Where Seasonal Weather Forecasts Actually Work in China ✎

Original: "Skill and reliability of seasonal forecasts for the Chinese energy sector"
arXiv:1703.06662 · 2017-03-20 · arXiv non-exclusive · independent analysis · 2 min
The GloSea5 climate model skillfully predicts winter winds off China's coast and summer heat in Beijing, but elsewhere its forecasts are barely better than a coin toss.

A map of China looks like a desert dotted with rare green oases. In those oases, the GloSea5 climate model can forecast weather three months ahead, while in the desert its predictions are no better than random guessing. Scientists spent twenty years testing the model against past data to find where it can be trusted.

The largest oasis is winter winds over the South China Sea. The model predicts them with an accuracy of 0.78 out of 1.00, because these winds depend on El Niño, the warm current in the Pacific that alters weather worldwide. When El Niño is active, winds weaken, and the model captures that.

The second oasis is summer heat in Beijing. Even after removing the overall climate warming trend, the model still predicts temperature better than a simple "business as usual" approach. That means it picks up a real signal, not just repeats a trend.

But beyond these spots lies desert. Summer winds and winter temperatures are hardly predictable anywhere. In the very South China Sea where the model excels in winter, its accuracy in summer drops to nearly zero. It is like a compass that works in clear weather but is useless in fog.

For China's energy sector, this matters. The country is building many solar and wind farms, and it needs to know in advance whether winters will be windy and summers hot. For now, only the oases can be trusted, but that is already enough to launch pilot services for the South China Sea and Beijing. If the model improves, the green patches will grow.

The correlation of 0.78 for winter winds in the South China Sea is comparable to the skill of El Niño forecasts, which is considered the gold standard of seasonal prediction. Yet in the same region in summer, the correlation drops to 0.13, meaning the forecast loses all skill.

BSS = (RES - REL) / UNC
BSS is positive if the forecast's resolution exceeds its reliability, normalized by the uncertainty of climatology
ROCSS = 2 * AROC - 1
The area under the ROC curve AROC is transformed so that 0 means no skill and 1 means perfect discrimination
Concepts
forecast skill NOAA CPC Niño 4 IRI forecasting seasonal cycle spring predictability barrier equatorial wave propagation forecast Westerly wind burst solar flare prediction Oceanic Niño index La Niña monsoon Jin-Timmermann model seasonal forcing sea surface temperature ERA5 ERSST Earth's energy imbalance