Introduction
Soils contain the largest terrestrial carbon store, estimated at around
2000 Pg in the top 2 m of soil . In
particular, permafrost regions contain a large amount of soil carbon, much of
which is old carbon that is prevented from decomposing due to the frozen
conditions . The
most recent estimate suggests that there is approximately 1035 Pg carbon in
the top 3 m of permafrost soil, and another 272 Pg carbon below 3 m in
e.g. yedoma deposits . This relatively inert carbon
has a critical role to play in the terrestrial feedbacks to climate change,
as it decomposes when permafrost thaws, releasing greenhouse gases to the
atmosphere and amplifying climate warming
.
Current estimates suggest that there will be 35–205 Pg of permafrost carbon
emissions by 2100 . However, the
magnitude and timing of carbon fluxes caused by permafrost degradation remain
highly uncertain, partly because of incomplete observations and partly
because modelling of many of the relevant processes is still in its infancy.
Earth system models (ESMs) play an important role in understanding feedbacks
and global impacts – aiming to include all significant links between
different components of the Earth system. There is currently a considerable
uncertainty in the soil carbon cycle feedbacks, and size and response of soil
carbon pools to a changing climate in ESM simulations. For example in the
CMIP5 models the soil carbon stocks correlate poorly with observations
, which is very likely due to missing
processes in the models . This in turn impacts the
sensitivity of soil organic matter to environmental change, leading, for
example, to the wide range of estimates of permafrost carbon emissions under
future warming . Only a few studies have explicitly
included permafrost carbon coupled with the climate in an ESM, e.g.
and .
Recent developments in permafrost physics such as including soil freezing,
organic soil properties, improved snow schemes, more realistic soil depths
and physical impacts of mosses and lichens
mean that the rate of permafrost thaw is now more realistic in many of the
land surface components of ESMs. Adding a vertical representation of soil
carbon is now required to enable a representation of permafrost carbon in
ESMs . Without a vertical representation, decomposition
rates are determined only by soil temperatures above the maximum summer thaw
depth, so the very slow turnover of deep carbon in the permanently frozen
soil is not represented. Vertically resolved soil carbon and nitrogen have
recently been introduced into the land surface schemes from several ESMs
, some
of which will participate in CMIP6. Other vertically resolved soil carbon
models have been applied on a site scale
, with a view to being included in
ESMs in future. It should be noted that any model that is included in an ESM
must be applicable globally as well as for permafrost regions.
Typically soil carbon within an ESM is “spun-up” using pre-industrial
climate until the soil carbon is relatively stable between spin-up
iterations. However, models are often missing many relevant burial processes
such as alluvial sedimentation; dust deposition; peat development and
cryoturbation
.
Therefore there will be biases in the soil carbon which will impact
projections of permafrost carbon emissions . One
method of reducing these biases is to initialize the soil organic carbon
stocks using the observed soil carbon distribution
. However, this may result in a
significant drift back towards the equilibrium state, and thus it is
important to check that this drift is not so large as to mask the climate
signal.
The purpose of this paper is to describe and evaluate a new vertically
resolved soil carbon scheme integrated within the Joint UK Land-Environment
Simulator (JULES at vn4.3_permafrost), which is the land surface component
of the UK Earth System Model (UKESM). We describe the model structure and
evaluate the results of global simulations over the 20th century against
observations of soil carbon stocks and respiration fluxes. The results are
also compared with the original standard zero-layer soil carbon scheme.
Although the ability of the vertically discretized soil carbon model to
represent the distribution of soil carbon is globally relevant, the current
assessment focuses particularly on permafrost regions.
Materials and methods
JULES model description
JULES is the land surface component of the new community ESM,
UKESM . It can also be run offline forced by observed
meteorology at a regional or point scale as well as globally. JULES is
described in and . It is a
community model with many users and ongoing developments. For recent
developments see, for example, and .
JULES includes a dynamic vegetation model (TRIFFID), surface energy balance,
a dynamic snowpack model (one dimensional), vertical heat and water fluxes,
soil freezing, large scale hydrology, and carbon fluxes and storage in both
vegetation and soil. It also includes specific representations of crops,
urban heat and water dynamics, fire diagnostics and river routing.
Model developments
RothC soil carbon model
The standard soil carbon model in JULES is based on RothC
, and described in detail in
. There are four pools: decomposable plant material (DPM),
resistant plant material (RPM), microbial biomass (BIO), and hummus (HUM). The
soil carbon dynamics are represented as follows:
dCDPMdt=fdpmΛc-RDPM,dCRPMdt=(1-fdpm)Λc-RRPM,dCBIOdt=0.46βRRtot-RBIO,dCHUMdt=0.54βRRtot-RHUM,
where Rtot=RDPM+RRPM+RBIO+RHUM is
the total respiration in kg C m-2 s-1, t is the time in s, the
Ci are the carbon pools in kg C m-2, fdpm is the fraction
of litter that goes into DPM (dependent on vegetation type), Λc is
the total litter input in kg C m-2 s-1, and βR is the
fraction of soil respiration which is emitted to the atmosphere. This depends
on soil texture.
The respiration for each pool (Ri, where i is one of (DPM, RPM, BIO,
HUM)) is given by:
Ri=kiCiFT(Tsoil)Fs(s)Fv(v),
where the ki are fixed constants in s-1 . The
functions of temperature [FT(Tsoil)] and moisture [Fs(s)]
depend on the temperature and moisture content near the surface. The function
for vegetation Fv(v) is a function of the vegetation cover.
There are two different functions available to represent the impact of
temperature on soil respiration. The first option for the temperature
function, FT,Q10 (Eq. ), is a commonly used exponential
function, and the second option, FT,Roth
(Eq. ), is based on the function from the original RothC
model:
FT,Q10(Tsoil)=Q10(Tsoil-298.15)/10,FT,Roth(Tsoil)=47.91+e106/(Tsoil-254.85),
where Tsoil is the soil temperature in K and Q10=2.
Figure shows that FT,Q10 allows much more respiration at
temperatures below freezing than FT,Roth.
The temperature response curves [FT(Tsoil)] from
Eqs. () and ().
The moisture function in the current version of JULES (vn4.3) is parametrized as
Fs(s)=1-0.8(s-s0);s>s00.2+0.8s-smins0-smin;smin<s≤s00.2;s≤smin,
where s and s0 are the unfrozen soil moisture content and the optimum
soil moisture, both expressed as a fraction of saturation. s0=0.5(1+sw), and smin=1.7sw where sw is the soil
moisture at wilting point also as a fraction of saturation. The unfrozen soil
moisture reduces in the winter and hence provides some additional constraint
on the temperature response of soil respiration.
Fv is a function of the vegetation fraction, v:
Fv(v)=0.6+0.4(1-v).
All of these modifying functions are poorly constrained, and of these the
temperature function has the largest impact on the simulation
. Therefore both versions of FT are
evaluated in our simulations.
Vertical discretization
In the new, vertically discretized version of the soil carbon model there is
a set of the four soil carbon pools (DPM, RPM, BIO, HUM) in every soil layer.
The respiration rate is determined for each soil layer depending on the
temperature and moisture conditions in that layer. Following
we also add a vertical mixing (diffusion) term, with
diffusivity D(z) in m2 s-1 (z is the vertical dimension in m).
The equations for each soil carbon pool become
∂CDPM(z)∂t=∂∂zD(z)∂CDPM(z)∂z+fdpmΛc(z)-RDPM(z),∂CRPM(z)∂t=∂∂zD(z)∂CRPM(z)∂z+(1-fdpm)Λc(z)-RRPM(z),∂CBIO(z)∂t=∂∂zD(z)∂CBIO(z)∂z+0.46βRRtot(z)-RBIO(z),∂CHUM(z)∂t=∂∂zD(z)∂CHUM(z)∂z+0.54βRRtot(z)-RHUM(z).
It is assumed that once respired, any carbon is instantly available to the
atmosphere. Both fdpm (the fraction of litter that goes into DPM)
and βR (the fraction of soil respiration emitted to the atmosphere)
remain independent of depth. However, the litter inputs, Λc(z), now
vary with depth. In reality, most of the litter enters at the top of the
soil, but there is a smaller amount of litter input into deeper soil layers,
for example from roots. In JULES, following , we
distribute the litter inputs declining exponentially with depth.
We modified the respiration terms in the new model version (from the
original, Eq. ), by including an extra reduction of
respiration with depth, based on and .
This accounts for factors that are currently missing in the model such as
priming effects, anoxia, soil mineral surface and aggregate stabilization.
The respiration terms are now a function of depth as is the total respiration
Rtot(z):
Ri(z)=kiCi(z)FT(Tsoil(z))Fs(s(z))Fv(v)exp(-τrespz).
FT(Tsoil(z)), Fs(s(z)) and Ci(z) are now all dependent on
depth. Tsoil(z) and s(z) are the simulated layered soil
temperature and soil moisture content and Ci(z) is the simulated soil
carbon content for each layer and pool i. The respiration is assumed to
additionally reduce exponentially with depth and τresp is an
empirical parameter (in m-1) which controls the amount of this
reduction. The larger the value of τresp, the more inhibited the
respiration is with increasing depth. In an equilibrium version of the
vertically discretized soil carbon model, it was shown that the soil carbon
vertical distribution and total amount is strongly dependent on the value of
τresp, more so than any other model parameter.
Since the transport of the respired soil carbon is not yet included in the
model, it is assumed that, once respired, the carbon is instantly transferred
to the atmosphere. Given that the soil carbon is assumed to be emitted as
CO2 rather than CH4, including gas transport processes are unlikely to
change the amount emitted but might change the timing of emissions.
The vertical mixing term in Eqs. ()–() represents
either bioturbation – mixing of the soil due to animals and plant roots –
or cryoturbation – where soil mixing occurs due to frost heave and
freeze–thaw processes. The diffusion coefficient, D(z), varies between grid
cells and with depth. We follow by changing the
coefficient depending on the presence of permafrost. Without permafrost,
there is a bioturbation mixing rate of 1 cm2 year-1. This is
constant with depth. When permafrost is present, the mixing represents
cryoturbation and the rate increases to 5 cm2 year-1, but drops off
linearly below 1 m and reaches zero at 3 m depth (Eq. )
D(z)=Do;z≤1mDo2(3-z);1m<z<3m0.0;z≥3m
Do is 5 cm2 year-1. Permafrost is diagnosed wherever the deepest
soil layer is below 0 ∘C, assuming that this layer is below the
depth of zero annual amplitude. Further modifications to these coefficients
could be considered in future work. For example, bioturbation rates may vary
with depth . There are few explicit measurements of
cryoturbation rates, but the available observations suggest that
5 cm2 year-1 may be a realistic value .
However, additional studies are required to better constrain soil mixing
processes. Some modelling studies have incorporated depth-dependent
bioturbation mixing, e.g. , and there are a few
detailed models of cryoturbation, e.g. .
The soil carbon and soil respiration do not currently feedback onto any of
the land surface processes within JULES.
Adding a soil carbon tracer
In order to more explicitly study soil carbon dynamics in transient
simulations, we added a tracer to the model. This works by labelling some of
the carbon at the start of the simulation and keeping track of the labels at
this carbon moves through the system, whether mixing into different layers or
leaving the soil through respiration.
Each soil carbon pool in each soil layer is assigned a fraction,
FroldC, at the start of the main run, representing the
fraction of carbon in that pool that is “labelled”. This fraction is then
updated whenever the soil carbon pools are updated, either due to input of
fresh carbon from litter (which reduces the fraction), or due to mixing of
carbon between two layers in which the fractions are different. The general
formula to update the old carbon fraction (FroldC) for
carbon pool Ci (kg m-2), with an increment of carbon Ci
→ Ci+ΔCi is
FroldC|Ci→FroldC|CiCi+FroldC|ΔCiΔCiCi+ΔCi.
ΔCi includes both incoming and outgoing fluxes from the pool. For
the outgoing fluxes in ΔCi, we assume that
FroldC is the same as for the Ci pool. For an
incoming litter flux we assume that FroldC is zero, and
incoming fluxes from other pools naturally take the FroldC
value from the corresponding pool. The fraction of labelled carbon in the
outgoing respiration flux is also output from the model. This respired
“old” carbon is assumed to be instantly transferred to the atmosphere.
Multiplying the carbon pools/fluxes by their corresponding fraction,
FroldC, gives the quantity of labelled carbon in the
pool/flux, allowing the user to follow it through the system.
The choice of which carbon is labelled and traced through the system depends
on the scientific question. For example, any carbon that is in permanently
frozen soil may be given a value FroldC=1 at the
beginning of the simulation, and carbon in other parts of the soil given a
value FroldC=0, allowing us to explicitly trace the
permafrost carbon. In this paper we label all carbon below 1.0 m with a
value of 1 to study the behaviour of the deep soil carbon.
JULES simulations
Global simulations were carried out using a permafrost version of JULES 4.3
(JULES vn4.3_permafrost). This included the changes to the physical model
described by . Developments include a
representation of moss and organic soils and the addition of bedrock. In
addition there was a higher resolution soil column with deeper soil. These
modifications result in a reduction in the annual cycle of soil temperatures
and a reduction in the summer thaw depth so that it better matches the
observations over the standard configuration of JULES (vn4.3).
The simulations discussed here followed the protocol for the S3 experiments
in TRENDY . Forcing consisted of time-varying CO2,
climate from the CRU-NCEP data set (v4, 1901–2012), and the fraction of
agriculture in each grid cell . The model
resolution was N96 (1.875∘ longitude × 1.25∘
latitude). Nine plant functional types (PFTs) were used: tropical broadleaf
evergreen trees (BET-Tr), temperate broadleaf evergreen trees (BET-Te),
broadleaf deciduous trees (BDT), needleleaf evergreen trees (NET), needleleaf
deciduous trees (NDT), C3 and C4 grass, evergreen shrubs (ESh), and deciduous
shrubs (DSh). These were parametrized following .
Plant competition was allowed, with TRIFFID updating vegetation fractions on
a daily time step.
Two different model simulations were carried out using the two alternative
parametrizations of the soil respiration. The first one is denoted JULES-Q10
and uses FT,Q10 (Eq. ), and τresp=2. The
second one is denoted JULES-Roth and uses FT,Roth
(Eq. ), and τresp=1.2. Respiration is more
suppressed at depth in JULES-Q10 than in JULES-Roth. For comparison purposes,
additional JULES simulations were carried out using the standard soil carbon
model (vn4.3), which uses the temperature and soil moisture from the first
layer of the soil to calculate one set of soil carbon pools representative of
the whole soil profile. These standard simulations are denoted
JULES-Q10onelyr and JULES-Rothonelyr.
The soil carbon distribution is the slowest part of JULES to reach
equilibrium. The “modified accelerated decomposition” technique
(modified-AD) described by was used to spin it up to an
initial distribution applicable for the year 1900. For the modified-AD the
decay rates for the four pools were set to that of the fastest pool. These
same factors were used as multipliers to accelerate the diffusion
coefficients. An initial equilibrium spin-up of 500 years was carried out to
get the vegetation distribution and soil physical properties approximately
correct. The model was then spun up by repeating the climate of 1901–1920
25 times. The decomposition rates and the diffusion coefficients were then
reset, the soil carbon pools rescaled by the relevant factors and the model
spun up until the change in soil carbon was less than
0.012 % decade-1 globally and 0.005 % decade-1 for the
permafrost region.
Evaluation data sets
The circum-Arctic map of permafrost and ground-ice conditions
gives a historical permafrost distribution, which can
be compared with the permafrost area simulated by the model. It records
continuous, discontinuous, sporadic, and isolated permafrost zones, for which
the estimated permafrost coverage is 90–100, 50–90, 10–50, and
< 10 % respectively. Since the model does not include subgrid-scale
information, the simulated extent was compared with the continuous and
discontinuous regions on the observed map.
There are no large-scale observations of litter available, but the annual
total litter will be approximately the same as the annual total net primary
productivity (NPP). Observations of NPP are derived from MODIS data using the
MOD17 algorithm . Here we assess the multiannual mean
NPP for the period 2000 to 2012. Three notable biomes were identified based
on 14 World Wildlife Fund terrestrial regions
: tundra; boreal and coniferous
forest; and tropical forest.
There are two different large-scale observationally based soil organic carbon
data sets used for evaluation. The WISE30sec data set was
created using the soil map unit delineations of the broad scale Harmonized
World Soil Database, version 1.21, with minor corrections, overlaid by a
climate zones map as covariate, and soil property estimates derived from
analyses of the ISRIC-WISE soil profile database
for the respective mapped “soil/climate” combinations. This is available
for soil layer depths of 0–20, 20–40, 40–60, 60–80, 80–100, 100–150,
and 150–200 cm. The Northern Circumpolar Soil Carbon Database Version 2
(NCSCDv2: ) is more appropriate for the northern
high latitudes because it includes more site observations than WISE30sec. It
is, however, restricted to the northern high latitudes and has a
lower-resolution depth structure. NCSCDv2 consists of spatially extrapolated
soil carbon data from more than 1700 soil core samples and gives soil organic
carbon for the following depths: 0–30, 0–100, 100–200, and 200–300 cm
depth.
The multiannual mean and seasonal cycle of observed soil respiration for the
period 2000–2012 was extracted from .
combined a global soil database with a
semi-empirical model to scale up the field observations of soil respiration
to the global scale and provide a data-oriented estimate of soil respiration.
Results
The four different JULES simulations – the two standard simulations
(JULES-Q10onelyr and JULES-Rothonelyr – vn4.3) and the
two vertically discretized simulations (JULES-Q10 and JULES-Roth –
vn4.3_permafrost) – all have the same soil physics and vegetation dynamics.
The only differences between the simulations are in the soil carbon and soil
respiration, which do not feed back onto any of the other land surface
processes when JULES is run “offline” driven by observed meteorology (as
here).
Figure shows the JULES simulation of the mean permafrost
extent for the period between 1961 and 1990, representative of the time
period over which the observations were made. The simulated area is
20.3 million km2 and the area of the discontinuous and continuous
permafrost calculated in a similar manner from the data
is 16.5 million km2. This slight overestimation by JULES is caused by an
overestimation in Eurasia, where the southernmost extent of the simulated
permafrost includes regions where there is only isolated or sporadic
permafrost, which JULES is not expected to capture.
JULES simulated permafrost extent is shaded grey. The boundaries of
the observed continuous and discontinuous permafrost are superimposed in
black and red.
MODIS observed and JULES simulated multiannual mean net primary
productivity in g C m-2 year-1 for the period 2000–2012. The
black contours highlight the region where JULES simulates permafrost.
The addition of the vertical representation of soil carbon is most likely to
impact model simulations in the permafrost region, because conditions there
are very different in the deeper soil compared with the surface. However, the
results must also be assessed both globally and in the tropics to ensure the
model remains appropriate for use in a global ESM.
Results are presented for three regions: (1) global, (2) tropical (latitudes
less than 23.5∘), and (3) the region where JULES simulates permafrost
and NCSCDv2 has soil carbon data (outlined by a black contour in
Fig. ).
MODIS observed and JULES simulated annual mean NPP for the regions
assessed in this paper (left-hand plot), and for the following observed
biomes defined by : tropical forest, boreal and
coniferous forest, and tundra (right-hand plot). Note the different scales.
Soil carbon stocks
The soil carbon quantity and distribution are highly dependent on the surface
input of soil organic carbon, which comes from plant litter. Since there are
limited observations of litterfall, the simulated NPP was compared with observations (Fig. ). The large-scale
spatial comparison between model and observations is visibly good with a
spatial correlation of 0.73. In much of the low and mid-to-high latitudes the
simulated NPP is higher than that observed whereas in the drier and colder
regions the NPP appears lower. These differences are summarized in
Fig. . Globally JULES overestimates the annual total NPP by
∼ 12 % compared with MODIS. Much of this overestimation occurs in
the tropics. In particular, the observed tropical forest biome (right-hand bar
plot) is about a third more productive in JULES than in the observations. In
contrast, JULES underestimates the productivity of the permafrost region
(highlighted by the black contours in Fig. ). In general, NPP
in the permafrost region is very low – the observed amount is
3.7 Pg C year-1. The JULES-simulated value of 2.5 Pg C year-1
is more than 30 % lower than observed. Most of this difference occurs in
the observed tundra biome, where the simulated productivity is almost half
that of the observations. As with the tropical forest, the boreal forest is
slightly too productive, which for the permafrost region as a whole
counteracts some of the simulated low-bias from the tundra. Errors in NPP
will impact the simulated soil carbon distribution – too low NPP means too
little input of organic carbon to the soil, resulting in a low soil carbon
whereas too high NPP will cause high soil carbon.
Soil carbon in the top 2 m in kg m-2 for the four different
model simulations. The new vertically discretized model versions are on the
left and the standard model versions are on the right. The WISE30sec observed
data set is shown at the bottom left and the NCSCDv2 at the bottom right.
Total soil carbon in top 2 m (Pg C) for the regions assessed in
this paper (top three lines) and for the following biomes defined by
Olson (2001): tropical forest; boreal and coniferous forest; and tundra
(bottom three lines). Bold font indicates the vertically discretized soil carbon models.
Region
WISE30sec
NCSCDv2
JULES-Roth
JULES-Rothonelyr
JULES-Q10
JULES-Q10onelyr
Global
1943
2545
1259
2976
1311
Permafrost
452
741
568
117
325
90
Tropical
542
491
356
832
446
Tropical forest
328
293
218
493
274
Boreal forest
567
959
325
759
275
Tundra
182
292
126
37
72
26
Table shows the total soil carbon simulated by JULES for the
different regions and biomes in the top 2 m of the soil. This can be
compared with both the WISE30sec data set and the NCSCDv2 data set. In
general both of the new vertically discretized JULES models perform better
than the standard models when compared with observations. Their global total
is more than twice that of the standard model versions and higher than the
total in the WISE30sec data set. However, over the NCSCDv2 region the
WISE30sec data set has 680 Pg C whilst NCSCDv2 has 1031 Pg C. Therefore
WISE30sec simulates 351 Pg C less than NCSCDv2. Roughly combining these two
data sets suggests that the global total could be nearer to 2300 Pg and
therefore only slightly lower than the new model estimates. The biggest
improvement in the vertically resolved model is the amount of soil carbon in
the permafrost region. The standard JULES versions
(JULES-Rothonelyr and JULES-Q10onelyr) have far too
little soil carbon in this region when compared with either the WISE30sec or
the NCSCDv2 data. This increases significantly, to a value comparable with
the observations, when using JULES-Roth or JULES-Q10. The soil carbon in the
cold deep soil takes a long time to reach equilibrium with its amount
depending on the balance between the litter input at the surface and the slow
soil respiration rate. Once in equilibrium it will not respond notably to
short-term climate fluctuations.
In terms of biomes (Table ), all four versions of the model have a
reasonable estimate of the soil carbon in the tropical forest biome. The
vertical discretization increases the soil carbon slightly. In the boreal
forest biome the soil carbon is slightly lower than observations for the
standard version of the model. This increases significantly for the
vertically discretized models, leading to an overestimation of the soil
carbon in the boreal forest by both model versions. The amount of soil carbon
in the tundra is significantly larger for the vertically resolved model
versions and closer to both observational data sets, but remains too low.
Some of the differences highlighted in Table are caused by
errors in the soil carbon input, i.e. NPP (Fig. ).
However, particularly in the cold regions, the errors are also related to
missing processes in the model such as dust deposition and peat accumulation.
In addition, the observations contain additional soil carbon which is not in
equilibrium with the current climate such as peatland and waterlogged soils
.
The spatial distribution of the soil carbon in the top 2m of the soil is
shown in Fig. . On first glance the modelled spatial
patterns are very similar, with higher levels of soil carbon in the boreal
forest region, eastern America and Europe. Spatial correlations between the
standard and layered soil carbon models are high (0.82 between JULES-Roth and
JULES-Rothonelyr and 0.92 between JULES-Q10 and
JULES-Q10onelyr). However, spatial correlations between the model
and WISE30sec observations are lower at 0.40, 0.27, 0.30, and 0.22 for
JULES-Roth, JULES-Rothonelyr, JULES-Q10, and
JULES-Q10onelyr respectively. There is a slight improvement in the
spatial correlation with WISE30sec of the vertically resolved model compared
with the standard model, but the values remain relatively low. In comparison with
the WISE30sec observations, the model has a greater area in the northern
mid-latitudes with high values of soil carbon, and a greater area in the
northern high latitudes and deserts with very small amounts of soil carbon.
The vertically resolved simulations show more soil carbon in some of these
cold regions compared with the standard model, making it more comparable to
the WISE30sec data than the standard simulations. The NCSCDv2 data have much
larger amounts of soil carbon in the northern high latitudes than the
WISE30sec data, more comparable with the vertically resolved model. This high
carbon density extends further north than any of the model simulations.
Zonal total of soil carbon (left-hand plot) and NPP (right-hand
plot) for the permafrost region highlighted in Fig. , expressed
as Pg C per degree of latitude.
The two main differences between JULES-Q10 and JULES-Roth are the
parametrization of FT(Tsoil) and the value of τresp. The
τresp exerts a strong control over both the total amount of soil
carbon present and the vertical distribution of soil carbon within the
profile. Respiration is more suppressed at depth in JULES-Q10
(τresp=2) than in JULES-Roth (τresp=1.2)
leading to a greater proportion of soil carbon deeper in the soil profile in
JULES-Q10 than in JULES-Roth. However τresp has little impact on
the spatial correlations between model and observations. In contrast
FT(Tsoil) affects the relative amounts of soil carbon in the
tropics compared with the polar regions (Fig. ).
Profile of soil carbon in kg m-3 for the three main regions in
this study.
Figure shows the zonal distribution of soil carbon for
the permafrost region (highlighted in Fig. ) for both NCSCDv2
and WISE30sec data. The vertically discretized models are significantly
improved over the standard model versions with much more soil carbon at
latitudes where the observations show more soil carbon. There is still a
mismatch between the latitude where the soil carbon is greatest – about
65∘ N for the observations, but only 60∘ N for the models.
The low simulated soil carbon in the region between 65 and 80∘ N is
partly caused by the low simulated NPP in those regions
(Fig. ).
Figure shows the profile of soil carbon for the regions
in this study. The WISE30sec data are only available down to 2 m, whilst the
NCSCDv2 is available from 0 to 3 m. In the permafrost region, the WISE30sec
and NCSCDv2 have different profiles, with the NCSCDv2 having a greater
proportion of its soil carbon between 1 and 2 m than the WISE30sec data and
a smaller proportion nearer the surface. In the modelled permafrost region
(highlighted in Fig. in grey) the vertical mixing of the
organic carbon through the soil profile represents cryoturbation, whereas for
the rest of the global land surface the mixing represents bioturbation, with
a smaller mixing rate (see Sect. ). The comparison of model and
observations suggests that the model simulates too much soil carbon near the
surface in the top 50 cm and not enough deeper in the soil both globally and
in the tropics, which may be in part due to the representation of
bioturbation. In the permafrost region, the model simulations approximately
follow the shape of depth distribution of the WISE30sec data, although the
soil carbon density is too low.
There are significant differences between the JULES-Roth and JULES-Q10
simulations. The FT,Roth rate-modifying function has a steeper
gradient with temperature than the FT,Q10 (Fig. ),
meaning that JULES-Roth tends to simulate less soil carbon in warm regions
(very high decomposition) and more soil carbon in cold regions (very low
decomposition), e.g. Table ; Fig. . These
JULES-Roth results overall compare better with the observations, for example
in Table , Figs. and .
JULES-Q10 simulates too little soil carbon in the permafrost regions, and
globally JULES-Roth has a better spatial correlation with the WISE30sec data
(0.4, compared with 0.3 for JULES-Q10).
Soil respiration
The addition of vertically discretized soil carbon has little impact on the
magnitude of the soil respiration (Fig. ). This is expected
because the climate is relatively stable at the beginning of the 21st century
and the inputs (litterfall) are expected to approximately equal the outputs
(respiration). Figure shows that the seasonal cycle is very
similar for all four model versions in the tropics. However, in the permafrost
region the seasonal cycle is slightly displaced, so that the peak of the
respiration happens later in the year in the vertically resolved simulation.
This shifts the peak so that it is approximately 20 days later for both
JULES-Q10 and JULES-Roth. In JULES-Q10onelyr and
JULES-Rothonelyr, the respiration increases with the warming of the
top soil layer in spring and reduces once the soil surface layer cools back
down early in the autumn. In JULES-Q10 and JULES-Roth the respiration is
dependent on the soil temperature profile – the soil warms up slowly from
the surface downwards during late spring leading to a slower increase in
respiration as the air temperature increases. At the end of the summer, the
deeper soil layers cool more slowly than the surface, so respiration
continues for longer. The delay in time of peak respiration is also notable
in the total global soil respiration. Including gas transport processes
within the soil will further delay the time of peak surface emissions – this
process will be included in a later version of JULES.
This change in the seasonal cycle of soil respiration impacts the seasonal
cycle of net ecosystem exchange (NEE – Fig. ). The annual
amplitude of the global NEE increases in the vertically resolved models. They
uptake more carbon in the Northern Hemisphere spring/summer and lose more in
the Northern Hemisphere autumn/early winter. In the permafrost region the
onset of carbon uptake is up to a month earlier in the vertically resolved
model when compared with the standard model.
Seasonal cycle of total monthly respiration for the three main
regions considered in this paper.
Seasonal cycle of net ecosystem exchange (NEE) for the three main
regions considered in this paper. Positive values represent an uptake of
carbon and negative values represent a loss of carbon.
JULES-Roth and JULES-Q10 have a different seasonality. JULES-Roth has its
peak uptake earlier in the year than JULES-Q10 and begins emitting carbon in
August. This emission in August is because the soil respiration in JULES-Roth
is higher than the NPP. JULES-Q10 has a smaller seasonal cycle of soil
respiration and smaller maximum summer value, resulting in an uptake of
carbon for a longer period during the Northern Hemisphere summer. The
difference in the annual cycle of soil respiration between JULES-Roth and
JULES-Q10 are due to the higher temperature sensitivity of the function
FT,Roth compared with FT,Q10. The larger seasonal cycle
in JULES-Roth is closer to the observed amplitude on Fig. .
Time series of change in soil carbon (in Pg C) over the 20th
century for the three regions: global, permafrost and tropical.
The changes in the simulated global seasonal cycle of NEE, when included in
the Earth system model, will feedback onto the atmospheric CO2 and
impact any climate simulations.
Soil carbon changes over the 20th century
Figure shows that the inclusion of vertically discretized
soil carbon only has a small impact on the change in soil carbon over the
20th century. Globally, the simulated soil carbon increases by around
1 Pg C year-1 for the period between 1960 and 2009, with some small
differences between model versions. The increase is about
0.15 Pg C year-1 greater for the vertically resolved model than the
standard soil carbon model and is a consequence of a slight decrease in the
overall sensitivity of soil respiration to temperature changes in the
discretized model. The net global response is the combination of an
increasing sensitivity of respiration to temperature changes in the warmer
regions (tropics – Fig. ) and a decreasing sensitivity in
the colder regions (permafrost – Fig. ).
The spatial plots show the deep soil carbon (defined as soil carbon
below 1 m in 1901) as a fraction of the total soil carbon for each grid
cell. The time series shows the change in labelled deep soil carbon for the
permafrost region in Pg C.
Labelled deep soil carbon and total soil carbon in the permafrost
region before and after adding the NCSCDv2 observed deep soil carbon for
depths below 1 m. Any differences between NCSCDv2 and the added labelled
deep soil carbon are caused by differences between interpolation
methodologies.
Vertically resolved simulations
NCSCDv2
JULES-Roth
JULES-Q10
Mean ratio: deep / total
0.59
0.53
0.60
SD of ratio: deep / total
0.11
0.07
0.06
Total soil carbon (Pg C)
1007
801
446
Labelled deep soil carbon (Pg C)
585
475
251
Added obs. of deep carbon
JULES-Roth
JULES-Q10
Mean ratio: deep / total
0.76
0.81
SD of ratio: deep / total
0.25
0.21
Total soil (Pg C)
855
724
Labelled deep soil carbon (Pg C)
543
528
In the permafrost region and between 1960 and 2009 the soil carbon increases
by 146–168 Tg C. This falls within the modelled spread found by
who used a range of land surface models and
showed the soil carbon increased over the permafrost region by 264
(42–637) Tg C year-1 for the period 1960–2009. The slightly faster
increase (by ∼ 10 to 25 Tg C year-1) in the vertically
discretized models are a consequence of the lower response of soil
respiration to temperature change – possibly caused by a lag in the response
of the deep soil temperatures to increasing air temperature. In the standard
model, the respiration only responds to the surface soil temperatures, which
will respond much more quickly to changes in air temperature than the deeper
soil. It should be noted that the difference in the soil carbon between the
standard and vertically discretized model are small compared with differences
between different models in, for example, .
The deep soil carbon was initialized in 1901 and tracked over the 20th
century (Fig. ). Figure shows the spatial
distribution of the fraction of the total soil carbon that is labelled as
“deep carbon”, i.e. the ratio of the carbon below 1 m depth to the total
soil carbon in the profile. In general JULES-Q10 has more deep soil carbon
than JULES-Roth because it has more suppression of respiration with depth
(τresp=2 compared with τresp=1.2). Both JULES
simulations give a large proportion of deep soil carbon in the permafrost
regions with 53 to 60 % at depths below 1 m (Table ) and a
much lower proportion over the rest of the land surface (particularly for the
temperate and tropical regions where it is ≲ 20 %). These
proportions are comparable with the proportion observed in the NCSCDv2 data
set (59 %), although the absolute magnitude of the soil carbon is too
low. However, the observations are much more spatially variable than the
model simulations (Table and Fig. ), with more
carbon in the deep soil in North America compared with Eurasia.
The change in the labelled deep soil carbon over the 20th century for the
permafrost region highlighted is shown as a time series in
Fig. . This represents, for example, original permafrost
carbon which may be released to the atmosphere in a changing climate
. Despite an increase in the total soil carbon in the
permafrost region (Fig. ), there is a decrease in the
labelled deep soil carbon in the soil profile of around
33–49 Tg C year-1. A further 41–80 Tg C year-1 labelled
deep soil carbon is mixed out of the deep soil and into the top 1 m of the
soil. Vertical mixing processes in JULES continue to add new deep soil carbon
resulting in a net increase in total deep soil carbon of
∼ 100 Tg C year-1 over the 20th century. However, this deep
soil carbon now consists of both “original” permafrost carbon and “newer”
active carbon which, in reality, are likely to have different qualities
. This tracking of soil carbon could also be used for more
detailed model evaluation – see, for example, .
Model initialization of permafrost carbon
The new soil carbon model has improved the simulated soil carbon distribution
for both versions of JULES. However, there are still considerable errors in
the spatial distribution of soil carbon, reflected by the low spatial
correlation between model and observations and most notable in the northern
polar regions in Figs. and . This is
caused, in part, by errors in the litter input to the soil – illustrated
here as differences between the observed and modelled NPP (Fig. ). Errors in the litter input are more
likely to cause errors in the soil carbon in the active layer which turns
over at shorter timescales. Any carbon frozen in the permafrost has been
buried over several thousand years by alluvial sedimentation; dust
deposition; peat development and cryoturbation
. The only
burial process included in the vertically resolved soil carbon model
discussed here is cryoturbation. Therefore the model should not be expected
to simulate the soil carbon stores introduced by these additional burial
processes. However, errors caused by these missing processes will bias
simulations of soil carbon and the response of the soil carbon to a changing
climate. This will have implications for any estimate of the permafrost
carbon feedback when JULES is used within UKESM.
One method of incorporating a spatially realistic quantity of relatively
inert permafrost carbon is to simply replace the simulated deep soil carbon
below 1 m with the observed soil carbon from the NCSCDv2 in the permafrost
region (“PFC added: JULES-Roth” and “PFC added: JULES-Q10”). JULES has
four soil carbon pools, whereas the observations only give total soil carbon.
Therefore the observed soil carbon was partitioned into the four pools based
on the model's simulation of partitioning for each grid cell. It was also
interpolated to the model soil levels. Table shows that the labelled
deep soil carbon in the two JULES simulations is approximately equal to that
in the NCSCDv2 data set. This has increased the total soil carbon in the
permafrost region so it more closely represents the total observed soil
carbon in the NCDSDv2. However, there is still a deficit of 122 to 283 Pg C
depending on model configuration. The main differences are in the top 1 m of
soil, which is simulated by the model. Figure shows
the zonal total soil carbon for the permafrost region. The thin red and black
lines (“PFC added: JULES-Roth” and “PFC added: JULES-Q10”) show that with
the addition of the observed soil carbon below 1m, JULES is closer to the
observations, particularly in the region between 65 and 75∘ N.
The global spatial correlation of the blended model and NCSCDv2 soil carbon
with the WISE30sec (top 2 m) increases from 0.40 to 0.53 for JULES-RothC and
from 0.3 to 0.43 for JULES-Q10.
Zonal profile of total soil carbon after re-initialization with
observed soil carbon at depths between 1 and 3 m – compared with
observations, and with the vertically resolved versions of JULES-Q10 and
JULES-Roth.
The addition of the permafrost carbon to the model could result in a
significant drift back towards the equilibrium state. In order to quantify
the size of this drift the model was re-spun up from 1901–1920 for
25 × 20 years. After 500 years the global soil carbon had increased
by 23 Pg C or 0.75 % of the global total for JULES-RothC and decreased by
2 Pg C or 0.06 % for JULES-Q10. The soil carbon in the permafrost
region had increased by 7.5 Pg C or 0.9 % of the total in that region
for JULES-RothC and decreased by 6.5 Pg C or 0.9 % of the total in that
region for JULES-Q10. Figure compares these spin-up
simulations with the simulated change in soil carbon over the 20th century,
both globally and for the permafrost region. Also shown are two additional
20th century simulations, one made directly after the permafrost carbon was
initialized (“20th century climate + permafrost carbon”) and one where
the models were re-spun-up for the additional 25 × 20 years after
the permafrost carbon was initialized (“20th century
climate + permafrost carbon + further spin-up”). In all cases the
change over the 20th century is larger than the trend in the spin-up. For the
global simulations, adding the carbon in the permafrost region has little
impact on the total simulated change. The very slightly smaller increase in
soil carbon is small compared with the differences between the model
configurations. In the permafrost region, the overall increase in soil carbon
is smaller, and as expected the addition of permafrost carbon has a greater
impact on the changes.
Change in soil carbon over 20th century with and without the
observed permafrost carbon added, and with and without further spin-up.
Conclusions
This paper presents a vertically resolved model of soil carbon developed as a
precursor to adding the permafrost carbon feedback into UKESM. This new model
includes a tracer so that specified soil carbon (such as permafrost carbon)
can be identified, labelled, and followed through the simulation. The
vertically resolved model improves the spatial representation of soil carbon
when compared to the standard non-vertically resolved model. The seasonal
peak of the soil respiration in the Northern Hemisphere summer is delayed by
leading to the NEE peaking slightly earlier in the year.
Once included within an ESM, the change in seasonal cycle will feed back onto
the model simulated climate. The change in soil carbon over the 20th century
is comparable with the standard model.
Given the two temperature-dependent rate-modifying functions available in
JULES, our results suggest that the RothC temperature function,
FT,Roth (Eq. ), should be used for the
vertically discretized version, in preference to the FT,Q10
(Eq. ), as it gives a better match with the observations both
for soil carbon distribution and the seasonal cycle of soil respiration.
However, there may be some compensating errors due to the incorrect
north–south gradient in NPP. The exact nature of the most relevant soil
temperature [FT(Tsoil)] and soil moisture [Fs(s)] functions and
their applicability across different biomes needs further investigation
. For example, a systematic analysis using a large
model ensemble with a range of respiration rate-modifying functions would be
very informative.
Two of the most notable remaining sources of model errors are (1) errors in
the model simulation of the litter input and (2) missing vertical processes
within the soil carbon model such as alluvial sedimentation; dust deposition;
peat development. These should all be considered in future model
developments. In order to reduce the influence of errors caused by litter, an
alternative method of model evaluation may be to use soil carbon turnover
times analogous to those used by . These may not
be appropriate for simulations of permafrost carbon, but may be useful in
determining the behaviour of the soil carbon within the active layer.
suggested that the observed carbon stocks could be
categorized based on whether the appropriate process is included within an
ESM and hence whether the ESM is expected to simulate soil carbon in that
region.
Initialization of the deep soil carbon according to the NCSCDv2 map allowed a
better match between the simulated soil carbon and the observed carbon
distribution. Although there is a slight drift in the regional and global
soil carbon, this is small compared with the change in soil carbon over the
20th century. This methodology may provide a way of initializing an ESM so
that the permafrost carbon feedback is more appropriately estimated. However,
this then limit the usefulness of the soil carbon observations for model
evaluation. Alternative methods of model evaluation need to be developed such
as those discussed by and
.