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To restore the historical sea surface temperatures (SSTs) better, it isimportant to construct a good calibration model for the associated proxies. Inthis paper, we introduce a new model for alkenone (${\rm{U}}_{37}^{\rm{K}'}$)based on the heteroscedastic Gaussian process (GP) regression method. Ournonparametric approach not only deals with the variable pattern of noises overSSTs but also contains a Bayesian method of classifying potential outliers.

Heteroscedastic Gaussian Process Regression on the Alkenone over Sea Surface Temperatures