关于CAS FGOALS-f3-L模式中青藏高原地表温度偏差的归因分析

中国科学院全球海洋-大气-陆地耦合模式(FGOALS-f3-L)参加了耦合模式比较计划的第六阶段(CMIP6)试验,但是其对关键气候敏感地区青藏高原的地表温度的再现能力还不清楚.这项研究用再分析资料CFSR评估了FGOALS-f3-L模式对青藏高原地表温度的再现能力.结果表明,FGOALS-f3-L可以合理模拟整个高原上年平均地表温度的空间分布,但低估了整个高原上年平均地表温度.模拟的地表温度在整个高原上冬春季表现为冷偏差,夏秋季表现为暖偏差.基于地表能量平衡方程的进一步定量分析表明,地表反照率反馈(SAF)项极大地贡献了高原西部年平均,冬春季平均地表温度的冷偏差,而对高原东部是暖偏差贡献.与SAF项相比,地表感热项对地表温度偏差的贡献几乎相反,这大大抵消了SAF项引起的偏差.云辐射强迫项对高原东部的年平均和季节平均弱冷偏差有很大贡献.与高估的水蒸气含量有关的长波辐射项造成了夏秋季整个高原上大部分的暖偏差.该研究表明,提高FGOALS-f3-L中的陆面和云过程对降低高原上地表温度偏差至关重要.

Analysis of surface temperature bias over the Tibetan plateau in the CAS FGOALS-f3-L model

The Chinese Academy of Sciences Flexible Global Ocean-Atmosphere-Land System atmospheric component model (FGOALS-f3-L) participated in Phase 6 of the Coupled Model Intercomparison Project,but its reproducibility of surface temperature (Ts) over the Tibetan Plateau (TP) as a key climatically sensitive region remains unclear.This study evaluates the capability of FGOALS-f3-L in reproducing the climatological Ts over the TP relative to the Climate Forecast System Reanalysis.The results show that FGOALS-f3-L can reasonably capture the spatial pattern of Ts but underestimates the annual mean Ts for the whole TP.The simulated Ts for the whole TP shows a cold bias in winter and spring and a warm bias in summer and autumn.Further quantitative analysis based on the surface energy budget equation shows that the surface albedo feedback (SAF) term strongly contributes to the annual,winter,and spring mean cold bias in the western TP and to the warm bias in the eastern TP.Compared with the SAF term,the surface sensible and latent heat flux terms make nearly opposite contributions to the Ts bias and considerably offset the bias due to the SAF term.The cloud radiative forcing term strongly contributes to the annual and seasonal mean weak cold bias in the eastern TP.The longwave radiation term associated with the overestimated water vapor content accounts for a large portion of the warm bias over the whole TP in summer and autumn.Improving land surface and cloud processes in FGOALS-f3-L is critical to reduce the Ts bias over the TP.