# An Assessment of ENSO Stability in CAMS Climate System Model Simulations

• Corresponding author: Lin CHEN, chenlin@nuist.edu.cn
• Funds:

Supported by the National Natural Science Foundation of China (41606011 and 91637210), National Key Research and Development Program (2016YFE0102400, 2016YFA0600602, and 2018YFC1506002), Basic Scientific Research and Operation Funds of the Chinese Academy of Meteorological Sciences (2017Y007), Startup Funds for Introduced Talents of Nanjing University of Information Science & Technology, Open Project Funds of the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, and Open Project Funds of the State Key Laboratory of Loess and Quartary Geology

• doi: 10.1007/s13351-018-8092-8
• We present an overview of the El Niño–Southern Oscillation (ENSO) stability simulation using the Chinese Academy of Meteorological Sciences climate system model (CAMS-CSM). The ENSO stability was quantified based on the Bjerknes (BJ) stability index. Generally speaking, CAMS-CSM has the capacity of reasonably representing the BJ index and ENSO-related air–sea feedback processes. The major simulation biases exist in the underestimated thermodynamic damping and thermocline feedbacks. Further diagnostic analysis reveals that the underestimated thermodynamic feedback is due to the underestimation of the shortwave radiation feedback, which arises from the cold bias in mean sea surface temperature (SST) over central–eastern equatorial Pacific (CEEP). The underestimated thermocline feedback is attributed to the weakened mean upwelling and weakened wind–SST feedback (μa) in the model simulation compared to observation. We found that the weakened μa is also due to the cold mean SST over the CEEP. The study highlights the essential role of reasonably representing the climatological mean state in ENSO simulations.
• Fig. 1.  Time series of Niño 3 index (°C) derived from (a) observation and (b) model simulation. The x-axis denotes time in month. (c) Standard deviations (STD; °C) of Niño 3 (blue) and Niño 3.4 indices (red).

Fig. 2.  Total BJ index and its associated contributing terms (units: yr–1), derived from observation (black) and model simulation (red). Difference between the model output and observation is indicated by green bar.

Fig. 3.  Qnet feedback (W m–2 K–1) and its four contributing terms, derived from observation (black) and model simulation (red). Difference between the model output and observation is indicated by green bar.

Fig. 4.  Climatological SST (°C) along the equator (averaged between 5°S and 5°N), derived from observation (black) and CAMS-CSM simulation (red).

Fig. 5.  (a) SWF (W m–2 K–1) along the equator (averaged between 5°S and 5°N), (b) PRF (mm day–1 K–1), and (c) TCCF (% K–1), derived from observation (black) and CAMS-CSM simulation (red).

Fig. 6.  Scatter diagrams of zonal wind stress anomaly (N m–2) plotted against SSTA (°C) derived from (a) observation and (b) CAMS-CSM simulation.

Fig. 7.  Horizontal pattern of PRF (color shaded; mm day–1 K–1) superimposed by horizontal structure of response of anomalous wind stress to SSTA (vector; N m–2 K–1) derived from (a) observation, (b) CAMS-CSM simulation, and (c) difference between the simulation and observation.

###### 通讯作者: 陈斌, bchen63@163.com
• 1.

沈阳化工大学材料科学与工程学院 沈阳 110142

## An Assessment of ENSO Stability in CAMS Climate System Model Simulations

###### Corresponding author: Lin CHEN, chenlin@nuist.edu.cn;
• 1. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing 100081
• 2. Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environmental Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044
• 3. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
Funds: Supported by the National Natural Science Foundation of China (41606011 and 91637210), National Key Research and Development Program (2016YFE0102400, 2016YFA0600602, and 2018YFC1506002), Basic Scientific Research and Operation Funds of the Chinese Academy of Meteorological Sciences (2017Y007), Startup Funds for Introduced Talents of Nanjing University of Information Science & Technology, Open Project Funds of the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, and Open Project Funds of the State Key Laboratory of Loess and Quartary Geology

Abstract: We present an overview of the El Niño–Southern Oscillation (ENSO) stability simulation using the Chinese Academy of Meteorological Sciences climate system model (CAMS-CSM). The ENSO stability was quantified based on the Bjerknes (BJ) stability index. Generally speaking, CAMS-CSM has the capacity of reasonably representing the BJ index and ENSO-related air–sea feedback processes. The major simulation biases exist in the underestimated thermodynamic damping and thermocline feedbacks. Further diagnostic analysis reveals that the underestimated thermodynamic feedback is due to the underestimation of the shortwave radiation feedback, which arises from the cold bias in mean sea surface temperature (SST) over central–eastern equatorial Pacific (CEEP). The underestimated thermocline feedback is attributed to the weakened mean upwelling and weakened wind–SST feedback (μa) in the model simulation compared to observation. We found that the weakened μa is also due to the cold mean SST over the CEEP. The study highlights the essential role of reasonably representing the climatological mean state in ENSO simulations.

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