An international team led by the University of Leicester has for the first time harnessed technology typically used to diagnose heart disease to measure the Earth's pulse — and has uncovered hidden patterns of climate change often overlooked by other types of measurement.
The statistical method is called 'multi-scale entropy analysis' and has not been used to study climate data before. It works by pattern matching and searches data for repetitive small chunks — or pattern templates — that appear over and over again.
If many of these chunks are found, then the data has low entropy and high regularity. If few are found, the entropy is high and the system is harder to predict, a university release said.
Heiko Balzter from the University of Leicester and lead author of the study explained: "The method has been applied a lot to diagnose heart disease, because it is good at detecting regularity and randomness in time-series data. We are literally taking the pulse of the planet".
"Imagine you roll a dice and write down the series of numbers it lands on. You expect these to be random. Say you roll a 1, 6, and 5. If the system has low entropy then we would expect other chunks of rolling 1 followed by 6 to have a higher probability of being followed by a 5. We have applied just that same method to European temperature data."
{{/usCountry}}"Imagine you roll a dice and write down the series of numbers it lands on. You expect these to be random. Say you roll a 1, 6, and 5. If the system has low entropy then we would expect other chunks of rolling 1 followed by 6 to have a higher probability of being followed by a 5. We have applied just that same method to European temperature data."
{{/usCountry}}The study shows that some scientific data analysis methods overlook subtle changes in the 'regularity' of the temperature data.
The unique way of analysing the data involves an analysis at several temporal scales. The data is aggregated to scales of months and analysed at each scale separately to find the time-scales at which the regularity of the data changes.
The results show that the temporal scales of the current temperatures (1961-2014) are different from the long-term average (1850-1960). At temporal scales longer than 12 months the researchers found a marked loss of regularity in the data for the past 54 years.
The study reveals that from 1961 to 2014, at time-scales from 12 to 70 months the air temperatures in Europe show less regularity compared to 1851-1960.