With ENSO, chaos rules, models drool

Per this 2011 paper;
“Real-time model predictions of ENSO conditions during the 2002–11 period are evaluated and compared to skill levels documented in studies of the 1990s. ENSO conditions are represented by the Niño- 3.4 SST index in the east-central tropical Pacific. The skills of 20 prediction models (12 dynamical, 8 statistical) are examined. Results indicate skills somewhat lower than those found for the less advanced models of the 1980s and 1990s. Using hindcasts spanning 1981–2011, this finding is explained by the relatively greater predictive challenge posed by the 2002–11 period and suggests that decadal variations in the character of ENSO variability are a greater skill-determining factor than the steady but gradual trend toward improved ENSO prediction science and models. After adjusting for the varying difficulty level, the skills of 2002–11 are slightly higher than those of earlier decades. Unlike earlier results, the average skill of dynamical models slightly, but statistically significantly, exceeds that of statistical models for start times just before the middle of the year when prediction has proven most difficult. The greater skill of dynamical models is largely attributable to the subset of dynamical models with the most advanced, highresolution, fully coupled ocean–atmosphere prediction systems using sophisticated data assimilation systems and large ensembles. This finding suggests that additional advances in skill remain likely, with the expected implementation of better physics, numeric and assimilation schemes, finer resolution, and larger ensemble sizes.”
http://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00111.1
ENSO forecasts are apparently getting worse. I built the WUWT ENSO Forecast Page a month ago;
http://wattsupwiththat.com/reference-pages/climatic-phenomena-pages/enso/enso-forecast-page/
but am hesitant to introduce given how bad we are at forecasting ENSO. The problem is that “The period from February through May is commonly referred to as the spring barrier. During this time, models generally have the least skill to predict the coming season.”
http://iri.columbia.edu/news/la-nina-still-hanging-on/
“Using predictions for the sea surface temperature (SST) generated by a Flexible Global Ocean-Atmosphere-Land System model of IAP/LASG (FGOALS-g), the season-dependent predictability of SST anomalies for El Nino/La Nina events is investigated by analyzing the forecast error growth in an imperfect model scenario. The results indicate that, for the predictions through the spring season in the growth phase of El Nino events, the prediction errors induced by both initial errors and model errors tend to have a prominent season-dependent evolution and yield a prominent spring predictability barrier (SPB). For the decay-phase predictions of El Nino events, a less prominent season-dependent evolution of prediction errors and then a less prominent SPB are observed. For the growth- and decay-phase predictions of La Nina events, the prediction errors do not exhibit a significant season-dependent evolution and yield a less prominent SPB phenomenon. These results indicate that the SPB phenomenon depends remarkably on the ENSO events themselves, particularly the phases of the El Nino/La Nina events. We also report that the initial SST errors that correspond to a significant SPB for El Nino events tend to have the dominant modes in a large-scale dipolar pattern with negative anomalies in the equatorial central-western Pacific and positive anomalies in the eastern Pacific, or vice versa. We further demonstrate that the error growth related to a significant SPB for El Nino prediction generated by the FGOALS-g model can result from two dynamical mechanisms: in one case, the prediction errors grow in a manner similar to El Niño; in the other, the prediction errors develop with a tendency opposite to El Niño.”
http://onlinelibrary.wiley.com/doi/10.1002/joc.3513/abstract
“Using the sea surface temperature (SST) predicted for the equatorial Pacific Ocean by the Flexible Global Ocean-Atmosphere-Land System Model-gamil (FGOALS-g), an analysis of the prediction errors was performed for the seasonally dependent predictability of SST anomalies both for neutral years and for the growth/decay phase of El Niño/La Niña events. The study results indicated that for the SST predictions relating to the growth phase and the decay phase of El Niño events, the prediction errors have a seasonally dependent evolution. The largest increase in errors occurred in the spring season, which indicates that a prominent spring predictability barrier (SPB) occurs during an El Niño-Southern Oscillation (ENSO) warming episode. Furthermore, the SPB associated with the growth-phase prediction is higher than that associated with the decay-phase prediction. However, for the neutral years and for the growth and decay phases of La Niña events, the SPB phenomenon was less prominent. These results indicate that the SPB phenomenon depends extensively on the ENSO events themselves. In particular, the SPB depends on the phases of the ENSO events. These results may provide useful knowledge for improving ENSO forecasting. ”
http://www.iapjournals.ac.cn/aosl/EN/abstract/abstract116.shtml