DOI: 10.1175/JCLI-D-11-00293.1
Scopus记录号: 2-s2.0-84865177279
论文题名: Nonlinear trends, long-range dependence, and climate noise properties of surface temperature
作者: Franzke C.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2012
卷: 25, 期: 12 起始页码: 4172
结束页码: 4183
语种: 英语
Scopus关键词: Auto regressive process
; Autocorrelated
; Climate trends
; Climate variability
; Degree of confidence
; England
; Ensemble empirical mode decomposition
; First order
; Generalized linear model
; Long range dependence
; Noise properties
; Non-Gaussian fluctuations
; Non-linear trends
; Null model
; Ordinary least squares
; Phase scrambling
; Stochastic trends
; Stockholm
; Surface temperatures
; Surrogate data
; Trend detection
; Trend tests
; Climatology
; Stochastic models
; Stochastic systems
; Time series
; Climate models
; climate variation
; surface temperature
; time series analysis
; trend analysis
; warming
; Alert
; Canada
; Ellesmere Island
; England
; Nunavut
; Queen Elizabeth Islands
; Stockholm [Stockholm (CNT)]
; Stockholm [Sweden]
; Sweden
; United Kingdom
英文摘要: This study investigates the significance of trends of four temperature time series-Central England Temperature (CET), Stockholm, Faraday-Vernadsky, and Alert. First the robustness and accuracy of various trend detection methods are examined: ordinary least squares, robust and generalized linear model regression, Ensemble Empirical Mode Decomposition (EEMD), and wavelets. It is found in tests with surrogate data that these trend detection methods are robust for nonlinear trends, superposed autocorrelated fluctuations, and non-Gaussian fluctuations. An analysis of the four temperature time series reveals evidence of long-range dependence (LRD) and nonlinear warming trends. The significance of these trends is tested against climate noise. Three different methods are used to generate climate noise: (i) a short-range-dependent autoregressive process of first order [AR(1)], (ii) an LRD model, and (iii) phase scrambling. It is found that the ability to distinguish the observed warming trend from stochastic trends depends on the model representing the background climate variability. Strong evidence is found of a significant warming trend at Faraday-Vernadsky that cannot be explained by any of the three null models. The authors find moderate evidence of warming trends for the Stockholm and CET time series that are significant against AR(1) and phase scrambling but not the LRD model. This suggests that the degree of significance of climate trends depends on the null model used to represent intrinsic climate variability. This study highlights that in statistical trend tests, more than just one simple null model of intrinsic climate variability should be used. This allows one to better gauge the degree of confidence to have in the significance of trends. © 2012 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/52361
Appears in Collections: 气候变化事实与影响
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作者单位: British Antarctic Survey, High Cross, Madingley Road, Cambridge CB3 0ET, United Kingdom
Recommended Citation:
Franzke C.. Nonlinear trends, long-range dependence, and climate noise properties of surface temperature[J]. Journal of Climate,2012-01-01,25(12)