An Investigation of the SST Annual Cycle in the Eastern Equatorial Pacific in CAS‐ESM2‐0

Abstract Coupled atmosphere‐ocean models exhibit significant biases in simulating the Sea Surface Temperature (SST) annual cycle, with substantial inter‐model discrepancies. The Chinese Academy of Sciences Earth System Model (CAS‐ESM2‐0), as one of the models contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6), displays a more pronounced SST annual cycle in the eastern equatorial Pacific when compared with observations. This study investigates the main components of the ocean mixed layer heat budget in this model and physical processes responsible for the SST bias using a diagnostic approach with controlled numerical experiments. We found that ocean dynamics dominates the model's SST annual cycle and its bias relative to observations, and the biases in ocean currents are caused by the annual cycle bias of surface winds in the standalone atmospheric model. Anomalous westerly bias in the boreal winter (DJF) drives eastward ocean currents, leading to meridional convergence of ocean currents and downwelling, thus excessive warming, while anomalous easterly in the boreal summer (JJA) drive divergence and upwelling that produce excessive cooling. The thermodynamics, although having a large impact on the SST heat budget, plays a passive role and a negative feedback in the model: It responds to SST, and the higher the SST, the larger the loss of the heat content of the mixed layer. The negative feedback is from both the latent heat flux and the shortwave radiation modulated by variations in clouds. These results offer insights into the causes of the equatorial Pacific SST annual cycle biases in coupled models.

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Publication Details

Journal
Journal of Geophysical Research Atmospheres
Published
2026-09-16
DOI
https://doi.org/10.1029/2026jd046330
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
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article

An Investigation of the SST Annual Cycle in the Eastern Equatorial Pacific in CAS‐ESM2‐0

Hailong Liu, Minghua Zhang, Wei Si, Xingwen Jiang
Journal of Geophysical Research Atmospheres
Climate variability and models
article

An Investigation of the SST Annual Cycle in the Eastern Equatorial Pacific in CAS‐ESM2‐0

Hailong Liu, Minghua Zhang, Wei Si, Xingwen Jiang
article en

Abstract

Abstract Coupled atmosphere‐ocean models exhibit significant biases in simulating the Sea Surface Temperature (SST) annual cycle, with substantial inter‐model discrepancies. The Chinese Academy of Sciences Earth System Model (CAS‐ESM2‐0), as one of the models contributing to the Coupled Model Intercomparison Project Phase 6 (CMIP6), displays a more pronounced SST annual cycle in the eastern equatorial Pacific when compared with observations. This study investigates the main components of the ocean mixed layer heat budget in this model and physical processes responsible for the SST bias using a diagnostic approach with controlled numerical experiments. We found that ocean dynamics dominates the model's SST annual cycle and its bias relative to observations, and the biases in ocean currents are caused by the annual cycle bias of surface winds in the standalone atmospheric model. Anomalous westerly bias in the boreal winter (DJF) drives eastward ocean currents, leading to meridional convergence of ocean currents and downwelling, thus excessive warming, while anomalous easterly in the boreal summer (JJA) drive divergence and upwelling that produce excessive cooling. The thermodynamics, although having a large impact on the SST heat budget, plays a passive role and a negative feedback in the model: It responds to SST, and the higher the SST, the larger the loss of the heat content of the mixed layer. The negative feedback is from both the latent heat flux and the shortwave radiation modulated by variations in clouds. These results offer insights into the causes of the equatorial Pacific SST annual cycle biases in coupled models.

Journal of Geophysical Research AtmospheresVol. 131(18)
China Meteorological Administration (CN), Chinese Academy of Meteorological Sciences (CN), Laoshan Laboratory, Stony Brook University (US)
National Natural Science Foundation of China
Life below water
Openalex Percentile: Top 14%
Climate variability and models
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