Cold air outbreaks (CAOs) enormously influence agriculture, environment, industry, and other socio-economic activities in East Asia. This study objectively identifies and categorizes CAOs in East Asia by employing 3-dimensional CAO detection algorithm and k-means clustering method. The analysis identifies a total of 106 CAOs which are classified into three distinct types based on core cold anomaly positions: South, Mixed, and North types. The South type, with the fewest occurrences and shortest lifetime, exhibits a weak warm lobe over Europe and northern Asian coast, with the coldest anomalies over southern China. The Mixed type, most frequent and longest-lived, accompanies the most extensive and coldest anomalies across Eurasia, uniformly distributed over East Asia. The North type shows the coldest anomalies in Northeast Asia with weaker effects in southern East Asia. The weakening trend in CAO intensity observed in East Asia is primarily due to long-term changes in the Mixed and North types. Temperature anomaly patterns and circulation variations are diverse, with the South type associated with a pre-existing strong stratospheric polar vortex state, and the Mixed and North types with a pre-existing week vortex state. Under those stratospheric backgrounds, even similar wave activities can result in dissimilar upper-tropospheric circulation anomalies over East Asia, leading to different extents and magnitudes of anomalous coldness. This study provides valuable insights into the diversity of East Asian CAOs, essential for forecasting and managing associated risks in East Asia.

Large-ensemble simulations of the atmosphere-only time-slice experiments for the Polar Amplification Model Intercomparison Project (PAMIP) were carried out by the model group of the Chinese Academy of Sciences (CAS) Flexible Global Ocean-Atmosphere-Land System (FGOALS-f3-L). Eight groups of experiments forced by different combinations of the sea surface temperature (SST) and sea ice concentration (SIC) for pre-industrial, present-day and future conditions were performed and submitted. The time-lag method was used to generate the 100 ensemble members, with each member integrating from 1st April 2000 to 30th June 2001 and the first two months as the spin-up period. The basic model responses of the surface air temperature (SAT) and precipitation were documented. The results indicate that Arctic amplification is mainly caused by Arctic SIC forcing changes. The SAT responses to the Arctic SIC forcing alone show an obvious meridional gradient over high latitudes, which is similar to the results from the combined forcing of SST and SIC. However, the change in global precipitation is dominated by the changes in the global SST rather than SIC, partly because tropical precipitation is mainly driven by local SST changes. The uncertainty of the model responses was also investigated through the analysis of the large-ensemble members. The relative roles of SST and SIC, together with their combined influence on Arctic amplification, are also discussed. All these model datasets will contribute to PAMIP multimodel analysis and improve the understanding of polar amplification.