On trend estimation and significance testing for non-Gaussian and serially dependent data: quantifying the urbanization effect on trends in hot extremes in the megacity of Shanghai

Cheng Qian

doi:10.1007/s00382-015-2838-0

Abstract

Quantifying the urbanization effect on trends in climate extremes is important both for detection and attribution studies and for human adaptation; however, a fundamental problem is how to accurately estimate a trend and its statistical significance, especially for non-Gaussian and serially dependent data. In this paper, the choice of trend estimation and significance testing method is suggested as important for these kinds of studies, as illustrated by quantifying the urbanization effect on trends in seven hot-extreme indices for the megacity of Shanghai during 1961–2013. Both linear and nonlinear trend estimation methods were used. The trends and corresponding statistical significances were estimated by taking into account potential non-Gaussian and serial dependence in the extreme indices. A new method based on adaptive surrogate data is proposed to test the statistical significance of the ensemble empirical mode decomposition (EEMD) nonlinear trend. The urbanization contribution was found to be approximately 34 % (43 %) for the trend in the non-Gaussian distributed heat wave index based on nonparametric linear trend (EEMD nonlinear trend) estimation. For some of the other six hot-extreme indices analyzed, the urbanization contributions estimated based on linear and nonlinear trends varied greatly, with as much as a twofold difference between them. For the linear trend estimation itself, the ordinary least squares fit can give a substantially biased estimation of the urbanization contribution for some of the non-Gaussian extreme indices.

Highlights

Change in the frequency and intensity of climate extremes, especially at regional or local scales, has great impact on human mortality, regional economics, and natural ecosystems
Ren and Zhou (2014) estimated that urbanization has led to a 37.8 % tropical nights increasing trend, a 10.1 % frost days decreasing trend, a 12.8 % summer days increasing trend, a 17.6 % cold nights decreasing trend, and a 26.4 % warm nights increasing trend, as observed based on China-averaged annual mean temperature
The urbanization effect on annual- and JJA-mean Tmean at Xujiahui station is first estimated by the ordinary least squares (OLS) trend, as commonly used in the previous studies, to give background information

Summary

Introduction

Change in the frequency and intensity of climate extremes, especially at regional or local scales, has great impact on human mortality, regional economics, and natural ecosystems. The detection and attribution of change in climate extremes at regional scales is important for effective risk management and adaptation, and worldwide efforts have been devoted to this area of study (Bindoff et al 2013; Stott et al 2010). As a typical type of human-induced land use change forcing, urbanization may have contributed a substantial influence on extreme temperature trends in rapidly developing regions, it is unlikely to have caused more than 10 % of the measured global land averaged centennial trend in surface temperature (Bindoff et al 2013; Hartmann et al 2013b). Sun et al (2014) reported about 24 % of the trend in summer temperature, which was highly correlated with heatwave days, could be attributed to urbanization in eastern China. At a smaller scale in eastern China, such as in the North China Plain, the contribution of urbanization to trends in

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