Yokohata et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-020-00366-8(2020) 7:56

Progress in Earth andPlanetary Science

RESEARCH ARTICLE

Open AccessFuture projection of greenhouse gas
emissions due to permafrost degradation
using a simple numerical scheme with a
global land surface model
Tokuta Yokohata1* , Kazuyuki Saito2, Akihiko Ito1, Hiroshi Ohno3, Katsumasa Tanaka1,4, Tomohiro Hajima2 andGo Iwahana5

Abstract
The Yedoma layer, a permafrost layer containing a massive amount of underground ice in the Arctic regions, isreported to be rapidly thawing. In this study, we develop the Permafrost Degradation and Greenhouse gasses Emission
Model (PDGEM), which describes the thawing of the Arctic permafrost including the Yedoma layer due to climatechange and the greenhouse gas (GHG) emissions. The PDGEM includes the processes by which high-concentration
GHGs (CO2 and CH4) contained in the pores of the Yedoma layer are released directly by dynamic degradation, as wellas the processes by which GHGs are released by the decomposition of organic matter in the Yedoma layer and otherpermafrost. Our model simulations show that the total GHG emissions from permafrost degradation in the RCP8.5
scenario was estimated to be 31-63 PgC for CO2 and 1261-2821 TgCH4 for CH4 (68th percentile of the perturbed model
simulations, corresponding to a global average surface air temperature change of 0.05–0.11 °C), and 14-28 PgC for CO2
and 618-1341 TgCH4 for CH4 (0.03–0.07 °C) in the RCP2.6 scenario. GHG emissions resulting from the dynamic
degradation of the Yedoma layer were estimated to be less than 1% of the total emissions from the permafrost in bothscenarios, possibly because of the small area ratio of the Yedoma layer. An advantage of PDGEM is that geographicaldistributions of GHG emissions can be estimated by combining a state-of-the-art land surface model featuring detailed
physical processes with a GHG release model using a simple scheme, enabling us to consider a broad range of
uncertainty regarding model parameters. In regions with large GHG emissions due to permafrost thawing, it may bepossible to help reduce GHG emissions by taking measures such as restraining land development.
Keywords: Permafrost degradation, Carbon cycle feedback, Climate change

1 Introduction“Permafrost” is the name given to areas where theground temperature has remained below 0 °C for morethan 2 years (IPCC 2013). Virtually all soil contains thebodies of dead organisms (mainly plants) in the form oforganic matter (Zimov et al. 2006a; Schuur et al. 2008;
Brown 2013). When the soil is not frozen, the organic* Correspondence: yokohata@nies.go.jp
1Center for Global Environmental Research, National Institute forEnvironmental Studies, 16-2 Onogawa, Tsukuba 305-8506, Japan
Full list of author information is available at the end of the article

matter is decomposed by microorganisms and released
from the surface to the atmosphere in the form of carbon
dioxide or methane (Zimov et al. 2006b; Walter et al. 2007;
Ciais et al. 2013). However, when the soil is frozen, the organic matter is trapped without being decomposed, as theactivity of these microorganisms is suppressed (Brown
2013; Hugelius et al. 2013; Hugelius et al. 2014). It is estimated that permafrost contains roughly twice the amountof carbon as the air and approximately three times as muchas land plants (Prentice et al. 2001; Ping et al. 2008; Tarnocai et al. 2009; Dlugokencky and Tans 2013). As the Earth’s© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
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(2020) 7:56

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surface temperature rises due to climate change, the frozen
soil in the polar region will thaw, thereby releasing in theform of greenhouse gases (GHGs) the organic substances
contained in the frozen soil (Collins et al. 2013; Koven
et al. 2013; Schuur et al. 2015). These GHGs will further
accelerate global warming (Lenton 2012; Köhler et al.
2014; Schuur et al. 2015). Given the large amount of carbon contained in the permafrost, positive feedback frompermafrost thawing is very likely to accelerate changes in
the climate system (Schaefer et al. 2014; Koven et al. 2015;
MacDougall et al. 2015; Schneider von Deimling et al.
2015; MacDougall and Knutti 2016; Steffen et al. 2018;
Gasser et al. 2018; McGuire et al. 2018; Kawamiya et al.
2020).
Still, there is a great deal of uncertainty regarding theprocess of GHG emissions from permafrost thawing(Schaefer et al. 2014). This is partly due to the lack ofobservational knowledge of basic permafrost processes(Schuur et al. 2015). Although permafrost exists invarious forms depending on its formation factors, what
has been attracting attention in recent years is thethawing of the Yedoma layer, a permafrost layer containing a large mass of ground ice, mostly found in Alaska
and Siberia (Strauss et al. 2013; Strauss et al. 2017). It haslong been known that the Yedoma layer exists in permafrost zones (Brouchkov and Fukuda 2002; Schirrmeister
et al. 2011; Kanevskiy et al. 2011), but it has only recently
been noted that this huge underground layer is thawing
rapidly (Vonk et al. 2012; Ulrich et al. 2014; Strauss et al.
2017). Analysis of satellite observations suggest that a
subsidence of the ground occurred at sites where tundra
fires have caused the heat insulation effect of vegetationon the surface to disappear (Iwahana et al. 2016) and thatfrozen soil and ground ice are being degraded by erosion
from rivers and ocean waves (Günther et al. 2013; Joneset al. 2011; Kanevskiy et al. 2016). Previous studies havereported that the ground ice and frozen soils in the
Yedoma layer contain high concentrations of carbon
dioxide, methane, and organic carbon (e.g., Saito et al.
2017; Strauss et al. 2017). To date, however, the impact on
the climate system of the dynamic degradation of theYedoma layer associated with ground subsidence has notbeen sufficiently evaluated, partly due to the difficulty ofmodeling it in global climate models (Schneider vonDeimling et al. 2015).
In this study, we developed a simple scheme todescribe the thawing process of the Yedoma layer
accompanied by vertical mechanical collapse due toground subsidence (hereinafter called “dynamic degradation”) based on in-situ observations conducted in
Alaska and Siberia. Using this model, we estimate the
GHG emissions due to the future degradation of theYedoma layers. We consider two pathways for GHG
emissions due to permafrost degradation: the process of

releasing GHGs (CO2 and CH4) trapped in the frozen
soil (referred to as “direct emissions”) and the process of
releasing GHG emissions produced by the decomposition of organic matter contained in the frozen soil(“secondary emissions”) caused by the thawing of thepermafrost. In addition to the dynamic degradation of
the Yedoma layers, we also estimate the GHG emissions
due to the thermodynamic degradation of the permafrost owing to the increase in ground temperature.
Finally, in the course of our study, we estimate theglobal mean temperature response caused by the GHG
emissions due to permafrost degradations using the simple climate model ACC2 (Tanaka and O’Neill 2018).

Yokohata et al. Progress in Earth and Planetary Science

2 Methods
Permafrost Degradation and Greenhouse gasses Emission Model (PDGEM) evaluates the GHG emissions dueto the degradation of the permafrost layer. PDGEM
describes the processes of dynamic (Section 2.1.1) andthermodynamic (Section 2.1.2) permafrost degradation
with a simple formulation and calculates the GHG
emissions globally with a resolution of 1 degree. The
parameters used in the formulation are varied (Table 1)
in order to describe the future possible behavior of
permafrost degradations. Details of the model formulation and experimental settings for the future projections
are summarized in the sections that follow.
2.1 Description of Permafrost Degradation and GHG
Emission Model
2.1.1 Dynamic degradation of the Yedoma Layer

FDy [kg year−1], the GHG emissions due to the dynamic
degradation of the Yedoma layer, is defined as
F Dy ¼ F Dy;dir þ F Dy; sec :ð1Þ

FDy, dir: GHG emissions due to the release of gases
trapped in the frozen soil [kg year−1]
FDy, sec: GHG emissions due to the decomposition oforganic matter [kg year−1]
The first term in Eq. (1) corresponds to direct emissions,while the second term represents secondary emissions due
to dynamic degradation. The direct emissions are formulated as follows:
F Dy;dir ¼ ΔV Dy  X GHG :ð2Þ

ΔVDy: Volume of thawed permafrost due to dynamic
degradation [m3 year−1]
XGHG, i: GHG mass in thawed permafrost [kg m−3]
Observational studies have measured the settling
velocity of the ground surface due to permafrost thawing
in the area where fire has occurred (e.g., Iwahana et al.
2016). In this study, the volume of dynamic permafrost

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Table 1 Model parameters for the calculation of GHG emissions due to the dynamic and thermodynamic permafrost degradations
Model parameter

VariableStandard value

Uncertainty range

References

Vdstrb [cm/yr]

Subsidence velocity of burnt area in Yedoma layer

2.4

± 2.1

a

rice

Ice contents in Yedoma layer

0.64± 0.15bdsoc [m]Depth of average soil organic layer

3.0

± 1.5

c

π

Fraction for the flux of soil organic carbon, fastpool slow pool

0.025
0.45

0.01–0.04
0.30–0.60

d, e, f, g, h, iτ [year]Time scale for slow decomposition at 5 °C

25

10-40

d, e, f, hΔrwtlnd, max [%]Maximum increase of wetland fraction

20

10-30

d

Q10

Temperature sensitivity parameter,
aerobic
anaerobic

2.5
3.0

1.5–3.5
4.0–6.0d, i, jrgas

GHG production ratio, CH4:CO2 in aerobic
CH4:CO2 in anaerobic
Fast pool
Slow pool

1:50
1:1
1:7

± 50%
± 20%
± 50%

k, m, n, p

oxd

CH4 oxidation rate, anaerobic

0.20

0.15–0.40h

The standard values and uncertainty ranges of the parameters are shown. References for the standard values and uncertainty ranges are as follows. a: Iwahana et al.
(2016), b: Strauss et al. (2017), c: Saito et al. (2020), d: Schneider von Deimling et al. (2015), e: Sitch et al. (2003), f: Dutta et al. (2006), g: Koven et al. (2011), h: Burke et al.
(2012), i: Schadel et al. (2014), j: Walter and Heimann (2000), k: Lee et al. (2012), m: Schuur et al. (2008), n: Segers (1998), p: Walter Anthony et al. (2014)

thawing is formulated based on this observational knowledge as follows:
ΔV Dy ¼ Pdstrb  Aydm  V dstrb :ð3Þ

Pdstrb: Probability of occurrence of fire
Aydm: Area of Yedoma layer in a 1-degree grid cell[m2]
Vdstrb: Settling velocity of the ground due to permafrost thawing [m year−1]
Equation (2) describes the processes of permafrost
thawing with land subsidence owing to the occurrenceof fire. We determine the fire area in the Yedoma layer
with the first and second terms (Pdstrb × Aydm) in Eq. (3).
The probability of fire, Pdstrb, is given as a function of
meteorological data based on the observed relationshipbetween past occurrences of fires and meteorologicalconditions. Veraverbeke et al. 2017 showed high correlations between fire occurrence and temperature, total precipitation and convective precipitation in the Northwest
territory (NT) and Alaska (AK) from 2001 to 2015. In thisstudy, the future fire area ratio, Pdstrb, is estimated using
future meteorological data and the relationship shown
below:
Pdstrb ¼ a þ b  T air þ c  P total þ d  P conv

ð4Þ

where Tair is surface air temperature [K], Ptotal is total
precipitation [kg/m2/s], and Pconv is convective precipitation [kg/m2/s], and the coefficients are a = − 0.495, b =0.00179, c = − 343.6, d = 204.4. The coefficients in Eq.
(4) are obtained using multiple regression of the fire area
ratios for 2001-2015 in NT and AK, from Veraverbeke
et al. 2017, and NCEP reanalysis data (Kalnay et al.

1996) for the same regions. To estimate the future firearea ratio, the bias of the global climate models (GCMs,
details of which are explained later) is corrected with
NCEP reanalysis data (Kalnay et al. 1996) by subtracting
the climatological error (the difference between model
results and the reanalysis data using 1980–2000 average).
As a result of this bias correction, the estimated fire area
ratio based on Eq. (4) is consistent with past observations (Veraverbeke et al. 2017). Given that Veraverbeke
et al. 2017 found correlation based on the NT and AK
regions, we estimate the future fire area ratio by averaging the climate model data at 10-degree resolution.
We also confirmed that the difference between the estimated value of the fire area obtained by Eq. (4) and theobserved value (Veraverbeke et al. 2017) has a normal
distribution (not shown, with standard deviation =0.00229). Considering that fires generally occur stochastically, a normal distribution with the above standard deviation, corresponding to the difference between theestimated and observed fire area ratio, was used to randomly assign values to each 1-degree grid.
With respect to the area of the Yedoma layer, Aydm,
we use the results of Saito et al. (2020) regarding the behavior of soil moisture and organic carbon from the lastinterglacial period (approximately 120,000 years ago) tothe present with 20 km resolution. Since the Yedoma
layer is considered to be a region where soil frozen waterand soil organic carbon are particularly concentrated(e.g., Strauss et al. 2017), in this study, we defined theYedoma layer by using a threshold value for soil frozen
water and soil organic carbon as calculated in Saito et al.
2020. We based our threshold value on the “vulnerability” measure defined in Saito et al. 2020 as (ICE/(2020) 7:56

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max(ICE) × SOC/max(SOC)), where ICE and SOC are
soil frozen water and soil organic carbon, respectively,
and “max” denotes the maximum value across the spatial
dimension. According to Strauss et al. 2017, the soil organiccarbon in the Yedoma layer is estimated to be 83–129 GtC.
In this study, the threshold of vulnerability was chosen sothat the soil organic matter of the Yedoma layer falls within
the range of Strauss et al. 2017 (Table 1).
The settling velocity, Vdstrb, in Eq. (2) is defined basedon observational studies. Table 1 of Iwahana et al. 2016
synthesized the annual ground subsidence rates atvarious fire-burnt sites. In this study, the average valueof Iwahana et al. 2016 is used; the range of the sedimentation velocity over the fire-bunt region is 2.4 ± 2.1 cm/
year, as shown in Table 1.
The GHG concentration, XGHG, in Eq. (1) can be
expressed by the following equation:

the dynamic degradation of permafrost (FDy, sec in Eq.1),
this study considers four types of decomposition, following Schneider von Deimling et al. 2015 and Gasser et al.
2018. Specifically, we differentiate decomposition types
based on two types of organic matter quality (fast orslow) and two types of soil moisture conditions (aerobic
or anaerobic). The following equations for the decomposition of thawed permafrost carbon are solved with a
global resolution of 1 degree:

Yokohata et al. Progress in Earth and Planetary Science

X GHG ¼ Rpore  C GHG  ρGHG :ð5Þ

Rpore: Volume fraction of bubbles in the permafrost[ratio]CGHG: GHG concentration in the permafrost pores[ratio]ρGHG: Mass density of GHG [kg m−3]
In this study, we consider CO2 and CH4 as the GHG
emissions and use data obtained by field observation in
the Yedoma layer in Alaska and Siberia (Saito et al.
2017) to set the values of Rpore and CGHG. As reported in
Saito et al. 2017, the Rpore and CGHG values obtained byfield observation have very large variation. Table 2 shows
the standard deviation of the observed values in Saito
et al. 2017. In calculating the dynamic degradation, theaverage value for the ground ice and frozen soil is used
for the calculation of XGHG. It is reported that the ice
content in the Yedoma layer (rice) is approximately 0.64
(Strauss et al. 2017). Accordingly, the ratios of groundice and frozen soil (rice and 1 − rice, respectively) are usedas multipliers for Rpore and CGHG. Field observations
revealed that the layer with high GHG concentration(Table 2) was above (approximately) 5 m in the soilcolumn and that the lower layer had very low GHG concentration. In this study, therefore, we assume that the
GHG concentration, as shown in Table 2, is zero below
5 m.
In order to estimate the GHG emissions associatedwith the decomposition of soil organic carbon due toi; jdC thaw
R j i; j¼ π i; j F thaw − i C thaw
dt
τ

ð6Þ

i: index for the quality of soil organic matter (fast or
slow decomposition)
j: index for the soil moisture state (aerobic and anaerobic decomposition)i; jC thaw : soil organic carbon content in the thawed
permafrost [kg]Fthaw: flux of soil organic carbon due to permafrost
thawing [kg/year]
πi, j: fraction of flux for the corresponding types
τi: turnover time of soil organic carbon [year]
Rj: changes in soil organic carbon decomposition rate
due to temperature rise
The model parameter, πi, j, i.e., the fraction of thawed
soil organic carbon, depends on the quality of organic
matter (i = 1: fast, i = 2: slow decomposition) and soil
water content (j = 1: aerobic, j = 2, anaerobic). The quality of organic matter is an important determinant for thetimescale of the carbon release (Strauss et al. 2015). We
subdivide the thawed permafrost carbon into a fast andslow decomposing fraction with annual and decadal
timescales (τi) based on the literature of soil organic
quality, as shown in Table 1 (Sitch et al. 2003; Dutta
et al. 2006; Koven et al. 2011; Burke et al. 2012; Schädel
et al. 2014).
The soil water content is also a key determinant in thedecomposition of soil organic carbon. In this study, thefraction of thawed permafrost carbon under the aerobic
or anaerobic condition is determined by the wetland
fraction, rwtlnd, obtained from the Global Lakes andWetland Database (Lehner and Döll 2004). The original
wetland fraction map is interpolated into 1-degree gridcells. The fraction of soil organic carbon for aerobic
decomposition is 1 − rwtlnd, while that for anaerobic
decomposition is rwtlnd in each grid cell. In the future

Table 2 Volume fraction of air bubbles in the ground ice and frozen soil (Rpore) and the concentration of GHGs in the air bubbles(CGHG)
Variable

CO2/ground ice

CH4/ground ice

CO2/frozen soil

CH4/frozen soil

Rpore [cc/cc]

0.044 (0.021)

0.031 (0.004)

0.019 (0.010)

0.014 (0.0074)

CGHG [ppmv]

2992 (5101)

21848 (38434)

7714 (11933)

131675 (139746)

The average value and standard deviation for CO2 and CH4 are shown(2020) 7:56

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simulations, extensions of the wetland area are represented as a function of surface air temperature rise, withreference to Schneider von Deimling et al. 2015. Specifically, we describe the increase in rwtlnd by linear scaling
with the surface air temperature anomaly, Δ Ta (theanomaly is calculated as the difference from the first 20years average). The wetland fraction reaches its maximum
extent (Δrwtlnd, max) for a warming Δ Ta of 10 K. For
further warming, the wetland fraction is kept constant at
the maximum extent. The uncertainty range of Δrwtlnd,
max is shown in Table 1.
The flux of soil organic carbon due to permafrost
thawing is formulated as

2008; Lee et al. 2012; Walter Anthony et al. 2014;
Schneider von Deimling et al. 2015). The oxdj term
corresponds to the fraction of released carbon that isoxidized (thus, oxdj = 0 for CO2), the range of which isdetermined from the literature (Burke et al. 2012;
Schneider von Deimling et al. 2015).

Yokohata et al. Progress in Earth and Planetary ScienceF thaw ¼ ΔV Dy  ρSOC

ð7Þ

Here, ρSOC is the density of soil organic carbon, calculated
as ρSOC ¼ dσ SOC, where σSOC is the soil organic carbon from
SOC
Saito et al. 2020, and dSOC is the depth of the soil organiccarbon. dSOC is a model parameter in the range shown in
Table 1. The changes in soil organic carbon decomposition
rate due to temperature change are formulated with reference to Schneider von Deimling et al. 2015 as follows:
R j ¼ Q10 j ðT g − 10Þ=10ð8ÞQ10j: temperature sensitivity parameter
Tg: soil temperature [°C]
Q10j is the temperature sensitivity of carbon decomposition due to the microbial soil activity rises that accompany increasing soil temperature. The Q10j parameter isdependent on the aerobic or anaerobic conditions; theparameter ranges are given based on the literature, as
shown in Table 1 (Walter and Heimann 2000; Shadel
et al. 2013; Schneider von Deimling et al. 2015). For Tg,
we use monthly mean soil temperature (averaged over the
top 4 m), calculated by land-surface model simulations
with 1-degree resolution (Yokohata et al. 2020a, 2020b).
The details of this are explained in Section 2.2.
The GHG emissions due to the decomposition of soilorganic carbon, FDy, sec, can be calculated by solvingi; jC thaw in Eq. (5) as follows:
F Dy; sec ¼
i; j

X
i; j

i; j

dC thaw− πi; j F thaw
dt

!



 r i;gasj 1 − oxd jð9Þr gas : production ratio of GHG (CO2 or CH4) due tosoil organic matter decomposition
oxdj: oxidation rate of CH4i; j
The production rate of CO2 and CH4, r gas , isdependent on the soil organic quality and aerobic or
anaerobic conditions. The ranges of the parameteri; jvalues for r gas are determined based on incubation studies under various conditions (Segers 1998; Schuur et al.

2.1.2 Thermodynamic degradation of the permafrost layer

In this section, the thermodynamic degradation of thepermafrost (i.e., the thickening of the active layer) due tothe rise in soil temperature in future climate change isformulated. In the formulation for the dynamic degradation of the Yedoma layer (Eq.1), direct emissions are
considered due to the presence of high-concentration
GHGs (Table 1). However, direct emissions are notconsidered in the thermodynamic degradation sincehigh-concentration GHGs are not expected to bepresent in the permafrost other than in the Yedoma
layer. Even in the thermodynamic degradation, (Eqs. 5,
6, 7 and 8) is used for the formulation of the secondary
GHG release. To establish the flux of soil organic carbon
due to the permafrost thawing associated with thermodynamic degradation, Eq. (6) is replaced by
F thaw ¼ ΔV thr  ρSOC

ð10Þ

ΔVDy: Volume of thawed permafrost due to thermodynamic degradation [m3 year−1]
Here, we use the same ρSOC as described in Eq. (6). For
the volume of thawed permafrost due to thermodynamic
degradation, numerical simulations using a global landsurface model (Yokohata et al. 2020a, 2020b) are used. The
formulation for ΔVTh [m3 yr-1] in the tth year is as follows:
ΔV Th ¼ ½ALT ðt Þ − MAXðALT ðt 0 Þ; t 0 ¼ 0; …t − 1Þ Agrid :ð11Þ
Here, ALT(t) is the active layer thickness [m] (ALT,
the annual maximum thaw depth) in the tth year. The
active layer is defined as the region where the groundtemperature exceeds 0 °C in summer seasons. Eq. (10) is
formulated in order to avoid counting the thawed region
multiple times due to the annual variability of ALT. Agridin Eq. (10) is the grid area of the global climate model
used for the simulation (1-degree latitude and longitude)
as described in the next section. If Eq. (10) produces anegative value, ΔVTh is set to zero.
2.2 Experimental setting

Table 2 shows the standard values and uncertainty
ranges for all parameters given in this study, as
explained in the previous sections. Each parameter was
randomly selected from the uniform distribution withthe uncertainty range shown in Table 2. In all, 500

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simulations were performed using the randomly selectedparameters.
In addition to the parameters shown in Table 2, one ofthe physical variables used in the Permafrost Degradation
and Greenhouse gasses Emission Model (PDGEM) modelis the soil temperature, Tg, which is used for changes inthe soil decomposition rate (Eq. 7) and the volume of thethermodynamic permafrost thawing (Eq. 9). Tg is calculated by the global land-surface climate model MIROCINTEG-LAND (MIROC INTEGrated LAND surface
model, Yokohata et al. 2020a, 2020b), which is based on
the land surface components of the global climate model
MIROC (Model for Interdisciplinary Research on Climate;
Watanabe et al. 2010; Takata et al. 2003). The results ofmulti-GCM simulations provided by the Inter-Sectoral
Impact Model Inter-comparison Project phase 1 (ISIM
IP1, Hempel et al. 2013) were used as the atmosphericforcings to drive this land surface model. Using atmospheric forcings generated by the five GCMs (GFDLES2M, Dunne et al. 2012; HadGEM2-ES, Jones et al. 2011;
IPSL-CM5A-LR, Dufresne et al. 2013; Nor-ESM, Bentsen
et al. 2013; MIROC-ESM-CHEM, Watanabe et al. 2011)of ISIMIP1, we performed historical simulations (1951–2005) and future simulations (2006–2100) based on representative concentration pathways (RCP, van Vuuren et al.
2011) RCP2.6 and RCP8.5. The resolution of the land
surface model was 1 degree (Nitta et al. 2014). In thisstudy, a model version with improved permafrost processes (Yokohata et al. 2020b) was used.
Another physical variable given to the PDGEM model
was the future temperature change, ΔTa, which is used
for the future extent of the wetland area. We use thefuture projections of the five ISIMIP1 GCMs (Hempel
et al. 2013) noted above, under the RCP2.6 and RCP8.5
scenarios for ΔTa.
The GHG emissions due to the dynamic and thermodynamic permafrost thawing are calculated with themodel parameters shown in Table 2. The GHG emissions are then integrated globally and given to a simplified climate model, ACC2 (Tanaka and O’Neill 2018),
which calculates the global mean surface air temperature
response to GHG emissions. By calculating the global
mean surface air temperature response with and without
the permafrost GHG emissions under RCP 8.5, the impact of permafrost thawing on the climate system can be
examined.

Yokohata et al. Progress in Earth and Planetary Science

3 Results and discussion
Figure 1 shows the area ratio of the Yedoma layer andthe distribution of soil organic carbon used to calculate
the dynamic degradation. As described in the previous
section, this study defines the Yedoma layer as permafrost having a particular abundance of soil organiccarbon and soil frozen water, based on data from Saito
et al. (2020). The total soil organic carbon in the
Yedoma layer, as shown in Fig. 1, is consistent with theestimates of Strauss et al. 2017 (106 GtC, the middle ofuncertainty range 83-129 GtC). Soil organic carbon in
the Arctic has accumulated in cold and humid environments where soil degradation is slow. It is distributed ineastern Siberia and Alaska, found mostly in coastal areasand near river basins (Saito et al. 2020). These areas arecharacterized by extremely low temperatures (Yokohata
et al. 2020b).

Fig. 1 (Left) Yedoma area used in the model simulation (unit = ratio to grid area); (right) soil organic carbon used in the model simulation (unit = kg/m2)

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Table 3 shows the cumulative emissions of CO2 andCH4 due to the dynamic and thermodynamic degradation of the permafrost in the RCP8.5 scenario. Before
conducting our future experiments, we confirmed thatthe average value of CH4 emission (3.9 TgCH4) for the
5-year period from the start year (2006) of the calculation is close to the estimate of present CH4 emission (~4 TgCH4, Walter Anthony et al. 2016; ~ 1 TgCH4, Saunois et al. 2020). As indicated in Table 3, the CO2 andCH4 releases due to the dynamic degradation (directplus secondary emissions) of the Yedoma layer areapproximately 0.1 PgC and 5 TgCH4, respectively. In
each case, this is less than 1% of the total release due todynamic and thermodynamic degradation (47 PgCO2
and 2067 TgCH4, respectively). Comparing the direct
release of GHGs trapped in the ground ice and frozen
soil and the secondary release of GHG due to thedecomposition of soil organic carbon, the latter is anorder of magnitude larger than the former (Table 3).
Even though very high concentrations of CO2 and CH4
are contained in the ground ice and frozen soil of theYedoma layer (Saito et al. 2017), their impact on the
climate is quite small when they are released into theatmosphere by the degradation of the permafrost. In thepresent formulation and over the study period (up to2100), the dynamic degradation of the Yedoma layer
does not significantly affect the carbon cycle feedback.
As shown in Table 3, the cumulative CO2 and CH4
emissions (the emissions due to dynamic and thermodynamic degradation) in the RCP8.5 scenario estimatedin the present study are 47 PgC (31–63 PgC, 68% range)and 2067 (1261–2821) TgCH4, respectively. For
comparison, Table 3 also shows the amount of GHG gasemissions estimated in various previous studies. As canbe seen in the table, these estimated emissions cover awide range. Notably, the GHG emissions for the RCP8.5
scenario estimated in the present study are within theindicated range of uncertainty. As shown in the table,
the aggregated carbon content of CO2 plus CH4 emissions due to permafrost degradation in the present study
is 48 (32–66) PgC, and the increase in surface airtemperature due to permafrost degradation is 0.08
(0.05–0.11) °C. Other studies (e.g., Schaefer et al. 2014;
Schneider von Deimling et al. 2015; Koven et al. 2015;
Gasser et al. 2018) have reported similar values. One
multi-model study featuring state-of-the-art processmodels reported that in some of the models, atmospheric carbon may actually be absorbed due to permafrost degradation owing to the effect of potential plant
growth after thawing (McGuire et al. 2018). The spreadin estimated GHG emissions in McGuire et al. 2018 islarger than in other studies, ranging from a carbon sinkof 41 PgC to a carbon source of 140 PgC at the end of
the twenty-first century. On the other hand, the amountof CH4 released in the RCP8.5 scenario in the present
study is larger than the 1474 TgCH4 reported by Schneider von Deimling et al. (2015).
Table 4 shows estimates of GHG emissions in the
RCP2.6 scenario. Even here, the dynamic degradation of
the Yedoma layer contributes less than 1% to total GHG
emissions, and the direct release of dynamic degradation
is an order of magnitude smaller than the secondary
release. As in the RCP8.5 scenario, the cumulativeemissions of CO2 and CH4 resulting from the combined
effect of dynamic and thermodynamic degradation are
similar to those in previous studies. In the present study,

Yokohata et al. Progress in Earth and Planetary Science

Table 3 Future predictions of GHG emissions from permafrost degradation in the RCP8.5 scenario
CO2 emissions[TgC or PgC]

CH4 emissions[GgCH4 or TgCH4]

Global mean temperaturechange [°C]

Reference–

This study

0.08 (0.05–0.11)

This study

0.09 (0.05–0.14)

aDynamic, Direct

3 (1–5) TgC

123 (43–203) TgCH4

Dynamic, Direct + secondary

0.1 (0.04–0.2) PgC

5 (2–8) TgCH4

Total: Dynamic + Thermo-dynamic

47 (31–63) PgC

2067 (1261–2821) TgCH4
48 (32–66) PgC

Total: previous studies

87 (42–141) PgC

1474 (836–2614) TgCH4

226 PgCb

27 (− 41 to 95) PgC

c59 (11–143) PgC

d57 (28–113) PgCe

37–174 PgC

f

37–343 PgC

0.29 (0.17–0.41)g

The cumulative CO2 and CH4 emissions are estimated at the end of twenty-first century. Direct emissions due to dynamic degradation, direct plus secondary
emissions due to dynamic degradation, and total emissions due to dynamic plus thermodynamic degradation in the current study are shown (average value and68th percentile range of model ensemble simulations). Some estimated values reported in previous studies are also shown in the far right column. References forthe estimate in the previous studies are as follows. a: Schneider von Deimling et al. (2015), b: MacDougall et al. (2015), c: McGuire et al. (2018), d: Gasser et al.
(2018), e: Koven et al. (2015), f: Schuur et al. (2015), g: Schaefer et al. (2014)

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Table 4 Same as Table 3, but for the RCP2.6 scenario
CO2 emissions[TgC or PgC]

CH4 emissions[GgCH4 or TgCH4]

Global mean temperaturechange [°C]

Reference

Dynamic, Direct

1 (0.4–2) TgC

48 (17–81) GgCH4–This study

Dynamic, Direct + secondary

0.07 (0.03–0.1) PgC

3 (1–5) TgCH4

Total: Dynamic + Thermo-dynamic

21 (14–28) PgC

986 (618–1341) TgCH4

0.05 (0.03–0.07)

This study

Total: Previous studies

36 (20–58) PgC

0.06 (0.03–0.10)a22 (15–29) PgC
446 (218–921) TgCH4

103 PgCb

27 (6–62) PgC

d56 (13–118) PgC

hReferences for the estimate in the previous studies are as follows. a: Schneider von Deimling et al. (2015), b: MacDougall et al. (2015), d: Gasser et al. (2018), h:
MacDougall and Knutti (2016)

the combined carbon content of CO2 and CH4 emissions
is 22 (15–29) PgC, which is similar to the total 27 PgC,
reported by Gasser et al. (2018). On the other hand, theamount of released CH4 is 986 (618–1341) TgCH4,
which is larger than the 446 TgCH4 estimated in
Schneider von Deimling et al. (2015). The increase insurface air temperature due to permafrost degradation is
0.05 (0.03–0.07) °C, which is similar to the 0.06 (0.03–
0.10) °C estimated in Schneider von Deimling et al.
(2015).
Figure 2 shows the cumulative GHG release due todynamic permafrost degradation. In the formulation of
dynamic degradation, GHG emissions are dependent on
the possibility of fire (Pdstrb in Eq. 4) and the subsidence
velocity of the land surface (Vdstrb), both of which are
based on present observation (Section 2.1.1). Since we
use the same Vdstrb for the RCP8.5 and RCP2.6 scenarios,

the difference between the scenarios in Fig. 2 can be
attributed to the difference in Pdstrb. In our study, the possibility of fire is increased mainly due to temperature rise,as shown in Fig. 3, since Pdstrb is estimated as a function of
meteorological data (Eq. 4) based on the relationshipestablished from historical data (Veraverbeke et al. 2017).
Figure 4 shows the results of the cumulative release of
GHG from the combination of dynamic and thermodynamic degradation. As described above, since thecontribution of dynamic degradation of the Yedoma
layer is less than 1% of the total, the cumulative emission
is essentially determined by thermodynamic degradation(Section 2.1.2). This thermodynamic degradation is
obtained by solving the equation of secondary release
shown in Eq. (8), based on Eqs. (9)–(10). Here, thechange in active layer thickness (ALT) simulated by the
global land surface model (Yokohata et al. 2020a, 2020b)

Fig. 2 Cumulative GHG flux due to dynamic degradation of the Yedoma layer for CO2 (left, unit = PgC) and CH4 (right, unit = TgCH4). The
simulations under the RCP2.6 (blue) and RCP8.5 (red) scenarios are shown. The width of the colored area represents the 68 percentiles of thesimulated results forced by five GCMs, each of which involved 300 simulations with different model parameters sampled from the uncertainty
ranges shown in Table 1. The average value of the model simulations is represented by the bold blue (RCP2.6) and red (RCP8.5) lines

Yokohata et al. Progress in Earth and Planetary Science(2020) 7:56

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Fig. 3 Time sequence for the probability of fire in the RCP8.5 (red) and RCP2.6 (blue) scenarios. Unit is the ratio (%) to the total land area above
50° N. The width of the colored area represents the 68 percentiles of the 300 simulations with different model parameters, as explained in
Section 2.1.1. The average value of the model simulations is represented by the bold blue (RCP2.6) and red (RCP8.5) lines

is used for the calculation of permafrost degradation. As
shown in Fig. 4, the cumulative release of CO2 frompermafrost degradation increases almost linearly in
RCP2.6, but the rate of increase rises in RCP8.5 in thelatter half of the twenty-first century. This is due to thefact that the permafrost area rapidly decreases in RCP8.5
in the latter half of the century in these simulations (thedetails of the land surface model simulation results areprovided in Yokohata et al. 2020b).

Figure 5 shows the CO2 and CH4 emissions at the endof the twenty-first century in the RCP8.5 scenario. We
found that CO2 emissions are more widespread compared to the confined emissions of CH4. This is related
to the fact that CH4 emissions can be larger in a wetland
region, and the regions with a high wetland ratio arelimited. The important factors that determine thermodynamic degradation are changes in the active layer
thickness (Eq. 10) and the rise of soil temperature (Eq. 7).

Fig. 4 Same as Fig. 2 but for the cumulative GHG flux due to the total (dynamic plus thermodynamic) degradation of permafrost for CO2 andCH4, respectively

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Fig. 5 The cumulative CO2 flux [TgC] (bottom, left) and CH4 flux [TgCH4] (bottom, right) due to the permafrost degradation (dynamic +thermodynamic) at the end of the twenty-first century in the RCP8.5 scenario. The average of all ensemble members is shown

In order to interpret the results in Fig. 5, the changes in
the active layer thickness, permafrost area, and wetland
fraction are shown in Fig. 6. As indicated in the figure, thechanges in active layer thickness are large in western andeastern Siberia, and in the North America coastal regionsof the Arctic Ocean. This distribution roughly corresponds to that of CO2 emissions (Fig. 5). In western andeastern Siberia, and the northern part of North America,
the amount of CH4 emission is large in regions with alarge wetland fraction (Fig. 6).
Figure 6 also shows the changes in the permafrost
area, which corresponds to the region with temperatures
below 0 °C throughout the year. In the regions where
the permafrost area decreased, the area below 0 °C
throughout the year decreased. Figure 6 indicates thatthe permafrost area decreases significantly in the western and southern part of eastern Siberia, while permafrost remains in a wide region from the center to eastern
Siberia. In other words, at the end of the twenty-first
century, permafrost will remain in the cold regions, withthe expectation that thawing will progress in the twentysecond century. Previous studies have reported that the
impact of permafrost degradation on the climate will begreater after the end of the twenty-first century (e.g.,
McGuire et al. 2018), which is consistent with our result.

4 Conclusions
In this study, we developed PDGEM, a model for estimating GHG emissions due to permafrost degradation.
Using the model, we produced future projections of thefollowing three processes:(a) Direct release of GHGs due to the dynamic
degradation of the Yedoma layer: The process inwhich high concentrations of CO2 and CH4 trappedin the ground ice and frozen soil of the Yedoma
layer are released due to dynamic degradation.
(b) Secondary release of GHGs due to the dynamic
degradation of the Yedoma layer: The process bywhich organic matter trapped in the Yedoma layer
is newly decomposed by the thawing of thepermafrost to release CO2 and CH4.
(c) Secondary release of GHGs due to thethermodynamic degradation of permafrost: Theprocess by which organic matter trapped in thepermafrost is newly decomposed by the thawing of
the permafrost to release CO2 and CH4.
In the RCP8.5 and RCP2.6 scenarios, numerical simulations through the twenty-first century showed that the
combination of (a) plus (b) contributed less than 1% of
the total emissions resulting from (a) + (b) + (c). It wasalso found that the contribution of (a) is an order ofmagnitude smaller than that of (b). The cumulative release of CO2 plus CH4 produced by (a) + (b) + (c) was
48 (32–66) PgC for RCP8.5, and 22 (15–29) PgC for
RCP2.6. This is consistent with a recent multi-model
study (− 41 to 95 GtC, McGuire et al. 2018) which
reported that in one of the ESMs, the land becomes acarbon sink owing to the effect of plant growth afterthawing.
In this study, dynamic degradation of the Yedoma
layer (defined as the location of high soil organic carbon

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Fig. 6 Total change in active layer thickness [m] (top left), decrease in the permafrost area (top right), the wetland fraction (bottom) at the end of
the twenty-first century in the RCP8.5 scenario. The average of all ensemble members is shownand soil frozen water) is formulated by the possibility offire (Pdstrb) and the present land surface subsidence
velocity (Vdstrb) as shown in Eq. (3). The contribution of
dynamic degradation ((a) + (b) above) is small since thearea ratio of the Yedoma layer (Aydm) is very small. The
contribution of dynamic degradation will be large if the
dynamic degradation (i.e., the subsidence of surface due
to dynamic collapse) occurs outside the Yedoma layer,
or if the subsidence velocity is higher than it is currently.
To estimate the probability of fire, the relationshipbetween the occurrence of fire and meteorologicalconditions (Eq. 4) constructed from observation data isused; however, if the relationship described in Eq. (4) isdifferent in the future, the frequency of fires will alsochange.
With PDGEM, the global distribution of GHG emissions can be estimated (e.g., Fig. 5) by using the thawing
process of permafrost obtained from a state-of-the-art
land surface model (Yokohata et al. 2020a, 2020b), taking into account the substantial uncertainties associatedwith the model’s parameters (Table 1) and future atmospheric changes. This represents a significant advantage
when compared to previous related studies (e.g., Schneider
von Deimling et al. 2015; Gasser et al. 2018; McGuireet al. 2018). The models of permafrost degradation in previous studies were unable to predict the geographic

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distribution of GHG emissions due to their simplificationof physical processes (Schneider von Deimling et al. 2015;
Gasser et al. 2018). On the other hand, for state-of-the-art
earth system models that incorporate advanced physicaland carbon cycle processes (McGuire et al. 2018), it isdifficult to fully consider the uncertainties in model
prediction such as the uncertainties in future atmospheric
responses. In this study, combining a simple scheme ofcarbon cycle processes with the results of the latest land
surface model makes it possible to project the geographical distribution of future GHG emissions due to permafrost degradation (Fig. 5) by considering, across a verybroad range, the uncertainties associated with the various
model parameters and future atmospheric responses.
In the previous studies (e.g., Gasser et al. 2018), it has
been shown that GHG emissions caused by the thawingof permafrost can be an obstacle to achieving the climate
stabilization called for in the Paris Agreement. In
addition, as described in Fig. 6, substantial permafrost
remains unthawed at the end of the twenty-first century,and thus the impact of GHG gas emissions from permafrost thawing on the climate system is expected to increase markedly after that time (McGuire et al. 2018).
As discussed in Section 4, the geographical distributionsof GHG emissions (Fig. 5) are connected to changes inground temperature, soil moisture status and wetland
distribution, and the soil carbon accumulated over timescales of past glacial cycles. The hotspots with particularly
large GHG emissions shown in Fig. 5 are determined by
the interactions between these factors investigated in thisstudy. In the regions of GHG emission hotspots shown in
Fig. 5, it may be possible to reduce GHG emissions by taking measures such as restricting land development.

Funding
This study was conducted as a part of the Environment Research andTechnology Development Fund project (2-1605, JPMEERF20162005)“Assessing and Projecting Greenhouse Gas Release from Large-scale
Permafrost Degradation” supported by the Ministry of Environment and theEnvironmental Restoration and Conservation Agency. Our research is also
supported by the “Integrated Research Program for Advancing Climate
Models (TOUGOU Program)” sponsored by the Ministry of Education, Culture,
Sports, Science and Technology (MEXT), Japan. This study was partly
supported by the NASA ABoVE (Arctic Boreal and Vulnerability Experiment(grant no. NNX17AC57A)). KT benefited from State assistance managed by
the National Research Agency in France under the “Programme d’Investissements d’Avenir” under the reference “ANR-19-MPGA-0008”.

Yokohata et al. Progress in Earth and Planetary Science

Abbreviations
ALT: Active layer thickness; CLAM: Circumpolar active layer monitoring;
CMIP: Coupled Model Inter-comparison Project; GCM: Global climate model;
GHGs: Greenhouse gases; IPA: International Permafrost Association;
ISIMIP: Inter-Sectoral Impact Model Inter-comparison Project; MATS
IRO: Minimal advanced treatments of surface interaction and runoff;
MIROC: Model for Interdisciplinary Research on Climate; PDGEM: Permafrost
Degradation and Greenhouse gases Emission Model; RCP: Representative
concentration pathways
Acknowledgements
We gratefully acknowledge the helpful discussions with Hideo Shiogama,
Tomoo Ogura, Nagio Hirota, Kaoru Tachiiri, and Michio Kawamiya. The authors
are much indebted to Keita Matsumoto, Kuniyasu Hamada, Kenryou Kataumi,
Eiichi Hirohashi, Futoshi Takeuchi, Nobuaki Morita, and Kenji Yoshimura at NEC
Corporation for their support in model development. Model simulations were
performed on the SGI UV20 at the National Institute for Environmental Studies.
NCEP Reanalysis Derived data provided by the NOAA/OAR/ESRL PSL, Boulder,
Colorado, USA, from their Web site at https://psl.noaa.gov/.
Authors’ contributions
TY and KS proposed the topic and conceived and designed the study. TY,
KS, and AI contributed to the formulation of the numerical model. TY and KT
carried out the experimental study and analyzed the results of the numericalsimulations. HO and GI provided the observational data. All authors haveread and approved the final manuscript.

Availability of data and materials
Data sharing is not applicable to this article as no datasets were generated
or analyzed during the current study. Please contact the authors for data
requests.
Competing interests
The authors declare that they have no competing interest.
Author details
1
Center for Global Environmental Research, National Institute forEnvironmental Studies, 16-2 Onogawa, Tsukuba 305-8506, Japan. 2ResearchCenter for Environmental Modeling and Application, Japan Agency for
Marine-Earth Science and Technology, 3173-25 Showamachi, Kanazawaku,
Yokohama 236-0001, Japan. 3School of Earth, Energy and Environmental
Engineering, Kitami Institute of Technology, 165 Koen-cho, Kitami 090-8507,
Japan. 4Laboratoire des Sciences du Climat et de l’Environnement (LSCE),
Commissariat à l’énergie atomique et aux énergies alternatives (CEA),
Gif-sur-Yvette, France. 5International Arctic Research Center, 739, The
University of Alaska Fairbanks, 2160 Koyukuk Dr, Fairbanks, AK 740
99775-7340, USA.
Received: 30 December 2019 Accepted: 20 August 2020

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