WWBs, ENSO predictability, the spring barrier and extreme events

Abstract

This study examines how semi-stochastic Westerly Wind Bursts (WWBs) affect El Niño Southern Oscillation (ENSO) predictability. An ensemble ENSO prediction experiment is presented in which the Community Climate System Model version 3 (CCSM3) and CCSM3 with a state-dependent WWB parameterization are used as both "truth" and as predictor systems. Inclusion of WWBs has little effect on ENSO predictability if the "truth" lacks WWBs. If the "truth" includes WWBs, the limit of ENSO predictability is larger for a forecast system that captures the correct statistics of WWBs. Predictability drops considerably if a forecast system that lacks WWB events is used to predict a "truth" that includes WWBs. At longer lead times, predictability is more dependent on the dynamical properties of the truth; that is, the importance of capturing the WWB statistics becomes less important and the statistics (e.g., signal-to-noise ratio) of the truth determine the limit of predictability. At short leads, ENSO predictability depends on the prediction system and the "truth." ENSO prediction skill is model and phase dependent. Predictability of extreme warm events remains a challenge as the number of ensemble members required to capture these events is on the order of 100 members. Finally, we examine real ENSO predictions with and without the WWB parameterization. It is found that including WWBs in the prediction system significantly increases ENSO prediction skill compared with a prediction system that lacks WWBs. Also, it is found that the so-called forecast spring prediction barrier is, at least partially, caused by the lack of WWB representation in the forecast system.

ENSO Bimodality and Extremes

“…The interaction between SST and WWBs has previously been shown to be an important contributor to adequate simulation and prediction of ENSO events (Gebbie et al, ). Previous studies show that models that represent state‐dependent noise can represent the extreme El Niño statistic better than the models that do not have a state‐dependent high‐frequency variability in winds (Lopez & Kirtman, , ; Takahashi & Dewitte, ). Our results show that the statistics of the zonal wind changes in response to the aforementioned ENSO feedbacks.…”

Westerly wind bursts simulated in CAM4 and CCSM4

“…Menkes et al (2014) showed that the lack of WWBs in boreal spring and summer is responsible for the weak El Niño in 2014. Lopez and Kirtman (2014) proposed that the spring prediction barrier of ENSO is partially caused by the lack of the WWBs in the forecast models. Improving the air-sea coupling may improve WWBs simulation in CCSM4, and may further improve the ENSO predictability in models.…”

The initial errors that induce a significant “spring predictability barrier” for El Niño events and their implications for target observation: results from an earth system model

“…westerly wind burst (WWB)] on the diversity and predictability of El Niño events (Hu et al 2014;Chen et al 2015). Particularly, Lopez and Kirtman (2014) indicated the SPB is due to the presence of WWB because significant WWB activities can contribute to a rapid drop off in signal-to-noise ratio of coupled system in spring. Actually, comparison between these studies associated with atmospheric effects and the present study proposes a new question to the SPB: is the SPB more sensitive to ocean state or variations in atmosphere?…”