Statistics of Regional Surface Temperatures after 1900: Long-Range versus Short-Range Dependence and Significance of Warming Trends

Abstract

Abstract This paper studies regional climate variability for the time period 1900–2013 using parsimonious stochastic models. Instrumental data records on 5° × 5°, 2° × 2°, and equal-area grids are examined. A long-range dependent (LRD) stochastic process is used as a simplified description of the multitude of response times in the climate system. Fitting a linear trend to the global mean surface temperature (GMST) implies a warming of 0.08 decade−1, which is highly significant under an LRD null hypothesis (p < 10−4). The regional trends are distributed around the global mean trend, while the fluctuation levels increases when going from global to regional scale. The temperature fluctuations of the tropical oceans are observed to be strongly influenced by El Niño–Southern Oscillation (ENSO) and, therefore, more consistent with autoregressive processes of order 1 [AR(1)]. A likelihood-ratio test is used to systematically determine the best null model [AR(1) or LRD]. About 80% of the regional warming trends are found to be significant (with a 5% significance level).