The main advancements of the Beijing Climate Center (BCC) climate system model from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to phase 6 (CMIP6) are presented, in terms of physical parameterizations and model performance. BCC-CSM1.1 and BCC-CSM1.1m are the two models involved in CMIP5, whereas BCC-CSM2-MR, BCC-CSM2-HR, and BCC-ESM1.0 are the three models configured for CMIP6. Historical simulations from 1851 to 2014 from BCC-CSM2-MR (CMIP6) and from 1851 to 2005 from BCC-CSM1.1m (CMIP5) are used for models assessment. The evaluation matrices include the following: (a) the energy budget at top-of-atmosphere; (b) surface air temperature, precipitation, and atmospheric circulation for the global and East Asia regions; (c) the sea surface temperature (SST) in the tropical Pacific; (d) sea-ice extent and thickness and Atlantic Meridional Overturning Circulation (AMOC); and (e) climate variations at different timescales, such as the global warming trend in the 20th century, the stratospheric quasi-biennial oscillation (QBO), the Madden–Julian Oscillation (MJO), and the diurnal cycle of precipitation. Compared with BCC-CSM1.1m, BCC-CSM2-MR shows significant improvements in many aspects including the tropospheric air temperature and circulation at global and regional scales in East Asia and climate variability at different timescales, such as the QBO, the MJO, the diurnal cycle of precipitation, interannual variations of SST in the equatorial Pacific, and the long-term trend of surface air temperature.
Changes in global climate and environment are the main challenges that human societies are facing with respect to sustainable development. Climate and environmental changes are often the consequence of the combined effects of anthropogenic influences and complex interactions among the atmosphere, hydrosphere, lithosphere, cryosphere, and biosphere of the Earth system. To better understand the behaviors of Earth's climate, and to predict its future evolution, appropriate new concepts and relevant methodologies need to be proposed and developed. Climate system models are effective tools to simulate the interactions and feedbacks in an objective manner, and to explore their impacts on climate and climate change. The Coupled Model Intercomparison Project (CMIP), organized under the auspices of the World Climate Research Programme's (WCRP) Working Group on Coupled Modelling (WGCM), started 20 years ago as a comparison of a handful of early global coupled climate models (Meehl et al., 1997). More than 30 models participated in phase 5 of CMIP (CMIP5, Taylor et al., 2012) and created an unprecedented dynamic in the scientific community to generate climate information and make it available for scientific research. Many of these models were then extended into Earth system models by including the representation of biogeochemical cycles. The Beijing Climate Center (BCC) effectively contributed to CMIP5 by running most of the mandatory and optional simulations.
The first generation of the Beijing Climate Center ocean–atmosphere Coupled Model BCC-CM1.0 was developed from 1995 to 2004 (e.g., Ding et al., 2002). It was mainly used for seasonal climate prediction. Since 2005, BCC has initiated the development of a new fully coupled climate modeling platform (Wu et al., 2010, 2013, 2014). In 2012, two versions of the BCC model were released: BCC-CSM1.1, with a coarse horizontal resolution T42 (approximately 280 km), and BCC-CSM1.1m, with a medium horizontal resolution T106 (approximately 110 km). The BCC model (both versions) was a fully coupled model with ocean, land surface, atmosphere, and sea-ice components (Wu et al., 2008; Wu, 2012; Xin et al., 2013). Both versions were extensively used for CMIP5. At the end of 2017, the second generation of the BCC model was released to run different simulations proposed by phase 6 of CMIP (CMIP6, Eyring et al., 2016). The purpose of this paper is to document the main efforts and advancements achieved in BCC with respect to its climate model transition from CMIP5 to CMIP6. We show improvements in both the model resolution and its physics. A relevant description of the model transition and experimental design are shown in Sects. 2 and 3. A comparison of the model performance is presented in Sect. 4. Conclusions and discussion are summarized in Sect. 5. Information regarding the code and the data availability is shown in Sect. 6.
Table 1 shows a summary of the different BCC models or versions used for CMIP5 and CMIP6; all of them are fully coupled global climate models with four components, atmosphere, ocean, land surface and sea ice, interacting with each other. They are physically coupled through fluxes of momentum, energy, and water at their interfaces. The coupling was realized using the flux coupler version 5 developed by the National Center for Atmosphere Research (NCAR). BCC-CSM1.1 and BCC-CSM1.1m are our two models involved in CMIP5. They differ mainly with respect to their horizontal resolutions. As shown in Table 1, BCC-CSM2-MR, BCC-CSM2-HR, and BCC-ESM1.0 are the three models developed for CMIP6.
BCC models for CMIP5 and CMIP6.
BCC-ESM1.0 is our Earth system configuration. It is a global fully coupled climate–chemistry–carbon model, and is intended to conduct simulations for the Aerosol Chemistry Model Intercomparison Project (AerChemMIP, Collins et al., 2017) and the Coupled Climate–Carbon Cycle Model Intercomparison Project (C4MIP, Jones et al., 2016), both endorsed by CMIP6. Its performance will be presented in a separated paper. BCC-CSM2-HR is our high-resolution configuration prepared for conducting simulations of the High Resolution Model Intercomparison Project (HighResMIP v1.0, Haarsma et al., 2016). It has 56 layers in the vertical and 0.092 hPa for the top of model. Its performance will also be presented separately.
In this paper, we focus on BCC-CSM1.1m and BCC-CSM2-MR. The two models are
representative of our climate modeling efforts in CMIP5 and CMIP6,
respectively. They have the same horizontal resolution (T106, about
The profiles of layer thickness against height for 26 vertical layers of the atmosphere in BCC-CSM-1.1m and 46 vertical layers in BCC-CSM2-MR.
The atmospheric component of BCC-CSM1.1m is BCC-AGCM2.2 (second generation). It is detailed in a series of publications (Wu et al., 2008, 2010, 2013; Wu, 2012). BCC-AGCM3-MR is its updated version (third generation), used as the atmosphere component in BCC-CSM2-MR. The dynamic core in the two models is identical and uses the spectral framework described in Wu et al. (2008); within this framework a reference stratified atmospheric temperature and a reference surface pressure are introduced into the governing equations to improve pressure gradient force and gradients of surface pressure and temperature, and the prognostic variables for temperature and surface pressure are separately replaced by their perturbations from their references. An explicit time difference scheme is applied to the vorticity equation, and an semi-implicit time difference scheme is applied to the divergence, temperature, and surface pressure equations. A semi-Lagrangian tracer transport scheme is used for water vapor, liquid cloud water, and ice cloud water. The main differences in the model physics used in the two models (BCC-AGCM2.2 and BCC-AGCM3-MR) are summarized in Table 2 and detailed in the following.
Main physics schemes in the atmospheric components (BCC-AGCM) of the BCC-CSM versions for CMIP5 and CMIP6.
Our second-generation atmospheric
model, BCC-AGCM2.2, operates with a parameterization scheme of deep cumulus
convection developed by Wu (2012). The main characteristics can be summarized as
follows:
Deep convection is initiated at the level of maximum moist static energy
above the boundary layer. It is triggered when there is positive convective
available potential energy (CAPE) and if the relative humidity of the air at
the lifting level of convective cloud is greater than
75 %. A bulk cloud model, taking the processes of
entrainment/detrainment into account, is used to calculate the convective updraft with
consideration of budgets for mass, dry static energy, moisture, cloud liquid
water, and momentum. The scheme also considers the lateral entrainment of
the environmental air into the unstable ascending parcel before it rises to
the lifting condensation level. The entrainment/detrainment amount for the
updraft cloud parcel is determined according to the increase/decrease of the
updraft parcel mass with altitude. Based on a total energy conservation
equation of the whole adiabatic system involving the updraft cloud parcel
and the environment, the mass change for the adiabatic ascent of the cloud
parcel with altitude is derived. The convective downdraft is assumed to be saturated and to have originated from
the level of minimum environmental saturated equivalent potential
temperature within the updraft cloud. The closure scheme determining the mass flux at the base of convective
cloud is that suggested by Zhang (2002). It assumes that the
increase/decrease of CAPE due to changes of the thermodynamic states in the
free troposphere resulting from convection approximately balances the
decrease/increase resulting from large-scale processes.
A modified version of Wu (2012) is used in BCC-AGCM3-MR for deep convection parameterization. The convection is only triggered when the boundary layer is unstable or when an updraft velocity in the environment exists at the lifting level of convective cloud, and there is simultaneously positive CAPE. This modification is aimed to connect the deep convection to the instability of the boundary layer. The lifting condensation level is set to above the nominal level of non-divergence (600 hPa) in BCC-AGCM2.2 and lowered to the level of 650 hPa in BCC-AGCM3-MR. These modifications in the deep convection scheme are found to improve the simulation of the diurnal cycle of precipitation and the Madden–Julian Oscillation (MJO).
Shallow convection is parameterized with a local convective transport scheme (Hack, 1994). It is used to remove any local instability that may remain after the deep convection scheme. This Hack convection scheme is largely used to typically represent shallow subtropical convection and midlevel convection that do not originate from the boundary layer.
The cloud macrophysics comprises physical processes to compute cloud fractions
in each layer, horizontal and vertical overlapping of clouds, and conversion
rates of water vapor into cloud condensates. In BCC-AGCM2.2, the cloud fraction
and the associated cloud macrophysics follow the NCAR Community
Atmosphere Model version 3 (CAM3, Collins et al., 2004) design. The total cloud
cover (
A new cloud scheme is developed and used in BCC-AGCM3-MR. It consists of
calculating convective cloud and the total cloud cover differently to
BCC-AGCM2.2. The total cloud fraction in each model grid cell is given
as
If no supersaturation exists in clouds, we can obtain the following from Eqs. (4)
and (5):
After the three moisture processes (i.e., deep convection, shallow
convection, and finally stratiform precipitation) are finished, the mean
temperature (
In BCC-AGCM2.2 and BCC-AGCM3-MR, the essential part of the stratiform cloud
microphysics remains the same and follows the framework of non-convective
cloud processes in CAM 3.0 (Collins et al., 2004), which is the scheme proposed
by Rasch and Kristjánsson (1998) and modified by Zhang et al. (2003).
However there is a noticeable difference in the cloud microphysics in the two
models concerning the treatments for indirect effects of aerosols through
mechanisms of clouds and precipitation. Indirect effects of aerosols were not
included in BCC-AGCM2.2 for CMIP5. That is, the effective
radius of cloud droplets was not related to aerosols or the precipitation
efficiency. The cloud droplets effective radius was either prescribed or was a simple function of
atmospheric temperature. The effective radius for warm clouds was specified
as 14
Aerosol particles influence clouds and the hydrological cycle by their
ability to act as cloud condensation nuclei and ice nuclei. This indirect
radiative forcing of aerosols is included in the latest version of
BCC-AGCM3-MR, with the effective radius of liquid water cloud droplets being
related to the cloud droplet number concentration
Aerosols also exert impacts on precipitation efficiency (Albrecht, 1989),
which is taken into account in the parameterization of non-convective cloud
processes. We use the same scheme as in CAM3 (Rasch and Kristjánsson,
1998; Zhang et al., 2003). There are five processes that convert condensate
to precipitate: auto-conversion of liquid water to rain, collection of cloud
water by rain, auto-conversion of ice to snow, collection of ice by snow, and
collection of liquid by snow. The auto-conversion of cloud liquid water to
rain (PWAUT) is dependent on the cloud droplet number concentration and
follows a formula that was originally suggested by Chen and Cotton (1987):
Gravity waves can be generated by a variety of sources including orography, convection, and geostrophic adjustment in regions of baroclinic instability (Richter et al., 2010). Gravity waves propagate upward from their source regions and break when large amplitudes are attained. This produces a drag on the mean flow. Gravity wave drag plays an important role in explaining the zonal mean flow and thermal structure in the upper atmosphere.
In previous versions of BCC models, the orographic gravity wave drag was parameterized as in McFarlane (1987), but non-orographic sources such as convection and jet-front systems were not considered. In BCC-AGCM3-MR, the gravity wave drag generated from convective sources is introduced as in Beres et al. (2004), but drag by frontal gravity waves and orographic blocking effects are still not involved. The key point of the Beres' scheme is relating the momentum flux phase speed spectrum to the convective heating properties. In the present version of BCC-AGCM3-MR, the convective gravity wave parameterization is only activated when the deep convective heating depth is greater than 2.5 km. Gravity waves generated by topography and fronts are important for higher latitudes. The efficiency parameter in the McFarlane scheme is set to 0.125 in BCC-AGCM2.2 and doubled to 0.25 in BCC-AGCM3-MR to obtain a better result for the polar night jet. In the future, it is planned to improve the orographic gravity wave scheme and to implement parameterizations of gravity waves emitted by fronts and jets.
In the convective gravity wave scheme, the uncertainty in the magnitude of
momentum flux arises from the horizontal scale of the heating and the
convective fraction. The convective fraction (CF) within a grid cell is an
important parameter and can be tuned to obtain the right wave
amplitudes. It is a constant and is valid for all latitudes where convection is active. Previous
studies from Alexander et al. (2004) show that the CF can vary from
The radiative transfer parameterization in BCC-AGCM2.2 follows the scheme initially implemented in CAM3 (Collins et al., 2004). Aerosol indirect effects on radiation are not taken into account, and the effective radius of cloud droplets is only a function of temperature for cold clouds and is prescribed different values for maritime, polar, and continental cases for warm clouds. However, in BCC-AGCM3-MR the aerosol indirect effects are fully included, and the effective radius of droplets for liquid clouds is calculated by Eq. (12) using the liquid cloud droplet number concentration.
BCC-AGCM3-MR basically inherits the boundary layer turbulence
parameterization used in BCC-AGCM2.2, which is based on the eddy diffusivity
approach (Holtslag and Boville, 1993). The eddy diffusivity is given by
The critical Richardson number
BCC-AVIM, the Beijing Climate Center Atmosphere–Vegetation Interaction Model, is a comprehensive land surface scheme developed and maintained in the BCC. Version 1 (BCC-AVIM1.0) was used as the land component in BCC-CSM1.1m, which participated in CMIP5 (Wu et al., 2013). It includes major land surface biophysical and plant physiological processes, and its origin could be traced back to the Atmosphere–Vegetation Interaction Model (AVIM) (Ji, 1995; Ji et al., 2008) with the necessary framework to include biophysical, physiological, and soil carbon–nitrogen dynamical processes. The biophysical module in BCC-AVIM1.0, with 10 layers for soil and up to 5 layers for snow, is almost the same as that used in the NCAR Community Land Model version 3 (CLM3) (Oleson et al., 2004). The terrestrial carbon cycle in BCC-AVIM1.0 consists of a series of biochemical and physiological processes modulating the photosynthesis and respiration of vegetation. Carbon assimilated by vegetation is parameterized by a seasonally varying allocation of carbohydrate to leaves, stem, and root tissues as a function of the prognostic leaf area index. Litter due to turnover and mortality of vegetation, and carbon dioxide release into the atmosphere via the heterogeneous respiration of soil microbes is taken into account in BCC-AVIM1.0. Vegetation litter falls to the ground surface and into the soil are divided into eight idealized terrestrial carbon pools according to the timescale of carbon decomposition of each pool and transfers among different pools, which are similar to those in the carbon exchange between vegetation, soil, and the atmosphere (CEVSA) model (Cao and Woodward, 1998).
BCC-AVIM1.0 has been updated to BCC-AVIM2.0 which serves as the land
component of BCC-CSM2-MR, which participates in CMIP6. As listed in Table 3,
several improvements have been implemented in BCC-AVIM2.0, such as the
inclusion of a variable temperature threshold to determine soil water
freezing–thawing rather than a fixed temperature of 0 Soil water freezes at a constant temperature of 0 In BCC-AVIM1.0, we took the snow aging effect on surface
albedo into account with a simple consideration using a unified scheme to mimic the
snow surface albedo decrease with time. In BCC-AVIM2.0, we assume different
reduction rates of snow albedo with actual elapsed time after snowfalls in
the accumulating and melting stages of a snow season (Chen et al., 2014).
Additionally, the variability of sub-grid topography is now taken into account to
calculate the snow cover fraction within a model grid cell. Unlike the empirical plant leaf unfolding and withering dates prescribed
in BCC-AVIM1.0, a dynamic determination of leaf unfolding, growth, and
withering dates according to the budget of photosynthetic assimilation of
carbon similar to the phenology scheme in CTEM (Arora and Boer, 2005) was implemented in BCC-AVIM2.0. Leaf
loss due to drought and cold stresses in addition to natural turnover are
also considered.
Main physics schemes in BCC-AVIM versions.
The four-stream solar radiation transfer scheme within the canopy in
BCC-AVIM2.0 is based on the same radiative transfer theory used in the atmosphere
(Liou, 2004). It adopts the analytic formula of Henyey–Greenstein for the
phase function. The vertical distribution of diffuse light within canopy is
related to the transmissivity and reflectivity of leaves, in addition to the average leaf
angle and the direction of incident direct beam radiation influence diffuse light
within the canopy. The upward and downward radiative fluxes are
determined by the phase function of diffuse light, the G-function, the leaf
reflectivity and transmissivity, the leaf area index, and the cosine of the solar
angle of incident direct beam radiation (Zhou et al., 2018). Considering the wide distribution of rice paddies in Southeast Asia and
the rather different characteristics of rice paddies and bare soil, a scheme
to parameterize the surface albedo, roughness length, and turbulent sensible and
latent heat fluxes over rice paddies is developed (a manuscript is currently in
preparation) and implemented in BCC-AVIM2.0. Finally, land use and land cover changes are explicitly involved in
BCC-AVIM2.0. An increase in crop area implies the replacement of natural
vegetation by crops, which is often known as deforestation.
There are no significant changes for the ocean and sea ice from BCC-CSM1.1m
to BCC-CSM2-MR. However, for the sake of completeness, we present a short
description of them here. The oceanic component is MOM4-L40, an oceanic GCM. It
is based on the
The concentration and thickness of sea ice are calculated using the Sea Ice Simulator (SIS) developed by the GFDL (Winton, 2000). The simulator is a global sea-ice thermodynamic model including the elastic–viscous–plastic dynamic processes and Semtner's thermodynamic processes. SIS has three vertical layers, including one snow cover and two ice layers of equal thickness. In each grid, five categories of sea ice (including open water) are considered, according to the thickness of sea ice. It also takes the mutual transformation from one category to another under thermodynamic conditions into account. The sea-ice model operates on the same oceanic grid and has the same horizontal resolution as MOM_L40. SIS calculates concentration, thickness, temperature, salinity of sea ice, and the motion of snow cover and ice sheets. There is no gas exchange through sea ice.
The atmosphere and sea/sea-ice interplay via the exchange of surface turbulent fluxes of momentum, heat, and water. An optimum treatment of the surface exchange, which is sound in physics and economic in computation, is very important in simulating the climate variability. Over the past several years, we have maintained a continuous effort to improve the turbulent exchange processes between air and sea/sea ice in different versions of the BCC models.
In BCC-CSM1.1m, the bulk formulas of turbulent fluxes over the sea surface originate from those used in CAM3, with some modifications to the roughness lengths and corrections to the temperature and moisture gradients considering sea-spray effects (Wu et al., 2010). The bulk formulas are updated in BCC-CSM2-MR. The coefficients of roughness length calculations were adjusted, and the arbitrary gradient corrections are not used. Instead, a gustiness parameterization is included to account for the sub-grid wind variability that is contributed by boundary layer eddies, convective precipitation, and cloudiness (Zeng et al., 2002).
In terms of turbulent exchange between air and sea ice, we proposed a new
bulk algorithm that aims to improve flux parameterizations over sea ice (Lu et
al., 2013). Based on theoretical and observational analysis, the new
algorithm employs superior stability functions for stable stratification as
suggested by Zeng et al. (1998), and features varying roughness lengths. All
three roughness lengths (
All BCC simulations presented in this work follow the protocols defined by CMIP5 and CMIP6. We aim for them to be comparable in spite of showing the transition of our climate system model from CMIP5 to CMIP6. The principal simulation to be analyzed is the historical simulation (hereafter historical) with prescribed forcings from 1850 to 2005 for CMIP5 (to 2014 for CMIP6).
Historical forcings data are based, as far as possible, on observations and
were downloaded from
(
The preindustrial initial state of BCC-CSM2-MR is preceded by a 500-year
piControl simulation following the requirement of CMIP6. The initial state of
the piControl simulation itself is obtained via individual spin-up runs
of each component of BCC-CSM2-MR in order for the piControl simulation to run
stably and quickly to its model equilibrium. Actually, the initial states of
atmosphere and land are obtained from a 10-year AMIP run forced with monthly
climatology of sea surface temperature (SST) and sea-ice concentration,
whereas the initial states of ocean and sea ice are derived from a 1000-year forced
run with a repeating annual cycle of monthly climatology of atmospheric state
from the Coordinated Ocean-ice Reference Experiment (CORE) data set version 2
(Danabasoglu et al., 2014). Figure 2 shows time series of the annual and
global mean of the net energy flux at top-of-atmosphere (TOA) and the sea
surface temperature for 600 years in the piControl simulation. The whole
system in BCC-CSM2-MR fluctuates around a
Time series of
Radiative fluxes at the top of the model atmosphere are fundamental variables
characterizing the Earth's energy balance. Satellite observations in modern
time allow us to monitor changes in the net radiation at top-of-atmosphere
(TOA) from 2001 onwards. The CERES (Clouds and Earth's Radiant Energy System)
project, with the lessons learned from its predecessor, the Earth's Radiation
Budget Experiment (ERBE), provides improved observation-based data products
of Earth's radiation budget (Wielicki et al., 1996). Recently, data of CERES
have been synthesized with EBAF (Energy Balanced and Filled) data to derive the
CERES-EBAF products, which are suitable for the evaluation of climate models (Loeb et
al., 2012). As shown in Table 4, the TOA shortwave and longwave components in
BCC-CSM2-MR are generally closer to CERES-EBAF than those in
BCC-CSM1.1m. Model results are for the 1986–2005 period, whereas the available
CERES-EBAF data are for the 2003–2014 period. Globally averaged TOA net energy is
0.85 W m
Energy balance and cloud radiative forcing at the top-of-atmosphere
(TOA) in the model with contrast to CERES-EBAF and CERES observations. (Units:
W m
Notes: the model data are the mean of 1986–2005, whereas the available observation data are for 2003–2014.
Clouds constitute a major modulator of the radiative transfer in the
atmosphere for both solar and terrestrial radiations. Their macro and micro
properties, including their radiative properties, exert strong impacts on the
equilibrium and variation of the radiative budget at TOA or at the surface.
Figure 3 displays the annual and zonal means of shortwave, longwave, and net cloud
radiative forcing for the BCC CMIP5 models (blue curves), the BCC CMIP6 (red curves) models,
and the observations (black curves). The data used in Fig. 3 are the same as in
Table 4. Although observations and models results cover different time
periods, they are still relevant to reveal climatological mean performance
of climate models. At low latitudes between 30
Zonal averages of the cloud radiative forcing (CRF) from the BCC CMIP5 and
CMIP6 models and the observations (in W m
The historical simulation allows us to evaluate the ability of models to
reproduce the global warming and climate variability in the 20th century.
The performance depends on both the model formulation and the time-varying
external forcings imposed on the models (Allen et al., 2000). Figure 4
presents global-mean (from 60
Time series of anomalies in the global (60
A remarkable feature in Fig. 4 is the presence of a global warming hiatus or pause for the period from 1998 to 2013 when the observed global surface air temperature warming slowed down. This is a hot topic, which is largely debated in the scientific research community (e.g., Fyfe et al., 2016; Medhaug et al., 2017). Two members (r1i1p1f1 and r2i1p1f1 in Fig. 4) of the historical simulations of the CMIP6 model show a hiatus towards the end of the simulation that resembles the observed pause. Although the third member (r3i1p1f1) simulated a global warming slowdown from 2004 to 2012, it is not comparable to the observed hiatus, as it has a short spell of colder years centered on 2010. Another warming hiatus occurred during the period from 1942 to 1974. The first and the third members (r1i1p1f1 and r3i1p1f1) of BCC-CSM2-MR only simulate the warming slowdown in the late period from 1958 to 1974, but the second member (r2i1p1f1) of BCC-CSM2-MR almost simulates this warming hiatus throughout the period from 1942 to 1974. Therefore, the simulation of a global warming hiatus in the BCC CMIP6 model clearly excludes any simple response to forcing, and makes internal variability a much more likely reason.
Annual-mean surface (2 m) air temperature biases (
The model response of the surface air temperature (SAT) to volcanic forcing is slightly stronger than
that estimated with HadCRU data. Evident global cooling shocks are coincident
with significant volcanic eruptions such as Krakatoa (in 1883), Mount Agung (in 1963), and Mount Pinatubo (in 1991). Each of these volcanic
eruptions significantly enriched stratospheric aerosols (available from
To keep the paper concise and of a reasonable length, only the first member
of the CMIP6 historical simulations of BCC-CSM2-MR will be presented hereafter.
Biases of the annual-mean surface air temperature (at 2 m) over the whole globe
for BCC-CSM2-MR and BCC-CSM1.1m are shown in Fig. 5. In both BCC models,
biases are generally within
The long trend of global warming in Fig. 4 depends on the climate
sensitivity which is an emblematic parameter to characterize the sensitivity
of a climate model to external forcing, with all feedbacks included. It
generally designates the variation of the global mean surface air temperature in
response to a forcing of doubled
We use the standard simulation of 1 %
Relationships between the change in the net top-of-atmosphere radiative
flux and the global-mean surface air temperature change simulated with an abrupt
The linear regression line shown in Fig. 6, as pointed out in Gregory et
al. (2012), also allows for the estimation of the instantaneous forcing due to
Annual-mean precipitation rate biases (mm d
The main spatial patterns of observed precipitation climatology are simulated
in BCC-CSM1.1m and BCC-CSM2-MR. Figure 7 shows model biases of annual-mean
precipitation for BCC-CSM1.1m and BCC-CSM2-MR around the globe. They are very
close to one another; their RMSE is also very close: 1.12 mm d
We now use the Taylor diagram (Fig. 8) to evaluate the general performance of our two models in terms of temperature at 850 hPa, precipitation, and atmospheric general circulation. The evaluation is carried out against the climatology of the ERA-Interim data set for the period from 1986 to 2005 (Dee et al., 2011). ERA-Interim is the latest global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF).
Taylor diagram for the global climatology (1980–2005) of sea level pressure (SLP), precipitation (PRCP), temperature at 850 hPa (T850), zonal wind at 850 hPa (U850), longitudinal wind at 850 hPa (V850), geopotential height at 500 hPa (Z500), and zonal wind at 200 hPa (U200). The radial coordinate shows the standard deviation of the spatial pattern, normalized by the observed standard deviation. The azimuthal variable shows the correlation of the modeled spatial pattern with the observed spatial pattern. The analysis is for the whole globe. The reference data set is ERA-Interim except for the precipitation from the Global Precipitation Climatology Project data set. The model results of BCC-CSM2-MR and BCC-CSM1.1m are the mean for the 1980–2000 period. Blue crosses represent BCC-CSM1.1m, and circles represent BCC-CSM2-MR.
For global fields, we calculate the spatial pattern correlations between models and ERA-Interim for the annual-mean climatology of sea level pressure (SLP), temperature at 850 hPa level (T850), zonal and meridional wind velocity at 850 hPa (U850 and V850), zonal wind velocity at 200 hPa (U200), geopotential height at 500 hPa (Z500), and precipitation from the Global Precipitation Climatology Project (PRCP in Fig. 8, Adler and Chang, 2003) over the period 1980–2000. Except for PRCP and U850, which have a lower correlation (less than 0.90) with the observations, other variables are have correlation coefficients above 0.90. The pattern correlation coefficient of Z500 with ERA-Interim is 0.995, which is the best correlation among these variables. Except for V850, correlations of all other variables in the CMIP6 model version (BCC-CSM2-MR) show an evident improvement compared with the CMIP5 version (BCC-CSM1.1m). The normalized standard deviations of most variables except for PRCP and T850 are obviously improved in BCC-CSM2-MR. As a whole, the performance of most variables in BCC-CSM2-MR is better than those in BCC-CSM1.1m.
Results shown in the Taylor diagrams in Fig. 8 regarding improvements in surface
climate and atmospheric general circulation at different vertical levels are
consistent with improvements in the vertical distribution of atmospheric
temperature. Figure 9 shows the yearly averaged zonal mean of atmospheric
temperature biases in BCC-CSM2-MR and BCC-CSM1.1m, with ERA-Interim for the
period from 1986 to 2005 as a reference. Overall, both BCC-CSM2-MR and BCC-CSM1.1m
have similar biases in their vertical structure: they are 1–3 K warmer in the
stratosphere (above 100 hPa) for most of the domain equatorward of
70
The improvement in tropospheric temperature induces naturally smaller biases
for the zonal wind throughout the troposphere in BCC-CSM2-MR (Fig. 9). However,
there are still westerly wind biases of 6 m s
Given a much higher vertical resolution and an advanced parameterization of
the gravity wave drag, the new model BCC-CSM2-MR is able to represent the
stratospheric quasi-biennial oscillation (QBO), as shown in Fig. 10 which
displays time–height diagrams of the tropical zonal winds averaged from
5
Pressure–latitude sections of
Tropical zonal winds (m s
The Madden–Julian Oscillation (MJO) is a very important atmospheric variability
acting within a periodicity between 20 and 100 d in the tropics with
considerable effects on regional weather and climate. It exerts significant
impacts on monsoonal circulations and the organization of tropical rainfall.
From the tropical Indian Ocean to the western Pacific, the MJO shows a pronounced
behavior of eastward propagation, as seen in Fig. 11a, in the form of
longitude–time, the lagged correlation coefficient of the rainfall in the
eastern Indian Ocean (75–85
The MJO can also exert impacts on the weather and climate of the extra-tropics, either through the emanation of Rossby waves, or the poleward propagation of the MJO itself. Figure 11d shows a latitude–time diagram for lagged correlation coefficients when rainfalls are filtered to retain only the 20–100 d variability. Panels (e) and (f) in Fig. 11 are the counterparts simulated by our two models. The new model presents a clear improvement.
Figure 12 shows the observed and simulated spatial pattern of the standard
deviation of SST anomalies in the tropical Pacific. Both BCC-CSM2-MR and
BCC-CSM1.1m can simulate the position of the strongest variation of SST,
situated in the central-eastern Pacific – east of the dateline. However,
cold SSTs in the eastern equatorial Pacific still extends too far west in both
models, and a cold tongue bias exists in the equatorial Pacific and even gets
a little worse in BCC-CSM2-MR. The annual mean SST in the coldest center near
110
The spatial distributions of the 1986–2005 annual mean sea surface
temperature (contour lines,
Figure 13 presents time series of the monthly Nino3.4 SST index from
observations and from simulations of BCC-CSM1.1m and BCC-CSM2-MR. Although
amplitudes of interannual variations of the Nino3.4 index in both models are
larger than in HadISST observations, it becomes weaker in BCC-CSM2-MR with
a standard deviation of 0.91
The time series of the Nino3.4 SST index from 1986 to 2005 for
Figure 14 shows time series of minimum sea-ice extent from 1851 to 2012 for
(a) the Arctic in September and (b) the Antarctic in March as simulated in
BCC-CSM2-MR and BCC-CSM1.1m. Based on the Hadley Centre Sea Ice and Sea Surface
Temperature data set (Rayner et al., 2003, shown by “Hadley” in Fig. 14), the observed minimum sea-ice extent in each September from 1851 to 2012
gradually shrinks, especially since the 1960s, which is attributed to global warming
(Fig. 4). The extent of Arctic sea ice in September in BCC-CSM1.1m is
about
Time series of the sea-ice extent from 1851 to 2012 for
Figure 15 shows the seasonal cycle of the sea-ice extent (SIE) and thickness
averaged for the period from 1980 to 2005 in the two polar regions in our
models. Observations of the sea-ice extent from the Hadley Centre data and sea-ice thickness from the
ECMWF are also plotted for the purpose of comparison.
Observations show that the Arctic sea-ice cover reaches a minimum extent of
The mean (1980–2005) seasonal cycle of sea-ice extent
(
The Atlantic Meridional Overturning Circulation (AMOC) plays a significant role in driving the global climate variation (Caesar et al., 2018). The AMOC consists of two primary overturning cells. In the upper cell, warm water flows northward in the upper 1000 m to supply the formation of the North Atlantic Deep Water (NADW), which returns southward in the depth range of approximately 1500 to 4000 m. In contrast, in the lower cell, the Antarctic Bottom Water (AABW) flows northward in the Atlantic basin beneath the NADW. Figure 16 shows the time-averaged AMOC simulated by the two coupled model versions. The two main cells are well depicted. The lower branch of the NADW is much deeper in BCC-CSM2-MR than in BCC-CSM1.1m, as indicated by the depth of the zero-contour line. Moreover, the central intensity of the NADW in BCC-CSM2-MR is over 22.5 Sv; this value is about 2.5 Sv stronger than that seen in BCC-CSM1.1m and is close to the observation-based value (25 Sv in Talley et al., 2013).
Zonally averaged streamfunction of the Atlantic Meridional
Overturning Circulation (AMOC) for the period from 1980 to 2005 in
BCC-CSM2-MR
A good simulation of climate over East Asia is always a challenging issue for
the climate modeling community, as the region is under the influences of complex
topography (the high Tibetan Plateau) and atmospheric circulations from low
latitudes (the tropical monsoon circulation) and from higher latitudes. Figure 17
plots a Taylor diagram to show the model performance of the main climate variables
over East Asia covering the following region: 100–140
Same as in Fig. 8, but for the domain covering East Asia
(20–50
Regional distribution maps of precipitation climatology (averaged
from 1980 to 2005) for December–January–February
Figure 18 shows the 1980–2005 climatology of December–January–February and June–July–August averaged precipitation over China and its surroundings. In boreal winter, GPCP precipitations show a rain belt from southeastern China to Japan and another rain belt along the southwestern flank of the Tibetan Plateau. In BCC-CSM1.1m the winter precipitation is too weak in southeastern China and too strong near Japan, compared with GPCP observations. This rain belt in BCC-CSM2-MR obviously spreads westward and is much closer to observations. However, the rain belt along the southwestern flank of the Tibetan Plateau in BCC-CSM2-MR becomes too strong. In boreal summer, large dry biases over eastern China are present in BCC-CSM1.1m. These biases are reduced in BCC-CSM2-MR. The center of precipitation around Japan is also well simulated in BCC-CSM2-MR.
The East Asian summer monsoon rainfall has a seasonal progression from south to north at the beginning of summer and then a quick retreat to the south when the summer monsoon terminates (as shown in Fig. 19a). This phenomenon is strongly related to the fact that the East Asian monsoon rainfall mainly takes place in the frontal zone between the warm and humid air mass from the south and cold and dry air mass from the north. This seasonal migration is also accompanied by a meridional movement of the Western North Pacific Subtropical High, an important atmospheric center of action controlling the climate of the region. In Fig. 19b and c, we compare the two models in terms of the seasonal migration of the monsoon rainfall. In the old model, rainfall was too weak. The new model produces more precipitation. In terms of seasonal match, both models show a delay of the peak rainfall by about 1 month, or even longer in BCC-CSM2-MR.
Latitude (from 20 to 25
Local times of the maximum frequency of rainfall occurrence in March–April–May
Finally, let us examine the rainfall diurnal cycle in summer. Figure 20 shows the timing of the rainfall diurnal cycle from observation and the two models. Main zones of nocturnal rainfall can be recognized on the south flank of the Tibetan Plateau, in the Sichuan Basin in the east of the Tibetan Plateau, and in the north of Xinjiang in central Asia. There is also a zone of nocturnal rainfall in the lower reach of the Yellow River, which is mainly under the influence of nocturnal rainfall in the Bohai Sea region. Other regions over land experience a diurnal rainfall peak in the afternoon after 16:00 LT. The diurnal cycle of rainfall was extensively studied in Jin et al. (2013) in terms of the physics causing the diurnal cycle; however, simulating the diurnal cycle well is always a major challenge for climate modeling. We can see that it is not very well simulated in our old model, and in East China the peak occurs from 00:00 to 04:00 LT. Nevertheless, the improvement is quite spectacular in our new model with the rainfall peak delayed in the afternoon. Such an improvement is due to the implementation of our new trigger scheme in convection parameterization.
This paper presents the main advancements of the BCC climate system models from CMIP5 to CMIP6 and focuses on the description of the CMIP6 version BCC-CSM2-MR and the CMIP5 version BCC-CSM1.1m, especially with respect to the model physics. Main updates to the model physics include a modification of the deep convection parameterization, a new scheme for the cloud fraction, indirect effects of aerosols through clouds and precipitation, and the gravity wave drag generated by deep convection. Surface processes in BCC-AVIM have also been significantly improved for the soil water freezing treatment, the snow aging effect on surface albedo, and the timing of vegetation leaf unfolding, growth, and withering. A four-stream radiation transfer within the vegetation canopy has replaced the two-stream radiation transfer. There is also a new treatment for rice paddy waters. Furthermore, new schemes for surface turbulent fluxes of momentum, heat, and water at the interface of atmosphere and sea/sea ice are used.
The evaluation of model performance in simulating present-day climatology is
presented for main climate variables, such as surface air temperature,
precipitation, and atmospheric circulation for the globe and for East Asia.
Emphasis is put on the comparison between the CMIP5 and CMIP6 model versions
(BCC-CSM2-MR versus BCC-CSM1.1m). The globally averaged TOA net energy budget
is 0.85 W m
Further evaluations are performed on climate variabilities at different timescales, including the long-term trend of global warming in the 20th century, the QBO, the MJO, and the diurnal cycle of precipitation. The globally averaged annual-mean surface air temperature from the historical simulation of BCC-CSM2-MR is much closer to the HadCRUT4 observations than BCC-CSM1.1m, and the observed global warming hiatus or warming slowdown in the period from 1998 to 2013 is captured in some realizations of BCC-CSM2-MR. With a higher vertical resolution and the inclusion of the gravity wave drag generated by deep convection, the new version BCC-CSM2-MR is able to reproduce the stratospheric QBO, whereas the QBO even does not exist in BCC-CSM1.1m. Further investigations on physical mechanisms controlling the QBO simulation in BCC-CSM2-MR will be reported in the future. The MJO is a very important atmospheric oscillation at intra-seasonal scales and main features are reproduced and improved in BCC-CSM2-MR, but with an intensity that is still weaker than its counterpart in the observations. At an interannual scale, the BCC-CSM1.1m shows overly strong variations of the Nino 3.4 SST index, but overly short and overly regular periodicity for ENSO. BCC-CSM2-MR shows a weaker amplitude for the Nino 3.4 SST index, which is an improvement and is closer to HadISST observations. The rainfall diurnal cycle in China has strong regional variations with pronounced nocturnal rainfalls in the Sichuan Basin and in northern China near the Bohai Sea and the coast. The diurnal rainfall generally peaks in the afternoon (local time) for most other land regions. BCC-CSM2-MR shows a clear improvement of the rainfall diurnal peaks compared with the CMIP5 model (BCC-CSM1.1m). This improvement of the rainfall diurnal variation is strongly related to the modification of the deep convection scheme.
Finally, we also evaluate the climate sensitivity to increasing
From our model evaluations, we find that although basic features of the QBO can be simulated in BCC-CSM2-MR, the magnitude between the westerly and easterly interchange is still too weak. We also note that there are large biases of air temperature and winds in the stratosphere. Therefore, improvement of the stratospheric temperature and circulation simulations is an important priority in the future development of BCC models. In addition, the sea-ice simulation in the Antarctic region has large biases, which need to be improved.
Source codes of the BCC models are freely available upon
request from Tongwen Wu (twwu@cma.gov.cn). Model output of the BCC models
for both the CMIP5 and CMIP6 simulations described in this paper is distributed
through the Earth System Grid Federation (ESGF) and is freely accessible via
the ESGF data portals after registration. Details regarding ESGF are presented on
the CMIP Panel website at
TW led the BCC-CSM development. TW and XX designed the experiments and carried them out. TW, LL, and XL wrote the final paper with contributions from all co-authors.
The authors declare that they have no conflict of interest.
This work was supported by The National Key Research and Development Program of China (grant no. 2016YFA0602100). Two anonymous reviewers are acknowledged for their constructive comments on earlier versions of the paper.
This paper was edited by Juan Antonio Añel and reviewed by two anonymous referees.