Deterministic skill of ENSO predictions from the North American Multimodel Ensemble

Abstract

the presence of calibration errors in some of the models. In particular, the amplitudes of some model predictions are too high when predictability is limited by the northern spring ENSO predictability barrier and/or when the interannual variability of the SST is near its seasonal minimum. The skill of the NMME system is compared to that of the MME from the IRI/CPC ENSO prediction plume, both for a comparable hindcast period and also for a set of real-time predictions spanning 2002-2011. Comparisons are made both between the MME predictions of each model group, and between the average of the skills of the respective individual models in each group. Acknowledging a hindcast versus real-time inconcsistency in the 2002-2012 skill comparison, the skill of the NMME is slightly higher than that of the prediction plume models in all cases. This result reflects well on the NMME system, with its large total ensemble size and opportunity for possible complementary contributions to skill.Abstract Hindcasts and real-time predictions of the eastcentral tropical Pacific sea surface temperature (SST) from the North American Multimodel Ensemble (NMME) system are verified for 1982-2015. Skill is examined using two deterministic verification measures: mean squared error skill score (MSESS) and anomaly correlation. Verification of eight individual models shows somewhat differing skills among them, with some models consistently producing more successful predictions than others. The skill levels of MME predictions are approximately the same as the two best performing individual models, and sometimes exceed both of them. A decomposition of the MSESS indicates This paper is a contribution to the special collection on the North American Multi-Model Ensemble (NMME) seasonal prediction experiment. The special collection focuses on documenting the use of the NMME system database for research ranging from predictability studies, to multi-model prediction evaluation and diagnostics, to emerging applications of climate predictability for subseasonal to seasonal predictions

Strong Relations Between ENSO and the Arctic Oscillation in the North American Multimodel Ensemble

“…In contrast to the AO, the correlations associated with ENSO are statistically significant for all seasons and leads (Figure , right column, top). Skills are in excess of 0.9 up to the lead‐4 forecasts (for targets outside of the summer/early fall) and minimize at ∼0.6 for the longest lead time (lead‐9) for the SON target, reflecting the weakness in skill for forecasts traversing the well documented “spring barrier” (Barnston et al, ; Tippett et al, ). In addition to Niño‐3.4 predictions having higher skill than the AO predictions, the expected Niño‐3.4 correlations (Figure , right column, middle) strongly resemble the actual correlations between the observations and ensemble mean (Figure , right column, top).…”

Assessing probabilistic predictions of ENSO phase and intensity from the North American Multimodel Ensemble

“…The RPSS is not statistically significantly greater than zero for these same late-spring through summer target months for forecast leads greater than 4 months, and is negative for many models, indicating average RPS values greater than that of the reference forecast. These negative RPSS values may reflect amplitude biases where forecast signals are disproportionately large relative to their skill level (Barnston et al 2017). CFSv2 forecasts show statistically significant RPSS values at long leads for May and June targets despite not having statistically significant skill at some shorter leads.…”

A Priori Identification of Skillful Extratropical Subseasonal Forecasts

“…Ours is not the only study to identify forecasts of opportunity using deterministic metrics of hindcast skill, despite the inherently probabilistic nature of the subseasonal forecast problem. For example, many studies have related state dependence of hindcast skill to specific climate phenomena such as El Niño–Southern Oscillation, the Madden‐Julian oscillation, and sudden stratospheric warmings (e.g., Barnston et al, ; DelSole et al, ; Kim et al, ; Tripathi et al, ; Vitart & Molteni, ). The LIM has also been used in this manner (Winkler et al, ; N2003), but here we exploited its ability to estimate ρ ∞ and thereby quantify the predictable signal from the combination of all such sources of predictability, which is different at every forecast time and forecast lead.…”