Seasonal predictability of winter ENSO types in operational dynamical model predictions

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.

Does increased atmospheric resolution improve seasonal climate predictions?

“…Seasonal predictability of El Nino Southern Oscillation (ENSO) has been well established for many years (Cane et al, 1986) and the high predictability of ENSO is now a cornerstone of current seasonal prediction capability (Smith et al, 2012). Nevertheless, outstanding questions remain (Tang et al, 2018), for example regarding the predictability of different ENSO types (Imada et al, 2015;Ren et al, 2019), or increases in skill as models improve (e.g., Luo et al, 2008). Although comprehensive models now appear to have the edge over simpler prediction models, there is also variation in prediction skill over time (Barnston et al, 2012).…”

Improving prediction of two ENSO types using a multi-model ensemble based on stepwise pattern projection model

“…In addition, Fig. 9 presents the center longitude index (CLI, proposed by Ren et al 2019a) that is defined as the longitude where the amplitude of equatorial-mean (5° S-5° N) SST anomalies reaches maximum, which collectively contrasts the center positions of the SST anomaly patterns as shown in Figs. 6, 7 and 8 for the regressed EP and CP ENSO patterns.…”

“…Hence, the hindcast datasets from the NMME system are used in this work. Ren et al (2019a) found that the MME has a limited ability to predict the different zonal positions of SST anomaly centers between the two types, despite considerable successes in prediction skill of the Niño indices. As another promising approach, empirical or statistical correction methods have been widely used in climate prediction (Feddersen et al 1999;Graham et al 1994;Kang et al 2004;Kug et al 2004Kug et al , 2007bKug et al , 2008aRen et al 2014;Sailor and Li 1999;Von Storch et al 1993;Ward and Navarra 1997;Zorita et al 1995;Zorita and Von Storch 1999).…”

“…Many previous studies revealed that the CP-type of El Niño has become more frequent since the late 1970s ; Kao and Yu 2009;Kug et al 2009;Larkin and Harrison 2005a, b;Ren and Jin 2013), and the frequency tends to increase under a warming climate (Yeh et al 2009). Large amounts of international studies have also focused on the prediction assessments and predictability for the two ENSO types using single models and multi-models (e.g., Hendon et al 2009;Imada et al 2015;Jeong et al 2012;Lee et al 2017;Ren et al 2019a;Yang and Jiang 2014). Their studies indicated that the CP ENSO may be more difficult to predict due to their smaller amplitude (Imada et al 2015;Zhu et al 2015), even though they tend to possess longer persistence or a weaker persistence barrier than the EP ENSO (Kim et al 2009;Ren et al 2016;Tian et al 2019).…”

“…Most dynamical models have difficulty in capturing the typical observed features of the two ENSO types (e.g., Yu and Kim 2010). Therefore, exploring how to improve the performance of the climate models in distinguishing the two types of ENSO is essential for further improving seasonal climate predictions (e.g., Ren et al 2019a).…”