El Nino–Southern Oscillation (ENSO), which is one of the most striking interannual variabilities in the tropical Pacific, has been extensively studied for several decades. Understanding the changes in its characteristics is still an important issue in environmental and socioeconomic spheres worldwide. Recently, a new type of El Nino [central Pacific (CP) El Nino] has emerged, in which maximum sea surface temperature (SST) anomalies are confined mostly to the CP—different to the canonical type [eastern Pacific (EP) El Nino], in which the maximum SST anomalies are located in the eastern Pacific. The more frequent occurrence of CP El Nino and its different impacts on global climate compared to EP El Nino have been well documented. However, a systematic examination of the performance of climate models in predicting the two types of El Nino had yet to be undertaken, and it remained controversial as to whether the predictability differs among state-of-the-art climate models.
Prof. Fei ZHENG from the Institute of Atmospheric Physics, Chinese Academy of Sciences and Prof. Jin-Yi YU, from the University of California, Irvine, explored the performance of the IAP’s ENSO ensemble prediction system with respect to the two types of El Nino, focusing on the nine EP El Nino and twelve CP El Nino events that have occurred since 1950.
In addition to the eastern Pacific El Nino which we have been quite familiar with, a second El Nino in the central Pacific has emerged. (Image by ZHENG Fei)
"We found that the skill scores for EP events were significantly better than those for CP events at all lead times," says ZHENG, “the possible reasons are related to the systematic forecast biases coming mostly from the prediction of CP events; and systematic error characterized by an overly warm eastern Pacific during the spring season, indicating a stronger spring prediction barrier for CP El Nino."
Further improvements to coupled atmosphere–ocean models in terms of CP El Nino prediction should be recognized as a key and high-priority task for the climate prediction community.
Zheng, F., and J.-Y. Yu, 2017: Contrasting the skills and biases of deterministic predictions for the two types of El Nino. Adv. Atmos. Sci., 34(12), https://doi.org/10.1007/s00376-017-6324-y .