Statistical Science


New data set quantifying uncertainties in past global temperatures created by Maryam Ilyas,

22 November 2017


Figure 1


Comparison of current temperatures to the pre-industrial era

A collaboration between UCL Statistical Science (PhD student Maryam Ilyas and her supervisor Professor Serge Guillas) and UCL Geography (co-supervisor Dr Chris Brierley) on temperatures over the globe has resulted in a paper recently published in Geophysical Research Letters (GRL)[1].

The study quantifies the uncertainties in monthly temperatures from 1850 to 2016 due to the gaps in coverage. Indeed, stations are sparsely located over the surface of the Earth. These uncertainties are added to the uncertainties due to observational issues at each station location, which had already been quantified in the past. A 10,000 member ensemble of monthly temperatures over the entire globe has been released that samples the combination of these two sources of uncertainties. It is freely available at: https://oasishub.co/dataset/global-monthly-temperature-ensemble-1850-to-2016.

The data set will enable studies of climatic variations that take into account the uncertainties in observations over this crucial period. See for instance Fig. 1 where we compare the current temperatures to the pre-industrial era.

Another application is the Probabilistic El Niño-Southern Oscillation (ENSO) index that has been invented in the GRL paper above: it assigns a probability to the ENSO status (e.g El Niño year or La Niña year) that reflects the uncertainties in temperatures used to compute the ENSO index, see Fig. 2. 

[1] Ilyas, M., Brierley, C. M., & Guillas, S. (2017). Uncertainty in regional temperatures inferred from sparse global observations: Application to a probabilistic classification of El Niño. Geophysical Research Letters, 44(17), 9068-9074. Link: http://onlinelibrary.wiley.com/doi/10.1002/2017GL074596/full.

Figure 2


Uncertainties in temperatures used to compute the ENSO index