ENSO Features, Dynamics, and Teleconnections to East Asian Climate as Simulated in CAMS-CSM

Abstract

This study evaluates the performance of CAMS-CSM (the climate system model of the Chinese Academy of Meteorological Sciences) in simulating the features, dynamics, and teleconnections to East Asian climate of the El Nino–Southern Oscillation (ENSO). In general, fundamental features of ENSO, such as its dominant patterns and phase-locking features, are reproduced well. The two types of El Nino are also represented, in terms of their spatial distributions and mutual independency. However, the skewed feature is missed in the model and the simulation of ENSO is extremely strong, which is found—based on Bjerknes index assessment—to be caused by underestimation of the shortwave damping effect. Besides, the modeled ENSO exhibits a regular oscillation with a period shorter than observed. By utilizing the Wyrtki index, it is suggested that this periodicity bias results from an overly quick phase transition induced by feedback from the thermocline and zonal advection. In addition to internal dynamics of ENSO, its external precursors—such as the North Pacific Oscillation with its accompanying seasonal footprinting mechanism, and the Indian Ocean Dipole with its 1-yr lead correlation with ENSO—are reproduced well by the model. Furthermore, with respect to the impacts of ENSO on the East Asian summer monsoon, although the anomalous Philippine anticyclone is reproduced in the post-El Nino summer, it exhibits an eastward shift compared with observation; and as a consequence, the observed flooding of the Yangtze River basin is poorly represented, with unrealistic air–sea interaction over the South China Sea being the likely physical origin of this bias. The response of wintertime lower-tropospheric circulation to ENSO is simulated well, in spite of an underestimation of temperature anomalies in central China. This study highlights the dynamic processes that are key for the simulation of ENSO, which could shed some light on improving this model in the future.