Temperature Anomalies, Long Memory, and Aggregation

Vera-Valdés, J. Eduardo

doi:10.3390/econometrics9010009

Cited by 11 publications

(5 citation statements)

References 44 publications

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“…Barreiro et al analyzed the monthly air temperature anomaly time series via ordinal pattern analysis and also detected LTC over EPWP [ 62 ]. Recently, Vera-Valdés investigated LTC in global monthly temperature anomalies by analyzing the autocovariance function and spectral density [ 63 ] and found that the degree of LTC were influenced by the long term dynamics of temperature anomalies in the Tropics. In this study, the imprint of ENSO on LTC of SAT anomaly time series is also very obvious [ 6 , 64 – 66 ].…”

Section: Discussionmentioning

confidence: 99%

Multiple serial correlations in global air temperature anomaly time series

Gao,

Fang,

et al. 2024

PLoS ONE

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Serial correlations within temperature time series serve as indicators of the temporal consistency of climate events. This study delves into the serial correlations embedded in global surface air temperature (SAT) data. Initially, we preprocess the SAT time series to eradicate seasonal patterns and linear trends, resulting in the SAT anomaly time series, which encapsulates the inherent variability of Earth’s climate system. Employing diverse statistical techniques, we identify three distinct types of serial correlations: short-term, long-term, and nonlinear. To identify short-term correlations, we utilize the first-order autoregressive model, AR(1), revealing a global pattern that can be partially attributed to atmospheric Rossby waves in extratropical regions and the Eastern Pacific warm pool. For long-term correlations, we adopt the standard detrended fluctuation analysis, finding that the global pattern aligns with long-term climate variability, such as the El Niño-Southern Oscillation (ENSO) over the Eastern Pacific. Furthermore, we apply the horizontal visibility graph (HVG) algorithm to transform the SAT anomaly time series into complex networks. The topological parameters of these networks aptly capture the long-term correlations present in the data. Additionally, we introduce a novel topological parameter, Δσ, to detect nonlinear correlations. The statistical significance of this parameter is rigorously tested using the Monte Carlo method, simulating fractional Brownian motion and fractional Gaussian noise processes with a predefined DFA exponent to estimate confidence intervals. In conclusion, serial correlations are universal in global SAT time series and the presence of these serial correlations should be considered carefully in climate sciences.

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Section: Discussionmentioning

confidence: 99%

Multiple serial correlations in global air temperature anomaly time series

Gao,

Fang,

et al. 2024

PLoS ONE

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“…Climate data have also been shown to possess long-range dependence. Several authors have argued that aggregation may be the reason behind the presence of long-range dependence in temperature data; see Baillie and Chung (2002);Gil-Alana (2005); Mills (2007);Vera-Valdés (2021a). Figure 6 shows an example using temperature data.…”

Section: Applicationmentioning

confidence: 99%

Nonfractional Long-Range Dependence: Long Memory, Antipersistence, and Aggregation

Vera-Valdés

2021

Econometrics

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This paper used cross-sectional aggregation as the inspiration for a model with long-range dependence that arises in actual data. One of the advantages of our model is that it is less brittle than fractionally integrated processes. In particular, we showed that the antipersistent phenomenon is not present for the cross-sectionally aggregated process. We proved that this has implications for estimators of long-range dependence in the frequency domain, which will be misspecified for nonfractional long-range-dependent processes with negative degrees of persistence. As an application, we showed how we can approximate a fractionally differenced process using theoretically-motivated cross-sectional aggregated long-range-dependent processes. An example with temperature data showed that our framework provides a better fit to the data than the fractional difference operator.

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“…Besides the R/S analysis, many other methods have been proposed for estimating the parameter H, such as the detrended fluctuation analysis (DFA), periodogram method, aggregated variance method, structure function method, etc. With these methods, fractal and multifractal properties in different climatic variables have been investigated by many researchers (Caballero et al 2002;Vyushin and Kushner 2008;Yuan et al 2014;Qiu et al 2020;Vera-Valdés 2021).…”

Section: Introductionmentioning

confidence: 99%

Long memory and trend in time series of precipitation in Mozambique

Bassrei

2023

Theor Appl Climatol

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Many climate studies in Mozambique have clearly identified signals of climate change, especially changes in the extreme temperatures. In this study, we have investigated the existence of long term correlations and trend in time series of precipitation. Two databases were used for this purpose: in situ observations along the period of 1960 -2020 and the Climate Hazards group InfraRed Precipitation with Stations dataset, which is a high resolution (0.05 o ) remote sensing data assimilated with in situ stations, along the period from 1981 to 2021. We have applied the rescaled-range analysis and the detrended fluctuation analysis for long memory investigation, and the linear regression and Mann-Kendall methods for trend analysis. Results have shown the existence of long memory in the time series of precipitation in most parts of Mozambique, being stronger in the southern and central regions, and weakening toward the north of the country. Precipitation has generally exhibited persistency in the southern and central regions, indicating that the observed climate behaviour during the evaluated period is likely to continue for a long period in future. On the other hand, some significant trend signals of precipitation were detected in some isolated areas of Mozambique, presenting an increase in some regions such as the southern part of Manica and eastern of Inhambane provinces, and a decrease in other regions such as the coastal areas of Zambezia and Nampula.

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Temperature Anomalies, Long Memory, and Aggregation

Cited by 11 publications

References 44 publications

Multiple serial correlations in global air temperature anomaly time series

Multiple serial correlations in global air temperature anomaly time series

Nonfractional Long-Range Dependence: Long Memory, Antipersistence, and Aggregation

Long memory and trend in time series of precipitation in Mozambique

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