Ito and Hajima Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-020-00372-w(2020) 7:54

Progress in Earth andPlanetary Science

RESEARCH ARTICLE

Open Access

Biogeophysical and biogeochemical
impacts of land-use change simulated by
MIROC-ES2L
Akihiko Ito1,2*

and Tomohiro Hajima2

Abstract
Land-use change is one of the focal processes in Earth system models because it has strong impacts on terrestrial
biogeophysical and biogeochemical conditions. However, modeling land-use impacts is still challenging because ofmodel complexity and uncertainty. This study examined the results of simulations of land-use change impacts by
the Model for Interdisciplinary Research on Climate, Earth System version 2 for long-term simulations (MIROC-ES2L)
conducted under the Land-Use Model Intercomparison Project protocol. In a historical experiment, the model
reproduced biogeophysical impacts such as decreasing trends in land-surface net radiation and evapotranspiration
by about 1970. Among biogeochemical impacts, the model captured the global decrease of vegetation and soil
carbon stocks caused by extensive deforestation. By releasing ecosystem carbon stock to the atmosphere, land-use
change shortened the mean residence time of terrestrial carbon and accelerated its turnover rate, especially in low
latitudes. Future projections based on Shared Socioeconomic Pathways indicated substantial alteration of land
conditions caused primarily by climatic change and secondarily by land-use change. Sensitivity experiments
conducted by exchanging land-use data between different future projection baseline experiments showed that, at
the global scale, the anticipated extent of land-use conversion would likely play a modest role in the future
terrestrial radiation, water, and carbon budgets. Regional investigations revealed that future land use would exert a
considerable influence on runoff and vegetation carbon stock. Further model refinement is required to improve its
capability to analyze its complicated terrestrial linkages or nexus (e.g., food, bioenergy, and carbon sequestration) toclimate-change impacts.
Keywords: Carbon cycle, Earth system model, Deforestation, Hydrology, Radiation budget, Terrestrial ecosystem

1 Introduction
Land-use change (e.g., expansion of cropland at the costof primary forest) associated with the human populationand economic growth is a critical driver of global change(Houghton 1994; Foley et al. 2005). In a broad sense,
land-use change encompasses various human activities
such as harvesting trees for fuel wood, shifting cultivation, pasturing of livestock, and selective logging. Extensive deforestation of tropical rainforests (e.g., in the* Correspondence: itoh@nies.go.jp
1
National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba
305-8506, Japan
2
Japan Agency for Marine-Earth Science and Technology, 3173-25
Showa-machi, Kanazawa-ku, Yokohama 236-0001, Japan

Amazon basin and Southeast Asia) has garnered particular attention (Woodwell et al. 1983; Malhi et al. 2008;
DeFries and Rosenzweig 2010). As a result of long-term
land use, most natural vegetation has been influenced byhuman activities, with the exception of limited reserved
land areas (Pongratz et al. 2009; Kaplan et al. 2010).
Drastic land cover changes have an immediate impact
on surface biophysical characteristics such as albedo androughness (Henderson-Sellers et al. 1993), which greatly
affect the net radiative and hydrological budgets at theland surface and subsequent propagation of these effects
leads to impacts on local to global atmospheric dynamics (Sud et al. 1996; Takata et al. 2009).

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(2020) 7:54

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Land-use change and associated biomass burning is
the second largest anthropogenic source of atmosphericcarbon dioxide (CO2) emissions; during 2001–2015, it
was responsible for 1.37 ± 0.18 Pg C year–1 of CO2 emissions to the atmosphere (Friedlingstein et al. 2019),
mainly from deforestation in tropical forests. In contrast,
the combination of afforestation and moderate forest
management is a cost-effective way to sequester carbon
from the atmosphere for the purpose of climate mitigation (Smith et al. 2016). Remarkably, the regrowth ofvegetation after deforestation and cropland abandonment results in net carbon sequestration at decadal tocentennial time scales.
Drivers of land-use change and its ecological, meteorological, and social impacts have been investigated with theaim of improving land management practices. Recent developments in satellite remote sensing have enabled thecontinuous detection of deforestation throughout the tropics (DeFries et al. 2002; Hansen et al. 2013). For example,
Saatchi et al. (2011) have used lidar remote sensing data
to quantify the aboveground biomass of tropical forests
and the carbon emissions associated with deforestation.
Field studies have also been conducted to clarify the impacts of land-use changes on local biodiversity and biogeochemical cycles (e.g., Newbold et al. 2016; Marques
et al. 2019). Nevertheless, our understanding of land-use
changes and their impacts is severely limited, especially at
decadal or longer time scales, and large uncertainties
therefore remain in present assessments of the impacts ofland-use change. For example, estimations of historicalcarbon emissions caused by land-use change differ widely
among studies (Ramankutty et al. 2007; Arneth et al.
2017). This uncertainty is attributable to both the qualityof land-use data and to the methodology of estimating
emissions. Following a systematic assessment, Pongratz
et al. (2014) concluded that ambiguities in the assumptions, definitions, and methods used in land-use emissions
studies are among the critical sources of uncertainty in assessments of the impacts of land-use changes.
Appropriate implementation of land-use change processes is critically important in Earth system models(ESMs), which are being increasingly used for future
projections of global environmental conditions. Early climate models considered the impacts of land-use change
simply by altering surface roughness, albedo, and stomatal conductance (e.g., Henderson-Sellers et al. 1993),
changes which led to immediate biogeophysical responses in the radiation and water budgets considered athourly to monthly time scales. Like ESMs, modern climate models include biogeochemical schemes in which
land-use changes affect the global carbon cycle by altering vegetation structure, productivity, and carbon stocks.
However, because the processes associated with land-use
change are complicated and occur heterogeneously overthe land area, it is difficult to include all such processes
in ESMs (Hibbard et al. 2010).
To cope with the issues of land-use simulations, theLand-Use Model Intercomparison Project (LUMIP) was
initiated as a part of the Coupled Model Intercomparison
Project phase 6 (CMIP6). As a part of the LUMIP, a seriesof experiments was proposed to evaluate the biophysicaland biogeochemical impacts of land-use change associatedwith different drivers. This study investigated land-use impacts simulated by the Model for Interdisciplinary Research on Climate, Earth System version 2 for Long-term
simulations (MIROC-ES2L) (Hajima et al. 2020), which
has been used for climate projections expected to contribute to the next assessment report of the Intergovernmental Panel on Climate Change. We first briefly describe theland-use scheme in the MIROC-ES2L, and then, we
present preliminary results of simulations of land-use impacts conducted under the LUMIP protocol.

Ito and Hajima Progress in Earth and Planetary Science

2 Methods
2.1 MIROC-ES2L

MIROC is a global climate model developed mainly by
the University of Tokyo and the Japan Agency for
Marine-Earth Science and Technology. Hajima et al.
(2020) developed MIROC-ES2L by incorporating biogeochemical processes into the well-tested MIROC general circulation model for atmospheric and oceanic
physical processes (Tatebe et al. 2019). The horizontal
resolution of the atmospheric component of MIROCES2L is approximately 2.8° for latitude and longitude (T42
spectral truncation), and the vertical resolution is 40 layers
up to 3 hPa. Land-surface physics is simulated by the Minimal Advanced Treatment of Surface Interaction and Runoff (MATSIRO) scheme (Takata et al. 2003), which
simulates the exchanges of momentum, heat energy, andwater between the atmosphere and land surface. The
scheme uses hydrological relationships to simulate canopy
transpiration, evaporation of canopy-intercepted water,
soil evaporation, runoff discharge, and soil water content,
and it is coupled with a river routine that enables it tosimulate freshwater transport from the land to the ocean.
The version used in this study takes into account sub-gridsnow distributions in a more physical and realistic mannerthan the original model (Nitta et al. 2014). The MATSIRO
scheme considers the vegetation canopy in an explicit
manner; it takes into account plant physiological (i.e., stomatal) regulation of CO2 and vapor exchange and includes
six soil layers.
The biogeochemical carbon cycle of terrestrial ecosystems was simulated by the Vegetation Integrative SImulator for Trace gases adapted for ESM (VISIT-e; Ito2019). See Ito and Oikawa (2002) and Ito and Inatomi(2012) for details of the carbon cycle in VISIT-e. The
simulation by this scheme of the atmosphere–land CO2

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exchange and terrestrial carbon stocks allowed us to include carbon-cycle feedbacks in MIROC-ES2L. In thisscheme, land cover in each grid is represented by fivetiles (primary land, secondary land, cropland, pasture,and urban areas), and fluxes are calculated separately foreach tile (Fig. 1a). The grid-average flux and carbon
stock are obtained as the fraction-weighted means of thecorresponding fluxes and carbon stocks of the five tiles.
In each tile, vegetation biomass is composed of three organs (leaf, stem, and root), and soil organic carbon iscomposed of two pools (litter and mineral soil). Carbon
stock and vegetation coverage in each grid, as well as responses to environmental changes such as atmospheric
CO2 increases and temperature rises, are therefore simulated dynamically at each time step. Land-use change isrepresented by the gross transitions among the fiveland-cover tiles; as a result, both vegetation loss by deforestation and regrowth by cropland abandonment andafforestation can be included (Fig. 1b). When deforestation (transition from primary or secondary forest to
cropland or pasture) occurs, carbon in the vegetation
biomass is exported out of the grid or transferred to thelitter pool at a certain rate. The exported biomass

represents the wood harvest, which is separated into
three pools with different residence times (1 to 100
years) before its consumption releases carbon to the atmosphere as CO2. Abandoned croplands and pastures
are transferred to secondary land, which absorbs CO2from the atmosphere as the vegetation regrows.
The land-surface biogeophysical scheme, MATSIRO,
considers sub-grid heterogeneity and land-use conversion, but in a simplified manner. The scheme aggregates
the five VISIT-e tiles of each grid into two (cropland
and non-cropland). The cropland fraction has its own
parameters, such as albedo and leaf conductance; this, aland-use change from non-cropland to cropland affects
both the radiation and water budgets at the land surface.
Importantly, VISIT-e provides leaf area index (LAI) data
to MATSIRO, and MATSIRO provides soil moisturedata to VISIT-e. Accordingly, the two schemes, which
use the same land-use data, react interactively to landuse change. Although the standalone VISIT-e model iscalibrated (i.e., parameters are adjusted) with observational data, the coupled model with interactive linkage
between models requires further calibrations. For example, vegetation productivity, biomass, and soil carbon

Ito and Hajima Progress in Earth and Planetary ScienceFig. 1 Schematic diagram of land-use change in MIROC-ES2L. a The two MATSIRO tiles and five VISIT-e tiles and their relationships within eachgrid. b Carbon flows associated with land-use change

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stocks are calibrated mainly by manually changing thevalues of photosynthetic capacity and carbon turnoverparameters.
Off-line simulation results of the VISIT-e scheme havebeen validated by comparing them with various observational data from point scale (Ichii et al. 2013, comparison with flux-tower measurement data) to global scale(Ito et al. 2017, comparison with satellite-observed productivity), and the terrestrial carbon cycle scheme has
been used to make land-use impact assessments. For example, Adachi et al. (2011) have applied VISIT (offline
version) to the simulation of temporal changes in the
carbon stock of a tropical rainforest in Malaysia associated with conversion to oil-palm plantations, and Hirata
et al. (2014) have used a similar model in a simulation of
the carbon balance of a cool-temperate, deciduous,
needle-leaved forest in Japan that included land-use conversion and thinning effects. In addition, Kato et al.
(2013) have used the scheme to evaluate carbon emissions
associated with land-use change at a global scale. Ito(2019) used global VISIT simulations to assess the impactsof minor carbon flows, including emissions due to landuse changes, on the global carbon budget. The simulated
emissions associated with land-use changes peaked at
1.2–1.4 Pg C year–1 around the 1950s and then gradually
decreased. This result is comparable to estimates obtainedby the book-keeping method (Houghton 2003) and toother model estimates (Fig. S1); for example, models using
contemporary data have estimated emissions of 1.5 ± 0.7
Pg C year–1 during 2009–2018 (Friedlingstein et al. 2019).
The results obtained by these studies allow us to apply
MIROC-ES2L to land-use studies with confidence.

Several baseline simulations using the common forcing
data have been conducted as a part of CMIP6 or othertasks. Note that the experiments in this study were conducted in the “concentration-driven” mode, and modelsimulated meteorological conditions were also analyzed.
LUMIP includes Tier 1 experiments with high priority
and Tier 2 experiments for investigation of specific features (hereafter, experiment names are shown in italics).
In this study, the historical experiment was conductedwith CMIP6, in which land models were driven by climate conditions derived from coupled climate models.
In contrast, the land-hist experiment was driven by observed climate conditions, so that land processes were
not influenced by model-specific land–atmosphere feedbacks. The land-hist experiment was a collaborative effort with the Land Surface, Snow, and Soil moisture
Model Intercomparison Project (LS3MIP; van den Hurket al. 2016). To remove the historical impact of land-use
change from the historical and land-hist experiments,
idealized experiments, hist-noLu and land-noLu, wereconducted in which a constant cropland distribution wasused throughout each simulation. In the future projection
period, two baseline experiments, ssp126 and ssp370, wereconducted that used Shared Socioeconomic Pathways(SSPs) based on certain socio-economic assumptions andmodels as land-use scenarios (van Vuuren et al. 2017).
These experiments were carried out collaboratively in
conjunction with the Scenario Model Intercomparison
Project (O’Neill et al. 2016). The ssp126 experiment assumed a sustainable, mitigation-oriented society and thus
represents the low end of the range of possible global
warming states. The ssp370 experiment assumed regional
rivalry and thus represents the high-end of the range ofpossible global warming states. The ssp370 scenario assumes a larger extent of cropland expansion at the cost offorests and other natural lands than the ssp126 scenario.
Importantly, however, the ssp126 scenario also assumes asubstantial amount of land-use conversion (e.g., loss ofprimary land), because it assumes that large areas are devoted to biofuel cultivation. To clarify the impacts of different land-use extents, data-exchange experiments areincluded in the LUMIP protocol. The ssp126_ssp370Lu
experiment uses mainly ssp126 forcing data but ssp370
land-use data. The difference between the ssp126 andssp126_ssp370Lu shows the impacts of extensive croplandexpansion. Similarly, the ssp370_ssp126Lu experimentuses mainly ssp370 forcing data but ssp126 land-use data.
In addition, an idealized deforestation experiment, deforest_glob, is included in the protocol. In that experiment,
20 × 106 km2 of forest is cleared at a constant rate during
the period 1850–1899. The 30% of grids with the highest
tree-cover fraction are selected for deforestation, to represent an extreme deforestation case. After 1899, a 30-year
restoration period with constant forest cover is simulated.

Ito and Hajima Progress in Earth and Planetary Science

2.2 LUMIP experiments

LUMIP is an endorsed activity of CMIP6 with the goalof exploring various issues related to land-use change(Eyring et al. 2016; Lawrence et al. 2016). Because
LUMIP was designed to address not only biophysical but
also biogeochemical aspects of climate change, most
ESMs participating in this task have sub-grid land fractions and carbon cycle schemes. Most LUMIP simulations use a dataset of land-use transitions (Land-Use
Harmonization data version 2, LUH2; Hurtt et al. 2011)
and corresponding management variables as forcing data
for model simulations. The land-use transition data aredeveloped by integrating country-based data for areas offorest, pasture, and cropland from the past to the future(harmonization). In this way, temporal expansion of thecropland area is captured in a consistent manner. The
dataset comprises data for both historical (850–2014)
and future (2015–2100) periods. In the present study,
we considered only changes in land-use conditions; thus,
technological innovations in urban and cropland areas
were excluded.

Ito and Hajima Progress in Earth and Planetary Science(2020) 7:54

2.3 Analyses of land-use impacts

We focused on the biophysical variables net radiation(rnet [CMIP6 variable name], W m–2 at grid scale andzettajoules [ZJ] at global scale), temperature (tas, K),evapotranspiration including sublimation (evspsbl, kg
H2O m–2 s–1 at grid scale and 103 km3 year–1 at global
scale), and total runoff discharge (mrro, kg H2O m–2 s–1
at grid scale and 103 km3 year–1 at global scale). We also
focused on the biogeochemical variables net primary
production (npp, kg C m–2 s–1 at grid scale and Pg C
year–1 at global scale), leaf area index (lai, m2 m–2), vegetation carbon stock (cVeg, kg C m–2 at grid scale and Pg
C at global scale), and soil carbon stock (cSoil, kg C m–2
at grid scale and Pg C at global scale). We compared thetemporal trajectories of the global means of these focal
variables and examined the spatial distributions of thedifferences between experiments.
Differences between the experiments indicated the impacts of land-use change, including both direct and indirect(e.g., via alteration of climatic conditions) effects. For thehistorical period, we analyzed the difference between historical and hist-noLu. For the future period, we analyzed thedifferences between ssp126 and ssp126_ssp370Lu, and between ssp370 and ssp370_22p126Lu. Regional analyses wereconducted for six regions (Fig. S2): Asia (20° S–80° N, 60–
180° E), Oceania (20–80° S, 60–180° E), Africa (45° S–35°
N, 30° W–60° E), Europe (35–85° N, 30° W–60° E), North
America (15–85° N, 30–180° W), and South America (15°N–60° S, 30–180° W).
In addition, the mean residence time and acceleration
factor of the vegetation carbon stock were analyzed toclarify land-use impacts on the terrestrial carbon cycle.
In accordance with the analyses of Erb et al. (2016), themean residence time and its acceleration factor were defined as follows:
Mean residence time ðith yearÞ ¼ cVegi =nppi

ð1Þ

Acceleration factor ðith yearÞ
¼ mean residence time ð1st year; pre − industrialÞ
=mean residence time ðith yearÞ
ð2Þ
For example, an acceleration factor of 2 indicates thatthe mean residence time of the vegetation carbon poolhas been halved.

3 Results and discussion
3.1 Land-use transitions

The historical land-use data indicated that in 1850, 95.4
× 106 km2 (73.4%) of the land area was occupied by primary lands such as natural forests, and 16.7 × 106 km2(12.9%) was occupied by secondary lands (Fig. 2).

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Croplands and pastures covered 5.8 × 106 km2 (4.5%)
and 12.0 × 106 km2 (9.2%), respectively (see Fig. S3 formaps). The urban extent was quite small: 0.04 × 106 km2(0.03%). By 2015, the extent of primary lands had decreased considerably, to 50.2 × 106 km2 (38.6%), becauseof deforestation for cultivation. Cropland and pastureareas had increased to 15.9 × 106 km2 (12.2%) and 32.8
× 106 km2 (25.2%), respectively. The extent of urban area
was still small at 0.59 × 106 km2 (0.45%), but it had increased rapidly (1470% of the extent in 1850).
Under the future scenarios, the area occupied by primary land continued to decrease to the end of the twentyfirst century, to 40.9 (ssp126) or 34.7 (ssp370) × 106
km2 in 2100. Both scenarios showed an expansion of thecropland area, by 1.95 (ssp126) and 5.65 (ssp370) × 106
km2 by 2100. The cropland area increased in SouthAmerica and central Africa in ssp126, whereas in ssp370,
it increased in western Africa, South Asia, and parts ofNorth America and South America (Fig. S3). Interestingly, the two scenarios differed with respect to the future change in pasture area: ssp126 predicted a 6.4 ×
106 km2, but ssp370 predicted a 1.6 × 106 km2 increase.
Under the ssp126 scenario, managed pasture decreased
greatly in Asia and moderately in other regions until
2100. By contrast, under the ssp370 scenario, pasture in
Asia decreased only weakly, and there was a considerable expansion of pasture in central Africa.
The urban area approximately doubled in both scenarios by the end of the twentyfirst century.
3.2 Biogeophysical impacts: historical

The correspondence between the MIROC-ES2L historicalsimulation of climatological state of land biogeophysialconditions (Table 1) and observations was good. For example, annual net radiation (rnet) at the land surface wassimulated to range from less than – 20 W m–2 on the Antarctic and Greenland ice sheets to > 160 W m–2 in tropicaland subtropical forests. This rnet distribution is consistent
with satellite observations (e.g., Wielicki et al. 1996). The
modeled global terrestrial water budget of precipitation,
evapotranspiration, and runoff compared favorably withthe budget inferred from a global hydrological synthesis(Oki and Kanae 2006).
Land-use change directly affected biogeophysical conditions by reducing vegetation coverage and associatedattributes. In the historical experiment, global rnet decreased gradually until the 1980s and then increasedslightly (Fig. 3a). Regionally, the decrease in rnet occurred mainly in Asia, Africa, and the low latitudes ofSouth America (Fig. 4a). In contrast, rnet increased in
middle to high latitudes, where extensive “greening” (i.e.,
an increase of vegetation with low albedo) occurred(Piao et al. 2020) and snow mass, which has high albedo,
decreased (Pulliainen et al. 2020).

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Ito and Hajima Progress in Earth and Planetary Science

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Fig. 2 Temporal changes in the global composition of land-use fractions derived from the LUH2 dataset under the historical and a ssp126 or b
ssp370 scenarios

Table 1 Land surface biogeophysical and biogeochemical conditions simulated by MIROC-ES2L (2005–2014 means). Regional (sixregions), zonal (tropical < 25° and extra-tropical > 25°), and global results are shown
rnet

evspsbl
3

3

mrro
–1

3

npp
3

–1

–1

cVeg

cSoil
Pg C

ZJ

10 km year

10 km year

Pg C year

Pg C

Asia

102.2

31.1

12.6

19.2

137.3

467.2

Africa

115.3

25.7

6.3

13.9

113.7

116.1

Oceania

23.2

4.9

0.3

1.8

16.4

23.6

Europe

27.5

8.6

2.1

7.0

49.8

163.5

N. America

54.4

15.8

5.9

12.2

95.7

298.4

S. America

78.4

24.5

9.0

14.0

136.4

123.0

Tropical

224.4

64.1

19.8

35.7

315.8

284.4

Extra-tropical

174.2

47.8

19.7

32.4

233.7

907.9

Global

398.6

111.9

39.5

68.1

549.4

1192.3

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Fig. 3 Global temporal changes in a net radiation (rnet), b evapotranspiration including sublimation (evspsbl), c total runoff (mrro), d net primary
production (npp), e vegetation carbon stock (cVeg), and f soil carbon stock (cSoil) in the CMIP6 and LUMIP experiments simulatedwith MIROC-ES2L

The temporal variation of rnet, that is, a decrease until
the 1970s and a subsequent increase, is consistent withthe global dimming and brightening revealed by observations of incoming solar radiation (Wild et al. 2005). The
simulated historical trend in evspsbl (Fig. 3b) was comparable to that in rnet and caused the simulated total
global evapotranspiration to decrease by 3 × 103 km3year–1 by ca. 1980. The decrease in net radiation until
the 1970s likely affected evspsbl, and the simulated global terrestrial precipitation also showed a weak decreasing trend during that time period (Fig. S4). Global runoff
discharge (mrro; Fig. 3c), however, showed a weak increasing trend with a wide range of interannual variability. Several studies have assessed the temporal changes
in evspsbl and increase in mrro during the historicalperiod (e.g., Gedney et al. 2006; Piao et al. 2007). For example, Gedney et al. (2006) attributed the historicalmrro increase, at least partly, to plant responses to elevated atmospheric CO2 concentrations. Namely, stomatal closure induced by elevated CO2 concentrations
resulted in a decrease of transpiration and hence anincrease of runoff discharge. In contrast, Piao et al.
(2007) have emphasized the impacts of land-use change(i.e., loss of forests) on historical runoff trends.

3.3 Biogeochemical impacts: historical

The climatological state of biogeochemical conditions
captured by MIROC-ES2L corresponded well with observations (Table 1; see Hajima et al. 2020, for benchmarking). For example, the simulated annual net
primary production (npp) during 1996–2005 was 66.1
Pg C year–1, which falls within the range of previous
data aggregation and model estimates (Ito 2011). Similarly, the simulated global vegetation biomass (cVeg) andsoil carbon stock (cSoil) of 535 Pg C and 1186 Pg C, respectively, are close to the values of a synthetic overview
of the global carbon cycle (IPCC 2013) and other Earthsystem models (Todd-Brown et al. 2013). The simulated
spatial distribution of the carbon budget was reasonable,in that tropical forests had high npp and cVeg and highlatitude ecosystems had high cSoil.

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Fig. 4 Regional changes in the simulated biophysical and biogeochemical variables: a net radiation (rnet), b evapotranspiration including
sublimation (evspsbl), c total runoff, d net primary production (npp), e vegetation carbon stock (cVeg), and f soil carbon stock (cSoil). Black bars
show the differences between 1850–1859 and 2005–2014 in the historical period. Colored bars show the differences between 2005–2014 and
2091–2100 in the future period experiments

Land-use conversion resulted in a decline of naturalvegetation, which was apparent in the historical decreaseof global vegetation biomass (Fig. 3e) until 1960. Similar
patterns of change in the global terrestrial carbon stock,
driven mainly by deforestation, have been simulated inprevious studies (e.g., McGuire et al. 2001; Devaraju
et al. 2016). Land-use change, however, did not cause adecline in net primary production (Fig. 3d), because (1)croplands established at the expense of naturalvegetation had comparable npp, and (2) elevated atmospheric CO2 concentration had a fertilization effect onnpp. Although cropland management practices such as
irrigation and fertilization were simplified in MIROCES2L, the high photosynthetic capability of crops would
have enhanced land productivity. As a result of the slowdown of the natural vegetation loss after 1960 (Fig. 2),
there was an acceleration of the npp increase, that led to
the recovery of cVeg (Fig. 3d, e). The aboveground(2020) 7:54

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carbon budget affected the soil carbon stock (cSoil) byaltering the input of litter carbon to the soil.
The simulated carbon cycle indicates that the terrestrial
biosphere provided a positive biogeochemical feedback tohuman-induced global change until 1960 by releasing carbon stock as CO2 to the atmosphere. After 1960, the biospheric feedback became negative because carbon fromthe atmosphere was taken up by the terrestrial biosphere;
the mean terrestrial carbon accumulation rate, 1.84 Pg C
year–1 (1.17 Pg C year–1 of cVeg and 0.67 Pg C year–1 ofcSoil) was comparable to values obtained by a global carbon synthesis (Friedlingstein et al. 2019). Regionally, theresponses in Oceania differed from those in other regions(Fig. 4) because cropland expansion in southern Australia,
where extensive farming is conducted, negatively influenced npp and carbon stocks.

increased in Europe, and cSoil had increased in central
North America, East Asia, and Europe. Cultivation withappropriate management was able to increase productivities and carbon stocks of semi-arid or infertile lands inthese regions.
The idealized global deforestation experiment revealed
strong impacts of land-use change on terrestrial carbon
stocks (Fig. 3e, f). During the 50-year deforestation phase,vegetation biomass and soil carbon stock decreased by 64
and 81 Pg C, respectively. The changes amounted to − 3.2
Pg C (106 km2)–1 for vegetation and − 4.1 Pg C (106 km2)–
1
for soil carbon. The recovery during the restoration
phase was remarkable in the vegetation carbon stock; theglobal total biomass recovered within 50 years to almost
the initial state in 1850. In contrast, accumulation of soil
carbon proceeded more slowly.

3.4 Impacts of global deforestation on carbon stocks

3.5 Biogeophysical impacts: future

Comparison between the historical and historical_noLu
experiments revealed spatiotemporal variations in theimpacts of land-use change (Fig. 5). In 1870, impacts on
cVeg and cSoil were not evident in most regions. By
1930, extensive deforestation had decreased cVeg andcSoil in eastern North America, Europe, and Southeast
Asia. By 2010, a decrease in carbon pools occurred inSouth America and Africa as well, but cVeg had

Under the ssp126 and ssp370 scenarios, biogeophysicalconditions simulated by MIROC-ES2L responded dramatically to the global drivers, but how they responded
differed between the scenarios (Fig. 3). In the ssp126 experiment, rnet increased rapidly from 2015 to 2050 andthen stabilized until the end of the twentyfirst century.
In the ssp370 experiment, rnet increased gradually
throughout the simulation period. These net radiation

Ito and Hajima Progress in Earth and Planetary ScienceFig. 5 Changes in carbon stocks in vegetation (cVeg: left side) and soil (cSoil: right side) between the historical and hist-noLu experiments
simulated with MIROC-ES2L for a, b 1870, c, d 1930, and e, f 2010

(2020) 7:54

Page 10 of 15changes affected land evspsbl in a similar manner, because evapotranspiration is driven primarily by net radiation energy. Total runoff was simulated to increase
moderately in the ssp126 experiment and markedly inthe ssp370 experiment (Fig. 3c). These clear mrro responses in the future experiments contrasted with the stability of mrro in the historical experiment. The temporal
responses of evapotranspiration differed between the vegetation components and the soil component (Fig. S5). In
the historical period, they compensated each other, leading to relatively stable runoff discharge.
In the land-use data-exchange experiments (ssp126_
ssp370Lu and ssp370_ssp126Lu), future patterns of rnet
and evspsbl were largely the same as those of ssp126 andssp370, respectively, at a global scale (Figs. 3 and 4).
However, regional differences were found between theexperiments with and without land-use data exchange(e.g., ssp126 and ssp126_ssp370Lu; Fig. 4). The use of thessp370 land-use scenario resulted in lower rnet in Africa,
northeastern North America, and a part of Eurasia,
whereas it resulted in higher rnet in Australia and central South America. Similar differences were found in
evspsbl, but only in low and middle latitudes. Thechanges of mrro differed from those of rnet and evspsbl;
mrro increased in southern Africa, Southeast Asia, andwestern South America, where precipitation generally
increased. Because more vegetation cover was removed
in the ssp126_ssp370Lu experiment, decreases of transpiration and evaporation of canopy-intercepted water
likely contributed to the increase of runoff (Fig. S5). Future temperature, precipitation, and radiation conditions
did not differ greatly between the experiments with andwithout land-use data exchange (Fig. S4).

scenario indicated an extensive increase of croplands inAfrica and South Asia, the npp increase was more limited by the land-use change in the ssp126_ssp370Lu experiment. Similar regional differences in the impacts ofland-use data exchange were found in cVeg and cSoil.

Ito and Hajima Progress in Earth and Planetary Science

3.6 Biogeochemical impacts: future

The future response of npp (Fig. 3d) showed that it was
driven primarily by atmospheric CO2 concentrations andonly secondarily by climate and land use. In the ssp126
experiment, npp increased and then reached a maximum
at around 2050; in the ssp370 experiment, it increased
steadily up to 90 Pg C year–1 in 2100. As a result ofphotosynthetic increase, cVeg and cSoil increases inboth scenarios. Because carbon in soil responds to external forcing and accumulates slowly, cSoil continued toincrease to the end of the twentyfirst century.
The impacts of land-use data exchange on the carbon
budget were small at the global scale, especially in thecase of npp; a similar weak response of npp to land use
in the future has been simulated by other ESMs (e.g.,
Lawrence et al. 2018). The npp response was not uniform across regions; however, npp increased more inEurope, eastern North America, central Africa, andSoutheast Asia in the ssp126_ssp370Lu experiment thanin the ssp126 experiment. Because the ssp370 land-use

3.7 Changes in carbon turnover

The mean residence time of carbon in terrestrial ecosystems is an important index of the carbon cycle and has
been used to characterize the results of carbon cycle
models, because it is related to the carbon accumulation
potential. For example, model intercomparison studies (e.g.,
Friend et al. 2014) have indicated that the carbon mean
residence time is a poorly constrained parameter in presently available models. Figure 6 shows the relationship between cVeg and npp (by Eq. 1, the slope, cVeg/npp,
corresponds to the mean residence time) as simulated by
CMIP6 models. The cVeg and npp estimates differedamong the ESMs, but all of them simulated an accelerationof carbon turnover; MIROC-ES2L gave intermediate resultsof npp, cVeg, mean residence time, and its acceleration
among the models. Similarly, Todd-Brown et al. (2013) analyzed the mean residence time of soil carbon simulated by
ESMs and found that it ranges from 10.8 to 39.3 years (ourestimate on the basis of the MIROC-ES2L results is 26.2
years). This remaining inter-model variability in the CMIP6
ESMs indicates a serious estimation uncertainty, whichshould be considered in depth in a forthcoming study.
Acceleration of carbon turnover as a result of biomass
exploitation and enhanced decomposition indicates a decline of carbon storage capacity. The mean residence
time of cVeg, calculated by using the definition of Erb
et al. (2016), indicated such an acceleration of terrestrial
carbon turnover (Fig. 7). Although the mean residence
time of carbon in tropical forests was relatively long (>15 years), it was simulated to be greatly reduced in the
future. The acceleration of the carbon turnover time bya factor of > 2 in Africa and Southeast Asia resulted in
the mean residence time of carbon in these regions being halved during the simulation period. Such acceleration of the terrestrial carbon turnover is consistent withthe results reported by Erb et al. (2016) for the historicalperiod, although they also found substantial accelerationin Europe, East Asia, and eastern North America. By
contrast, only our future analysis showed the clear acceleration in central and southern Africa. On the basis of
their data analyses, they concluded that the mean residence time of terrestrial biomass was halved during thehistorical period and that 59% of the acceleration was attributable to land-use change.
3.8 Advantages and limitations

This study examined how well MIROC-ES2L simulated
the impacts of land-use change on biogeophysical andIto and Hajima Progress in Earth and Planetary Science(2020) 7:54

Page 11 of 15

Fig. 6 Relationship between global net primary production (npp) and vegetation biomass carbon stock (cVeg) simulated by CMIP6 models. ACCE
SS (ACCESS-ESM1-5), BCC (BCC-CSM2-MR), CanESM (CanESM5), CESM2, CMCC (CMCC-ESM2), CNRM (CNRM-ESM2-1), EC-Earth (EC-Earth-Veg), IPSL(IPSL-CM6A-LR), MIROC-ES2L, MPI (MPI-ESM1-2-LR), MRI (MRI-ESM2-0), NorESM (NorESM2-LM), and UKESM (UKESM1-0-LL). The dashed line showswhere cVeg/npp = 10 (i.e., mean residence time of about 10 years). Arrows show the temporal change in each ESM; a rightward-directed arrow
indicates acceleration of carbon turnoverbiogeochemical conditions. By integrating physical andbiological processes, the Earth system model captured thehistorical variations in radiation, water, and carbon budgets
over the land surface. Figure 8, for example, illustrates
linkages among impacts caused by land-use change, as demonstrated by the difference between the ssp126 and ssp126_
ssp370Lu experiments. As shown in previous studies (e.g.,
Arora and Montenegro 2011), such an integration of land
processes facilitates capturing the land–atmosphere interactions under shifting environmental conditions. Change in
vegetation cover had sequential impacts on albedo, roughness, the radiation budget, temperature, and then evapotranspiration and runoff. This sequence of impacts wascaptured by the land surface scheme implemented in
MIROC-ES2L, although the accuracy of the simulated impacts needs further examination. Subsequently, biogeochemical processes such as organic carbon production anddecomposition were affected, directly by the loss of vegetation cover and indirectly by the change in environmental
conditions. These biogeochemical impacts, which occurredat decadal time scales, were captured well by the ecosystemcarbon cycle scheme implemented in the model. These results are reasons to have confidence in simulations of future
land conditions by MIROC-ES2L.

However, the present study had several limitations.
First, this study used the results of concentration-driven
simulations, but, emission-driven simulations are required to assess biogeochemical feedback effects on the
climate system of altered atmospheric CO2 concentrations. The emission-driven LUMIP experiment (esmssp585-ssp126Lu experiment; Lawrence et al. 2016),
which we could not include in the present study, would
be useful for this purpose. Second, because of the coarse
model grid resolution (> 200 km), land-use data were aggregated before use. Sub-grid land-cover heterogeneity
was considered by using a simple tile-mosaic approximation (Fig. 1), but this approximation probably led tounder- or over-representation of the impacts. Future improvements of high-resolution models, aided by computational advances and remote sensing data, would
alleviate this problem. Third, the present ESMs, including MIROC-ES2L, do not fully account for biogeochemical feedbacks of land-use changes such as those
associated with non-CO2 greenhouse gases and biogenic
volatile organic compounds (Arneth et al. 2010). Landuse change should affect not only the carbon cycle but
also the nitrogen cycle (e.g., accelerated mineralizationand nitrous oxide emissions), but this study focused on

Ito and Hajima Progress in Earth and Planetary Science(2020) 7:54

Page 12 of 15

Fig. 7 Mean residence time (MRT) of vegetation biomass and its acceleration: mean residence time in the a 1850s and b 2090s in the ssp370
experiment and c the acceleration factor based on the definition by Erb et al. (2016)

only the carbon cycle. These limitations are unsolved
problems that provide opportunities for future studies.

4 Concluding remarks
This study examined land-use impacts simulated by
MIROC-ES2L and showed that this model gave seasonable results under different scenarios. One important

implication of the study relates to the possibility of future terrestrial carbon sequestration as a means of climate change mitigation. In the present study, the ssp126
and ssp370 scenarios showed that terrestrial ecosystems
might accumulate an additional 100 to 280 Pg C globallyby the end of the twentyfirst century and that this sequestration capacity is unlikely to be greatly diminishedIto and Hajima Progress in Earth and Planetary Science(2020) 7:54

Page 13 of 15

Fig. 8 Distribution of the sequential impacts of land-use change simulated with MIROC-ES2L. The maps show differences between ssp126 andssp126-ssp370Lu during 2091–2100 (blue, lower values in ssp126-ssp370Lu; red, higher values). The arrows indicate the directions of impact

by land-use change. This persistent carbon sequestrationof the future terrestrial ecosystem has important implications for climate–carbon cycle feedbacks, which play aconsiderable role in future climate projections.
Considering the simplicity of the land-use scenarios used inthis study, more targeted land-use planning should result in
additional carbon sequestration. For example, the present study
did not consider soil management options such as no-tillage
cultivation and bio-char amendment, for improving soil carbon
capacity (Paustian et al. 2016; Soussana et al. 2019). Also, thestrengthening of specific mitigation policies such as the Reduced Emission from Deforestation and forest Degradation(REDD) policy (Obersteiner et al. 2010) and forest management(e.g., Law et al. 2018) should enhance carbon sequestration interrestrial ecosystems more than was simulated in this study.
Accounting for these mitigation options, in addition to othersocio-economic factors, will allow Earth system model results
to be used to make more policy-relevant recommendations.

5 Supplementary information
Supplementary information accompanies this paper at https://doi.org/10.
1186/s40645-020-00372-w.
Additional file 1: Fig. S1. Comparison of land-use change emissions
between VISIT estimation and others used in the global CO2 budget

analysis (Friedlingstein et al. 2019). For keeping consistency with othermodel estimates, VISIT (offline version) was driven by historical climate
data at spatial resolution of 0.5° × 0.5°.
Additional file 2: Fig. S2 Regional boundaries used in this study
overlaid on the MIROC-ES2L land fraction map: Asia (856 land grids, 41.7
× 106 km2), Africa (462 land grids, 35.9 × 106 km2), Oceania (112 land
grids, 7.0 × 106 km2), Europe (347 land grids, 13.6 × 106 km2), North
America (653 land grids, 25.2 × 106 km2), and South America (271 land
grids, 18.7 × 106 km2).
Additional file 3: Fig. S3. Changes in the cropland fraction in (a) 1861,(b) 2010, (c) 2100 by ssp126, and (d) 2100 by ssp370. Data were derived
by using Land Use Harmonization version 2.
Additional file 4: Fig. S4. Temporal changes in global terrestrial-meansof (a) annual near-surface air temperature, (b) mean annual precipitation,(c) downward shortwave radiation, and (d) downward longwave
radiation.
Additional file 5: Fig. S5. Temporal changes in global water flows. (a)Vegetation transpiration (tran), (b) evaporation from canopy-intercepted
precipitation (evspsblveg), and (c) evaporation from soil (evspsblsoi).

Abbreviations
LUMIP: Land-Use Model Intercomparison Project; MIROC-ES2L: Model forInterdisciplinary Research on Climate, Earth System version 2 for Long-term
simulations
Acknowledgements
We acknowledge the World Climate Research Programme, which, throughits Working Group on Coupled Modelling, coordinated and promoted CMIP6.
We thank the climate modeling groups for producing and making available
their model output, the Earth System Grid Federation (ESGF) for archiving

Ito and Hajima Progress in Earth and Planetary Science(2020) 7:54

the data and providing access, and the multiple funding agencies who
supported CMIP6 and ESGF. A.I. and T.H. were supported by the Integrated
Research Program for Advancing Climate Models, Ministry of Education,
Culture, Sports, Science and Technology, Japan.
Authors’ contributions
AI drafted the paper and TH supervised the analyses. Both authors
contributed significantly to the LUMIP simulations, the interpretation of theresults, and the writing of the manuscript. The author(s) read and approved
the final manuscript.
Funding
A.I. and T.H. were supported by the Integrated Research Program for
Advancing Climate Models (JPMXD0717935715), Ministry of Education,
Culture, Sports, Science and Technology, Japan.
Availability of data and materials
The datasets supporting the conclusions of this article are available on the
Earth System Grid Federation website [URL: https://esgf-node.llnl.gov/
projects/cmip6/].
Competing interests
The authors declare that they have no competing interests.
Received: 25 February 2020 Accepted: 4 September 2020

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