Articles | Volume 3, issue 1
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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Cited articles

Andsager, K., Ross, T., Kruk, M.C., and Spinar, M. L.: Climate database modernization program: pre-20th century task – key climate observations recorded since the founding of America, 1700s–1800s, in: Combined preprints: 84th AMS annual meeting : 20th Conference on Weather Analysis and Forecasting/16th Conference on Numerical Weather Prediction, Seattle Washington, Boston, MA, American Meteorological Society, 2004.
Barboza, L., Li, B., Tingley, M., and Viens, F.: Reconstructing past temperatures from natural proxies and estimated climate forcings using short-and long-memory models, Ann. Appl. Stat., 8, 1966–2001, 2014.
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Bernardo, J. M. and Smith, A.: Bayesian Theory, vol. 405, John Wiley & Sons, 2009.
Brázdil, R., Kundzewicz, Z., and Benito, G.: Historical hydrology for studying flood risk in Europe, Hydrolog. Sci. J., 51, 739–764, 2006.
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.