scholarly journals Long-Range Dependence and Climate Noise Characteristics of Antarctic Temperature Data

2010 ◽  
Vol 23 (22) ◽  
pp. 6074-6081 ◽  
Author(s):  
Christian Franzke
Keyword(s):  
Time Series ◽  
Long Range ◽  
Short Range ◽  
Null Model ◽  
Null Models ◽  
Environmental Research ◽  
Noise Characteristics ◽  
Dependent Process ◽  
Nonlinear Trends ◽  
Climate Noise

Abstract This study examines the long-range dependency, climate noise characteristics, and nonlinear temperature trends of eight Antarctic stations from the Reference Antarctic Data for Environmental Research (READER) dataset. Evidence is shown that Antarctic temperatures are long-range dependent. To identify possible nonlinear trends, the ensemble empirical mode decomposition (EEMD) method is used, and then the question of whether the observed trends can arise from internal atmospheric fluctuations is examined. To answer this question, surrogate data are generated from two paradigmatic null models: a standard first-order autoregressive process representing a short-range dependent process and a fractional integrated process representing a long-range dependent process. It is found that three of the eight stations show statistically significant trends when tested against the short-range dependent process while only the Faraday–Vernadsky station temperature time series shows a significant trend when tested against the long-range dependent null model. All other considered stations show no trends that are statistically significant against the two null models, and thus they can be explained by internal atmospheric variability. These results imply that more attention should be given to assessing the correlation structure of climate time series.

scholarly journals Nonlinear Trends, Long-Range Dependence, and Climate Noise Properties of Surface Temperature

2012 ◽  
Vol 25 (12) ◽  
pp. 4172-4183 ◽  
Author(s):  
Christian Franzke
Keyword(s):  
Time Series ◽  
Climate Variability ◽  
Null Model ◽  
Warming Trend ◽  
Temperature Time Series ◽  
Trend Detection ◽  
Detection Methods ◽  
Nonlinear Trends ◽  
Temperature Time ◽  
Climate Noise

Abstract 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.

scholarly journals Multi-scale analysis of teleconnection indices: climate noise and nonlinear trend analysis

2009 ◽  
Vol 16 (1) ◽  
pp. 65-76 ◽  
Author(s):  
C. Franzke
Keyword(s):  
Time Series ◽  
Decadal Variability ◽  
Statistical Significance ◽  
Stationary Time Series ◽  
Intrinsic Mode Functions ◽  
Multi Scale ◽  
The North ◽  
Teleconnection Indices ◽  
Nonlinear Trends ◽  
Climate Noise

Abstract. The multi-scale nature and climate noise properties of teleconnection indices are examined by using the Empirical Mode Decomposition (EMD) procedure. The EMD procedure allows for the analysis of non-stationary time series to extract physically meaningful intrinsic mode functions (IMF) and nonlinear trends. The climatologically relevant monthly mean teleconnection indices of the North Atlantic Oscillation (NAO), the North Pacific index (NP) and the Southern Annular Mode (SAM) are analyzed. The significance of IMFs and trends are tested against the null hypothesis of climate noise. The analysis of surrogate monthly mean time series from a red noise process shows that the EMD procedure is effectively a dyadic filter bank and the IMFs (except the first IMF) are nearly Gaussian distributed. The distribution of the variance contained in IMFs of an ensemble of AR(1) simulations is nearly χ2 distributed. To test the statistical significance of the IMFs of the teleconnection indices and their nonlinear trends we utilize an ensemble of corresponding monthly averaged AR(1) processes, which we refer to as climate noise. Our results indicate that most of the interannual and decadal variability of the analysed teleconnection indices cannot be distinguished from climate noise. The NP and SAM indices have significant nonlinear trends, while the NAO has no significant trend when tested against a climate noise hypothesis.

Correlation models with long-range dependence

2002 ◽  
Vol 39 (2) ◽  
pp. 370-382 ◽  
Author(s):  
Chunsheng Ma
Keyword(s):  
Time Series ◽  
Long Range ◽  
Long Memory ◽  
Short Range ◽  
Sufficient Conditions ◽  
Long Range Dependence ◽  
Simple Method ◽  
Summable Sequence ◽  
Correlation Models ◽  
Necessary And Sufficient

This paper is concerned with the correlation structure of a stationary discrete time-series with long memory or long-range dependence. Given a sequence of bounded variation, we obtain necessary and sufficient conditions for a function generated from the sequence to be a proper correlation function. These conditions are applied to derive various slowly decaying correlation models. To obtain correlation models with short-range dependence from an absolutely summable sequence, a simple method is introduced.

The Multi-Dependent Hurst Exponent in Traffic Time Series

2010 ◽  
Vol 20-23 ◽  
pp. 346-351
Author(s):  
Ke Qiang Dong ◽  
Peng Jian Shang ◽  
Hong Zhang
Keyword(s):  
Time Series ◽  
Long Range ◽  
Hurst Exponent ◽  
Short Range ◽  
A Priori ◽  
Correlated Data ◽  
Nonstationary Time Series ◽  
Traffic Time Series ◽  
Correlation Exponent ◽  
Correlation Properties

We propose a new method called the multi-dependent Hurst exponent to investigate the correlation properties of the nonstationary time series. The method is validated with the artificial series including both short-range correlated data and long-range correlated data. The results indicate that the multi-dependent Hurst exponents fluctuate around the a-priori known correlation exponent H. Application to traffic time series is also presented, and comparison is made between the artificial time series and traffic time series.

Correlation models with long-range dependence

2002 ◽  
Vol 39 (02) ◽  
pp. 370-382 ◽  
Author(s):  
Chunsheng Ma
Keyword(s):  
Time Series ◽  
Long Range ◽  
Long Memory ◽  
Short Range ◽  
Sufficient Conditions ◽  
Long Range Dependence ◽  
Simple Method ◽  
Summable Sequence ◽  
Correlation Models ◽  
Necessary And Sufficient

This paper is concerned with the correlation structure of a stationary discrete time-series with long memory or long-range dependence. Given a sequence of bounded variation, we obtain necessary and sufficient conditions for a function generated from the sequence to be a proper correlation function. These conditions are applied to derive various slowly decaying correlation models. To obtain correlation models with short-range dependence from an absolutely summable sequence, a simple method is introduced.

scholarly journals Interpreting Null Models of Resting-State Functional MRI

2021 ◽  
Author(s):  
Raphaël Liégeois ◽  
B. T. Thomas Yeo ◽  
Dimitri Van De Ville
Keyword(s):  
Time Series ◽  
Dynamic Properties ◽  
Null Model ◽  
Model Testing ◽  
Null Models ◽  
Statistical Properties ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Fmri Time Series

AbstractNull models are necessary for assessing whether a dataset exhibits non-trivial statistical properties. These models have recently gained interest in the neuroimaging community as means to explore dynamic properties of functional Magnetic Resonance Imaging (fMRI) time series. Interpretation of null-model testing in this context may not be straightforward because (i) null hypotheses associated to different null models are sometimes unclear and (ii) fMRI metrics might be ‘trivial’, i.e. preserved under the null hypothesis, and still be useful in neuroimaging applications. In this commentary, we review several commonly used null models of fMRI time series and discuss the interpretation of the corresponding tests. We argue that, while null-model testing allows for a better characterization of the statistical properties of fMRI time series and associated metrics, it should not be considered as a mandatory validation step to assess their relevance in neuroimaging applications.

scholarly journals Null models for community dynamics: Beware of the cyclic shift algorithm

2019 ◽  
Author(s):  
Michael Kalyuzhny
Keyword(s):  
Time Series ◽  
Community Dynamics ◽  
Null Model ◽  
Temporal Patterns ◽  
Null Models ◽  
Type I ◽  
Cyclic Shift ◽  
Initial State ◽  
Temporal Autocorrelation ◽  
Compositional Changes

AbstractAimTemporal patterns of community dynamics are drawing increasing interest due to their potential to shed light on assembly processes and anthropogenic effects. However, interpreting such patterns considerably benefits from comparing observed dynamics to the reference of a null model. For that aim, the cyclic shift permutations algorithm, which generates randomized null communities based on empirically observed time series, has recently been proposed. The use of this algorithm, which shifts each species time series randomly in time, has been justified by the claim that it preserves the temporal autocorrelation of single species. Hence it has been used to test the significance of various community patterns, in particular excessive compositional changes, biodiversity trends and community stability.InnovationHere we critically study the properties of the cyclic shift algorithm for the first time. We show that, unlike previously suggested, this algorithm does not preserve temporal autocorrelation due to the need to “wrap” the time series and assign the last observations to the first years. Moreover, this algorithm scrambles the initial state of the community, making any dynamics that results from deviations from equilibrium seem excessive. We exemplify that these two issues lead to a highly elevated type I error rate in tests for excessive compositional changes and richness trends.ConclusionsCaution is needed when using the cyclic shift permutation algorithm and interpreting results obtained using it. Interpretation is further complicated because the algorithm removes all correlations between species. We suggest guidelines for using this method and discuss several possible alternative approaches. More research is needed on the best practices for using null models for temporal patterns.

Análise da relação entre O3, NO e NO2 usando técnicas de regressão linear múltipla.

GEOgraphia ◽  
2018 ◽  
Vol 20 (43) ◽  
pp. 124
Author(s):  
Amaury De Souza ◽  
Priscilla V Ikefuti ◽  
Ana Paula Garcia ◽  
Debora A.S Santos ◽  
Soetania Oliveira
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Nitrogen Dioxide ◽  
Multiple Linear Regression ◽  
Quality Parameters ◽  
Environmental Pollutant ◽  
Environmental Research ◽  
Linear Regression Models ◽  
Mato Grosso ◽  
The Impact

Análise e previsão de parâmetros de qualidade do ar são tópicos importantes da pesquisa atmosférica e ambiental atual, devido ao impacto causado pela poluição do ar na saúde humana. Este estudo examina a transformação do dióxido de nitrogênio (NO2) em ozônio (O3) no ambiente urbano, usando o diagrama de séries temporais. Foram utilizados dados de concentração de poluentes ambientais e variáveis meteorológicas para prever a concentração de O3 na atmosfera. Foi testado o emprego de modelos de regressão linear múltipla como ferramenta para a predição da concentração de O3. Os resultados indicam que o valor da temperatura e a presença de NO2 influenciam na concentração de O3 em Campo Grande, capital do Estado do Mato Grosso do Sul. Palavras-chave: Ozônio. Dióxido de nitrogênio. Séries cronológicas. Regressões. ANALYSIS OF THE RELATIONSHIP BETWEEN O3, NO AND NO2 USING MULTIPLE LINEAR REGRESSION TECHNIQUES.Abstract: Analysis and prediction of air quality parameters are important topics of current atmospheric and environmental research due to the impact caused by air pollution on human health. This study examines the transformation of nitrogen dioxide (NO2) into ozone (O3) in the urban environment, using the time series diagram. Environmental pollutant concentration and meteorological variables were used to predict the O3 concentration in the atmosphere. The use of multiple linear regression models was tested as a tool to predict O3 concentration. The results indicate that the temperature value and the presence of NO2 influence the O3 concentration in Campo Grande, capital of the State of Mato Grosso do Sul.Keywords: Ozone. Nitrogen dioxide. Time series. Regressions. ANÁLISIS DE LA RELACIÓN ENTRE O3, NO Y NO2 UTILIZANDO MÚLTIPLES TÉCNICAS DE REGRESIÓN LINEAL.Resumen: Análisis y previsión de los parámetros de calidad del aire son temas importantes de la actual investigación de la atmósfera y el medio ambiente, debido al impacto de la contaminación atmosférica sobre la salud humana. Este estudio examina la transformación del dióxido de nitrógeno (NO2) en ozono (O3) en el entorno urbano, utilizando el diagrama de series de tiempo. Las concentraciones de los contaminantes ambientales de datos y variables climáticas fueron utilizadas para predecir la concentración de O3 en la atmósfera. El uso de múltiples modelos de regresión lineal como herramienta para predecir la concentración de O3 se puso a prueba. Los resultados indican que el valor de la temperatura y la presencia de NO2 influyen en la concentración de O3 en Campo Grande, capital del Estado de Mato Grosso do Sul.Palabras clave: Ozono. Dióxido de nitrógeno. Series de tiempo. Regresiones.

scholarly journals Structural basis of long-range to short-range synaptic transition in NHEJ

Nature ◽  
2021 ◽  
Author(s):  
Siyu Chen ◽  
Linda Lee ◽  
Tasmin Naila ◽  
Susan Fishbain ◽  
Annie Wang ◽  
...  
Keyword(s):  
Long Range ◽  
Short Range ◽  
Structural Basis

scholarly journals Comparison of short-range order in irradiated dysprosium titanates

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Roman Sherrod ◽  
Eric C. O’Quinn ◽  
Igor M. Gussev ◽  
Cale Overstreet ◽  
Joerg Neuefeind ◽  
...  
Keyword(s):  
Long Range ◽  
Short Range ◽  
Structural Model ◽  
Ion Irradiation ◽  
Function Analysis ◽  
Ion Beam ◽  
Structural Response ◽  
Heavy Ion ◽  
Range Order ◽  
Radiation Resistant

AbstractThe structural response of Dy2TiO5 oxide under swift heavy ion irradiation (2.2 GeV Au ions) was studied over a range of structural length scales utilizing neutron total scattering experiments. Refinement of diffraction data confirms that the long-range orthorhombic structure is susceptible to ion beam-induced amorphization with limited crystalline fraction remaining after irradiation to 8 × 1012 ions/cm2. In contrast, the local atomic arrangement, examined through pair distribution function analysis, shows only subtle changes after irradiation and is still described best by the original orthorhombic structural model. A comparison to Dy2Ti2O7 pyrochlore oxide under the same irradiation conditions reveals a different behavior: while the dysprosium titanate pyrochlore is more radiation resistant over the long-range with smaller degree of amorphization as compared to Dy2TiO5, the former involves more local atomic rearrangements, best described by a pyrochlore-to-weberite-type transformation. These results highlight the importance of short-range and medium-range order analysis for a comprehensive description of radiation behavior.

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