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| "Multi-scale sample entropy" of the CRUTEM4v temperature anomaly data over Central Europe. Time-scales at which the data are aggregated are shown as the scale factor (in months). Black=1850 to 1960. Red=1961 to 2014. Bold lines show the mean of all grid boxes and thin lines show the upper and lower bounds of the 95 percent confidence interval. The data show that the aggregated temperature data with scale factors greater than about 12 months have a markedly higher sample entropy in the recent past (red) than before 1960 (black) [Credit: Balzter et al./Climate (2015)] |
The results show hidden patterns of climate change which are often overlooked by other types of analysis.
Data from UEA’s Climatic Research Unit was used in the project, which was led by the University of Leicester.
Prof Phil Jones, from CRU and UEA’s School of Environmental Sciences, said: “This method harnesses technology typically used to diagnose heart disease. It literally takes the pulse of the planet to detect the regularity of climate change data.
“The results reveal subtle changes in Europe’s climate, and we can now see the time scales in which climate has changed he most,” he added.
Multi-scale entropy analysis works by pattern-matching and searches data for repetitive small chunks - or pattern templates - that appear over and over again.
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| A map of Central Europe showing where the CRUTEM4v temperature data show a change in entropy. Non-significant values are set to zero. (Map 1) Colors show the largest change in entropy over all scale factors. Negative values show a decrease from 1850-1960 to 1961-2014, positive ones an increase [Credit: Balzter et al./Climate (2015)] |
Lead author Prof Heiko Balzter from the University of Leicester, said: “I had the idea to apply a new method to the climate data. It has been applied a lot to diagnose heart disease, because it is good at detecting regularity and randomness in time-series data.”
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 are 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 researchers analysed the Central European variance-adjusted mean monthly air temperature anomalies (CRUTEM4v) produced at UEA.
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.
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| A map of Central Europe showing where the CRUTEM4v temperature data show a change in entropy. Non-significant values are set to zero. (Map 2) Temporal scale factor with the largest difference in entropy (in units of months) [Credit: Balzter et al./Climate (2015)] |
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.
This may be a sign that the regional temperatures are now influenced by more complex forces at work – and the researchers suggest that it could be possible that climate system feedbacks express themselves in altered temporal scales of European temperatures.
The study also provides a map visualisation of the entropy results for the first time, showing the time scales which have changed most.
The study, entitled ‘Multi-scale entropy analysis as a method for time-series analysis of climate data’, is published in the Multidisciplinary Digital Publishing Institute journal Climate.
Source: University of East Anglia [March 16, 2015]
