A comparison of the variability of local and global daily mean temperatures shows why the global perspective is important. 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 distribution of daily mean values overlaps quite considerably in the two periods.
Application to the hydrological cycle
The findings could have broad implications for climate science. “Weather at the global level carries important information about climate,” says Knutti. “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 study stems from a collaboration between ETH researchers and the Swiss Data Science Center (SDSC), which ETH Zurich operates jointly with its sister university EPFL. “The current study underlines how useful data science methods are in clarifying environmental questions, and the SDSC is of great use in this,” says Knutti.
Data science methods not only allow researchers to demonstrate the strength of the human “fingerprint”, they also show where in the world climate change is particularly clear and recognisable at an early stage. This is very important in the hydrological cycle, where there are very large natural fluctuations from day to day and year to year. “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,” says the ETH professor.