TCThe CryosphereTCThe Cryosphere1994-0424Copernicus PublicationsGöttingen, Germany10.5194/tc-10-465-2016The importance of a surface organic layer in simulating permafrost
thermal and carbon dynamicsJafarovElchinelchin.jafarov@colorado.eduhttps://orcid.org/0000-0002-8310-3261SchaeferKevinInstitute of Arctic and Alpine Research, University of Colorado at
Boulder, Boulder, CO 80309, USANational Snow and Ice Data Center, Cooperative Institute for
Research in Environmental Sciences, University of Colorado at Boulder,
Boulder, CO 80309, USAElchin Jafarov (elchin.jafarov@colorado.edu)1March20161014654754May201512June20153February201610February2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://tc.copernicus.org/articles/10/465/2016/tc-10-465-2016.htmlThe full text article is available as a PDF file from https://tc.copernicus.org/articles/10/465/2016/tc-10-465-2016.pdf
Permafrost-affected soils contain twice as much carbon as currently exists in
the atmosphere. Studies show that warming of the perennially frozen ground
could initiate significant release of the frozen soil carbon into the
atmosphere. Initializing the frozen permafrost carbon with the observed soil
carbon distribution from the Northern Circumpolar Soil Carbon Database reduces
the uncertainty associated with the modeling of the permafrost carbon
feedback. To improve permafrost thermal and carbon dynamics we implemented a
dynamic surface organic layer with vertical carbon redistribution, and
introduced dynamic root growth controlled by active layer thickness, which
improved soil carbon exchange between frozen and thawed pools. These changes
increased the initial amount of simulated frozen carbon from 313 to 560 Gt C,
consistent with observed frozen carbon stocks, and increased the spatial
correlation of the simulated and observed distribution of frozen carbon from
0.12 to 0.63.
Introduction
Warming of the global climate will lead to widespread permafrost thaw and
degradation with impacts on ecosystems, infrastructure, and emissions that
amplify climate warming (Oberman, 2008; Callaghan et al., 2011; Schuur et al.,
2015). Permafrost-affected soils in the high northern latitudes contain
1300 ± 200 Gt of carbon, where ∼ 800 Gt C is preserved frozen
in permafrost with ∼ 550 Gt C in the top 3 m of soil (Tarnocai
et al., 2009; Hugelius et al., 2014). As permafrost thaws, organic matter
frozen within permafrost will thaw and decay, which will initiate the
permafrost carbon feedback, releasing an estimated 120 ± 85 Gt
of carbon emissions by 2100 (Schaefer et al., 2014). The wide range of
estimates of carbon emissions from thawing permafrost depends, to a large
extent, on the ability of models to simulate present permafrost extent (Brown et al.,
1997). For example, the simulated permafrost in some models is significantly
more sensitive to thaw, with corresponding larger estimates of carbon
emissions (Koven et al., 2013). Narrowing the uncertainty in estimated carbon
emissions requires improvements in how land surface models (LSMs) represent
permafrost thermal and carbon dynamics.
The active layer in permafrost regions is the surficial soil layer overlying
the permafrost, which undergoes seasonal freeze–thaw cycles. Active layer
thickness (ALT) is the maximum depth of thaw at the end of summer. LSMs used
to estimate emissions from thawing permafrost typically assume that the
frozen carbon is located in the upper permafrost above 3 m depth and below
the maximum ALT (Koven et al., 2011; Schaefer et al., 2011; MacDougall et
al., 2012). Thus, the simulated ALT determines the volume of permafrost in
the top 3 m of soil, and thus the initial amount of frozen carbon.
Consequently, any biases in the simulated ALT will influence the initial
amount of frozen carbon, even if different models initialize the frozen
carbon in the same way. In addition, the same thermal biases that lead to
deeper simulated active layers lead to warmer soil temperatures, making the
simulated permafrost more vulnerable to thaw and resulting in higher
emissions estimates (Koven et al., 2013).
The surface organic layer (SOL) is the surface soil layer of nearly pure
organic matter that exerts a huge influence on the thermodynamics of the
active layer. The organic layer thickness (OLT) usually varies between 5 and 30 cm,
depending on a balance between the litter accumulation rate relative to
the organic matter decomposition rate (Yi et al., 2009; Johnstone et al.,
2010). A recent model intercomparison study shows that LSMs need more
realistic surface processes such as an SOL and better representations of
subsoil thermal dynamics (Ekici et al., 2015). The low thermal conductivity
of the SOL makes it an effective insulator, decreasing the heat exchange
between permafrost and the atmosphere (Rinke et al., 2008). The effect of the
SOL has been well presented in several modeling studies. For example,
Lawrence and Slater (2008) showed that soil organic matter affects the
permafrost thermal state in the Community Land Model, and Jafarov et
al. (2012) discussed the effect of the SOL in the regional modeling study for
Alaska, United States. Recently, Chadburn et al. (2015a, b) incorporated an SOL
in the Joint UK Land Environment Simulator (JULES) model to illustrate its
influence on ALT and ground temperatures both at a site-specific study in
Siberia, Russia, and globally. In essence, the soil temperatures and ALT
decrease as the OLT increases. Consequently, how (or if) LSMs represent the
SOL in the simulated soil thermodynamics will simultaneously determine the
initial amount of frozen permafrost carbon and the vulnerability of the
simulated permafrost to thaw.
In this study we improved present-day frozen carbon stocks in the Simple
Biosphere/Carnegie-Ames-Stanford Approach (SiBCASA) model to reduce biases
in initial permafrost carbon stocks and improve the dynamics of future
permafrost carbon release. To achieve this we introduce three improvements
into the SiBCASA model: (1) improve the soil thermal dynamics and ALT,
(2) improve soil carbon dynamics and build-up of carbon stocks in soil, and
(3) initialize the older, frozen carbon using observed circumpolar soil carbon
(Hugelius et al., 2014).
Methods
We used the SiBCASA model (Schaefer et al., 2008) to evaluate current soil
carbon stocks in permafrost affected soils. SiBCASA has fully integrated
water, energy, and carbon cycles and computes surface energy and carbon
fluxes at 10 min time steps. SiBCASA predicts the moisture content,
temperature, and carbon content of the canopy, canopy air space, and soil
(Sellers et al., 1996; Vidale and Stockli, 2005). To calculate plant
photosynthesis, the model uses a modified Ball–Berry stomatal conductance
model (Ball, 1998; Collatz et al., 1991) coupled to a C3 enzyme kinetic
model (Farquhar et al., 1980) and a C4 photosynthesis model (Collatz et al.,
1992). It predicts soil organic matter, surface litter, and live biomass
(leaves, roots, and wood) in a system of 13 prognostic carbon pools as a
function of soil depth (Schaefer et al., 2008). The model biogeochemistry
does not account for disturbances, such as fire, and does not include a
nitrogen cycle. SiBCASA separately calculates respiration losses due to
microbial decay (heterotrophic respiration) and plant growth (autotrophic
respiration).
SiBCASA uses a fully coupled soil temperature and hydrology model with
explicit treatment of frozen soil water originally from the Community
Climate System Model, version 2.0 (Bonan, 1996; Oleson et al., 2004). To
improve simulated soil temperatures and permafrost dynamics, Schaefer et al. (2009)
increased the total soil depth to 15 m and added the effects of soil
organic matter on soil physical properties. Simulated snow density and
depth, and thus thermal conductivity, significantly influence simulated
permafrost dynamics, so Schaefer et al. (2009) added the effects of depth
hoar and wind compaction on simulated snow density and depth. Recent model
developments include accounting for substrate availability in frozen soil
biogeochemistry (Schaefer and Jafarov, 2015).
We spun SiBCASA up to steady-state initial conditions using an input weather
data set from the modified Climatic Research Unit National Center for
Environmental Prediction (CRUNCEP)
(Wei et al., 2014) for the
entire permafrost domain in the Northern Hemisphere (Brown et al., 1997).
CRUNCEP is modeled weather data at 0.5 × 0.5 degree latitude and longitude
resolution, optimally consistent with a broad array of observations. The
CRUNCEP data set used in this study spans 110 years, from 1901 to 2010. We
selected the first 30 years from the CRUNCEP data set (1901 to 1931) and
randomly distributed them over 900 years. To run our simulations we used
JANUS High Performance Computing (HPC) Center at the University of Colorado at
Boulder. The 900-year time span was chosen in order to make optimal use of the
computational time, which allowed us to finish one spin-up simulation on JANUS
HPC without interruptions.
Frozen carbon initialization
We initialized the frozen carbon stocks using the Northern Circumpolar Soil
Carbon Dataset version 2 (NCSCDv2) (Hugelius et al., 2014). The NCSCDv2
includes soil carbon density maps in permafrost-affected soils available at
several spatial resolutions ranging from 0.012 to 1∘. The data set
consists of spatially extrapolated soil carbon data from more than 1700 soil
core samples. We used three layers from the NCSCDv2 data set, each 1 m in
depth, distributed between ground surface and 3 m depth.
We placed the frozen carbon within the top 3 m of simulated
permafrost, ignoring deltaic and loess deposits that are known to extend
well beyond 3 m of depth (Hugelius et al., 2014). The bottom of the
permafrost carbon layer is fixed at 3 m, while the top varies spatially,
depending on the simulated ALT during the spin-up run. We initialized the
permafrost carbon by assigning carbon from the NCSCDv2 to the frozen soil
carbon pools below the maximum thaw depth. These frozen pools remained
inactive until the layer thaws.
We initialized frozen carbon between the permafrost table and 3 m depth
using two scenarios: (1) spatially uniform distribution of the frozen carbon
throughout the permafrost domain (Schaefer et al., 2011), and (2) observed
distribution of the frozen carbon according to the NCSCDv2. It is important
to know the “stable” depth of the active layer before initializing frozen
carbon. We ran the model for several years in order to calculate ALT, and
then initialized frozen carbon below the maximum calculated ALT. The frozen
carbon was initialized only once after the first spin-up simulation. For the
next simulation we used the previously calculated permafrost carbon. We
defined an equilibrium point when changes in overall permafrost carbon were
negligible or almost zero.
The total initial frozen carbon in each soil layer between the permafrost
table and 3 m is
Cfri=ρcΔzi,
where Cfri is the total permafrost carbon within the ith soil
layer, ρc is the permafrost carbon density, and Δzi is the thickness of the ith soil layer in the model. For
the uniform permafrost carbon distribution, spatially and vertically uniform
ρc of 21 kg C m-3 (Schaefer et al., 2011). For the
observed distribution from the NCSCDv2, ρc varies both with
location and depth (Hugelius et al., 2013).
The permafrost carbon in each layer is divided between slow (Cslow),
metabolic (Cmet), and structural (Cstr) soil carbon pools as
follows:
Cslowi=0.8CfriCmeti=0.2froot2meetCfriCstri=0.2froot2strtCfri,
where froot2met and froot2strt are the simulated fractions of root
pool losses to the soil metabolic and structural pools respectively (Schaefer
et al., 2008). The nominal turnover time is 5 years for the slow pool, 76
days for the structural pool, and 20 days for the metabolic pool. Schaefer et
al. (2011) state a 5 % loss to the metabolic pool and a 15 % loss to the
structural pool based on observed values in Dutta et al. (2006). The
simulated fractions are actually 5.6 % to the metabolic pool and 14.4 %
to the structural pool. We found it encouraging that the numbers calculated
with the SiBCASA metabolic fractions resulted in numbers that are close to
the observed values in Dutta et al. (2006).
Dynamic SOL
We modified SiBCASA to include a dynamic SOL by incorporating the vertical
redistribution of organic material associated with soil accumulation.
SiBCASA calculates the soil physical properties as a weighted average of
those for organic matter, mineral soil, ice, and water (Schaefer et al.,
2009). The physical properties include soil porosity, hydraulic
conductivity, heat capacity, thermal conductivity, and matric potential. The
model calculates the organic fraction used in the weighted mean as the ratio
of simulated carbon density to the density of pure organic matter. The model
does not account for the compression of organic matter. Since the prognostic
soil carbon pools vary with depth and time, the organic fraction and the
physical properties all vary with time and depth. We only summarized these
calculations here since the calculations are covered in detail in Schaefer
et al. (2009).
As live, above-ground biomass in the model dies, carbon is transferred into
the first layer as litter. Without the vertical redistribution we describe
here to create a surface organic layer, the top layer of the model tended to
accumulate carbon in excess of that expected for pure organic matter. To
allow vertical movement and build up a SOL, we placed a maximum limit on the
amount of organic material that each soil layer can hold. When the simulated
carbon content exceeds this threshold, the excess carbon is transferred to
the layer below. This is a simplified version of the Koven et al. (2009)
carbon diffusion model, which accounts for all sedimentation and
cryoturbation processes. This simplified model is better suited for our
application because we wanted to focus only to the buildup of a SOL.
We calculate the maximum allowed carbon content per soil layer, Cmax,
as
Cmax=ρmaxΔz1000MWC,
where ρmax is the density of pure organic matter or peat, Δz is the soil layer thickness (m), MWC is the molecular weight of carbon
(12 g mol-1), and the factor of 103 converts from grams to
kilograms. Based on observations of bulk densities of peat, we assume that ρmax
is 140 kg m-3 (Price et al., 2005). The MWC term
converts the expression into mol C m-2, the SiBCASA internal units for
carbon. The simulated organic soil fraction per soil layer, forg, is
defined as
forg=CCmax,
where C is the carbon content per soil layer (mol m-2). To convert to
carbon we assume that the fraction of organic matter is 0.5, which means
that half of the organic matter by mass is carbon. The original formulation
allowed forg to exceed 1.0 such that the excess organic material was
essentially “compressed” into the top soil layer, resulting in a 2 cm
simulated SOL. We place an upper limit of 0.95 on forg and transfer the
excess carbon to the layer below. The OLT is defined as the bottom of the
lowest soil layer where forg is 0.95.
Maximum thaw depth (ALT) averaged over the last 5 years after spin-up
from (a) Schaefer et al. (2011) and (b) this study, in
meters.
Coupling growth to thaw depth
We coupled simulated gross primary productivity (GPP), plant phenology, and
root growth to simulated thaw depth as a function of time. The model assumes
that root growth decreases exponentially with depth based on observed vertical
root distributions (Jackson et al., 1996; Schaefer et al., 2008). The
maximum rooting depth for completely thawed soil is defined as the soil
depth corresponding to 99 % of the observed vertical root distribution or
1.1 m for the tundra and boreal forest biomes. In real life, growing roots
cannot penetrate frozen soil (Tryon and Chapin, 1983; Van Cleve et al.,
1983), so we restricted simulated root growth to occur only within the
thawed portion of the active layer. Typically, the date of snowmelt
determines the start date of the growing season (Grøndahl et al., 2007;
Wipf and Rixen, 2010). However, in permafrost-affected soils, the start date
of the growing season could be delayed by thawing of the active layer. Since
fine root and leaf growth are coupled (Schaefer et al., 2008), constraining
root growth to thawed soil also constrains spring leaf out to occur after the
active layer starts thawing. In real life plants cannot photosynthesize
without liquid water in the soil, so we scaled simulated GPP based on the
fraction of thawed roots in the root zone.
The previous version of the model distributed fine and coarse root growth
vertically within the soil column based on observed root distributions. As
the roots died, carbon was transferred to the soil carbon pools for that
layer. Thus, the maximum rooting depth determined the maximum depth of
“current” or “active” carbon in the model. Of course, if the maximum rooting
depth fell below the permafrost table, the model would incorrectly grow
roots directly into frozen soil and consequently accumulate permafrost
carbon.
In order to restrict simulated root growth to thawed soil layers, we first
calculated the fraction of thawed roots within the root zone defined by
Rth=∑i=1nrootRfi1-Ficei,
where Rth is the fraction of total roots that are thawed, nroot
is the soil layer corresponding to the maximum root depth, Rfi is
the reference root fraction for the ith soil layer based on observed
root distributions, and Ficei is the ice fraction calculated from
the simulated ice content for the ith soil layer. When Rth equals
1, the entire root zone is thawed, and when Rth is zero, the entire
root zone is frozen. We assume evenly distributed liquid water in each layer
such that Fice equals the frozen soil fraction. We then calculated
Reffi, the effective root fraction for the ith soil layer:
Reffi=Rfi1-Ficei/Rth.
We use Reffi to distribute new fine and coarse root growth
within the soil column. When Reffi equals zero, the soil layer is
frozen with no root growth. Dividing by Rth ensures Reffi
sums to 1 within the soil column to conserve mass. This formulation makes
the effective maximum rooting depth equal to the thaw depth.
To couple GPP to thaw depth, we treated the reference root zone distribution
for completely thawed soil as the maximum root growth capacity defining the
maximum potential GPP. When Rth < 1, the root zone is partially
frozen and GPP is less than its full potential. We defined a GPP scaling
factor, Ssoilfrz, as
Ssoilfrz=RthforRth≥0.010forRth<0.01.
This assumes that at least 1 % of the roots must be thawed for GPP to
occur, corresponding to about ∼ 1 cm of thawed soil. Ssoilfrz is
applied along with the drought stress and temperature scaling factors to
constrain photosynthesis (Schaefer et al., 2008). SiBCASA assumes that the
factors that control GPP also control wood and leaf growth, so we also
included Ssoilfrz as a new scaling factor in addition to the drought
stress and temperature scaling factors that control wood and leaf growth.
Comparison of ALT from 76 Circumpolar Active Layer Monitoring
stations with the averaged ALT from the last 5 years after spin-up from
(a) Schaefer et al. (2011) and (b) this study. r is a
Pearson's correlation coefficient and p is a significance value;
p < 0.05 stands for the 95 % confidence level.
Root growth and GPP without (a) and with (b) the
frozen soil constraint on growth. GPP is normalized to a maximum value of
1.0. The root growth fraction is relative to total plant growth.
The average soil carbon distribution from 200 grid cells for
(a) a tundra region in continuous permafrost zone,
(b) boreal forest on the boundary between continuous and
discontinuous zones, and (c) low carbon soil at the southern border of
the discontinuous permafrost zone. The solid blue curve indicates the mean,
and the white blue shading indicates the spread in the simulated soil carbon
density.
The frozen carbon maps obtained assuming a uniform frozen carbon
distribution at the initial time step, and averaged over 5 years at the
end of the steady-state run: (a) from Schaefer et al. (2011), and
(b) from the current run, correspondingly.
The soil carbon maps averaged over top 3 m: (a) from
SiBCASA at the end of the steady-state run with constant permafrost carbon
density, (b) from SiBCASA at the end of the steady-state run with
spatially varying permafrost carbon density, and (c) from the
NCSCDv2.
(a) The near-surface air temperature averaged over the first
2 months of the fall season. (b) The downwelling long-wave
radiation, averaged yearly over 10 years. (c) The maximum snow depth
obtained over 10 years for the steady-state run, and (d) the soil
wetness fraction (dimensionless fraction of 1), representing overall
near-surface soil wetness, averaged yearly over 10 years.
Results
The dynamic SOL decreased the simulated ALT on average 50 % across the
domain and allowed the model to simulate permafrost in discontinuous zones
where it could not before (Fig. 1). The area of near surface permafrost
simulated with the current version of the model is equal to
13.5 mil km2, which is almost 38 % greater than without the dynamic SOL (Schaefer et
al., 2011). This area is closer to the observed area from the International
Permafrost Association: 16.2 mil km2 (Brown et al., 1997). Simulated
ALT less than 2 m covers about 92 % of the area in the new simulations
(Fig. 1b) in comparison to 66 % of the area in the Schaefer et al. (2011)
simulations (Fig. 1a). The previous version of SiBCASA could not simulate
permafrost in many parts of the discontinuous zone with relatively warm
climate. Adding the dynamic SOL essentially decreased the thermal
conductivity of the surface soil, allowing SiBCASA to simulate permafrost
where the mean annual air temperatures are close to 0 ∘C.
To illustrate the improvement of the simulated ALT with respect to the
observed data, we compared simulated ALT with measured values from
Circumpolar Active Layer Monitoring (CALM) stations. The CALM network is a
part of the Global Terrestrial Network for Permafrost (Burgess et
al., 2000). The monitoring network measures ALT either using a mechanical
probe or a vertical array of temperature sensors (Brown et al., 2000;
Shiklomanov et al., 2010). After matching up the CALM coordinates with the
coordinates of previously simulated ALT (Schaefer et al., 2011), we excluded
sites with no measurements or ALT greater than 3 m depth, ending up with 76
CALM stations. Figure 2 shows simulated vs. observed ALT for the 76 CALM
sites. The current simulations have a higher resolution than Schaefer et
al. (2011) simulations, which allowed us to reach a higher order of
heterogeneity between measured and simulated ALTs. The Pearson's correlation
coefficient, R, is negative and not significant for the Schaefer et
al. (2011) simulations (Fig. 2a), but is positive and statistically
significant for the current simulations assuming p < 0.05
(Fig. 2b). The dynamic SOL greatly improves the simulated ALT, but SiBCASA
still tends to overestimate ALT.
Figure 3 illustrates the effect of the frozen soil restrictions on phenology
and GPP at a single point in central Siberia. Before applying a frozen soil
restriction, SiBCASA maintained fine roots even in winter, resulting in root
growth all year with a peak in spring corresponding to simulated leaf out
(Fig. 3a). Simulated GPP was restricted by liquid water availability and was
closely tied to thawing of the active layer, resulting in a lag as high as 60
days between leaf out and start of GPP in spring. Restricting growth and GPP
to when the soil is thawed essentially synchronizes all phenological events
to occur at the same time (Fig. 3b).
Restricting growth and GPP to when the soil is thawed delayed the onset of
plant photosynthesis in spring in permafrost-affected regions. Introduction
of the thawed root fraction in the model reduced GPP primarily in early
spring. To illustrate the difference between unconstrained and restricted
root growth (Fig. 3), we ran the model for 10 years for both cases. The
difference between unconstrained and restricted root growth resulted in an
overall ∼ 9 % reduction in annual GPP for the entire permafrost
domain, nearly all of which occurred in spring.
To illustrate soil carbon distribution with depth we selected three
representative areas: a continuous permafrost area corresponding to tundra
type biome above the Arctic Circle, an area in the boundary of continuous and
discontinuous permafrost corresponding to the boreal forest biome, and an
area near the south border of the discontinuous permafrost corresponding to
poorly vegetated–rocky areas. We calculated the mean and standard deviation
of the carbon density distribution with depth for 200 grid points around each
of the three selected locations. Simulated typical carbon densities from the
selected locations are shown in Fig. 4. All profiles shown in Fig. 4 show a
similar pattern: a 20–30 cm SOL with reduced carbon content at the bottom
of the active layer. The SOL and permafrost carbon content matches observed
values (Harden et al., 2012), but carbon content near the bottom of the
active layer does not, most likely because our model does not include
cryoturbation processes.
The decrease in ALT resulting from a dynamic SOL increases the volume of
permafrost in the top 3 m of soil, greatly increasing the initial amount
of frozen permafrost carbon in the simulations. Schaefer et al. (2011),
without the dynamic SOL, assumed a uniform permafrost carbon density of
21 kg C m-3, resulting in a total of 313 Gt of
permafrost carbon at the start of their transient run (Fig. 5a). To compare
with the results of Schaefer et al. (2011), we initialized the permafrost carbon
using the same assumed uniform carbon density and ran SiBCASA to steady-state
initial conditions (Fig. 5b). Assuming the same uniform carbon density, the
current version with the dynamic SOL results in a total of ∼ 680 Gt C
compared to 313 Gt C in Schaefer et al. (2011). The dynamic SOL effectively
doubled the volume of permafrost in the top 3 m of soil and the
amount of simulated frozen carbon.
Initializing SiBCASA with the observed spatial distribution of permafrost
carbon from the NCSCDv2 resulted in ∼ 560 Gt C of carbon stored in
permafrost after spin-up, close to the observed value ∼ 550 Gt C in the
top 3 m of soil (Hugelius et al., 2014). This does not mean that
after the spin-up-simulated permafrost carbon stocks exactly matched the
NCSDCv2 data. In discontinuous zones, for example, if the model simulated
permafrost, it tended to produce a deeper ALT and thus less permafrost carbon
than the NCSCDv2. Assuming a uniform permafrost carbon density does not
account for the spatial heterogeneity in permafrost carbon and overestimates
the total amount of permafrost carbon compared to the NCSCDv2 (680 Gt C vs.
550 Gt C, see Fig. 6a and b). The spatial correlation between simulated
and observed permafrost carbon is 0.63 when initializing with the NCSCDv2
(Fig. 6c), compared with a spatial correlation of 0.12 for the uniform
permafrost carbon density. The amount and spatial distribution of permafrost
carbon significantly improves when initializing with NCSCDv2.
Discussion
Failure to simulate soil carbon in southeast Canada and southwest Siberia
(see Fig. 6b and c) is attributed to deep ALT. These areas correspond to
the peat lands. Our model uses the Harmonized World Soil Carbon Database (HWSD)
(FAO et al., 2009) to initialize soil texture and related thermal
properties. Deep layers of peat have low thermal conductivities, providing an
ideal condition for permafrost existence. However, the HWSD does not address
peat lands in southeast Canada and southwest Siberia.
The overestimation of soil organic carbon (SOC) in central Siberia results from coupling between
GPP and ALT. The dynamic SOL and rooting depth strengthens the feedback
between GPP and ALT (Koven et al., 2009). Higher GPP produces greater litter
fall, which increases the input soil carbon at the surface and results in a
thicker SOL. The dynamic SOL changes the properties of the near surface
soil, resulting in a shallower ALT and cooler soil temperatures. The dynamic
rooting depth accounts for a shallower ALT and modulates GPP accordingly.
The cooler soil temperatures slow microbial decay and increase the carbon
accumulation rate, which in turn increases the SOL and reduces ALT further.
Eventually, this feedback results in the development of a peat bog. The
changes we describe here indicate that SiBCASA can simulate the dynamics of
peat bog development, but the model does not yet include a dynamic
vegetation model to account for conversions between biome types, such as
boreal forest to peat bog.
The overall amount of permafrost carbon is less than that calculated
assuming a uniform frozen carbon distribution. It is important to note that
the SOL, ALT, and the permafrost thickness are the same for both cases
(Fig. 6a and b). This is due to the fact that in both cases soil carbon
is added in the permafrost layer below the active layer. Consequently, the
ALT does not change between simulations, and the volume of permafrost in the
top 3 m of soil does not change as well. The smaller permafrost
carbon stock simulated for the nonuniform case is mainly due to the fact
that we did not initialize frozen carbon in regions where it is not present according to the
NCSCDv2, such as the Brooks Range in Alaska.
The dynamic SOL insulates ALT from air temperature, allowing SiBCASA to
simulate permafrost in many discontinuous permafrost regions where it could
not before, consistent with previous results where changes in thermal
properties associated with the presence of soil organic matter cooled the
ground (Lawrence and Slater, 2008; Yi et al., 2009; Ekici et al., 2014;
Chadburn et al., 2015a, b). In addition, our work confirms findings by Koven
et al. (2009) showing that including SOL dynamics in the model improves
agreement with the observed permafrost carbon stocks. However, to better
simulate known permafrost distribution in the discontinuous permafrost zone,
it is important to know the exact OLT. Unfortunately, in situ measurements of
OLT are scarce and essentially lacking in most areas of continuous and
discontinuous permafrost.
To investigate the influence of the environmental factors on ALT further, we
looked at the relationship between ALT and near surface air temperature
(NSAT), soil wetness fraction (SWF), downwelling long-wave radiation (DLWR),
and snow depth (SD). The simulated ALT is most influenced by NSAT and soil
SWF, with a slightly smaller influence by DLWR, and nonlinearly influenced by
SD (Fig. 7). To show the influence of the NSAT, we averaged two early fall
months over 10 years. The areas with deep simulated ALT correspond to annual
NSAT > 1 ∘C in southwest Siberia and
NSAT > 5 ∘C in southeast Canada with a statistically significant
correlation of 0.62 (Fig. 7a). DLWR showed a similar, but slightly weaker
relationship with ALT, with higher DLWR values in southeast Canada and
southwest Siberia and statistically significant correlation of 0.45 (Fig. 7b).
Figure 7c shows maximum simulated snow depth calculated over the last 10 years
of the steady-state run. Zhang (2005) indicates that SDs of less than 50 cm
have the greatest impact on soil temperatures. Our results show no
correlation between SD and ALT, but the effects of snow on ALT are less
obvious and depend on different physical processes, such as wind, snow
metamorphism, and depth hoar formation (Sturm et al., 1997; Ekici et al.,
2015; Jafarov et al., 2014). We also observe high SWF in southwest Siberia
and southeast Canada (see Fig. 7d) where SiBCASA simulates deep ALT with a
statistically significant correlation of 0.68, suggesting that wet soils modulate
the insulating effects of the SOL (Lawrence and Slater, 2008). This work
does not address the impacts of fire on soil thermodynamics and recovery from
fire, both of which are strongly influenced by the changes in the SOL
(Jafarov et al., 2013). Studies show that wildfires and climate change could
substantially alter soil carbon storage (Yuan et al., 2012; Yi et al., 2010).
In the current version of the model the topsoil carbon stays in the system
and provides resilience to permafrost. However, in reality, the upper SOL
could be removed by fire, which would alter soil thermal properties and
perturb permafrost carbon stability.
Conclusion
This work shows that the dynamic organic layer directly improves the distribution
of carbon in soil, as well as indirectly through the improved ALT.
Initialization of the carbon according to the NCSCDv2 map allowed us to
better match simulated soil carbon with the observed carbon distribution. Restriction of the root
growth within the thawed layer prevented artificial accumulation of
permafrost carbon. Our model developments improved both the total amount and
the spatial distribution of simulated permafrost carbon. The total permafrost
carbon increased from 313 to 560 Gt C, compared to the observed
value of 550 Gt C, and the spatial correlation with the observed
distribution increased from 0.12 to 0.63. These improvements indicate the
importance of including these developments in all land surface models.
In addition, most of the LSMs calculate soil properties based on prognostic
soil carbon and soil texture from HWSD. We found that HWSD does not include
thermal properties of peat lands, which resulted in inaccurate modeling of
the ALT at the southern boundaries of the permafrost domain in Canada and
Russia.
Acknowledgements
This research was funded by NOAA grant NA09OAR4310063 and NASA grant
NNX10AR63G. This work utilized the Janus supercomputer, which is supported
by the National Science Foundation (award number CNS-0821794) and the
University of Colorado, Boulder. We thank K. Gregory at NSIDC for reviewing
the manuscript. Software tools used in this study include m_map MATLAB
package and shadedErrorBar.m MATLAB script.Edited by: J. Boike
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