UCL Department of Geography



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Paper of the month

Uncertainty in regional temperatures inferred from sparse global observations: Application to a probabilistic classification of El Niño

By Maryam Ilyas, Chris Brierley and Serge Guillas (2017)

This work uses a novel statistical approach to fill in the missing data in the instrumental global surface temperature record. Whilst that in itself has been done before, the new approach provides proper quantification of the uncertainty in the local temperatures. To demonstrate the impact of the in-filling uncertainties, we looked that Niño 3.4 region, used to classify El Niño (see picture above). In the recent record, we are confident whether it was an El Niño (red), La Nina (blue) or a Neutral state (green and cyan).

However, just after World War II and before 1920 there are many years where the temperature record is ambiguous (pink) and we can't tell whether it is was an El Niño or an La Niña. The global ensemble of surface temperatures is freely downable from the Oasis HUB.

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