A Bayes Factor Model for Detecting Artificial Discontinuities via Pairwise Comparisons

Abstract

n this paper, the authors present a Bayes factor model for detecting undocumented artificial discontinuities in a network of temperature series. First, they generate multiple difference series for each station with the pairwise comparison approach. Next, they treat the detection problem as a Bayesian model selection problem and use Bayes factors to calculate the posterior probabilities of the discontinuities and estimate their locations in time and space. The model can be applied to large climate networks and realistic temperature series with missing data. The effectiveness of the model is illustrated with two realistic large-scale simulations and four sensitivity analyses. Results from applying the algorithm to observed monthly temperature data from the conterminous United States are also briefly discussed in the context of what is currently known about the nature of biases in the U.S. surface temperature record.