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A collaboration between ¹û¶³Ó°Ôº Statistical Science (PhD student Maryam Ilyas and her supervisorÌýProfessor Serge Guillas) and ¹û¶³Ó°Ôº Geography (co-supervisorÌý) 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.ÌýA 10,000Ìýmember 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:Ìý.

TheÌýdata setÌýwill 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-industrialÌýera.

Another application is the ProbabilisticÌýEl 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.Ìý

[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: .