Weather vs. Climate: How to Tell Noise from Signal express
In winter, one part of town gets three times as much snow as the neighborhood next door. One year is a scorcher, the next is a cold snap. That's the whims of weather. But over decades, a different, slow shift emerges: winters, on average, get milder. Telling the two apart is the central challenge for climate scientists.
In a new study, researchers propose a way to count how much of the temperature or precipitation data is random fluctuation and how much is a persistent change. They use computer models run many times with slightly different starting conditions, producing a whole fan of possible climates. Then they compare the spread among these runs with the shift caused by an external forcing, such as rising carbon dioxide.
It turns out the new approach works more reliably than previous ones, even when the data are heavily "scattered" or contain rare extreme values. The key is that the ensemble must include at least forty simulation runs. As the forcing strengthens, the share of randomness drops, and this becomes especially clear.
Such a tool helps separate the wheat from the chaff: understanding what in climate projections is truly driven by human activity and what is just natural variability, like an unexpected May snowstorm.
In one modeling scenario, the warming was so strong that random temperature fluctuations almost stopped influencing the overall picture, the climate change signal became obvious.