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In this study, the radiation scheme BCC-RAD (Beijing Climate Center RADiative transfer model) developed for global climate models is implemented into the Global–Regional Integrated SysTem (GRIST) model as an alternative to the default RRTMG (general circulation model (GCM) version of the Rapid Radiative Transfer Model) scheme. Its impact on the simulated climate is comprehensively evaluated under different physics parametrization packages, in comparison with both the CERES (partly from ERA5 reanalysis) observations and multi-model results from CMIP6. The results indicate that under the default physics parameterization package of GRIST (PhysC), BCC-RAD improved the simulated global mean cloud cover by ~3% and the clear-sky outgoing longwave radiation by ~5.6 W/m2. Upon the inclusion of the PhysCN parameterization package, BCC-RAD exhibited further improvement in simulated cloud cover and radiative forcing (particularly longwave radiative forcing, the bias of which decreases from −9.2 W/m2 to −1.8 W/m2), leading it to be closer to observations than RRTMG. Additionally, BCC-RAD improved the simulation of atmospheric temperature and hence notably diminished the apparent overestimation of atmospheric humidity seen in RRTMG. This study demonstrates the advantages of BCC-RAD over RRTMG in certain aspects of the GRIST-simulated climate, verifying its capability for the climate-oriented configuration of GRIST. [ABSTRACT FROM AUTHOR]

Wind and solar energy are crucial for meeting the growing energy demand and mitigating the impact of climate change, and their sources show a climate‐dependence. Here, based on the outputs from two regional climate models (RCMs) driven by three global climate models within the Coordinated Regional Climate Downscaling Experiments‐East Asia (CORDEX‐EA‐II), the effects of future climate change on wind power density (WPD) and photovoltaic power potential (PVP) in China under the Representative Concentration Pathway (RCP) 2.6 scenario and RCP8.5 scenario are comprehensively investigated, and the sources of uncertainty are also quantified. Results show that all RCMs can reproduce the observed WPD and PVP in China by employing the Quantile Delta Mapping method for wind speed simulations. For the future projections, the annual averages of WPD and PVP in China tend to decrease by −11.67% to −1.7% and −4.6% to −1.12%, respectively, with more significant reduction under the RCP8.5 than under the RCP2.6. Note that uncertainties exist among the RCMs' simulations in terms of future projections and long‐term trends. Further analysis reveals that the uncertainty in both WPD and PVP projections are primarily driven by the internal variability in most sub‐regions except the Tibetan Plateau, where GCM uncertainty and emission scenarios play the dominant role in the uncertainty of WPD and PVP projection, respectively. This study highlights the potential benefits of controlling greenhouse gas emissions in enhancing and stabilizing renewable energy in China. Plain Language Summary: China has set an ambitious goal of achieving carbon neutrality by 2060, which requires an accelerated energy transition toward wind power and photovoltaic. However, the sources of wind and solar energy are sensitive to weather and climate, and future potential of wind and solar energy in China under a warming climate remains uncertain, posing a significant concern for the decision‐makers. Based on high‐resolution multiple regional climate models (RCMs) projections, we found that the annual average of wind power density (WPD) and photovoltaic power potential (PVP) are expected to decrease in the future by approximately −11.67% to −1.7% and −4.6% to −1.12%, respectively. There are notable uncertainties among the multi‐RCM projections, and the internal variability is the main source of uncertainty for most sub‐regions except the Tibetan Plateau, where GCM uncertainty and emission scenarios play the dominant role in the uncertainty of WPD and PVP projection, respectively. Our results highlight the advantages of reducing greenhouse gas emissions to enhance and stabilize renewable energy sources in China. Key Points: The annual average wind and solar energy in China are expected to decrease in the future based on a multi‐RCM ensembleThe internal variability plays a dominated role in the uncertainty of WPD and PVP projections in most sub‐regionsThe uncertainty in WPD and PVP projections over the TP is driven primarily by the different GCMs and emission scenarios, respectively [ABSTRACT FROM AUTHOR]

Cloud albedo is expected to influence cloud radiative forcing in addition to cloud fraction, and inadequate description of the cloud overlapping effects on the cloud fraction may influence the simulated cloud fraction, and thus the relative cloud radiative forcing (RCRF) and cloud albedo. In this study, we first present a new formula by extending that presented previously to consider multilayer clouds directly in the relationship between cloud albedo, cloud fraction, and RCRF, and then quantitatively evaluate the effects of different cloud vertical overlapping structures, represented by the decorrelation length scales (Lcf), on the simulated cloud albedos. We use the BCC_AGCM2.0_CUACE/Aero model with simultaneous validation by observations from the Clouds and the Earth's Radiation Energy System (CERES) satellite. When Lcf < 4 km (i.e., the cloud overlap is closer to the random overlap), the simulated cloud albedos are generally in good agreement with the satellite-based albedos for December–February and June–August; when Lcf ≥ 4 km (i.e., the cloud vertical overlap is closer to the maximum overlap), the difference between simulated and observed cloud albedos became larger, due mainly to significant differences in cloud fractions and RCRF. Further quantitative analysis shows that the relative Euclidean distance, which represents the degree of overall model–observation disagreement, increases with the Lcf for all three variables (cloud albedo, cloud fraction, and RCRF), indicating the importance of cloud vertical overlapping in determining the accuracy of the calculated cloud albedo for multilayer clouds. Significance Statement: The purpose of this study is presenting a new formula to consider multilayer clouds directly in the relationship between cloud albedo, cloud fraction, and relative cloud radiative forcing (RCRF). This is important because the effects of different cloud vertical overlapping structures, represented by the decorrelation length scales (Lcf), can affect the simulated cloud albedos. Our results provide a guide on the importance of cloud vertical overlapping in determining the accuracy of the calculated cloud albedo for multilayer clouds. [ABSTRACT FROM AUTHOR]