Cloud Radiative Feedbacks during the ENSO Cycle Simulated by CAMS-CSM

Chen, Lin; Hua, Lijuan; Rong, Ximin; Li, Jian; Wang, Lu; Zhang, Guo; Sun, Mingdong; Ge, Zi-An

doi:10.1007/s13351-019-8104-3

Cited by 5 publications

(1 citation statement)

References 51 publications

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“…The reduced low cloud over the tropical eastern Pacific in the Hindcast‐A is closely associated with the equatorial cold tongue bias in FGOALS‐s2 (Figure a) through following two processes. First, the excessive cold tongue prevents the total SST from exceeding the convection threshold in the tropical eastern Pacific even during El Niño year (Chen, Hua, et al, ; Graham & Barnett, ; Zhang, ). Second, the stronger climatological cold tongue leads to more stable marine boundary layer and thus more amount of marine stratiform (Figure d).…”

Section: Resultsmentioning

confidence: 99%

Improved ENSO Prediction Skill Resulting From Reduced Climate Drift in IAP‐DecPreS: A Comparison of Full‐Field and Anomaly Initializations

Zhou

2020

J Adv Model Earth Syst

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When initiated from observational conditions, coupled climate models used in seasonal predictions generally experience climate drifts. How climate drift affects El Niño–Southern Oscillation (ENSO) prediction is important but not clearly understood. Here, we investigate this issue by comparing seasonal hindcasts using two distinct initialization approaches, namely, anomaly and full‐field initializations, based on a climate prediction system named IAP‐DecPreS. The differences between the two approaches are mainly evident in the drift behavior. We find that the hindcasts based on anomaly initialization (Hindcast‐A) have higher ENSO prediction skill compared to those based on full‐field initialization (Hindcast‐F). The climate drifts are largely reduced in the Hindcast‐A as expected. In contrast, the Hindcast‐F features a growing warming of the equatorial central eastern Pacific with increasing lead times. To investigate the impact of drift on the prediction, the 1997/1998 and 2015/2016 El Niño cases are analyzed. At a 7‐month lead, the Hindcast‐A reasonably predicts the two events, while the Hindcast‐F shows large errors in both evolution and amplitude. Budget analyses show that the underestimation of warming tendency in the Hindcast‐F is caused by cooling effects of excessive anomalous surface shortwave radiative flux and anomalous temperature advection by mean horizontal currents, both of which are associated with the climate drift. Our results imply that the use of the AI scheme can improve ENSO predictions through a reduction in climate drift in IAP‐DecPreS. The drifts can dynamically influence ENSO predictions, and their impact cannot be thoroughly removed via the empirical bias correction. Thus, reducing drift impacts is necessary.

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Section: Resultsmentioning

confidence: 99%

Improved ENSO Prediction Skill Resulting From Reduced Climate Drift in IAP‐DecPreS: A Comparison of Full‐Field and Anomaly Initializations

Zhou

2020

J Adv Model Earth Syst

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Evaluation and attribution of shortwave feedbacks to ENSO in CMIP6 models

Huang,

Li,

et al. 2024

Clim Dyn

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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.

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