A fisherman in Queensland notices that in some years snapper are harder to catch, even though the nets and boat are the same. The reason hides not in the catch itself but in the water temperature several months earlier, when the fry were just hatching. A warm current arrived not for the whole year but only for a few weeks, and that short episode decided the fate of an entire fish generation. Scientists have long struggled to pull such an episode out of a noisy stream of measurements without losing its strength or smearing it across the whole series.

The authors propose a universal sieve. Imagine a kitchen sieve used to sift flour: fine particles fall through, while larger lumps remain. The IMPIT index works similarly, but instead of flour, you pass a time series through it, and instead of mesh size, you tune two parameters: memory length and sensitivity to intensity. A short memory catches sharp spikes, while a long memory gathers smooth waves. Sensitivity decides which episodes count as large lumps and which are let through as dust.

Tests on real data show that the sieve works. For snapper in Queensland waters, an index based on sea surface temperature explained catch fluctuations better than raw temperature values. For scallops, a warm-water episode in a specific season proved significant, and for wheat yield in New South Wales, an index built on the Southern Oscillation Index (a number describing the El Niño phase) captured the influence of El Niño and La Niña episodes on the final harvest. In all three cases, the model with the IMPIT index had higher explanatory power than the model with the original series.

The Queensland fisherman can now know in advance what the catch will be, without waiting for the season to end. Just run the water temperature from the past months through the IMPIT sieve, and the output number will tell whether the warm current was worth changing course for. Nature keeps making noise, but now we have a tool that hears individual voices within it.