Yokohata et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-020-00380-w(2020) 7:69

Progress in Earth andPlanetary Science

RESEARCH ARTICLE

Open Access

Model improvement and future projectionof permafrost processes in a global land
surface model
Tokuta Yokohata1* , Kazuyuki Saito2, Kumiko Takata1,3, Tomoko Nitta4, Yusuke Satoh1, Tomohiro Hajima2,
Tetsuo Sueyoshi2,5 and Go Iwahana6

Abstract
To date, the treatment of permafrost in global climate models has been simplified due to the prevailing
uncertainties in the processes involving frozen ground. In this study, we improved the modeling of permafrost
processes in a state-of-the-art climate model by taking into account some of the relevant physical properties of soil
such as changes in the thermophysical properties due to soil freezing. As a result, the improved version of the
global land surface model was able to reproduce a more realistic permafrost distribution at the southern limit of
the permafrost area by increasing the freezing of soil moisture in winter. The improved modeling of permafrost
processes also had a significant effect on future projections. Using the conventional formulation, the predictedcumulative reduction of the permafrost area by year 2100 was approximately 60% (40–80% range of uncertainty
from a multi-model ensemble) in the RCP8.5 scenario, while with the improved formulation, the reduction wasapproximately 35% (20–50%). Our results indicate that the improved treatment of permafrost processes in global
climate models is important to ensuring more reliable future projections.
Keywords: Permafrost degradation, Global climate model, Climate change

1 Introduction
Global warming is expected to produce a thawing of thepermafrost (Koven et al. 2013; Slater and Lawrence 2013;
Vaks et al. 2013; Koven et al. 2015; McGuire et al. 2018).
As a result, large amounts of organic matter confined in
the permafrost will decompose, releasing greenhousegases (GHGs) such as carbon dioxide and methane(Zimov et al. 2006; Schuur et al. 2008; Brown 2013;
Schaefer et al. 2014; 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; Saito et al. 2020; Yokohata
et al. 2020). Unfortunately, the permafrost processes represented in current global climate models (GCMs) tend
* 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

to be rather simplistic because of the significant uncertainties regarding the dynamics of the permafrost due
mainly to a lack of observational knowledge (Alexeev
et al. 2007; Nicolsky et al. 2007; Lawrence et al. 2008;
Rinke et al. 2008; Koven et al. 2009; Gouttevin et al.
2012). Projections of permafrost thawing vary substantially in distribution and magnitude depending on the
climate model used (Koven et al. 2013; Slater andLawrence 2013). This wide variation in projections canbe explained by the fact that the rate of degradation of
the permafrost is closely related to a variety of factors in
the climate system, including changes in the surface airtemperature, precipitation, and evaporation in the highlatitudes, all of which involve a great deal of uncertainty.
In this study, we improved the modeling of permafrost
processes in the global land surface model MATSIRO(Minimal Advanced Treatments of Surface Interaction
and Runoff, Nitta et al. 2014), which is used in the global© 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) 7:69

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climate model MIROC (Model for Interdisciplinary Research on Climate, Watanabe et al. 2010). We conducted
historical simulations by using both conventional
schemes and our proposed scheme and compared simulation results to observations. We also conducted future
simulations using the two schemes and assessed the impact of our model improvement, as well as the sourcesof uncertainty in future simulations.
Previous research (Lawrence et al. 2008; Saito 2008a;
Saito 2008b; Saito 2011; Chadburn et al. 2015; Melton
et al. 2019) has recognized the importance of taking into
account changes in thermodynamic quantities due to thefreezing of soil moisture and the physical characteristicsof the organic layer near the ground surface, and presence of unfrozen water in order to obtain more realistic
permafrost distributions in simulations using global climate models. However, this previous research with
MATSIRO (Saito 2008a, 2008b, 2011) involved only historical or ideal future simulations at particular points
where observational data were available (e.g., Utqiagvik,
formerly known as Barrow) or conducted only historicalglobal simulations. Consequently, neither the impact ofthe proposed improvements in the modeling of thephysical properties of permafrost processes nor the uncertainties in the projection of future atmospheric forcing were evaluated.
This study directly addresses both issues. We firstevaluate the effects of the improvements in the modelingof the physical processes related to permafrost on the reproducibility of the observed permafrost distribution andanalyze the effect of these improvements on the future
projection of the permafrost distribution. We also assess
the effect of the uncertainties associate with the atmospheric forcings on the simulations of the permafrost
distributions. For this purpose, we apply atmosphericforcings generated by bias-corrected global climate
models to the global land surface model and then perform multiple future simulations.

soil heat capacity C and thermal conductivity K (Takata
et al. 2003). However, frozen water has a smaller heat
capacity and greater thermal conductivity than liquidwater. To reflect this, in the improved scheme that we
propose, when water is frozen the heat capacity issmaller and the thermal conductivity is greater than isthe case in the conventional scheme (Saito 2008a,
2008b). Similarly, unfrozen water content is present but
decreases
exponentiallywiththesubfreezing
temperature where the exponent coefficient is predetermined by the soil types (e.g., sand, silt, and clay).
Saito (2011) gives details of the improved treatment of
the organic layer as described in consideration (2). In
the current study, the soil parameter values are further
modified to increase model performance. The land surface model MATSIRO determines the vegetation andsoil type for each grid. The six physical properties of soil
(volumetric specific heat, thermal conductivity, porosity,
saturated matrix potential, saturated hydraulic conductivity, void size distribution index) are then set according
to the soil type (Takata et al. 2003; Saito 2011). In thisstudy, we first determined the uncertainty ranges forthese soil parameters based on expert judgement, andthen perturbed these parameters one by one within theuncertainty ranges. Then, we found a parameter set for
which the permafrost area obtained by the land surface
model (as illustrated in Fig. 2) is close to the permafrost
region estimated from observations by the International
Permafrost Association (IPA; Brown et al. 1998).
For both the conventional and improved schemes described here, six vertical layers and a bottom depth of 14
m were used (Nitta et al. 2014). In the conventional
scheme, constant physical properties are given according
to soil type regardless of the vertical depth (Takata et al.
2003). The definition of soil types and the values for thesix physical properties are the same as those in Nitta
et al. (2014). In the improved scheme, in high-latitude
regions with Tundra vegetation types, the top threelayers (0–1 m) are organic layers, the middle two layers(1 m–4 m) are mineral soil layers, and the bottom layer(4–14 m) is the bedrock layer, mainly consisting of rocks
as shown in Table 1. In the regions with taiga vegetation
type, the top four layers (0–2 m) are organic, the fifthlayer (2–4 m) is mineral soil, and the bottom layer (4–14m) is the bedrock layer (Table 1). The values for thephysical parameters are different depending on the typeof soil layer. In the grids for regions other than the Taiga
or Tundra regions, the top five layers are mineral soil
layers, and the bottom layer is the bedrock layer. The
soil type in the improved scheme is given in the same
way as in Saito (2011). However, the values for the six
physical properties have been improved. Table 1 summarizes these values for the organic, mineral, and baselayers used in the improved and conventional scheme.

Yokohata et al. Progress in Earth and Planetary Science

2 Methods
2.1 Improvement of permafrost processes in a global
climate model

In this study, the proposed improvements in the modeling of the permafrost processes include the following
considerations:(1) Consideration of the heat capacity and thermal
conductivity of frozen soil.
(2) Consideration of the organic layer near the surface
in the high-latitude Taiga and Tundra regions.
(3) Consideration of unfrozen water in regions where
the ground temperature is below 0 °C
The details of consideration (1) and (3) are provided
in Saito (2008a). In brief, in the conventional scheme, allsoil moisture is treated as liquid water when calculating

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Table 1 Parameter values for the organic, mineral soil, and base layers used in the improved scheme. The values for the minerallayers are dependent on the soil type. Here, the values of the mineral layers for the “sand” soil type are shown. The layers for theorganic (moss and soil), mineral soil, and bed lock layer for the improved and conventional schemes are also shown
Organic (moss)

Organic (soil)

MineralBedrock

Layers in the improved scheme (Tundra)

Top layer (0–5 cm)

2nd–3rd layers (5–1 m)

4th–5th layers (1–4 m)

Bottom layer (4–14 m)

Layers in the improved scheme (Taiga)

Top two layers (0–25 cm)

3rd–4th layers (25 –2m)

5th layer (2–4 m)

Bottom layer (4–14 m)

Layers in the original scheme––

All layers (0–14 m)–

3

Volumetric specific heat [MJ/m K]

2.5

2.5

2.1

2.3

Thermal conductivity [W/m K]

0.12

0.25

2.0

3.0

Saturated soil moisture (porosity) [−]

0.8

0.7

0.48

0.3

Saturated hydraulic conductivity 10−6 [m/s]

15

2.0

1.71

5.0

Saturated hydraulic potential [m]− 0.085− 0.12− 0.0563− 0.300

Void size distribution index [−]

2.1

4.0

3.6

3.3

2.2 Experimental settings

To investigate the effect of our improvements in themodeling of the permafrost processes, we performed
offline simulations using a global land surface model
MATSIRO (Nitta et al. 2014). In these simulations,
atmosphere-ocean processes in the global climate
model MIROC (Watanabe et al. 2010) were not calculated but were given as an atmospheric forcing todrive the land surface model. For the atmosphericforcing, we used the data provided by the InterSectoral Impact Model Inter-comparison Project,
Phase 1 (ISIMIP1, Hempel et al. 2013). In the ISIMIP1 forcing, atmospheric variables were calculated by
five GCMs (GFDL-ES2M, 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), andmodel bias was corrected in the historical and future
simulations.
In the present study, we performed our historicaland future simulations by giving the atmosphericforcings of the five GCMs to MATSIRO. The climate
scenarios in the future simulations were based on
Representative Concentration Pathways (RCP, vanVuuren et al. 2011); specifically, we used the lowcarbon scenario (RCP2.6) and the high-warming
business-as-usual scenario (RCP8.5). The global average of the surface air temperature anomaly given as
the atmospheric forcing for RCP2.6 and RCP8.5 isshown in Fig. 1.

3 Results and discussion
3.1 Comparison of model simulations with observations

Figure 2 shows the permafrost area results obtained by
our MATSIRO offline simulation. The permafrost area
is defined as the regions where the soil temperature is
kept below 0 oC in any of the soil layers throughout theyear. In Fig. 2, the permafrost region estimated fromobservations by the IPA (Brown et al. 1998) is outlined
in black and compared to the areas estimated in themodel simulations. In the MATSIRO offline simulation,
the results of the GCM simulations were used as the atmospheric forcing (Section 2.2). As shown in Fig. 2, thepermafrost area estimated with the improved scheme islarger than that in the conventional scheme, especiallyin southern Siberia and southern Canada. Importantly,
the estimated area under the improved scheme shows
better agreement with the observation-based estimate.
Consideration of three additional factors accounts forthe superior performance of the improved version.
These include (a) the heat insulation effect of the organic layer, (b) the increase in heat conduction in winterFig. 1 Time sequence of the global mean surface air temperature
anomaly given as atmospheric forcing to the land surface model.
The width of the lines represents the maximum and minimum
values of the simulated results forced by the five GCMs; the averagevalue is represented by the bold lines. The global mean surface airtemperature under RCP2.6 (blue) and RCP8.5 (red) scenarios isshown. Anomaly of surface air temperature is calculated withrespect to the 1995–2014 average

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Fig. 2 Permafrost area of the historical model simulation (blue) and observation (outlined in black). The historical simulation was performed with
the MIROC atmospheric forcing. The permafrost area is determined using the soil temperature for the year 2000. The observed permafrost area isfrom Brown et al. (1998)

due to the presence of ice, and (c) the decrease in
ground temperature in winter due to the presence of unfrozen water. In summer, factor (a) suppresses the soil
temperature rise in the improved version compared tothe conventional version. On the other hand, in winter,(b) and (c) promote a decrease in soil temperature.
Figure 3 shows the latitude-depth soil temperature distribution in the present climate. The upper panel ofFig. 3 shows the JJA, DJF, and annual average soil
temperature distribution in the improved version. The
soil temperature near the surface is higher than in thelower layer in the summer, but lower in winter. The

vertical gradient of the soil temperature near the surface(to a depth of approximately 3 m) is large, but becomes
smaller with increasing depth. The annual average soil
temperature is essentially determined according to latitude. The lower panel of Fig. 3 shows the difference in
JJA, DJF, and annual average soil temperature between
the improved and conventional versions. In summer, thesoil temperature is lower in the improved version due tofactor (a), the heat insulation effect of the organic layer.
In winter, the soil temperature is higher in places due tothe effect of (a), but is lower in many regions due to theeffects of (b) and (c). For these reasons, in terms of theFig. 3 Latitudinal distributions of soil temperature in the historical simulations. Upper panels: the JJA, DJF, and annual average of soil temperature
for the year 2000 in the improved version. Lower panels: the same as the upper panels but showing the differences between the improved andconventional versions. Vertical axis represents the soil depth [m], and the longitudinal average over land is shown. The average simulations withthe atmospheric forcings of all five GCMs is shownYokohata et al. Progress in Earth and Planetary Science(2020) 7:69

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Fig. 4 Permafrost area of the historical model simulation (blue) and observation (outlined in black) for the simulations with the atmosphericforcings of all five GCMs (GFDL-ES2M, 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). The upper panel shows the results with the improved scheme; the lower panel shows theresults with the conventional scheme

annual average value, the soil temperature is lower in
the improved version over the entire area. As a consequence, the improved version has a larger distribution of
permafrost, as shown in Fig. 2.
Figure 2 shows the simulation results when the atmospheric forcing of MIROC is given. The permafrost
areas forced by all the various GCMs are shown in
Fig. 4. The tendency of the permafrost area in the
improved scheme to be larger and closer to the observed area than is the case for the conventional
scheme is evident in all the simulation results forcedby the five GCMs. More specifically, from the easternpart to the western part of Siberia, the improved version reproduces the southern limit of permafrost very
well (reproducibility is especially high in GFDL and
MIROC). On the other hand, from the eastern partto the western part of Siberia, the conventional version underestimates the permafrost distribution in all
the simulations with different atmospheric forcings.
Similarly, the improved version reproduces the southern limit of permafrost very well in central North
America in all the simulations, while the permafrost
area is underestimated in eastern and western Canada
and the Himalayan region. This may be related to thefact that the global land surface model has a resolution of 1° and that topographical details cannot be
expressed, and thus permafrost is not reproduced adequately in the simulations.
Figure 5 compares the active layer thickness (ALT) in
the model simulations with the observed values. We
used the observation data obtained by Circumpolar Active Layer Monitoring (CALM) at the same forty sitesthat were used in Saito (2011). Here, a simulated ALT
result in the grid cell closest to a CALM site was chosen.
For the calculation of the ALT value from the model
simulations, we chose the maximum depth of the model
layers each year at which the soil temperature (monthly
average) is larger than 0 oC, following the procedure of
Koven et al. (2015). The ALT value for the improved

Fig. 5 Active layer thickness for model results (vertical axis) versus
observed ALT values (horizontal axis). The results of the improved(red) and conventional (blue) schemes are shown. The box andwhisker plot indicates the minimum, lower quartile, median, upper
quartile, and maximum values of observation. The correlation
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scheme is smaller than that for the conventional scheme,
which is consistent with the results of the permafrost
area comparison shown in Fig. 2. The correlation between the ALT values in the model simulations and theobserved values in the improved and conventional
schemes is 0.688 and 0.331, respectively. Thus, the simulated ALT values for the improved scheme appear to be
in better agreement with the observations than those in
the conventional scheme. Given these results, we concluded that a more realistic treatment of the soil freezing
processes can improve the reproducibility of the permafrost area in the model simulations.

(average of the ensemble members) by 2100, with a highestimate of 50% (maximum of the ensemble members),
while with the conventional scheme, the average decrease is roughly 60%, with a high estimate of 80%.
In order to investigate the differences in the permafrost
distribution between the improved and conventional
schemes, the time sequence of vertical distribution of soil
temperature is shown in Fig. 7. As described above, thesoil temperature near the surface is high in summer andlow in winter, and the vertical gradient of soil temperatureis small below 3 m. As the air temperature rises throughthe twenty-first century, the soil temperature increases at
all depths. As discussed above, the improved scheme considers (a) the organic layer near the surface, (b) the thermal conductivity of ice, and (c) unfrozen water. The effectof (a) suppresses the increase in soil temperature especially in summer and the effect of (a) is canceled by that of(b) and (c) in winter; thus, the annual average soil
temperature in the improved scheme is lower than that in
the conventional scheme over a wider area.
As shown in Fig. 7, the difference in summer soil
temperature determines the difference in annual meansoil temperature between the improved and conventional schemes. Due to the effect of (a) in summer, thetotal heat flux reaching deep in the ground is larger inthe conventional scheme than in the improved scheme.
Since the difference in soil temperature is determined by
the difference in the integrated amount of the heat flux
reaching to the ground layers, the difference in soil
temperature between the improved and conventional
scheme increases with time. For this reason, the amountof permafrost reduction is larger in the conventional
scheme than in the improved scheme.
Next, the difference in the permafrost change between
the RCP2.6 and RCP8.5 scenarios in Fig. 4 is investigated. In this study, the future projection of surface air

Yokohata et al. Progress in Earth and Planetary Science

4 Future simulations
Figure 6 shows future projections of the permafrost area
using the improved and conventional schemes. Here, thepermafrost area is diagnosed in the same way as in Fig. 2.
Overall, the simulated response of the permafrost area
to global warming is consistent with the features described above (Figs. 2, 3, 4, and 5). As shown in Fig. 6,
the permafrost area decreases more rapidly under theconventional scheme than under the improved scheme.
Figure 6 indicates that changes in the way in which thefreezing processes are represented in the model greatly
influence the model’s projections of the future, as well as
its description of the present.
A detailed comparison of the simulated changes in thepermafrost area provides additional useful information.
In both the improved and conventional schemes, thepermafrost area decreases monotonically until the year
2040 (approximately) for both the RCP2.6 and RCP8.5
scenarios. After 2040 (approximately), the reduction rateof the permafrost area slows in the RCP2.6 scenario,
while it accelerates in RCP8.5. More specifically, with
the improved scheme, in the RCP8.5 scenario, thepermafrost area decreases by an average of roughly 35%

Fig. 6 Changes in the total permafrost area across the world as projected by the study’s future simulations. The width of the line represents the
maximum and minimum values of the simulated results forced by the five GCMs; the average value is represented by the bold lines. The simulations
with the improved (left) and conventional (right) schemes under RCP2.6 (blue) and RCP8.5 (red) scenarios are shown. Values on the vertical axis were
determined by setting the permafrost area at the initial year (2005) of the future simulations to 1.0

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Fig. 7 Time sequence of soil temperature. Upper panels: soil temperature for JJA, DJF, and annual average in the improved scheme. Lower
panels: The same as upper panels, but showing the differences between improved and conventional scheme. Vertical axis represents the soil
depth [m], and the longitudinal average of the soil temperature north of 50° is shown.

temperature by five GCMs is applied to the land surface
model as the atmospheric forcing (Fig. 1). In the future
projection of surface air temperature, the average valuesof the five GCMs under RCP2.6 and RCP8.5 are nearly
the same until around 2030. Changes in the permafrost
area (Fig. 4) are a delayed response to changes in surface
air temperature, and the changes in permafrost areas in
the two scenarios are nearly the same until around 2050
in the improved scheme. Figure 8 shows the differencein the vertical distributions of soil temperature between
the RCP2.6 and RCP8.5 scenarios in the improved
scheme. As indicated in Fig. 8, a difference in soil
temperature between RCP2.6 and RCP8.5 begins tooccur near the surface at around 2040, and it takes approximately 10 years to reach the bottom layer. As aresult, differences in the permafrost area between
RCP2.6 and RCP8.5 will appear roughly 20 to 30 years
after the difference in surface air temperature between
these scenarios occurs.
We also investigated the relationship between permafrost changes and global mean surface air temperature(Fig. 9). Burke et al. (2020) reported that permafrost sensitivity—the change in permafrost volume per degree of
global mean surface air temperature change—is 20–30%
using CMIP5/CMIP6 data. Although these authors analyzed the uncertainty ranges in multi-model ensemblesof CMIPs, the present study could examine the responseof a global land surface model by considering the uncertainty in atmospheric model projection. As shown in
Fig. 9, permafrost sensitivity is similar for RCP2.6 andFig. 8 Difference in soil temperature between RCP2.6 and RCP8.5. The time sequences of the vertical distribution of soil temperature for
JJA, DJF, and annual average are shown. Vertical axis represents the soil depth [m], and the longitudinal average of the soil temperaturenorth of 50° is shownYokohata et al. Progress in Earth and Planetary Science(2020) 7:69

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Fig. 9 Time sequence of permafrost sensitivity. The change in permafrost volume divided by the change in global mean surface air temperature[ratio/degree] is shown. The width of the colored bands represents the maximum and minimum values of the simulated results forced by thefive GCMs; the average value is represented by the bold lines. The simulations with the improved (left) and conventional (right) schemes under
RCP2.6 (blue) and RCP8.5 (red) scenarios are shown. Anomaly of permafrost volume and surface air temperature is calculated with respect to the
1995–2014 average.

RCP8.5 until around 2020. After 2040, a difference
emerges, with RCP2.6 showing higher permafrost sensitivity. The reason for this higher sensitivity in RCP2.6 isthat, in this scenario, the global mean surface airtemperature stabilizes in the latter half of the twentyfirst century, which means changes in permafrost volume also stabilize during this time. In contrast, in
RCP8.5, the global average temperature continues to risethroughout the century, and thus the response of soil
temperature and permafrost volume is not stabilized.
With the five atmospheric forcings in the improved
scheme, the permafrost sensitivity in RCP2.6 is 0.2–0.3/°C at the end of the current century. On the other hand,the permafrost sensitivity for the RCP8.5 scenario in the
improved scheme is 0.11–0.14/°C, which is approximately half that for the RCP2.6 scenario.

Figure 10 shows the changes in the distribution of thepermafrost regions with the improved and conventional
schemes. As can be seen in the figure, the permafrost regions estimated with the conventional scheme retreats
to higher latitudes than is the case for the improved
scheme. On the North American continent, the permafrost regions with the conventional scheme decreases at
the higher latitudes over most of the longitudinal band.
In Siberia, the permafrost area is reduced around a latitude of 60 °N within longitudes 90–135 °E with the conventional scheme, but not so with the improved scheme(Fig. 10). On the other hand, the permafrost area does
not decrease very much within longitudes of 90–135 °E.
Interestingly, reductions in the permafrost area differ
depending on longitude, as shown in Fig. 10. The features of permafrost thawing must be related to theFig. 10 Changes in the permafrost distribution projected by the study’s future projection simulations. The current permafrost area is set at 1.0.
Average values for the simulations with atmospheric forcings by the five GCMs are plotted. The difference between the average for 2081–2090
and 2006–2015 under RCP8.5 was calculated

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different types of permafrost under the current climate,which varies greatly among different regions due to geological, geographical, and historical reasons. As indicatedin Fig. 2, in the eastern part of the Eurasian continent(around longitudes 90–135 °E), permafrost exists in thelower latitudes to roughly 45 °N (owing to the Tibetan
Plateau). On the other hand, there is no permafrost areain the western part of the continent (around 0–90 oE)
below latitude 60 oN (approximately) because of the temperate marine climate due to the Gulf Stream. These temperate climate conditions make the western part of theEurasian continent (around 0–90 °E) more prone to permafrost thawing (Fig. 10). Conversely, the permafrost regionsin the eastern part of the Eurasian continent (or east
Siberia, roughly 90–135 oE) are less likely to be reduced because of the low surface air temperature,even at relatively low latitudes due to elevation. Figure 11 shows the future projections of the nearsurface soil temperature (top 5 cm) in the RCP8.5
scenario simulated with the improved scheme. Although the changes in soil temperature are large inthe eastern part of the Eurasian continent (90–135 °E,
Fig. 11a), the soil temperature remains below 0 °C,
even at the end of the twenty-first century (Fig. 11b).
In all, these findings confirm the proposition that future changes in the permafrost area are closely related
to the features of current climate conditions andpermafrost characteristics.

In general, in order to ensure more reliable future projections, it is critical that a predictive model have theability to accurately reproduce reality. The analysis in
this study showed that the reproducibility of the featured
model was enhanced by improving the sophistication of
the basic freeze and thaw processes included in themodel, particularly soil heat capacity and thermal conductivity. The findings reported here have the potential
to greatly improve future projections of the permafrost
area, which is an important physical quantity in climatechange research.
According to a study evaluating the future projection
results produced by the GCMs of the 5th Coupled
Model Inter-comparison Project (CMIP5, Koven et al.
2015), the rate of decrease in the permafrost area for the
RCP8.5 scenario was 30 to 100%, with a high degree of
uncertainty attached to the projections. Koven et al.
(2015) used the results of 18 GCMs to quantify the uncertainties. However, the structure of the models (i.e.,model parameterization, resolution) is quite different
among the various CMIP5 GCMs (i.e., Knutti et al.
2013; Yokohata et al. 2013), making it difficult to precisely identify the causes of the uncertainties in themodel projections. On the other hand, the analysis in
this study revealed that the thermophysical properties(heat conduction and heat capacity) of the soil greatly
affect the reproducibility of the current permafrost distribution (Figs. 2 and 4). Based on our analysis, it seems
clear that a realistic treatment of such physical properties (e.g., thermophysical variables) is important to producing more realistic future projections.
A web page showing an animation of future permafrost change (Fig. 12, https://ads.nipr.ac.jp/node/dagik/?type=MIROC5/PERMAFROST_SEP) has been createdYokohata et al. Progress in Earth and Planetary Science

5 Conclusions
By refining the process associated with the freezing andthawing of soil moisture—which has been simplified inprevious global climate models—the reproducibility ofthe model used here improved noticeably (Figs. 2 and 4).

Fig. 11 Future projections of soil temperature changes. a Changes in the soil temperature (top 5 cm) between 2081–2090 and 2006–2015 [K]; b Soil
temperature (top 5 cm) averaged over 2081–2090. The average values for the simulations with atmospheric forcings by the five GCMs are plottedYokohata et al. Progress in Earth and Planetary Science(2020) 7:69

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Fig. 12 Snapshot of an animation of future projections of the permafrost area based on data from simulations using the improved scheme with
the MIROC atmospheric forcing under the RCP8.5 scenario. The animation was generated based on a tool developed by the Dagik Earth project(https://www.dagik.net/). The full animation is available at https://ads.nipr.ac.jp/node/dagik/?type=MIROC5/PERMAFROST_SEP

based on the future projection data that the authors provided to a research team at the National Institute ofPolar Research specializing in such visualization (Arctic
Data archive System). The animation was generated
based on a tool developed by the Dagik Earth project(https://www.dagik.net/). On the created page, the center
coordinates of the display (i.e., the angle of the projectedmap) can be freely changed by the viewer. With theseanimations, future projections indicating permafrost area
reductions of as much as one-half are expressed in aneasy-to-understand manner, not just for the general public, but for climate researchers as well.
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; RCP: Representative
Concentration Pathways
Acknowledgements
We gratefully acknowledge the helpful discussions with Hideo Shiogama,
Nagio Hirota, Kaoru Tachiiri, and Michio Kawamiya. The authors are muchindebted 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.

Authors’ contributions
TY and KS proposed the topic and conceived and designed the study. TY
and KS carried out the experimental study and analyzed the results of thenumerical simulations. TN provided the original source code of the global
land surface model. YS provided the forcing data for the land surface
models. All authors have read and approved the final manuscript.
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 the Environmental Restoration and Conservation Agency. Our research was 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 work was partially supported by the Arctic Challenge for Sustainability (ArCS), and the Arctic Challenge for Sustainability II (ArCS II), Program Grant Number JPMXD1420318865
sponsored by the MEXT, Japan. This study was partly supported by the NASA
ABoVE (Arctic Boreal and Vulnerability Experiment (grant no. NNX17AC57A)).
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 corresponding
author for data requests.
Competing interests
The authors declare that they have no competing interests
Author details
1
Center for Global Environmental Research, National Institute forEnvironmental Studies, 16-2 Onogawa, Tsukuba 305-8506, Japan. 2ResearchInstitute for Global Change, Japan Agency for Marine-Earth Science andTechnology, 3173-25 Showa-machi, Yokohama 236-0001, Japan. 3School ofYokohata et al. Progress in Earth and Planetary Science(2020) 7:69

Life and Environmental Science, Azabu University, 1-17-71 Fuchinobe,
Chuo-ku, Sagamihara-shi, Kanagawa 252-5201, Japan. 4Institute of Industrial
Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba
277-8574, Japan. 5International Affairs and Research Development Office,National Institute for Polar Research, 10-3 Midori-cho, Tachikawa 190-8518,
Japan. 6International Arctic Research Center, The University of Alaska
Fairbanks, 2160 Koyukuk Dr, Fairbanks, AK 99775-7340, USA.
Received: 30 December 2019 Accepted: 9 October 2020

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