Trend Detection in Climate Change Indicators Using Non-Parametric Statistics: A Case Study of Abu Dhabi, United Arab Emirates

Başarir, Aydın; Arman, Hasan; Hussein, Saber; Murad, Ahmed; Aldahan, Ala; Al-Abri, M. Abdulla

doi:10.12693/aphyspola.132.655

Cited by 3 publications

(3 citation statements)

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“…Many other methods to identify discontinuities in a time series have been posited over the years (see Weatherhead et al ., 1998; Reeves et al ., 2007; Mudelsee, 2019; Muthuramu and Maheswari, 2019). Such methods usually seek to identify structural breaks or changepoints using a variety of different approaches including autoregressive and autoregressive‐moving averages (Karl et al ., 2000; Seidel and Lanzante, 2004; Davis et al ., 2006), regression‐based coefficients and methods (Jouini and Boutahar, 2005; Dixon and Moore, 2011; Aue and Horváth, 2013; Lyubchich et al ., 2013; Guo et al ., 2018; Elder and Fong, 2019; Tharu and Dhakal, 2020), t or F test with hypothesis testing (Lund et al ., 2007; Rienzner and Gandolfi, 2011; Gallagher et al ., 2013), higher‐order moments of the distribution (Hilas et al ., 2013; Xie et al ., 2019) and cumulative sums (Shao and Zhang, 2010), Bayesian methods (Ruggieri, 2013; Chen et al ., 2017; Yu and Ruggieri, 2019), bootstrap approaches (Bickel and Ren, 2001; Noguchi et al ., 2011; Lyubchich et al ., 2013), classification analysis (Anders et al ., 2013), manifold learning models (Xie et al ., 2013), functional data analysis (Alaya et al ., 2020), an informational approach (Beaulieu et al ., 2012), and nonparametric methods (Lanzante, 1996; Douglas et al ., 2000; Burn and Elnur, 2002; Xiong and Guo, 2004; McKitrick and Vogelsang, 2014; Xie et al ., 2014; Basarir et al ., 2017; Guo et al ., 2018; Hajani and Rahman, 2018; Zhou et al ., 2019; Bagniewski et al ., 2021). Most of these techniques work directly with the series in the time domain, usually searching for changes in parameters of the distribution or trend or shifts in the moments of the time series (e.g., mean, kurtosis) across a change point, although other researchers have explored the use of Hurst statistics to changepoint analyses (Outcalt et al ., 1997; Tan and Gan, 2017).…”

Section: Discussionmentioning

confidence: 99%

Detection of climate transitions and discontinuities by Hurst rescaling

Legates

Outcalt²

2022

Intl Journal of Climatology

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The method of Outcalt et al., based on work developed originally by Hurst, is re-examined to evaluate its efficacy in delineating changes in trends and identifying regime shifts in climatic-related time series. This technique is based on the concept of the normalized rescaled running sum where temporal changes in the Hurst exponent can be used to identify climatic trends from one regime to another as each regime has a characteristic distribution that differs from the statistical characteristics of the complete time series. An examination of the temporal change in the amplitude of the normalized rescaled running sum can be used as a method to identify these regime changes, which may be either real (i.e., a true climatic shift) or induced (i.e., through a change in measurement bias, station location, or other nonclimatic influence). Examples shown here focus on examining time series of the Pacific Decadal Oscillation, Arctic thaw depth, the Northern Hemisphere snow cover extent, treeflow data from Lees Ferry (AZ), North Atlantic tropical cyclone frequency, and central England air temperatures.

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Section: Discussionmentioning

confidence: 99%

Detection of climate transitions and discontinuities by Hurst rescaling

Legates

Outcalt²

2022

Intl Journal of Climatology

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“…Finally, the RI quantifies the average factor by which two measuring techniques differ from each other. A high RI value suggests a significant divergence between the s and therefore less reliability Basarir et al, 2017;Rahman & Pekkat, 2024). In temperature analysis, accuracy might be used to evaluate how well a model predicts temperature categories (e.g., hot, warm, cold) or specific temperature thresholds (Aguilera et al, 2019;Liu et al, 2021).…”

Section: Data Processingmentioning

confidence: 99%

Validation of the Accuracy of the GNSS RO Temperature Data for Climate Monitoring over Africa

Sa’i,

Musa,

Aleem

et al. 2024

Preprint

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Africa is facing significant impacts from temperature and climate change, with a pronounced warming trend surpassing the global average over the past century. This trend is reshaping climates and ecosystems, leading to increased temperatures, more frequent heatwaves, and altered precipitation patterns across different regions. Severe weather events like droughts, cyclones, and floods pose risks to human lives and well-being. The decline in radiosonde stations across Africa and the lack of collocated meteorological sensors at a majority of GNSS ground-based stations present substantial hurdles in effectively monitoring and responding to climate change in the region. This study thus evaluates the validity of temperature data based on the GNSS Radio Occultation (RO) technique as an alternative to the in-situ radiosonde observations for climate change monitoring over Africa. Three datasets which include GNSS RO, Radiosonde, and ERA5, were analyzed using Python programming, focusing on temperature as the key variable. The validation process involved the use of ten (10) different performance metrics for assessing temperature data from the datasets, including NRMSE (%), Bias (mm), SDR, MAE, ACC, VC, MEF, RI, -squared, and SD Residuals (mm). Most of these metrics exhibited improved performance when dealing with a temperature discrepancy of approximately 1K, although a few stations showed weak or no correlation with certain metrics. Some stations displayed temperature differences as high as 7K due to the number of Radio Occultation Events (ROE) around the stations or problematic Radiosonde data, while most stations exhibited temperature variances of around 1K. These findings suggest that the GNSS RO technique could be considered for Africa due to the current status of the Radiosonde stations over the region.

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“…The Mann-Kendall trend test method is widely used in the analysis of the change trend of time series of climate factors [30]. In this paper, the Mann-Kendall trend test method based on MATLAB is used to analyze the mutability of soil temperature, and the sequence is constructed for the time series x with n samples:…”

Section: Mann-kendall Nonparametric Testmentioning

confidence: 99%

Characteristics of Soil Temperature Change in Lhasa in the Face of Climate Change

Jia,

Dai,

et al. 2024

Atmosphere

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Soil temperature is an important index of climate change, and the analysis of soil temperature change is of great significance for understanding climate change and ecohydrological processes. This study was based on the measured meteorological data of a meteorological station, combined with the soil temperature data of 0–10, 10–40, 40–100 and 100–200 cm from the Global Land Data Assimilation System (GLDAS-NOAH). The Mann–Kendall test, wavelet analysis, linear tendency estimation and other methods were used to analyze the variability, periodicity and trend of soil temperature in Lhasa from 2006 to 2022. The results showed that the soil temperature of different soil layers had abrupt changes in annual and seasonal time series, and all showed a warming phenomenon after abrupt changes. In terms of periodicity, the average annual soil temperature of different soil layers has similar periodic changes, and the periodic oscillation is strong around 10a, which is the main cycle of soil temperature change. The soil temperature in Lhasa showed a significant rising trend in the interannual and seasonal time series, and the average annual rising trend of soil temperature was greater than that of air temperature. The correlation between soil temperature and mean air temperature (MAT), maximum air temperature (Tmax), minimum air temperature (Tmin) and snow depth (SD) was investigated by Pearson correlation analysis. Soil temperature in spring, autumn and winter had a strong correlation with MAT, Tmax and Tmin, showing a significant positive correlation. The negative correlation between soil temperature and SD in 0–40 cm soil in spring and winter was more severe. The research results show that Lhasa has experienced a rise in air temperature and soil temperature in the past 17 years, and reveal the specific changes in soil temperature in Lhasa against the background of climate change. These findings have reference significance for understanding the impact of climate change on the natural environment.

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Trend Detection in Climate Change Indicators Using Non-Parametric Statistics: A Case Study of Abu Dhabi, United Arab Emirates

Cited by 3 publications

References 10 publications

Detection of climate transitions and discontinuities by Hurst rescaling

Detection of climate transitions and discontinuities by Hurst rescaling

Validation of the Accuracy of the GNSS RO Temperature Data for Climate Monitoring over Africa

Characteristics of Soil Temperature Change in Lhasa in the Face of Climate Change

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