Ensemble prediction has been an important tool for weather forecasting, sub-seasonal to seasonal prediction, seasonal prediction, interannual prediction and even simulation of climate change, which has garnered widespread attention in the field of meteorology. This paper introduces the ensemble prediction scheme of China Meteorological Administration Climate Prediction System version 3 (CMA-CPSv3). In this scheme, we adopt the approach of combining stochastic perturbations of physical process tendencies in the atmosphere and the air-sea flux with time-lagged initial value perturbation. Based upon the version 2 of High-Resolution Beijing Climate Centre Climate System Model (BCC-CSM2-HR), we have developed a multi-layer random perturbation ensemble prediction system with relatively good ensemble sample dispersion, stability, and reliability. Results of evaluation for hindcasts over the past 20 years show that this ensemble prediction system significantly improves the prediction of precipitation and 2 m air temperature over China, as well as the El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Asian Monsoon. In particular, the random perturbation of air-sea flux shows a positive effect on improving the prediction skills of ENSO and Southeast Asian Monsoon (SEAM) and Western North Pacific Summer Monsoon (WNPSM) indices. This study offers useful insights for further characterizing uncertainty in other component models of the climate system.

Atmospheric CO₂ concentration is characterized by spatial inhomogeneity and seasonal variability. The response of surface air temperature (SAT) to the inhomogeneity in CO₂ concentration globally and regionally remains elusive. In this study, the BCC-CSM2-MR climate model was used to investigate the differences in global SAT in response to the spatially inhomogeneous distribution of atmospheric CO₂ concentration. The analysis was based on three historical experiments (Hist_1dCO₂, Hist_2dCO₂, and Hist_3dCO₂) conducted separately under the forcing of globally homogeneous, zonally homogeneous, and wholly spatially inhomogeneous CO₂ concentrations from 1850 to 2014, derived from 12 Earth System Models of the Coupled Model Intercomparison Project Phase 6. The simulation results revealed similar trends of evolution in the global mean SATs in the 20th century under the three CO₂ concentration distributions, and showed that the simulated historical SATs considering the meridional inhomogeneity of CO₂ concentration in Hist_2dCO₂ and the wholly spatial inhomogeneity in Hist_3dCO₂ were more consistent with the observations. Compared with the results of Hist_1dCO₂, the SATs in Hist_2dCO₂ were warmer over land in the mid–high latitudes of the Northern Hemisphere (NH) than over other land areas. Further consideration of the zonally inhomogeneous CO₂ concentration in Hist_3dCO₂ revealed generally colder SATs over the NH mid–high-latitude ocean than over land at the same latitudes, and even the zonal mean SATs in the NH were slightly colder than those in Hist_2dCO₂. These differences are ascribed to the uneven distribution of CO₂ concentration along the same latitude in the NH in Hist_3dCO₂, which leads to strong large-scale fluctuations in the atmospheric circulation. Eurasia is the region with the highest concentration of atmospheric CO₂, which leads to remarkable regional SAT warming owing to enhanced downward longwave radiation. Warmer SATs in Eurasia in winter will further strengthen the northwesterly winds over eastern Asia, resulting in an increase in sea ice and strengthened cold SAT anomalies over the northern North Pacific. The simulated varied responses of the atmospheric circulation and SAT to inhomogeneous CO₂ forcing highlight the imperative need for refined representation of the inhomogeneity of the atmospheric CO₂ distribution in climate models for more accurate assessment of climate change.