Using Historical Climate Data to Evaluate Climate Trends: Issues of Statistical Inference

Abstract

A strong case for global warming has been made based on reconstructed global climate histories. However, certain unique features of paleoclimate data make statistical inference problematic. Historical climate data have dating error of such a magnitude that combined series will really represent very long-term averages, which will flatten peaks in the reconstructed series. Similarly, dating error will prevent peaks (e.g., of the Medieval Warm Period) from multiple series from lining up precisely. Meta-analysis is proposed as a tool for dealing with dating uncertainty. While it is generally assumed that a proper null model for twentieth-century climate is no trend, it is shown that the proper prior expectation based on past climate is that climate trends over a century period are likely. Climate data must be detrended before analysis to take this prior expectation into account.