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Geosci. Model Dev. Discuss., 4, 1063–1128, 2011
www.geosci-model-dev-discuss.net/4/1063/2011/
doi:10.5194/gmdd-4-1063-2011
© Author(s) 2011. CC Attribution 3.0 License.
Geoscientific
Model Development
Discussions
This discussion paper is/has been under review for the journal Geoscientific Model
Development (GMD). Please refer to the corresponding final paper in GMD if available.
MIROC-ESM: model description and basic
results of CMIP5-20c3m experiments
S. Watanabe1, T. Hajima1, K. Sudo2, T. Nagashima3, T. Takemura4, H. Okajima1,
T. Nozawa2,3, H. Kawase3, M. Abe3, T. Yokohata3, T. Ise1, H. Sato2, E. Kato1,
K. Takata1, S. Emori1,3, and M. Kawamiya1
1Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
2Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan
3National Institute for Environmental Studies, Tsukuba, Japan
4Research Institute for Applied Mechanics, Kyushu University, Kasuga, Japan
Received: 25 April 2011 – Accepted: 10 May 2011 – Published: 17 May 2011
Correspondence to: S. Watanabe (wnabe@jamstec.go.jp)
Published by Copernicus Publications on behalf of the European Geosciences Union.
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Abstract
An earth system model (MIROC-ESM) is fully described in terms of each model com-
ponent and their interactions. Results for the CMIP5 (Coupled Model Intercomparison
Project phase 5) historical simulation are presented to demonstrate the model’s per-
formance from several perspectives: atmosphere, ocean, sea-ice, land-surface, ocean5
and terrestrial biogeochemistry, and atmospheric chemistry and aerosols. An atmo-
spheric chemistry coupled version of MIROC-ESM (MIROC-ESM-CHEM) reasonably
reproduces transient variations in surface air temperatures for the period 1850–2005,
as well as the present-day climatology for the zonal-mean zonal winds and temper-
atures from the surface to the mesosphere. The historical evolution and global dis-10
tribution of column ozone and the amount of tropospheric aerosols are reasonably
simulated in the model based on the Representative Concentration Pathways’ (RCP)
historical emissions of these precursors. The simulated distributions of the terrestrial
and marine biogeochemistry parameters agree with recent observations, which is en-
couraging to use the model for future global change projections.15
1 Introduction
The establishment of long-term mitigation goals against climate change should be
based on sound information from scientific projections on a centennial time scale.
Tools that have been developed for reliable projection include numerical climate mod-
els (e.g., K-1 model developers, 2004), future scenarios (Moss et al., 2010), and model20
experimental design (Hibbard et al., 2007; Meehl and Hibbard, 2007; Taylor et al.,
2009). These efforts are mutually cooperative and expected to enhance collaboration
among different communities working on model development, impact assessment and
scenario development (Moss et al., 2010). Projections made up to year 2300 using
this approach will aid the refinement of policies for greenhouse gas (GHG) reduction25
by, say, 2050 (Miyama and Kawamiya, 2009).
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Interactions between climate change and biogeochemical processes should be taken
into account when performing centennial projections. Cox et al. (2000) pointed out that
there could be a significant positive feedback between climate change and the carbon
cycle, implying that future temperature rise projected by “traditional” climate models
without a built-in carbon cycle may have been underestimated. Further study is needed5
on this issue because the strength of the feedback shows complex spatial variability
(Yoshikawa et al., 2008), varies considerably among different models (Friedlingstein
et al., 2006) and may be altered by incorporation of novel processes as suggested
by recent studies (Bonan, 2008). Moreover, the behavior of atmospheric constituents
such as tropospheric and stratospheric ozone may trigger changes in the carbon cycle10
(Sitch et al., 2007; Le Qu ´
er´
e et al., 2007; Lenton et al., 2009). Furthermore, some phe-
nomena that involve stratospheric processes, such as ozone exchange between the
stratosphere and troposphere, could have a significant impact on the surface climate
(Sudo et al., 2003; Solomon et al., 2010). It is therefore desirable that comprehensive
models for global change projection represent the dynamics of non-CO2GHGs, as well15
as that of carbon, with a sophisticated treatment of the stratosphere.
In response to these issues, earth system models (ESMs), which is often used as a
synonym for coupled climate models with biogeochemical components, are now being
developed at leading institutes for climate science (e.g., Tjiputra et al., 2010; Weaver
et al., 2001; Hill et al., 2004; Redler et al., 2010). This work describes the struc-20
ture and performances of an ESM developed on the basis of the version presented by
Kawamiya et al. (2005) at the Japan Agency for Marine-Earth Science and Technol-
ogy (JAMSTEC) in collaboration with, among others, the University of Tokyo and the
National Institute for Environmental Studies (NIES).
2 Model description25
Our ESM, named “MIROC-ESM”, is based on a global climate model MIROC (Model
for Interdisciplinary Research on Climate) which has been cooperatively developed by
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the University of Tokyo, NIES, and JAMSTEC (K-1 model developers, 2004; Nozawa et
al., 2007). A comprehensive atmospheric general circulation model (MIROC-AGCM)
including an on-line aerosol component (SPRINTARS), an ocean GCM with sea-ice
component (COCO), and a land surface model (MATSIRO) are interactively coupled in
MIROC as illustrated in Fig. 1. These atmosphere, ocean, and land surface compo-5
nents, as well as a river routine, are coupled by a flux coupler (K-1 model developers,
2004).
On the basis of MIROC, MIROC-ESM further includes an atmospheric chemistry
component (CHASER), a nutrient-phytoplankton-zooplankton-detritus (NPZD) type
ocean ecosystem component, and a terrestrial ecosystem component dealing with10
dynamic vegetation (SEIB-DGVM). Table 1 shows the modeled variables that are ex-
changed among the components of MIROC-ESM, and the numbered arrows in Fig. 1
indicate the pathways of these variables.
As a total time integration period of many thousands of years was requested for the
series of CMIP5 (Coupled Model Intercomparison Project phase-5) experiments on15
long-term future climate projections (Taylor et al., 2009), the number of experiments
that would be performed with the full version of MIROC-ESM had to be limited. There-
fore, a limited number of experiments were performed using the CHASER-coupled
version of MIROC-ESM (MIROC-ESM-CHEM), while all of the requested experiments
were performed using a version without the coupled atmospheric chemistry, referred to20
as MIROC-ESM hereafter. By comparing results of these two versions, the importance
of chemistry climate interactions on the transient climate system may be estimated,
although this is beyond the scope of the present paper. Each component of MIROC-
ESM-CHEM will be described in the following subsections.
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2.1 Atmospheric model
2.1.1 MIROC-AGCM
The atmospheric general circulation model (MIROC-AGCM) is based on the previous
CCSR (Center for Climate System Research, University of Tokyo)/NIES/FRCGC (Fron-
tier Research Center for Global Change, JAMSTEC) AGCM (K-1 model developers,5
2004; Nozawa et al., 2007). The MIROC-AGCM has a spectral dynamical core, and
uses a flux-form semi-Lagrangian scheme for the tracer advection. The horizontal tri-
angular truncation at a total horizontal number of 42 (T42; equivalent grid interval is
approximately 2.8125 degrees in latitude and longitude) is used in the present study.
Unlike other setups of the MIROC-AGCM, MIROC-ESM has the fully resolved strato-10
sphere and mesosphere (Watanabe et al., 2008a). The hybrid terrain-following (sigma)
pressure vertical coordinate system is used, and there are 80 vertical layers between
the surface and about 0.003 hPa. In order to obtain the spontaneously generated equa-
torial quasi-biennial oscillation (QBO), a fine vertical resolution of about 680 m is used
in the lower stratosphere.15
The MIROC-AGCM has a suite of physical parameterizations that are detailed in K-1
model developers (2004) and Nozawa et al. (2007). Watanabe et al. (2008a) describes
the modifications and inclusions of physical parameterizations from MIROC-AGCM to
MIROC-ESM that are crucial for the representation of the large-scale dynamical and
thermal structures in the stratosphere and mesosphere. A brief summary of the physi-20
cal parameterization is given in the following.
The radiative transfer scheme adopted in MIROC-ESM follows Sekiguchi and Naka-
jima (2008) and is an updated version of the k-distribution scheme used in the previous
versions of MIROC-AGCM. Watanabe et al. (2008a) illustrated the improvements of the
simulated thermal structure in MIROC-ESM-CHEM by replacing the old scheme with25
the new one. The present scheme considers 29 and 37 absorption bands in MIROC-
ESM and MIROC-ESM-CHEM, respectively. The spectral resolution in visible and ul-
tra violet regions is increased from 15 in MIROC-ESM to 23 in MIROC-ESM-CHEM,
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because detailed calculations are required for photolysis. Direct and indirect effects of
aerosols are considered in the radiation scheme, which will be described in Sect. 2.1.2.
The cumulus parameterization is based on the scheme presented by Arakawa and
Schubert (1974). A prognostic closure is used in the cumulus scheme, in which cloud
base mass flux is treated as a prognostic variable. An empirical cumulus suppression5
condition is introduced (Emori et al., 2001), by which cumulus convection is suppressed
when cloud mean ambient relative humidity is less than a critical value. This is a param-
eter by which the spatio-temporal distribution of the parameterized cumulus precipita-
tion, and hence characteristics of vertically propagating atmospheric waves generated
by cumulus convection, are strongly controlled. In the present setup of MIROC-ESM,10
a value of 0.7 is used for this parameter to generate moderate convective precipitation
and a moderate wave momentum flux associated with the resolved atmospheric waves.
The large-scale (grid-scale) condensation is diagnosed based on the scheme of Le
Treut and Li (1991) and a simple cloud microphysics scheme. In MIROC-ESM, the
cloud phase (solid or liquid) is diagnosed according to the temperature, T:15
fliq =exp(−((Ts−T)/Tf)2) (T > Tm),
fliq =1 (T < Tm),
where fliq is the ratio of liquid cloud water to total cloud water, and Tm,Ts, and Tfare
set to 235.15 K, 268.91 K, and 12.0 K, respectively.
The sub-grid vertical mixing of prognostic variables is calculated on the basis of the20
level 2 scheme of the turbulence closure model by Mellor and Yamada (1974, 1982).
MIROC-ESM uses ∇6horizontal hyper viscosity diffusion to suppress the effect of extra
energies at the largest horizontal wave number. The horizontal diffusion is not adapted
to the tracers. The e-folding time for the smallest resolved wave is 0.5 days. In order
to prevent extra wave reflection at the top boundary, a sponge layer is added to the top25
level, which causes the wave motions to be greatly dampened.
The effects of orographically and non-orographically generated subgrid-scale inter-
nal gravity waves are parameterized following McFarlane (1987) and Hines (1997),
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respectively (Watanabe et al., 2008a). As documented in Watanabe et al. (2008a) and
Watanabe (2008), the present-day climatology of non-orographic gravity wave source
spectra estimated using results of a gravity wave-resolving version of MIROC-AGCM
(Watanabe et al., 2008b) are launched at 70 hPa in the extratropics of MIROC-ESM. In
the tropics, an isotropic source of non-orographic gravity waves is launched at 650hPa5
in the present version. The strength of the tropical source is arbitrarily tuned so that
the QBO with a realistic period of 27–28 months on average can be reproduced under
present-day (2000s) conditions.
2.1.2 Aerosol module – SPRINTARS
An aerosol module in MIROC, SPRINTARS, predicts mass mixing ratios of the main10
tropospheric aerosols: carbonaceous (black carbon (BC) and organic matter (OM)),
sulfate, soil dust, and sea salt, and the precursor gases of sulfate, i.e., sulfur dioxide
(SO2) and dimethylsulfide (DMS). The aerosol transport processes include emission,
advection, diffusion, sulfur chemistry, wet deposition, dry deposition, and gravitational
settling. Emissions of soil dust, sea salt, and DMS are calculated using the internal15
parameters of the model, and external emission inventories are used for the other
aerosol sources. SPRINTARS is coupled with the radiation and cloud/precipitation
schemes for calculating the direct, semi-direct, and indirect effects of aerosols. In the
calculation of the direct effect, the refractive indices depending on wavelengths, size
distributions, and hygroscopic growth are considered for each type of aerosol. The20
aerosol semi-direct effect is also included as a consequence of a link between the GCM
and aerosol module. Number concentrations for cloud droplets and ice crystals are
prognostic variables as well as their mass mixing ratios, and changes in their radii and
precipitation rates are calculated for the indirect effect. More detailed descriptions of
SPRINTARS can be found in Takemura et al. (2000) for the aerosol transport, Takemura25
et al. (2002) for the aerosol direct effect, and Takemura et al. (2005, 2009) for the
aerosol indirect effect on water and ice clouds. Some improvements to each process
are described in the later references.
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2.1.3 Chemistry module – CHASER
Simulations of atmospheric chemistry in MIROC-ESM-CHEM are based on the chem-
istry model CHASER (Sudo et al., 2002a, 2007) which has been developed mainly at
Nagoya University in coorporation with the University of Tokyo, JAMSTEC, and NIES
(Fig. 2). The CHASER model version used in MIROC-ESM-CHEM considers the de-5
tailed photochemistry in the troposphere and stratosphere by simulating tracer trans-
port, wet and dry deposition, and emissions. The original version of CHASER (Sudo
et al., 2002a) focused mainly on tropospheric chemistry, and has been extended to
include the stratosphere by incorporating halogen chemistry and related processes.
In its present configuration, the model considers the fundamental chemical cycle of10
Ox-NOx-HOx-CH4-CO with oxidation of volatile organic compounds (VOCs) and halo-
gen chemistry calculating concentrations of 92 chemical species with 262 chemical
reactions (58 photolytic, 183 kinetic, and 21 heterogeneous reactions). For VOCs, the
model includes oxidation of ethane (C2H6), ethene (C2H4), propane (C3H8), propene
(C3H6), butane (C4H10), acetone, methanol, isoprene, and terpenes. The model adopts15
the condensed isoprene oxidation scheme of P¨
oschl et al. (2000) which is based on
the Master Chemical Mechanism (MCM, Version 2.0) (Jenkin et al., 1997). Terpene
oxidation is largely based on Brasseur et al. (1998). The model also includes de-
tailed stratospheric chemistry, calculating ClOx, HCl, HOCl, BrOx, HBr, HOBr, Cl2, Br2,
BrCl, ClONO2, BrONO2, CFCs, HFCs, and OCS. The formation of PSCs and asso-20
ciated heterogeneous reactions on their surfaces (13 reactions for halogen species
and N2O5) are calculated based on the schemes adopted in the CCSR/NIES strato-
spheric chemistry model (Akiyoshi et al., 2004; Nagashima et al., 2001). The pho-
tolysis rates (J-values) are calculated on-line using temperature and radiation fluxes
computed in the radiation component of the MIROC-AGCM (Sekiguchi and Nakajima,25
2008) considering absorption and scattering by gases, aerosols, and clouds as well as
the effect of surface albedo. In MIROC-ESM-CHEM, influences of short-wave radiative
forcing associated with the solar cycle, volcanic eruptions, and subsequent changes
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in stratospheric ozone are also taken into account for the calculation of the photol-
ysis rate. In the original MIROC-AGCM, the wavelength resolution for the radiation
calculation is relatively coarse in the ultraviolet and the visible wavelength regions as
in general GCMs. Therefore, the wavelength resolution in these wavelength regions
is improved for the photochemistry in CHASER (see Sect. 2.1.1). In addition, repre-5
sentative absorption cross-sections and quantum yields for individual spectral bins are
evaluated depending on the optical thickness computed in the radiation component. In
a similar manner to Landgraf and Crutzen (1998), we optimized the averaging (weight-
ing) function for each spectral bin differently for the troposphere and stratosphere. The
simulated distributions of trace gases are generally well in line with the observations10
(Sudo et al., 2002b).
In the default configuration of the MIROC-ESM-CHEM model, sulfate formation from
oxidation of SO2and DMS is basically simulated in the SPRINTARS model component
using concentrations of oxidants (OH, O3, and H2O2) calculated by the CHASER chem-
istry. Alternatively, the CHASER model component can simulate sulfate and nitrate15
aerosols on-line in cooperation with the aerosol thermodynamics model ISORROPIA
(Nenes et al., 1998; Fountoukis et al., 2007) by considering the ammonia chemistry. It
should be noted that sulfate simulation in CHASER considers neutralization of acidity of
cloud water by ammonium and dust cations and its influences on liquid phase oxidation
of S(IV) to form sulfate, but such processes are not included in the SPRINTARS sulfate20
simulation (which assumes a constant pH value for cloud water). The latest version of
CHASER also includes chemical formation of secondary organic aerosol (SOA) from
oxidation of VOCs (isoprene, terpenes, and aromatics) with a “two product” scheme
based on Odum et al. (1996). However, our present experiments for the CMIP5 and
related projects do not use this on-line SOA simulation mainly because it is yet to be25
adequately validated.
The spatial and temporal resolutions for the chemistry and aerosol calculations are
linked to the main dynamical and physical cores of the model (MIROC-AGCM), and
grid/sub-grid scale tracer transport is simulated in the framework of the GCM.
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For the CMIP5 related experiments, surface and aircraft emissions of BC/OC and
precursor gases (NOx, CO, VOCs, and SO2) are specified from the RCP database
(Lamarque et al., 2010, etc.). Lightning NOx emission, calculated in the convection
scheme of the MIROC-AGCM, is changeable from year to year responding to the inter-
annual variability and climatic trends. Although MIROC-ESM-CHEM includes the land5
surface model MATSIRO and the land ecosystem model SEIB-DGVM, biogenic emis-
sions of VOCs, such as isoprene or terpenes, are not curently linked to the vegetation
processes in these models.
2.2 Ocean and sea-ice model with biogeochemistry
The ocean component of MIROC-ESM is developed at CCSR, University of Tokyo,10
and is called COCO, the acronym of CCSR Ocean COmponent model. The COCO
solves the primitive equations under hydrostatic and Boussinesq approximations with
an explicit free surface. The surface mixed layer parameterization is based on Noh
and Kim’s turbulence closure scheme (Noh and Kim, 1999), a derivative of Mellor and
Yamada level 2.5 (Mellor and Yamada, 1982). The sea-ice is based on a two-category15
thickness representation, zero-layer thermodynamics (Semtner, 1976), and dynamics
with elastic-viscous-plastic rheology (Hunke and Dukowicz, 1997).
The horizontal resolution for COCO is finer than for the atmospheric model. The lon-
gitudinal grid spacing is about 1.4 degrees, while the latitudinal grid intervals gradually
vary from 0.5 degrees at the equator to 1.7 degrees near the North/South Pole. The20
vertical coordinate is a hybrid of sigma-z, resolving 44 levels in total: 8 sigma-layers
near the surface, and 35 z-layers at depth, plus one bottom layer for the boundary
parameterization (K-1 model developers, 2004).
A simple biogeochemical process is employed to simulate the ocean ecosystem.
A type of Nitrogen-Phytoplankton-Zooplankton-Detritus model (NPZD, Oschlies et al.,25
2001) is sufficient to resolve the seasonal variation of oceanic biological activities at a
basin-wide scale (Kawamiya et al., 2000).
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2.3 Land surface models
2.3.1 Physical land component – MATSIRO
MIROC-ESM includes a land surface model: Minimal Advanced Treatments of Sur-
face Interaction and RunOff(MATSIRO; Takata et al., 2003), coupled to a river routing
model, TRIP (Oki and Sud, 1998), for calculating river discharge. In MATSIRO, the5
heat and water exchanges between the land and atmosphere are calculated, as are
the thermal and hydrological conditions in the soil. The model consists of a single layer
canopy, three layers of snow, and six layers of soil to a depth of 14 m.
The aging effect on snow albedo (Yang et al., 1997) has been applied to MATSIRO.
The effects of dirt in snow had been considered as a constant after Yang et al. (1997),10
but was modified to vary in accordance with the dirt concentration at the snow surface
to mimic the observed relation between snow albedo and dirt concentration (Aoki et
al., 2006). The dirt concentration in snow is calculated from the deposition fluxes of
dust and soot calculated in the aerosol module, SPRINTARS (Sect. 2.1.2). Since the
absorption coefficients of dust and soot are very different, the relative strength of their15
absorption coefficients (0.012 for soil dust and 0.988 for black carbon) are weighted to
the deposition fluxes to obtain a radiatively effective amount of dirt in snow.
The surface albedo of an ice sheet had been assumed to be constant, but has been
modified to consider the effects of melt water on the surface (Bougamont et al., 2005).
Here, the ice sheet albedo is a function of the water content above the ice for visible20
and near-infrared radiation, and is a fixed value of 0.05 for the infrared band.
2.3.2 Land ecosystem model – SEIB-DGVM
The process-based terrestrial ecosystem model SEIB-DGVM (Spatially Explicit
Individual-Based Dynamic Global Vegetation Model; Sato et al., 2007; Ise et al., 2009)
was coupled to MIROC-ESM to simulate global vegetation dynamics and terrestrial25
carbon cycling. Under global climate change, terrestrial ecosystems will be affected
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by aspects including shifts in vegetation types, changes in living biomass, alterations
of vegetation structure and energy balance, and accumulation and decomposition of
soil organic carbon. These changes will in turn influence the climate, thereby forming
a terrestrial-atmosphere feedback. In order to appropriately reproduce these terres-
trial ecological processes, SEIB-DGVM adopts an individual-based simulation scheme5
that explicitly captures light competition among trees, while other terrestrial ecosystem
models (e.g., Sitch et al., 2003) rely heavily on parameterization for plant competition.
Incorporating ecological realities of competition for light is fundamentally important to
strengthen DGVM predictions (Purves and Pacala, 2008). SEIB-DGVM has been val-
idated in various regions with different biomes (Ise and Sato, 2008; Sato, 2009; Sato10
et al., 2010). In this model, the ecological processes – ecophysiology, population,
community, and ecosystem dynamics – are simulated in an integrated manner.
In SEIB-DGVM, vegetation is classified into 13 plant functional types (PFTs), con-
sisting of 11 tree PFTs and 2 grass PFTs. Each PFT has different ecophysiological pa-
rameters such as maximum photosynthetic rates, optimal temperatures for photosyn-15
thesis, and minimum temperatures for frost-related mortality. Allometry relationships
and carbon allocation patterns also differ, resulting in differential growth patterns and
competition among PFTs under the environmental conditions in each grid cell. Photo-
synthesis is calculated daily as a function of air temperature, photosynthetically active
radiation, and atmospheric CO2concentration, and modified by air humidity through20
stomatal control and soil moisture availability. Plant respiration is controlled by the vol-
ume of plant tissues (i.e., leaves, stems, and root), growth rates of each tissue, and
air temperature with a Q10 function. Population dynamics (establishment, growth, and
mortality) and community dynamics (competition and succession) are then simulated
from the daily gain from photosynthesis by each tree.25
Dynamics of soil organic carbon is determined by inputs (turnover of plant tis-
sues and mortality) and the output (decomposition by heterotrophic respiration). Het-
erotrophic respiration responds linearly to the soil water content and exponentially to
the soil temperature via an Arrhenius-type equation. SEIB-DGVM in MIROC-ESM
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contains two soil organic carbon pools (fast- and slow-decomposing) based on the
Roth-C scheme (Coleman and Jenkinson, 1999). The ecosystem carbon balance is
then calculated by adding changes in living biomass and soil organic carbon.
In order to represent the effects of anthropogenic land use change, SEIB-DGVM
incorporates land use datasets of RCPs scenarios (Hurtt et al., 2009) for the period5
1500–2100. The spatial resolution of the datasets is converted to T42 and land use
types are summarized into five categories: primary vegetation, secondary vegetation,
pasture, cropland, and urban area. Transitions are reproduced by a dataset of frac-
tional changes of land use area in each grid of MIROC-ESM and computed using an
annual time step. The secondary vegetation is formed as a result of logging or burn-10
ing of primary forests or abandonment of agricultural land. Regrowth of forest PFTs is
then simulated by the individual-based forest dynamics scheme. Carbon in harvested
biomass is transferred into carbon pools of linear decay (with turnover times of 1, 10,
and 100 yr) according to the Grand Slam Protocol described in Houghton et al. (1983).
We simulate the ecosystem dynamics of agricultural land using the processes for natu-15
ral grassland, but the biomass of cropland is harvested annually and partly transferred
into the grand slam carbon pools. The anthropogenic land use changes alter the veg-
etation structure and carbon cycle in terrestrial ecosystems, and resultant changes of
land surface conditions and atmospheric CO2will affect the climate through biophysi-
cal/biogeochemical processes.20
3 Spin-up and experimental designs
3.1 Spin-up and initial condition
Figure 3 schematically illustrates the spin-up procedures of MIROC-ESM. The terres-
trial and ocean carbon cycles require a long time to reach equilibrium compared to
physical climate systems. In our approach, the terrestrial carbon cycle component in-25
cluding the vegetation dynamics (SEIB-DGVM) and the ocean carbon cycle component
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embedded in the ocean GCM were separately spun-up for 2000 and 1245yr, respec-
tively (Fig. 3a). In these first off-line spin-up runs, surface physical quantities such as
winds, temperature, moisture, precipitation, and radiation flux, which are the climatol-
ogy of a pre-industrial run of MIROC, were recursively adapted to each model. Next, we
inputted the resultant equilibrated carbon cycle data into a low-top version of MIROC-5
ESM, in which the L80-AGCM is replaced by a L20-AGCM for computational efficiency,
as initial conditions, and the on-line terrestrial and ocean carbon cycles was integrated
for 200 yr (Fig. 3b). The resultant terrestrial carbon cycle data was again input into the
off-line SEIB-DGVM, which was integrated for 4350yr to adapt to the land-use corre-
sponding to 1850 (Fig. 3c). Using the terrestrial carbon cycle data, the second on-line10
spin-up was conducted using the low-top MIROC-ESM for 180 yr (Fig. 3d), from which
the final states of carbon cycle are used as the initial conditions for the final on-line
spin-up of MIROC-ESM with the L80-AGCM (Fig. 3e). After the spin-up had finished,
we conducted the pre-industrial control run of MIROC-ESM for 530 yr, and the first day
of the 20th year of the control run was used as the initial condition of the historical15
simulation of MIROC-ESM-CHEM, whose results are described in Sect. 4.
The atmospheric chemistry component (CHASER) of MIROC-ESM-CHEM was
spun-up separately from the carbon cycles because the atmospheric chemistry does
not need thousands of years to reach equilibrium. Some chemical species important in
the stratosphere required a few tens of years to reach equilibrium if the surface emis-20
sion of source gases was substantially changed. Since we had only run the current
version of CHASER under present-day conditions before CMIP5, we first needed to
prepare appropriate initial conditions for 1850 utilizing the existing present-day dataset:
(1) concentrations of halogen compounds such as halocarbons, inorganic chlorine and
bromine were set to zero, (2) concentrations of the nitrogen family such as nitrogen25
dioxide was scaled on the basis of present-day values with reference to the surface con-
centration of nitrous oxide, and (3) concentration of methane and moisture were scaled
with reference to the surface concentration of methane. After a spin-up of about 15 yr,
the concentrations of all chemical compounds in the troposphere and stratosphere
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reached equilibrium. The final results of the chemistry spin-up were added to the initial
condition of the historical simulation of MIROC-ESM-CHEM described in the previous
paragraph.
3.2 Experimental designs
The historical simulation was performed for the period from 1850 to 2005 using a set5
of external forcings recommended by the CMIP5 project. Spectral changes in solar
irradiance are considered according to Lean et al. (2005). Historical changes in op-
tical thickness of volcanic stratospheric aerosols are given by Sato et al. (1993) and
subsequent updates. Unlike our previous simulations, the temporal evolution of the
optical thickness in latitude-altitude cross section is considered. From 1998, the opti-10
cal thickness of volcanic stratospheric aerosols is exponentially reduced with one year
relaxation time. Atmospheric concentrations of well-mixed greenhouse gases are pro-
vided by Meinshausen et al. (2011). Surface emissions of tropospheric aerosols and
ozone precursors are provided by Lamarque et al. (2010).
To appropriately incorporate the effects of anthropogenic land use, the RCPs dataset15
(Hurtt et al., 2009) was implemented in the SEIB-DGVM. The RCP land use dataset
provided global land use type transitions among five types (primary vegetation, sec-
ondary vegetation, cropland, pasture, and urban area) in fractions annually at a res-
olution of 0.5 degrees and was prepared by integration of four socioeconomic studies
(IMAGE, MINICAM, AIM, and MESSAGE) for historical data (1500–2005). The origi-20
nal RCP data were converted to T42 to fit the spatial resolution of this study by taking
weighted means. The quality of the conversion was checked graphically for each sce-
nario. Implementation of RCPs in SEIB-DGVM is described in Sect. 2.3.2.
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4 Results of historical simulation
4.1 Transient variations
Temporal variations of global and annual mean surface air temperature (SAT) are
shown in Fig. 4 for the MIROC-ESM-CHEM simulation as well as for the observations
(Brohan et al., 2006). The MIROC-ESM-CHEM simulation well captures the observed5
multi-decadal variations throughout the whole simulation period. The simulated SAT in-
crease in the first and the second half of the 20th century is about 0.8 and 1.0 K/century
respectively, which is slightly less than that in the observations. These global annual
mean SAT trends are similar to those of our previous simulations (Nozawa et al., 2007),
although we use different forcing datasets than previously.10
Figure 5 shows the geographical distributions of linear SAT trends for the first and
second half of the 20th century. In comparison with the observed distributions (Fig. 5a,
c), the overall SAT trend for the MIROC-ESM-CHEM simulation shows a realistic geo-
graphical pattern, although the simulated SAT trends are slightly smaller than those in
observations. For the second half of the 20th century, the simulated SAT trend pattern15
over the northern Pacific is very similar to that in observations (Fig. 5c, d), although
the simulated trends over the Southern Hemisphere are slightly smaller than those in
observations.
4.2 Climatology in the late 20th century
Figure 6a and b shows geographical distributions of climatological values of SAT aver-20
aged for the 1961–1990 periods for the MIROC-ESM-CHEM simulation and its biases
against observational dataset (Jones et al., 1999), respectively. Overall, the clima-
tological SAT distributions are realistic but their differences from observations show
systematic biases: the simulated SAT is warmer in the mid and high latitudes of the
Northern Hemisphere and over Antarctica. On the other hand, the simulated SAT is25
slightly cooler in the tropics and in the mid latitudes of the Southern Hemisphere.
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The MIROC-ESM-CHEM shows a realistic distribution of annual mean precipitation
for the 1981–2000 period (Fig. 6c). However, there are some differences compared
with the GPCP observational dataset (Adler et al., 2003) (Fig. 6d): the precipitation is
underestimated along the South Pacific convergence zone (SPCZ), over the eastern
side of the Maritime Continent, and over Central America, whereas it is overestimated5
over the Maritime Continent, the northwestern Indian Ocean, and the western side
of South America. These shortcomings are similar to those in our previous model
(MIROC3.2) because these two models have almost the same atmospheric physics
components.
There is significant interest in how many years the Arctic and Antarctic sea-ice can10
last in a warming globe. Supposing a slightly warming ocean, both the sea-ice extent
and the surface albedo would decrease and more solar radiation would be absorbed
by the ocean, and hence initial warming is accelerated. Therefore, Arctic and Antarctic
sea-ice are very sensitive in a changing climate and can provide a good benchmark for
a climate model.15
Figure 7 compares the sea-ice concentration between the reanalysis (Reynolds et
al., 2002) and simulation by seasons and hemispheres. MIROC-ESM-CHEM fairly
well simulates the sea-ice seasonality in both hemispheres. The sea-ice distribution is
basically alike between the reanalysis and simulation. During the boreal summer (JJA),
the Arctic Ocean has less sea-ice and the Southern Ocean has more sea-ice compared20
with the other seasons, and vice versa for the boreal winter (DJF).
However, some unrealistic features can be found in the simulation, especially over
shallow oceans. Hudson Bay, for example, has thin sea-ice during the boreal sum-
mer in the reanalysis while no sea-ice is formed in the simulation. The southwestern
Okhotskoe Sea and northern Barrentsovo Sea also have less sea-ice during the bo-25
real winter in the simulation. Therefore, MIROC-ESM-CHEM underestimates a small
amount of sea-ice over shallow oceans and such sea-ice may have disappeared by the
1990s, a bit earlier than in the observations, but the model adequately resolves most
other sea-ice, which has more importance in a large-scale climate simulation.
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The annual mean shortwave (SW) and longwave (LW) radiation at the top of the
atmosphere (TOA), and their cloud radiative forcing (CRF) are shown in Figs. 8 and 9.
The observational dataset from the Earth Radiation Budget Experiment (ERBE), Earth
Radiant Fluxes and Albedo for Month S-9 for the period 1986 to 1990 (Barkstrom et al.,
1989) is used for comparison with the model simulation. As shown in Fig. 8, negative5
bias in SW radiation (the model has too much reflection) and CRF can be seen in the
central Pacific, western Atlantic, and Indian Ocean, and positive bias is seen in the
eastern Pacific and Atlantic. These features are similar to the bias of low-level cloud
albedo in Fig. 12 of Yokohata et al. (2010), and thus these biases are likely caused by
the model feature of low-level cloud.10
As for LW radiation, the bias is relatively small compared to that in SW radiation. A
positive bias in LW radiation and LW CRF can be seen over the intertropical conver-
gence zone (ITCZ) in the equatorial Pacific. Since this feature is similar to that of the
precipitation bias shown in Fig. 6d, this positive bias in LW radiation is likely caused by
a problem with the model precipitation or convection. Similarly, the negative bias in the15
LW radiation and LW CRF over the SPCZ may be related to the negative precipitation
bias there (Fig. 6d).
The meridional cross sections of simulated zonal mean temperatures, specific hu-
midity, and relative humidity are shown in Fig. 10, along with those biases against
ERA-40 (Uppala et al., 2005). A cold bias is seen near the surface except in the Arctic.20
This is consistent with that in SAT (Fig. 6b). At higher altitudes, a cold bias is seen in
the middle troposphere between 40◦N and 50◦S, and a larger cold bias is seen in the
extratropical upper troposphere and lower stratosphere.
A large negative (dry) bias is seen in the lower troposphere, especially at latitudes
lower than 30 degrees. Reasons for the dry bias in these regions are related to the25
cold bias near the surface (Fig. 10b) and the dry bias in the relative humidity centered
around 850 hPa (Fig. 10d). In the region where the dry bias in relative humidity is
maximal, the dry bias in specific humidity is also maximal. In the upper troposphere
and lower stratosphere, a large positive (wet) bias in relative humidity is seen. This
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bias could cause the negative temperature bias (Fig. 10b) owing to an increase in LW
emission to outer space.
The meridional cross sections of simulated zonal mean eastward winds and tem-
peratures are shown in Fig. 11, along with those for ERA-40 (below 1 hPa) and the
1986 Committee on Space Research (COSPAR) International Reference Atmosphere5
(CIRA) data (above 1 hPa) (Fleming et al., 1990). The model qualitatively reproduces
the observed meridional structure of the zonal mean winds and temperatures in each
month (January, April, July, and October). Relatively large discrepancies between the
model and observations are found in the winter hemisphere high latitudes. More fre-
quent occurrence of stratospheric sudden warming than that in the observations in the10
1990s results in weak winds in the northern winter upper stratosphere and mesosphere
(Fig. 11a). Less gravity wave drag owing to a lack of lateral propagation in gravity wave
drag parameterizations causes strong winds in the southern winter upper stratosphere
and mesosphere (Fig. 11e, see Watanabe, 2008).
Figure 12 compares time-height cross-sections for the reanalysis (ERA-40) and sim-15
ulated equatorial eastward winds. They both show alternation of downward propa-
gating eastward and westward wind shear zones, known as the equatorial QBO. The
mean periods of the observed and simulated QBO are similar to each other, i.e., ap-
proximately 28 months, while the simulated QBO behavior is somewhat regular com-
pared to the reanalysis. The QBO in MIROC-ESM will be detailed in a forthcoming20
paper (Watanabe and Kawatani, 2011).
4.3 Aerosols
Significant changes in atmospheric aerosol loading from pre-industrial times to the
present result from industrial activities and biomass burning (Fig. 13). BC, OM, and
sulfate from anthropogenic sources are emitted from East and South Asia, North Amer-25
ica, and Europe and then transported to the outflow regions. They also originate
from biomass burning caused by deforestation in Southeast Asia, central and south-
ern Africa, and the Amazon. Figure 14 shows the time evolution of the global mean
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increasing rate of mass column loading for each aerosol component. BC and sulfate
gradually increased after the Industrial Revolution to the first half of the 20th century
mainly due to consumption of fossil fuel, and then accelerated after 1950s along with
OM due to rapid industrialization and deforestation. Global mean aerosol mass in the
atmosphere at the end of the 20th century is about three times and twice as large as5
that in 1850 for BC and sulfate, respectively. Atmospheric dust slightly increased in the
20th century relative to the previous century. Anthropogenic aerosols from urban ar-
eas in the mid-latitudes of the Northern Hemisphere concentrate in the boundary layer,
while biomass burning aerosols from tropical and subtropical regions are injected to
higher altitudes due to convection and fire heat (Fig. 15). Figure 16 shows the di-10
rect radiative forcing at the tropopause due to anthropogenic aerosols under all-sky
condition, which have strong negative values over industrialized and biomass burning
regions. The radiative forcings of the direct and indirect effects due to anthropogenic
aerosols are −0.2 W m−2and −0.9 W m−2for the global mean, respectively.
4.4 Atmospheric chemistry15
Figure 17 displays the temporal evolution of the global mean ozone column reproduced
by our past simulation with MIROC-ESM-CHEM using the RCP dataset for CMIP5. The
total ozone column rapidly decreases after 1980 in response to the increased halo-
gen loading, exhibiting the large influence of volcanic eruptions in 1980s and 1990s.
The decreasing trend of total ozone in 1980s, however, appears to be significantly20
underestimated by the model in view of the long-term trend in stratospheric ozone
observed during this time period (about −1 % yr−1) (e.g., World Meteorological Organi-
zation (WMO), 2007). In comparison with the total ozone measurements by the total
ozone mapping spectrometer (TOMS), we found a large underestimation of ozone in
the northern high latitudes. The model clearly simulates impacts of the 11-yr solar cy-25
cle on total ozone. In the troposphere, the model calculates a large increase of ozone
from 24 DU in 1850 to 33 DU in 2000 due to enhanced emissions of precursors. This
increasing ozone trend in the troposphere appears to be contributing to the positive
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trend of total ozone in the period before 1980. The model may overestimate present-
day tropospheric ozone abundance in comparison with the global mean ozone derived
by the global ozone monitoring experiment (GOME) satellite measurements (Liu et
al., 2005, 2006). Our estimated radiative forcing of tropospheric ozone (0.41 W m−2)
is, however, in good agreement with the range suggested by IPCC (2007). The spa-5
tial and seasonal distributions of tropospheric ozone calculated by the model are well
consistent with the GOME observations (Fig. 18). Both the model and observation
show large ozone increases in the northern mid-latitudes in spring (MAM) due to en-
hanced chemical production of ozone and downward transport of stratospheric ozone.
In summer in the Northern Hemisphere, the model well captures the ozone enhance-10
ments over the continental regions of Eurasia and North America. In spring time for the
Southern Hemisphere (SON), outflow of high ozone in the mid-latitudes from biomass
burning in South America and Africa is also well reproduced by the model. On the other
hand, the model appears to underestimate ozone abundances in the northern high lat-
itudes, including the Arctic for spring and summer. We found that this is due principally15
to underestimation of lower stratospheric ozone in high latitudes as described above.
These features of our model simulation suggest that further improvement and validation
of chemical processes are still required for the modeling framework of MIROC-ESM-
CHEM.
4.5 Land surface and terrestrial ecosystems20
The leaf area index (LAI) fraction predicted by MIROC-ESM-CHEM in each grid is
shown in Fig. 19a. The color indicates fractional coverage of LAI from three ecosystem
components, trees, natural grass, and agricultural, and these components may coexist
in the same grid cell. The first component, “trees”, is the fraction of LAI comprising all
woody PFTs in primary and secondary vegetation. The second, ”natural grass”, which25
is the LAI fraction of grasslands without anthropogenic land use. The third component
is “agricultural”, which is the LAI fraction consisting of crops and pasture. Except for
the case when total LAI is zero, the sum total of these three fractions is 1. Therefore,
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this map indicates the relative abundance of the above three ecosystems as a result of
competition among PFTs and of historical human influence on vegetation through the
land use change.
Total vegetation biomass consisting of leaf, stem, and root was 353 PgC for the
2000–2005 average, which is at the lower end of previous reports: 560 PgC in Aj-5
tay et al. (1979), 359 PgC in Dixon et al. (1994), and 466–654 PgC in Prentice et
al. (2001). This is because artificial disturbances to vegetation carbon through land
use change, forest cutting, gradual forest recovery from abandoned agricultural area,
annual harvesting of crops, continuous livestock grazing in pasture and its recovery,
are incorporated in the present model, while previous models deal with the potential10
vegetation only in the absence of artificial disturbance (Friedlingstein et al., 2006; Sitch
et al., 2008) or only consider the natural vegetation and croplands (Kato et al., 2009;
Matthews et al., 2005; Meissner et al., 2003). As a result, the predicted value of vegeta-
tion carbon tends to be lower than that of previous applications of terrestrial ecosystem
models. Recent aggregated results of forest statics considering human impacts re-15
vealed that the forest carbon stock is less than 300 PgC (Kindermann et al., 2008),
which is even lower than the 338 PgC estimated from our model.
The distribution pattern of forest biomass was well reproduced by MIROC-ESM-
CHEM compared with the observation (Fig. 19b, c). Highly accumulated biomass is
found in the tropical forests of the Amazon, Southeast Asia, and tropical Africa. The ex-20
tent of forests and moderate accumulations of carbon in temperate and boreal regions
were also well reproduced. Regions with low forest biomass are located in deserts,
semi-arid regions, and agricultural areas of Europe, Asia, and North America. Although
the model can correctly predict the location of deserts in the Northern Hemisphere, it
failed to capture the semi-arid or desert areas in Australia and steppe and savanna in25
southern Africa. This failure is due to the high sensitivity of plants to soil moisture in
the terrestrial ecosystem model and a positive precipitation bias in these areas.
The total simulated soil organic carbon including litter was 2511 PgC, which is slightly
higher than the estimates of soil inventories to a few meters depth (2376–2456 PgC
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in Batjes, 1996, 2344 PgC in Jobbagy and Jackson, 2000). After linearly scaling to
1 m depth (in our model, the soil grid system for soil decomposition assumes 1.5 m
depth), the amount of soil organic carbon was 1717 PgC, which is within the range of
1395–2070 PgC previously reported (Ajtay et al., 1979; Batjes, 1996; Post et al., 1982;
Prentice et al., 2001; IGBP-DIS, 2000).5
The model was able to capture the global trend of large accumulations of soil organic
carbon in boreal and tundra regions in Eurasia and North America, and small accumu-
lations in tropical and extra-tropical regions (Fig. 19e). Compared with the observation
(IGBP-DIS, 2000; Fig. 19d), the model overestimated the soil carbon in mountainous or
plateau areas such as the Rocky Mountain, Tibetan plateau, and a chain of mountains10
in east Siberia, likely due to the overestimation of vegetation biomass. The reason for
these overestimate in vegetation carbon is that the growth conditions for vegetation in
complicated terrain with high altitude cannot be correctly represented in coarse grid
systems.
Compared with the satellite-based observations (Zhao et al., 2005), the spatial dis-15
tribution and its magnitude of GPP (Gross Primary Production) was well reproduced
by our model (Fig. 19f, g). The global GPP of terrestrial ecosystems averaged over
2000–2005 was 134 PgC yr−1, which is comparable with other model estimates and
present day observations (127.9 PgC yr−1in Ito, 2005; 137 PgC yr−1in Krinner et al.,
2005; 184–187 PgC yr−1in Meissner et al., 2003; 109.29 PgC yr−1in Zhao et al.,20
2005; 119.6 PgC yr−1in Sarmiento and Gruber, 2002). The global total of NPP (Net
Primary Production) averaged over 2000–2005 was 64.3PgC yr−1, which also falls
within the ranges of several model estimates (44.4–66.3 PgC yr−1under 1931–1960
climate conditions: Cramer et al., 1999) and compare reasonably with other estimates
(59.9 PgC yr−1in Ajtay et al., 1979; 59.6 PgC yr−1in Sarmiento and Gruber, 2002;25
56.0 PgC yr−1in Zhao et al., 2005). Global emissions without autotrophic respiration
(i.e., the sum of heterotrophic respiration and gross land use emissions) was 62.8
PgC yr−1. The resultant net carbon fluxes from the atmosphere to land (net biome pro-
duction) in the 1990s and 2000–2005 were 1.34 and 1.50 PgC yr−1, respectively, which
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are within the range reported by Denman et al. (2007).
In MIROC-ESM-CHEM, the distribution and communities of vegetation, biomass,
and the leaf amount are dynamically determined by SEIB-DGVM. It is therefore im-
portant to reproduce the relation between climate and terrestrial ecosystems properly
in evaluating the climate sensitivity including those feedback processes. Suzuki et5
al. (2006) examined the relation between climate zones and vegetation distribution us-
ing the global observation datasets. The climatological condition for vegetation growth
was evaluated by the warmth index (WAI) and the wetness index (WEI), and they were
compared with the normalized difference vegetation index (NDVI), which represents
the greenness of land ecosystems. Figure 20a is a scatter plot of the WEI and the WAI10
with the color tones according to the NDVI, after Fig. 2 of Suzuki et al. (2006). The
NDVI was large where both WEI and WAI were high, while the NDVI was small where
either the WEI or the WAI were low.
Figure 20b shows a scatter plot of WEI and WAI with color tones according to the LAI
calculated by MIROC-ESM-CHEM. The colors for the logarithmic LAI are adapted to15
follow the colors for the NDVI in Fig. 20a, since the increasing rate of NDVI is generally
lower for larger LAI. (Myneni et al., 2002). The general shape of the scatter plot for
WEI and WAI is similar to that in Fig. 20a. MIROC-ESM-CHEM also reproduces the
features of large LAI in regions where WEI and WAI are high, and small LAI where
WEI or WAI is low. However, points with a small WAI and a large WEI were fewer than20
observation. That is probably due to the overestimation of surface air temperature in
the warm season over the continents in MIROC-ESM-CHEM.
4.6 Ocean and marine ecosystems
The ocean circulation model is three dimensional and driven by the surface wind stress
and tracer stratification. The properties of sea water may vary by vertical and horizontal25
mixing of different water masses, but the tracer distribution over a large scale is difficult
to change in the model after the spin-up. In order to investigate the properties of sea
water and their formation, the tracer vertical distributions are analyzed.
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Figure 21 displays the Atlantic meridional sector of sea temperature, salinity, and nu-
trients. The Atlantic surface water has the warmest and saltiest water and is also poor-
est in nutrients compared to deeper layers. With the subtropical gyres in both hemi-
spheres divided by the equatorial upwelling, the meridional vertical section of these
tracers describes a W-shape for a few hundred meters depth from the surface.5
The next layer of subsurface water is called Antarctic Intermediate Water (AAIW),
which slides down from the surface in the Southern Ocean to 1 km depth in the tropics.
The AAIW is relatively fresh (low salinity), rich in nutrients, and the lowest in tempera-
ture at around 100–200 m depth.
Below the AAIW, at a depth of 1000–4000 m, the North Atlantic Deep Water (NADW)10
fills most of the Atlantic basin with more saline and low nutrient water which originates
from the high northern latitudes, but reaches as far south as 40◦S and beyond.
At the bottom of the Atlantic Ocean, the Antarctic Bottom Water (AABW) consists of
the coldest and nutrient rich but less saline water.
Compared to the observations (Conkright et al., 2002), the AAIW and AABW are15
reasonably well simulated by the model, while the NADW looks to be weakly formed,
probably due to stable stratification near the surface. The Atlantic meridional overturn-
ing cell is also somewhat weakly resolved, as the maximum stream function across
30◦N is around 15 ±1 Sv, but is still in a valid range.
Phytoplankton are the microscopic organisms responsible for ocean primary pro-20
duction, and play an important role in controlling the ocean-atmosphere CO2flux, and
hence the global carbon cycle. In order to validate the biogeochemistry-capable gen-
eral circulation model, the spatio-temporal variability of phytoplankton is analyzed.
Figure 22 illustrates the seasonal variation of sea surface chlorophyll in the central
Pacific, around the international dateline. Both the satellite observation and model25
simulation show the sinusoidal curves of meridional migration with time because the
solar insolation is a factor for the chlorophyll growth. Another chlorophyll growth factor
is the nutrient supply. The phytoplankton spring bloom initiates around March (Octo-
ber) in the North (South) subpolar Pacific, as solar insolation and the nutrient supply
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increases and the vertical mixing activates. The North (South) Pacific spring bloom
is underestimated (overestimated) in the simulation mainly due to the underestimated
(overestimated) nutrient distributions beneath.
Other biases are visible at the equator and around 15◦N. These biases are caused
by unrealistic strong trade winds in the central to western Pacific which often appear in5
atmosphere-ocean coupled general circulation models. The anomalous strong easterly
winds further enhance the equatorial upwelling and the development of a cold tongue
in the boreal summer, and also enhance upwelling where the easterly wind velocity is
at a maximum, by enhancing the northward (southward) transport to the north (south).
In summary, some biases are found in the ocean circulation model simulation results10
but these arise from errors in inputs from other components of the model, and all are
reasonable. The seasonality in ocean processes is fairly well simulated.
5 Concluding remarks
In this study, the MIROC-ESM has been fully described and results for the CMIP5
historical simulation have been evaluated from several perspectives: atmosphere,15
ocean, sea-ice, land-surface, ocean and terrestrial biogeochemistry, and atmospheric
chemistry and aerosols. The atmospheric chemistry coupled version of MIROC-ESM
(MIROC-ESM-CHEM) reasonably reproduces the transient variations in global mean
SAT throughout 1850–2005, as well as the geographical distribution of SAT trends. The
climatological distribution of SAT generally agrees with observations, but shows a sys-20
tematic warm bias in the northern mid- and high latitudes and over Antarctica and a cold
bias over the tropics. These temperature biases are likely associated with over-/under-
estimations of low-level clouds, as seen in the TOA OSR biases. The model has the
systematic dry bias in the tropical lower troposphere, which could cause the underes-
timation of low-level clouds. The simulated present-day climatology of the zonal-mean25
zonal winds and temperatures in the stratosphere and mesosphere generally agree
with the observations, and the model self-consistently generates the equatorial QBO
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in the lower stratosphere. The aerosol module simulates the historical evolution of
aerosols in terms of changes in the optical thickness, column mass loading, extinction
coefficient, and direct effect, based on the RCP historical emissions. The simulated
present-day tropospheric column ozone distribution shows reasonable agreement with
satellite observations, although several systematic errors are pointed out. The terres-5
trial carbon cycle component simulates realistic geographical distributions of LAI, GPP,
vegetation biomass, and soil organic carbon. The ocean GCM and marine biogeo-
chemistry component generally simulates the observed latitude-depth distributions of
potential temperatures, salinity, and nutrients in the Atlantic sector, as well as seasonal
variations in chlorophyll over the Pacific sector.10
Overall, MIROC-ESM-CHEM generally shows good performance in the reproduction
of the earth system in the historical period. The model has also been used for several
mandatory simulations of CMIP5, while the atmospheric chemistry uncoupled version
(MIROC-ESM) has been used to conduct a wider variety of simulations. Although
the general results of MIROC-ESM and MIROC-ESM-CHEM agree with each other in15
terms of historical evolution of SAT, aerosols, sea-ice, land surface, and the terrestrial
and marine biogeochemistry, there are several fundamental differences between the
two versions. For instance, ozone concentration is predicted in MIROC-ESM-CHEM,
while prescribed in MIROC-ESM. The prescribed ozone includes effects of historical
evolution of tropospheric ozone precursors and halogen species destroying the strato-20
spheric ozone, but effects of the solar cycle and QBO on the ozone concentration are
neglected in the current setup (Kawase et al., 2011). In this context, MIROC-ESM-
CHEM seems to have a higher potential to realistically reproduce climate variability in
the stratosphere. Further evaluations of the simulated climate fields are required for
both versions, along with the simulated biogeochemistry parameters.25
Several papers on the analyses of our CMIP5 simulations have already been sub-
mitted to peer-reviewed journals, and may be available in the near future. For exam-
ple, Watanabe et al. (2011) and Watanabe and Yokohata (2011), respectively, demon-
strate the projected future evolution of the surface ultraviolet radiation and attribute its
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potential changes to future changes in column ozone, aerosols, clouds, and surface
albedo. Watanabe and Kawatani (2011) focus on the future evolution of the equatorial
QBO associated with climate change.
Acknowledgements. The authors thank Team-MIROC for their support and encouragement
throughout the project. Discussions with Hideharu Akiyoshi were helpful to improve the at-5
mospheric chemistry component. We thank Rikie Suzuki for providing the global datasets
of observation-based climatology and vegetation indices. This study was supported by the
Innovative Program of Climate Change Projection for the 21st Century, MEXT, Japan. The nu-
merical simulations in this study were performed using the Earth Simulator, and figures were
drawn using GTOOL and the GFD-DENNOU Library. This study is supported in part by the10
Funding Program for Next Generation World-Leading Researchers by the Cabinet Office, Gov-
ernment of Japan (GR079).
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Table 1. Variables exchanged between each model component.
Within Atmosphere
(1) Climate (MIROC-AGCM) ⇒Aerosols (SPRINTARS)
Specific Humidity
Mass Mixing Ratio of Cloud (Water plus Ice)
Mass Mixing Ratio of Aerosols (Each Component)
Cloud Droplet Number Concentration
Ice Crystal Number Concentration
Surface Air Pressure
Air Temperature
Surface Altitude
Land Area Fraction
Near-Surface Air Temperature
Eastward Near-Surface Wind Speed
Northward Near-Surface Wind Speed
Diffusion Coefficient
Near-Surface Wind Speed due to Dry Convection
Omega
Solar Zenith Angle
Soil Moisture
Snow Amount
Surface Downwelling Shortwave Radiation
Sea Ice Concentration
Total Cloud Fraction
Leaf Area Index
Precipitation
Convective Cloud Area Fraction
Stratiform Cloud Area Fraction
Mass Mixing Ratio of Cloud Liquid Water
Mass Mixing Ratio of Cloud Ice
Mass Fraction of Cloud Liquid Water
Tendency of Air Temperature due to Radiative Heating
time
time step
(2) Aerosols (SPRINTARS) ⇒Climate (MIROC-AGCM)
Specific Humidity
Mass Mixing Ratio of Cloud (Water plus Ice)
Mass Mixing Ratio of Aerosols (Each Component)
Cloud Droplet Number Concentration
Ice Crystal Number Concentration
Mass Mixing Ratio of Aerosols for Radiation Code
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Table 1. Continued.
Within Atmosphere
(3) Climate (MIROC-AGCM) ⇒Chemistry (CHASER)
Air temperature (3-D & surface)
Specific humidity
Relative humidity
Eastward wind
Northward wind
Vertical wind
Convective mass flux
cloud area fraction (3-D & surface)
atmosphere cloud condensed water content
atmosphere cloud ice content
precipitation flux (3-D & surface)
snowfall flux (3-D & surface)
convective precipitation flux
tendency of cloud condensed water content
tendency of cloud ice content
subgrid diffusion coefficients
upward shortwave flux (3-D & surface)
downward shortwave flux (3-D & surface)
(4) Chemistry (CHASER) ⇒Climate (MIROC-AGCM)
specific humidity
mole fraction of O3in air
mole fraction of CH4in air
mole fraction of N2O in air
mole fraction of Halocarbons in air
(5) Aerosols (SPRINTARS) ⇒Chemistry (CHASER)
aerosol surface density in air
mole fraction of dust aerosol in air
(6) Chemistry (CHASER) ⇒Aerosols (SPRINTARS)
mole fraction of OH in air
mole fraction of O3in air
mole fraction of H2O2in air
#mole fraction & number density of SO4in air
#mole fraction & number density of aerosol nitrate in air
#mole fraction & number density of SOA in air
#aerosol water in air
# CHASER on-line aerosol simulation (not used in CMIP5 simulations)
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Table 1. Continued.
(7) Atmosphere ⇒Ocean
Eastward Wind (lowest layer)
Northward Wind (lowest layer)
Air Temperature (lowest layer)
Specific Humidity (lowest layer)
Air Pressure
Surface Air Pressure
Surface Height
Net Downward Shortwave Radiation at Sea Water Surface
Solar Zenith Angle
Mole Fraction of CO2in Air
Henry constant (for CHASER)
precipitation flux: cumulus (for CHASER)
precipitation flux: Large Scale Condensation (for CHASER)
latitude
(8) Ocean ⇒Atmosphere
Albedo
Surface Temperature
Surface Upward CO2Flux
Bulk Coefficient
Sea Ice Mass
deposition velocity for CHASER
biological emission flux for CHASER
(9) Ocean ⇒Ocean biogeochemistry
Sea Water Potential Temperature
Net Downward Shortwave Radiation at Sea Water Surface
Solar Zenith Angle
Surface Upward CO2Flux
Sea Surface Height Above Geoid
Dissolved Nitrate Concentration
Phytoplankton Carbon Concentration
Zooplankton Carbon Concentration
Detrital Organic Carbon Concentration
Calcite Concentration
Calcium
Dissolved Inorganic Carbon Concentration
Total Alkalinity
Sea Water Salinity
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Table 1. Continued.
(10) Ocean biogeochemistry ⇒Ocean
Surface Aqueous Partial Pressure of CO2
Sea Water CO2Solubility
(11) Atmosphere ⇒Land (MATSIRO)
Eastward Wind (lowest layer)
Northward Wind (lowest layer)
Air temperature (lowest layer)
Specific humidity (lowest layer)
Air pressure (Lowest layer/Surface)
Downward radiation fluxes (6 components: Visible/Near Infrared/Infrared, Direct/Diffuse)
Solar Zenith Angle (for parameterization of radiation transfer in canopy
Mole Fraction of CO2in Air (lowest layer)
Henry constant (from CHASER)
Precipitation (including snowfall, 2 types: cumulus/large-scale condensation)
Surface deposition of soil dust (from SPRINTARS)
Surface deposition of black carbon (from SPRINTARS)
(12) Land (MATSIRO) ⇒Atmosphere
Surface Upward Eastward Wind Stress
Surface Upward Northward Wind Stress
Surface Upward Sensible heat flux
Surface Upward Latent heat flux
Upward radiation fluxes (Short wave/Long wave)
Albedo (6 components: Visible/Near Infrared/Infrared, Direct/Diffuse)
Surface temperature
Evapotranspiration (6 components: Transpiration/Interception/Ground, Evaporation/Sublimation)
Snow sublimation
10 m Wind (to SPRINTARS, CHASER)
2 m temperature (to SPRINTARS, CHASER)
2 m Specific humidity (to SPRINTARS, CHASER)
Surface wetness (to SPRINTARS)
Snow water equivalent (to SPRINTARS)
Bulk coefficient for eddy transfer (to SPRINTARS)
Deposition fluxes of tracers (lowerst layer/surface) (to CHASER)
Emission (to CHASER)
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Table 1. Continued.
(13) MATSIRO ⇒SEIB-DGVM
Precipitation
Downward short wave radiation
Mole fraction of CO2in air
2 m temperature
Eastward 10 m wind speed
Northward 10 m wind speed
2 m Specific humidity
Soil temperature
(14) SEIB-DGVM ⇒MATSIRO
Leaf Area Index
Atmosphere-Land carbon flux (Net carbon balance) (Through to Atmosphere)
(15) MATSIRO ⇒Ocean
River runoff
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40
1
Figure 1. Structure of MIROC-ESM. The numbers refer to the variables in Table 1. 2
3
4
Fig. 1. Structure of MIROC-ESM. The numbers refer to the variables in Table 1.
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41
1
Figure 2. Coupling of chemistry and aerosol calculations (based on the CHASER and 2
SPRINTARS models) in the MIROC-ESM-CHEM modeling framework. Note that SOA 3
production from VOCs and nitrate aerosol (NO3-) are considered in the CHASER component 4
in cooperation with the aerosol thermodynamics module ISORROPIA, but are not included in 5
the simulation for the CMIP5 and other related experiments. 6
7
Fig. 2. Coupling of chemistry and aerosol calculations (based on the CHASER and SPRINT-
ARS models) in the MIROC-ESM-CHEM modeling framework. Note that SOA production from
VOCs and nitrate aerosol (NO−
3) are considered in the CHASER component in cooperation with
the aerosol thermodynamics module ISORROPIA, but are not included in the simulation for the
CMIP5 and other related experiments.
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42
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Figure 3. Spin-up procedures of MIROC-ESM. 2
3
4
Fig. 3. Spin-up procedures of MIROC-ESM.
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43
1
Figure 4. Temporal variations of global annual mean surface air temperature (SAT). 2
Anomalies from the 1851-1900 mean for the observations (Brohan et al., 2006; black line) 3
and the MIROC-ESM-CHEM simulation (red line). In calculating the global annual mean 4
SAT, modeled data are projected onto the same resolution as the observations, discarding 5
simulated data at grid points where there was missing observational data. At each location, 6
more than two months of data were required to calculate the seasonal mean value, and all four 7
seasons of data were required to calculate the annual mean value. 8
9
Fig. 4. Temporal variations of global annual mean surface air temperature (SAT). Anomalies
from the 1851–1900 mean for the observations (Brohan et al., 2006; black line) and the MIROC-
ESM-CHEM simulation (red line). In calculating the global annual mean SAT, modeled data are
projected onto the same resolution as the observations, discarding simulated data at grid points
where there was missing observational data. At each location, more than two months of data
were required to calculate the seasonal mean value, and all four seasons of data were required
to calculate the annual mean value.
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44
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Figure 5. Geographical distributions of linear surface air temperature trends (K/century) in the 2
(a,b) first and (c,d) second half of the 20th century for the (a,c) observations (Brohan et al., 3
2006) and (b,d) the MIROC-ESM-CHEM simulation. Trends were calculated from annual 4
mean values only for those grid points where the annual data is available in at least 2/3 of the 5
50 years and distributed in time without significant bias. 6
7
Fig. 5. Geographical distributions of linear surface air temperature trends (K/century) in the
(a, b) first and (c, d) second half of the 20th century for the (a, c) observations (Brohan et
al., 2006) and (b, d) the MIROC-ESM-CHEM simulation. Trends were calculated from annual
mean values only for those grid points where the annual data is available in at least 2/3 of the
50 yr and distributed in time without significant bias.
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Figure 6. (a) Annual mean climatology of surface air temperature (SAT) for the 1961-1990 2
period for the MIROC-ESM-CHEM simulations and (b) biases in the annual mean SAT 3
climatology against the observational dataset (Jones et al., 1999). (c) Annual mean 4
climatology of precipitation for the 1981-2000 period for the MIROC-ESM-CHEM 5
simulations and (d) biases in the annual mean precipitation climatology against the GPCP 6
observational dataset (Adler et al., 2003). 7
8
Fig. 6. (a) Annual mean climatology of surface air temperature (SAT) for the 1961–1990 period
for the MIROC-ESM-CHEM simulations and (b) biases in the annual mean SAT climatology
against the observational dataset (Jones et al., 1999). (c) Annual mean climatology of precip-
itation for the 1981–2000 period for the MIROC-ESM-CHEM simulations and (d) biases in the
annual mean precipitation climatology against the GPCP observational dataset (Adler et al.,
2003).
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Figure 7. Seasonal climatology of Arctic and Antarctic sea-ice concentration in the 1990s: for 2
the boreal (JJA) and austral (DJF) summer, and the reanalysis (OISST) and simulation 3
(MIROC-ESM-CHEM). 4
5
Fig. 7. Seasonal climatology of Arctic and Antarctic sea-ice concentration in the 1990s: for the
boreal (JJA) and austral (DJF) summer, and the reanalysis (OISST) and simulation (MIROC-
ESM-CHEM).
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Figure 8. Annual mean net downward (a,b) shortwave (SW) radiation at the top of the 2
atmosphere (TOA), and (c,d) SW cloud radiative forcing (CRF) at the TOA. Results of the 3
model simulation are shown in (a,c), and the model bias is shown in (b,d) (see text). 4
5
Fig. 8. Annual mean net downward (a, b) shortwave (SW) radiation at the top of the atmosphere
(TOA), and (c, d) SW cloud radiative forcing (CRF) at the TOA. Results of the model simulation
are shown in (a, c), and the model bias is shown in (b, d) (see text).
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Figure 9. Same as Figure 8, but for the longwave (LW) radiation. 2
3
Fig. 9. Same as Fig. 8, but for the longwave (LW) radiation.
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Figure 10. Zonal mean of annual averaged model climatology (1980-1999) for (a) 2
atmospheric temperature, (c) specific humidity, and (e) relative humidity. Differences 3
between the model climatology and ERA-40 data are displayed in (b), (d), and (f), 4
respectively. 5
6
Fig. 10. Zonal mean of annual averaged model climatology (1980–1999) for (a) atmospheric
temperature, (c) specific humidity, and (e) relative humidity. Differences between the model
climatology and ERA-40 data are displayed in (b),(d), and (f), respectively.
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Figure 11. Zonal-mean zonal winds and temperatures in (a, b) January, (c, d) April, (e, f) July, 2
and (g, h) October. Left column shows model climatology (1980-1999), and the right column 3
shows observed climatology: ERA-40 (1980-1999) below the 1 hPa level and CIRA86 (1975-4
1978) above. 5
6
Fig. 11. Caption on next page.
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Fig. 11 . Zonal-mean zonal winds and temperatures in (a, b) January, (c, d) April, (e, f) July,
and (g, h) October. Left column shows model climatology (1980–1999), and the right column
shows observed climatology: ERA-40 (1980–1999) below the 1 hPa level and CIRA86 (1975–
1978) above.
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Figure 12. Zonal-mean zonal winds over the equator for (a) ERA-40 and (b) the model result. 2
3
Fig. 12. Zonal-mean zonal winds over the equator for (a) ERA-40 and (b) the model result.
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Figure 13. Anomaly of the aerosol optical thickness averaged for the period 1991-2000 2
relative to the period 1851-1860. 3
4
Fig. 13. Anomaly of the aerosol optical thickness averaged for the period 1991–2000 relative
to the period 1851–1860.
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Figure 14. Time evolution of global annual mean increasing rate of mass column loading for 2
each aerosol component relative to the mean of the period 1851-1860. 3
4
Fig. 14. Time evolution of global annual mean increasing rate of mass column loading for each
aerosol component relative to the mean of the period 1851–1860.
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Figure 15. Anomaly of the zonal-mean aerosol extinction coefficient averaged for the period 2
1991-2000 relative to the period 1851-1860. 3
4
Fig. 15. Anomaly of the zonal-mean aerosol extinction coefficient averaged for the period
1991–2000 relative to the period 1851–1860.
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Figure 16. Annual mean radiative forcing of the aerosol direct effect under all-sky conditions 2
in the year 2000 relative to 1850. 3
4
Fig. 16. Annual mean radiative forcing of the aerosol direct effect under all-sky conditions in
the year 2000 relative to 1850.
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Figure 17. Time series of global mean total and tropospheric ozone column abundance in 2
Dobson Units simulated by the MIROC-ESM-CHEM (blue and red lines, respectively). 3
Triangles represent tropospheric column ozone derived from satellite measurements (GOME) 4
for around 2000. 5
6
Fig. 17. Time series of global mean total and tropospheric ozone column abundance in Dob-
son Units simulated by the MIROC-ESM-CHEM (blue and red lines, respectively). Triangles
represent tropospheric column ozone derived from satellite measurements (GOME) for around
2000.
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Figure 18. Tropospheric column ozone distributions (a) observed by GOME for 2000 and (b) 2
calculated by the model for distinct seasons. The modeled ozone is shown as an average of 3
simulations for 2000-2003 (using the averaging kernel for GOME 2000). 4
5
Fig. 18. Tropospheric column ozone distributions (a) observed by GOME for 2000 and (b) cal-
culated by the model for distinct seasons. The modeled ozone is shown as an average of
simulations for 2000–2003 (using the averaging kernel for GOME 2000).
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Figure 19. (a) LAI fraction predicted by the model. (b) Forest carbon from Kindermann et al. 2
(2008). (c) Model-predicted forest carbon. (d) Soil carbon to 1 m depth from IGBP-DIS 3
(2000). (e) Model-predicted soil carbon linearly scaled to 1 m depth. (f) Gross primary 4
production from Zhao et al. (2005). (g) Model predicted gross primary production. All model 5
outputs are the results averaged over 2000-2005. 6
7
Fig. 19. (a) LAI fraction predicted by the model. (b) Forest carbon from Kindermann et
al. (2008). (c) Model-predicted forest carbon. (d) Soil carbon to 1 m depth from IGBP-DIS
(2000). (e) Model-predicted soil carbon linearly scaled to 1 m depth. (f) Gross primary produc-
tion from Zhao et al. (2005). (g) Model predicted gross primary production. All model outputs
are the results averaged over 2000–2005.
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Figure 20. (a) Observation-based relation of WEI (wetness index), WAI (warmth index), and 2
NDVI (normalized differential vegetation index) over the global continents at 1 degree 3
resolution. The NDVI value is shown by color at the intersection of WEI and WAI. (b) As in 4
(a), but for the model result. The colors are for the logarithmic LAI, and at the model 5
resolution (T42). 6
7
Fig. 20. (a) Observation-based relation of WEI (wetness index), WAI (warmth index), and NDVI
(normalized differential vegetation index) over the global continents at 1 degree resolution. The
NDVI value is shown by color at the intersection of WEI and WAI. (b) As in (a), but for the model
result. The colors are for the logarithmic LAI, and at the model resolution (T42).
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Figure 21. Annual mean climatology of the Atlantic latitude-depth sector at 25W: sea water 2
potential temperature (T, degC), salinity (S, PSU), and nutrients (N, mmolN/m3), for the 3
observations (WOA01) (left column) and simulation (MIROC-ECM-CHEM) (right column). 4
5
Fig. 21. Annual mean climatology of the Atlantic latitude-depth sector at 25◦W: sea water
potential temperature (T, degC), salinity (S, PSU), and nutrients (N, mmolN m−3), for the ob-
servations (WOA01) (left column) and simulation (MIROC-ECM-CHEM) (right column).
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Fig. 22. Time-latitude Hovmoeller diagram of the sea surface chlorophyll density (mg Chl m−3)
around the international dateline (zonally averaged for 170◦E–170◦W) for (a) satellite obser-
vation (SeaWiFS) and (b) the simulation (MIROC-ECM-CHEM). Note that the satellite obser-
vation is missing data in the polar night or under sea-ice because the SeaWiFS measures the
chlorophyll remotely by the ocean surface color.
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