated hydraulic conductivity, dry
thermal conductivity and saturated soil water suction were calculated using
an appropriate non-linear aggregation. As a result, the organic components of
the dry thermal conductivity and saturated water suction have a larger effect
than if they were calculated via a linear weighted average.
The parametrisation of soil thermal conductivity was
extended to take account of organic soils using a modified relationship
between saturated and dry thermal conductivity.
Moss layer at surface
In order to include the insulating effects of mosses, the thermal
conductivity of the top soil layer was modified to account for their
presence. The thermal parameters for the moss layer are based on
. These are also consistent with purely organic soils.
It is assumed that the water potential in the moss layer is in equilibrium
with that of the top soil layer. At present, hydrological processes within
the moss are not explicitly represented in JULES.
Change to snow scheme
In the original multilayer snow scheme, numerical stability requires
that the layered snow is only used when the snow depth is
10 cm or greater, and the old zero-layer snow scheme is used for
shallower snow. The modification introduced in
allows the multilayer snow scheme to run with arbitrarily thin layers,
thus removing the zero-layer snow scheme from the model altogether.
In the zero-layer snow scheme the heat capacity of snow is neglected and melt
water is passed directly to the soil model to be partitioned into
infiltration and runoff. In the multilayer snow scheme the snow is treated as
a separate layer with its own heat capacity, and a fraction of the mass in a
snow layer can be retained as liquid water instead of passing straight into
the soil model. This water will freeze if the layer temperature falls below
0 ∘C. Thus the snow mass will be slightly different in the
multilayer scheme, and in general the model's behaviour is more realistic.
Applying model developments on a global scale
The following sections describe how the spatial distribution of organic
matter and moss was determined for a large-scale simulation. The depth of the
soil column was fixed across the globe, although there is scope for further
improvement to this, for example using a spatially variable depth from soil-surface to bedrock, as in
recent work by .
Global organic matter distribution
The organic fractions were calculated from a combination of the Northern
Circumpolar Soil Carbon Database (NCSCD) where available and
the Harmonized World Soil Database (HWSD) for the rest of the
land surface. These databases include some rather limited information about
the vertical distribution of soil carbon. Using this, an approximate vertical
profile of soil carbon was prescribed for each grid cell (see
Eq. ) and the soil properties calculated accordingly for each
model level.
Organic carbon quantities can be obtained from both the NCSCD and HWSD
data sets for the top 30 cm (C30) and top 1 m
(C100). The profile of soil carbon was assumed to be a constant
plus an exponential term. The total for the top 1 m adds up to the
observed amount.
C(z)=C100-C300.7+C300.3-C100-C300.7exp-z0.3,
when z<3 m, and C(z)=0 otherwise.
This form of the profile is based on the generic profiles in
(Fig. 2). It assumes that an exponential distribution
of carbon is appropriate and that there is no carbon below 3 m. In
reality some carbon will be found below 3 m, but it is not likely to
have a great impact on the soil properties, which are somewhat uncertain
anyway for the deeper ground. Figure shows profiles
generated using this method for a warm soil grid cell and a high-latitude
grid cell.
Dynamic moss
There are no data sets showing the pan-Arctic distribution of mosses. In
addition in a changing climate the distribution of moss may also change.
Therefore, moss was implemented in JULES so that it can be run either with
a static map that is input at the start of the run or with dynamic growth
determined by environmental conditions in the model.
Soil carbon profiles generated using Eq. (). Left:
119.75∘ E, 72.25∘ N; a grid cell with high soil carbon
(Siberia). Right: -70.25∘ E, 4.25∘ N; a warm location
with most of the carbon near the surface (South America).
Conditions for moss growth and dieback. For light saturation and
compensation curves (Lsat and Lcomp) see
Eqs. () and ().
Health
Water suction
Temperature
Light
Snow mass
Wind speed
+3
< 200 m
0<Ts< 27.5 ∘C
>Lsat
< 5 kgm-2
–
-3
> 1000 m
–
–
–
–
-3
–
> 35 ∘C
–
–
–
-3
–
< -5 ∘C
–
< 0.1 kgm-2
> 12 ms-1
-1
< 1000 m
< 35 ∘C
–
> 40 kgm-2
–
-1
< 1000 m
< 35 ∘C
<Lcomp
–*
–*
-1
< 1000 m
< -5 ∘C
–
–*
–*
+1
None of the above.
* Must not simultaneously satisfy snow
mass < 0.1 kgm-2, wind speed > 12 ms-1 and
temperature < -5 ∘C.
In order to determine the presence of moss in any grid cell, the model takes
account of the local temperature, moisture, light, snow cover and, in some
cases, wind speed (see Table ). The moss cover is then
determined by a “health” variable, whose value is updated once a day
depending on the conditions over the past 24 h. Good conditions add to
health and bad conditions subtract from it. It is contrained within bounds
that result in maximum health within a year given optimum growing conditions.
The conditions for good and poor growth are given in
Table . The water suction is taken as the minimum of
water suctions in the top soil layer and the atmosphere, the temperature,
Ts, is that of the top soil layer, and the light is the radiation
at the bottom of the canopy. These values are chosen for being closest to the
soil surface where moss is located.
The temperature, moisture and light ranges for good growth are based
on the values in . The light saturation and
compensation curves (Lsat and Lcomp
respectively) were estimated from and are given by
Lsat=19+exp(0.161Ts),Lcomp=0.1exp(-0.5Ts)+0.4exp(0.13Ts),
where light compensation is the level at which photosynthesis balances
respiration, and light saturation is the level at which photosynthesis
is no longer light limited (increasing radiation levels no longer
increase the rate of photosynthesis).
The heat and moisture conditions that cause the moss to die off are
also taken from . As well as dying in very hot or
dry conditions, moss can suffer badly from wind damage when it is
frozen . Thus the model includes a third scenario
in which moss dies off: when it is cold, windy and there is no
protective snow cover.
Snow protects moss from harsh conditions in winter, but of course it cannot
actually grow under deep snow, so a small value is subtracted from the health
under deep snow. The same occurs when it is too dark or too cold for
photosynthesis to take place. Moss growth has been observed up to 2 weeks
before the end of snowmelt , so the threshold
value for growth under snow was assumed based on snow mass values in JULES
2 weeks before the end of snowmelt. The magnitude of the values added to
and subtracted from the health variable were calibrated at several sites
where the moss cover was known.
When the moss health is positive, it is taken to have maximum cover in the
grid cell. When the health becomes negative, the percentage cover drops off
linearly to 0, and the cover is 0 for the lowest quartile of health
values. If there is other vegetation present, the fractional cover of moss is
capped at 0.7 for those vegetation tiles. This value was pragmatically
chosen given that moss can have around 50–100 % cover in forests
. Moss cover is assumed to be 0 for the urban or
ice fraction of a grid box. The relationship of moss health to moss cover
would benefit from further calibration.
Figure shows a moss distribution simulated by JULES in
the northern high latitudes and compares it with data from the
land-cover map. In general the most densely
moss-covered areas correspond to the tundra land-cover classes, which are
shown in bright green. Our scheme gives some general representation of this
low vegetation cover, which is otherwise missing in JULES. Moss also grows in
the boreal forest (shown in purple on the Euskirchen map), but in JULES it
does not grow in the deciduous needleleaf zone, which may require some
investigation. Evaluating the distribution in lower latitudes is a subject
for future work.
Data sets for model forcing and evaluation
Historical meteorological forcing data
The Water and Global Change (WATCH) project produced a meteorological forcing
data set (WATCH Forcing Data Era-Interim, WFDEI) for use with land-surface and
hydrological models . It is
based on Era-Interim reanalysis data , with corrections
generated from Climate Research Unit and
Global Precipitation Climatology Centre data
(http://gpcc.dwd.de). It covers the time period 1979–2012 at half-degree
resolution globally and at 3 hourly temporal resolution. Rainfall and
snowfall are provided as separate variables.
Upper plot: land-cover map using data from
. “Shrub tundra” includes prostrate, dwarf shrub
and low shrub tundra, and “boreal forest” includes boreal evergreen
needleleaf and boreal broadleaf deciduous. Lower plot: mean moss cover
simulated in JULES for the year 2000, from orgmossD historical simulation
(see Table ).
List of JULES simulations carried out.
Simulation
Soil
Soil
Bedrock
Moss
Organic
Modified
layers
depth
soils
snow
min4l
4
3 m
N
N
N
N
min14l
14
3 m
N
N
N
N
minD
28
10 m
50 m
N
N
N
minmossD
28
10 m
50 m
Y
N
N
orgD
28
10 m
50 m
N
Y
N
orgmossD
28
10 m
50 m
Y
Y
N
orgmossDS
28
10 m
50 m
Y
Y
Y
Future meteorological forcing data
Meteorological forcing for the years 2006–2100 was created by adding
modelled future climate anomalies to the historical meteorological forcing.
The monthly climate anomalies are from version 4 of the Community Climate
System Model (CCSM4) and available at a resolution of
0.9∘latitude×1.25∘longitude. These were provided for the permafrost
carbon model intercomparison project (MIP) (for more information see
http://www.permafrostcarbon.org/, D. Lawrence, personal communication,
2013). Seven variables are provided, including a combined precipitation
variable rather than separate rain and snow, for two future scenarios –
RCP4.5 and RCP8.5. As described in the protocol for the permafrost carbon
MIP, these anomalies are combined with historical data either by addition
(temperature, pressure, humidity, wind speed) or multiplication (short-wave
and long-wave radiation, precipitation). The anomalies for air temperature and
precipitation are shown in Fig. . There is not a large
trend in precipitation, although in RCP8.5 there is a small increase, along
with an increase in variability. Air temperature, however, shows a
clear increase by the end of the century, up to 10 ∘C in RCP8.5. The
change in air temperature is much larger in winter than in summer.
For the simulations discussed in this paper, the anomalies were re-gridded to
0.5∘ resolution and applied to a repeating sequence of 8 years of
WFDEI reanalysis data (1998–2005). Using anomalies rather than directly
using climate model data makes the climate variability in the future
simulations more consistent with the historical data. The main disadvantage
is that small-scale features are not captured, since sub-monthly variability
comes from the base data set .
CCSM4 anomalies for air temperature and precipitation. Values given
are the areal mean for the region north of 25∘ latitude (the same
region as the JULES simulations).
CALM Network
The Circumpolar Active Layer Monitoring Network (CALM)
is a network of over 100 sites at which
on-going measurements of the end-of-season thaw depth (the ALT) are taken.
Measurements are available from the early 1990s, when the network was
formed. The data are available from
http://www.gwu.edu/~calm/data/north.html.
Historical soil temperatures
The Russian historical soil temperature data set is described in
. Soil temperatures were measured at 242 stations, over
different time periods starting as early as 1890. The measurements used in
this paper were taken at depths of 0.2, 0.4, 0.8, 1.6 and 3.2 m using
extraction thermometers, with additional measurements at 0.6, 1.2 and
2.4 m. At some of the sites the natural vegetation cover was removed
and at others there is some possibility of site disturbance, however the
majority of these measurement sites retained their natural vegetation and
snow cover.
International Polar Year Thermal State of Permafrost (IPY-TSP)
borehole inventory data were compiled in 2007–2009 from both new and
existing boreholes, achieving a wide spatial coverage of soil
temperature data . Data are available in the
most part from 2006 to 2009 at a daily resolution, with temperatures
measured at a variety of depths.
Globsnow
The European Space Agency snow water equivalent (SWE) product,
GlobSnow covers the years 1978–2010 and is
available on an EASE-Grid at 25 km resolution. GlobSnow is
produced using a combination of satellite-based microwave radiometer
and ground-based weather station data.
The GlobSnow documentation records an exponentially
increasing bias with larger snow mass, which is particularly significant
at snow mass greater than 200 kgm-2.
Permafrost distribution data
The Circum-Arctic map of permafrost and ground-ice conditions
gives a historical permafrost distribution,
which can be compared with permafrost area in the model. The data set contains
information on the distribution and properties of permafrost and ground ice
in the Northern Hemisphere (20–90∘ N), with gridded data available at 12.5, 25 km and
0.5∘ resolution. It records continuous, discontinuous,
sporadic and isolated permafrost regions, for which the estimated
permafrost area is 90–100, 50–90, 10–50 and < 10 %
respectively. In this work, an estimate of the observed permafrost
area is used to compare with the simulated area. For the maximum we
assume that permafrost would be simulated in the whole of the
continuous and discontinuous area, plus a fraction of permafrost in
the areas of sporadic permafrost and isolated patches – the fractions
being 0.05 and 0.3 respectively. For the minimum we assume that no
permafrost would be found in the sporadic and isolated regions and
that in the continuous and discontinuous zones only a fractional
coverage of permafrost is found: 0.95 and 0.7 respectively
for the two zones. This gives a maximum area of
17.0 millionkm2 and a minimum of
13.7 millionkm2. It is important to note that there is
considerable uncertainty in these data which means that the true value
could fall outside of this estimate.
Simulation set-up
For the historical period, JULES was driven by the WFDEI reanalysis
data at a resolution of 0.5∘ (Sect. ). JULES was driven by precipitation (sum of rain and
snow in WFDEI), which was converted internally within the model to
rain and snow. This maintains consistency with the future driving
data. The model was spun up for 60 years by repeating the
first 10 years of driving data (1979–1988) and then run over
the period 1979–2009 for the historical runs. After spin-up, the soil
temperature and moisture contents were fully stabilised at the vast
majority of points, i.e. there was no residual model drift. The
future runs began at the start of 2006, taking their initial state
from 2006 in the historical simulations and simulating both RCP4.5
and RCP8.5 scenarios until 2100.
In order to capture all the main permafrost regions in the Northern Hemisphere, the simulations were run for the region north of 25∘. The mineral soil properties, land-cover fractions and topographic index data (needed for LSH, see Sect. ) were
taken from HadGEM2-ES ancillary data . Organic
soil ancillaries were generated using the method described in
, with the spatial distribution as described
in Sect. .
The simulations carried out are given in
Table . This includes the standard JULES set-up
(min4l), a higher-resolution soil column (min14l), a deeper soil
column (minD), the effects of moss and organic soils both separately
and in combination (minmossD, orgD, orgmossD) and finally the
modified snow scheme (orgmossDS). When deeper soil is added in minD,
this includes both the extension of the soil column to 10 m and the
addition of a 50 m bedrock column. The distribution of moss was
determined dynamically in the model as described in Sect. .
Evaluation methods
The maximum summer thaw depth or ALT was
calculated by taking the unfrozen water fraction in the deepest layer
that has begun to thaw and assuming that this same fraction of the
soil layer has thawed. This gives significantly more precise estimates
of the ALT than temperature interpolation (see
).
The ALT was then used to derive the
near-surface permafrost extent. Our definition of near-surface permafrost
is a grid cell with ALT less than 3 m for 2 or more consecutive years. There is no representation of sub-grid heterogeneity in the soils so any 0.5∘ grid cell either contains 100 %
near-surface permafrost or no near-surface permafrost at all.
calculated a range of metrics for soil
temperature dynamics, which are also used here. They include the
offset between the mean air, 0 and 1 m temperatures and the
attenuation of the annual cycle between each of these levels. The
values were calculated as in by first
calculating the annual mean and seasonal cycle at the available
depths and then interpolating these to 0 and 1 m depth. The
annual cycle was interpolated between layers by assuming it falls off
exponentially with depth. The model values were taken from the grid
cell containing the measurement point. All IPY-TSP sites with
sufficient data were used, along with the colder Russian soil
temperature sites (those with a mean soil temperature below
0 ∘C as an indicator of permafrost). This gives
a total of 86 sites with reasonable circumpolar coverage (see Sect. for description of observational data).
In order to analyse future permafrost degradation, the sensitivity of
near-surface permafrost area loss to climate warming was calculated via a linear
regression of near-surface permafrost area against the annual mean air temperature
over the historical permafrost region region defined by the
observed map in.
Results
Active layer and near-surface soil temperatures in
historical simulation
In Figs. and , the simulated ALT from
the JULES simulations (Sect. ) is compared with
observations from the CALM active layer monitoring programme
(Sect. ). The low resolution in min4l significantly impairs the
capacity of the model to simulate the active layer. This was seen for the
point site simulation in and is even clearer in
these large-scale results: the active layer in min4l has very little
variability and little apparent correlation with the observations (see
Fig. a).
Simulated and observed range of active layer depths for CALM sites
(Sect. ). Black dots are means, the boxes shows the
interquartile range (IQR) and the horizontal line is the median. The
whiskers indicate the most extreme data point that is no more than 1.5 times
the IQR. Outliers are not shown. Points at which either the simulated or
observed active layer was very large (greater than 6 m) were removed.
The model point for each CALM site is the grid box containing that site.
Active layer model values plotted against measurements from CALM
data set (as in Fig. ). The dashed lines show an ALT of
1 m. Logarithmic axes are used.
Most of the new model processes reduce the active layer, bringing it into
much better agreement with the observations, as shown in
Fig. . Simulating a deeper soil column reduces the active
layer mean for the CALM sites by 0.12 m (min14l to minD). The
insulating effects of organic matter and moss have a greater impact, reducing
the ALT by a further 0.58 m (orgmossD). The inclusion of organic
matter has the single greatest effect, reducing the mean ALT by
0.44 m. Figure d shows that with the inclusion
of all of the new model processes, the full range of ALT values are captured
and the points fall around the one-to-one line. There is still an outlying block
of points where the active layer in JULES is greater than 3.5 m and
much deeper than the measurements. Many of these sites fall along the course
of the Mackenzie River in northern Canada (where JULES simulates very little
permafrost – see Fig. ). Precipitation gauges are sparse
in this region so there may be large uncertainties in hydrology
, and the observed soil temperatures vary greatly from
around -1 to -7 ∘C as a result of various influences including
land cover and snow . This is a subject for future
investigation.
The active layer thickness is determined by both the annual cycle of soil
temperatures and the thermal offset between the air and the soil.
Table compares these dynamics in JULES with
observations from the IPY-TSP data set and cold sites from the Russian soil
temperature data set (see Sects. and ). The
root mean squared error (RMSE) is calculated using the mean value of the
metric for each site, so it quantifies the extent to which the variability
between the sites is correctly simulated. In this table the most relevant
values are the offset and attenuation of the annual cycle between the air and
1 m depth in the soil, since the soil surface is not so well defined
in the observations. In the standard JULES set-up (min4l) the total offset is
approximately correct, suggesting that the mean soil temperatures are
simulated well. However, the annual cycle is nearly 25 % too large at
1 m depth.
The introduction of a well-resolved soil has a cooling effect of
approximately 0.7 ∘C, and organic soils and moss have a further
cooling effect of approximately 1 ∘C. As in
, the improvements to the snow scheme then compensate
for the cooling from the other model changes, resulting in a 1 m
ground temperature approximately the same in the final simulation (orgmossDS)
as the original one (min4l). There is no significant improvement in the RMSE
between the first and last simulation (quantifying the extent to which we
capture the spatial variability), but the RMSE is between 2 and
2.5 ∘C for all simulations, suggesting that the mean soil
temperatures are captured fairly accurately.
The annual cycle, however, is reduced overall by the model
improvements, with the attenuation value in orgmossDS being very close to the
observed value (less than 2 % different to be exact). The RMSE in these
values is also reduced (by approximately 20 %) by the model developments,
showing that spatial patterns are better simulated. This shows a significant
improvement in the simulation.
Figure shows the mean annual cycle of soil temperatures at
90 cm for the sites used in Table .
Figure a shows that the main effect of increasing soil
resolution is to reduce the winter temperatures. This is very likely because
of the way the snow is simulated in the standard snow scheme, where the
insulating effect of shallow snow is incorporated into the top soil layer –
this means that when the top soil layer is thinner the insulation will be
less effective. The deeper soil gives a slight attenuation of the annual
cycle, which is expected of an additional heat sink.
The attenuation of the annual cycle and the thermal offset in the
JULES simulations, between the air and the top of the soil, and the top of
the soil and 1 m depth, calculated as in . This
includes IPY-TSP and Russian data for cold sites. The RMSE (root mean squared
error) values are based on the mean value of the metric at each site and thus
give an indication of how well the variability between sites is captured. The
bold numbers mark the observations as distinct from simulated
values.
Simulation
Attenuation (fraction of amplitude)
Offset (∘C)
Air–0 m
0–1 m
Total
RMSE
Air–0 m
0–1 m
Total
RMSE
Observations
0.62
0.40
0.25
–
7.7
-0.25
7.4
–
min4l
0.62
0.50
0.31
0.18
8.5
-1.00
7.5
2.5
min14l
0.67
0.52
0.35
0.20
7.3
-0.57
6.8
2.3
minD
0.68
0.48
0.33
0.20
7.4
-0.72
6.7
2.2
minmossD
0.66
0.48
0.32
0.18
7.2
-0.68
6.5
2.2
orgD
0.65
0.45
0.29
0.17
6.9
-0.92
6.0
2.4
orgmossD
0.62
0.45
0.28
0.16
6.7
-0.87
5.8
2.5
orgmossDS
0.54
0.47
0.25
0.15
8.1
-0.87
7.3
2.4
Figure b shows that the main effect of organic soils and moss
is to reduce the summer soil temperatures. Finally, the main effect of the
modified snow scheme is to increase the winter temperatures. Overall,
a reduction in summer temperatures and an increase in winter temperatures
lead to a reduced annual cycle which matches better with the observations in
Fig. c.
Comparison of annual cycle of soil temperatures at 90 cm
depth, from the Russian historical soil temperature and IPY-TSP data
(Sect. ) and JULES simulations.
First two rows: mean active layer thickness in JULES simulations,
1979–1989. All grid cells with active layer ≤ 6 m are shown, with
mean ALT indicated by colour. Bottom: observed permafrost map
, based on maps made between approximately 1960 and
1990. On all plots the 0∘ air temperature isotherm is shown in red
(1979–1989 from WFDEI air temperature, see Sect. ).
There are still significant differences between the model and observations,
shown by the size of the RMSE in Table . The error in
winter snow depth when compared with the closest grid cells in the Globsnow
data set (see Sect. ) has a significant correlation of 0.3
(for about 260 points) with the error in winter soil temperatures in
orgmossDS, suggesting that snow explains at least some of the remaining
error. show that uncertainty in the simulated active layer
depth comes from the uncertainty in soil composition, particularly ground-ice
contents. Some variability is also expected when comparing a large grid cell
with a point site, and this is difficult to quantify. While the RMSE in
offset is reasonably small, the RMSE for the attenuation of the annual cycle
is a significant fraction of the value itself. This suggests that the annual
cycle is more difficult to simulate than the mean temperature.
In this section a comparison with CALM observations has shown that one
essential feature for simulating the ALT is the resolution of the soil
column, without which the active layer variability is not resolved (see
Fig. ). For capturing the near-surface soil temperature
dynamics, the effects of organic soils and the improved snow scheme are both
particularly significant in making the annual temperature cycle more
realistic (Fig. and Table ). Organic
soils also greatly improve the ALT (Figs.
and ), so this process is a particularly useful addition
to the model. The importance of organic soils has also been shown in e.g. .
Permafrost distribution in historical simulation
The simulated permafrost in JULES is shown in Fig. , along
with observations from the Circum-Arctic map of permafrost and ground-ice
conditions (Sect. ). The observed map
shows areas with continuous, discontinuous and sporadic permafrost and
isolated patches. There is no equivalent of discontinuous permafrost in
JULES because each grid box has only a single soil column, so in order to
compare the two maps we assume that a deeper active layer in JULES may
correspond to discontinuous or sporadic permafrost. With this assumption, all
the simulations match the observations fairly well in most areas. We can see
that introducing the model developments brings in much more spatial
variability in ALT, which generally matches with the patterns of
continuous/discontinuous permafrost. The correlation between the ALT in JULES
and the percentage cover of permafrost from (100 %
for continuous, 90 % for discontinuous, 50 % for sporadic and
10 % for isolated patches) is high, ranging between -0.37 and -0.51.
However, there are still places where continuous permafrost is observed but
JULES does not simulate permafrost. Figure shows that in
most of these areas, JULES simulates far too much snow, which will mean too
much insulation in winter leading to soils that are too warm. This is
particularly noticeable in north-east Canada and two areas in north-west
Russia. In north-east Canada, however, it has been shown that the GlobSnow
data set underestimates the SWE
, so the over-estimation in JULES may not be as large
as Fig. suggests. However, the permafrost in this region
is unstable to thawing , so a small bias in the model
could make the difference between simulating permafrost or not. For most of
the remaining land surface, JULES slightly underestimates the SWE.
showed that JULES generally underestimates SWE when
driven by reanalysis data sets.
Comparison of Globsnow and JULES. Mean snow water equivalent (SWE)
in Globsnow was subtracted from the JULES values over the same time periods.
See Sect. .
In the observations, the edge of the permafrost-affected zone corresponds
quite closely with the 0∘ isotherm, although there is a gap in
western Russia (red lines in Fig. ). In the JULES
simulations this relationship is less consistent, suggesting more spatial
heterogeneity in the relationship between air and soil temperatures. For
example, excessive snow cover such as that seen in north-east Canada in
Fig. could contribute to this effect.
It is also possible to consider the vertical distribution of permafrost.
Figure shows a breakdown of active layer depths for all
near-surface permafrost points (a) and all points (b) in the simulations. The
first thing that is clear from this plot is that the discretisation of the
soil column has a very large effect on the simulation of permafrost. A series
of kinks corresponding to the model discretisation is apparent in all the
curves, which for the higher-resolution simulations does not significantly
impact the overall shape of the curve, but for the low-resolution soil
changes it almost beyond recognition. For about 50 % of the near-surface
permafrost points in min4l, the active layer depth is between 1.8 and
2.2 m, where 2 m is the centre of the bottom model layer.
Figure shows that the vertical distribution of permafrost
is affected by all the model improvements, but the most significant impact is
when organic soils and moss are included. Here, the permafrost is
generally found nearer to the surface.
The vertical distribution of permafrost, shown by the fraction of
points for which the soil thaws below a given depth.
(a) Near-surface permafrost points only: this includes only those
points with ALT less than 3 m, and hence 0 % of them thaw to below
3 m. (b) All points are included, so about 70 % thaw to
greater than 3 m or have no permafrost at all. There is one point for
each grid cell for each year of the historical simulation.
In Fig. b the amount of near-surface permafrost in each
simulation is apparent from the fraction of points that thaw to less than
3 m (generally about 30 % of points). This shows that some
simulations have a shallower active layer (so colder soils) but less
near-surface permafrost; for example, compare min14l with min4l and orgmossDS
with minD. This is related to the vertical profile of soil temperatures. For
example, due to the combination of processes, the maximum soil temperature in
orgmossDS compared with minD is colder near the surface but warmer in the
deeper soil. This is seen in the soil temperature profiles in
Fig. .
The total near-surface permafrost area in each simulation is more
clearly seen by looking at the historical period in
Fig. . For consistency with the definition of near-surface
permafrost, these values include any grid cells where the ALT was less
than 3 m for the past 2 years (so for example in the first
year that a grid cell is frozen, it is not included in the permafrost
area but after the second year it is added to the area, so the area
changes from year to year). Comparing the deep-soil simulations, minD,
minmossD, orgD and orgmossD, we see that adding insulation from
organic soils and moss increases the near-surface permafrost area, which is
consistent with the cooling effect seen in
Table . In orgmossDS, the near-surface
permafrost area is significantly reduced compared with orgmossD, which
was also apparent in Fig. b. Finally, in the shallow
(3 m) simulations (min4l and min14l), the near-surface permafrost area is
smaller, but this is not really meaningful. This is because the
zero-heat-flux boundary condition is not correct at 3 m, which
leads to “edge effects” close to the soil boundary. This is seen in
Fig. , where the soil temperatures in the mineral
soil simulations (min4l, min14l and minD) are very similar near the
top of the soil, but the annual cycle in the shallow simulations
(min4l and min14l) does not continue to fall off with depth, resulting
in a maximum temperature that is much too high at the base of the
soil. This shows that diagnosing permafrost as the area with ALT less
than 3 m requires a soil column significantly deeper than
3 m.
The annual maximum, minimum and mean of simulated soil temperatures
(right-hand, left-hand and centre lines respectively), averaged over the
period 1979–1989, for the land area north of 50∘ latitude. Comparing
the simulations with different soil depth is of particular interest here.
A range for the observed permafrost area is also shown in
Fig. . This is estimated from using
assumptions described in Sect. . According to this, the
simulated near-surface permafrost area in the mineral soil simulations
(min4l, min14l, minD) falls inside the observed range, and the addition of
organic soils and moss results in a simulated near-surface permafrost area
that is somewhat too large. However, in the final simulation with all model
improvements (orgmossDS), the near-surface permafrost area once again falls
within the observed range.
Time series of future projections of permafrost area. Left: RCP4.5.
Right: RCP8.5. The grey area represents an estimate of the observed area from
(see Sect. ).
Rate of loss of near-surface permafrost per degree of high-latitude
warming in future JULES simulations. The temperature change is calculated
over the historical permafrost area (observed).
Simulation
Historical area
Rate of loss (106 km2∘C-1)
Rate of loss (fraction ∘C-1)
(106 km2)
RCP4.5
RCP8.5
RCP4.5
RCP8.5
min4l
15.5
-1.52
-1.47
0.101
0.097
min14l
14.3
-1.36
-1.32
0.106
0.101
minD
17.0
-1.34
-1.37
0.085
0.087
minmossD
17.7
-1.33
-1.34
0.080
0.080
orgD
18.0
-1.17
-1.21
0.069
0.071
orgmossD
18.7
-1.08
-1.19
0.060
0.066
orgmossDS
16.1
-1.15
-1.11
0.077
0.074
HadGEM2-ES
22–23a
-1.5a
-1.46b
0.065a
a ,
b .
Coloured area shows near-surface permafrost at the start of the
simulation: green regions have disappeared by the end of the simulation
(2090–2100) and other colours (reddish) show active layer deepening in the
remaining permafrost.
Future permafrost degradation
Section showed that the model improvements make the
simulation of permafrost more realistic. Although further development is
needed, these are important processes to include and it is worthwhile to
study their impact on long-term permafrost dynamics; hence in this section we
study the loss of near-surface permafrost and the active layer deepening over
the next century.
Figure shows the time series of total near-surface
permafrost area over the next century. Comparing minD and orgmossD shows that
organic soils and moss reduce the interannual variability of the
near-surface permafrost area. Although this variability cannot be measured on
a global scale, permafrost tends to degrade or form over a number of years,
so a high level of variability from year to year is probably unrealistic. In
orgmossDS, although the near-surface permafrost area is significantly reduced
compared to orgmossD, the interannual variability is similar. However, the shallow (3 m) simulations (min4l and min14l) have a much
higher interannual variability in near-surface permafrost area than the deep
soil simulations, indicating the importance of the thermal inertia from
a deep heat sink in the soil, which has been shown already in e.g. .
Table shows the rate of near-surface permafrost loss
per degree of warming (calculated by a linear regression between future mean
air temperature, averaged over the historical permafrost region, and
permafrost area). The sensitivity is reduced by the new model developments,
from approximately 1.5×106 km2∘C-1 in the
standard JULES set-up (min4l) to between 1.1×106 and 1.2×106 km2∘C-1 in orgmossDS: a reduction of about
25 %. In the rate of permafrost loss in the Community Land Model is
reduced by over 25 % by the inclusion of organic matter and a deeper soil
column, which is an even larger effect than is found in JULES. In that study
an even deeper soil column down to 125 m was used.
The loss of near-surface permafrost by the end of the 21st century is very
large in all the JULES simulations, particularly in RCP8.5. Even in orgmossD
and orgmossDS, the simulations with the lowest sensitivity to temperature,
the area with near-surface permafrost has approximately halved by the end of
the 21st century in RCP8.5 (see Fig. ). In orgmossDS it
decreases from approximately 16 millionkm2 for the historical
period down to approximately 7 millionkm2 in RCP8.5 and
12 millionkm2 in RCP4.5 by 2100.
In terms of area lost, the values for the standard JULES set-up are
approximately the same as for HadGEM2-ES. However, the fractional loss in
HadGEM2-ES is smaller, because the near-surface permafrost area itself is
significantly larger (22.3 millionkm2 for the historical period
compared with approximately 15 millionkm2 in min4l). This is
predominantly because HadGEM2-ES uses the zero-layer snow scheme, leading to
significantly colder soils. This suggests that the snow scheme does not have
a great effect on the actual rate of permafrost degradation in JULES, which
is supported by comparing orgmossD and orgmossDS in
Table . A study of historical permafrost in JULES
showed a loss of 0.55–0.81 millionkm2 per
decade. In our historical simulations the loss rates generally fall within
this range except for orgmossDS, for which the mean near-surface permafrost loss
per decade is slightly lower at 0.43 millionkm2. Compared with the
other CMIP5 models, the rates of near-surface permafrost loss in the improved
version of JULES (orgmossDS) are now lower than most of the other models
.
Figure shows the near-surface permafrost distribution at
the end of the future simulations for the “standard” JULES set-up (min4l)
and the final improved model version (orgmossDS). In RCP4.5 the near-surface
permafrost retreats only from the edges of the permafrost zone, but there is
some thickening of the active layer across the whole near-surface permafrost
area of the order of 0.5 m, which is a significant change. In the
more southern permafrost regions, the active layer deepens more in orgmossDS
– as much as 1 m – which may reflect the fact that the ALT is
initially shallower, so more deepening is possible.
In RCP8.5, much of the near-surface permafrost thaws in both simulations.
However, in the improved model version, there is significantly more
near-surface permafrost remaining at the end of the century, particularly in
northern Russia, reflecting the reduced sensitivity to warming. However, the
areas where near-surface permafrost remains in orgmossDS show a strong active
layer deepening, significantly more than 1 m in some areas. This
suggests that although less near-surface permafrost is lost in this
simulation, with a further increase in temperature it could disappear. Note
that this refers just to permafrost at depths of up to 3 m – deeper
permafrost may remain longer.
Conclusions
Large-scale simulations have shown improved physical permafrost
dynamics in JULES, thanks to a deeper and better-resolved soil column,
including the physical effects of moss and organic soils and an improvement
to the snow scheme. The model developments reduce the simulated summer thaw
depth and the amplitude of the annual cycle of soil temperatures and bring
both to more realistic values. The rate of near-surface permafrost loss under
future climate warming is also reduced, as is the interannual variability of
the near-surface permafrost area.
It is important to simulate a reasonable ALT before beginning to consider
permafrost carbon feedback. For this we have shown that the depth and
resolution of the soil column and the effects of organic soils are the most
important considerations for the model. JULES is now able to simulate
large-scale patterns in ALT, as seen in Fig. , where
points with shallower ALT are now generally simulated with shallower ALT in the model.
A well-resolved soil is absolutely essential for simulating active layer
dynamics. With a poorly resolved soil it is not possible to simulate ALT
variability: the thaw depth depends strongly on the model layers,
Fig. , and there is very little spatial variability in ALT
(Fig. ), which is unrealistic (see
Fig. ). The importance of soil resolution has not often
been emphasised in the literature.
We have confirmed the importance of including a deep soil column, showing
that the thermal inertia from deeper soil has a significant impact on the
permafrost dynamics (see e.g. Figs. and
). We also find that with a 3 m soil column it is
not possible to meaningfully diagnose permafrost at 3 m depth due to
the edge effects close to the bottom boundary of the soil
(Fig. ).
Organic soils and moss both act to insulate the soil. They reduce the ALT and
increase the near-surface permafrost area (see for example
Fig. and Table ) due to their
insulating effect in summer, which helps to make the annual cycle of soil
temperatures more realistic (Fig. b). Of these two processes,
organic soils have the larger effect. Including moss has a smaller but
significant physical impact, as seen for example by the increase in
near-surface permafrost area of 0.7 millionkm2 when it is included
(Table ). High-latitude vegetation is an important
component that is currently missing in JULES. This simple insulating layer is
only the first step and more work is needed to fully incorporate it into the
vegetation model.
The improvement to the snow scheme has a large winter warming effect
(Fig. c), and as a result it warms the deep soil and reduces
the near-surface permafrost area, bringing it closer to the observational
estimate. It also contributes to a more realistic annual cycle
(Fig. c). However, the snow is less important for simulating
the correct ALT (Fig. ).
Snow has a very strong effect on soil temperatures
, and there is
certainly more scope to improve the snow model in JULES, for example with
additional compaction processes and lateral redistribution. However, there
are high uncertainties associated with global precipitation data in
observations, reanalysis products and climate model output, particularly
for the Arctic , so
these limit the potential to improve the snow representation at present.
Soil moisture is also important for soil temperatures, and the two are linked
in a complex manner. Water has a higher thermal conductivity than air, so in
wetter soils more heat will penetrate and leave the ground. However, if
there is soil freezing and thawing, the latent heat will reduce the rate of
heat penetration, counteracting this effect. Furthermore, if there is soil
freezing the mean temperature in the deeper soil is colder than that at the
surface, since the thermal conductivity of ice is greater than that of water,
so more heat moves upwards in winter than downwards in summer. The influence
of soil temperature on soil moisture mainly comes from freezing, as this
prevents moisture running out of the soil and may also hold liquid water
above a permafrost layer.
Between the runs in this paper, the main differences in soil moisture come
from organic soils, which increase the soil moisture content overall. Disentangling the complex impacts requires more specific experiments than the
ones in this study, such as an experiment where specific influences of soil
moisture on temperature are removed. Further investigation of the hydrology
in JULES is vital and this is the subject of ongoing work.
Important improvements have been made in JULES and other global land-surface
models e.g., but these models are now somewhat limited by their coarse
spatial resolution, since permafrost processes are heterogeneous on small
spatial scales and have non-linear effects. There is some progress being made
towards upscaling small-scale processes
, and large-scale data sets are improving
over time, which is important for simulating realistic carbon fluxes.
In this paper we have analysed the large-scale degradation of permafrost
under two future climate scenarios. This shows a significant reduction in
near-surface permafrost area, with up to 1.5 millionkm2 of
near-surface permafrost loss per degree of high-latitude warming, although
this is reduced to approximately 1.1 millionkm2 in the improved
model version, showing the importance of these model developments in
assessing future permafrost thaw. The impact of organic matter is
particularly large, as this alone reduces the sensitivity by approximately
15 % (Table ). In RCP4.5, near-surface permafrost
is only lost from the edges of the permafrost zone by the end of the century,
but in RCP8.5 the near-surface permafrost disappears entirely from some large
regions, with large areas of near-surface permafrost remaining only in
northern Canada and some parts of Russia. In areas where near-surface
permafrost remains, there is a significant thickening of the active layer,
which is relevant for consideration of the permafrost carbon feedback.