Abstract
AbstractZonal wind stress plays an important role in the evolution of El Nino–Southern Oscillation (ENSO) events; however, a comprehensive comparison and analysis in terms of model performance and related bias in the interannual variability of zonal wind stress across the tropical Pacific has yet to be performed. In this study, the authors evaluate how well the individual atmospheric models participating in phase 5 of the Coupled Model Intercomparison Project simulate zonal wind stress. It is found that the wind stress anomalies simulated by the multi-model ensemble are weaker than those in the observation in both El Nino and La Nina events, with a larger bias in the former. Further analysis indicates that the bias associated with El Nino events may be mainly attributable to the weaker negative precipitation anomalies in the AMIP simulations, compared with observations, over the eastern Indian Ocean. Through the Gill-like responses in atmospheric circulation, the rainfall bias over the eastern Indian Ocea...
Highlights
El Niño–Southern Oscillation (ENSO) is characterized by the irregular occurrence of warm or cold sea surface temperature (SST) anomalies in the equatorial eastern Pacific, and usually reaches its peak in boreal winter (Rasmusson and Carpenter 1982)
One of the typical characteristics of ENSO is a seesaw pattern in sea level pressure, associated with a modulation of the trade winds and a shift in tropical Pacific precipitation
The reason for the severely weaker zonal wind stress produced in El Niño events is examined and is attributed to the apparent underestimation of negative precipitation anomalies over the eastern Indian Ocean during warm events
Summary
El Niño–Southern Oscillation (ENSO) is characterized by the irregular occurrence of warm or cold sea surface temperature (SST) anomalies in the equatorial eastern Pacific, and usually reaches its peak in boreal winter (Rasmusson and Carpenter 1982). Being able to predict ENSO events well in advance has practical significance for economic development and social stability. Over the last 30 years, the theoretical understanding of ENSO has advanced significantly with the increasing availability and quality of observational data and paleo proxies (Wang and Picaut 2004); plus, climate model performance has continually improved (Zhang and Jin 2012; Chen, Yu, and Sun 2013; Bellenger et al 2013). It remains difficult to accurately simulate and predict ENSO with coupled atmosphere–ocean general circulation models (CGCMs)—even the state-of-theart models involved in phase 5 of the Coupled Model Intercomparison Project (CMIP5) (Bellenger et al 2013; Chen et al 2017)—because of the complex interplay between various oceanic and atmospheric processes and the relatively short observational record.
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