For generations, climate scientists have educated the public that ‘weather is not climate,’ and climate change has been framed as the change in the distribution of weather that slowly emerges from large variability over decades. In short, the variability of local weather masks long-term trends in the global climate.
Their study suggests that the climate signal – that is, the long-term warming trend– can be observed in daily weather data, for example, surface air temperature and humidity, gave that global spatial example are considered.
Sebastian Sippel, a postdoc working in Knutti’s research group, said, “Uncovering the climate change signal in daily weather conditions calls for a global perspective, not a regional one.”
To detect the climate signal in daily weather records, scientists used statistical learning techniques to combine simulations with climate models and data from measuring stations. Analytical learning techniques can extricate a “fingerprint” of climate change from a combination of temperatures of different regions and the ration of expected warming and variability. By systematically assessing the model simulations, they can distinguish the climate fingerprint in the global measurement data on some random day since spring 2012.
A comparison of the variability of local and global daily mean temperatures shows why the global perspective is essential. Whereas locally measured daily mean temperatures can fluctuate widely (even after the seasonal cycle is removed), global daily mean values show a very narrow range.
If the distribution of global daily mean values from 1951 to 1980 are then compared with those from 2009 to 2018, the two distributions (bell curves) barely overlap. The climate signal is thus prominent in the global values but obscured in the local values since the delivery of daily mean values overlaps quite considerably in the two periods.
Knutti said, “Weather at the global level carries important information about climate. This information could, for example, be used for further studies that quantify changes in the probability of extreme weather events, such as regional cold spells. These studies are based on model calculations, and our approach could then provide a global context of the climate change fingerprint in observations made during regional cold spells of this kind. This gives rise to new opportunities for the communication of regional weather events against the backdrop of global warming.”
“The current study underlines how useful data science methods are in clarifying environmental questions, and the SDSC is of great use in this.”
Data science strategies not just enable analysts to exhibit the strength of the human “fingerprint,” they likewise show where in the world climate change is exceptionally bright and conspicuous at an early stage. This is significant in the hydrological cycle, where there are huge normal variances from every day and year to year.
The ETH professor said, “In future, we should, therefore, be able to pick out human-induced patterns and trends in other more complex measurement parameters, such as precipitation, that are hard to detect using traditional statistics.”
The study is recently published in Nature Climate Change.