Evaluation and Attribution of Shortwave Feedbacks to ENSO in CMIP6 models

Author:

Huang Junjie1,Li Lijuan1,Ran Haiyan1,Liu Juan2,Wang Bin1,Feng Tao3,Chang Youli3

Affiliation:

1. Institute of Atmospheric Physics

2. Beijing Institute of Applied Meteorology

3. Yunnan University

Abstract

Abstract The shortwave (SW) feedback to El Niño–Southern Oscillation (ENSO) is one of the largest biases in climate models, as the feedback includes atmosphere–ocean interactions and cloud processes. In this study, the performance of SW feedback in 19 models from the 6th Coupled Model Intercomparison Project (CMIP6) is evaluated and the biases are attributed using the historical and Atmospheric Model Intercomparison Project (AMIP) runs. The results demonstrate that most CMIP6 models underestimate the strength of SW feedback, although 11 models (~ 58%) show the observed negative signs in the Niño-3 region, a superior result to that (7 of 17, ~ 41%) of CMIP5. The underestimates of SW feedback arise mainly from the biased feedbacks to El Niño in the four models with relatively better skills, while from both underestimated negative feedbacks to El Niño and overestimated positive feedbacks to La Niña in other 15 models, which reproduce better seasonal variations than corresponding CMIP5 models. Furthermore, the SW feedback bias is connected to weak convective/stratiform rainfall feedback, which is sensitive/insensitive to sea surface temperature (SST) biases during El Niño/La Niña. There are different biases among the factors contributing to SW feedback, such as erroneous compensations between underestimated cloud fraction feedback and overestimated liquid water path feedback in the four best-performing models, whereas both are underestimated in the other models, and weakened dynamical feedbacks are observed in all models. The rainfall feedbacks in the AMIP runs are much closer to the observations than those in CMIP5, although they are greatly reduced in the historical runs, indicating that the atmospheric models may be over-tuning under given observed SSTs.

Publisher

Research Square Platform LLC

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