

FGOALS-g3 Model Datasets for CMIP6 Flux-Anomaly-Forced

Model Intercomparison Project

Yaqi WANG

1,2

, Zipeng YU

1,2

, Pengfei LIN

1,2

, Hailong LIU

1,2

, Jiangbo JIN

, Lijuan LI

, Yanli TANG

Li DONG

, Kangjun CHEN

, Yiwen LI

1,2

, Qian YANG

1,2

, Mengrong DING

1,2

, Yao MENG

1,2

Bowen ZHAO

1,2

, Jilin WEI

1,2

, Jinfeng MA

, and Zhikuo SUN

State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics,

Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China

International Center for Climate and Environment Sciences, Institute of Atmospheric Physics,

Chinese Academy of Sciences, Beijing 100029, China

School of Atmospheric Sciences, Guangdong Province Key Laboratory for Climate Change and Nature Disaster Studies,

Sun Yat-sen University, Guangzhou 510275, China

(Received 27 February 2020; revised 3 June 2020; accepted 18 June 2020)

ABSTRACT

The Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) is an endorsed Model Intercomparison Project

in phase 6 of the Coupled Model Intercomparison Project (CMIP6). The goal of FAFMIP is to investigate the spread in the

atmosphere–ocean general circulation model projections of ocean climate change forced by increased CO

, including the

uncertainties in the simulations of ocean heat uptake, global mean sea level rise due to ocean thermal expansion and

dynamic sea level change due to ocean circulation and density changes. The FAFMIP experiments have already been

conducted with the Flexible Global Ocean–Atmosphere–Land System Model, gridpoint version 3.0 (FGOALS-g3). The

model datasets have been submitted to the Earth System Grid Federation (ESGF) node. Here, the details of the experiments,

the output variables and some baseline results are presented. Compared with the preliminary results of other models, the

evolutions of global mean variables can be reproduced well by FGOALS-g3. The simulations of spatial patterns are also

consistent with those of other models in most regions except the North Atlantic and the Southern Ocean, indicating large

uncertainties in the regional sea level projections of these two regions.

Key words:FAFMIP,CMIP6,global mean sea level rise,dynamic sea level change

Citation: Wang, Y. Q., and Coauthors, 2020: FGOALS-g3 Model Datasets for CMIP6 Flux-Anomaly-Forced Model

Intercomparison Project. Adv. Atmos. Sci., 37(10), 1093−1101, https://doi.org/10.1007/s00376-020-2045-8.



1.Background and summary

Understanding changes in global and regional sea

levels is of paramount importance, as they both reflect the nat-

ural and anthropogenic changes in the climate system and

affect the livelihoods of people in coastal areas (Church et

al., 2013). One of the main causes of global mean sea level

rise (GMSLR) is ocean thermal expansion, with the rest

mostly due to the loss of land ice. Based on phase 5 of the

Coupled Model Intercomparison Project (CMIP5) mul-

timodel mean, thermosteric sea level related to ocean

thermal expansion accounts for 30%–50% of GMSLR dur-

ing the 21st century (Church et al., 2013). In contrast, the

redistribution of ocean salt content makes no significant con-

tribution to GMSLR or its uncertainty (Gregory et al.,

2016). On a regional basis, sea level change can deviate signi-

ficantly from the global mean. Changes in dynamic sea

level (DSL) induced by ocean circulation and density

changes are the main contributors to the deviation. Sea level

projections from CMIP5 have a considerable model spread

at the regional scale, mostly caused by the differences in

ocean density and redistribution by ocean circulation

(Gregory et al., 2016).

To investigate the spread and isolate the uncertainty in

sea level projections at both the global and regional scales,

the Flux-Anomaly-Forced Model Intercomparison Project

(FAFMIP) has been proposed by comparing results from

atmosphere–ocean general circulation model (AOGCM)



* Corresponding author: Hailong LIU

Email: [email protected]



ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 37, OCTOBER 2020, 1093–1101



• Data Description Article •



©The Author(s) 2020. This article is published with open access at link.springer.com.



experiments with independent surface flux perturbations

imposed on the sea surface (Gregory et al., 2016). FAFMIP

is an endorsed Model Intercomparison Project of phase 6 of

the Coupled Model Intercomparison Project (CMIP6). The

Flexible Global Ocean–Atmosphere–Land System model,

gridpoint version 3.0 (FGOALS-g3) (Li et al., 2020)

developed at the State Key Laboratory of Numerical Model-

ing for Atmospheric Sciences and Geophysical Fluid Dynam-

ics (LASG), Institute of Atmospheric Physics (IAP),

Chinese Academy of Sciences (CAS), is one of the climate

system models contributing to CMIP6. The FAFMIP experi-

ments are also conducted using FGOALS-g3 following the

standard protocol of Gregory et al. (2016), and the data

have been submitted to the Earth System Grid Federation

(ESGF) data server (https://esgf-node.llnl.gov/projects/

cmip6/). The required diagnostics of FAFMIP are provided

in the format of the CMIP6 Ocean Model Intercomparison

Project (Griffies et al., 2016).

The purpose of this paper is to provide a comprehens-

ive description of the FGOALS-g3 FAFMIP datasets for the

users of CMIP6 datasets. The remainder of this paper is organ-

ized as follows. Section 2 presents the model descriptions

and experimental design. Section 3 presents the basic tech-

nical validation of the FGOALS-g3 experiments. Section 4

describes the datasets. The fifth part provides usage notes.

2.Model and experiments

2.1.Introduction to the model

FGOALS-g3 has four component models: the Grid-

Point Atmospheric Model of LASG–IAP, version 3

(GAMIL3) for the atmosphere (Li et al., 2013), the

LASG–IAP Climate System Ocean Model, version 3

(LICOM3) for the ocean (Lin et al., 2016; Yu et al., 2018;

Lin et al., 2020), the Los Alamos sea ice model, version 4

(CICE4) for the sea ice, and the CAS Land Surface Model

(CAS-LSM) for the land (Xie et al., 2018). All the compon-

ents are coupled with version 7 of the flux coupler

developed at the National Center for Atmospheric Research

(Craig et al., 2005).

GAMIL3 uses a finite difference dynamical core,

which conserves mass and effective energy under the stand-

ard stratification approximation. The horizontal resolution

of GAMIL3 is ~2° (180×80) and the number of vertical lay-

ers used in GAMIL3 is 26. The land component is CAS-

LSM with the same grid as GAMIL3. With regard to the phys-

ical processes of GAMIL3 and CAS-LSM, the details can

be found in Li et al. (2020).

The ocean component, LICOM3, has also been extens-

ively improved (Liu et al., 2012; Lin et al., 2016, 2020; Yu

et al., 2018; Lin et al., 2020). Its dynamic core with a latit-

ude–longitude grid structure is replaced by arbitrary ortho-

gonal curvilinear coordinates (Yu et al., 2018). Therefore,

the tripolar grid from Murray (1996) can be applied in

LICOM3 with two North Poles on the Eurasian (65°N,

65°E) and North American (65°N, 115°W) continents. The

introduction of the tripolar grid can directly improve the

effectiveness of the dynamic core by both enlarging the

time steps and removing the zonal filter for momentum and

tracers. An Arakawa B-grid is used for the horizontal grid

with 360×218 grid points. The eta coordinates with 30 or 80

layers are used in the vertical direction, but only 30 layers

are used for the DECK and FAFMIP experiments of

CMIP6. With regard to the physical processes, the St.

Laurent et al. (2002) internal tidal mixing is introduced into

LICOM3 (Yu et al., 2017), and the buoyancy frequency-

related thickness diffusivity of Ferreira et al. (2005) is

applied in the eddy-induced advection of Gent and McWilli-

ams (1990). In addition, the chlorophyll-a-dependent solar

penetration of the Ohlmann (2003) scheme and vertical mix-

ing of Canuto et al. (2002) are inherited from LICOM2.

CICE4 is the sea ice component of FGOALS-g3, with the

same horizontal resolution as the ocean component.

2.2.Experimental design

Five experiments are carried out in FAFMIP: faf-water,

faf-stress, faf-heat, faf-all and faf-passiveheat (Table 1). In

the first three experiments, the surface momentum, freshwa-

ter and heat flux perturbations are applied, while in faf-all,

all three perturbations are applied together. All the forcing

data are from Gregory et al. (2016), which are monthly flux

anomalies of the ensemble mean of the 61st–80th years

1pctCO2 experiments from 13 CMIP5 models. For compar-

ison between the perturbation and preindustrial control

(piControl) experiments, all other conditions of the experi-

ments are the same as those of the setup of the piControl

run, including the point to branch the experiment, the concen-

tration of CO

(280 ppm), etc. The experiments are all 70

years long, and the scale of the CO

concentration is doub-

ling. The control experiment used in this paper is the faf-pass-

iveheat experiment, which is equivalent to the piControl run

but with an extra diagnostic tracer. The details of the five

FAFMIP experiments are described as follows:

Table 1.  Descriptions of the FAFMIP experiments.

Name Ocean surface flux perturbation Integration/Year

faf-stress Zonal and meridional momentum 70

faf-water Freshwater 70

faf-heat Heat 70

faf-all Zonal and meridional momentum, heat and freshwater 70

faf-passiveheat Heat as in faf-heat, but added as a passive tracer 70

1094 FGOALS-g3 MODEL DATASETS FOR CMIP6 FAFMIP VOLUME 37



In the faf-stress experiment, the perturbations of sur-

face downward fluxes of eastward and northward

momentum derived from CMIP5 are applied in the surface

zonal and meridional momentum flux. The stress perturba-

tions are directly added to the momentum balance of the sea-

water but not to the ocean subgrid processes and the

momentum balance of the sea ice. Figure 1a shows the

annual mean surface momentum flux perturbations for

FAFMIP. Its dominant feature is the increase in westerly

wind stress in the Southern Ocean, which indicates that

large changes in faf-stress will occur in the Southern Ocean.

In the faf-water experiment, a perturbation of freshwa-

ter anomalies is applied to the freshwater flux into the sea sur-

face. The anomalies are the sum of all possible sources in

the CMIP5 AOGCM, including precipitation, evaporation,

river inflow and water fluxes between floating ice (sea ice

and icebergs) and seawater. Figure 1b shows the annual

mean surface water flux perturbations for FAFMIP. We find

that its pattern is dominated by that of precipitation

changes, which are positive near the equator and at mid to

high latitudes and negative in the subtropics.

In the faf-heat experiment, a perturbation of the sur-

face downward heat flux in seawater is applied to the heat

flux into the sea surface. The anomalies are the sum of all pos-

sible sources in the CMIP5 AOGCM, including the net down-

ward radiative fluxes, sensible and latent heat fluxes to the

atmosphere, and heat fluxes between sea ice and seawater.

In previous studies, we found that there is a negative feed-

back due to the air–sea interaction at the surface, which will

reduce the increase in temperature by approximately 50%.

To avoid this effect, a passive tracer, which cannot feel heat

perturbation, has been introduced to compute the surface

heat flux instead of the sea surface temperature (SST), as pro-

posed by Bouttes et al. (2014). The passive tracer is initial-

ized to the ocean temperature at the start of the experiment

and subsequently transported by all the same processes as

ocean temperature, except for the heat anomalies. Figure 1c

shows the annual mean surface heat flux perturbations for

FAFMIP. Large positive anomalies occur in the North

Atlantic Ocean and the Southern Ocean.

In the faf-all experiment, the surface flux perturbations

of momentum, heat and freshwater are all applied simultan-

eously into the seawater. The method of computing surface

flux uses the same method as that in the faf-heat experi-

ment. The purpose of the faf-all experiment is to quantify

the nonlinearities of the effects of the three perturbations. If

the ocean response to CO

forcing may be interpreted as the

sum of the effects, the effects of the three perturbations are lin-

ear.

In faf-passiveheat, the heat flux perturbation is applied

instead to a passive tracer to diagnose the effect of added

heat on the ocean temperature through processes other than

heat transport due to circulation. The tracer here is initial-

ized to zero and does not affect the processes. Therefore,

the faf-passiveheat experiment is the same as the standard

piControl but with an additional passive tracer for dia-

gnosis. The results of this experiment are used as a refer-

ence in the present paper.

Three additional experiments, faf-heat-NA50pct, faf-

heat-NA0pct and faf-antwater-stress for FAFMIP, which

were further proposed by the FAFMIP meeting in April

2019, have not been conducted so far. Therefore, they are

not included and discussed in the present paper. The former

two experiments reduce the double-counted surface heat

flux in the North Atlantic due to the change in SST. The lat-

ter experiment is to investigate the effects of both wind

stress and freshwater in the Southern Ocean. Further details

of the implementation of each of the experiments can be

found at the following website: http://www.fafmip.org.

3.Validation

Some preliminary results from the experiments are valid-

ated here, and the usefulness of this dataset is demonstrated.

The metrics shown here follow Gregory et al. (2016), includ-

ing the global mean of SST changes, ocean heat content

(OHC) change, DSL change and the maximum transport

change of Atlantic meridional overturning circulation

(AMOC), as well as the spatial pattern of zonal mean temper-



Fig. 1. Surface flux perturbations of (a) momentum (10

−3

Pa,

color indicates the magnitude of the vector, arrow indicates

direction), (b) water (10

−6

kg m

−2

−1

) and (c) heat (W m

−2

)

from FAFMIP.

OCTOBER 2020 WANG ET AL. 1095



ature change, OHC change and DSL change. All the

changes are relative to the piControl (or faf-passiveheat)

state, the values of the 70-year mean or values of the corres-

ponding year are used for the time series of changes, and

the values of the 61–70-year mean are used for the spatial pat-

terns of changes.

3.1.Time series of changes

3.1.1.Global mean SST

The global mean SST change with respect to piControl

reaches approximately 0.9 K after the 70th year in the faf-

all experiments (Fig. 2a, black line). The change in faf-all is

mainly dominated by faf-heat but with a slightly smaller mag-

nitude (approximately 0.8 K after the 70th year, Fig. 2a, red

line), while the changes in global mean SST are almost negli-

gible in both faf-stress (Fig. 2a, orange line) and faf-water

(Fig. 2a, blue line). Under faf-stress, the changes in global

mean SST slow down gradually and decrease by 0.07 K

after the 70th year. In faf-water, the global mean SST levels

off within approximately 30 years, showing a decrease of

almost the same magnitude as that of faf-stress (−0.06 K)

after the 70th year.

3.1.2.Global OHC

The OHC is defined as the vertical integration of ocean

temperature from the sea surface to the bottom, multiplied

by the reference density (1026 kg m

−3

) and the specific heat

capacity (3992 J kg

−1

°C

−1

). Changes in globally integrated

OHC are crucial for the GMSLR induced by thermal expan-

sion. The time series of globally integrated OHC change in

faf-all (Fig. 2b, black line) is still dominated by faf-heat

(Fig. 2b, red line), reaching approximately 0.9 YJ (1 YJ =

J) after the 70th year for both experiments. Changes in

globally integrated OHC are also negligible in faf-stress

(Fig. 2b, orange line) and faf-water (Fig. 2b, blue line), in

which they both increase only approximately 0.1 YJ at the

end of the experiments.

The globally integrated OHC change is proportional to

the global mean ocean temperature change, which is also

shown in Gregory et al. (2016, second row of their Fig. 5).

Their values have a range of 0.2–0.3 K in faf-heat for five

CMIP5 models in the last year of integration, while the

value in our experiment is relatively small at approximately

0.16 K. This might be due to the larger negative changes in

the sea ice cover or SST of FGOALS-g3 than that of other

models.

3.1.3.DSL

¯η

ζ = η − ¯η

In the present study, DSL ( ) is defined as the devi-

ation of the sea surface height ( ) from the global mean ( );

that is, . To quantify the DSL change in FAFMIP

experiments, we compute the time series of the area-

weighted spatial standard deviation of the annual mean DSL

change of each experiment (Fig. 2c). The large values mean

that the spatial pattern of forced change in DSL can be detec-

ted from the background of unforced variability. The DSL

rises above the control value in all four experiments. It is



Fig. 2. Global annual time series for faf-stress (orange), faf-water (blue), faf-heat (red) and faf-all (black)

experiments: (a) SST change (K); (b) ocean heat content change (YJ); (c) the spatial standard deviation of dynamic

sea level change; and (d) maximum of the Atlantic meridional overturning streamfunction (Sv). The changes in (a–d)

are with respect to the 70-year mean of piControl (or faf-passiveheat), while (b) is with respect to the corresponding

year of the piControl run.

1096 FGOALS-g3 MODEL DATASETS FOR CMIP6 FAFMIP VOLUME 37



also the case for the above two variables that the large DSL

changes in faf-heat (Fig. 2c, red line) can explain most of

the changes in faf-all (Fig. 2c, black line), with values of

0.10 m and 0.11 m, respectively. The magnitudes of the

DSL change of faf-all in this study are near the upper limit

of the values in Gregory et al. (2016). The faf-stress and faf-

water values do not differ significantly from those of the con-

trol, with values less than half of those of the other two

runs, faf-stress and faf-water.

3.1.4.AMOC

The evaluations of the maximum transport of AMOC

change for FAFMIP experiments are shown in Fig. 2d,

which is crucial to determine the changes in OHC and sea

level in the North Atlantic. The weakened AMOC can be

seen in both faf-heat (Fig. 2d, red line) and faf-all (Fig. 2d,

black line), in which the maximum AMOC weakens nearly

14 Sv at the end of the experiments. The change in faf-heat

is also within the range of 5–15 Sv in Gregory et al. (2016,

third row of their Fig. 5). In faf-stress (Fig. 2d, orange line)

and faf-water (Fig. 2d, blue line), the changes in the max-

imum AMOC relative to the control are less than 1 Sv at the

70th year, indicating that the perturbations to the surface

momentum and water fluxes do not cause significant

changes in the AMOC.

The weakened AMOC in faf-heat is larger than the expec-

ted response for the 1pctCO2 experiments. This is due to

the positive feedback between the surface heat flux and the

weakening of the AMOC. When the AMOC declines, the

associated heat transport decreases, and the SST cools in the

North Atlantic. The cooling SST inhibits the heat flux from

being released into the atmosphere, and the ocean absorbs

more heat flux, which will further decrease the AMOC.

Another reason for a larger response than that in 1pctCO2 is

that the heat flux perturbation consistent with double CO

con-

centration is applied at the beginning of the faf-heat experi-

ment, while the CO

concentration in 1pctCO2 is gradually

increased to double

3.2.Spatial patterns of changes

3.2.1.Zonal mean ocean temperature

Figure 3 shows the change in zonal mean ocean temperat-

ure in the time mean of the 61st–70th year relative to the

piControl run. The warming occurs in faf-all in the upper

1000 m at most latitudes (Fig. 3d), with the maximum mag-

nitude of changes over 1 K at the surface at approximately

60°N. The warming of faf-all is mainly dominated by faf-

heat (Fig. 3c), and faf-water contributes 0.2 K between 10°

and 60°N at depths of approximately 200–600 m (Fig. 3b).

It is unusual that the significant warming induced by the sur-

face heat perturbation penetrates 4000 m in the Arctic

Ocean, which cannot be found in the results of Gregory et

al. (2016). We presume that this might be due to the large ver-

tical mixing in this region, and it is worth investigating fur-

ther.



Fig. 3. Changes in zonal mean ocean temperature (K) as a function of depth and latitudes of the time mean during the

last 10 years (61–70) of (a) faf-stress, (b) faf-water, (c) faf-heat and (d) faf-all relative to the piControl run.

OCTOBER 2020 WANG ET AL. 1097



The cooling occurs in faf-all in three regions: south of

60°S from the surface to 4000 m, approximately 70°N in

the subsurface from 1000 m to 3500 m, and at the bottom

(Fig. 3d). Three kinds of perturbations at the surface all pos-

sibly contribute to the negative changes. The cooling south

of 60°S is attributed to both faf-stress and faf-water, while

the cooling in the high latitudes of the Northern Hemi-

sphere is dominated by both faf-stress and faf-heat. The

small negative temperature changes at the bottom are due to

faf-heat. The cooling in the high latitudes might be related

to the weakened circulation. Most of the cooling patterns

are also consistent with the multimodel mean results from

Gregory et al. (2016), while cooling below 1000 m in the

Northern Hemisphere high latitudes in faf-heat does not

appear in FGOALS-g3, and cooling south of 60°S in faf-

water does not appear in the multimodel mean.

3.2.2.DSL and OHC

Change patterns of the DSL and OHC in the time mean

of the 61st–70th year of the FAFMIP experiments relative

to the piControl run are shown in Fig. 4 and Fig. 5, respect-

ively. The heat flux perturbation produces the most changes

in the spatial pattern of the DSL (Figs. 4c and d), while the

wind stress and freshwater perturbations dominate the posit-

ive anomaly in the Southern Ocean and in the Arctic, respect-

ively. Previous studies point out that the simulated com-

mon DSL features are the dipole in the North Atlantic, the

enhanced sea level rise in the Arctic and the increase in the

gradient across the Antarctic Circumpolar Current (ACC)

(Church et al., 2013; Bouttes and Gregory, 2014). The pat-

tern of DSL changes and the contribution of three flux per-

turbations are also the same as the results from Gregory et

al. (2016).

There is a strong correlation between the patterns of

OHC and DSL change in both faf-heat (Fig. 4c and Fig. 5c)

and faf-all (Fig. 4d and Fig. 5d). This is consistent with previ-

ous studies, suggesting that the patterns of OHC change and

corresponding DSL change are largely driven by surface

heat flux perturbations (Gregory et al., 2016). As discussed

in section 3.1.4, there is also a similarity of the AMOC

response between faf-heat and faf-all. The regional OHC

changes are relatively small in faf-stress (Fig. 5a) and faf-

water (Fig. 5b) in comparison to those of faf-heat (Fig. 5c)

and faf-all (Fig. 5d).

The differences between FGOALS-g3 and the mul-

timodel mean of Gregory et al. (2016) appear in the faf-

water experiment. First, the water flux perturbation makes

an opposite contribution to the dipole in the multimodel

mean of Gregory et al. (2016), while it makes no signific-

ant contribution in FGOALS-g3. Second, the increase in the

gradient across the ACC in the Southern Ocean is mainly

caused by momentum flux perturbation and somewhat by

the water flux perturbation in FGOALS-g3. The results of

Gregory et al. (2016) show that freshwater perturbations

reduce the gradient in the Southern Ocean. Combining these

differences with the deviation in the zonal mean ocean temper-

ature from the multimodel mean results discussed in sec-

tion 3.2.1, we can find that the North Atlantic and the South-

ern Ocean are the two regions with large uncertainties in sea

level projections.



Fig. 4. Changes in dynamic sea level (m) averaged duing the last 10 years (61–70) of (a) faf-stress, (b) faf-water, (c)

faf-heat and (d) faf-all relative to the piControl run.

1098 FGOALS-g3 MODEL DATASETS FOR CMIP6 FAFMIP VOLUME 37



4.Data records

FAFMIP datasets have been uploaded onto the ESGF

node and can be found at https://esgf-node.llnl.gov/projects/

cmip6/. The dataset format is Network Common Data Form

(NetCDF), version 4. Although the model outputs have

double precision, we converted all the variables into single

precision for analysis. These data can be easily dealt with

by common computer programming languages and profes-

sional software such as Climate Data Operators (CDO,

https://code.mpimet.mpg.de/projects/cdo/) or NetCDF Oper-

ator (NCO, http://nco.sourceforge.net).

5.Usage notes

The original model outputs are on a tripolar grid with

two poles in the Northern Hemisphere on the continents.

The horizontal grid numbers are 360 and 218 in the zonal

and meridional directions, respectively. The original grid dis-

tribution is kept and the format is slightly changed to Cli-

mate Model Output Rewriter (CMOR) file structure as

required by FAFMIP. The data have 30 vertical levels, and

the original vertical level is not changed on the ESGF note.

The first level is at a depth of 5 m with a thickness of 10 m.

The variables of Priority 1 for FAFMIP are shown in Table 2.

Table 2.  Descriptions of output variables of Priority 1 for FAFMIP.

Name Description

zos Sea surface height above geoid

zostoga Global average thermosteric sea level

thetao Sea water potential temperature

thetaoga Global average sea water potential temperature

so Sea water salinity

msftmz Ocean meridional overturning mass streamfunction

hfds Downward heat flux at sea water surface

wfo Water flux into sea water

pathetao Sea water additional potential temperature

prthetao Sea water redistributed potential temperature

opottempdiff Tendency of sea water potential temperature expressed as heat content due to parameterized dianeutral mixing

opottemppadvect Tendency of sea water potential temperature expressed as heat content due to parameterized eddy advection

opottemppmdiff Tendency of sea water potential temperature expressed as heat content due to parameterized mesoscale diffusion

opottemprmadvect Tendency of sea water potential temperature expressed as heat content due to residual mean advection

opottemptend Tendency of sea water potential temperature expressed as heat content

osaltdiff Tendency of sea water salinity expressed as salt content due to parameterized dianeutral mixing



Fig. 5. Changes in ocean heat content (GJ m

−2

) averaged duing the last 10years (61–70) of (a) faf-stress, (b) faf-

water, (c) faf-heat and (d) faf-all relative to the piControl run.

OCTOBER 2020 WANG ET AL. 1099



Acknowledgments.  This study was supported by National

Key R&D Program for Developing Basic Sciences

(2018YFA0605703), the Strategic Priority Research Program of

Chinese Academy of Sciences (Grant No. XDB42010404) and the

National Natural Science Foundation of China (Grants 41976026,

41776030 and 41931183, 41931182). The authors acknowledge

the technical support from the National Key Scientific and Technolo-

gical Infrastructure project "Earth System Science Numerical Simu-

lator Facility" (EarthLab).

Data availability statement

The data that support the findings of this study are avail-

able from https://esgf-node.llnl.gov/projects/cmip6/.

The citation faf-stress is “CAS FGOALS-g3 model out-

put prepared for CMIP6 FAFMIP faf-water. Earth System

Grid Federation. http://doi.org/10.22033/ESGF/CMIP6.

3299”.

The citation faf-water is “CAS FGOALS-g3 model out-

put prepared for CMIP6 FAFMIP faf-stress. Earth System

Grid Federation. http://doi.org/10.22033/ESGF/CMIP6.

3297”.

The citation faf-heat is “CAS FGOALS-g3 model out-

put prepared for CMIP6 FAFMIP faf-heat. Earth System

Grid Federation. http://doi.org/10.22033/ESGF/CMIP6.

3293”.

The citation faf-all is “CAS FGOALS-g3 model output

prepared for CMIP6 FAFMIP faf-all. Earth System Grid Fed-

eration. http://doi.org/10.22033/ESGF/CMIP6.3291”.

The citation faf-passiveheat is “CAS FGOALS-g3

model output prepared for CMIP6 FAFMIP faf-passiveheat.

Earth System Grid Federation. http://doi.org/10.22033/

ESGF/CMIP6.3295”.

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