Publié le 5 février 2020
Last October, weather researchers in Utah measured the lowest temperature ever recorded in the month of October in the US (excluding Alaska): -37.1°C. The previous low-temperature record for October was -35°C, and people wondered what had happened to climate change.
Until now, climate researchers have responded that climate is not the same thing as weather. Climate is what we expect in the long term, whereas weather is what we get in the short term—and since local weather conditions are highly variable, it can be very cold in one location for a short time despite long-term global warming. In short, the variability of local weather masks long-term trends in global climate.
A paradigm shift
Now, however, a group led by ETH professor Reto Knutti has conducted a new analysis of temperature measurements and models. The scientists concluded that the weather-is-not-climate paradigm is no longer applicable in that form.
According to the researchers, the climate signal—that is, the long-term warming trend—can actually be discerned in daily weather data, such as surface air temperature and humidity, provided that global spatial patterns are taken into account.
In plain English, this means that—despite global warming—there may well be a record low temperature in October in the US. If it is simultaneously warmer than average in other regions, however, this deviation is almost completely eliminated. "Uncovering the climate change signal in daily weather conditions calls for a global perspective, not a regional one," says Sebastian Sippel, a postdoc working in Knutti's research group and lead author of a study recently published in Nature Climate Change.
North American surface temperatures for Dec. 26, 2017-Jan. 2, 2018: even if it is extremely cold in a region, this does not mean that climate change has stopped.
Statistical learning techniques extract climate change signature
In order to detect the climate signal in daily weather records, Sippel and his colleagues used statistical learning techniques to combine simulations with climate models and data from measuring stations.
Statistical learning techniques can extract a "fingerprint" of climate change from the combination of temperatures of various regions and the ratio of expected warming and variability. By systematically evaluating the model simulations, they can identify the climate fingerprint in the global measurement data on any single day since spring 2012.