A spatiotemporal analysis of U.S. station temperature trends over the last century

V Capparelli,M P Freeman,N W Watkins,V Carbone,A Vecchio,C Franzke

doi:10.1002/jgrd.50551

Abstract

This study presents a nonlinear spatiotemporal analysis of 1167 station temperature records from the United States Historical Climatology Network covering the period from 1898 through 2008. We use the empirical mode decomposition method to extract the generally nonlinear trends of each station. The statistical significance of each trend is assessed against three null models of the background climate variability, represented by stochastic processes of increasing temporal correlation length. We find strong evidence that more than 50% of all stations experienced a significant trend over the last century with respect to all three null models. A spatiotemporal analysis reveals a significant cooling trend in the South‐East and significant warming trends in the rest of the contiguous U.S. It also shows that the warming trend appears to have migrated equatorward. This shows the complex spatiotemporal evolution of climate change at local scales.

Highlights

[2] Changes in climate have significant implications for societies, future generations, the economy, ecosystems, and agriculture
empirical mode decomposition (EMD) decomposes a time series into a finite number of intrinsic mode functions (IMFs) and a residual by using an adaptive basis derived from the time series through a so-called “sifting” process, namely, X m–1
If the empirical trend is outside the 2.5th or 97.5th percentiles of the surrogate distribution, we identify that this station has a trend that is unlikely to have arisen from the background climate variability described by the corresponding null model

Summary

Introduction

[2] Changes in climate have significant implications for societies, future generations, the economy, ecosystems, and agriculture. The simplest definition of a trend, and the one most often used in climate research, is a straight line fitted to the data This may be illogical and physically meaningless in the real nonlinear and nonstationary world [Wu et al, 2007; Franzke, 2009]. Another definition of trend is a running mean of the data, which requires a predetermined time scale to carry out the smoothing operation. We analyze the statistical significance of the temperature trends (section 2), and we present the results (section 3) and draw conclusions (section 4)

Methods

Results

Conclusions