A Statistical Analysis of Daily Snow Depth Trends in North America

Abstract

Several attempts to assess regional snow depth trends have been previously made. These studies estimate trends by applying various statistical methods to snow depths, new snowfalls, or their climatological proxies such as snow water equivalents. In most of these studies, inhomogeneities (changepoints) were not accounted for in the analysis. Changepoint features can dramatically influence trend inferences from climate time series. The purpose of this paper is to present a detailed statistical methodology to estimate trends of a time series of daily snow depths that account for changepoint features. The methods are illustrated in the analysis of a daily snow depth data set from North America.

Application of Homogenization Methods for Climate Records

“…Another difficult task, the homogenization of snow depth time series, is the topic of the third paper [3]. Snow depth is one of the essential climate indicators, because we need accurate snow depth data to find its trends with climate change, but the density of observing stations does not allow performing relative homogenization.…”