A new method for abrupt dynamic change detection of correlated time series

He, Wenping; Feng, Guolin; Wu, Qiong; He, Tao; Wan, Shiquan; Jifan, Chou

doi:10.1002/joc.2367

Cited by 56 publications

(30 citation statements)

References 23 publications

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“…Observed climate abrupt changes in a homogenous climate time series are caused only by variations in weather and climate [44].…”

Section: Data Processing and Adjustmentmentioning

confidence: 99%

Trend Analysis of the Mean Annual Temperature in Rwanda during the Last Fifty Two Years

Safari¹

2012

JEP

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Climate change and global warming are widely recognized as the most significant environmental dilemma the world is experiencing today. Recent studies have shown that the Earth's surface air temperature has increased by 0.6˚C -0.8˚C during the 20 th century, along with changes in the hydrological cycle. This has alerted the international community and brought great interest to climate scientists leading to several studies on climate trend detection at various scales. This paper examines the long-term modification of the near surface air temperature in Rwanda. Time series of near surface air temperature data for the period ranging from 1958 to 2010 for five weather observatories were collected from the Rwanda National Meteorological Service. Variations and trends of annual mean temperature time series were examined. The cumulative sum charts (CUSUM) and bootstrapping and the sequential version of the Mann Kendall Rank Statistic were used for the detection of abrupt changes. Regression analysis was performed for the trends and the Mann-Kendall Rank Statistic Test was used for the examination of their significance. Statistically significant abrupt changes and trends have been detected. The major change point in the annual mean temperature occurred around [1977][1978][1979]. The analysis of the annual mean temperature showed for all observatories a not very significant cooling trend during the period ranging from 1958 to 1977-1979 while a significant warming trend was furthermore observed for the period after the [1977][1978][1979] where Kigali, the Capital of Rwanda, presented the highest values of the slope (0.0455/year) with high value of coefficient of determination (R 2 = 0.6798), the Kendall's tau statistic (M-K = 0.62), the Kendall Score (S = 328) with a two-sided p-value far less than the confidence level α of 5%. This is most likely explained by the growing population and increasing urbanization and industrialization the country has experienced, especially the Capital City Kigali, during the last decades.

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“…Observed climate abrupt changes in a homogenous climate time series are caused only by variations in weather and climate [44].…”

Section: Data Processing and Adjustmentmentioning

confidence: 99%

Trend Analysis of the Mean Annual Temperature in Rwanda during the Last Fifty Two Years

Safari¹

2012

JEP

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“…When climate changes, the system jumps from one state to another, and it experiences a process (Li et al, 1996;Yan et al, 2012Yan et al, , 2013. Most traditional theories and detection methods (Wei, 1999;Feng et al, 2011;He et al, 2012) have been focused on the changes in statistics before and after climate change, such as Yamamoto (Yamamoto et al, 1986), Mann-Kendall (Mann, 1945;Kendall, 1955;Kendall et al, 1976), moving t test, moving cut data-approximate entropy (He et al, 2009;Jin et al, 2015), and the duration has been ignored. Therefore, it is urgent to study the transition process of climate change to understand how the system changes abruptly.…”

Section: Introductionmentioning

confidence: 99%

Transition process of abrupt climate change based on global sea surface temperature over the past century

Yan

Hou

Feng

2016

Nonlin. Processes Geophys.

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Abstract.A new detection method has been proposed to study the transition process of abrupt climate change. With this method, the climate system transiting from one stable state to another can be verified clearly. By applying this method to the global sea surface temperature over the past century, several climate changes and their processes are detected, including the start state (moment), persist time, and end state (moment). According to the spatial distribution, the locations of climate changes mainly have occurred in the Indian Ocean and western Pacific before the middle twentieth century, in the 1970s in the equatorial middle-eastern Pacific, and in the middle and southern Pacific since the end of the twentieth century. In addition, the quantitative relationship between the transition process parameters is verified in theory and practice: (1) the relationship between the rate and stability parameters is linear, and (2) the relationship between the rate and change amplitude parameters is quadratic.

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“…Feng et al (2006Feng et al ( , 2008 systematically studied abrupt dynamic change detection methods and presented several new methods. Based on the long-range correlation of the climate system, He et al (2008He et al ( , 2010He et al ( , 2012 proposed three methods, i.e., moving detrended fluctuation analysis, moving cut data detrended fluctuation analysis, and moving cut data rescaled range analysis. They found that these three types of methods performed well in detecting abrupt changes in correlated time series.…”

Section: Introductionmentioning

confidence: 99%

Detecting abrupt dynamic change based on changes in the fractal properties of spatial images

Liu

et al. 2016

Theor Appl Climatol

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Many abrupt climate change events often cannot be detected timely by conventional abrupt detection methods until a few years after these events have occurred. The reason for this lag in detection is that abundant and long-term observational data are required for accurate abrupt change detection by these methods, especially for the detection of a regime shift. So, these methods cannot help us understand and forecast the evolution of the climate system in a timely manner. Obviously, spatial images, generated by a coupled spatiotemporal dynamical model, contain more information about a dynamic system than a single time series, and we find that spatial images show the fractal properties. The fractal properties of spatial images can be quantitatively characterized by the Hurst exponent, which can be estimated by twodimensional detrended fluctuation analysis (TD-DFA). Based on this, TD-DFA is used to detect an abrupt dynamic change of a coupled spatiotemporal model. The results show that the TD-DFA method can effectively detect abrupt parameter changes in the coupled model by monitoring the changing in the fractal properties of spatial images. The present method provides a new way for abrupt dynamic change detection, which can achieve timely and efficient abrupt change detection results.

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A new method for abrupt dynamic change detection of correlated time series

Cited by 56 publications

References 23 publications

Trend Analysis of the Mean Annual Temperature in Rwanda during the Last Fifty Two Years

Trend Analysis of the Mean Annual Temperature in Rwanda during the Last Fifty Two Years

Transition process of abrupt climate change based on global sea surface temperature over the past century

Detecting abrupt dynamic change based on changes in the fractal properties of spatial images

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