Articles | Volume 3, issue 1
https://doi.org/10.5194/ascmo-3-1-2017
https://doi.org/10.5194/ascmo-3-1-2017
27 Jan 2017
 | 27 Jan 2017

Reconstruction of spatio-temporal temperature from sparse historical records using robust probabilistic principal component regression

John Tipton, Mevin Hooten, and Simon Goring

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Latest update: 19 Jun 2024
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Short summary
We present a statistical framework for the reconstruction of historic temperature patterns from sparse, irregular data collected from observer stations. A common statistical technique for climate reconstruction uses modern era data as a set of temperature patterns that can be used to estimate the spatial temperature patterns. We present a framework for exploration of different assumptions about the sets of patterns used in the reconstruction while providing statistically rigorous estimates.