%0 Journal Article %1 ren2018seasonal %A Ren, Hong-Li %A Scaife, Adam A. %A Dunstone, Nick %A Tian, Ben %A Liu, Ying %A Ineson, Sarah %A Lee, June-Yi %A Smith, Doug %A Liu, Changzheng %A Thompson, Vikki %A Vellinga, Michael %A MacLachlan, Craig %D 2018 %J Climate Dynamics %K colleagues seasonal skill ENSO %R 10.1007/s00382-018-4366-1 %T Seasonal predictability of winter ENSO types in operational dynamical model predictions %U https://doi.org/10.1007/s00382-018-4366-1 %X The El Niño-Southern Oscillation (ENSO) events of recent decades have been divided into the two different types based on their spatial patterns, the Eastern Pacific (EP) type and Central Pacific (CP) type. Their most significant difference is the distinguished zonal center locations of sea surface temperature (SST) anomalies in the equatorial Pacific. In this study, based on six operational climate models, we evaluate predictability of the two types of ENSO events in winter to examine whether dynamical predictions can distinguish between the two spatial patterns at lead time of 1 month and tell us more than simply whether an event is on the way. We show that winter EP and CP El Niño and La Niña events can only be distinguished in a minority of these models at 1-month lead, and the EP type tends to has a more realistic zonal positions of SST pattern centers than the CP type. Compared to the SST patterns, the differences between the two types are less apparent in precipitation especially for the two La Niña types in the models. Examinations of the extratropical teleconnections to the two ENSO types show that some of the models can reproduce the differences between EP and CP teleconnections. Evaluations of model predictions show that the EP El Niño event has the same level hit rate with the CP El Niño and the CP La Niña event has much higher hit rate than the EP La Niña. While the multi-model ensemble increases Niño index prediction skill, it does not help to improve forecast skill of center longitude index of the SST patterns and distinguish the two types of ENSO events. Although ENSO skill is very high at this lead time, the rapid loss of the initialized information on the different ENSO types in most of the models severely limits the predictability of the two types of winter ENSO events and more research is needed to improve the performance of climate models in forecasting the two ENSO types.

@article{ren2018seasonal, abstract = {The El Niño-Southern Oscillation (ENSO) events of recent decades have been divided into the two different types based on their spatial patterns, the Eastern Pacific (EP) type and Central Pacific (CP) type. Their most significant difference is the distinguished zonal center locations of sea surface temperature (SST) anomalies in the equatorial Pacific. In this study, based on six operational climate models, we evaluate predictability of the two types of ENSO events in winter to examine whether dynamical predictions can distinguish between the two spatial patterns at lead time of 1 month and tell us more than simply whether an event is on the way. We show that winter EP and CP El Niño and La Niña events can only be distinguished in a minority of these models at 1-month lead, and the EP type tends to has a more realistic zonal positions of SST pattern centers than the CP type. Compared to the SST patterns, the differences between the two types are less apparent in precipitation especially for the two La Niña types in the models. Examinations of the extratropical teleconnections to the two ENSO types show that some of the models can reproduce the differences between EP and CP teleconnections. Evaluations of model predictions show that the EP El Niño event has the same level hit rate with the CP El Niño and the CP La Niña event has much higher hit rate than the EP La Niña. While the multi-model ensemble increases Niño index prediction skill, it does not help to improve forecast skill of center longitude index of the SST patterns and distinguish the two types of ENSO events. Although ENSO skill is very high at this lead time, the rapid loss of the initialized information on the different ENSO types in most of the models severely limits the predictability of the two types of winter ENSO events and more research is needed to improve the performance of climate models in forecasting the two ENSO types.}, added-at = {2018-08-03T19:27:56.000+0200}, author = {Ren, Hong-Li and Scaife, Adam A. and Dunstone, Nick and Tian, Ben and Liu, Ying and Ineson, Sarah and Lee, June-Yi and Smith, Doug and Liu, Changzheng and Thompson, Vikki and Vellinga, Michael and MacLachlan, Craig}, biburl = {https://www.bibsonomy.org/bibtex/286c60da8963ced980cf98768d6e8ffa9/pbett}, day = 25, description = {Seasonal predictability of winter ENSO types in operational dynamical model predictions | SpringerLink}, doi = {10.1007/s00382-018-4366-1}, interhash = {1555906441fe34c0458682c572b63dce}, intrahash = {86c60da8963ced980cf98768d6e8ffa9}, issn = {1432-0894}, journal = {Climate Dynamics}, keywords = {colleagues seasonal skill ENSO}, month = jul, timestamp = {2020-04-15T09:29:49.000+0200}, title = {Seasonal predictability of winter ENSO types in operational dynamical model predictions}, url = {https://doi.org/10.1007/s00382-018-4366-1}, year = 2018 }