Predictability of two types of El Niño and their climate impacts in boreal spring to summer in coupled models
The predictability of the two El Niño types and their different impacts on the East Asian climate from boreal spring to summer have been studied, based on coupled general circulation models (CGCM) simulations from the APEC Climate Center (APCC) multi-model ensemble (MME) hindcast experiments. It was found that both the spatial pattern and temporal persistence of canonical (eastern Pacific type) El Niño sea surface temperature (SST) are much better simulated than those for El Niño Modoki (central Pacific type). In particular, most models tend to have El Niño Modoki events that decay too quickly, in comparison to those observed. The ability of these models in distinguishing between the two types of ENSO has also been assessed. Based on the MME average, the two ENSO types become less and less differentiated in the model environment as the forecast leadtime increases. Regarding the climate impact of ENSO, in spring during canonical El Niño, coupled models can reasonably capture the anomalous low-level anticyclone over the western north Pacific (WNP)/Philippine Sea area, as well as rainfall over coastal East Asia. However, most models have difficulties in predicting the springtime dry signal over Indochina to South China Sea (SCS) when El Niño Modoki occurs. This is related to the location of the simulated anomalous anticyclone in this region, which is displaced eastward over SCS relative to the observed. In boreal summer, coupled models still exhibit some skills in predicting the East Asian rainfall during canonical El Nino, but not for El Niño Modoki. Overall, models’ performance in spring to summer precipitation forecasts is dictated by their ability in capturing the low-level anticyclonic feature over the WNP/SCS area. The latter in turn is likely to be affected by the realism of the time mean monsoon circulation in models.
The authors appreciate those institutes participating in prediction of the APCC multi-model ensemble operating system for providing the Tier-1 hindcast experimental data. We thank Profs. Johnny Chan and Wen Zhou for discussions, and Dr. Hoffman Cheng for technical assistance. W.-K. Lee is supported by the Focused Innovations Scheme of The Chinese University of Hong Kong (Project No.: 1907001).
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