CAS FGOALS-f3-L Large-ensemble Simulations for the CMIP6 Polar Amplification Model Intercomparison Project

• Published in: Advances in Atmospheric Sciences
• Main Authors: He, Bian, Zhang, Xiaoqi, Duan, Anmin, Bao, Qing, Liu, Yimin, Hu, Wenting, Li, Jinxiao, Wu, Guoxiong
• Format: Article in Journal/Newspaper
• Language: English
• Published: Springer Science and Business Media LLC 2021
• Subjects: Atmospheric Science; Arctic; Sea ice
• Online Access: http://dx.doi.org/10.1007/s00376-021-0343-4; https://link.springer.com/content/pdf/10.1007/s00376-021-0343-4.pdf; https://link.springer.com/article/10.1007/s00376-021-0343-4/fulltext.html

Abstract 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 published. The time-lag method was used to generate the 100 ensemble members, with each member integrating from 1 April 2000 to 30 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 decrease alone show an obvious increase 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 of these model datasets will contribute to PAMIP multi-model analysis and improve the understanding of polar amplification.