Kawamiya et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-020-00369-5(2020) 7:64

Progress in Earth andPlanetary Science

REVIEW

Open AccessTwo decades of Earth system modelingwith an emphasis on Model forInterdisciplinary Research on Climate(MIROC)
Michio Kawamiya1* , Tomohiro Hajima1, Kaoru Tachiiri1, Shingo Watanabe1 and Tokuta Yokohata2

Abstract
The past 20 years of research using Earth system models (ESMs) is reviewed with an emphasis on results from the
ESM based on MIROC (Model for Interdisciplinary Research on Climate) developed in Japan. Earth system models
are climate models incorporating biogeochemical processes such as the carbon cycle. The development of ESM
was triggered by studies of the feedback between climate change and the carbon cycle. State-of-the-art ESMs aremuch more realistic than the first ESMs. They now include various biogeochemical processes other than carbon,
such as atmospheric chemistry and the nitrogen and iron cycles as well as nutrient transport by atmospheric dust
and rivers. They are used to address many practical issues, such as evaluating the amount of carbon dioxide
emissions that is consistent with climate change mitigation targets, and are indispensable tools for thedevelopment of climate change mitigation policies. Novel, ambitious attempts to use ESMs include couplingsocioeconomics with Earth systems, and projecting the carbon cycle on decadal timescales. Development of ESMs
requires ongoing integration of multiple aspects of climate science. Emerging applications of ESMs can bring forth
meaningful insights, and should be directed toward expanding connections with fields outside climate science, e.g.,socioeconomics.
Keywords: Earth system model, Climate change, IPCC, Carbon cycle, Biogeochemistry, Interdisciplinary project,
Remaining carbon budget, Socioeconomics, Nitrogen, Iron

1 Introduction
1.1 Manifestations of climate change

The effects of climate change are now noticeable. For
example, the World Meteorological Organization(WMO) pointed out that climate change is one of thefactors underlying the floods and disastrous heatwave inJapan in 2018 (WMO 2018). The general public is also
starting to identify manifestations of climate change. To
meet the demands of society, it is increasingly important
to avoid the dangerous consequences of climate change* Correspondence: kawamiya@jamstec.go.jp
1
Japan Agency for Marine-Earth Science and Technology, 3173-25,
Showamachi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan
Full list of author information is available at the end of the article

on the basis of scientific knowledge. Climate models cancontribute by, for example, providing estimates ofchanges in frequency of extreme events under climatechange (cf., Imada et al. 2019).
Up until the early 2000s, the priority for many climate
scientists was to focus on improving our scientific understanding of climate change, such as detection of human impacts and validation of climate models for
projection. In 2007, the 4th Assessment Report (AR4) of
the Intergovernmental Panel on Climate Change (IPCC)
reported dominant human influence on the warming of
the atmosphere and the ocean since the mid-twentiethcentury (IPCC 2007). This was perhaps the turning
point; it put an end to the question of whether climate© 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
permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Kawamiya et al. Progress in Earth and Planetary Science(2020) 7:64

Page 2 of 13

change was real, and encouraged scientists to direct
more efforts toward obtaining reliable data that could be
used for planning mitigation and adaptation processes,
rather than proving human influence on climate.

feedback, and then continues to cover other aspects
while highlighting the MIROC-based ESM. Nevertheless,the overall direction of ESM research clearly involves
the interests of the international community.

1.2 Contribution of climate models to the design ofclimate change mitigation and adaptation strategies

2 Review

Detailed information on climate change tailored to individual regions is needed to support regional planning efforts. For example, high-resolution climate models arebeing used to establish regional adaptation strategies(JMA 2017), not only in Japan, but also worldwide. The
Working Group I contribution to IPCC’s 6th Assessment
Report (AR6) will be published in 2021. Three of thetwelve chapters are devoted to regional-scale projections,and will discuss regional effects linked to global scalechanges, extreme events (such as tropical cyclones), andthe impacts of hazards (IPCC 2017).
Projections of climate change can underpin measuresagainst climate change, and Earth system models(ESMs), in which biogeochemical processes such as the
carbon cycle are coupled with the physical climate system, are often used to design climate change mitigation
strategies to reduce anthropogenic greenhouse gas emissions. While conventional climate models require carbon
dioxide (CO2) concentration data as inputs, ESMs candirectly use anthropogenic CO2 emissions as inputs, andare therefore able to express more explicitly the link between environmental change and climate change mitigation policies.
In this review, we focus on possible contributions of
ESMs to the design of climate change mitigation policies, accentuating results obtained using the Model forInterdisciplinary Research on Climate (MIROC)-based
ESM, which is currently being developed in Japan. Earthsystem models can be classified into three categories according to their degree of complexity (Hajima et al.
2014a): conceptual models, ESMs with intermediate
complexity (EMICs), and ESMs based on general circulation models (GCMs). This review incorporates developments that have been published since the review ofHajima et al. (2014a). In the present review, unlessotherwise specified, the term ESM(s) refers to the third
category, i.e., ESM(s) based on GCM(s), which are themodels that are used most frequently to understand potential consequences of greenhouse gas emission scenarios. Claussen et al. (2002) provides an excellent review of
EMICs, which can also be used for climate change projections, especially over longer timescales, such as
millennia.
Feedback resulting from interactions between climatechange and the carbon cycle is a dominant motivation
behind ongoing development of ESMs. Therefore, thispresent review provides an overview that starts with this

2.1 The dawn of Earth system models: climate–carbon
cycle feedback

One of the reasons ESMs first attracted attention is that
feedback from the carbon cycle was shown to have thepotential to accelerate climate change (Cox et al. 2000;
Friedlingstein et al. 2001). Since then, several institutes
have developed their own ESMs by introducing many
component models into their GCMs; these include thedynamic global vegetation model (DGVM), which projects changes in vegetation cover of natural land system,and the carbon cycle model with fixed vegetation types,
which focuses on forecasting carbon exchange (e.g., Friedlingstein et al. 2006, and references therein). These terrestrial component models affect climate via thebiogeochemical (carbon cycle) processes but also
through the impact of biophysical processes such aschanges in leaf area index on surface albedo (e.g., Abeet al. 2017).
In Japan, the Meteorological Research Institute (MRI)
has been developing an ESM based on its GCM (Yukimoto et al. 2011). The Japan Agency for Marine–Earth
Science and Technology (JAMSTEC), in collaboration
with the National Institute of Environmental Studies, Atmosphere Ocean Research Institute of the University ofTokyo, and a community of other universities (TeamMIROC) has been developing another GCM-based ESM(Hajima et al. 2020; Kawamiya et al. 2005; Watanabe
et al. 2011a). Figure 1 depicts the structure of the ESM
that Team MIROC first developed. This ESM consistsof: The atmosphere–ocean coupled climate model







MIROC (Hasumi and Emori 2004; Tatebe et al.
2019; Watanabe et al. 2010)
The terrestrial ecosystem model Sim-CYCLE (Itoand Oikawa 2002)
The simple NPZD-type ocean ecosystem model,
which includes dissolved inorganic nitrogen (N),phytoplankton (P), zooplankton (Z), and detritus (D;Oschlies and Garçon 1999)
The land surface model MATSIRO (Takata et al.
2003), andThe aerosol transport model SPRINTARS(Takemura et al. 2000)

A full atmospheric chemistry component model,
known as CHASER (Sudo et al. 2002), was integrated
into the ESM at a subsequent stage (Watanabe et al.

Kawamiya et al. Progress in Earth and Planetary Science(2020) 7:64

Fig. 1 Structure of the original version of the Earth system model(ESM) used for the Coupled Model Intercomparison Project (CMIP)
Phase 3, developed by Team MIROC. From CMIP Phase 5 onwards,
the full atmospheric chemistry component model CHASER wasadded. The land biogeochemical model was later replaced by SEIBDGVM for CMIP Phase 5 and then by VISIT for CMIP Phase 6. All
model acronyms are listed in the Abbreviations

2011a) (see Fig. 3). All model acronyms are spelled out
in full in the Abbreviations section. Component models
have been developed separately in different scientificfields. In ESMs, they are often updated, and sometimes
replaced with other component models that may be
more appropriate for specific purposes (Hasumi 2000;
Inatomi et al. 2010; Sato et al. 2007). The fundamental
structure of Team MIROC’s ESM, as outlined in Fig. 1,
is common to that of other ESMs being developed atother institutes.
The Coupled Climate–Carbon Cycle Model Intercomparison Project (C4MIP) was initiated in the early 2000s
to explore issues related to climate–carbon cycle feedback in participating models (Friedlingstein et al. 2006).
Climate–carbon cycle feedback has a mainly positive(accelerating) effect on climate change because higher
temperature enhances decomposition of soil organic
matter and plant respiration, and inhibits photosynthesis. Because of thermodynamic effects on air–sea gas
exchange, higher temperature also suppresses ocean carbon uptake, but global impact of this marine process isminor compared with that of terrestrial processes.
Feedback intensity—defined as the difference between
CO2 concentrations at year 2100 with climate–carbon

Page 3 of 13

cycle interactions and CO2 concentrations at year 2100
without these interactions—is highly dependent onmodels. They range from 20 to 224 ppm (Friedlingstein
et al. 2006), which correspond to temperature differences of ~ 0.2 °C to > 2 °C. It is difficult to obtain precise
temperature conversions though, because of the manyfactors that are model specific. Various factors are involved in determining the feedback intensity, including
equilibrium climate sensitivity, transient climate response of the base climate model, and properties of theembedded carbon cycle model. Thompson et al. (2004)
pointed out that models with strong CO2 fertilization
tend to have moderate to weak feedback, because carbon
added to the atmosphere will be immediately absorbed
by terrestrial vegetation. Although CO2 fertilization itself
is considered as a negative feedback on climate change,
it is not explicitly linked to temperature rise. Instead, elevated CO2 concentrations lead to more vigorous CO2
uptake, counteracting climate change. Hajima et al.
(2014b) later showed that the range in this model feedback intensity is a result of the choice of formulationand parameter values for modeled photosynthesis.
The Coupled Model Intercomparison Project (CMIP)
has an ongoing critical role in contributing to IPCC reports. As a result of C4MIP contributions to the IPCC’s
5th Assessment Report (AR5), the scientific community
became aware of the importance of incorporating thecarbon cycle into climate change projections, and incorporated experiments using ESMs with carbon cycle in
the protocols of the 5th (CMIP5) and subsequent phasesof CMIP (Hibbard et al. 2007; Taylor et al. 2011).
Concentration-driven experiments were formally
adopted for C4MIP under CMIP5; CO2 concentrations,
not emissions, were used as input data for integrating
ESMs, and carbon cycle components were activated; onthe basis of global carbon balance, ESM output was used
to determine anthropogenic CO2 emission, which wasthen used to evaluate climate change impact on the carbon cycle.
Because the causal relationship in reality starts fromCO2 emission rather than concentration, the CMIP
protocol is rather counterintuitive for experiments involving the carbon cycle. However, by making
concentration-driven experiments accessible to models
without carbon cycle, it allows more models to participate in CMIP. It also allows for direct comparison between ESM outputs and socioeconomic emission
scenarios, as explored in the Representative Concentration Pathways (RCPs; Moss et al. 2010) from Integrated
Assessment Models (IAMs). On average, there is a good
match between ESM outputs and IAM projections.
Under the highest emission scenario of RCP8.5, however, mean ESM-based emission is 85% that of mean
IAM-based emission (Jones et al. 2013), implying that

Kawamiya et al. Progress in Earth and Planetary Science(2020) 7:64

terrestrial and oceanic carbon sinks in ESMs are smaller
than those in IAMs. This may reflect insufficient expression of the carbon cycle feedback in IAMs, or differences
between ESMs and IAMs in terms of the implementation of land use and land cover change. Jones et al.
(2013) highlighted that the IAM used for developing
RCP8.5 was calibrated using an ESM with a relatively
large oceanic sink.
While there is a good match between average values
from ESMs and IAMs, ESM output scatter is large because of uncertainties in the global carbon cycle, making
it impossible to identify specific emission levels evenwhen different ESMs target the same CO2 concentration
scenario. Matsumoto et al. (2016) evaluated impacts ofEarth system uncertainties, including those of the carbon
cycle on socioeconomics, especially mitigation policy;
under RCP4.5, inefficient carbon uptake and/or high climate sensitivity would result in stringent emission cuts
and future carbon prices that are roughly 300% of thoseunder efficient carbon uptake and/or low climate sensitivity. Since the disciplines of natural sciences and socioeconomics on climate change are related, there should
be a room for expanding collaborations between researchers from these different disciplines. The study ofMatsumoto et al. (2016) is one of the successful initiatives, in which ESM development facilitated collaboration between researchers from climate science andsocioeconomics, enabling the conversion of uncertainties
in natural processes into those related to socioeconomics. Sokolov et al. (2005), Yang et al. (2016), andMercure et al. (2018) also developed platforms linking
ESMs (including EMICs) with socioeconomic models,catalyzing interactions between different disciplines thatare related.
2.2 Sophistication of Earth system models
2.2.1 Incorporating the nitrogen cycle

While most ESM studies report a positive climate–carbon cycle feedback that accelerates climate change,
Thornton et al. (2007) showed that feedback became
negative when the nitrogen cycle was incorporated into
their model. They reported that higher temperatures enhance remineralization of soil organic nitrogen, which inturn supplies more nutrients, and stimulates photosynthesis of terrestrial vegetation. Two of the nine models
in CMIP5 included the nitrogen cycle. Both of themwere based on the community land model (CLM4) ofLawrence et al. (2011), and the climate–carbon cycle
feedback was considerably weaker in these models relative to that in the other CMIP5 models (Arora et al.
2013). While net primary production should be strongly
regulated by heterotrophic respiration according toThornton et al. (2007), decomposition of carbon–concentration feedbacks into land ecophysiological and soil

Page 4 of 13

processes showed no conclusive supporting evidence(Hajima et al. 2014b). Nevertheless, models with the nitrogen cycle behave in a distinct manner, as demonstrated by the results of CESM1-BGC and NorESM1ME, which are both models with the nitrogen cycle andoutliers in CMIP5 (Fig. 2 with data from Hajima et al.
2014b). Zaehle et al. (2014) suggested that CMIP5
models without the nitrogen cycle possibly overestimate
uptake of anthropogenic carbon, and Wieder et al.
(2015) highlighted effects of interactions between thephosphorus and carbon cycles.
These studies clearly support the incorporation of interactions between the cycles of carbon and other elements, especially nitrogen, into ESMs. Several of the
ESMs joining CMIP6 (Eyring et al. 2016; Jones et al.
2016; Lawrence et al. 2016; Orr et al. 2017) are performing experiments that require the incorporation of the nitrogen cycle. This includes MIROC-ES2L, which is thefirst version of the MIROC-based ESM to include theterrestrial nitrogen cycle. Figure 2 shows CMIP6 values
from Arora et al. (2020). As indicated by Arora et al.
(2020), CMIP6 models with explicit nitrogen cycle (filled
blue squares in Fig. 2) show moderate response of net
primary productivity (NPP) to CO2 increase and the resultant increase of land total carbon; the smallest response was given by the model that has incorporated thephosphorous cycle and limitation on plant growth(ACCESS-ESM1.5). Carbon cycle behavior in MIROCES2L—the latest version of MIROC—is similar to that in
MIROC-ESM—the former version of MIROC used for
CMIP5. This can be seen in Fig. 2, where MIROC-ES2L
lies far from the origin, and is, in fact, further away fromthe origin than MIROC-ESM. This may be at least partly
because, in MIROC-ESM, parameter values were set to
produce a modest CO2 fertilization effect, implicitly taking soil nitrogen limitation into consideration. Like
MIROC-ESM, CO2 fertilization effect in MIROC-ES2L
is moderate (see Fig. 2). Therefore, relative to MIROCESM, the enhanced sensitivity of NPP and terrestrial carbon of MIROC-ES2L is likely attributable to the longer
soil carbon residence time and higher carbon use efficiency in MIROC-ES2L (Arora et al. 2020). Wenzel et al.
(2016) examined the validity of the CO2 fertilization effect that emerged in CMIP5 models; they comparedmodel outputs with observations and found that
MIROC-ESM responds realistically to CO2 increase. Incorporation of the nitrogen cycle into the terrestrial
component model of the ESM enables evaluation of impacts of human activities, such as addition of nitrogen to
ecosystems through agricultural fertilization. Bonan andDoney (2018) indeed pointed out that taking agriculture
into consideration with nitrogen dynamics is a key for
utilizing ESMs to establish mitigation policy. ESMs are
becoming a tool to assess the impacts of various humanKawamiya et al. Progress in Earth and Planetary Science(2020) 7:64

Page 5 of 13

Fig. 2 Relationship between net primary production increments (ΔNPP) and terrestrial carbon accumulation stimulated by atmospheric CO2
increase in Coupled Model Intercomparison Project (CMIP) Phase 5 (black) and 6 (blue) models based on results from an idealized CMIP5 (Hajima
et al. 2014a, 2014b) and CMIP6 (Arora et al. 2020) experiment in which CO2 concentration was increased every year by 1% and the carbon cycle
was assumed to be unaffected by temperature change. Increments were calculated as the difference between the last and first conditions of theexperiments. Filled symbols indicate results from models with terrestrial carbon–nitrogen interactions. Solid curve indicates results from theanalytical, simple algebraic model based on parameters of CMIP5 models, as proposed by Hajima et al. (2014b). Red ovals indicate results from
MIROC series of ESMs

activities, in addition to CO2 emission, on global
systems.
2.2.2 Connecting land and ocean via nitrogen

River transport is incorporated into MIROC-ES2L; acertain portion of active nitrogen on land is carriedby rivers into the ocean; this terrestrial component
becomes part of the marine inorganic nitrogen, whichis consumed in oceanic phytoplankton photosynthesis(Hajima et al. 2020). For CMIP5, MIROC-ESM only
had a closed nitrogen cycle in the ocean. For CMIP6,
nitrogen cycles on land and ocean are linked in
MIROC-ES2L; the modeled ocean has an additionalsource of nitrogen, necessitating explicit treatment of
nitrogen sinks and other sources within the ocean.
Like many other ESMs in CMIP6, MIROC-ES2L takes
nitrogen fixation and denitrification into consideration. Dust deposition of nitrogen from the atmosphere is externally provided as input data. The
impacts on oceanic net primary production of rivertransport and atmospheric deposition have been examined by Hajima et al. (2020). The effect of anthropogenic nitrogen river loading is becoming
detectable in some coastal regions, albeit not at basin
or larger scales (Gruber and Galloway 2008; Rabalais
2002). However, it would be desirable for future

model development to include physical transport processes near coasts, thereby enabling evaluation of impacts of nitrogen loading, which can undergo
explosive increases related to population growth (Bodirsky et al. 2012).
2.2.3 Introducing the iron cycle into the ocean

Along with nitrogen fixation and denitrification, iron
cycling is another process that is often neglected in
ESMs, despite its importance. Martin and Fitzwater(1988) first presented convincing data to illustrate theindispensable role of iron as a nutrient. Research on iron
cycling has shown that CO2 concentration variation overglacial–interglacial cycles was at least partly triggered byiron transported as dust, which oscillated insynchronization with the climate cycle (Ohgaito et al.
2018). It was thought that perhaps climate warming
could be somewhat mediated by distributing iron over
the ocean (so-called geoengineering; Boyd 2008). Although subsequent field experiments have indicated thatiron fertilization is not an efficient way of slowing down
climate change (Williamson et al. 2012), the effect ofiron on CO2 fluctuation over geological time scales of ~100 ka is now widely accepted (Yamamoto et al. 2019).
There is also firm recognition that iron is the key limiting factor for phytoplankton growth in oceanic regions

Kawamiya et al. Progress in Earth and Planetary Science(2020) 7:64

having high nutrient and low chlorophyll levels, such as
the Southern Ocean, Equatorial Pacific, and Northernmost Pacific regions (Sarmiento and Gruber 2006).
Iron supply via atmospheric dust is a vital part ofthe oceanic iron cycle. Influxes of iron to the ocean
have conventionally been evaluated by multiplying
dust deposition (e.g., Duce and Tindale 1991; Jickells
et al. 2005) by a constant parameter (Duce et al.
1991). This simple approach was adopted because ofa lack of adequate observation data, although clearly
this is insufficient to reproduce the geographical distribution of iron inputs. Currently, process-basedmodels are being developed that may be able to capture the geographical distribution. One example is themodel by Ito et al. (2019), which takes into accountthe key process associated with pyrogenic ironchanges. In MIROC-ES2L, pyrogenic and lithogenic
iron are differentiated, although emission of pyrogenic
iron is prescribed as input data. Treating pyrogenic
and lithogenic iron separately is a characteristic feature of the MIROC series of ESMs, and enables special variation in solubility of iron supplied to theocean to be reproduced (Hajima et al. 2020).
Besides nitrogen and iron cycling, another important biogeochemical process yet to be incorporated
into many (including the MIROC series) of the existing ESMs is CO2 or CH4 emissions related to permafrost thawing, as pointed out by the IPCC (2018).
Despite this issue being known for many years, model
development has been hampered by the scarcity ofdata. Some modeling groups have started to embed
detailed treatment of permafrost, as observation data
become available (Xia et al. 2017).
2.2.4 Incorporation of short-lived climate forcers

As low-emission scenarios to limit global warming to 2
°C or 1.5 °C below preindustrial levels are emphasized in
discussions of climate change mitigation measures, attention is focused on the treatment of climate driving
forcers other than CO2. The Special Report on Global
warming of 1.5 °C (SR1.5) by the IPCC (2018) estimatedthe total future CO2 emissions consistent with the 1.5 °C
target (this quantity is often referred to as the remaining
carbon budget and will be discussed later in more detail). Contributions from non-CO2 forcings and responseof the earth system to them are considered as the largest
sources of uncertainty underlying remaining carbon
budget estimates.
These forcing agents are termed as short-lived climate forcers (SLCFs) since they tend to have shorter
residence times than CO2 because of their high chemical reactivity in or rapid deposition out of the atmosphere. At its 49th plenary in 2019, the IPCC decided
to revise SLCF inventory methodology to improve thePage 6 of 13

emissions dataset (IPCC 2019). This development canserve as a timely stimulus for the development of
ESMs that explicitly incorporate full atmosphericchemistry.
The MIROC-ESM-CHEM (Watanabe et al. 2011a)
was the first version of Team MIROC’s ESM (Fig. 3a)with a full atmospheric chemistry component (Sudoet al. 2002). It participated in CMIP5, and was also
used to project future changes in UV-B reaching the
Earth’s surface (Watanabe et al. 2011b; Watanabe andYokohata 2012) because of its detailed treatment ofstratospheric chemistry and a top of the atmospherethat extends to a height of 85 km. However, incorporation of full atmospheric chemistry requires following the reactions and transport of more than fifty
three-dimensional tracers, which is computationallydemanding. While MIROC-ESM-CHEM simulations
were at the coarse horizontal resolution of T42 (280km), they only covered a small portion of CMIP5
experiments.
The MIROC-ES2H is the latest version of the MIROC
series for CMIP6 with full chemistry (Sudo et al. 2002).
Except for the treatment of atmospheric chemistry andhorizontal resolution, it has inherited most of the features of MIROC-ES2L. It is at T85 (140 km) resolution,
which is relatively high for such a complex model. Because computational load would be unrealistically high if
the chemical component were to be directly coupledwith the main body of the ESM, the atmosphere-onlymodel with full chemistry is run at T42 in parallel withthe main coupled model at T85 (Fig. 3b). The model
coupling software Jcup (Arakawa et al. 2020) exchanges
data between the T42 and T85 models, and computational requirements are maintained at a level that can befeasibly met.
The MIROC-ES2H has been used to develop SLCF reduction scenarios (Nakajima et al. 2020), and covers
parts of CMIP6 experiments. Comparison between
Figs. 1 and 3a, b illustrates how an ESM becomes elaborate over time and needs increasingly advanced programming techniques. Chemical processes in the atmospherecan have impacts on the biosphere through, for instance,
deposition of nitrate and ammonium on land and ocean.
While depositions are provided as external inputs in
MIROC-ES2L, they are computed in MIROC-ES2H. On
land, this nitrogen accumulation eventually leads to release of N2O, N2, and NH3 out of the land cycle (Gruber
and Galloway 2008), which is implemented in MIROCES2H (and MIROC-ES2L) only diagnostically without
feedbacks to atmospheric chemistry (Hajima et al. 2020).
Introducing more sophisticated interactions between atmospheric chemistry, biosphere, and the carbon cycle
would be the next step in ESM development beyond
CMIP6.

Kawamiya et al. Progress in Earth and Planetary Science(2020) 7:64

Page 7 of 13

Fig. 3 Coupling of component models in a MIROC-ESM-CHEM used for CMIP5 and b MIROC-ES2H used for CMIP. T42 and T85 indicate horizontal
resolutions that correspond to approximately 280 and 140 km, respectively. L80 and L81 indicate 80 and 81 vertical layers in theatmospheric model.

2.3 Expanding areas of application
2.3.1 Contributions to decision making

The Paris Agreement—the framework of measures from2021 to 2030 to reduce climate change—entered into
force in 2016. It aims to hold global temperature increase to below 2 °C above preindustrial levels, and pursue efforts to limit warming to below 1.5 °C (UNFCCC
2015). While the 2 °C target was often mentioned ininternational negotiations prior to the Paris Agreement,
the 1.5 °C target was relatively new and hence studies of
the impacts of 1.5 °C warming were relatively scarce.
Therefore, since the signing of the Paris Agreement, numerous studies have been conducted to investigate thedifferences between impacts of a 1.5 °C warming andthose of a 2 °C warming (e.g., Schleussner et al. 2016;
Tachiiri et al. 2019b), and their results are summarized
in SR1.5 (IPCC 2018).
The Agreement also stipulates that participating countries should reduce their greenhouse gas emissions in accordance with the goals set out in their Nationally
Determined Contributions (NDCs). As part of theAgreement, a Global Stocktake (GST) is to be carriedout to confirm global progress toward NDCs, and across-checking of NDC consistency with the 1.5 °C and
2.0 °C targets. The GST will be based on the latest scientific knowledge, and scientific studies that contribute to
it are expected to be very valuable.
The demonstration of a fairly linear relationship between cumulative anthropogenic CO2 emission andtransient temperature rise is one of the most wellknown results of ESM studies that have contributed toclimate change mitigation policies (IPCC 2013). The
proportionality constant for this relationship is called
the transient climate response to cumulative carbon
emissions (TCRE). The allowable future carbon emission
level consistent with a given target is termed theremaining carbon budget (IPCC 2018). The value of
TCRE is crucial for quantifying the upper limit of theremaining carbon budget consistent with a 2 °C target,
which has often been addressed in international climate
negotiations, and a 1.5 °C target, which was stressed in
the Paris Agreement.
To be consistent with the 2 °C target, the amount of
cumulative remaining anthropogenic carbon emissions
since the preindustrial era was estimated to be 790–900
GtC at the time of the publication of AR5 (IPCC 2013).
Given that cumulative emission between the preindustrial era and 2013 was approximately 550 PgC (Le Quéréet al. 2018), the remaining carbon budget consistent withthe 2 °C target in 2013 was estimated to be 240–350
PgC. This roughly corresponds to 25–40 years of anthropogenic carbon emissions at the current (2008–2017mean) rate of 9.4 ± 0.5 PgC/y (Le Quéré et al. 2018).
The value is much smaller for the 1.5 °C target, whichgave rise to a pessimistic view that the 1.5 °C target isunattainable geophysically, even without considering thepolitical difficulties.
This view was shared by some climate scientists wellbefore the publication of SR1.5. In recent years, however,there is accumulating evidence from ESM studies thatthe remaining carbon budget could be considerably larger than that estimated in AR5 (Goodwin et al. 2018;

Kawamiya et al. Progress in Earth and Planetary Science(2020) 7:64

Holden et al. 2018; Millar et al. 2017; Tokarska and Gillett 2018). One of the reasons for this revision is thatsimulation results published in AR5 had a tendency tooverestimate temperature rise per unit carbon emission.
These revised results are incorporated into SR1.5, which
postulates a higher remaining carbon budget.
Table 1 shows remaining carbon budget estimates in
SR1.5 and AR5 converted to values for 2018. There areclearly large uncertainties in the estimates. Temperature
data type used in the estimation influences estimatedvalues; global mean surface air temperature (GSAT) uses
air temperature near the surface (often at 2 m height),
while global mean surface temperature (GMST) combines 2-m temperature with sea surface temperaturefrom some oceanic regions where observations of 2-m
temperature are sparse (Richardson et al. 2018). The
TCRE used for evaluating the remaining carbon
budget also has major uncertainties that are scenario
dependent (e.g., Tachiiri et al. 2019a). As part of theirreview of existing literature, Rogelj et al. (2019) summarized the sources of uncertainty in remaining carbon
budget estimates; these include considerable impacts of
non-CO2 greenhouse gases, as well as issues related tothe use of different temperature data types and carbon
cycle feedbacks.
Arora et al. (2020) reported a TCRE value of 1.78 ±0.41 °C EgC–1 for a subset of CMIP6 ESMs, which is 9%
higher than that for CMIP5 (1.63 ± 0.48 °C EgC–1). Thisimplies a lower value of remaining carbon budget, although its estimate depends on many other factors as
stated above and the mean values are not statistically different given the small sample size of available models.
While it is beyond the scope of this review to provide
precise estimate of remaining carbon budget, it is possible that its value is again lowered when evaluatedbased on a full set of CMIP6 ESMs.

Table 1 Estimates of the remaining carbon budgets for early2018 for different mitigation targets. Units: GtCO2
TargetAR5SR1.5

1.5 °C110 (*1, GSAT-based, lower end)

420–580 (*3, GSAT-based)
570–770 (*4, GMST-based)

2.0 °C

720–830 (*2, GSAT-based)

1320–1690 (*4, GMST-based)

Lower and upper bounds correspond to 66%- and 50%-tile values,
respectively. (*1) This value is based on Chapter 2 of the Special Report on
Global Warming of 1.5 °C (SR1.5) (IPCC 2018), which only provides the 66%-tilevalue from the beginning of 2011. This was converted to a value for the
beginning of 2018 by using a medium estimate of annual anthropogenic
emission of 42 GtCO2, as adopted in SR1.5. (*2). These values are based on theSummary for Policymakers (SPM) of the 5th Assessment Report (AR5) (IPCC2013). Estimates of the 66%- and 50%-tile values given for the beginning of2011 in the SPM were converted to values for the beginning of 2018, asabove. (*3) These values are taken from Table 2.2 of SR1.5, in which historicalwarming is defined by global mean surface air temperature (GSAT). (*4) As for*3, but in this case, historical warming is defined by global mean surface
temperature (GMST). See text for the definitions of GSAT and GMST

Page 8 of 13

In addition to remaining carbon budget estimates,
ESM studies also provide the society with other useful
information, for example for climate impact assessmentsof land use changes associated with various socioeconomic scenarios, and detailed analysis of annual balancesof greenhouse gases. Working toward AR6, research institutes from across the world are evaluating these issues
using their cutting-edge ESMs and land use models(Alexander et al. 2018; Meiyappan et al. 2014; Olin et al.
2015; Rolinski et al. 2018; Yokohata et al. 2019a). An attempt is being made to organize the efforts of modelingthe effects of land use change on climate (Lawrence
et al. 2016).
2.3.2 Decadal prediction of Earth systems

It is plausible that scientists may be requested to provide
projections of air temperature and greenhouse gas concentrations over periods of 5–10 years for the GST. If
possible, it would be desirable to have annual- to
decadal-scale projections in addition to projections on
the centennial scale. Studies predicting trends over timescales of annual to decadal periods are gaining in popularity (Boer et al. 2016). Unlike traditional centennial
projections, decadal predictions require explicit consideration of the natural variability of initial conditions.
Data assimilation has been developed for predictions
over relatively short timescales, e.g., for numerical weather prediction. It is being further developed to provide
a description of the global environment at a certain timethat is as close to reality as possible to be used as input
for decadal predictions.
Given the increasing demand for detailed global warming projections over long timescales that involve biogeochemical processes, there is a clear need for moreaccurate predictions on shorter timescales. Using hindcasting and initial fields obtained from data assimilation,a group in Germany demonstrated 4–7 years of predictability in air–sea CO2 exchange over the northern North
Atlantic Ocean (Li et al. 2016). Using a perfect model
approach, where simulated fields were treated as if they
were observations to examine predictability, anothergroup in France has shown 6 years of predictability inthe global carbon cycle (Séférian et al. 2018). In Japan,
the JAMSTEC is working on a research project to predict air–sea CO2 flux from the equatorial Pacific region
using a data assimilation technique (Watanabe et al.
2020).
Decadal prediction with traditional types of Atmosphere–Ocean GCMs has been rapidly growing. Methodologies to address issues such as removing model drift
have been compiled (International CLIVAR Poject Office2011). This knowledge is needed to develop biogeochemical decadal prediction with ESMs, necessitatinginterdisciplinary collaboration.

Kawamiya et al. Progress in Earth and Planetary Science(2020) 7:64

2.3.3 Links to socioeconomic models

Human activities affect climate not only through emissions of greenhouse gases from industry and deforestation but also via other mechanisms such as changing
surface albedo through expansion of urban areas andcroplands. Impacts of temperature rise on labor productivity can also lead to changes in greenhouse gas emission(Matsumoto 2019), thus forming a feedback loop between climate change and human society. Collins et al.
(2015) described a possible feedback loop between climate change and society: changes in agricultural productivity related to global warming may affect the amountof cropland needed to support the population, whichfurther alters surface albedo and the degree of global
warming. Such interactions could have considerable impacts on climate change projections, especially at regional scales, and need to be addressed as one of thecross-cutting issues between socioeconomics and climate
science (Woodard et al. 2019; Yokohata et al. 2019a). An
ESM and an IAM developed for socioeconomic

Page 9 of 13

projections could be coupled to conduct consistent andcomprehensive studies of such interactions. On the basisof the National Center for Atmospheric Research(NCAR)’s Community Climate System Model (CCSM)(Bitz et al. 2011), Jones et al. (2012) developed such a
coupled model and analyzed differences between imposing carbon tax only on industrial emissions and on bothindustrial and deforestation emissions. They found thattemperature rise by 2100 could be reduced considerably
by taxing only industrial emissions. These results are rather counterintuitive, and are a result of albedo increases
and water vapor decreases from deforestation activities.
A group in Japan (Matsumoto et al. 2016) reported
that uncertainties in projections of the Earth system,
related to climate sensitivity and the carbon cycle,
may lead to considerable uncertainties in future carbon price. Indeed, carbon price is often identified as
one of the socioeconomic parameters that is the mostsensitive to environmental alteration (Su et al. 2018;
Yamamoto et al. 2014), although, compared with

Fig. 4 Climate risks and their cause–effect relationships across different sectors, adapted from Yokohata et al. (2019b). Arrow thickness represents
number of risk interconnections. Arrows connecting different sectors indicate intersectoral causal relationships, and those looping back to thesame sector represent causal links within each sector. Node size reflects number of risks identified within the sector. Node color indicates the
proportion of intersector causes (orange), effects (blue), and intrasector causes and effects (gray). Oval with a red dashed line shows the range
covered by Earth system models, as reviewed here. Ovals with blue and green dashed lines show the ranges covered by integrated assessment
models and dedicated models in the field of impact assessment, respectively, illustrating the complementarity of the diverse models used inenvironmental scienceKawamiya et al. Progress in Earth and Planetary Science(2020) 7:64

uncertainties associated with other more encompassing socioeconomic projections, those associated with
future carbon price are relatively small. This conclusion is largely consistent with the study by Silva Herran et al. (2019), which investigated the effect ofclimate uncertainties on structural transformations in
the global energy system. Although the models usedin such studies do not tightly couple socioeconomic
and climate models, they indicate a need for hardcoupling between models originally developed in totally different fields. On the basis of their survey,
Tachiiri et al. (Identifying key processes and sectors
in the interaction between climate and socioeconomic systems: a review towards integrating
Earth–human systems, submitted) concluded that terrestrial productivity and labor efficiencies are some of
the key processes to be included in such coupling.
However, parameterization of processes involves large
errors. Clearly, such attempts need to proceed withcareful consideration of the advantages, disadvantages,
and model limitations, as outlined by van Vuuren
et al. (2012) and Calvin and Bond-Lamberty (2019).

3 Conclusions: models as a tool for evolving Earthsystem science
This review has briefly described the current status ofresearch applications involving ESMs. Earth systemmodels can be used to study climate change related toanthropogenic greenhouse gas emissions, and also otherassociated environmental problems, such as ocean acidification through ocean uptake of emitted carbon (Watanabe and Kawamiya 2017; Yamamoto et al. 2012), andimpacts of land use change related to biofuel production,
cropland expansion, and urbanization (e.g., Smith et al.
2015).
These issues are interconnected and often extend beyond the domain of traditional climate science, necessitating synergetic cooperation among various areas ofscience and social science, such as agriculture and socioeconomics. Yokohata et al. (2019a, 2019b) developed ascheme to visualize the interlinkages of risks due to climate change, based on an expert review on literature onsuch individual risks. Figure 4 lumps those risks by sector, such as energy and food, and showing their interand intra-sector impacts. For example, the relationshipbetween the risk of increase in flooding and that of increase in damage to agricultural land is shown by anarrow from “water” to “food” sector, with the former
regarded as a cause and the latter an effect. The relationship between change in food distribution leading todestabilization of food supply is represented by an arrowlooping within the sector “food.”
It can be seen from Fig. 4 that the processes typicallydealt with in ESMs have significant impacts on those in

Page 10 of 13

other models such as IAMs and impact assessment
models, and vice versa, as most notably illustrated by theinteraction between food production and ecosystems. A
comprehensive approach to global environmental
change would require integration of issues traditionallytreated in separate disciplines and consideration of theirinterlinkages.
Furthermore, the search for solutions to global environmental change is a social problem, which requires
stakeholder involvement. While scientific problems havebeen traditionally identified by people with highly specific scientific expertise, identification of scientific problems to be addressed in ESM studies should involve thegeneral public. Ongoing development of ESMs shouldtake an inter- and trans-disciplinary approach, requiringsynergetic cooperation through enhanced communications across diverse fields and sectors.
Abbreviations
AGCM: Atmospheric General Circulation Model; CHASER: Chemical
Atmospheric General Circulation Model (AGCM) for Study of Atmospheric
Environment and Radiative forcing; CMIP5(6): 5th (6th) Phase of the Coupled
Model Intercomparison Project; COCO: Centre for Climate System Research(CCSR) Ocean Component Model; CCSM: Community climate system model;
CTM: Chemical transport model; ESM: Earth system model; EMIC: ESM withintermediate complexity; GCM: General circulation model; GMST: Global
mean surface temperature; GST: Global stocktake; GSAT: Global mean surface
air temperature; IPCC: Intergovernmental Panel on Climate Change;
JAMSTEC: Japan Agency for Marine-Earth Science and Technology;
JMA: Japan Meteorological Agency; MATSIRO: Minimal advanced treatmentsof surface interaction and runoff; MIROC: Model for Interdisciplinary Researchon Climate; NCAR: National Center for Atmospheric Research;
NDC: Nationally Determined Contributions; NPZD: Nutrient, Phytoplankton,
Zooplankton, and Detritus; SEIB-DGVM: Spatially Explicit Individual Based
Dynamic Global Vegetation Model; Sim-CYCLE: Simulation model of the
carbon CYCLE in land ecosystems; SPRINTARS: Spectral Radiation-Transport
Model for Aerosol Species; SR1.5: Special Report on Global Warming of 1.5 °C(IPCC 2018); TCRE: Transient Climate Response to cumulative carbon
Emissions; TRIP: Total Runoff Integrating Pathways (Oki and Sud 1998); VISI
T: Vegetation Integrative Simulator for Trace Gases; WMO: World
Meteorological Organization
Acknowledgements
This work is supported by TOUGOU, the “Integrated Research Program for
Advancing Climate Models” of the Ministry of Education, Culture, Sports,
Science and Technology of Japan (Grant Number JPMXD0717935715).
Discussion with Junichi Tsutsui of Central Research Institute of Electric Power
Industry greatly improved our understanding of the remaining carbon
budget and helped us compile Table 1. Authors are grateful to Michio
Watanabe and Rumi Ohgaito of the JAMSTEC, who provided usefulcomments on the manuscript. We thank Dr. Trudi Semeniuk and Dr. Tina Tin
from Edanz Group (https://en-author-services.edanzgroup.com/) for editing a
draft of this manuscript, and Kazuya Nishina for preparing Fig. 4.
Authors’ contributions
MK proposed the topic and composed the main text. TH examined the text
regarding ESM development and provided revisions. KT and TY checked theparts concerning collaboration between Earth system and socioeconomic
modeling. SW provided information on MIROC-ES2H. All authors read andapproved the final manuscript.
Authors’ information
MK is the director-general of Research Center for Environmental Modelingand Application of the JAMSTEC, which is the section responsible for the development of the MIROC series of models including its Earth system version.

Kawamiya et al. Progress in Earth and Planetary Science(2020) 7:64

Funding
This work is supported by TOUGOU, the “Integrated Research Program for
Advancing Climate Models” of the Ministry of Education, Culture, Sports,
Science and Technology of Japan (Grant Number JPMXD0717935715).

Availability of data and materials
Not applicable.

Competing interests
The authors declare that they have no competing interests.
Author details
Japan Agency for Marine-Earth Science and Technology, 3173-25,
Showamachi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan. 2National
Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506,
Japan.
1

Received: 24 November 2019 Accepted: 31 August 2020

References
Abe M, Takata K, Kawamiya M, Watanabe S (2017) Vegetation masking effect on
future warming and snow albedo feedback in a boreal forest region ofnorthern Eurasia according to MIROC-ESM. J Geophys Res 122:9245–9261
Alexander P, Rabin S, Anthoni P, Henry R, Pugh TAM, Rounsevell MDA, Arneth A(2018) Adaptation of global land use and management intensity to changes
in climate and atmospheric carbon dioxide. Glob Change Biol 24:2791–2809
Arakawa T, Inoue T, Yashiro H, Satoh M (2020) Coupling library Jcup3: its
philosophy and application. Prog Earth Planet Sci 7:6. https://doi.org/10.1186/
s40645-019-0320-z
Arora VK, Boer GJ, Friedlingstein P, Eby M, Jones CD, Christian JR, Bonan G, Bopp
L, Brovkin V, Cadule P, Hajima T, Ilyina T, Lindsay K, Tjiputra JF, Wu T (2013)
Carbon–concentration and carbon–climate feedbacks in CMIP5 Earth systemmodels. J Climate 26:5289–5314
Arora VK, Katavouta A, Williams RG, Jones CD, Brovkin V, Friedlingstein P,
Schwinger J, Bopp L, Boucher O, Cadule P, Chamberlain MA, Christian JR,
Delire C, Fisher RA, Hajima T, Ilyina T, Joetzjer E, Kawamiya M, Koven C,
Krasting J, Law RM, Lawrence DM, Lenton A, Lindsay K, Pongratz J, Raddatz T,
Séférian R, Tachiiri K, Tjiputra JF, Wiltshire A, Wu T, Ziehn T (2020) Carbonconcentration and carbon-climate feedbacks in CMIP6 models and theircomparison to CMIP5 models. Biogeosciences 17:4173–4222. https://doi.org/
10.5194/bg-17-4173-2020
Bitz CM, Shell KM, Gent PR, Bailey DA, Danabasoglu G, Armour KC, Holland MM,
Kiehl JT (2011) Climate sensitivity of the community climate system model,
Version 4. J Climate 25:3053–3070
Bodirsky BL, Popp A, Weindl I, Dietrich JP, Rolinski S, Scheiffele L, Schmitz C,
Lotze-Campen H (2012) N2O emissions from the global agricultural nitrogen
cycle - current state and future scenarios. Biogeosciences 9:4169–4197
Boer GJ, Smith DM, Cassou C, Doblas-Reyes F, Danabasoglu G, Kirtman B, Kushnir
Y, Kimoto M, Meehl GA, Msadek R, Mueller WA, Taylor KE, Zwiers F, Rixen M,
Ruprich-Robert Y, Eade R (2016) The Decadal Climate Prediction Project(DCPP) contribution to CMIP6. Geosci Model Dev. 9:3751–3777
Bonan GB, Doney SC (2018) Climate, ecosystems, and planetary futures: thechallenge to predict life in Earth system models. Science 359:eaam8328.
https://doi.org/10.1126/science.aam8328
Boyd PW (2008) Ranking geo-engineering schemes. Nature Geosci 1:722–724
Calvin K, Bond-Lamberty B (2019) Integrated human-earth systemmodeling—state of the science and future directions. Environ Res Lett 13:
063006
Claussen M, Mysak LA, Weaver AJ, Crucifix M, Fichefet T, Loutre M-F, Weber SL,
Alcamo J, Alexeev VA, Berger A, Calov R, Ganopolski A, Goosse H, Lohmann
G, Lunkeit F, Mokhov II, Petoukhov V, Stone P, Wang Z (2002) Earth systemmodels of intermediate complexity: closing the gap in the spectrum ofclimate system models. Climate Dyn 18:579–586
Collins WD, Craig AP, Truesdale JE, Di Vittorio AV, Jones AD, Bond-Lamberty B,
Calvin KV, Edmonds JA, Kim SH, Thomson AM, Patel P, Zhou Y, Mao J, Shi X,
Thornton PE, Chini LP, Hurtt GC (2015) The integrated Earth system modelversion 1: formulation and functionality. Geoscientific Model Dev 8:2203–
2219

Page 11 of 13

Cox PM, Betts RA, Jones CD, Spall SA, Totterdell IJ (2000) Acceleration of global
warming due to carbon-cycle feedbacks in a coupled climate model. Nature
408:184–187
Duce RA, Liss PS, Merrill JT, Atlas EL, Buat-Menard P, Hicks BB, Miller JM, Prospero
JM, Arimoto R, Church TM, Ellis W, Galloway JN, Hansen L, Jickells TD, Knap
AH, Reinhardt KH, Schneider B, Soudine A, Tokos JJ, Tsunogai S, Wollast R,
Zhou M (1991) The atmospheric input of trace species to the world ocean.
Glob Biogeochem Cycles 5:193–259
Duce RA, Tindale NW (1991) Atmospheric transport of iron and its deposition in
the ocean. Limnol Oceanography 36:1715–1726
Eyring V, Bony S, Meehl GA, Senior CA, Stevens B, Stouffer RJ, Taylor KE (2016)
Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6)
experimental design and organization. Geosci Model Dev 9:1937–1958
Friedlingstein P, Bopp L, Ciais P, Dufresne J-L, Fairhead L, LeTreut H, Monfray P,
Orr J (2001) Positive feedback between future climate change and thecarbon cycle. Geophys Res Lett 28:1543–1546
Friedlingstein P, Cox P, Betts R, Bopp L, von Bloh W, Brovkin V, Cadule P, Doney
S, Eby M, Fung I, Bala G, John J, Jones C, Joos F, Kato T, Kawamiya M, Knorr
W, Lindsay K, Matthews HD, Raddatz T, Rayner P, Reick C, Roeckner E,
Schnitzler KG, Schnur R, Strassmann K, Weaver AJ, Yoshikawa C, Zeng N(2006) Climate–Carbon Cycle Feedback Analysis: Results from the C4MIP
Model Intercomparison. J Climate 19:3337–3353
Goodwin P, Katavouta A, Roussenov VM, Foster GL, Rohling EJ, Williams RG(2018) Pathways to 1.5 °C and 2 °C warming based on observational andgeological constraints. Nature Geosci 11:102–107
Gruber N, Galloway JN (2008) An Earth-system perspective of the global nitrogen
cycle. Nature 451:293–296
Hajima T, Kawamiya M, Watanabe M, Kato E, Tachiiri K, Sugiyama M, Watanabe S,
Okajima H, Ito A (2014a) Modeling in Earth system science up to andbeyond IPCC AR5. Prog Earth Planet Sci 1:29
Hajima T, Tachiiri K, Ito A, Kawamiya M (2014b) Uncertainty of concentration–
terrestrial carbon feedback in earth system models. J Climate 27:3425–3445
Hajima T, Watanabe M, Yamamoto A, Tatebe H, Noguchi MA, Abe M, Ohgaito R,
Ito A, Yamazaki D, Okajima H, Ito A, Takata K, Ogochi K, Watanabe S,
Kawamiya M (2020) Description of the MIROC-ES2L Earth system model andevaluation of its climate–biogeochemical processes and feedbacks.
Geoscientific Model Dev. https://doi.org/10.5194/gmd-2019-275
Hasumi H (2000) CCSR Ocean Component Model (COCO) Version 2.1, CCSR
Report No. 13. Center for Climate System Research, University of Tokyo,
Tokyo, Japan.
Hasumi H, Emori S (2004) K-1 Coupled GCM (MIROC) Description, K-1 technicalreport. Center for Climate System Research (Univ. of Tokyo), Kashiwa, Japan.
Hibbard KA, Meehl GA, Cox PM, Friedlingstein P (2007) A strategy forclimate change stabilization experiments. Eos Transact Am Geophys
Union 88:217–221
Holden PB, Edwards NR, Ridgwell A, Wilkinson RD, Fraedrich K, Lunkeit F, Pollitt
H, Mercure JF, Salas P, Lam A, Knobloch F, Chewpreecha U, Viñuales JE(2018) Climate–carbon cycle uncertainties and the Paris Agreement. Nat
Climate Change 8:609–613
Imada Y, Watanabe M, Kawase H, Shiogama H, Arai M (2019) The July 2018high temperature event in Japan could not have happened without
human-induced global warming. SOLA 15A:8–12. https://doi.org/10.2151/
sola.15A-002
Inatomi M, Ito A, Ishijima K, Murayama S (2010) Greenhouse gas budget of acool-temperate deciduous broad-leaved forest in Japan estimated using aprocess-based model. Ecosystems 13:472–483
International CLIVAR Project Office (2011) Data and bias correction for decadal
climate predictions, CLIVAR Publication Series 150, International CLIVAR
Project Office, Qingdao, China.
IPCC (2007) Climate Change 2007: The physical science basis. Contribution ofWorking Group I to the Fourth Assessment Report of the Intergovernmental
Panel on Climate Change. Cambridge University Press, Cambridge and New
York
IPCC (2013) The physical science basis. Contribution of Working Group I to theFifth Assessment Report of the Intergovernmental Panel on Climate Change
in: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A,
Xia Y, Bex V, Midgley PM (Eds.), Cambridge and New York, p. 1535.
IPCC (2017) Chapter outline of the working group I contributions to the IPCC
sixth assessment report (AR6).
IPCC (2018) Global warming of 1.5°C. An IPCC special report on the impacts ofglobal warming of 1.5°C above pre-industrial levels and related global

Kawamiya et al. Progress in Earth and Planetary Science(2020) 7:64

greenhouse gas emission pathways, in the context of strengthening the
global response to the threat of climate change, sustainable development,
and efforts to eradicate poverty, in: Masson-Delmotte V, Zhai P, Pörtner HO,
Roberts D, Skea J, Shukla PR, Pirani A, Moufouma-Okia W, Péan C, Pidcock R,
Connors S, Matthews JBR, Chen Y, Zhou X, Gomis MI, Lonnoy E, Maycock T,
Tignor M, Waterfield T (Eds.), Cambridge, United Kingdom and New York, NY,
USA, p. 616.
IPCC (2019) Decisions adopted by the Panel. 49th Session of the IPCC. https://www.ipcc.ch/site/assets/uploads/2019/05/IPCC-49_decWoSons_adopted.pdf
Ito A, Myriokefalitakis S, Kanakidou M, Mahowald NM, Scanza RA, Hamilton DS,
Baker AR, Jickells T, Sarin M, Bikkina S, Gao Y, Shelley RU, Buck CS, Landing
WM, Bowie AR, Perron MMG, Guieu C, Meskhidze N, Johnson MS, Feng Y,
Kok JF, Nenes A, Duce RA (2019) Pyrogenic iron: The missing link to highiron solubility in aerosols. Sci Adv 5. https://doi.org/10.1126/sciadv.aau7671
Ito A, Oikawa T (2002) A simulation model of the carbon cycle in land
ecosystems (Sim-CYCLE): a description based on dry-matter production
theory and plot-scale validation. Ecol Model 151:143–176
Jickells TD, An ZS, Andersen KK, Baker AR, Bergametti G, Brooks N, Cao JJ, Boyd
PW, Duce RA, Hunter KA, Kawahata H, Kubilay N, laRoche J, Liss PS,
Mahowald N, Prospero JM, Ridgwell AJ, Tegen I, Torres R (2005) Global ironconnections between desert dust, ocean biogeochemistry, and climate.
Science 308:67–71
JMA (2017) Global Warming Projection Information vol.9, climate change
projection for Japan using non-hydrostatic regional climate model under
IPCC’s RCP8.5 scenario, p 79
Jones AD, Collins WD, Edmonds J, Torn MS, Janetos A, Calvin KV, Thomson A,
Chini LP, Mao J, Shi X, Thornton P, Hurtt GC, Wise M (2012) Greenhouse Gas
Policy Influences Climate via Direct Effects of Land-Use Change. J Climate 26:
3657–3670
Jones C, Robertson E, Arora V, Friedlingstein P, Shevliakova E, Bopp L, Brovkin V,
Hajima T, Kato E, Kawamiya M, Liddicoat S, Lindsay K, Reick CH, Roelandt C,
Segschneider J, Tjiputra J (2013) Twenty-First-Century Compatible CO2Emissions and Airborne Fraction Simulated by CMIP5 Earth System Models
under Four Representative Concentration Pathways. J Climate 26:4398–4413
Jones CD, Arora V, Friedlingstein P, Bopp L, Brovkin V, Dunne J, Graven H,
Hoffman F, Ilyina T, John JG, Jung M, Kawamiya M, Koven C, Pongratz J,
Raddatz T, Randerson JT, Zaehle S (2016) C4MIP—The Coupled Climate
Carbon Cycle Model Intercomparison Project: experimental protocol for
CMIP6. Geoscientific Model Dev 9:2853–2880
Kawamiya M, Yoshikawa C, Kato T, Sato H, Sudo K, Watanabe S, Matsuno T (2005)
Development of an Integrated Earth System Model on the Earth Simulator. J
Earth Simulator 4:18–30
Lawrence DM, Hurtt GC, Arneth A, Brovkin V, Calvin KV, Jones AD, Jones CD,
Lawrence P, De Noblet-Ducoudré N, Pongratz J, Seneviratne SI, Shevliakova E(2016) The Land Use Model Intercomparison Project (LUMIP) contribution to
CMIP6: rationale and experimental design. Geoscientific Model Dev 9:29732998.
Lawrence DM, Oleson KW, Flanner MG, Thornton PE, Swenson SC, Lawrence PJ,
Zeng X, Yang Z-L, Levis S, Sakaguchi K, Bonan GB, Slater AG (2011)
Parameterization improvements and functional and structural advances in
Version 4 of the Community Land Model. J Adv Model Earth Syst 3. https://doi.org/10.1029/2011MS00045
Le Quéré C, Andrew RM, Friedlingstein P, Sitch S, Hauck J, Pongratz J, Pickers PA,
Korsbakken JI, Peters GP, Canadell JG, Arneth A, Arora VK, Barbero L, Bastos A,
Bopp L, Chevallier F, Chini LP, Ciais P, Doney SC, Gkritzalis T, Goll DS, Harris I,
Haverd V, Hoffman FM, Hoppema M, Houghton RA, Hurtt G, Ilyina T, Jain AK,
Johannessen T et al (2018) Global Carbon Budget 2018. Earth Syst Sci Data
10:2141–2194
Li H, Ilyina T, Müller WA, Sienz F (2016) Decadal predictions of the North Atlantic
CO2 uptake. Nature Commun 7:11076. https://doi.org/10.1038/ncomms11076
Martin JH, Fitzwater SE (1988) Iron deficiency limits phytoplankton growth in thenorth-east Pacific subarctic. Nature 331:341–343
Matsumoto K (2019) Climate change impacts on socioeconomic activities
through labor productivity changes considering interactions between
socioeconomic and climate systems. J Cleaner Production 216:528–541
Matsumoto K, Tachiiri K, Kawamiya M (2016) Impact of climate model
uncertainties on socioeconomics: A case study with a medium mitigation
scenario. Comput Oper Res 66:374–383
Meiyappan P, Dalton M, O’Neill BC, Jain AK (2014) Spatial modeling of agriculturalland use change at global scale. Ecol Model 291:152–174

Page 12 of 13

Mercure J-F, Pollitt H, Edwards NR, Holden PB, Chewpreecha U, Salas P, Lam A,
Knobloch F, Vinuales JE (2018) Environmental impact assessment for climatechange policy with the simulation-based integrated assessment model
E3ME-FTT-GENIE. Energy Strategy Rev 20:195–208
Millar RJ, Fuglestvedt JS, Friedlingstein P, Rogelj J, Grubb MJ, Matthews HD, Skeie
RB, Forster PM, Frame DJ, Allen MR (2017) Emission budgets and pathways
consistent with limiting warming to 1.5 °C. Nature Geosci 10:741–747
Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, van Vuuren DP, Carter
TR, Emori S, Kainuma M, Kram T, Meehl GA, Mitchell JFB, Nakicenovic N, Riahi
K, Smith SJ, Stouffer RJ, Thomson AM, Weyant JP, Wilbanks TJ (2010) The
next generation of scenarios for climate change research and assessment.
Nature 463:747–756
Nakajima T, Ohara T, Masui T, Takemura T, Yoshimura K, Goto D, Hanaoka T,
Itahashi S, Kurata G, Kurokawa J, Maki T, Masutomi Y, Nakata M, Nitta T, Sudo
K, Suzuki K, Tsuruta H, Ueda K, Watanabe S, Xerxes S, Yu Y, Yumimoto K,
Zhao S, S-12 Project (2020) A development of reduction scenarios of theshort-lived climate pollutants (SLCPs) for mitigating global warming andenvironmental problems. Progress in Earth and Planetary Science 7:33,
https://doi.org/10.1186/s40645-020-00351-1
Ohgaito R, Abe-Ouchi A, O'Ishi R, Takemura T, Ito A, Hajima T, Watanabe S,
Kawamiya M (2018) Effect of high dust amount on surface temperatureduring the Last Glacial Maximum: a modelling study using MIROC-ESM.
Climate Past 14:1565–1581
Oki T, Sud YC (1998) Design of total runoff integrating pathways (TRIP)—a global
river channel network. Earth Interact 2:1–37. doi.https://doi.org/10.1175/10873562
Olin S, Schurgers G, Lindeskog M, Wårlind D, Smith B, Bodin P, Holmér J, Arneth
A (2015) Modelling the response of yields and tissue C : N to changes inatmospheric CO2 and N management in the main wheat regions of western
Europe. Biogeosciences 12:2489–2515
Orr JC, Najjar RG, Aumont O, Bopp L, Bullister JL, Danabasoglu G, Doney SC,
Dunne JP, Dutay J-C, Graven H, Griffies SM, John JG, Joos F, Levin I, Lindsay
K, Matear RJ, McKinley GA, Mouchet A, Oschlies A, Romanou A, Schlitzer R,
Tagliabue A, Tanhua T, Yool A (2017) Biogeochemical protocols anddiagnostics for the CMIP6 Ocean Model Intercomparison Project (OMIP).
Geosci Model Dev 10:2169–2219. https://doi.org/10.5194/gmd-10-2169-2017
Oschlies A, Garçon V (1999) An eddy-permitting coupled physical-biologicalmodel of the North Atlantic: 1. Sensitivity to advection numerics and mixed
layer physics. Glob Biogeochem Cycles 13:135–160
Rabalais NN (2002) Nitrogen in Aquatic Ecosystems. Ambio 31:102–112
Richardson M, Cowtan K, Millar RJ (2018) Global temperature definition affects
achievement of long-term climate goals. Environ Res J 13:054004. https://doi.
org/10.1088/1748-9326/aab305
Rogelj J, Forster PM, Kriegler E, Smith CJ, Séférian R (2019) Estimating andtracking the remaining carbon budget for stringent climate targets. Nature
571:335–342
Rolinski S, Müller C, Heinke J, Weindl I, Biewald A, Bodirsky BL, Bondeau A, BoonsPrins ER, Bouwman AF, Leffelaar PA, te Roller JA, Schaphoff S, Thonicke K(2018) Modeling vegetation and carbon dynamics of managed grasslands at
the global scale with LPJmL 3.6. Geosci Model Dev. 11:429–451
Sarmiento JL, Gruber N (2006) Ocean biogeochemical dynamics. Princeton
University Press, New York
Sato H, Itoh A, Kohyama T (2007) SEIB-DGVM: a new dynamic global vegetation
model using a spatially explicit individual-based approach. Ecol Model 200:
279–307
Schleussner C-F, Lissner TK, Fischer EM, Wohland J, Perrette M, Golly A, Rogelj J,
Childers K, Schewe J, Frieler K, Mengel M, Hare W, Schaeffer M (2016)
Differential climate impacts for policy-relevant limits to global warming: thecase of 1.5 °C and 2 °C. Earth Syst Dyn 7:327–351
Séférian R, Berthet S, Chevallier M (2018) Assessing the decadal predictability ofland and ocean carbon uptake. Geophys Res Lett 45:2455–2466
Silva Herran D, Tachiiri K, Matsumoto K (2019) Global energy systemtransformations in mitigation scenarios considering climate uncertainties.
Appl Energy 243:119–131
Smith P, Davis SJ, Creutzig F, Fuss S, Minx J, Gabrielle B, Kato E, Jackson RB,
Cowie A, Kriegler E, van Vuuren DP, Rogelj J, Ciais P, Milne J, Canadell JG,
McCollum D, Peters G, Andrew R, Krey V, Shrestha G, Friedlingstein P, Gasser
T, Grübler A, Heidug WK, Jonas M, Jones CD, Kraxner F, Littleton E, Lowe J,
Moreira JR et al (2015) Biophysical and economic limits to negative CO2
emissions. Nature Climate Change 6:42–50

Kawamiya et al. Progress in Earth and Planetary Science(2020) 7:64

Sokolov AP, Schlosser CA, Dutkiewicz S, Paltsev S, Kicklighter DW, Jacoby HD,
Prinn RG, Forest CE, Reilly JM, Wang C, Felzer B, Sarofim MC, Scott J, Stone
PH, Melillo JM, Cohen J (2005) The MIT Integrated Global System Model(IGSM) Version 2: Model Description and Baseline Evaluation. Joint Program
Report Series Report 124:40
Su X, Shiogama H, Tanaka K, Fujimori S, Hasegawa T, Hijioka Y, Takahashi K, Liu J(2018) How do climate-related uncertainties influence 2 and 1.5 °C
pathways? Sustainability Sci 13:291–299
Sudo K, Takahashi M, Kurokawa J, Akimoto H (2002) CHASER: A global chemical
model of the troposphere 1. Model description. J Geophys Res 107:ACH 7-1ACH 7-20. https://doi.org/10.1029/2001JD001113
Tachiiri K, Hajima T, Kawamiya M (2019a) Increase of the transient climate
response to cumulative carbon emissions with decreasing CO2 concentration
scenarios. Environ Res Lett. https://doi.org/10.1088/1748-9326/ab57d3
Tachiiri K, Silva Herran D, Su X, Kawamiya M (2019b) Effect on the Earth system ofrealizing a 1.5°C warming climate target after overshooting to the 2°C level.
Environ Res Lett: doi:https://doi.org/10.1088/1748-9326/ab5199
Takata K, Emori S, Watanabe T (2003) Development of the minimal advanced
treatments of surface interaction and runoff. Glob Planet Change 38:209–222
Takemura T, Okamoto H, Maruyama Y, Numaguti A, Higurashi A, Nakajima T(2000) Global three-dimensional simulation of aerosol optical thickness
distribution of various origins. J Geophys Res 105:17853–17873
Tatebe H, Ogura T, Nitta T, Komuro Y, Ogochi K, Takemura T, Sudo K, Sekiguchi
M, Abe M, Saito F, Chikira M, Watanabe S, Mori M, Hirota N, Kawatani Y,
Mochizuki T, Yoshimura K, Takata K, O'ishi R, Yamazaki D, Suzuki T, Kurogi M,
Kataoka T, Watanabe M, Kimoto M (2019) Description and basic evaluation ofsimulated mean state, internal variability, and climate sensitivity in MIROC6.
Geosci Model Dev. 12:2727–2765
Taylor KE, Stouffer RJ, Meehl GA (2011) An overview of CMIP5 and theexperiment Design. Bull Am Meteorol Soc 93:485–498
Thompson SL, Govindasamy B, Mirin A, Caldeira K, Delire C, Milovich J, Wickett M,
Erickson D (2004) Quantifying the effects of CO2-fertilized vegetation on
future global climate and carbon dynamics. Geophys Res Lett 31. https://doi.
org/10.1029/2004GL021239
Thornton PE, Lamarque J-F, Rosenbloom NA, Mahowald NM (2007) Influence ofcarbon-nitrogen cycle coupling on land model response to CO2 fertilizationand climate variability. Glob Biogeochem Cycles 21. https://doi.org/10.1029/
2006GB002868
Tokarska KB, Gillett NP (2018) Cumulative carbon emissions budgets consistent
with 1.5 °C global warming. Nature Climate Change 8:296–299
UNFCCC (2015) Paris Agreement/CP/2015/L.9/Rev.1, in: Secretariat, U. (Ed.).
Van Vuuren DP, Batlle Bayer L, Chuwah C, Ganzeveld L, Hazeleger W, Van Den
Hurk B, Van Noije T, O’Neill B, Strengers BJ (2012) A comprehensive view on
climate change: coupling of earth system and integrated assessment models.
Environ Res Lett 7:024012. https://doi.org/10.1088/1748-9326/7/2/024012
Watanabe M, Kawamiya M (2017) Remote effects of mixed layer development on
ocean acidification in the subsurface layers of the North Pacific. J
Oceanography 73:771–784
Watanabe M, Suzuki T, O’ishi R, Komuro Y, Watanabe S, Emori S, Takemura T,
Chikira M, Ogura T, Sekiguchi M, Takata K, Yamazaki D, Yokohata T, Nozawa T,
Hasumi H, Tatebe H, Kimoto M (2010) Improved climate simulation by
MIROC5: mean states, variability, and climate sensitivity. J Climate 23:6312–
6335
Watanabe M, Tatebe H, Koyama H, Hajima T, Watanabe M, Kawamiya M (2020)
Importance of El Niño reproducibility for reconstructing historical CO2 flux
variations in the equatorial Pacific. Ocean Sci Discuss 2020:1–21. https://doi.
org/10.5194/os-2020-32
Watanabe S, Hajima T, Sudo K, Nagashima T, Takemura T, Okajima H, Nozawa T,
Kawase H, Abe M, Yokohata T, Ise T, Sato H, Kato E, Takata K, Emori S,
Kawamiya M (2011a) MIROC-ESM 2010: model description and basic resultsof CMIP5-20c3m experiments. Geoscientific Model Dev 4:845–872
Watanabe S, Sudo K, Nagashima T, Takemura T, Kawase H, Nozawa T (2011b)
Future projections of surface UV-B in a changing climate. J Geophys Res 116.
https://doi.org/10.1029/2011JD015749
Watanabe S, Yokohata T (2012) Future Increase in the All-sky UV-B Radiation over
Asia Projected by an Earth System Model. J Meteorol Soc Japan 90A:295–305
Wenzel S, Cox PM, Eyring V, Friedlingstein P (2016) Projected land photosynthesis
constrained by changes in the seasonal cycle of atmospheric CO2. Nature
538:499–501
Wieder WR, Cleveland CC, Smith WK, Todd-Brown K (2015) Future productivity andcarbon storage limited by terrestrial nutrient availability. Nature Geosci 8:441–444

Page 13 of 13

Williamson P, Wallace DWR, Law CS, Boyd PW, Collos Y, Croo P, Denman K,
Riebesell U, Takeda S, Vivian C (2012) Ocean fertilization for geoengineering:a review of effectiveness, environmental impacts and emerging governance.
Process Saf Environ Prot 90:475–488
WMO (2018) July sees extreme weather with high impacts. https://public.wmo.
int/en/media/news/july-sees-extreme-weather-high-impacts, retrieved August
22nd, 2019.
Woodard DL, Davis SJ, Randerson JT (2019) Economic carbon cycle feedbacks
may offset additional warming from natural feedbacks. Proc Natl Acad Sci
USA 116:759–764
Xia JY, McGuire AD, Lawrence D, Burke E, Chen GS, Chen XD, Delire C, Koven C,
MacDougall A, Peng SS, Rinke A, Saito K, Zhang WX, Alkama R, Bohn TJ, Ciais
P, Decharme B, Gouttevin I, Hajima T, Hayes DJ, Huang K, Ji DY, Krinner G,
Lettenmaier DP, Miller PA, Moore JC, Smith B, Sueyoshi T, Shi Z, Yan LM et al(2017) Terrestrial ecosystem model performance in simulating productivity
and its vulnerability to climate change in the northern permafrost region. J
Geophys Res Biogeosci 122:430–446
Yamamoto A, Abe-Ouchi A, Ohgaito R, Ito A, Oka A (2019) Glacial CO2 decrease
and deep-water deoxygenation by iron fertilization from glaciogenic dust.
Climate Past 15:981–996
Yamamoto A, Kawamiya M, Ishida A, Yamanaka Y, Watanabe S (2012) Impact of
rapid sea-ice reduction in the Arctic Ocean on the rate of ocean acidification.
Biogeosciences 9:2365–2375
Yamamoto H, Sugiyama M, Tsutsui J (2014) Role of end-use technologies in longterm GHG reduction scenarios developed with the BET model. Climatic
Change 123:583–596
Yang S, Dong W, Chou J, Feng J, Wei Z, Yan G, Wen X, Wei T, Tian D, Zhu X,
Yang Z (2016) Global warming projections using the human–earth systemmodel BNU-HESM1.0. Sci Bull. https://doi.org/10.1007/s11434-016-1176-x
Yokohata T, Kinoshita T, Sakurai G, Pokhrel Y, Ito A, Okada M, Satoh Y, Kato E,
Nitta T, Fujimori S, Felfelani F, Masaki Y, Iizumi T, Nishimori M, Hanasaki N,
Takahashi K, Yamagata Y, Emori S (2019a) MIROC-INTEG-LAND version 1: A
global bio-geochemical land surface model with human water management,
crop growth, and land-use change . Geosci Model Dev. in press, https://doi.
org/10.5194/gmd-2019-184
Yokohata T, Tanaka K, Nishina K, Takahashi K, Emori S, Kiguchi M, Iseri Y, Honda Y,
Okada M, Masaki Y, Yamamoto A, Shigemitsu M, Yoshimori M, Sueyoshi T,
Iwase K, Hanasaki N, Ito A, Sakurai G, Iizumi T, Nishimori M, Lim WH, Miyazaki
C, Okamoto A, Kanae S, Oki T (2019b) Visualizing the Interconnections
Among Climate Risks. Earth’s Future 7:85–100
Yukimoto S, Yoshimura H, Hosaka M, Sakami T, Tsujino H, Hirabara M, Tanak TY,
Deushi M, Obata A, Nakano H, Adachi Y, Shindo E, Yabu S, Ose T, Kitoh A(2011) Meteorological Research Institute Earth System Model Version 1 (MRIESM1) —Model Description—, Technical Report of the MeteorologicalResearch Institute, p 83
Zaehle S, Jones CD, Houlton B, Lamarque J-F, Robertson E (2014) Nitrogen
availability reduces CMIP5 projections of twenty-first-century land carbon
uptake. J Climate 28:2494–2511

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.