The Network That Hears El Niño a Year in Advance ✎

Original: "Very early warning signal for El Niño in 2020 with a 4 in 5 likelihood"
arXiv:1910.14642 · 2019-10-31 · arXiv non-exclusive · independent analysis · 1 min
A climate network predicted the return of El Niño in 2020 with an 80% probability.

The Pacific Ocean is like a vast orchestra, where each point on the map is a musician. Usually they play out of tune: somewhere it warms, somewhere it cools, and the overall picture resembles noise. But every few years, the instruments suddenly begin to sound in unison, and then droughts, floods, and heatwaves strike across the globe. This phenomenon is called El Niño, and it always catches people off guard.

For a long time, scientists could not predict El Niño in advance because their models went blind every spring. Then, at the Potsdam Institute for Climate Impact Research, they came up with the idea of monitoring not the ocean itself, but the air above it. They placed 207 points across the Pacific and began measuring how synchronously the temperature changes at these points. When the points start behaving alike, it means the orchestra is tuning up, and a storm is about to break.

The method worked: since 2012, it has correctly predicted all quiet years and all major El Niños. In September 2019, the system again heard the tuning and announced that El Niño would return in 2020.

Such an early forecast gives people time to prepare. Farmers can plant different crops, builders can reinforce dams, and rescue teams can pre-position equipment in vulnerable areas. For now, the method cannot predict how strong El Niño will be, but scientists are already working on that. If they succeed, we will be able to hear the storm coming a year ahead and stop fearing its sudden blow.

An average El Niño raises the global temperature anomaly by about 0.1°C. If a strong event develops in late 2020, 2021 could become a new record year for temperature, since the atmospheric response lags by three months.

The idea of predicting global catastrophes from barely perceptible signs echoes Kim Stanley Robinson's novel "Forty Signs of Rain," where scientists try to convince politicians of an impending climate shift based on early indicators.

C_{ij}^{(t)}(-\tau) = \frac{\langle T_i(t) T_j(t-\tau)\rangle - \langle T_i(t)\rangle \langle T_j(t-\tau)\rangle}{\sqrt{\langle (T_i(t) - \langle T_i(t)\rangle)^2\rangle} \sqrt{\langle (T_j(t-\tau) - \langle T_j(t-\tau)\rangle)^2\rangle}}
Measure of statistical association between temperature series, accounting for time shift
S_{ij}(t) = \frac{\max_{\tau} |C_{ij}^{(t)}(\tau)| - \text{mean}_{\tau} |C_{ij}^{(t)}(\tau)|}{\text{std}_{\tau} |C_{ij}^{(t)}(\tau)|}
Signal-to-noise ratio for the correlation function, reflecting the prominence of the teleconnection
S(t) = \frac{1}{N} \sum_{i,j} S_{ij}(t)
Integral indicator of cooperativity of atmospheric temperatures in the Pacific
Concepts
ENSO El Niño-Southern Oscillation spring predictability barrier El Niño Multivariate ENSO Index recharge oscillator model La Niña ERSST Southern Oscillation Jin-Timmermann model Westerly wind burst equatorial wave propagation TAO/TRITON moorings seasonal cycle NOAA CPC natural variability monsoon onset NOAA PMEL / TAO forecast interannual variability