This article describes the new Earth system model (ESM), the Model for
Interdisciplinary Research on Climate, Earth System version 2 for Long-term
simulations (MIROC-ES2L), using a state-of-the-art climate model as the
physical core. This model embeds a terrestrial biogeochemical component with
explicit carbon–nitrogen interaction to account for soil nutrient control
on plant growth and the land carbon sink. The model's ocean biogeochemical
component is largely updated to simulate the biogeochemical cycles of carbon,
nitrogen, phosphorus, iron, and oxygen such that oceanic primary
productivity can be controlled by multiple nutrient limitations. The ocean
nitrogen cycle is coupled with the land component via river discharge
processes, and external inputs of iron from pyrogenic and lithogenic sources
are considered. Comparison of a historical simulation with observation
studies showed that the model could reproduce the transient global climate
change and carbon cycle as well as the observed large-scale spatial patterns
of the land carbon cycle and upper-ocean biogeochemistry. The model
demonstrated historical human perturbation of the nitrogen cycle through
land use and agriculture and simulated the resultant impact on the
terrestrial carbon cycle. Sensitivity analyses under preindustrial
conditions revealed that the simulated ocean biogeochemistry could be
altered regionally (and substantially) by nutrient input from the atmosphere
and rivers. Based on an idealized experiment in which
Originally, global climate projections using climate models were based on simulations using atmosphere-only physical models (Manabe et al., 1965). Numerical climate models evolved through the integration or improvement of component models on ocean circulation (Manabe and Bryan, 1969), land hydrological processes (Sellers et al., 1986), sea ice dynamics (e.g., Meehl and Washington, 1995), and aerosols (e.g., Takemura et al., 2000), most of which focus on physical aspects that affect how climate is formed. Cox et al. (2000) attempted to couple a carbon cycle model and a climate model to investigate the roles of biophysical and biogeochemical (carbon cycle) feedbacks on climate. Their results showed that such interactions are significant in projecting future climate due to processes and feedbacks beyond those incorporated in traditional climate models. Models that incorporate biogeochemical processes, such as that by Cox et al. (2000), are often called Earth system models (ESMs). Currently, the most comprehensive state-of-the-art ESMs include component models of the land and ocean carbon cycle, atmospheric chemistry, dynamic vegetation, and other biogeochemical cycles (e.g., Watanabe et al., 2011; Collins et al., 2011).
Among many processes and possible interactions in the Earth system, the
carbon cycle and its feedback on climate remain the focus of simulation
studies using ESMs because of the importance of anthropogenic
The feedback of the carbon cycle on climate is manifested through the regulation
of the atmospheric
Quantifications of the strength of the carbon cycle feedbacks and their
comparison among ESMs were first made by Friedlingstein et al. (2006), who
showed that all ESMs agreed with the positive sign of the climate–carbon
feedbacks for both land and ocean. The latest comparison using CMIP5 ESMs
was made by Arora et al. (2013). They found that the widest spread between
the models was in the land carbon response to
Compared with land, the oceans showed better agreement among the CMIP5 ESMs
(Arora et al., 2013) in terms of the strength of both
The ecological response of the ocean in ESMs remains far from certain. A benchmark study by Anav et al. (2013) revealed that all CMIP5 ESMs underestimate net primary productivity (NPP) in the high latitudes of the Northern Hemisphere, where seawater temperature and N availability likely limit primary production (e.g., Moore et al., 2013). They also found that most models overestimate NPP in the Southern Hemisphere high latitudes, where the nutrient supply is sufficient because of strong upwelling but the iron supply is limited (Moore et al., 2013). Globally, the CMIP5 ESMs simulate NPP with different magnitudes, even in preindustrial conditions, and the global NPP response among the models to past and future climate change is largely divergent (Laufkötter et al., 2015), as is the sinking particle flux (Fu et al., 2016). Although such problems regarding oceanic NPP might be partly attributable to an inaccurate reproduction of oceanic physical fields by the models (Frölicher et al., 2015; Laufkötter et al., 2015), it is critical in simulations to accurately reproduce the relative abundances of nutrients in the euphotic zone and their availability to microorganisms. In particular, nutrients in the upper ocean are sustained by upwelling from the deeper ocean and inputs from external sources. Some studies suggest that nutrient availability to marine ecosystems could decline in the future through the reduction of nutrient upwelling because of intensified stratification (e.g., Ono et al., 2008; Whitney et al., 2013; Yasunaka et al., 2016). Conversely, other studies suggest that nutrient supply through atmospheric deposition and river discharge processes could be amplified in the future because of human activities (Gruber and Galloway, 2008; Mahowald et al., 2009) unless robust mitigation policies are adopted. Thus, to project the effects of biogeochemical feedback on climate, it is necessary to consider the response of ecological processes to changing nutrient inputs as well as the physical response.
On the basis of the above, we previously reviewed the CMIP5 exercises and
discussed the perspective for new ESM development (Hajima et al., 2014a). In
our ESM development, we prioritized the incorporation of explicit C–N
interaction in the land biogeochemical component. The terrestrial nitrogen
cycle regulates the carbon cycle by modulating soil nutrient availability to
plants, regulating leaf N concentration and photosynthetic capacity, and
changing the
For the ocean, the biogeochemical component in our previous model (MIROC-ESM; Watanabe et al., 2011) was unchanged from that used for the first
stage of the Coupled Climate Carbon Cycle Model Intercomparison Project
(C4MIP; Friedlingstein et al., 2006; Yoshikawa et al., 2008). The ocean
component simulated C and N cycles only, using simple parameterizations of
ocean ecosystem dynamics with four types of N tracer and five C tracers
(Watanabe et al., 2011) with fixed
The third priority in developing a new ESM was the incorporation of Fe cycle processes. Fe is an essential micronutrient for phytoplankton. Thus, any model lacking consideration of the Fe cycle potentially overestimates primary productivity, especially in regions in which the subsurface macronutrient supply is enhanced but Fe availability is limited, e.g., the main oceanic upwelling “high-nutrient, low-chlorophyll” (HNLC) regions (Martin and Gordon, 1988; Moore et al., 2013). Similar to the N cycle, the ocean Fe cycle is also an open system. One of its main external sources is dissolved Fe from continental margins and from hydrothermal vents along mid-ocean ridges (Tagliabue et al., 2017). Thus, the continental and hydrothermal Fe supply is important in terms of determining the background Fe concentration in seawater. Additionally, the ocean Fe cycle is also connected to the land through the atmosphere (Jickells et al., 2005; Mahowald et al., 2009; Ito et al., 2019a). Fe-containing aerosols are emitted from dry land surfaces, open biomass burning, and fossil fuel combustion, and they are delivered to marine ecosystems via dry and wet deposition processes. These processes have been perturbed by climate change, land use change (LUC), and air pollution (Jickells et al., 2005; Mahowald et al., 2009; Ito et al., 2019a). Thus, consideration of atmospheric Fe deposition, in particular, is necessary to reflect the anthropogenic impact on future marine ecosystem dynamics via Fe cycle processes.
Here, we present a description of a new ESM, the Model for Interdisciplinary
Research on Climate, Earth System version 2 for Long-term simulations
(MIROC-ESL2), which considers explicit carbon and nitrogen cycles for land
and carbon, nitrogen, iron, phosphate, and oxygen cycles for the ocean. In
the model, the biogeochemical components are coupled interactively with
physical climate components, enabling consideration of
climate–biogeochemical feedbacks. The model description and experimental
settings are presented in Sect. 2. The basic performance of the model,
evaluated by executing a historical simulation and comparison of the results
with observation-based studies, is presented in Sect. 3.1. To evaluate the
sensitivity of the biogeochemical processes, experiments for sensitivity
analysis were performed and the results compared with existing studies. In
particular, the global temperature response to cumulative anthropogenic
To comprehensively describe the MIROC-ES2L structure (Fig. 1), we first present the physical core of MIROC5.2, which is an updated version of MIROC5 used in CMIP5. Only a brief summary is presented here because a detailed description of the modeling of MIROC5 can be found in Watanabe et al. (2010), and an account of a simulation study performed by MIROC5.2 can be found in Tatebe et al. (2018). Additionally, a description of MIROC6, which shares almost the same structure and many of the characteristics of MIROC5.2 except for the atmospheric spatial resolution and cumulus treatments, can be found in Tatebe et al. (2019). In this paper, a description of the land and ocean biogeochemistry is presented in detail because those two components represent the main modifications from the previous version of the ESM (i.e., MIROC-ESM; Watanabe et al., 2011).
Schematic of component models in the new MIROC-ES2L Earth system model, the
biogeochemical and biophysical interactions, and external forcing. The physical
core of the model is MIROC5.2, which comprises an atmospheric climate model
(CCSR-NIES AGCM or MIROC-AGCM) with an aerosol module (SPRINTARS), an ocean
physical model (COCO) with a sea ice model, and a land physical model
(MATSIRO) with a river submodel. The land biogeochemistry component
(VISIT-e) simulates carbon and nitrogen cycles with an LUC submodel, and the
ocean biogeochemistry component (OECO) simulates the cycles of carbon,
nitrogen, iron, phosphorus, and oxygen.
Color-boxed arrows indicate external forcing. Solid (dashed) black arrows
represent biogeochemical (physical) variables exchanged between the
component models (the exchanges of physical variables are almost the same as
in MIROC-ESM; see Table 1 of Watanabe et al., 2011). Variables in square
brackets represent the prognostic biogeochemical cycles and aerosol species
(black carbon, BC; organic matter, OM; sulfate (including precursors), SU;
dust, DU; sea salt, SA). The names of exchanged variables within parentheses are
diagnosed variables, i.e., ocean–land riverine P flux diagnosed from the N
flux and simulated land and ocean
The MIROC5.2 physical core comprises component models of the atmosphere,
ocean, and land. The atmospheric model is based on a spectral dynamical
core originally named the Center for Climate System Research–National
Institute for Environmental Studies atmospheric general circulation model
(CCSR-NIES AGCM; Numaguti et al., 1997), which is interactively coupled with
an aerosol component model called the Spectral Radiation-Transport Model for
Aerosol Species (SPRINTARS; Takemura et al., 2000, 2005). For the ocean, the
CCSR Ocean Component (COCO) model (Hasumi, 2006) is used in conjunction with
a sea ice component model. For land, the Minimal Advanced Treatments of
Surface Interaction and Runoff (MATSIRO) model (Takata et al., 2003) is
coupled to simulate the atmosphere–land boundary conditions and freshwater
input into the ocean. Considering the application possibility of the ESM to
long-term climate simulations of more than hundreds of years, e.g.,
paleoclimate studies (Ohgaito et al., 2013; Yamamoto et al., 2019), the
horizontal resolution of the atmosphere is set to have T42 spectral
truncation, which is approximately 2.8
For the AGCM, the schemes used for the dynamical core, radiation, cumulus convection, and cloud microphysics are mostly the same as in MIROC5; the major update of processes mainly concerns the aerosol module. The version used here treats atmospheric organic matter (OM) as one of the prognostic variables, and emissions of primary OM and precursors for secondary OM are diagnosed in the component. For land, the scheme for subgrid snow distribution is replaced by one incorporating a physically based approach (Nitta et al., 2014; Tatebe et al., 2019), and wetland formed temporarily in the snowmelt season is newly considered to reduce the warm bias in temperature in the European region during spring–summer (Nitta et al., 2017; Tatebe et al., 2019). The ocean and sea ice components are mostly the same as in MIROC5.
The model of the land ecosystem–biogeochemistry component in MIROC-ES2L is
the Vegetation Integrative SImulator for Trace gases model (VISIT; Ito and
Inatomi, 2012a). This model simulates carbon and nitrogen dynamics on land
(schematics for the carbon cycle can be found in Ito and Oikawa, 2002, and
for the nitrogen cycle in Supplement Fig. S1). It has been used for
ecological studies of the site–global scale (e.g., Ito and Inatomi, 2012b),
impact assessments of climate change (e.g., Warszawski et al., 2013; Ito et
al., 2016a, b),
For the nitrogen cycle, the model considers two major nitrogen influxes to
the ecosystem: biological nitrogen fixation (BNF) simulated based on the
scheme of Cleveland et al. (1999) and external nitrogen sources such as
fertilizer and atmospheric nitrogen deposition, which are prescribed in the
forcing data. The fluxes of nitrogen out of the land ecosystem are simulated
through
Although the original land component model covers most major carbon–nitrogen
processes, for the purposes of inclusion in the new ESM and making fully
coupled climate–carbon–nitrogen projections, the land model was modified
for this study (hereafter, the modified version is called VISIT-e). First,
the modified model represents the close interaction between carbon and
nitrogen in plants. This is because the original model has only a loose
interaction between these two cycles, and thus it cannot precisely predict
the nitrogen limitation on primary productivity. To achieve this, the
photosynthetic capacity in VISIT-e is modified to be controlled by the
amount of nitrogen in leaves (leaf nitrogen concentration), which is
determined by the balance between the nitrogen demand of plants and
potential supply from the soil. Thus, if sufficient inorganic nitrogen is
not available for plants, the leaf nitrogen concentration is gradually lowered,
which reduces photosynthetic capacity and the plant production rate. This
process is required to simulate the observed downregulation in elevated
Second, although the original VISIT incorporates LUC and associated
The land ecosystem component runs with a daily time step in the ESM. It has
fixed spatial distribution patterns of 12 vegetation categories (see
Supplement Fig. S2), and the land biogeochemistry is affected by daily
averaged atmospheric conditions (
The new ocean biogeochemical component model OECO2 (see Supplement Fig. S3 for a schematic) is a nutrient–phytoplankton–zooplankton–detritus-type model that is an extension of the previous model (Watanabe et al., 2011). Although only an overview of OECO2 is presented here, a detailed description can be found in Appendix B.
In OECO2, ocean biogeochemical dynamics are simulated with 13 biogeochemical
tracers. Three of them are associated with cycles of macronutrients (nitrate
and phosphate) and a micronutrient (dissolved Fe). The model has four
organic tracers of “ordinary” nondiazotrophic phytoplankton, diazotrophic
phytoplankton (nitrogen fixer), zooplankton, and particulate detritus. All
OM in these four tracers is assumed to have an identical nutrient, oxygen, and
micronutrient iron composition following the Redfield ratio of
The nitrogen cycle in OECO2 is similar to that in the previous version
(Yoshikawa et al., 2008; Watanabe et al., 2011), except the new model
accounts for nitrogen influxes such as nitrogen deposition from the
atmosphere (as external forcing), input of inorganic nitrogen from land via
rivers, and BNF by diazotrophic phytoplankton (Fig. 1). Additionally,
denitrification is also modeled as the dominant process of oceanic nitrogen
loss, with an explicit distinction between the gaseous forms of
The structure of the ocean iron cycle is also similar to that of nitrogen,
except the following processes are modeled as iron input into the ocean. Two
major sources of iron deposition from the atmosphere are included in the new
model: lithogenic and pyrogenic sources. Mineral dust emission is diagnosed
by the aerosol component module, depending on the near-surface wind speed,
soil dryness, and bare ground cover, while iron emitted from biomass burning
and the consumption of fossil fuel and biofuel follows external forcing. The
latter emission dataset used in this study is shown in Supplement Fig. S4.
The iron emissions from pyrogenic sources are estimated based on the iron
content and emissions of particulate matter (Ito et al., 2018). A shift from
coal to oil combustion is considered in relation to shipping (Fletcher,
1997; Endresen et al., 2007). The iron content of mineral dust is prescribed
at 3.5 % (Duce and Tindale, 1991). The iron deposition from biomass
burning is calculated from black carbon (BC) deposition and a ratio of 0.04 gFe gBC
In addition to the Fe input from the atmosphere, recent studies suggest
contributions of Fe supply from sediment and hydrothermal vents to ecosystem
activities (Tagliabue et al., 2017). The contributions of these two natural
Fe sources to the determination of the atmospheric
Ocean ecosystem dynamics are simulated based on the nutrient cycles of
nitrate, phosphorous, and iron. The nutrient concentration, in conjunction with
the controls of seawater temperature and the availability of light, regulates
the primary productivity of the two types of phytoplankton. The model
assumes that diazotrophic phytoplankton can prosper in regions in which
phosphate is available but the nitrate concentration is small (
The ocean carbon cycle is formed by atmosphere–ocean
Finally, the flux of dimethyl sulfide (DMS) from the ocean, which is
produced by plankton and is a precursor of atmospheric sulfate aerosols, is
diagnosed in the original aerosol module from the surface downward shortwave
radiation flux. In MIROC-ES2L, this emission scheme is modified and the flux
is calculated from the sea surface DMS concentration that is diagnosed from
the simulated surface water chlorophyll concentrations and the corresponding
mixed-layer depth (Appendix B). In the present model, this is the only
pathway via which ocean biogeochemistry affects climate if the model is
driven by a prescribed
To evaluate the performance and sensitivities of MIROC-ES2L, we conducted four groups of experiments comprising 11 experiments in total (Tables 1 and 2). The first group was a control run that comprised two types of experiments: a normal control run (CTL) in which the external forcing was set to preindustrial conditions and an alternative control run (CTL-D) used for sensitivity analysis of the ocean biogeochemistry, which is described later.
The second group, used for historical simulations, comprised three types of
experiments during the period 1850–2014. All three experiments were driven
by the Coupled Model Intercomparison Project Phase 6 (Eyring et al., 2016)
official forcing datasets (version 6.2.1; details on the forcing datasets used
in the simulations are summarized in Appendix C), and the
The third experimental group was used to evaluate the climate and carbon
cycle feedbacks. This group comprised three types of idealized experiments,
following experimental designs proposed by Eyring et al. (2016) and Jones et
al. (2016). In the three experiments, the
The final group comprised a set of experiments to evaluate ocean biogeochemistry, focusing mainly on the processes newly introduced in MIROC-ES2L. This group comprised three types of experiments. The first experiment (NO-NR) was configured similarly to the CTL run, except the ocean component did not receive any riverine N input. Through this experiment, the impact of riverine N on ocean biogeochemistry could be evaluated. The second experiment (NO-NRD) was the same as NO-NR, except atmospheric N deposition additionally had no effect on ocean biogeochemistry. By evaluating the difference between NO-NR and NO-NRD, the impact of nitrogen deposition on ocean biogeochemistry could be evaluated. The final experiment (NO-FD) was configured with atmospheric Fe deposition onto the ocean surface switched off. To detect slight signals of ocean biogeochemistry arising from switching off the three processes (i.e., riverine N, N deposition, and Fe deposition), it was necessary to maintain consistency in the ocean physical fields between these experiments because a slight difference in the ocean physical fields produces perturbation on ocean biogeochemistry. In MIROC-ES2L, the ocean DMS emissions represent the feedback process of ocean biogeochemistry on the atmospheric physical processes; thus, biogeochemical change induced by the switching-off manipulations must change the DMS emission, which leads to inconsistency in the physical fields between the experiments. To avoid this occurrence, the DMS emission scheme in all three experiments was reverted to that used in the original aerosol component model, which is independent of the ocean ecosystem state (Appendix B). Similarly, the special control run (CTL-D), which was based on CTL, also had the DMS emission scheme changed to the same as NO-NR, NO-NRD, and NO-FD.
To conduct the experiments described above, preindustrial spin-up was performed in advance. Land and ocean biogeochemical components were decoupled from the ESM, and the spin-up run was conducted for 3000 years for the ocean component and 30 000 years for land by prescribing model-derived physical fields and other external forcing for the component models. In the final phase of the spin-up procedure, continuous spin-up, forced by the 1850-year condition of CMIP6 forcing, was performed for the entire system for 2483 years (Supplement Fig. S5). All the experiments listed in Table 1 were initiated from the final condition of this spin-up procedure.
Summary of experimental details.
Biogeochemical configurations in experiments, summarized as biogeochemical process settings. Bold characters represent the major differences between experiments within an experimental group.
To evaluate the climate and carbon cycle response to
First, TCRE is defined as the ratio of global mean near-surface air
temperature change (
To evaluate the strength of carbon cycle feedbacks in the model, the
feedback strength is quantified by the so-called
The quantity
To evaluate the physical fields reproduced by MIROC-ES2L, the temporal
evolutions of the global mean net radiation balance at the top of atmosphere
(TOA) and anomalies of near-surface air temperature (SAT), sea surface
temperature (SST), and upper-ocean (0–700 m) temperature were compared with
observation datasets; the results are shown in Fig. 2. The model simulates a
reasonably steady state of net TOA radiation balance in the CTL run, showing
a trend of
Following the net increase in incoming radiation, the SAT anomaly increases
in the latter half of the 20th century (Fig. 2b). The warming trend during
1951–2011 is simulated as 0.1 K per decade, which is consistent with that
of HadCRUT4 (version 4.6; Morice et al., 2012), i.e., 0.11 K per decade
(Stocker et al., 2013). Observation datasets of SST (HadSST version 3.1.1;
Kennedy et al., 2011) and upper-ocean temperature (Levitus et al., 2012)
clearly display increasing trends in the corresponding period, which are
successfully reproduced by the model (Fig. 2c and d). In addition to the
warming trend in the latter half of the 20th century, the model captures the
slowdown of SAT increase both in the 1950s and in the 1960s. These changes
are likely induced by increased anthropogenic aerosol emissions and
resultant cooling through indirect aerosol effects, together with cooling
attributable to large volcanic eruptions in the 1960s (Wilcox et al., 2013;
Nozawa et al., 2005). However, distinct deviations of the model results from
HadCRUT4 are found for SAT and SST in the 1860s and particularly in the
1900s. This might be due to inevitable asynchronization between the
simulation and observations on the phasing of the internal variability of
the climate, as identified by Kosaka and Xie (2016). They reported that there
should have been four major cooling events due to tropical Pacific
variability in the 20th century, one of which was found in the 1900s. They
also reported that the other three events were around 1940, 1970, and 2000;
however, discrepancies arising from these three events are not so evident in
this study, likely because of the single ensemble simulation. The model also
exhibits a short-term response of the TOA radiation balance following episodic
volcanic events (Fig. 2a, vertical dashed lines), with resultant cooling of
SAT and SST (Fig. 2a–c) and further propagation into the deeper ocean with
an extended cooling duration (Fig. 2d). Overall, the historical SAT increase in
MIROC-ES2L, taking the difference between the averages of 1850–1900 and
2003–2012, is 0.69 K, while the HadCRUT4-based estimate by Stocker et al. (2013) is 0.78 K for the corresponding period. The model shows good
performance in reproducing global physical fields. This is likely
attributable to the inherited robust performance of the physical core of the
model (MIROC5.2) because MIROC-ES2L has only two feedback pathways of
biophysical processes on climate (DMS emissions from the ocean and
terrestrial processes associated with LAI dynamics) when the model is driven
by a prescribed
In addition to the radiation and temperature responses against historical
external forcing, we briefly describe here the El Niño–Southern
Oscillation (ENSO) and Atlantic meridional overturning circulation (AMOC)
strength in MIROC-ES2L, both of which can affect interannual–multidecadal
carbon cycle processes (Zickfeld et al., 2008; Pérez et al., 2013;
Friedlingstein, 2015). In the HIST experiment, the standard deviation of
the monthly SST anomaly in the Niño-3 region (5
Comparison of HIST simulation results by MIROC-ES2L with observations:
anomalies of
Hereafter, we present an overview of the performance of the mean state of
the physical fields, atmosphere, and land–ocean basic variables of the model
in comparison with various observational-based data. The variables examined
here are SAT, precipitation, SST, sea ice concentration, land snow cover,
and mixed-layer depth, all of which are representative physical states
associated with biogeochemical processes. The mixed-layer depth is defined
as the depth at which the potential density becomes larger than that of the
sea surface by 0.125 kg m
Figure 4 shows the precipitation distribution in the HIST experiment in comparison with the Global Precipitation Climatology Project (GPCP) dataset (Adler et al., 2003). Generally, the precipitation distribution is reasonably well represented in the model. The Intertropical Convergence Zone is reproduced well in the experiment, except that the simulated South Pacific Convergence Zone is shifted equatorward relative to the GPCP, which is the so-called double Intertropical Convergence Zone syndrome (Bellucci et al., 2010). Over continental areas, the model is effective in capturing the spatial pattern of both the annual mean precipitation and the seasonality. However, positive precipitation biases are evident in some tropical land regions such as central Africa, South and Southeast Asia, and South America. Additionally, arid and semiarid regions of central Asia, Australia, and the western side of North America also show a positive precipitation bias, although it is unclear in the bias map (see Supplement Fig. S6 for a comparison with the absolute precipitation rate of GPCP).
Air temperature at 2 m of height (
Precipitation distributions (mm d
When projecting future climate change, it is important for a model to reproduce the observed climatological patterns of key physical variables, as suggested by Ohgaito and Abe-Ouchi (2009). The biogeochemical tracers are also affected by the representation of the physical fields. Figure 5 presents the modeled SST and its bias with respect to the World Ocean Atlas 2013 (Locarnini et al., 2013). Generally, the model performs well, confirmed by the large extent of the area with minimal bias (colored white in Fig. 5). However, obvious bias is evident, e.g., the warm bias in the Southern Ocean, as already explained above (Fig. 3). A cold bias is also evident over the western North Pacific Ocean, which is attributable to the lack of narrow and swift western boundary currents owing to the coarse horizontal resolution in the ocean parts of the present ESM.
The model performance in simulating sea ice concentration and snow cover over land for both March and September is shown in Fig. 6 in comparison with observational data (Special Sensor Microwave Imager (SSM/I; Kaleschke et al., 2001) for sea ice concentration and the Moderate-resolution Imaging Spectroradiometer (MODIS; Hall et al., 2006) for snow cover. Sea ice extent in the Northern Hemisphere is represented well for both months, although the summertime concentration minimum is slightly smaller than observed. In the Southern Hemisphere, however, the sea ice extent is unrealistically underestimated because of the persistent warm bias described above. The extent of the snow-covered area is also represented well, likely owing to the updated scheme for subgrid snow representation (Nitta et al., 2014; Tatebe et al., 2019). However, the fine structure of the snow cover is lost in the simulation, which is likely attributable to the coarse resolution of the modeled atmosphere and land. The reasonable performance in reproducing land snow seasonality in the boreal region is important for land biogeochemistry and the physical climate because snowmelt (accumulation) and leaf flush (shedding) processes are mutually associated (Supplement Fig. S7).
Figure 7 shows the mixed-layer depth in comparison with the mixed-layer dataset of Argo with grid point value (MILA_GPV; Hosoda et al., 2010). The HIST simulation captures both the spatial pattern and the seasonality change in mixed-layer depth. In the Northern Hemisphere winter, the structure of the deep mixed layer over the western North Pacific is consistent with observations; however, the actual depth is overestimated owing to the lack of mesoscale eddies. The deep mixed layer in the subarctic North Atlantic is also consistent with observations, except there is less deep water formed in the Labrador Sea. Additionally, the shallow mixed layer in low latitudes is generally captured well by the simulation, and the depth that is maintained at around 100 m over the Southern Ocean is consistent with observations. In austral winter, MILA_GPV shows that the mixed layer develops to more than 200 m over the Indian Ocean and the Pacific sector of the Southern Ocean, whereas it is shallow (around 50 m) in the tropics and the Northern Hemisphere (Fig. 7d). The model captures the general pattern in austral winter, although the extent of the simulated deeper mixed-layer depth of more than 200 m in the Southern Ocean is larger than that of MILA_GPV (Fig. 7c).
SST (
Northern Hemisphere sea ice concentration and land snow fraction (%) in
the HIST simulation presented as a 2003–2013 climatology and in comparison
with SSM/I (Kaleschke et al., 2001) and MODIS (Hall et al., 2006) data for
Mixed-layer depth (m) in the HIST simulation presented as a 2000–2010
climatology and comparison with the MILA_GPV dataset (Hosoda et
al., 2010) for
The simulated net
Through being affected by both environmental changes and LUCs, MIROC-ES2L
demonstrates in the HIST simulation that land carbon is reduced by
approximately 60 PgC from the beginning of the simulation until the
middle of the 20th century (black line in Fig. 8a). This reduction should
reflect LUC during this period because HIST-NOLUC does not show such a trend
of decrease in the corresponding period (dashed gray line in Fig. 8a). From
the 1960s, the model shows continuous carbon sequestration on land, which
results in a positive net
For the ocean, the model shows an increase in carbon accumulation in the CTL
run (Fig. 8b). This is partly because of carbon removal by the sedimentation
process that is newly introduced into MIROC-ES2L. In this process, an amount
of carbon is extracted from the ocean bottom, which should be compensated for by
an equivalent input of carbon from the atmosphere through gas exchange
processes. In the CTL run, the rate of carbon extracted from the ocean
bottom is 0.068 PgC yr
The HIST run shows the cumulative carbon uptake by the ocean, which is
predominantly driven by
Overall, MIROC-ES2L qualitatively captures the temporal evolution of carbon
dynamics in the historical period; the cumulative carbon uptake by both land
and ocean is within the range of the estimates by Le Quéré et al. (2018). However, the model might overestimate the net carbon uptake by the land
and/or ocean or underestimate LUC emissions. This is because the cumulative
fossil fuel emissions, diagnosed from the simulated atmosphere–land–ocean
Land and ocean carbon change (i.e., cumulative net carbon uptake by land and
ocean) in historical simulations.
Key variables of global land biogeochemistry: preindustrial condition (average of 10 years) and the 2000s in the historical run (HIST).
Key global ocean biogeochemical fluxes and concentrations under the preindustrial control simulation and the 2000s.
MIROC-ES2L can simulate the global nitrogen cycle under interaction with
the climate and carbon cycle, and the global N budget for land and ocean in
the HIST simulation is shown in Fig. 9 as the component fluxes. Comparison of
the terrestrial nitrogen budget in the 2000s with the preindustrial
condition (Table 3) reveals that the annual inputs of nitrogen via deposition and
fertilizer, which are controlled by forcing data, increase to 65.5 and 114 TgN yr
For terrestrial nitrogen efflux, Gruber and Galloway (2008) reported
Although it is difficult to obtain observation-based estimates of how much
nitrogen was accumulated by the land ecosystem in the historical period, the
model demonstrates net nitrogen uptake by land in the 2000s as 37 TgN yr
Compared with land, the model simulates relatively stable dynamics of the
oceanic nitrogen budget but with larger interannual variation (Fig. 9b). In
the 2000s, oceanic BNF is simulated as 126 TgN yr
Rate of change of the global nitrogen budget in the
Model performance in relation to land biogeochemistry is evaluated based on
the spatial distributions of three fundamental variables of the land carbon
cycle in comparison with observation-based products. First, GPP in the HIST
simulation is compared with the global product by Jung et al. (2011) (Fig. 10a–c). The model simulates high productivity (
To evaluate the simulated vegetation carbon, we compare the model results of
forest carbon, not total vegetation carbon, with those of Kindermann et al. (2008) (Fig. 10d–f). The model reproduces the reasonably high density of
biomass in tropical forests, although the values are smaller than the
observation product (Fig. 10f). This is partly attributable to the
underestimation of GPP in this region, as described above. In high-latitude
regions of the Northern Hemisphere (around 50
In Fig. 10g–i, the model results of soil organic carbon (SOC) are compared
with two different types of SOC products: harmonized soil property values
for broad-scale modeling (WISE30sec) by Batjes (2016) and the Northern
Circumpolar Soil Carbon Database version 2 (NCSCDv2) by Hugelius et al. (2013). The former is a global dataset that represents soil column SOC down
to the depth of 2 m, whereas the latter targets only the high-latitudinal
region of the Northern Hemisphere at different soil depths (
Comparison of the carbon flux and storage of the land ecosystem between the HIST
simulation by MIROC-ES2L and an observation-based dataset.
In this section, we evaluate the simulated surface and vertical
distributions of nitrate, phosphate, dissolved Fe, NPP, oxygen, DIC, and
alkalinity against observations (Fig. 11). Additionally, the ocean
Owing to the long spin-up, the drift in global averaged concentrations of
biogeochemical tracers becomes close to zero. The linear drift of dissolved
oxygen,
The simulated surface distributions of nitrate and phosphate are generally
in agreement with the WOA2013 datasets (Fig. 11a and b). The surface
macronutrient concentrations in HNLC regions (e.g., the Southern Ocean,
North Pacific Ocean, and eastern equatorial Pacific Ocean) are higher than
those produced by the ocean biogeochemical component of our previous model
(Watanabe et al., 2011), and they are more consistent with the observed
values. This increase in macronutrients in HNLC regions is reasonable
because the implementation of the iron cycle and the iron limitation on
phytoplankton growth can reduce macronutrient utilization in these regions.
Ocean circulation also influences the distribution of nutrient
concentrations. In the Southern Ocean, the deep mixed-layer depths simulated
by the model can cause an overestimation of nutrient entrainment to the surface
and thus produce a high nutrient bias (Fig. 7). The simulated global mean
vertical profile of nitrate concentrations compares reasonably well with
observed values, likely because the ocean circulation is represented
adequately (Fig. 11a). To check the influence of ocean circulation on the
tracer distributions, we compared the apparent oxygen utilization (AOU)
between the model and observations (Supplement Fig. S10). Although the
model captures the observed AOU distributions, the strong and deep AMOC
causes an underestimation of AOU values in the Atlantic Ocean deep water. The
largest bias is an underestimation in the North Pacific Ocean, which is caused
by the strong deep circulation of the Pacific Ocean. It should be noted that
the difficulty of simulating the Pacific Ocean deep circulation appears to
be a general problem in present coarse-resolution models (Hasumi et al.,
2010). Model–data agreement on vertical nitrate concentrations is also the
result of the near balance between nitrogen cycle sources (i.e., nitrogen
fixation, atmospheric nitrogen deposition, and riverine nitrogen input) and
sinks (i.e., denitrification,
The concentration of dissolved iron in the open ocean is highest in the
subtropical North Atlantic Ocean and in the Arabian Sea (Fig. 11c), which is
consistent with the pattern observed in GEOTRACES. Such high concentrations
are caused by enhanced dust deposition from the Sahara. In the
remainder of the open ocean, dissolved iron concentrations are generally
Reproducing the spatial pattern of nutrient limitation on phytoplankton growth is crucial for the accurate prediction of primary production and for reflecting in the simulations the consequences of ongoing anthropogenic perturbations to oceanic nutrient cycles (Moore et al., 2013). The model reasonably reproduces the HNLC regions because of the iron limitation in the subarctic North Pacific Ocean, the equatorial Pacific Ocean, and the Southern Ocean (Supplement Fig. S11), although the subarctic North Pacific Ocean and the equatorial Pacific Ocean have larger HNLC zones than observed upwelling regions. This is likely because of an underestimation of surface iron concentrations and/or a relatively high half-saturation constant for iron uptake (Appendix B). Nitrogen limitation occurs throughout much of the low-latitude surface ocean where the nitrogen supply from the subsurface is relatively slow.
Based on the distribution pattern of nutrients and the limitations, annual
NPP is simulated as 28.6 PgC yr
The simulated surface distribution of dissolved oxygen compares reasonably well
with observations (not shown). This is because the surface oxygen
concentration is close to its solubility value, and it is strongly
constrained by SST. At depth, oxygen minimum zones in the eastern equatorial
Pacific Ocean, eastern tropical Atlantic Ocean, Arabian Sea, and Bay of
Bengal are reproduced well (Fig. 11f). However, the model produces oxygen
concentration values higher than observed; thus, it underestimates the
hypoxic volume ([
The model also captures the global-scale patterns of observed DIC and
alkalinity (Fig. 11d and e). High values of these tracers in subtropical
gyres (and in the Southern Ocean for DIC) are found in the model output and
observations. Salinity bias and the parameterization of calcium carbonate
production in the model can contribute to the alkalinity bias.
Overestimation of alkalinity in subtropical gyres leads to an overestimation of
DIC because alkalinity affects the ocean's capacity to take up and store
atmospheric
Figure 12a shows the simulated annual mean air–sea
Comparison between model output and observations for key oceanic
biogeochemical tracers. Simulated annual mean surface
To evaluate the sensitivities of modeled land biogeochemistry, we focus on
GPP and its response to external forcing in the terrestrial system because
this carbon flux is the primary driver of land carbon input. GPP change was
calculated by taking the difference of the 2005–2014 averages between the
HIST and CTL runs. Then, as diagnosed in Fig. 8c, the GPP change was
decomposed into the response to (1)
Figure 13d–f shows that
In addition to the responses to
By responding to
In this section, we investigate the sensitivity of oceanic NPP to external
nutrient inputs from atmospheric deposition and river discharge processes
under preindustrial conditions because these processes are newly
incorporated into the ESM. Through the combination of the simulation results of
CTL-D, NO-NR, NO-NRD, and NO-FD (Tables 1 and 2), the impacts of nutrient
input on both the nutrient concentration and primary productivity are analyzed
(Fig. 14 for N input assessment and Fig. 15 for Fe), and the spatial
patterns of simulated nutrient limitation on NPP in the four experiments are
examined (Fig. 16). Here,
First, the impacts of riverine N input on the surface nutrient concentration
and NPP are assessed by subtracting the zero-input scenario NO-NR from the
control experiment CTL-D (Tables 1 and 2). Surface NPP is increased by
riverine N input (by
Second, the effects of atmospheric N deposition on the surface nutrient
concentration and NPP are evaluated by subtracting the zero-input scenario
NO-NRD from the NO-NR experiment (Tables 1 and 2). Similar to riverine N
input, atmospheric N deposition causes an increase in NPP in N-limited
regions and a global increase in
Changes in
Finally, changes in the surface nutrient concentration and NPP, driven by
atmospheric Fe deposition, are calculated by subtracting the zero-input
scenario NO-FD from the control experiment CTL-D (Tables 1 and 2). In
contrast to N input, atmospheric Fe deposition causes an increase in NPP in
Fe-limited regions and a decrease in N-limited regions (Figs. 15, 16a and
d). A significant Fe increase is found in N-limited regions. Global NPP and
export production increase by 1.8 and 0.8 PgC yr
Changes in
Limiting nutrient map for phytoplankton for
Here, we examine model sensitivity against global inputs of both N and Fe
into the ocean through atmospheric deposition and river discharge in the
preindustrial condition. We note, however, that these two types of nutrient input
have increased significantly since the preindustrial era because of human
activities (Duce et al., 2008; Seitzinger et al., 2010; Krishnamurthy et
al., 2010). Additionally, ongoing nutrient input increase can lead to a future
increase in biological production, which might partly negate the production
decrease driven by global warming. Conversely, the resultant increase in
the export of OM would accelerate
The coupling of land and ocean ecosystems via riverine nitrogen is one of
the new features of MIROC-ES2L, and the potential impact of the process on
ocean biogeochemistry has already been examined and discussed in Sect. 3.2.2. Here, we examine the response of river nitrogen loading itself
against anthropogenic forcing by comparing the results of the CTL,
HIST-NOLUC, and HIST simulations.
As mentioned in Sect. 3.1.3, the global flux of riverine nitrogen input into
the ocean is simulated at 17.5 TgN yr
Another possible reason for the above overestimation is precipitation bias, which results in the overestimation of BNF on land. As mentioned in Sect. 3.1.1, the model has a positive precipitation bias on land in arid and desert regions (Supplement Fig. S6). As the scheme for the natural BNF flux employed in MIROC-ES2L is modeled to be controlled by the actual evapotranspiration rate (Cleveland et al., 1999), the precipitation bias in arid regions could easily lead to an overestimation of the BNF flux and an increase in riverine nitrogen loading. This is also evident when decomposing the global riverine flux into river basins and comparing the findings with a previous study by Dumont et al. (2005) (Fig. 17). MIROC-ES2L overestimates the DIN fluxes of large rivers such as the Amazon, Mississippi, and Yangtze rivers, even in the CTL experiment, in which all anthropogenic forcings are fixed at preindustrial levels. This suggests the necessity of improvement of the baseline flux of riverine nitrogen in the model. For more in-depth discussion, it will be necessary to explicitly simulate the organic and particulate nitrogen fluxes in rivers, and it might be necessary to simulate the explicit sedimentary and chemical reaction processes in freshwater and coastal zone systems.
In Fig. 17, the difference between the results of CTL and HIST-NOLUC mainly
reflect the change induced by nitrogen deposition (and historical climate
change) (Table 2), and the model demonstrates that deposition has increased
N fluxes in many rivers. Additionally, the difference between HIST-NOLUC and
HIST demonstrates the impact of LUC and agricultural management change
(Table 2), and regions that have intensive agriculture within their
watersheds (e.g., the basins of the Mississippi, Indus, Yellow, and Yangtze
rivers) are simulated as strongly affected by the forcing change. The DIN
discharge in each river is not always smaller in HIST-NOLUC than in HIST.
This is because LAI in HIST-NOLUC is different to that in HIST, which
is sometimes accompanied by a slight change in the surface climate via
biophysical feedback. If the soil temperature is slightly warmer in HIST-NOLUC
than in HIST, the soil mineralization rate in HIST-NOLUC should be
accelerated, and thus the DIN loadings of rivers could be increased. This
simulated trend in the historical period is qualitatively consistent with
previous studies (Gruber and Galloway, 2008). Furthermore, the model
simulates the global riverine flux to be increased by 16.4 TgN yr
Simulated and observed DIN load per river basin: sorted by simulated
Here, the model sensitivity of the global climate–carbon cycle against
The TCR, AF, and TCRE derived from the 1PPY simulation are displayed in
Table 5. The TCR of MIROC-ES2L is 1.5 K, which is lower than the multimodel
mean of the CMIP5 ESMs but within the range of spread (
Comparison of TCR, AF, and TCRE between MIROC-ES2L, MIROC-ESM, MIROC5.2, and
CMIP5 ESMs in the 1PPY simulation. For MIROC-ES2L, both TCR and AF are
calculated based on 20-year means of T2, CL, and CO centered on the 70th
year of the 1PPY simulation (i.e., the time when the
To further explore why AF is lowered in MIROC-ES2L, the strengths of the
carbon cycle feedbacks were analyzed using the 1PPY-BGC and 1PPY-RAD
simulation results (Table 6), and the findings were compared with the CMIP5
ESMs (Arora et al., 2013). The strength of the
As the quantities
Comparison of
Comparison of the strength of
In this study, a new Earth system model (MIROC-ES2L) was developed using a state-of-the-art climate model (MIROC5.2) as the physical core. This new ESM embeds a terrestrial biogeochemical component with explicit carbon–nitrogen interaction (VISIT-e) that accounts for the nutrient limitation of nitrogen on plant growth and therefore the change in the land carbon fluxes. Additionally, the ocean biogeochemical component (OECO2) is largely updated to simulate the biogeochemical cycles of carbon, nitrogen, phosphorus, iron, and oxygen such that oceanic primary productivity in the model is now controlled by multiple nutrient limitations. As a new challenge, land and ocean nitrogen cycles were coupled via river discharge processes; thus, marine productivity is now also affected by the riverine nitrogen input. Furthermore, iron-related processes such as emission, atmospheric transport, deposition, and utilization in the marine ecosystem are newly included to represent the micronutrient limitation on phytoplankton productivity. This is necessary to reproduce the HNLC regions and simulate ecosystem variability in response to changes in external iron inputs.
To evaluate the performance of the new model, a historical simulation
following CMIP6 protocols and forcing datasets was performed for the
1850–2014 period, and the results were compared with observation-based
products. The model reasonably reproduces the global changes in net TOA
radiation balance, SAT, SST, and upper-ocean temperature. Considering the few
biophysical feedbacks on climate in the model, the MIROC-ES2L good
performance in simulating the physical fields is inherited from its original
climate model (MIROC5.2), although persistent problems remain such as the
warm bias in the Southern Ocean, as found in some climate models. Global
carbon and nitrogen budgets in the historical simulation were also examined
and discussed by comparing the results with existing studies. The model
successfully captured the observation-based estimates of contemporary
air–sea and air–land carbon fluxes in terms of cumulative values. The
component fluxes of global nitrogen between land, atmosphere, and ocean are
also reasonably reproduced by the model. The spatial distributions of
fundamental variables of the land carbon cycle were also assessed through
comparison with observation-based products, and the model produced
reasonable patterns for primary productivity, forest carbon, and SOC. The
spatial patterns of oceanic macronutrients and micronutrients, total inorganic
carbon, alkalinity, oxygen, primary productivity, and oceanic
To assess the global climate–carbon cycle feedback in MIROC-ES2L, a
sensitivity analysis was performed in which the atmospheric
In the new model, the terrestrial nitrogen cycle processes and the
interaction with the carbon cycle are modeled explicitly. By performing
several types of simulations, it was clearly demonstrated that agricultural
management such as fertilizer application has changed the carbon cycle (GPP)
in the historical period, which suggests that the nitrogen cycle in the
model alters the land carbon cycle. The model simulated the change in the
total land carbon content during 1850–2014 at 44 PgC, which is within the
estimated range of Le Quéré et al. (2018). However, historical
terrestrial carbon change is highly uncertain because the change is
processed by multiple responses against the external forcing of
In the new model, the ocean nitrogen cycle is modified to be an open system, and thus the model can reflect the influences of external sources of nitrogen via atmospheric deposition and river discharge. Our sensitivity analyses under the preindustrial condition suggested minor contributions of these two external sources to primary productivity on the global scale. However, regions in which primary productivity is constrained by nitrogen availability showed a strong positive NPP response to the relaxation of nitrogen limitation. It accelerates the use of other nutrients within the marine ecosystem in such regions and reduces iron and phosphorus availability in other regions. Furthermore, by switching on the process of iron deposition into the ocean, the model showed an increase of approximately 7 % in primary production under the preindustrial condition, which suggests that iron input has a relatively stronger impact than nitrogen. Coupling iron cycle processes in the model led to the successful reproduction of HNLC regions, and it will enable the model to project future biogeochemical changes induced by anthropogenic iron emissions associated with the use of fossil fuels and biomass burning. We note, however, that as an atmospheric chemistry module is not included in MIROC-ES2L, the atmospheric chemical reaction of iron-containing aerosols is ignored and the iron solubility to seawater is simply assumed constant. Considering the relatively strong impact of iron deposition on marine primary productivity in the model, we need further detailed evaluation and modification of the iron cycle processes in terms of both aerosol transport and marine biogeochemical responses.
In addition to such improvements in terms of the iron cycle, other factors should also be improved and extended in the ESM for future simulation study. First, a freshwater biogeochemistry module is required. In the present model, the chemical form of riverine nitrogen is assumed inorganic, but actual river flow contains OM and particulate matter that undergo biogeochemical processing during transport. Thus, inclusion of the transport of organic–inorganic matter and the modeling of freshwater biogeochemistry might be necessary. This conclusion is supported by the sensitivity analysis that showed a relatively strong regional-scale impact of riverine nitrogen on marine primary productivity, although the global-scale impact was demonstrated to be minor. Second, MIROC-ES2L can simulate natural emissions of nitrous oxide; however, the emissions did not change the radiative balance in the atmosphere. Nitrous oxide is one of the strongest greenhouse gases with a long lifetime. As diagnosed in this study, future nitrous oxide emissions could be controlled by land use and agriculture, as well as climate change. Therefore, full coupling of the nitrous oxide cycle with other associated atmospheric chemical processes should be incorporated in the next-generation ESM, together with the methane cycle, as suggested in previous studies (e.g., Collins et al., 2018). Third, a mechanistic model for the denitrification process in ocean sediment should be included in a future model. The present model simulates only the denitrification rate of the water column, and the flux from sediment is likely imposed on the water-column denitrification. As the timescale of the sedimentary process is likely longer than that of water-column denitrification, explicit modeling of sedimentary denitrification will be important, particularly for long-term simulations over timescales of millennia. Finally, we partly demonstrated the importance of external sources of nutrients for marine productivity, although its evaluation was performed under the preindustrial condition. As anthropogenic nutrient inputs under that condition are much smaller than under the present-day condition and could be amplified or mitigated in the future, a similar set of sensitivity simulations should be undertaken for present-day and future conditions.
ESMs represent powerful tools to investigate interactions between the climate, biogeochemistry, and human activities, and they have facilitated climate projections and quantifications of future emissions of greenhouse gases for achieving climate change mitigation goals. Such models are also valuable for examining how Earth system components might respond to different levels of mitigation policies and scenarios spanning from the business-as-usual scenario to one employing intensive measures such as geoengineering techniques. Furthermore, state-of-the-art ESMs can reproduce some of the dominant long-term environmental changes on Earth that are becoming evident or doubted in association with climate change, e.g., ocean acidification and hypoxia, global nitrogen loading, air pollution, and habitable zone changes in ecosystems. ESMs can simulate such problems and their interactions in a holistic and consistent manner. Such simulations have the potential to elucidate sustainable ways to mitigate climate change with less environmental stress. To support such applications, further efforts should be made to improve ESMs and to constrain model performance in collaboration with observation studies.
The structure of carbon and nitrogen compartments and the flux calculations in VISIT-e mostly follow the original version of the model (Ito and Inatomi, 2012a). For N cycle and LUC processes, some major changes were brought to VISIT-e to couple the model with MIROC-ES2L; the details are described below.
Terrestrial N dynamics in VISIT are simulated based on three major
compartment groups of N storage: vegetation N (
The component
The inorganic nitrogen is assumed to consist of N pools of
The budget equation for
In the above two equations,
BNF is calculated based on the actual evapotranspiration rate (Cleveland et
al., 1999). In the original version of VISIT, all nitrogen fixed through BNF
(
The mineralization rate of litter is the same as that in the original version,
and it is calculated as follows:
The humus N mineralization rate is similar to that of litter, but it is
modified to be dependent on the humus
The immobilization rate is simplified in VISIT-e, and it is modeled as a function
of the mineralization rate of litter N, depending on the
N flux by humification (N flow from litter to humus,
Abiotic N loss from soil (
To simulate soil nutrient (soil inorganic nitrogen) control on plant growth, VISIT-e is modified from the original model as follows.
First, the photosynthetic capacity (
Second, actual N uptake by plants (
LUC by external forcing and its impact on land biogeochemistry are
simulated with five main types of tiles (primary vegetation, secondary
vegetation, urban, cropland, and pasture) in each land grid. The same
structure of C and N compartments is shared among the tiles, and each tile
has its own areal fraction in a grid (
The carbon and nitrogen in biomass removed by crop harvesting and by land
use conversion (
Even if the areal fraction of each land use tile were fixed in a simulation,
there could still be impacts of land use on land biogeochemistry, referred
to here as the status-driven impact. This impact is specific to each tile,
and it is summarized as follows:
prohibition of plant growth on an urban tile; increased mortality of plants by grazing pressure on pasture tiles,
assuming a 20 % increase in mortality rate for foliage; annual crop harvesting on crop tiles (assuming 10 % of foliage is
harvested) and loss of C and N from the product pools; nitrogen fixation by N-fixing crop on crop tiles.
For (4), the total BNF rate on crop tiles (
When the areal fractions of tiles are made to change following the forcing
dataset, the apparent mass densities of C and N on a grid can be changed.
For example, when a portion of a grid area is converted from category
The ocean ecosystem component (OECO2) embedded within the ocean circulation
model is based on nutrient–phytoplankton–zooplankton–detritus (NPZD) type
with four prognostic variables: nitrate (
Each variable changes its concentration
First, the source minus sink term for
The term
Then,
Using the molar
where Scav represents scavenging (Moore et al., 2004; Moore and Braucher, 2008), Dustin is the iron input from dust, Sedin is the iron input from sediment following both Moore et al. (2004) and Aumont and Bopp (2006), and HTin is the hydrothermal dissolved iron flux following Tagliabue et al. (2010).
The source minus sink term for
The source minus sink term for DIC can be expressed as follows:
Then,
To simply evaluate the effect of iron limitation on the growth of
ordinary nondiazotrophic phytoplankton and diazotrophic phytoplankton
(nitrogen fixers), we modify the equations of phytoplankton growth rate by
Keller et al. (2012) as follows. First, we estimate the maximum potential
growth rate of phytoplankton (
Once the maximum potential growth rate has been calculated, the realized
growth rate of phytoplankton (
Model parameters.
Definitions of parameters and variables not mentioned specifically in the text.
The external forcing used for the HIST experiment is summarized in Table C1.
List of forcing datasets for the HIST simulation: categories, variables, and references for the data creation and a description of how the datasets are applied in the HIST simulation in MIROC-ES2L.
The global carbon budget can be written as follows:
If we can obtain the prescribed emission (CE
As in Appendix D, the global carbon budget can be written as follows:
The code of MIROC-ES2L is not publicly archived because of the copyright
policy of the MIROC community. Readers are requested to contact the
corresponding author if they wish to validate the model configurations of
MIROC-ES2L and conduct replication experiments. The model outputs of the control (
The supplement related to this article is available online at:
TH was responsible for the development and description of MIROC-ES2L and VISIT-e, executed the spin-up and experiments, and undertook global climate–biogeochemistry and terrestrial analyses. MW, AY, and MAN contributed to the development and description of OECO2, as well as the analysis of ocean biogeochemistry. HT developed MIROC5.2 and supervised the physical modeling and engineering. MA contributed to the DMS emission modeling, preparation of the forcing dataset, and conversion and archiving of the output. RO contributed to the examination of model performance, post-processing of the output, and analysis of the physical fields. AkinI contributed to the development of atmospheric iron transport, preparation of iron emission forcing, and its description. DY contributed to river nitrogen modeling and its analysis. HO contributed to the coupling of OECO2. AkihI provided the original model VISIT and supervised the modeling and analysis of the terrestrial biogeochemistry. KT supervised the modeling of the terrestrial physical processes. KO supervised and supported the software engineering. SW determined the primitive design of MIROC-ES2L and supervised the entire system. MK organized the project, supervised the entire system, and contributed to the background section.
The authors declare that they have no conflict of interest.
This work was supported by TOUGOU/SOUSEI, the Integrated Research Program
for Advancing Climate Models (grant number JPMXD0717935715)/Program for
Risk Information on Climate Change, through the Ministry of Education, Culture,
Sports, Science, and Technology of Japan. This work was also partly
supported by JSPS KAKENHI grant number 17K12820 and by scientific
collaboration in GCOM-C RA (JX-PSPC-500211). The Earth Simulator and JAMSTEC
Super Computing System were used for the simulations, and the administration
staff provided much support. The authors are grateful for the programming
support provided by Tsuyoshi Hasegawa and Shinichi Toshimitsu and for the
engineering advice offered by Hiroaki Kanai. Osamu Arakawa provided powerful
support and services on data archiving and server management. Kengo Sudo and
Tomoko Nitta kindly provided the forcing data and the forcing preparation
system, respectively. Kaoru Tachiiri and Prabir Patra provided helpful and
encouraging comments. This work was based on a long-term endeavor of
members of the MIROC community. We greatly appreciate the valuable comments
from the two reviewers – Jerry Tjiputra and an anonymous referee. We
thank James Buxton MSc from the Edanz Group (
This research has been supported by the The Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan (Integrated Research Program for Advancing Climate Models (grant no. JPMXD0717935715)) and the JSPS KAKENHI (grant no. 17K12820).
This paper was edited by Paul Halloran and reviewed by Jerry Tjiputra and one anonymous referee.