Every few years, the water in the Pacific Ocean off the coast of South America becomes warmer than usual. This phenomenon is called El Niño. It affects weather around the world, and especially rainfall in India. When El Niño arrives, monsoon rains often weaken, leading to droughts.
Scientists wanted to know whether this connection changes over time. They ran the same climate model 63 times with slight differences in initial conditions, producing 63 possible futures. In every one, the link between El Niño and Indian rainfall strengthened. But it did so unevenly: faster in some years, slower in others. Like a rope braided in fits and starts, it becomes stronger, but not smoothly.
This finding matters for forecasts. If the connection shifts unevenly, historical data can mislead. Models need constant updates to better predict droughts and floods in India, since these rains determine the harvest and the lives of hundreds of millions of people.
The non-ergodicity test showed that even if the correlation changes linearly over time, biases can arise if mean values or standard deviations change. In this case, these trivial contributions turned out to be insignificant, confirming the true nonlinearity of the forced response.