Decomposition of Fast and Slow Cloud Responses to Quadrupled CO₂ Forcing in BCC–AGCM2.0 over East Asia

Clouds cover approximately 60% of the Earth's surface and have an important role in balancing the radiation budget (Liou, 1992; Zhang et al., 2017, 2021). There are two main ways in which clouds affect the radiation budget (Ramanathan et al., 1989). First, clouds strongly reflect solar shortwave radiation, reducing the solar radiation reaching the Earth’s surface, and leading to cooling of the earth–atmosphere system, which is referred to as the albedo effect of clouds. Second, clouds can absorb large amounts of infrared longwave radiation emitted by the surface and the lower troposphere, reducing the energy lost from the earth–atmosphere climate system and causing a warming effect, which is referred to as the greenhouse effect of clouds. Changes in cloud cover can lead to changes in their radiation effects, which can then affect climate. For example, a decrease in low cloud cover (LCC) will decrease the albedo of the earth–atmosphere system, thereby increasing the solar radiation reaching the surface, which has a warming effect on climate (Wang et al., 2021).

Cloud cover is one of the most considerable sources of uncertainty in climate change research (Wielicki et al., 1995; Colman, 2003; Potter and Cess, 2004; Randall et al., 2007; Zhang et al., 2022). A 4% increase in LCC could offset the 2°C–3°C warming caused by a doubling of the atmospheric CO₂ concentration, and the reverse relationship is also applicable (Randall et al., 1984). Norris et al. (2016) found that anthropogenic greenhouse gases are the most important external forcings for cloud cover changes in recent years. A simulation by Schneider et al. (2019) concerning a small area of stratocumulus (approximately 5 km²) showed that the stratocumulus will completely disappear when the CO₂ concentration reaches 1200 ppmv.

The responses of climate systems (including clouds) to forcing factors can be divided into fast and slow responses. Fast responses are the direct effects of changes in atmospheric radiative heating caused by forcings, while slow responses comprise a series of climate responses caused by forcing-induced changes in the global surface temperature, particularly the sea surface temperature (SST) (Gregory et al., 2004; Hansen et al., 2005; Andrews and Forster, 2010; Bala et al., 2010). Fast responses are adjustments of the stratosphere, troposphere, and land surface prior to changes in global surface temperature over a time scale of days to months; slow responses are the responses to global surface temperature changes over a time scale of years to decades (Zhang et al., 2022). The increased atmospheric CO₂ will absorb more longwave radiation, heating the atmosphere and changing the vertical distribution of the atmospheric temperature and humidity profiles. This will lead to changes in cloud cover, which represents the fast response of clouds to the forcing of greenhouse gases. An increase in the CO₂ concentration will heat the land and sea surfaces, which changes atmospheric circulation patterns and impacts on the hydrological cycle, ultimately affecting cloud cover. This represents the slow response of clouds to the forcing of greenhouse gases.

In recent years, multiple studies have focused on the global and regional changes in cloud cover under the background of global warming (Norris, 2005; Wylie et al., 2005; Liu et al., 2008; Sun et al., 2015; Zhou et al., 2016; Zhang et al., 2019). These studies showed that clouds changed differently in different regions. General circulation models (GCMs) are important tools for studying global climate change. Many studies have simulated and assessed the impact of an increasing CO₂ concentration in the atmosphere on cloud cover. However, they have obtained different or completely opposite results (Miller, 1997; Dai et al., 2001; Vavrus, 2004; Ogura et al., 2008; Bretherton and Blossey, 2014), which indicates that there remains considerable uncertainty regarding the cloud responses to global warming. Wyant et al. (2012) used a superparameterized climate model to simulate the fast cloud response caused by a quadrupled CO₂ concentration; they found that both the global and tropical mean total cloud cover (TCC), LCC, and high cloud cover (HCC) increased slightly, whereas the midlevel cloud cover decreased slightly. Studies of the differences among models have indicated that when the CO₂ concentration is doubled, models with a low surface air temperature (SAT) sensitivity to the doubled CO₂ (i.e., the SAT increases are smaller when the CO₂ concentration is doubled) produce increases in LCC in the tropical and subtropical regions, while models with a high SAT sensitivity to the doubled CO₂ produce decreases in low clouds in those regions (Wetherald, 2011).

Most studies of cloud responses to changes in CO₂ concentration have been conducted for the global and tropical ocean regions, whereas there has been a lack of research in East Asia (EA). In this study, we used a GCM, BCC–AGCM2.0, to investigate the changes in cloud cover and cloud radiation effects caused by quadrupling the atmospheric CO₂ concentration relative to pre-industrial levels. Schneider et al. (2019) used a large eddy simulation and only simulated a small area of stratocumulus. In this study, we investigated the changes in LCC and HCC separately over EA using a global climate model. Previous studies have not considered the decomposition of the fast and slow responses of clouds. To explore the differences between fast and slow cloud responses to increasing greenhouse gas concentrations, we decomposed the simulation results into fast and slow responses for respective analyses.

In section 2, we describe the BCC–AGCM2.0 model and the experimental design. The simulation results (i.e., fast and slow responses of clouds in EA to quadrupled CO₂ forcing) are presented in section 3, along with an analysis of the mechanisms involved. Section 4 provides the conclusions of the study.

The GCM used in this study (BCC–AGCM2.0) was developed by the Beijing Climate Center of the China Meteorological Administration and was based on the Community Atmospheric Model version 3.0 (CAM3.0) from the National Center for Atmospheric Research (NCAR). Wu et al. (2008) introduced a new reference atmosphere into BCC–AGCM2.0, in which the vertical temperature profile is close to that of the US midlatitude standard atmosphere. The Community Land Model version 3.0 (CLM3.0) was coupled to the atmospheric model to calculate the land surface processes (Oleson et al., 2004). A detailed description of BCC–AGCM2.0 can be found in Wu et al. (2010). A 42-wave triangular truncation scheme (T42) was adopted in the horizontal direction with a resolution of approximately 2.8° × 2.8°. In the vertical direction, a hybrid σ-pressure coordinate was adopted with 26 layers, and the top pressure of the model was approximately 2.9 hPa, which represented a rigid boundary. The model used a radiation scheme, BCC_RAD, to calculate the radiative transfer process in the atmosphere, including the absorption of greenhouse gases such as H₂O, CO₂, O₃, N₂O, CH₄, CFCs (three types), and O₂, as well as the scattering and absorbing processes of clouds and aerosols (Zhang et al., 2003, 2006a, b; Zhang, 2016). The wavelength ranges from 0.2 μm to 1000 μm were divided into 17 bands, including nine bands in the shortwave range (0.2–3.73 μm) and eight bands in the longwave range (3.73–1000 μm). The dynamical framework of BCC–AGCM2.0 was described in Wu et al. (2008), and the parameterization schemes for convection, dry adiabatic adjustment, and turbulent fluxes over the ocean surface were developed by Wu et al. (2010). The cloud fraction and the associated cloud macrophysics were designed by Collins et al. (2004). The Monte Carlo Independent Column Approximation was used to describe the subgrid cloud structure (Pincus et al., 2003; Jing and Zhang, 2012; Zhang et al., 2013, 2014; Zhang and Jing, 2016). The use of Monte Carlo Independent Column Approximation ensured that the cloud cover simulation results were similar to the International Satellite Cloud Climatology Project (ISCCP) observation results, particularly in the 30°–60°N and 30°–60°S regions; the deviation of the global mean cloud cover was only 1.4% (Jing, 2012).

Four experiments were conducted (Table 1) in this study. These experiments were divided into control experiments (CTL), where the CO₂ concentration was maintained at the pre-industrial level (284.7 ppmv), and quadrupled CO₂ forcing experiments (4CO₂), where the CO₂ concentration was abruptly increased to four times the pre-industrial level (1139 ppmv). The quadrupled CO₂ concentration was close to the CO₂ level when stratocumulus disappeared in Schneider et al. (2019). The aerosol, volcanic, and ozone datasets in the four experiments were all kept in the default configuration. The aerosol dataset, including sulfate, sea salt, carbonaceous, and soil–dust aerosols, was derived from a chemical transport model constrained by assimilation of satellite retrievals of aerosol depth (Collins et al., 2001), and the climatology was obtained from an aerosol assimilation for the period 1995–2000 (Collins et al., 2006). The volcanic dataset was from Ammann et al. (2003) with a period of 1870–1999. The ozone dataset from 1870 to 1990 was produced by Kiehl et al. (1999).

To obtain the total response of clouds, we coupled BCC–AGCM2.0 with a slab ocean model (SOM) in both the control and quadrupled CO₂ forcing experiments (CTL_Som and 4CO₂_Som). The calculations of SST and sea ice (SI) in the SOM were based on energy exchange with the atmosphere, ocean heat transport, and ocean mixed layer heat capacity (Hansen et al., 1984). The difference between the results of these two experiments was the total response:

To obtain the fast cloud responses to quadrupled CO₂ forcing, we fixed the SST and SI in both the control and quadrupled CO₂ forcing experiments (CTL_Fix and 4CO₂_Fix). The SST and SI data were derived from observational climatological monthly mean data from 1991 to 2015 (Hurrell et al., 2008). The difference between these two experiments was the fast response:

The slow response was obtained by subtracting the fast response from the total response (Hansen et al., 2005; Ganguly et al., 2012; Samset et al., 2016; Wang et al., 2017; Duan et al., 2018):

The experimental period in the model with fixed SST (coupled SOM) was 15 (70) years, with the previous 5 (30) years allowed for CO₂ perturbations; therefore, the last 10 (40) years of data in the fixed SST (coupled SOM) experiments were used to estimate the fast (total) response (refer to Wang et al., 2013; Zhang et al., 2018).

In this study, we used Coupled Model Intercomparison Project Phase 6 (CMIP6) (Eyring et al., 2016) multimodels ensemble mean total cloud cover to compare with the simulations of BCC–AGCM2.0. The CMIP6 data are available online at https://esgf-node.llnl.gov/projects/cmip6/. Four experiments were used, including two experiments with the coupled atmosphere–ocean general circulation models (abrupt-4×CO₂ and piControl), and two experiments with fixed SSTs (piClim-4×CO₂ and piClim-control). All forcings (including CO₂) in the control experiments (e.g., piControl and piClim-control) were set to the level of year 1850, while the CO₂ concentrations in abrupt-4×CO₂ and piClim-4×CO₂ were set to four times those of the control experiments. In CMIP6 experiments, piClim-4×CO₂ and piClim-control have provided 30 years of data for calculating the fast response; while abrupt-4×CO₂ and piControl have provided data for 150 or more years, and we used the data from year 121 to year 150 to calculate the total response. The slow response was calculated by subtracting the fast response from the total response. Ten typical models used in this study are shown in Table 2.

As one of the most important anthropogenic greenhouse gases in the atmosphere, an increase in the CO₂ concentration will lead to an increase in global SAT. Figure 1 shows the responses of SAT over EA (0°–60°N, 70°–150°E) to a quadrupled CO₂ concentration. The change in SAT became more positive with an increase in latitude, and the land warmed more than the ocean because the heat capacity of the ocean is greater than the land. The Tibetan Plateau had the largest temperature increase, reaching more than 10 K. The mean SAT increase in EA was 7.19 K. The SST remained unchanged, and the mean SAT of EA increased by 0.79 K as a consequence of land warming in the fast response, while the slow response contributed 6.40 K. These findings indicate the importance of the ocean in climate change caused by increasing concentrations of atmospheric greenhouse gases.

Figure 1.  Total, fast, and slow responses of the surface air temperature (units: K) over East Asia. Black dots represent significance at the ≥ 95% confidence level from a t-test.

An increase in SAT can change the atmospheric temperature, circulation, and moisture content, further affecting the cloud cover. Cloud cover is defined as the percentage of clouds in the sky. Figure 2 shows the responses of TCC, LCC, and HCC to a quadrupled CO₂ concentration over EA. In this study, low clouds are defined as clouds with a cloud top pressure from surface to 680 hPa, while high clouds are defined as clouds with a cloud top pressure from 440 hPa to the model top. The spatial resolution of the model while estimating cloud cover is approximately 2.8° × 2.8°. In the total response (Figs. 2a, d, and g), TCC, LCC, and HCC all increased north of 40°N and decreased south of 20°N, although LCC increased over the South China Sea. At 20°–40°N, TCC and LCC decreased in most areas; HCC increased over the Tibetan Plateau and the eastern ocean, and decreased in other areas. In the fast response (Figs. 2b, e, and h), TCC and HCC increased south of 40°N; LCC increased at 20°–40°N and over the Indian Peninsula, and decreased over oceans at low latitudes; TCC and LCC decreased north of 20°N, whereas the change of HCC was not obvious. In the slow response (Figs. 2c, f, and i), the distribution patterns of TCC, LCC, and HCC were opposite to those in the fast response, but consistent with those in the total response, so the total response of cloud cover was dominated by the slow response; however, the slow response of HCC in the Tibetan Plateau was opposite to the total response. The results for the tropical TCC and HCC were consistent with findings by Wetherald (2011) in the total response and Wyant et al. (2012) in the fast response, respectively. Table 3 provides the mean changes in cloud cover of EA. The mean changes in TCC, LCC, and HCC were 0.74%, 0.38%, and –0.38% in the total response, respectively; 1.05%, –0.03%, and 1.63% in the fast response, respectively; and –1.79%, 0.41%, and –2.01% in the slow response, respectively.

Figure 2.  Total, fast, and slow responses of (a–c) total cloud cover, (d–f) low cloud cover, and (g–i) high cloud cover over East Asia (units: %). Black dots represent significance at the ≥ 95% confidence level from a t-test.

The Tibetan Plateau greatly affects the climate of China due to its high sea level altitude, and the HCC there largely increased in the fast response; the cloud cover changes in south China and northeast China were significant and opposite; therefore, we selected the Tibetan Plateau (TP), south China (SC), and northeast China (NE) as three typical regions for the detailed analysis. Their geographic ranges are as follows: TP: 27°–38°N, 78°–100°E; SC: 22°–33°N, 108°–123°E; NE: 40°–55°N, 120°–135°E.

The mean variations of TCC in the TP, SC, and NE in the total response were –5.43%, –4.81%, and 5.54%, respectively (Table 3). Changes in the TCC in the three regions in the fast response (4.38%, 2.21%, and –0.47%, respectively) and slow response (–9.81%, –7.02%, and 6.01%, respectively) were opposite of each other, and the total response was dominated by the corresponding slow response. The TP had minimal LCC in the model because of its high altitude, so the change in its LCC was small. The fast response of HCC in the TP (6.56%) exceeded the slow response (–4.45%); therefore, the changes in HCC in the TP was dominated by the fast response. In SC and NE, the total responses of LCC were –5.34% and 7.14%, respectively, and the total responses of HCC were –0.59% and 2.01%, respectively, and all were dominated by the slow responses.

The CMIP6 ensemble mean responses of TCC over EA are shown in Fig. 3. The TCC decreased over EA in the total and slow responses, with mean values of –4.47% [–12.22% to –0.77%] and –3.77% [–11.54% to –0.15%], respectively. In the fast response, the TCC increased over land between 20°N and 40°N and decreased over other areas, with a mean change of –0.70% [–2.31% to 0.33%]. By comparison, we found that the mean changes in TCC in BCC–AGCM2.0 over EA were within the uncertainty ranges of the CMIP6 10 typical model results in the total and slow responses, and the spatial distribution patterns of BCC–AGCM2.0 and CMIP6 were similar south of 40°N. In the fast response, the mean change in TCC (1.05%) in BCC–AGCM2.0 over EA was larger than the uncertainty range of the CMIP6 10 typical model results, but their spatial distribution patterns were similar over land.

Figure 3.  Total, fast, and slow responses of the CMIP6 ensemble mean total cloud cover over East Asia (units: %). Black dots represent significance at the ≥ 95% confidence level from a t-test.

In the following sections, we discuss the mechanisms of the changes in LCC and HCC in the fast and slow responses, respectively.

Figures 4a–c show the fast responses of the pressure–latitude vertical profiles of relative humidity (RH), atmospheric temperature, and meridional circulation over EA (0°–60°N, 70°–150°E), respectively. The atmospheric temperature increased the most between 30°N and 40°N (Fig. 4b), which resulted in an intense ascending motion anomaly (Fig. 4c) and promoted water vapor to condense into clouds. Therefore, the TCC, LCC, and HCC all exhibited the greatest increase in this latitude zone (Figs. 2b, e, and h). Meanwhile, the decrease in atmospheric temperature at 100–250 hPa caused saturation vapor pressure reduction in this latitude zone, which substantially increased the RH of the upper atmosphere (Fig. 4a) and the HCC. The anomalous descending motion (i.e., a weakening of the convection) south of 10°N (Fig. 4c) resulted in a decrease in LCC. In the upper atmosphere (100–300 hPa), water vapor was transported from higher latitudes to lower latitudes with the circulation, causing a slight increase in HCC south of 10°N. The anomalous descending motion occurred predominantly north of 40°N, and the temperature increase in the lower atmosphere increased the saturated vapor pressure, leading to a decrease in LCC.

Figure 4.  Fast responses of the pressure–latitude vertical profiles of (a) relative humidity (units: %), (b) atmospheric temperature (units: K), and (c) meridional circulation (v, −ω) over East Asia, where v is the meridional velocity (units: m s^–1) and −ω is the vertical velocity (units: 10^–2 Pa s^–1). Black dots represent significance at the ≥ 95% confidence level from a t-test.

Figure 5a shows the changes in the 850-hPa wind field and 500-hPa vertical velocity (ω₅₀₀). A positive anomaly of ω₅₀₀ represents an anomalous descending motion. A significant negative anomaly of ω₅₀₀ occurred over north China (30°–40°N, 110°–130°E), which resulted in a great increase in LCC and HCC. A clockwise anticyclonic circulation occurred over northeast China and produced a positive anomaly of ω₅₀₀, which led to a reduction in LCC. At 10°–20°N, the anomalous ascending motion over the Indian Peninsula and near the Philippines was beneficial to the increase in LCC and HCC. Water vapor supply is an important atmospheric condition for the formation and maintenance of clouds (Li et al., 2020). Figure 5b shows the fast responses of the column horizontal water vapor flux (i.e., QF_1000-680) and divergence (i.e., QD_1000-680) integrated from 1000 hPa to 680 hPa. The QD_1000-680 over the Indian Peninsula and near the Philippines was greatly reduced (i.e., water vapor convergence was greatly enhanced), which led to the increase in LCC (Fig. 2e). Figure 5b shows the changes of the column horizontal water vapor flux (i.e., QF_440-100) and divergence (i.e., QD_440-100) integrated from 440 hPa to 100 hPa. The enhanced water vapor convergence resulted in the increase in HCC near Japan (Fig. 2h).

Figure 5.  Fast responses of (a) the 850-hPa horizontal wind field (vector, units: m s^–1) and 500-hPa vertical velocity (ω₅₀₀, color shading, units: 10^–2 Pa s^–1, positive values represent descending motion anomaly); (b) column horizontal water vapor flux (vector, units: kg m^–1 s^–1) and divergence (color shading, units: 10^–5 kg m^–2 s^–1, positive values represent diffusion) integrated from 1000 hPa to 680 hPa; and (c) corresponding column horizontal water vapor flux and divergence integrated from 440 hPa to 100 hPa over East Asia.

Figures 6a–c show the slow responses of the pressure–latitude vertical profiles of RH, atmospheric temperature, and meridional circulation, respectively. The land was warmed to a greater extent than the ocean in the slow response (Fig. 1c), and the temperature difference between the lower (surface to 700 hPa) and upper (400 hPa to 100 hPa) atmosphere north of 40°N was further increased (Fig. 6b). This increased the instability of the atmosphere, causing the anomalous ascending motion north of 40°N (Fig. 6c), which led to the increase of LCC and HCC in this latitude zone (Figs. 2f and i). South of 40°N, the atmosphere mainly exibited anomalous descending motion (Fig. 6c), resulting in a reduction in LCC and HCC. In particular, the descending motion anomaly south of 20°N was intense, and the severe warming of the upper atmosphere (100–400 hPa) (Fig. 6b) increased the saturated water vapor pressure, leading to a significant decrease in the upper atmospheric RH (Fig. 6a), along with a decrease in the HCC.

Figure 6.  Same as Fig. 4, but for the slow response.

Figure 7 shows the changes in the 850-hPa wind field, ω₅₀₀, and the column horizontal water vapor flux and divergence. The enhancement of anticlockwise cyclonic circulation over northeast China and the negative anomalies of ω₅₀₀ and QD_1000−680 north of 40°N (Figs. 7a and b) caused a significant increase in LCC. Anomalous descending motion occurred mainly in the land area between 20°N and 40°N except in the TP (Fig. 7a), and the moisture was transported from land to ocean (Figs. 7b and c), leading to decreases in both LCC and HCC in this latitude zone. In particular, the significant positive anomalies of ω₅₀₀ and QD_1000−680 from southwest China to south of the Korean Peninsula resulted in the greatest decrease in LCC in these areas. The increase in LCC over the South China Sea was attributed to the increased water vapor convergence caused by the negative anomaly of QD_1000−680 (Fig. 7b).

Figure 7.  Same as Fig. 5, but for the slow response.

Figure 8 shows the changes of RH, atmospheric temperature, and vertical velocity with pressure in the three regions: TP (solid line), SC (dotted line), and NE (dashed line). The first row shows the fast responses. The negative vertical velocity represents an ascending motion. Because of the high altitude in the TP, we only considered changes from 600 hPa to 100 hPa. In the fast response, the maximum increase of RH in all three regions occurred at 200 hPa (Fig. 8a), and the atmospheric temperatures in all regions decreased at 100–250 hPa (Fig. 8b). The ascending motion increased strongly in the TP and reached a maximum vertical velocity at 450 hPa (approximately –0.63 × 10^–2 Pa s^–1, Fig. 8c); furthermore, the atmospheric temperature decreased at 100–250 hPa. Therefore, the RH of the upper atmosphere in the TP increased substantially, reaching a maximum value at 200 hPa (approximately 8%), and the increase in HCC in the TP was the greatest among the three regions (Fig. 2h). The anomalous ascending motion in SC occurred from the surface to 100 hPa, and the maximum change in vertical velocity occurred at 850 hPa (approximately –0.55 × 10^–2 Pa s^–1). Moisture was transported from the ocean to SC, leading to a decrease in QD_1000-680 (–0.55 × 10^–5 kg m^–2 s^–1, indicating enhanced water vapor convergence) in SC (Fig. 5b). Therefore, the LCC increased over SC. The negative anomaly of the temperature at 100–250 hPa resulted in the increase in the upper atmospheric RH and HCC over SC. The anomalous descending motion in NE occurred from the surface to 100 hPa (Fig. 8c), causing a decrease in LCC. However, because of the decrease in atmospheric temperature at 100–250 hPa, the RH in the upper atmosphere and HCC increased over NE.

Figure 8.  Fast (a–c) and slow (d–f) responses of (a, d) relative humidity (units: %), (b, e) atmospheric temperature (units: K), and (c, f) vertical velocity (units: 10^–2 Pa s^–1, positive values represent a descending motion anomaly) in three typical areas of East Asia. Changes in the TP, SC, and NE are indicated by solid, dotted, and dashed lines, respectively.

The second row in Fig. 8 shows the slow responses. The intense atmospheric warming (Fig. 8e) and a strong water vapor diffusion in the upper atmosphere (mean QD_440−100 anomaly = 0.42 × 10^–5 kg m^–2 s^–1, Fig. 7c) substantially reduced the HCC over the TP. The anomalous descending motion occurred throughout the atmosphere in SC (Fig. 8f), and the largest increase in vertical velocity (approximately 0.7 × 10^–2 Pa s^–1) occurred at 700 hPa. Furthermore, the moisture from 1000 hPa to 680 hPa flowed from land to ocean, which caused a significant increase in QD_1000-680 (mean = 1.61 × 10^–5 kg m^–2 s^–1, Fig. 7b) in SC. Thus, the LCC decreased over SC. The atmospheric temperature increased sharply at 100–400 hPa (7–9 K, Fig. 8e) in SC, resulting in a decrease in HCC over SC. The anomalous ascending motion extended from the surface to 300 hPa in NE (Fig. 8f), and the atmospheric warming was significantly weakened at 100–300 hPa (Fig. 8e). Thus, the LCC and HCC both increased over NE.

The radiative effect of clouds is an important factor that impacts the energy budget of the earth–atmosphere climate system (Liou, 1992). It is usually expressed by cloud radiative forcing (CRF), which is defined as the difference in net radiative flux between all sky and clear sky in a particular atmosphere (Imre et al., 1996). The absorption of gases and the scattering and absorbing processes of aerosols were included in the calculations of radiative fluxes in both all sky and clear sky conditions, while the radiative processes of clouds were only considered in all sky condition. Shortwave cloud forcing (SWCF) is defined as the difference between all sky net downward shortwave flux (SW) and clear sky net downward shortwave flux (SW_clear) at the top of the atmosphere (Wang et al., 2012):

Longwave cloud forcing (LWCF) is defined as the difference between clear sky net upward longwave flux (LW_clear) and all sky net upward longwave flux (LW) at the top of the atmosphere:

Thus, the sum of SWCF and LWCF is the net cloud forcing (NCF):

More detailed calculations of CRF are described in Shi (2007).

Figure 9 shows the changes in SWCF, LWCF, and NCF with a quadrupled CO₂ concentration over EA. In most areas, the fast and slow responses resulted in opposite changes in cloud forcing, such that the slow response was stronger than the fast response; therefore, the total response was dominated by the slow response. These characteristics were consistent with the changes in cloud cover. The changes in SWCF and LWCF were also opposite of each other. The shortwave radiative effect of clouds was greater than the longwave radiative effect, and the change in SWCF was larger than the change in LWCF. The distribution pattern of the change in NCF was therefore similar to the distribution pattern of the change in SWCF. The distribution patterns of the changes in SWCF and LCC were opposite of each other because low clouds have a strong reflection effect on shortwave radiation, which can reduce the shortwave radiation received by the surface. The LCC significantly increased north of 40°N, where SWCF decreased substantially (Fig. 9a). The SWCF of the TP decreased in both the fast and slow responses; therefore, the SWCF caused by the total response decreased substantially. The LCC exhibited the greatest decrease from southwest China to Japan, accompanied by the largest increase in SWCF (Fig. 9a). The changes in LWCF and HCC had similar distribution patterns because high clouds have a strong absorption effect on longwave radiation, which can reduce the longwave radiation emitted from the Earth. The HCC and LWCF both increased in the TP and north of 40°N, while they both decreased in SC and low-latitude ocean areas (Fig. 9d).

Figure 9.  Total, fast, and slow responses of (a–c) shortwave cloud forcing, (d–f) longwave cloud forcing, and (g–i) net cloud forcing over East Asia (units: W m^–2). Black dots represent significance at the ≥ 95% confidence level from a t-test.

Table 4 lists the mean values of changes in SWCF, LWCF, and NCF in EA. The respective mean changes in SWCF and LWCF in EA were –0.72 W m^–2 and –1.08 W m^–2 in the total response, which indicates that both forcings can offset part of the warming caused by a quadrupled CO₂ concentration. The respective mean NCF changes in EA in the total, fast, and slow responses were –1.80 W m^–2, –0.75 W m^–2, and –1.05 W m⁻², respectively, indicating that the fast and slow cloud responses in EA caused by a quadrupled CO₂ concentration both have a cooling effect. The changes in cloud forcing in the different areas in the slow response varied substantially, but they were all much higher than the changes in the fast response. Therefore, the NCF in the different areas was mainly determined by the slow response. Decreases in the NCF occurred in the low-latitude ocean, the TP, and north of 40°N, leading to a cooling effect; an increase in NCF occurred from southwest China to Japan, leading to a warming effect (Fig. 9g). The changes in NCF in the TP, SC, and NE in the total response were –6.74 W m^–2, 6.11 W m^–2, and –7.49 W m^-2, respectively, indicating that the cloud changes in the TP and NE had a strong cooling effect and considerably offset the warming effect caused by the quadrupled CO₂ concentration, while the cloud changes in SC amplified the warming effect caused by the quadrupled CO₂ concentration.

We used BCC–AGCM2.0 to simulate the cloud responses in three regions of EA to extreme global warming caused by a quadrupled CO₂ concentration. The total responses were decomposed into the fast and slow responses for comprehensive and thorough analyses. The conclusions were as follows.

First, the fast responses of the cloud cover were generally opposite to the slow responses in EA. The mean changes in TCC, LCC, and HCC were –0.74%, 0.38%, and –0.38% in the total response, respectively; 1.05%, –0.03%, and 1.63% in the fast response, respectively; and –1.79%, 0.41%, and –2.01% in the slow response, respectively. In the total response, the TCC and LCC over EA decreased south of 40°N and increased north of 40°N; the HCC increased over the area north of 40°N, the TP, and the eastern ocean, while it decreased in other areas. The spatial distributions of the slow responses in TCC, LCC, and HCC were all similar to the total responses, which indicated that the changes in TCC, LCC, and HCC over EA were all dominated by the slow response. However, the change in HCC in the TP was dominated by the fast response.

Second, atmospheric temperature and circulation can affect the condensation and transmission of water vapor, resulting in changes in clouds. In the fast response, the anomalous ascending motion at 30°–40°N caused a significant increase in TCC, LCC, and HCC. The largest increase in HCC occurred in the TP, which was caused by the strong ascending motion anomaly and the decrease in the upper atmospheric temperature in the TP. The enhanced water vapor convergence in SC was beneficial to the increase in HCC. The atmospheric descending motion anomaly reduced the LCC north of 40°N, and the change in HCC was insignificant because of the upper atmospheric cooling. As a consequence of the slow response, LCC and HCC increased north of 40°N due to the anomalous ascending motion, while they decreased south of 40°N due to the anomalous descending motion. The sharp warming of the upper atmosphere resulted in a significant reduction in HCC south of 20°N. In the TP, there was a substantial reduction in HCC, which was attributed to the intense water vapor diffusion in the upper atmosphere. The enhanced water vapor diffusion in SC also contributed to the decrease in LCC and HCC.

Third, the changes in the SWCF and LWCF over EA in the fast and slow responses were opposite to each other, and the total response was dominated by the slow response. The change in NCF was dominated by the change in SWCF. The mean change in NCF in EA was –1.80 W m^–2, indicating that changes in cloud cover over EA because of a quadrupled CO₂ concentration could induce a cooling effect, thereby partially offsetting the warming caused by the quadrupled CO₂ concentration. The changes in cloud forcing in the TP, SC, and NE in the fast and slow responses were opposite of each other; changes in NCF in the total response were –6.74 W m^–2, 6.11 W m^–2, and –7.49 W m^–2, respectively. Therefore, the cloud changes in the TP and NE had a strong cooling effect, which offset the warming effect caused by the quadrupled CO₂ concentration, while the changes in cloud cover in SC amplified the warming effect caused by the quadrupled CO₂ concentration.

In this study, BCC–AGCM2.0 was used to simulate the changes in cloud cover in EA to a quadrupled CO₂ concentration. Our results showed that cloud cover will change to some extent, but the conclusion that cloud cover will disappear by Schneider et al. (2019) was not obtained. Generally, the spatial distribution patterns of the changes in TCC over EA between BCC–AGCM2.0 and the CMIP6 10 typical model results were similar south of 40°N in the total and slow responses, and they were similar over land in the fast response; the mean changes in TCC of BCC–AGCM2.0 in the total and slow responses were within the uncertainty ranges of the CMIP6 results.

Acknowledgements. This work was financially supported by the National Key R&D Program of China (2017YFA0603502), the National Natural Science Foundation of China (Grant No. 41905081), and S&T Development Fund of CAMS (2021KJ004&2022KJ019).