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Climate Test Bed

NMME prediction of the 2015-2016 El Niño

Michelle L'Heureux of Climate Prediction Center reviewed NMME prediction of the 2015-2016 El Niño in the November NMME teleconference. Overall, the official CPC/IRI forecasts captured both the strength and the timing of the event. The development of El Niño was declared in March 2015, followed by the official forecast of a potentially strong event broadcast in June 2015 and further alert of the seasonal Niño-3.4 SST exceeding 2.0°C, a top 3 event in history, issued in early August 2015. A final El Niño Advisory with the return of ENSO-neutral condition was sent out in early June 2016. Apparent CFSR cold biases in the tropical Atlantic Ocean impacted CSFv2 forecasts and consequent skill scores during the 2015-16 event. The "target period slippage" problem, i.e. the model forecasts were slow to transition into and out of ENSO event, a long-standing problem, was prominent in forecasts by both NMME/dynamical models and statistical models from the IRI/CPC plumes (Fig. 1). It appeared to affect longest lead forecasts the most and be more severe during lower skill/lower variability ENSO periods. Despite the difficulties, North American Multi-Model ensemble (NMME) and other multi-model plumes were valuable, providing a backstop for ENSO forecasts.

Fig. 1 Lead-lag correlation of 8-month lead forecast and observation. Ideally, the correlation curves are symmetric in shape with the largest correlation at zero lag. Most models tend to “slip”, having elevated correlations when the observations lead (right skewed).

Evaluating predictability of monthly means using the NMME

Kathy Pegion of GMU gave a talk entitled “Which predictability estimates are most realistic?” in the September NMME teleconference. Using metrics of spread/error and autocorrelation, a fidelity assessment of predictability estimates revealed a common problem of overestimation of signals of Niño 3.4 SST and US temperature and precipitation, and showed the skill limit of Niño 3.4 prediction at 3-months lead had almost been reached.

Update about NAO prediction by all eight NMME models; testing 'unequal weights' on the NAO prediction

Huug van den Dool of NCEP/CPC, taking NAO prediction as an example, made an update on the unequal weights project of NMME, in which Community Earth System Model (CESM) had been added recently. It showed using distributional information could help weighting relative to using ensemble means, but it was tough to calculate trustworthy weights, or to beat equal weights or skill-based weights. Some hopes were seen in special solutions and sub-sampling by deleting certain models ‘upfront’ with negative weights.