A New Global Climate Model of the Meteorological Research

Journal of the Meteorological Society of Japan, Vol. 90A, pp. 23--64, 2012. 23

DOI:10.2151/jmsj.2012-A02

A New Global Climate Model of the Meteorological Research Institute: MRI-CGCM3

—Model Description and Basic Performance—

Seiji YUKIMOTO, Yukimasa ADACHI, Masahiro HOSAKA, Tomonori SAKAMI,

Hiromasa YOSHIMURA, Mikitoshi HIRABARA, Taichu Y. TANAKA, Eiki SHINDO,

Hiroyuki TSUJINO, Makoto DEUSHI, Ryo MIZUTA, Shoukichi YABU, Atsushi OBATA,

Hideyuki NAKANO, Tsuyoshi KOSHIRO, Tomoaki OSE, and Akio KITOH

Meteorological Research Institute, Tsukuba, Japan

(Manuscript received 16 March 2011, in final form 4 August 2011)

Abstract

A new global climate model, MRI-CGCM3, has been developed at the Meteorological Research Institute(MRI). This model is an overall upgrade of MRI’s former climate model MRI-CGCM2 series. MRI-CGCM3 iscomposed of atmosphere-land, aerosol, and ocean-ice models, and is a subset of the MRI’s earth system modelMRI-ESM1. Atmospheric component MRI-AGCM3 is interactively coupled with aerosol model to represent di-rect and indirect e¤ects of aerosols with a new cloud microphysics scheme. Basic experiments for pre-industrialcontrol, historical and climate sensitivity are performed with MRI-CGCM3. In the pre-industrial control experi-ment, the model exhibits very stable behavior without climatic drifts, at least in the radiation budget, the temper-ature near the surface and the major indices of ocean circulations. The sea surface temperature (SST) drift is suf-ficiently small, while there is a 1 W m�2 heating imbalance at the surface. The model’s climate sensitivity isestimated to be 2.11 K with Gregory’s method. The transient climate response (TCR) to 1 % yr�1 increase of car-bon dioxide (CO2) concentration is 1.6 K with doubling of CO2 concentration and 4.1 K with quadrupling ofCO2 concentration. The simulated present-day mean climate in the historical experiment is evaluated by compar-ison with observations, including reanalysis. The model reproduces the overall mean climate, including seasonalvariation in various aspects in the atmosphere and the oceans. Variability in the simulated climate is also eval-uated and is found to be realistic, including El Nino and Southern Oscillation and the Arctic and Antarctic oscil-lations. However, some important issues are identified. The simulated SST indicates generally cold bias in theNorthern Hemisphere (NH) and warm bias in the Southern Hemisphere (SH), and the simulated sea ice expandsexcessively in the North Atlantic in winter. A double ITCZ also appears in the tropical Pacific, particularly in theaustral summer.

1. Introduction

Climate models have been advanced to simulatemany aspects of the observed climate. In theFourth Assessment Report of the Intergovernmen-tal Panel on Climate Change (IPCC) (hereafter,IPCC-AR4), the projections of future climatechange were based on numerous experiments with

more than 20 state-of-the-art climate models, andyielded results with quantitative confidence levels.

The range of uncertainties in the projections,however, remained as large as in the 3rd Assess-ment Report (IPCC 2001). Bony and Dufresne(2005) suggested that a major source of uncertaintyin climate sensitivity is feedback in tropical lowclouds. The uncertainty related to radiative forcingwas also a major factor. Many questions remain re-garding the modeling of the indirect e¤ects of aero-sols (e.g., Forster et al. 2007), which must take intoaccount sophisticated cloud microphysics (involv-ing high computational costs). No models thus far

Corresponding author: Seiji Yukimoto, Climate Re-search Department, Meteorological Research Institute,1-1 Nagamine, Tsukuba, 305-0052, Japan.E-mail: [email protected] 2012, Meteorological Society of Japan

have involved a fully interactive atmospheric chem-istry of aerosols with cloud microphysics for cli-mate change experiments, although major uncer-tainties are associated with the aerosol e¤ects.

The Meteorological Research Institute (MRI),with the former climate model MRI-CGCM2.3.2(Yukimoto et al. 2006), contributed to the IPCC-AR4 by providing results from numerous experi-ments for the third phase of the Coupled ModelIntercomparison Project (CMIP3). For the experi-ments, MRI-CGCM2.3.2 used global flux adjust-ments (Manabe and Stou¤er 1988) for heat andfreshwater, and partially for momentum. The mainreason for using the flux adjustments is that themodel output is used as boundary conditions ofthe regional climate model (RCM) for downscalingthe future climate change in the vicinity of Japan(Kurihara et al. 2005), which requires high preci-sion and reproducibility of climate. Except for theRCM’s boundary condition, the model exhibitedsu‰cient performance without flux adjustments insimulating the mean climate and variability onglobal and sub-continental scales (Kitoh 2004).

We have developed a new climate model, MRI-CGCM3. This model is an overall upgrade of theMRI-CGCM2 series. This climate model is a coresubset of MRI’s earth system model MRI-ESM1(Yukimoto et al. 2011). MRI-CGCM3 consists ofthe atmosphere-land model (MRI-AGCM3), theocean and sea ice model (MRI.COM3), and theaerosol model (MASINGAR mk-2). These compo-nent models are coupled with a simple and flexiblecoupler ‘‘Scup’’, which enables us to make a varietyof combinations of the component models with ar-bitrary resolutions and grid coordinates.

Phase five of the CMIP (CMIP5) experiments isplanned (Taylor et al. 2011; available at http://cmip-pcmdi.llnl.gov/cmip5/experiment_design.html). These experiments are divided into twomajor categories, long-term experiments and near-term (decadal prediction) experiments. The long-term experiments are further divided into an exper-iment group driven by concentrations of the GHGsand other forcing agents (C-driven), and an experi-ment group driven by emissions (E-driven). MRIwill perform all these experiments with this unifiedmodel. The E-driven experiments will be performedwith the fully configured MRI-ESM1, includingthe atmospheric chemistry climate model (MRI-CCM2), and submodels of the carbon cycle pro-cess, representing terrestrial and marine ecology.The other experiments will be performed with

MRI-CGCM3, which has the same configurationas the MRI-ESM1 but without MRI-CCM2 andthe carbon cycle. We will perform all the experi-ments for CMIP5 without any flux adjustments.

Atmospheric aerosols influence the climate byperturbing the Earth’s radiation budget in severalways. A direct radiative e¤ect is caused by the di-rect scattering and absorption of atmospheric radi-ation by aerosols. An indirect radiative e¤ect isaerosols acting as cloud condensation nuclei and af-fects cloud albedo (Twomey 1974; Twomey 1991),precipitation formation, and cloud lifetime charac-teristics (Albrecht 1989). Absorptive aerosols, suchas black carbon (BC) or mineral dust, warm the at-mosphere and reduce solar radiation at the surface,thus increasing atmospheric stability. Absorptiveaerosols can locally inhibit cloud formation or re-duce cloud cover by heating cloud droplets. This iscalled the semi-direct aerosol e¤ect (Hansen et al.1997). Moreover, absorptive aerosols deposited ona snow surface reduce the albedo of the snow sur-face and enhance the melting of the snow (Hansenand Nazarenko 2004). The aerosol model is interac-tively incorporated into MRI-CGCM3, which ex-plicitly presents the interaction between such e¤ectsof aerosols and climate perturbation.

We conducted a pre-industrial control experi-ment (piControl in the CMIP5 syntax) and a his-torical experiment. With the piControl experiment,we evaluate the stability of the model’s climate.The experiment is also used as a reference for vari-ous experiments. By comparing the instrument ob-servations with the historical experiment of 1850through 2005, we can examine the capability of themodel to reproduce the historical climate changeas well as the present-day climate. In particular, itis important to assess the reproducibility of recentdecades since 1979 from a comprehensive perspec-tive, since observations are enhanced with advancesin various satellites for this period. Based on theresults of some of the climate sensitivity experi-ments, we also examine the climate sensitivity ofthe model.

This paper is structured as follows. Section 2describes the model’s configuration. Section 3 de-scribes the experiment design and the spin-up ofthe model. Section 4 demonstrates the stability andclimate drift of the model in the piControl experi-ment. Section 5 examines the climate sensitivity ofthe model and the reproducibility of historical cli-mate change, and Section 6 evaluates how the cli-mate in the recent past is reproduced, including the

24 Journal of the Meteorological Society of Japan Vol. 90A

mean field and the variability. Section 7 presents asummary and discussion.

2. Model description

The model we describe in this paper is MRI-CGCM3, which is a subset of MRI-ESM1 (Yuki-moto et al. 2011). It is a coupled atmosphere-oceanglobal climate model that is composed of MRI-AGCM3 and MRI.COM3, whose atmosphericpart (MRI-AGCM3) is interactively coupled withaerosol model MASINGAR mk-2. See Yukimotoet al. (2011) for a detailed description of the model.

2.1 Atmospheric model (MRI-AGCM3)

Newly developed atmospheric model MRI-AGCM3 is based on JMA’s operational forecastmodel. Its dynamics frame is a global spectralmodel with hydrostatic primitive equations as prog-nostic equations. A two-time-level semi-implicitsemi-Lagrangian scheme is used for time integra-tion. This scheme permits a longer time-step thanthe formerly used semi-implicit Eulerian scheme(JMA 2002) and realizes high e‰ciency. The semi-Lagrangian advection scheme is vertically conser-vative (Yoshimura and Matsumura 2003, 2005)as well as globally conservative. The model weuse here has a horizontal resolution of TL159(A120 km), with 48 layers in the vertical eta co-ordinate. The top of the atmosphere (TOA) is0.01 hPa, so the stratosphere is fully covered.

This subsection describes each physical processscheme of MRI-AGCM3 used in MRI-CGCM3 toparticipate in the CMIP5. Table A1 summarizes theconfiguration of the atmospheric model. For refer-ence, a di¤erent model configuration is also pre-sented. This configuration uses the same MRI-AGCM3 series to participate in the CMIP5 as adi¤erent modeling group with a set of very-high-resolution atmospheric model experiments (MRI-AGCM3.2S) (Mizuta et al. 2011).

a. Cumulus convection

A new mass-flux cumulus scheme has been devel-oped (Yoshimura et al. in preparation; hereafter,the Yoshimura cumulus scheme; see also Yukimotoet al. 2011, for a detailed description). In thisscheme, convective updrafts are calculated as de-tailed entraining and detraining plumes, as inTiedtke (1989). It also represents multiple convec-tive updrafts with di¤erent heights, as in an AS-type scheme (Arakawa and Schubert 1974), byconsidering continuous convective updrafts be-tween the minimum and maximum turbulent

entrainment/detrainment rates (lmin and lmax). Atthe cloud bottom, lmin is set to 0:5� 10�4 m�1 andlmax is set to 3:0� 10�4 m�1. Magnitudes of theconvective updrafts are determined by using a clo-sure assumption (Nordeng 1994), which is basedon convective available potential energy (CAPE).Convective updrafts are assumed to have virtualtemperature, water vapor mixing ratios, and othervariables linearly interpolated between the two up-drafts with lmin and lmax.

In addition to turbulent entrainment, two kindsof organized entrainment are considered: entrain-ment from a layer with high moist static energyfrom which updrafts originate and entrainmentnearly proportional to the grid-scale mass conver-gence and the convective updraft mass flux. Whenthe convective updraft becomes negatively buoyant,an organized detrainment occurs at that level.

The scheme represents a convective downdraftwhen a detrained air mixed with the environmentair becomes negatively buoyant. The turbulententrainment/detrainment rate for the convectivedowndraft is 2:0� 10�4 m�1. The mass flux of theconvective downdraft is limited so as not to exceed0.3 times the sum of the mass fluxes of the con-vective updrafts. When the convective downdraftbecomes positively buoyant at some level, the en-tire downdraft mass flux is detrained at that levelas organized detrainment. If the downdraft doesnot become positively buoyant, organized detrain-ment from the downdraft occurs at the sub-cloudlayer.

The vertical transport of horizontal momentumby convection is calculated. The e¤ect of the sub-grid-scale horizontal pressure gradient force is in-troduced based on Gregory et al. (1997). This e¤ectacts to adjust the direction of the horizontal wind inconvection toward that of the wind in the environ-ment. Without this e¤ect, the momentum transportis overestimated. The pressure gradient force is setproportional to the vertical wind shear in the envi-ronment.

b. Radiation

The radiation scheme is basically the same asthat of JMA’s operational global model (see detailsin JMA 2007), but with some di¤erences (e.g., inthe interaction with aerosols in MRI-AGCM3). In-frared (i.e., longwave (LW)) radiation of up to3000 cm�1 and solar (shortwave (SW)) radiationare treated separately. The radiative flux is calcu-lated in 9 LW bands and 22 SW bands. In the LW

February 2012 S. YUKIMOTO et al. 25

and SW schemes, major absorptions due to watervapor (line and continuum absorption), carbon di-oxide (CO2) (e.g., in the 15 mm band and near-infrared region), and ozone (O3) (in the 9.6 mmband and the visible and ultraviolet regions) areconsidered. In addition, absorptions due tomethane (CH4), dinitrogen monoxide (N2O), andchlorofluorocarbons (CFCs; CFC-11, CFC-12,and HCFC-22) are taken into account in the LWscheme because of their greenhouse gas (GHG) ef-fect. Absorption by oxygen (O2) and Rayleigh scat-tering by molecules of atmospheric gas are also cal-culated in the SW scheme.

To represent the direct e¤ects of aerosols, opticalparameters are configured for five aerosol species,corresponding to those in aerosol model MASIN-GAR mk-2. The extinction and absorption coe‰-cients and asymmetry factors of these species arecomputed under the assumption of Mie scatteringby spherical particles, by using the complex refrac-tion index data of OPAC software (Hess et al.1998). The dependence of hygroscopic species onambient relative humidity is also considered (Chinet al. 2002).

The e¤ect of aerosols on the optical propertiesof clouds (i.e., the first indirect e¤ect) is consideredin the configuration of the e¤ective radius of cloudparticles. The e¤ective radius of a liquid watercloud particle is computed as a function of clouddroplet number density, based on Liu et al. (2006)and Peng and Lohmann (2003). Since simple cloudmicrophysics is introduced for the cloud scheme(see the next subsection), the aerosol indirect e¤ecton ice-cloud particles is also considered (Lohmann2002).

In terms of the optical properties of cloud par-ticle, LW emissivity is parameterized depending oncloud water content (Kiehl and Zender 1995), andthe corresponding absorption coe‰cient is parame-terized as a function of e¤ective radius (Hu andStamnes 1993, for liquid water clouds, and Ebertand Curry 1992, for ice clouds). Optical depth,single-scattering albedo, and the asymmetry factorin SW are similarly parameterized by cloud watercontent and e¤ective radius (Slingo 1989, for waterclouds and Ebert and Curry 1992, for ice clouds).

The vertical overlap of clouds greatly influencesestimates of radiative fluxes in a cloudy column.In the LW scheme, maximum-random overlap (Ge-leyn and Hollingsworth 1979) is assumed. In theSW scheme, total cloudiness in a column is firstcomputed according to the maximum-random

overlap assumption, and then random overlap isassumed to solve radiative fluxes in a cloudy sub-column.

Because of relatively high computational costs,full radiation computations are calculated for everytwo grids in the zonal direction, for every hour inthe SW region, and for every three hours in theLW region.

c. Cloud model

Cloud droplet and ice crystal concentrations areimportant variables for representing the detailed in-direct e¤ects of aerosols. A new two-moment bulkcloud scheme (the MRI-TMBC scheme) has beendeveloped by expanding the Tiedtke cloud scheme(Tiedtke 1993; ECMWF 2004; Jakob 2000). Thisscheme predicts the cloud liquid water (CLW) mix-ing ratio, cloud-ice mixing ratio, and cloud dropletand ice crystal concentrations. It represents forma-tion of cloud droplets and ice crystals by convectiveprocesses (i.e., cumulus detrainment) and stratiformprocesses, and phase changes by immersion freez-ing, contact freezing, and homogeneous freezing.

The aerosol model is coupled to the MRI-TMBCscheme for activation of aerosol species into clouddroplets and ice crystals. The aerosol species em-ployed in the cloud scheme are SOxþ dimethyl sul-fide (DMS), BC, OC, sea-salt (two bins of particlesize), and mineral dust (six bins of particle size).The activation of some aerosols into cloud dropletsis based on the parameterizations of Abdul-Razzakand Ghan (2000, 2002) and Takemura et al. (2005).The activation of some aerosols into ice crystals isbased on the parameterizations of Bigg (1953) forimmersion freezing, Lohman and Diehl (2006) forcontact freezing, and Karcher et al. (2006) for cir-rus clouds. Cloud droplet and ice crystal concentra-tions evaluated by the scheme reflect radiation pro-cesses through their e¤ective radii.

The deposition terms are based on Murakami(1990), the depositional growth terms are basedon Rutledge and Hobbs (1983), and the condensa-tion and condensation growth terms are based onTiedtke (1993). Melting occurs when the atmo-spheric temperature is above 273.15 K, and homo-geneous freezing occurs at temperatures below235.0 K. A semi-Lagrangian scheme is used for theadvection process. The precipitation process is basi-cally the same as that of Tiedtke (1993), except theparameterization of Rotstayn (2000) is adopted forthe rainfall term. The Bergeron-Findeisen process isalso incorporated into the MRI-TMBC scheme.


This process occurs when the cloud-ice mixing ratioexceeds 0.5 mg kg�1 (Lohman et al. 2007). Further-more, a saturation adjustment proposed by Tao(1989) is introduced.

d. Planetary boundary layer

The planetary boundary layer (PBL) scheme inJMA’s operational model is the turbulence modeladvocated by Mellor and Yamada (1974, 1982)(the MY scheme), which is a second-order closuremodel based on the Reynolds averaging method.Nakanishi (2001) and Nakanishi and Niino (2004,2006, 2009) proposed an improved version of theMY scheme (called the MYNN scheme) for deter-mining closure constants, mixing length, and thestability of time integration. We introduced the firsttwo improvements into MRI-AGCM3 as follows.First, based on data obtained by large eddy simula-tion (LES), the MYNN scheme re-evaluates theclosure constants and introduces terms for the e¤ectof buoyancy and wind shear, which are neglected inthe MY model. Second, a new diagnostic equationfor the mixing length is proposed; it evaluates thestability of the surface layer by using the Monin-Obukhov length and the e¤ect of buoyancy (Hara2007a, 2007b).

e. Land surface model

A new land surface model named the Hydrology,Atmosphere and Land (HAL) model (Hosaka et al.in preparation) has been developed for MRI-AGCM3. HAL consists of three submodels: SiByl(vegetation), SNOWA (snow), and SOILA (soil).

Vegetation submodel SiByl has surface vegeta-tion processes similar to those of JMA/SiB (Hiraiet al. 2007). SiByl has two vegetation layers (can-opy and grass) and calculates heat, moisture, andmomentum fluxes between the land surface and theatmosphere. Precipitation interception and evapo-transpiration are included as moisture processes.Implemented heat processes include SW radiation(direct/di¤use, visible/infrared), LW radiation, sen-sible heat, latent heat, and ground heat fluxes. Abulk formulation scheme (Louis 1979) is used forestimating surface flux. Air surface information(e.g., air temperature at 2 m height) is diagnosed.

Snow submodel SNOWA can have any numberof snow layers; the maximum value is set to eightfor the CMIP5 experiments. The number of layersdepends on the snow water equivalent (SWE) andthe snow accumulation history. The predicted vari-ables for snow are temperature, SWE, density,grain size, and the aerosol deposition contents of

each layer. Water phase change (snow melting) oc-curs when the temperature of each layer exceeds�1�C. The bottom snow melts when the tempera-ture of the uppermost underlying soil layer is above1�C. Snow properties, including grain size, are pre-dicted due to snow metamorphism (Niwano et al.in preparation), and the snow albedo is diagnosedfrom the aerosol mixing ratio, the snow properties,and the temperature (Aoki et al. 2011).

Soil submodel SOILA can have any number ofsoil layers. In the CMIP5 experiments, it is com-posed of 14 soil layers with depths of 2, 3, 5, 10,10, 20, 30, 30, 40, 100, 100, 150, 200, and 300 cm,for a total depth of 10 m. The temperature of eachlayer is predicted by solving heat-conduction equa-tions. The water phase change occurs between�1�C and 1�C. The number of layers in which soilmoisture is predicted depends on the vegetationtype (350 cm in forest grids to 50 cm in desertgrids). The infiltration flux of liquid water is esti-mated by solving the Darcy equation, in whichhydraulic conductivity depends on soil moisture.Drainage (gravitational runo¤ ) occurs from thebottom layer, and surface runo¤ occurs from thetop layer.

f. Ocean-surface process

An important function of the ocean-surfacescheme is calculating turbulent heat, moisture, andmomentum fluxes. A simple skin sea surface tem-perature (SST) scheme is used in MRI-AGCM3as the lower boundary over the sea surface. Thisscheme is designed to represent short-term tempera-ture variation (e.g., diurnal variation) at the air–seainterface, caused by short-term variations in windand solar radiation. The scheme has one sub-skinlayer with a linear temperature profile. Bulk SST isthe temperature at the bottom of the sub-skin layer,which is given by the first layer temperature of theOGCM in MRI-CGCM3, and is fixed during eachtime step in the calculation of the fluxes over thesea surface.

The interface temperature is the temperature atthe top of the sub-skin layer, which is estimatedfrom heat fluxes (including radiation) to the atmo-sphere and from the bottom of the sub-skin layer.The interface temperature and the sensible and la-tent heat fluxes to the atmosphere are determinedat the same time by an implicit method from verti-cal di¤usivity, which is calculated in the PBLscheme. The bulk coe‰cients for the heat flux esti-mation follow Louis (1979) and Louis et al. (1982),


except those for turbulent fluxes in an unstable statefollow Miller et al. (1992).

Since the sub-skin layer is mixed well with theunderlying layer under windy conditions (and viceversa), the empirical coe‰cient for the heat fluxfrom the bottom of the layer depends on windspeed; it is larger (smaller) when the wind isstronger (weaker) (Kawai and Wada 2007; Gente-mann et al. 2003; Castro et al. 2003). See Yukimotoet al. (2011) for the detailed formulation. TheOGCM is thermally driven by the heat flux at thebottom of the sub-skin layer.

g. Sea ice surface process

The sea ice surface temperature is a prognosticvariable in MRI-AGCM3, and its variation is cal-culated at the same time as the sensible and latentheat fluxes by the implicit method. The snow sur-face processes are calculated in the same way asthose for snow on land when snow (either partialor full coverage) is on the sea ice. The roughnesslength is set to a constant (0.001 m). The other pa-rameters are fundamentally the same as those forthe sea surface. The snow amount (water equiva-lent) on sea ice is also a prognostic variable inMRI-AGCM3. When there are multiple categoriesfor sea ice thickness in a single grid box, thesevalues are calculated for each category.

Albedos of ice and snow are separately esti-mated, and the former is similar to that by Hunkeand Lipscomb (2006). The parameterization ofAoki and Tanaka (2008) is applied for estimatingsnow albedo, which accounts for the decline of al-bedo due to pollution by aerosol deposition. More-over, the e¤ect of solar penetration into the snowon albedo is taken into account for both visiblewavelengths for ice and near-infrared wavelengthsfor snow.

h. River-lake model and ice discharge from

ice-sheet

The river-flow component in MRI-AGCM3 isthe Global River model using TRIP (GRiveT).The river-channel dataset we use for MRI-CGCM3 is the 1� � 1� version of the Total RiverIntegrated Pathway (TRIP) (Oki and Sud 1998).River runo¤ is calculated by the land surface modelin MRI-AGCM3 and is transported by GRiveT tothe river mouths via TRIP. GRiveT also has a lakein each TRIP grid, which is connected to the lakesurface component in MRI-AGCM3.

The river and lake water masses and their inter-nal energies are predicted. The water runo¤ esti-

mated by the land surface component and pre-cipitation minus evaporation by the lake-surfacecomponent are input to GRiveT. In each grid, riverwater flows at a constant velocity of 0.4 m s�1. Halfof the river water flows into the lake, and the lakewater returns to the river, depending on the waterlevel, with a 10-day e-folding timescale. The inter-nal energies are a¤ected only in the lake by the sur-face heat fluxes estimated at the lake-surface com-ponent, and are reflected on the ocean temperaturewhen they are discharged at the river mouth. Freez-ing of the lake is not considered.

As MRI-CGCM3 does not include a dynamicice-sheet model, a simple model for snow on icesheet (SMIST) is used as the ice-sheet componentof MRI-CGCM3 in order to balance the globalocean water mass in the unforced climate. Whenthe SWE is greater than 10 m over the land surface,the excess snow is taken away from the land surfacemodel and passed to SMIST. The ice mass andits energy are transported to the ocean coast bySMIST and discharged into the ocean as an ice-berg. The algorithm is almost the same as theGRiveT algorithm, but without any lake.

2.2 Ocean-ice model (MRI.COM3)

The ocean-ice component of MRI-CGCM3is MRI Community Ocean Model Version 3(MRI.COM3). Users may refer to its referencemanual (Tsujino et al. 2010) for details. The free-surface, depth-coordinate ocean component solvesprimitive equations using Boussinesq and hydro-static approximation. A split-explicit algorithm isused for the barotropic and baroclinic parts ofthe equations (Killworth et al. 1991). MRI.COM3can be used to simulate ocean and sea ice withvarious specific configurations. Here, we describethe MRI.COM3 specifications used in the MRI-CGCM3 (or MRI-ESM1) component. Since thespecification is very close to Tsujino et al. (2011),see their description also.

a. Resolution and topography

Horizontal resolutions are 1� longitude and 0.5�

latitude. A generalized orthogonal coordinate sys-tem is used in the Arctic region (latitudes higherthan 64�N) with polar singularities at Siberia(64�N, 80�E), Canada (64�N, 100�W), and theSouth Pole (tripolar grid).

The ocean model consists of 50 vertical levelsplus a bottom boundary layer (BBL) (Nakano andSuginohara 2002). The surface layer is 4 m thick,and the upper layers above 1000 m are resolved


by 30 layers. Vertical levels shallower than 32 mfollow the surface topography, as in s-coordinatemodels (Hasumi 2006). BBLs with a thickness of50 m are added in dense-water formation regionsof the present climate (i.e., the northern North At-lantic (50�N to 70�N and 60�W to 0�E) and theSouthern Ocean around the Antarctica (south of60�S)), assuming that the dense-water formationregions do not change significantly in a warmerclimate.

Model topography is constructed from theGlobal Gridded 2-minute Database (ETOPO2v2;National Geophysical Data Center). The topogra-phy of the model is modified to represent importantoceanic current systems, including those aroundcomplex archipelagos such as the Philippine Is-lands.

b. Transport algorithm

The generalized Arakawa scheme as describedby Ishizaki and Motoi (1999) is used to calculatethe momentum advection terms. This momentumadvection scheme conserves total momentum andenergy for three-dimensionally non-divergent flowsover arbitrary topographies, and total quasi-

enstrophy (ðqv=qxÞ2 or ðqu=qyÞ2) for horizontallynon-divergent flows. A numerical advection schemebased on conservation of second-order moments(SOMs) (Prather 1986) is employed for advectionof all tracers (temperature, salinity, and biogeo-chemical tracers). The SOM scheme is computa-tionally stable and almost free of numerical di¤u-sion; therefore, it can reproduce relatively realistictracer distributions in OGCMs (e.g., Hofmann andMaqueda 2006).

c. Sub-grid scale mixing

A flow-dependent anisotropic horizontal viscos-ity scheme (Smith and McWilliams 2003) isadopted to reduce viscosity in the direction normalto the flow (nN ). Viscosity in the flow direction (nF )is set as Smagorinsky harmonic viscosity (Smagor-insky 1963), nF ¼ ð4D=pÞ2jDj, where D is the gridsize and jDj is the strain rate. Anisotropic viscosity,nN ¼ 0:2� nF , allows the equatorial undercurrentto be narrow and swift, as observed. At the lateralboundary, nN is set to be half of nF in order toproduce a Munk boundary layer. Isopycnal tracerdi¤usion (Redi 1982; Cox 1987) is used with a coef-ficient 1000 m2 s�1. Eddy-induced transport is par-ameterized as isopycnal layer thickness di¤usion(Gent and McWilliams 1990) with a coe‰cientvarying in space and time (Visbeck et al. 1997).

Vertical di¤usivity and viscosity are set by the tur-bulence closure model (Noh and Kim 1999; Nohet al. 2005). Background vertical di¤usivity consistsof a horizontally uniform vertical profile, as pro-posed by Tsujino et al. (2000), and parameteriza-tion for the tidally driven mixing (St. Laurent et al.2002) near the Kuril Islands and the Sea ofOkhotsk. The latter treatment is to realize the inter-mediate layer ventilation in the northern North Pa-cific (e.g., Nakamura et al. 2004) while avoidingunexpected side e¤ects that might arise when thisparameterization is applied globally. Seawater den-sities are calculated by an accurate equation of state(Tsujino et al. 2010). The vertical gravitational in-stabilities calculated by the model are completelyeliminated at each time step by a convective adjust-ment scheme.

d. Sea ice model

The sea ice model solves the evolution of frac-tional area, heat content, and thickness; the trans-port of ice categorized according to its thickness;and the dynamics of the grid-cell-averaged ice pack.Using the coupler, the sea ice model sends surfacetemperature, interior temperature, snow and icethicknesses, and fractional area to MRI-AGCM3,and receives surface fluxes calculated by MRI-AGCM3. The ice model is part of MRI.COM3,and ice–ocean exchange processes are internal.

The thermodynamic part is based on Mellorand Kantha (1989). For processes that are neitherexplicitly discussed nor included by Mellor andKantha (1989) (e.g., categorization by thickness,ridging, and rheology), we adopt those of the LosAlamos sea ice model (CICE) (Hunke and Lips-comb 2006). Fractional area, snow volume, ice vol-ume, ice energy, and ice-surface temperature ofeach thickness category are transported using themultidimensional positive definite advection trans-port algorithm (MPDATA) (Smolarkiewicz 1984).See the MRI.COM3 reference manual (Tsujinoet al. 2010) for details.

2.3 Aerosol model (MASINGAR mk-2)

In MRI-CGCM3, atmospheric aerosols are cal-culated with the Model of Aerosol Species in theGlobal Atmosphere (MASINGAR) mk-2, which iscoupled with the Scup coupler library (Yoshimuraand Yukimoto 2008). This improved version of theMASINGAR aerosol model (Tanaka et al. 2003)treats five aerosol species: non-sea-salt sulfate, BC,OC, sea-salt, and mineral dust. The grid resolutionof the model is variable, and the horizontal resolu-


tion is set as TL95 (A180 km) for MRI-CGCM3,which di¤ers from that of the coupled AGCM(TL159, A120 km). The vertical layers are set thesame as those of AGCM.

a. Coupling with MRI-AGCM3

The aerosol model receives the meteorologicalfields and surface conditions from MRI-AGCM3through the coupler. The meteorological fields in-clude horizontal wind components, air temperature,specific humidity, convective mass flux, precipita-tion and evaporation with convective and large-scale clouds, the vertical eddy di¤usion coe‰cient,and surface pressure. The surface conditions in-clude near-surface wind speed, surface air temper-ature (SAT), ground temperature, SST, sea icecoverage, snow amount, land-use type, vegetationamount, and leaf area index. MASINGAR mk-2sends the concentrations of the five aerosol speciesand the deposition fluxes of absorptive aerosols(BC and mineral dust) to MRI-AGCM3 to beused in calculating the direct and indirect radiativee¤ects of the aerosols and of the snow albedo. Sea-salt aerosol is calculated in six size bins in theaerosol model, but is sent in two bins (smaller andlarger than 1 mm).

b. Atmospheric transport

Atmospheric transport is calculated using a semi-Lagrangian advection scheme and schemes for sub-grid turbulent vertical di¤usion and convectivetransport. The vertical eddy di¤usion coe‰cientis taken from that of water vapor, calculated inMRI-AGCM3. Convective transport is calculatedusing the updraft mass flux from the cumulus con-vection scheme in MRI-AGCM3.

Aerosol particles are subject to gravitational set-tling relative to air motion and are assumed to fallwith terminal velocity Vs. The terminal velocity Vs

is calculated under the assumption that the particlesare spherical and is proportional to the square ofthe radius of the particle and the Cunningham slip-flow correction.

c. Dry and wet deposition processes

Dry deposition is parameterized by theresistance-in-series model (Seinfeld and Pandis1997), which includes turbulent impaction andgravitational settling. Wet deposition is distin-guished between in-cloud and below-cloud scaveng-ing and is categorized by cloud type (convective orlarge-scale) and species (aerosol or gas; accommo-dation with water droplets). For in-cloud scaveng-

ing by large-scale precipitation, we use the parame-terization developed by Giorgi and Chameides(1986). Both the dry and wet deposition schemesfor sea-salt and mineral dust aerosols are particle-size-dependent.

For water-soluble gases such as SO2, wetscavenging is calculated simultaneously with theaqueous-phase chemistry. The fraction of a water-soluble gas that is in liquid water is assumedto follow Henry’s law of equilibrium, which istemperature- and pH-dependent. Evaporation ofrainwater is considered when calculating the wetdeposition rate. When evaporation occurs, a frac-tion of the trace elements is released back to theair.

d. Emission processes and chemical reactions

Prescribed emission inventories are used for theemission processes of anthropogenic sulfur com-pounds and carbonaceous aerosols. The emissionsof oceanic DMS, sea-salt, and mineral dust are cal-culated from the meteorological and surface condi-tions. See Yukimoto et al. (2011) for the detailedformulations.

2.4 Coupler (Scup)

Scup (Yoshimura and Yukimoto 2008) was de-veloped at MRI as a simple general-purpose cou-pler for coupling component models for integrationinto an ESM. Each component model in MRI-CGCM3 (or MRI-ESM1) (atmospheric, ocean-ice,aerosol, and atmospheric chemistry models) usesScup to exchange data with the other componentmodels. Scup makes it easy to develop an inte-grated model composed of an arbitrary combina-tion of these component models. Distributing thecommunications reduces the amount of transferreddata and leads to high computational e‰ciency.Using the settings in the Scup configuration file,the models can be executed in parallel or sequen-tially; accordingly, we can distribute the execu-tion of the component models in the most e‰cientway on the computer being used. For the CMIP5experiments, the coupling interval is 1 hour foratmosphere-ocean coupling (including the sea icemodel) and 30 minutes for atmosphere-chemistrycoupling (in the aerosol model).

3. Spin-up and basic experiments

In this section, we first describe how the initialstate of the climate system in the model is made(i.e., spin-up procedure) for the base-line (pi-Control) experiment. We then describe the design


of the basic experiments introduced in the presentpaper.

3.1 Spin-up procedure

Before coupling the atmosphere and the ocean,initial atmospheric and land surface data is takenfrom JMA’s operational analysis for 00UTC, July9, 2002, and data on the initial oceanic state aretaken from the 2360-year integration of theOGCM forced by the interannually varying forcingboundary conditions of Version 2 of the Coordi-nated Ocean-ice Reference Experiments (CORE-2)datasets (Large and Yeager 2009). The initial distri-butions of aerosol species and their precursor gasesare set to zero, except carbonyl sulfide (OCS) below100 hPa, which is set to 500 pptv.

A spin-up run with the coupled model MRI-CGCM3 was then started and continued for 305years under the present-day forcing agents (e.g.,GHG concentration and solar irradiance). Afterthe spin-up of the present-day condition, GHGconcentration is gradually decreased (during 53years) to that of the year 1850. An additional 62-year spin-up is then conducted under the fixed year1850 condition in order to derive the initial stateof the piControl experiment. During this spin-upphase, we tune some parameters slightly. We alsotry to improve the sea ice distribution bias by re-storing sea surface salinity (SSS) to the observed cli-matology for only five years at the beginning of thelast 62-year spin-up, but with no apparent e¤ect onthe final spin-up state. Since MRI-CGCM3 uses avegetation map from the USGS-GLCC land-usedata and SAGE Global Potential Vegetation Data-set (Ramankutty and Foley 1999) as the standard(preset-day) condition, the spin-up starts with thestandard condition but is replaced with the 1850condition at the beginning of the last 62-year spin-up run.

3.2 Experiment design

Numerous coordinated experiments in CMIP5(Taylor et al. 20112) have been conducted. Ofthese, some of the core experiments for evaluat-ing the model’s basic performance are describedhere.

a. piControl experiment

The piControl experiment serves as the baselinefor all other experiments in CMIP5. According to

the CMIP5 protocol, all external forcing agentshave the same values at year 1850 as for the his-torical experiment (see below). Concentrations ofGHGs and anthropogenic aerosols or their precur-sors are fixed at 1850 values of the RepresentativeConcentration Pathways (RCP) database.3 Forexample, the concentrations of CO2, CH4, andN2O are 284.725 ppmv, 790.97924 ppbv, and275.42506 ppbv. Emissions from eruptive volca-noes are not included in the piControl experiment.

The initial condition of the piControl experimentis the spin-up state described above. The simulationperiod for the piControl experiment is planned tobe 500 years or more. In the present study, we pres-ent results for the first 500 years.

b. historical experiment

The historical experiment is designed to evaluatehow realistically the model can simulate the presentclimate and the recent past climate changes. Also, itprovides initial conditions for future RCP scenarioexperiments and decadal prediction experiments ofthe CMIP5. The length of the experiment is 156years, starting from 1850 (1850 through 2005). Thereproducibility of the era since the mid-nineteenthcentury is particularly important, since instrumentobservations are available for that era.

Historical records of concentrations of GHGsand anthropogenic aerosols or their precursors inthe RCP database are used. Emission fluxes of sea-salt, mineral dust, and oceanic DMS are calculatedin the aerosol model, as described in the previoussection. Emission of terrestrial biogenic DMS isadopted from the inventories compiled by Spiroet al. (1992). Historical SO2 emission from sporadi-cally erupting volcanoes is compiled from theGlobal Emissions Inventory Activity (GEIA) data-base (Andres and Kasgnoc 1998), total ozone map-ping spectrometer (TOMS) satellite measurement(Bluth et al. 1997), and ground-based observationsof aerosol characteristics from pre-satellite era spec-tral extinction measurements (Stothers 1996, 2001).The SO2 emission from non-eruptive volcanoes isset as invariant throughout the piControl and his-torical experiments. Historical ozone concentrationis taken from the Atmospheric Chemistry and Cli-mate (AC&C) and Stratospheric Processes Andtheir Role in Climate (SPARC) database.4 The his-

2 available from http://cmip-pcmdi.llnl.gov/cmip5/experiment_design.html

3 available from http://www.iiasa.ac.at/web-apps/tnt/RcpDb

4 available from http://www.pa.op.dlr.de/CCMVal/AC&CSPARC_O3Database_CMIP5.html


torical solar forcing data is taken from the databasereconstructed for 1850 through 2005 based on Leanet al. (2005).

Change of land-use is evaluated by using theland-use type (LUT) datasets (gcrop and gpast)provided by the CMIP5 land-use group for 1700through 2100. A normalized and monotonically in-creasing ‘‘gcropþ gpast’’ function is calculated ateach model grid as the index of vegetation typechange from forest to grass. By using the index,land-use changes are reflected in the standard vege-tation type (assumed as for year 1990).

The historical experiment consists of three mem-ber ensemble runs. Each initial state is taken fromyears 100, 130, and 160 of the piControl experi-ment. Conditions other than those described aboveare the same as in the piControl experiment.

c. abrupt4xCO2 experiment

An experiment with abrupt quadrupling of CO2

concentration (abrupt4xCO2) is executed to evalu-ate the equilibrium climate sensitivity of the modelfollowing the Gregory regression approach (Greg-ory et al. 2004). The experiment consists of 5-year,12-member ensemble runs initiated from eachmonth of year 40 in the piControl run. The evalua-tion becomes more stable when ensemble runs areused, since each run has a short response that tendsto be disturbed by interannual variations. Onlythe first run (started in January) is extended to 150years long to examine the long response. Condi-tions other than those described above are thesame as in the piControl experiment.

d. 1pctCO2 experiment

A run with the idealized 1 % yr�1 increase ofCO2 concentration is executed to measure the tran-sient climate response (TCR). This run allows anidealized climate response without such complica-tions as aerosols and land-use changes. The resultsof this experiment can be compared with previousCMIP model results (e.g., CMIP3). The initial stateof the experiment is taken from year 40 of the pi-Control experiment. Except for the increased CO2

concentration, all the conditions are the same as inthe piControl experiment.

4. Model stability and drift

The climate system requires millennia to achievean equilibrium state, even if imbalance of the globalradiation budget at the TOA is very small. The sta-bility and drift should be carefully checked for the

piControl run, to determine whether the spin-up issu‰cient or not.

The temporal variation of the globally averagedannual mean SAT (2-m screen level) for the pi-Control experiment is presented in Fig. 1a. It issu‰ciently stable for a long term (at least 500years), and there is almost no climate drift. The500-year average temperature is 13.6�C, and thelinear trend of þ0.016�C yr�1 over 500 years isnot statistically significant at the 99% confidencelevel.

Figure 2a presents the global radiation budgetat TOA, which is downward solar irradiance minusreflected solar radiation and outgoing LW radia-

Fig. 1. Time series of globally averagedannual-mean surface air temperature(SAT) for the (a) piControl experiment(thin solid line) and (b) historical experi-ment (solid line, ensemble mean; ‘x’, eachmember), and for the observation (greylines). Eleven-year running mean (thickline in panel a) and linear trend (dashedlines) for the piControl experiment arealso plotted.


tion (OLR). The 0.5 W m�2 downward net imbal-ance seems very stable except for interannual varia-tion. In addition, there is an unknown energysource of 0.5 W m�2 in the atmosphere, due to thelack of strict energy conservation. We do not be-lieve that this unknown energy source is a criticalissue in evaluating climate change, since the valuedoes not change throughout the period or in otherexperiments (e.g., the 1pctCO2 experiment, notshown). Consequently, there is a net energy input(or downward flux) of about 1 W m�2 at the sur-face. The land surface model is well-conserved,and the annual mean energy budget is almost zero,which leads to net energy absorption by the oceandue to the surface energy imbalance.

We next examine the drift of the ocean. Figure2b depicts temporal variations of globally averaged

annual mean SST, and vertically averaged oceantemperature for the upper 635 m (VAT635) andfor full depth (VATFULL). The global SST is18.1�C on average and exhibits no significant trend.However, a warming trend is apparent in the timeseries of VAT635, which represents the ocean sub-surface layer. A relatively large warming trend of0.1�C yr�1 occurs 200 years from the beginning,though after that the trend becomes much smaller.The variation of VATFULL reflects an energy im-balance at the surface, since the ocean-ice modelconserves heat (and mass) with high precision. Asmall warming trend (0.017�C/100yr) is found inthe first 200 years, followed by a reduced warmingtrend. The temporal change of VATFULL towardequilibrium is likely to be a logarithm function,and the trend is expected to become graduallyweaker. We estimate the logarithm function for the500-year VATFULL to be 0:0112 lnðnÞ þ 4:1837,where n is years from the start. Thus, VATFULLis estimated to be 4.279�C (4.287�C), and in-creases 0.095�C (0.103�C) by 5000 (10000) yearslater. It is reasonable to assume that the SST in-creases similarly to VATFULL in the equilibriumstate. The temperature change (< 0.1�C in 10000years) is su‰ciently small compared to the climatechange in the 20th and 21st Centuries that we aretargeting.

The climate system cannot be stable if the gen-eral circulation of the oceans is not stable, eventhough the drift in the globally averaged oceantemperature is su‰ciently small. It is necessary toexamine how realistic and how stable the modelsimulates meridional overturning circulation(MOC) and other important volume transports inthe oceans, particularly for evaluating long-termclimate change. Figure 3 illustrates temporal varia-tions of volume transports for the Atlantic MOC at45�N, the Antarctic MOC at 70�S–65�S, and theAntarctic Circumpolar Current (ACC). The MOCsare closely related to formation of the North Atlan-tic deep water (NADW) and the Antarctic bottomwater (AABW), and are associated with a majorocean heat up-take. The Atlantic MOC fluctuatesfrom 12 through 18 Sv (106 m3 s�1) and is ratherstable at an average of 15 Sv throughout the simu-lation. The observation by Talley et al. (2003) is18 Sv with a 3 to 5 Sv error. The simulated valueis within the observation error and is hence realis-tic. The Antarctic MOC is only around 4 Sv at thebeginning of the experiment; however, it increaseswithin the next 50 years and then almost stabilizes

Fig. 2. Time series of globally averaged an-nual mean (a) radiation imbalances (posi-tive downward) at the top (solid line) andthe bottom (dashed line) of the atmo-sphere, (b) sea surface temperature (SST)(solid line, left axis), upper 635 m (dashedline, right inner axis), and total (grey line,right outer axis) averaged ocean tempera-ture of the piControl experiment. Eleven-year running means are also plotted withthick solid lines.


at 8 Sv. Estimated values (e.g., Talley et al. 2003;Sloyan and Rintoul 2001) range from 20 Sv to50 Sv, though they are thought to have much uncer-tainty. The simulated value, however, is somewhatsmaller than the observed value. The simulatedACC demonstrates a small but very long-term vari-ation between 110 Sv and 120 Sv. Transport of theACC is estimated at 134 Sv, based on observationby Cunningham et al. (2003). The simulated trans-port of the ACC is also probably underestimated.

We next examine the global water budget.Global sea level change would occur with even asmall water budget imbalance, since the OGCM ex-changes real freshwater flux in MRI-CGCM3 (notvirtual salt flux, as in MRI-CGCM2.3.2). It hasbeen confirmed that the model conserves the globalwater with high precision. The trend of the sea levelattributable to water budget imbalance in thepiControl experiment is less than 2 mm/100yr,which we believe is su‰ciently small to evaluatesea level change due to thermal expansion and ahydrological cycle change by anthropogenic GHGincrease. Combining the e¤ects of thermal imbal-

ance in the piControl experiment results in anincreasing trend of approximately 5 mm/100yr inglobal sea level.

5. Climate sensitivity and historical climate change

Various methods of estimating the climate sensi-tivity of models have been proposed since the 1980s(e.g., Hansen et al. 1984). Each method has bothadvantages and disadvantages. The method withthe slab mixed layer ocean coupled to AGCM wasgenerally used to obtain an equilibrium response,but has some drawbacks, especially in includingthe e¤ects of oceanic circulations. Recently, Greg-ory et al. (2004) proposed a new, simple methodby regressing the radiative flux at TOA against theglobal average SAT, which is recommended byCMIP5.

Figure 4 depicts the Gregory plot (Gregory et al.2004) for the abrupt4xCO2 ensemble simulationswith MRI-CGCM3. The regression line crosses atthe change in global SAT DT ¼ 4:22 K with theaxis of net radiative flux change at TOA DR ¼ 0,which means that the radiative flux balancesFig. 3. Time series of meridional overturn-

ing volume transport (unit: Sv ¼106 m3 s�1) in (a) the Atlantic (45�N) and(b) the Antarctic (70�S to 65�S), and (c)ACC in the piControl experiment.

Fig. 4. Scatter plot and its linear regressionof globally averaged annual mean values.Downward radiation flux change at TOAversus SAT change from 12-member en-semble (5 years each) abrupt4xCO2 experi-ment, for net radiation (RT), clear-skylongwave (LN), clear-sky shortwave (SN),longwave cloud forcing (LC), and short-wave cloud forcing (SC).


(DR ¼ 0) if the SAT change achieves equilibrium(with DT ¼ 4:22 K) for the quadrupling of CO2

concentration. The regression line crosses at DR ¼7:66 W m�2 with the axis of DT ¼ 0, which meansthe radiative forcing is 7.66 W m�2 for quadru-pling of CO2 concentration. The radiative forcingfor doubling CO2 concentration is estimated as3.83 W m�2, since radiative forcing is known to beapproximately linear relative to CO2 concentration.The radiative forcing is comparable to 3.7 W m�2

of MRI-CGCM2.3.2 using a di¤erent radiationscheme (Shibata and Aoki 1989; Shibata andUchiyama 1992). Consequently, the climate sensi-tivity of MRI-CGCM3 is 2.11 K (¼ 4.22/2) fordoubling of CO2 concentration. This value is mar-ginally in the lower range of multi-model results(2.1 K to 4.4 K) estimated from slab mixed layerocean equilibrium in IPCC-AR4 and is only 73%of the climate sensitivity 2.9 K (equilibrium re-sponse with slab ocean) of MRI-CGCM2.3.2 (Yu-kimoto et al. 2006). It is di‰cult to estimate realclimate sensitivity from the observations, due tocomplicated variations induced by various forcingsand internal climate variability. Therefore, the re-sults do not necessarily mean that the model under-estimates climate sensitivity.

Another orthodox measure of climate responseto a forcing is evaluating TCR with an ideal 1 %yr�1 CO2 increase. The result (not shown) demon-strates global SAT increases 1.6 K for doublingand 4.1 K for quadrupling CO2 concentration.These increases are 84% and 89% relative to thosefor MRI-CGCM2.3.2.

Yukimoto et al. (2006) concluded that improvingthe cloud basic state results in positive cloud feed-back in the tropics and leads to greater climate sen-sitivity. In MRI-CGCM3, the cloud model is so-phisticated, and the cloud variables are predictedbased on physical theories, keeping satisfactory re-alistic distributions for the cloud forcings for thepresent climate (presented later). Furthermore, thecloud microphysics in the model is fully interactivewith variations of aerosols, taking into accountdirect and indirect aerosol e¤ects. More detailedanalysis of the experiments of MRI-CGCM3 willlead to a better understanding of the mechanismassociated with cloud and climate sensitivity. Thissubject will be investigated in future studies.

The globally averaged SAT variations in thethree-member ensemble historical experiment areplotted in Fig. 1b. In contrast with the piControlexperiment, the simulated SAT in the historical

run demonstrates a moderate increase from the be-ginning through the middle of the 20th Centuryafter recovery from consistently downward spikesin 1883 and 1902 associated with the eruptions ofKurakatau and Santa Marıa. A slight decreaseof SAT is observed during the 1940s through the1960s, followed by a rapid increase since the 1970s.These multi-decadal tendencies are consistently si-mulated in all the members and are similar to theobservation (HADCRUT3v) (Brohan et al. 2006).The ensemble mean of the 2001 to 2005 average is14.17�C, and the increase relative to the 1850 to1899 average is 0.59�C. This value is 0.17�C smallerthan that in IPCC-AR4 (0:76G 0:19�C), though itis marginally in the lower limit of the estimationerror range.

6. Mean climate and variability

Reproducing the present-day climate is an essen-tial requirement for models used for future climatechange projection. A recent period of a few decadesis favorable for comparing the simulated fields withthe observation, since abundant instrument obser-vations, including satellite observations, are avail-able for this period. Available multiple reanalysisdata for this period enable us to verify the modelrather conveniently. We use JRA-25 (Onogi et al.2007) for the present verification.

6.1 Mean climate

We compare the simulated mean climate of thelast 27 years of the historical experiment (1979through 2005) with observations of the same pe-riod. To evaluate the mean climate, we use the en-semble mean of three members of the historicalexperiment.

Representative globally averaged mean valuesare compared among the observations, MRI-CGCM3, and MRI-CGCM2.3.2 (Table 1). Thedownward solar radiation at TOA of 341.7 W m�2

in MRI-CGCM3 is reasonably accurate, since itdepends only on the solar input (forcing) and theEarth’s orbital parameters for the present day. AtTOA, the upward SW radiation is 103.3 W m�2,and the LW radiation is 237.6 W m�2, in agree-ment with the satellite observations of the ISCCPFD dataset (Zhang et al. 2004) and CERES obser-vation adjusted by Loeb et al. (2009) within theerror range of satellite observations. The upwardSW and LW radiation at TOA are also close tothose of the present-day control experiment ofMRI-CGCM2.3.2 (di¤erences within 2 W m�2).


The net radiation at TOA is downward0.85 W m�2, indicating slight heating of the system.

Generally, SW radiation at TOA dependsstrongly on clouds, particularly tropical and sub-tropical low-level clouds. LW radiation at TOA,however, depends mainly on the atmospheric tem-perature and high clouds associated with tropicalconvective activity. Agreement of both SW andLW radiation with the observation supports therealistic simulation of cloud height distribution.Cloud forcing is simulated to be �46:3 W m�2 forSW radiation and þ23.7 W m�2 for LW radiation,in reasonable agreement with the observations.

The energy budget at the surface exhibits down-ward 1.30 W m�2 that includes an unknown energysource of 0.45 W m�2 in the atmosphere, which isas large as in the piControl experiment. The oceanis heated by an additional 0.31 W m�2 in 1979through 2005, compared to the average in the pi-Control experiment (0.99 W m�2 for the corre-sponding period). Therefore, the change in surfaceenergy budget corresponds to the ocean heat up-take due to the simulated climate change. The valueis one third of a recent observation of 0.9 W m�2

(Trenberth et al. 2009). However, other recent ob-

servations of the upper ocean heat content changeare 0:582G 0:151� 1022 J yr�1 for 1993 through2005 (Ishii and Kimoto 2009) and 0:73� 1022 Jyr�1 for 1993 through 2007 (Levitus et al. 2009).These values correspond to 0.36 W m�2 and0.45 W m�2 for ocean heat up-take.

Each energy flux at the surface (downward SWand LW radiation) reflects SW and upward LW ra-diation, and sensible and latent heat fluxes. Theseresults are satisfactorily close to the new estimationby Trenberth et al. (2009) based on the new satelliteobservations and three reanalyses. The latent flux,however, is somewhat overestimated, which is con-sistent with the larger global mean precipitation(2.90 mm day�1) compared to the CMAP observa-tion (Xie and Arkin 1997) of 2.67 mm day�1 forthe same period. This overestimation of global pre-cipitation is common in many models and reanaly-ses (e.g., Onogi et al. 2007). More precipitationmeans a larger global water cycle that probablyleads to overestimated poleward water transport,which in turn suggests freshening the upper oceansat high latitudes.

Meridional distribution of the radiation budgetat TOA regulates meridional energy transport by

Table 1. Globally averaged radiation and cloud radiative forcing (CRF) at TOA and radiative and heat fluxes at thesurface (SFC), and SAT and precipitation for the observation, the MRI-CGCM3 experiments ‘piControl’ and en-semble mean of historical, and the present-day control experiment with MRI-CGCM2.3.2. Units for radiation(heat) fluxes are W m�2.

Experiment ObservationsMRI-CGCM3

piControlMRI-CGCM3

historicalMRI-CGCM2.3.2

present-day

Incoming solar radiation 341.3 341.61 341.72 342Reflected solar at TOA 101.9 101.04 103.26 102Outgoing longwave radiation 238.5 240.05 237.61 236Net downward radiation at TOA 0.9 0.51 0.85 4SW CRF at TOA �51.0a/�46.6b �44.93 �46.26 �49LW CRF at TOA 26.5a/29.5b 23.29 23.65 22Net CRF at TOA �24.5a/�17.1b �21.65 �22.61 �27Downward SW radiation at SFC 184 199.89 197.58 197Absorbed SW radiation at SFC 161 168.56 166.02 165Downward LW radiation at SFC 333 329.86 333.00 337Net LW radiation at SFC �63 �64.41 �63.16 �59Sensible heat flux at SFC 17 17.97 17.34 31Latent heat flux at SFC 80 85.19 84.23 75Net downward energy at SFC 0.9 0.99 1.30 0SAT (�C) 14 13.62 13.99 13.65Precipitation (mm day�1) 2.67 2.94 2.90 2.6

a: ISCCP FD (Zhang et al. 2004)b: CERES adjusted (Loeb et al. 2009)other values: (Trenberth et al. 2009)


the climate system. Figure 5 depicts the simulatednorthward energy transports by the system, the at-mosphere, and the oceans, along with those esti-mated based on the observation (Trenberth andCaron 2001; Fasullo and Trenberth 2008). Oceanicenergy transport is implied from the integratedocean surface heat flux. Atmospheric transport iscalculated by subtracting the implied oceanic trans-port from the total energy transport produced bythe system. The unknown energy source in the at-mosphere is also subtracted, on the assumptionthat it is globally uniform. We confirmed that resultfrom another assumption (where the unknown en-ergy source is proportional to the SAT) is very sim-ilar to this one.

The simulated energy transport by the systemagrees well with the observational estimations inthe Northern Hemisphere (NH) but is under-estimated approximately 1 PW in the tropical andmid-latitude Southern Hemisphere (SH). This biasarises from overestimated SW radiation input inthe Southern Ocean. The atmospheric transport,however, is realistically simulated at all latitudes.Results indicate a 4.9 PW northward peak at 40�Nand a 5.0 PW southward peak at 40�S. Due to theradiation bias in the SH, the oceanic transport indi-

cates a large (1 PW) deficit in SH low latitudes.The model simulates only a 0.4 PW peak of thesouthward transport, while 1 PW is observed at10�S. The oceanic transport in the NH is generallywell-simulated, despite a slightly weaker northwardtransport than observed in the subtropics.

Distribution of simulated annual mean SST iscompared with the observation (COBE) (Ishii et al.2005) (Fig. 6). The model generally reproduces thebasin-scale SST distribution fairly well. The warmpool in the Indo-Pacific region and the relativelyhigh SST in the western tropical Atlantic are simu-lated. Northward bulges of the contours around theKuroshio and the Gulf Stream are slightly exagger-ated compared to those observed. The cold tonguealong the equator in the Pacific is simulated, al-though it is too strong.

A hemispheric bias is apparent in the di¤erencemap (Fig. 6c); it is generally colder in the NH andwarmer in the SH than actually observed. TheKuroshio Extension region and the Labrador Seathrough the Greenland-Iceland-Norwegian (GIN)Seas exhibit strong cold biases exceeding 4�C. Thelatter cold bias is accompanied by an excessiveextent of sea ice in winter (discussed later). TheSST in most of the tropical oceans exhibits smallbiases of less than 1�C, except for a slightly coldequatorial bias associated with the exaggeratedcold tongue in the Pacific. Warm biases are com-mon in the subtropical eastern part of the southernbasins, where marine low-level clouds predominate.Warm bias is also dominant across the SouthernOcean.

Some of the SST errors are related to radiationbiases. Figure 7 depicts the simulated annual meanSW and LW upward radiation at TOA and theirdi¤erence from the ERBE observation (Barkstromet al. 1989). The simulated overall distribution ofSW and LW radiation agrees with the observations.Large reflected SW appears in the tropical regions(e.g., over the Maritime Continent and the Amazonbasin). Consistent with the large SW, the regionsdisplay smaller OLR, suggesting dominant activeconvection. The reflected SW is low and the OLRis high over the tropical and subtropical oceanswhere subsidence is dominant.

The di¤erences from the observation for bothSW and LW are generally small, with magnitudesof less than 10 W m�2 in most global areas. How-ever, there are some noticeable biases, which arethought to be consistent with errors in cloud distri-bution. Reflected SW is overestimated and OLR is

Fig. 5. Northward heat transport by the sys-tem (black), the atmosphere (red), and theoceans (blue) for 1979 through 2005 aver-age of the historical experiment (solid lines)and observational estimations (dots, Tren-berth and Caron 2001; crosses, Fasulloand Trenberth 2008).


Fig. 6. Annual mean SST (a) observed and (b) simulated in the historical experiment, and (c) its bias(simulation–observation) for 1979 through 2005 average. Units are �C.


Fig. 7. Annual mean radiation fluxes at the top of the atmosphere (TOA) for (a) reflected shortwave, (b) outgoing longwave in the histor-ical experiment (1979 through 2005 average), and di¤erences from the ERBE observation (c, d). Units are W m�2.

February

2012

S.YUKIM

OTO

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underestimated over the Maritime Continent, thetropical western Indian Ocean, and the tropicalAtlantic coast of South America, suggesting toostrong a cloud forcing. In the equatorial westernPacific, a distinct overestimated OLR implies alack of convection, possibly attributable to the ex-aggerated cold tongue. Across the Southern Ocean,the cloud forcing is underestimated, as impliedfrom the negative bias in SW reflection and theoverestimated OLR. This underestimated cloudforcing leads to an excessive energy input in theSH mid-latitude and a consequently underesti-mated required poleward energy transport in theSH (Fig. 5).

Geographical SAT distributions reproduced bythe model for June through August (JJA) and De-cember through February (DJF) seasonal averagesare presented in Fig. 8. To facilitate evaluating themodel, di¤erences from observations over the land(the latest CRU TS3.1 dataset based on Mitchelland Jones (2005)) are also depicted. SAT over landis generally subject to hydrological conditions (e.g.,precipitation, snow cover, and soil wetness), de-pending on the season. The model realistically re-produces the overall SAT distributions for bothwinter and summer. In JJA, the bias is less than2�C in most of the continental regions. Cold biasesexceeding 2�C in Scandinavia through western Rus-sia, northeastern Canada, and the subtropical NHin DJF may be associated with the dominant SSTcold bias in the NH (Fig. 6c) and overestimatedsea ice in the North Atlantic.

Distributions of seasonally averaged precipita-tion for JJA and DJF are illustrated in Fig. 9. Thelarge-scale precipitation pattern associated with thesummer Asian monsoon in JJA is fairly well simu-lated. Active convection regions over the Indochi-nese Peninsula and the South China Sea throughthe western tropical Pacific east of the Philippinesare qualitatively realistic. The precipitation bandassociated with the Baiu front is satisfactorily real-istic. The northern intertropical convergence zone(ITCZ) along the 10�N to 15�N latitude bandis also qualitatively well simulated, with a con-centrated precipitation band extending from themiddle-eastern Pacific through northern SouthAmerica, the tropical Atlantic, and western Africa.

For the austral summer (DJF), the heavy precip-itation associated with South American and theSouth African monsoons is fairly well simulated.The distinct precipitation in the North Pacific andthe North Atlantic associated with winter storm

tracks is also realistic.Precipitation biases exceeding 1 mm day�1 are

limited but prominent in the tropics, where strongprecipitation is primarily observed. The IndianMonsoon precipitation (JJA) is significantly under-estimated over the Indian subcontinent, the Bay ofBengal, and the western coast of India. The warmbias in the SAT in JJA (Fig. 8c) is possibly associ-ated with dry bias due to the shortage of IndianMonsoon precipitation. The cold SST bias in thenorthern Arabian Sea (Fig. 6c) is a possible reasonfor this shortage. In contrast, the western PacificITCZ has too much precipitation. Separate testruns with modified cumulus convection parameters(not shown) exhibit a negative correlation betweenIndian and western Pacific precipitation. There isunrealistic precipitation along the south o¤ theequatorial Pacific, extending from east of NewGuinea. The overestimated precipitation o¤ theequator in the SH becomes more distinct in aus-tral summer (DJF), and underestimated equatorialwestern Pacific precipitation results in the doubleITCZ. This problem is discussed in the next section.

Snow coverage, one of the most important fac-tors a¤ecting the SAT over land, is evaluated forgeographical distributions for the boreal fall (Sep-tember through November (SON)), winter (DJF),and spring (March through May (MAM)) (Fig.10). In comparison with the observations (Arm-strong and Brodzik 2005; dataset obtained fromthe National Snow and Ice Data Center (NSIDC)),the model reasonably reproduces the observed dis-tribution with its seasonal march. However, thesnow cover is slightly excessive in spring and fall inthe western part of the Eurasian continent. Also,the simulated snow cover is excessive over the Tibe-tan Plateau during all seasons.

The horizontal distribution of column-integratedCLW content is realistically simulated comparedwith Special Sensor Microwave/Imager (SSM/I)observations (Wentz 1997) (Fig. 11). The simu-lated values are 0.10 through 0.12 kg m�2 in theregions along the storm tracks, and 0.06 through0.08 kg m�2 in the regions of subtropical subsi-dence in the North Pacific and the North Atlantic.The values in these regions are quantitatively con-sistent with those of the observations. The overallgeographical pattern is also similar to that of theobservations, though it is available only over theocean. However, the model overestimates the CLWamount in the tropical western North Pacific andthe o¤-equator area of the Southern Pacific. These


Fig. 8. SAT distribution for (a) JJA and (b) DJF simulated in the historical experiment (1979 through 2005 average), and (c, d) di¤erencesfrom the observation (CRU TS3.1) over land. Units are �C.

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Fig. 9. Precipitation distribution for (a) JJA and (b) DJF simulated in the historical experiment (1979 through 2005 average), and (c, d)di¤erences from the CMAP observation. Units are mm day�1.

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Fig. 10. Snow coverage climatological distributions (1979 through 2005 average; in percent; data obtained from NSIDC) observed inthe NH in (a) fall (SON), (b) winter (DJF), (c) spring (MAM), and (d) summer (JJA). (e–h) same as (a–d) but for simulated dis-tributions in the historical experiment.

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biases indicate distributions similar to those ofthe biases in precipitation (Fig. 9), implying over-estimated convections with significant cloud dropletdetrainment, although there is less CLW along theITCZ in the middle-eastern Pacific. In the SouthernOcean, however, the model generally underesti-mates the CLW, which is consistent with underesti-mated reflected SW radiation and overestimatedOLR at TOA in the region (Figs. 7c, d).

Figure 12 compares the simulated aerosol opticaldepth (AOD) at 550 nm with the satellite-retrievedAOD with the MODerate resolution Imaging Spec-trometer (MODIS) (e.g., Remer et al. 2008). Thespatial pattern of the simulated AOD is consistentwith that of the satellite retrievals. Large AOD val-ues are found over a dust belt (Prospero et al. 2002)that extends from the west coast of North Africa

over the Middle East, Central Asia, and SouthAsia to East Asia. The simulated AOD is, however,systemically less than the satellite retrieved valuesover both the oceans and land. The globally aver-aged simulated AOD (0.6) is within the range butat the lower end of the values among the aerosolmodels in AeroCom (Kinne et al. 2006). Compari-sons with satellite retrievals suggest that the simula-tion underestimates the biomass burning aerosol inthe tropics. The negative bias is also possibly dueto the treatment of the dependence of AOD onhygroscopicity, especially sea-salt aerosol, or maybe partly the result of overestimating the satelliteretrievals of sea-salt, due to possible cloud contam-ination (Kaufman et al. 2005).

Figure 13 displays mean sea-level pressure (SLP)distributions simulated for JJA and DJF, with dif-

Fig. 11. Annual mean column-integratedcloud liquid water (CLW) (in kg m�2) for(a) SSM/I observations (the Defense Mete-orological Satellite Program) and (b) simu-lation in the historical experiment. Eachclimatology is for 1988 through 2005. TheSSM/I data for January 1988 to November1991 is from the F8 satellite, that for De-cember 1991 to April 1997 is from the F11satellite, and that for May 1997 to Decem-ber 2005 is from the F14 satellite.

Fig. 12. Annually averaged global distribu-tions of the aerosol optical depth (AOD)from 2001 through 2005, obtained from(a) simulation (historical experiment), and(b) MODIS-Terra 5.1 observation.


ferences from JRA-25. The Pacific and Atlanticsubtropical highs in the boreal summer (JJA) areappropriately simulated in their location and cen-tral pressure. In the boreal winter (DJF), the majorclimatological cyclones of the Aleutian Low andthe Icelandic Low are satisfactorily simulated,though they are stronger than the reanalysis with anegative bias southeast of the climatological center.These low SLP biases imply a tendency of strongersubpolar gyres in the North Pacific and the NorthAtlantic. There are relatively weaker SLP biases inthe SH in both seasons, despite the systematic radi-ation bias, except for a slightly stronger meander-ing south of Australia in winter.

We now examine the simulated atmosphericmeridional-vertical structure. Figure 14 depicts thesimulated zonally averaged temperature and zonalwind for the seasonal mean (JJA and DJF). The

overall temperature structure agrees well with thereanalysis for both seasons. In particular, biases inthe tropics are su‰ciently small, with di¤erences ofless than 1 K. The tropopause height is accuratelysimulated, though the tropopause temperature islower in JJA and higher in DJF by 2 to 3 K com-pared with the reanalysis. A cold bias with a baro-tropic structure is outstanding at 40�N in summer.In contrast, an overall cold bias of 2 to 3 K is dom-inant in winter mid-high latitudes in the NH, witha strong cold bias (> 5 K) near the surface at highlatitude.

The zonal wind structure is also well-simulatedfor both seasons. Each subtropical jet is accuratelysimulated in position and strength in JJA and DJF.Separation between the subtropical jet and the stra-tospheric polar night jet is also properly simulated,implying a realistic vertical propagation of plane-

Fig. 13. Mean sea-level pressure for (a) JJA and (b) DJF simulated in the historical experiment (contour,c.i. ¼ 4 hPa) and di¤erences from the JRA-25 reanalysis (shading). The di¤erences are masked in thehigh-altitude region where the climatological surface pressure is less than 850 hPa. Each climatology is for1979 through 2005 average.


Fig. 14. Zonally averaged atmospheric temperature (unit: K) for (a) JJA and (b) DJF, and zonal wind (unit: m s�1) for (c) JJA and(d) DJF. Black and dark green contours denote simulated and JRA-25 reanalysis values, and shading denotes simulated biases rel-ative to the JRA-25 reanalysis. Each climatology is for the 1979 through 2005 average.

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tary waves and divergence. Associated with thetemperature bias in the NH there is a weak easterlybias in the JJA equatorial side of the subtropical jetaxis.

SSS can be an important measure for evaluatingthe water cycle simulated in the climate model,since it is strongly a¤ected by precipitation minusevaporation, river runo¤, and sea ice formation/melting. Figure 15 illustrates the simulated annual-mean SSS for the 1979 through 2005 averages,along with a comparison with the observations forthe same period (Ishii et al. 2006). The basin-scalepatterns (e.g., the saline regions in the subtropicaloceans, the higher SSS in the Atlantic than in thePacific, and the low SSS in the Arctic and aroundthe Maritime Continent) are realistic. In most oceanareas, the SSS is close to that observed, with biasesof less than 1 psu. A remarkable low SSS bias,however, is found in the region around the NorthAtlantic Current (NAC), particularly east of New-foundland. This SSS bias can be attributed to aweaker northward transport of saline water by theNAC and implies the possibility of suppressed con-vection, leading to weaker thermohaline circulationor MOC (15 Sv) in the North Atlantic. Some sig-nificant biases in limited regions in the Bay ofBengal (positive bias) and in the tropical South Pa-cific (negative bias) are consistent with precipitationbiases (Fig. 9).

Zonal mean ocean temperature and salinity arepresented in Fig. 16a, along with their biases fromthe Polar science center Hydrographic Climatology(PHC) version 3 (Steele et al. 2001). The overallstructure for both temperature and salinity is rea-sonably simulated. A warm bias of more than 2�Cnorth of 60�N suggests that the GIN Seas (notshown) are warmly biased beneath the subsurface,in contrast with the cold surface bias. Anotherwarm bias of more than 1�C in the abyss is at-tributed to a warmly biased AABW. Similar butweaker warm biases are also found in a long-termintegration of MRI.COM3 by using CORE-2 inter-annual forcing (Tsujino et al. 2011). Intrusions ofsaline water into the Weddell and Ross Seas arenot su‰cient in the present simulation, comparedto the CORE-2 simulation.

Temperature biases above 1000 m mainly reflectNorth Pacific characteristics (not shown). The ther-mocline sinks (rises) at low (mid) latitudes, proba-bly because the subtropical gyre is biased towardthe equator. Large salinity biases seem to be con-fined to upper layers. Fresh (saline) biases are

found at high (low) latitudes in the Atlantic, whilea fresh (saline) bias is found in the South (North)Pacific. We assume that these biases are attribut-able mainly to the biased ocean current for the At-lantic (e.g., insu‰cient northward transport by theNAC) and to the biases in precipitation pattern forthe Pacific, which are also consistent with the abovesuggestion for the SSS biases.

Meridional overturning of each basin (i.e., theNorth Atlantic, Pacific, and Indian Oceans) is pre-sented in Fig. 16b. Estimates of MOCs based onthe inverse method are 16G 2 Sv at 48�N for theNorth Atlantic, 21G 6 Sv for the Southern Ocean,and 18G 6 Sv for the deep Indo-Pacific Ocean(Ganachaud 2003). These values are consistentwith the simulated MOCs. In the Southern Ocean,however, part of the AABW formation process isabsent as noted above, resulting in a northward-shifted meridional overturning cell around Antarc-tica (Fig. 16b), consistent with a warmer AABWthan that in the CORE-2 simulation (Tsujino et al.2011).

Sea ice interacts with the ocean currents due toinfluences on freshwater transport as well as stronginfluences on the local climate with well-known ice-albedo feedback. Therefore, realistically reproduc-ing the sea ice distribution is a crucial element inclimate modeling. Figure 17 depicts the simulatedsea ice coverage (concentration or compactness) inSeptember and March for the NH and the SH. Theobserved sea ice extent (region where the mean seaice concentration exceeds 15%) from HadISST1.1(Rayner et al. 2003) is also presented for com-parison.

The geographical distribution in March indicatesgood simulation in the North Pacific sector, butan excessive extent in the Labrador Sea throughthe GIN and Barents Seas. This unrealistic seaice expansion in the North Atlantic is associatedwith the fresher sea surface (Fig. 17b) and thepossibly weaker and southward-shifted NAC, andthey possibly interact each other. The strongerIcelandic Low (Fig. 13b) is possibly somewhat re-lated to this sea ice bias; however, the influenceseems relatively small, based on a brief examinationof the relationship with the interannual variation ofthe Icelandic Low (not shown). In the Antarctic,the sea ice extent is generally well-simulated inboth seasons.

The seasonal cycle of the sea ice area (sum ofthe ocean area multiplied by the sea ice concentra-tion) for the NH and the SH is also plotted in


Fig. 15. Annual mean sea surface salinity (SSS) (unit: psu) (a) observed and (b) simulated in the historicalexperiment, and (c) its bias (simulation–observation), for the 1979 through 2005 average.


Fig. 16. (a) Zonally averaged annual mean global ocean (left) temperature [unit: �C] and (right) salinity[unit: psu] (contours) and biases (color shading) relative to the observation (PHC; Steele et al. 2001). (b)Meridional overturning stream function [units: Sv] in (left) the Southern Ocean, (center) the AtlanticOcean, and (right) the Indian plus Pacific Oceans.


Fig. 17. Simulated sea ice concentration distributions in September for (a) NH and (b) SH, and in March for(c) NH and (d) SH. Red contours denote 15% lines of the observed concentration (HadISST). Seasonalcycle of simulated (black) and observed (red) sea ice area for (e) NH and (f ) SH. Each climatology is forthe 1979 through 2005 average.


Fig. 17. The simulated sea ice area in the NH ex-hibits a 19� 106 km2 maximum in March and a5� 106 km2 minimum in August. The months ofmaximum and minimum are close to those ob-served; however, the model simulates too large asea ice area in winter through spring. In the SH,despite the well-simulated distributions, the sea-sonal variation of sea ice area has an overestimated18� 106 km2 maximum area in September, incomparison with the observed maximum area of15� 106 km2. This result suggests that the Antarc-tic sea ice compactness is higher than indicated byobservation.

6.2 Variability

In projecting future climate change, projectinghow the variabilities, including El Nino and South-ern Oscillation (ENSO) change, is becoming as im-portant as, or more important than, projecting themean climate. Improving the reliability of the pro-jected change in variabilities requires realistic simu-lation of the variability in the present-day climate.Here, we present SST variations as representativeocean variabilities, including ENSO, the Pacificdecadal oscillation (PDO) (Mantua et al. 1997),and the Atlantic multi-decadal oscillation (AMO)(Kerr 2000). In addition, we present the mostdominant atmospheric variability, known as theArctic Oscillation (AO) or northern annular mode(NAM), and the Antarctic oscillation (AAO) orsouthern annular mode (SAM). To examine thesevariabilities, we use the results from one memberin the ensemble historical experiment. Other mem-bers have similar results.

Figure 18 presents the standard deviation of themonthly SST anomalies observed and simulatedby the historical experiment. We evaluated theSST anomalies for 116 recent years (1890 through2005) with respect to the 116-year climatologywithout detrending, in order to capture the inter-annual, decadal, and multi-decadal variations. Adistinct variation in the equatorial Pacific suggestsSST variation associated with ENSO. Relativelylarger variation is found around the Kuroshio Ex-tension, from which the variation seems to spreadaround the subtropical gyre in a horseshoe-like pat-tern. Decadal variation in this region is related toPDO, which is an important variability in decadalprediction for the near future (Mochizuki et al.2010). In the North Atlantic, however, an unrealis-tically strong variation meanders along the simu-lated NAC. The simulated NAC shifts southward

in the east o¤ Newfoundland. This problem will bediscussed later.

We examine the simulated variations associatedwith ENSO (Fig. 19) for 1890 through 2005. Thesimulated time series of the NINO3 region (90�W–150�W, 5�S–5�N) exhibits interannual variationwith a standard deviation of 0.65�C, which is 14%smaller than the observation (0.76�C) for the sameperiod. The oscillation period seems similar to theobserved one, and there is no unrealistically con-centrated dominant period (e.g., biennial oscillationwas rather dominant in MRI-CGCM2.3.2). Theobserved NINO3 SST indicates an apparent posi-tively skewed variation in recent several decades,while the simulated one does not. Stronger ENSOvariability tends to induce a stronger El Nino, dueto its non-linear e¤ects (Yukimoto and Kitamura2003).

The SST anomaly with regression on the NINO3SST anomaly is presented in Fig. 19b. A warmanomaly in the equatorial Pacific extends from o¤Peru, and a cold anomaly is observed in the mid-latitude North Pacific. This pattern is consistentwith the observation (Fig. 19e), though the simu-lated signal is generally weaker than the observedone.

Associated with El Nino, the simulated precipi-tation increases in the equatorial Pacific and de-creases around it, with a relatively strong signal inthe Maritime Continent. This overall characteristicroughly agrees with the observation. The observedprecipitation increase with El Nino is located inthe central Pacific along the equator (Fig. 19f );however, the simulated one shifts westward. Themodel indicates a much weaker east-west contrastof the SLP anomaly (i.e., Southern Oscillation) as-sociated with El Nino. The negative SLP anomalysimulated in the northern North Pacific is consis-tent with the observation.

The simulated and observed SLP and SATanomalies are regressed on the AO index that isthe first EOF mode calculated for the month-to-month SLP in November through March in 20�Nto 90�N. The observed (JRA-25) SLP anomalypattern (Fig. 20a) associated with AO indicates alow-pressure anomaly in the polar region, and sur-rounding high-pressure anomalies in the mid-latitude. The simulated SLP anomaly pattern (Fig.20b), however, only roughly agrees with the ob-served pattern, indicating a zonally symmetric low-pressure anomaly in the Arctic. The minimum ofthe low-pressure anomaly is simulated around the


North Pole, while it is observed around Iceland.The observed high-pressure anomalies in the Atlan-tic and the Pacific are simulated faint and shiftedwestward. The simulated AO accounts for morevariance (29.2%) than the observation (20.4%).Miller et al. (2006) suggested that the AO in modelstend to overestimate the variance.

In accordance with the SLP anomaly, warm SATanomalies over the Eurasian continent from north-ern Europe through northern Japan, and colderSAT anomalies in northeastern Canada throughGreenland, the Labrador Sea, and southern Alaskaare observed. This overall feature associated withAO in MRI-CGCM3 is roughly consistent withthe observations.

The Antarctic oscillation, the counterpart ofAO in the SH, is also examined. Its index is thefirst mode of the month-to-month 700 hPa geo-potential height at 90�S to 20�S. The plots forAAO, which are similar to those for AO, aredepicted in Fig. 21. This figure indicates a low-pressure anomaly with a center of action aroundthe Pacific coast of Western Antarctica, and sur-rounding high-pressure anomalies with a tri-polarpattern in the mid-latitude. In addition, it indi-cates cold anomalies in the Pacific and IndianOcean sectors, and a warm anomaly around theAntarctic Peninsula. These characteristics are con-sistent with the observations. The simulated AAOalso overestimates the fraction of the accounted

Fig. 18. Standard deviations of (a) observed and (b) simulated monthly SST anomalies for 1890 through2005. The anomalies are deviations from the 1890 through 2005 climatology without detrending. Unitsare �C.


variance (31.8%) compared to the observed one(24.7%).

7. Summary and discussion

A new climate model, MRI-CGCM3, has beendeveloped as a subset of the new earth system model

MRI-ESM1 at MRI. A set of CMIP5 experimentsis performed using MRI-CGCM3. As a base-linefor detailed analysis of the results of various experi-ments, we describe formulations of the model andevaluate the basic performance of MRI-CGCM3from the results of a set of basic experiments.

Fig. 19. Simulated (left column) and observed (right column) variations associated with ENSO. (a) Time se-ries of SST anomaly in the NINO3 region (90�W to 150�W, 5�S to 5�N), and anomalies of (b) SST (�C)and (c) precipitation (mm day�1; color shading) and SLP (hPa; contours) regressed on the NINO3 SSTanomaly. The regressions are calculated over 116 years (1890 through 2005) for SST and 27 years (1979through 2005) for precipitation and SLP. (d–f ) Same as (a–c) but for the observations (the COBE SST,the JRA-25 SLP, and the CMAP precipitation).


Fig. 20. Anomaly patterns of mean sea-level pressure (SLP) (hPa; contours) and SAT (K; color shading) as-sociated with the normalized AO index (1979 through 2005) for (a) JRA-25 reanalysis and (b) simulation inthe historical experiment.

Fig. 21. Same as Fig. 20 but for AAO.


To develop MRI-CGCM3, the AGCM of theformer climate model MRI-CGCM2 series hasbeen substantially upgraded. The upgrade includesthe dynamics frame, and physical parameteriza-tions of cumulus convection, radiation, clouds,PBL, land surface, and ocean surface processes.The ocean-ice model is also a new model MRI.COM3, which is a fundamental replacement ofthe former ocean component in the MRI-CGCM2series. In MRI-CGCM3, the AGCM is coupled in-teractively with a new version of aerosol modelMASINGAR mk-2, which enables the model to ex-plicitly represent the direct and indirect e¤ects ofaerosols on the climate system.

In the piControl experiment, the model exhibitsvery stable behavior without climatic drifts, at leastin the radiation budget and the temperature nearthe surface. Some of major indices associated withocean circulation (i.e., MOCs and ACC) are alsofound to be stable with fairly realistic values.

A small net radiation imbalance at TOA of0.5 W m�2 exists in the piControl experiment, andan unknown energy source of 0.5 W m�2 in the at-mosphere results in an increasing trend of the aver-aged temperature of the global ocean. Since thetrend in SAT is su‰ciently small (0.016 K/100yr),it can be neglected in analyses of at least centennialtimescale climate change. The global water is con-served with a high precision of less than 2 mm/100yr of the sea level trend.

Climate sensitivity is estimated with the Gregorymethod (Gregory et al. 2004) and found to be2.11 K, which is rather low compared with MRI-CGCM2.3.2. The global SAT increase for the1pctCO2 experiment is also less than that in MRI-CGCM2.3.2, but exhibits relatively closer valuesto MRI-CGCM2.3.2 compared to the ratio of cli-mate sensitivities, implying a possible di¤erence inocean heat up-take. In the historical experimentfor reproducing climate change of 1850 through2005 with all the forcing agents including concen-tration of GHGs and emission of aerosols, theglobal SAT increase during this period is underesti-mated by 0.17�C compared with the observation.The contribution of cloud radiative forcing to cli-mate sensitivity is also evaluated with a similarregression method (Fig. 4), and the results indicatealmost neutral SW cloud feedback and negativeLW cloud feedback (�0:38 W m�2 K�1) to theGHG forcing. The LW cloud feedback has a sig-nificantly greater negative value than those for themodels in Gregory and Webb (2008). Further de-

tailed analysis of this subject will be required in fu-ture studies.

For comparison with the recent observations andreanalysis, the mean climate simulated by MRI-CGCM3 is evaluated for its ability to reproducethe present-day climate (1979 through 2005 aver-age) of the historical experiment.

The simulated global-mean radiation at TOAagrees well with the new satellite observations forSW and LW. The cloud forcing is also realistic inthe global average. However, biases in the meri-dional distribution of the radiation budget lead tounderestimation of 1 PW in the implied oceanicsouthward heat transport in the SH, although theatmospheric meridional heat transport generallyagrees with the observational estimations.

The global mean and geographical distributionof the simulated SST indicate overall proper repro-duction of the global-scale climate, even withoutflux adjustment. The SST indicates small biases inthe tropics but contrasting biases in the mid-highlatitudes with cold NH and warm SH.

The distribution of radiation, which is stronglya¤ected by the distribution of clouds, indicatessmall biases in the large-scale pattern, except for re-gions with errors in tropical convection and theSouthern Ocean. The basic mean fields that regu-late the atmospheric general circulation (includingprecipitation, SLP, and meridional-vertical struc-ture) are evaluated by comparison with the obser-vation and JRA-25 reanalysis. Consequently, themodel demonstrates generally reasonable reproduc-tion of these mean fields, except for some importantissues in precipitation (i.e., a lack of Indian Mon-soon precipitation and the double ITCZ in the Pa-cific). These basic performances and features arealso consistent with the simulated CLW content.

The simulated ocean temperature and salinitystructures and important circulations are found tobe reasonable by considering the atmospheric andsurface states. Sea ice distribution generally agreeswith the observations, except for the excessive ex-tension in the winter Atlantic and overestimatedAntarctic sea ice compactness.

For variability, the model simulates comparableSST variation, including ENSO, with the observa-tion for 1890 through 2005. The model suggests re-alistic SST variability, including ENSO and PDO.However, an unrealistic SST variation in the NorthAtlantic seems to be associated with biased oceancurrent and sea ice distribution. The dominant at-mospheric variability for AO and AAO is found


to be realistic for AAO in the model. The simu-lated SLP pattern with AO is not so realistic partic-ularly over the mid-latitude oceans (Fig. 20), whichmeans discussing the decadal variability of AO,that would involve interactions with the oceans,may not be suitable for this model.

In many respects, MRI-CGCM3 is capable ofreproducing the basic mean state and variability inthe climate system for investigating global and sub-continental scale variability and climate change.However, special attention should be paid to thefollowing issues, which need improvement.

There are systematic biases in the simulated SST.The cold bias in the North Pacific concentratesalong the Kuroshio Extension and its return cur-rent in the subtropical gyre (Fig. 6c). The regiondownstream of the strong western boundary cur-rent of the Kuroshio is dominated by eddies. Onepossible reason for this cold bias is insu‰cientnorthward eddy heat transport, since many climatemodels with a low-resolution (non-eddy-resolving)ocean model tend to indicate a similar bias (e.g.,Delworth et al. 2006; Watanabe et al. 2011). Moresophisticated treatment, including parameterizationof the oceanic eddies, may be required.

Another possible factor is overestimation of thesubpolar gyre, which is attributable to the simu-lated Aleutian Low (Fig. 13b) being stronger thanthat observed. The stronger subpolar gyre in theNorth Pacific possibly leads to a stronger south-ward cold advection east of Japan, which is oneof the factors of the cold bias along the KuroshioExtension. As a result of cooling in the KuroshioExtension, the region of return current in the sub-tropical gyre becomes colder.

The cold SST bias in the northern part of thesubtropical gyre results in overestimated low cloudsin summer and subsequent underestimation of solarradiation at the sea surface (Fig. 7c) and decreasedSST, yielding a positive feedback for a colder SST.

Cold SST biases also dominate in the North At-lantic (Fig. 6c), with a particularly large bias in theLabrador and GIN Seas through the Barents Sea.This region is along the NAC, which transportswarm and saline water. Possibly, the lack of trans-port tends to result in a larger sea ice extent in thisregion in winter (Fig. 17c). The overestimated seaice extent again results in a decreased SST withice-albedo feedback. Furthermore, once the sea iceexpands, a very stable layer with low SSS (Fig. 15)is formed near the surface, due to melt water of thesea ice in spring through summer. This very stable

sea surface prevents the deep convective mixingthat is active in the region, including the LabradorSea (e.g., Pickart et al. 2002), which again creates afavorable situation for sea ice formation. Themodel seems to simulate unrealistically large fluctu-ations along the NAC (Fig. 18b). This unstablefluctuation of the current is possibly a trigger forthe expansion of sea ice and entering the feedbackloop mentioned above.

The present MRI-CGCM3 ocean model has lowresolution and cannot resolve oceanic barocliniceddies in the mid-high latitudes. Improvements inthe parameterization of the ocean model will be re-quired to simulate a realistic transport of heat andsalt by the eddy e¤ects in these important regions.Also, parameters, including oceanic eddy viscosity,must be set carefully so as to avoid unrealistic ed-dies or meandering. This is an important problemto be solved.

Warm SST biases dominate in the eastern basinsin the subtropical SH, where low-level clouds mustbe realistically represented. The warm SST bias ex-tends broadly in the Southern Ocean, partly due tooverestimated solar absorption (Fig. 7c). Resultsfrom a separate test run with a cloud satellite simu-lator (not shown) suggest underestimation of low-level clouds, in addition to underestimation of col-umn CLW content (Fig. 11b) in the mid-latitudeSH. The atmospheric circulation in the SH is fairlyrealistic in the mean fields (Figs. 13 and 14), thussupporting realistic heat transport (Fig. 5) by theatmosphere, including by synoptic disturbances.Reflected solar radiation is also underestimated inatmosphere-only experiments. Tuning parametersof the PBL scheme to increase low-level clouds didnot resolve the problem since the SW bias worsenedin the NH when parameters were adjusted for bet-ter estimates in the SH. Once the SST increases,the low-level cloud decreases in the region; as a re-sult, the SST increases further due to excessive solarheating, forming a positive feedback loop.

The double ITCZ is another serious problem inthe simulation by MRI-CGCM3. Less precipitationis simulated on the equator in the western Pacific,and excessive precipitation is simulated at theITCZ in the winter hemisphere. Many models havesu¤ered from the double ITCZ problem (e.g., Lin2007), although some models indicate improvement(e.g., Watanabe et al. 2011). We also have madesome progress in addressing this problem by intro-ducing new cumulus parameterization with sometuning. Experiments with the atmosphere-only


model (not shown) yield much more precipita-tion on the equator in the western Pacific and adiminished double ITCZ feature. However, tuningfurther decreases the Indian summer monsoon pre-cipitation, which may be related to the excess ofprecipitation in the western Pacific ITCZ.

For the ocean-coupled MRI-CGCM3, we ad-justed cloud forcing to agree with the observationby tuning cloud water detrainment from the cumu-lus convection. The model simulates fairly well theprecipitation pattern in the boreal summer, al-though it generally overestimates precipitation inthe tropics. In the austral summer, however, theprecipitation pattern still forms a double ITCZ,since the precipitation is simulated along the south-ern edge of the cold tongue, not in the South PacificConvergence Zone (SPCZ) as observed. A possiblecause of this precipitation error is that the simu-lated cold tongue is stronger than the observed one(Fig. 6b). The double ITCZ tends to produce astronger easterly wind along the equator, which fur-ther enhances the cold tongue, again forming a pos-itive feedback loop (Lin 2007).

The exaggerated cold tongue may a¤ect the sim-ulation of ENSO. The lack of precipitation in theequatorial central Pacific could cause the weak ElNino by reducing anomalous Bjerknes feedback inthe El Nino, as implied from the weaker zonal pres-sure anomaly in the simulated El Nino (Fig. 19c).

MRI-CGCM3’s present ocean resolution(1� � 0:5�) is probably insu‰cient to resolve equa-torial instability waves, which may contribute tothe stronger cold tongue. Some special treatmentwill be required to resolve this problem, which isalso an important subject for future study.

Acknowledgments

The atmospheric model development is basedupon JMA’s operational weather prediction model,which is the result of enormous e¤orts by numer-ous personnel at JMA, especially in the NumericalPrediction Division. Other component models arebased on the results of research e¤orts by the per-sonnel at MRI, especially in the Climate ResearchDepartment, Oceanographic Research Department,and Atmospheric Environment and Applied Mete-orology Research Department. This work was con-ducted under the framework of the ‘‘Comprehen-sive Projection of Climate Change around Japandue to Global Warming’’ supported by JMA, andpartly supported by the KAKUSHIN Program ofthe Ministry of Education, Culture, Sports, Science,and Technology (MEXT). The calculations wereperformed on the HITACHI SR16000 located atMRI.

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