scholarly journals Long-range memory in millennium-long ESM and AOGCM experiments

2014 ◽  
Vol 5 (1) ◽  
pp. 363-401 ◽  
Author(s):  
L. Østvand ◽  
T. Nilsen ◽  
K. Rypdal ◽  
D. Divine ◽  
M. Rypdal
Keyword(s):  
Long Range ◽  
Time Scales ◽  
General Circulation ◽  
Climate Model ◽  
Climate System ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
External Forcing ◽  
Internal Dynamics ◽  
Temperature Records

Abstract. Northern Hemisphere (NH) temperature records from a reconstruction and a number of millennium-long climate model experiments are investigated for long-range memory (LRM). The models are two Earth system models and two atmospheric-ocean general circulation models. The periodogram, detrended fluctuation analysis and wavelet variance analysis are applied to examine scaling properties and to estimate a scaling exponent of the temperature records. A simple linear model for the climate response to external forcing is also applied to the reconstruction and the forced climate model runs, and then compared to unforced control runs to extract the LRM generated by internal dynamics of the climate system. With one exception the climate models show strong persistent scaling with power spectral densities of the form S(f) ~ f−β with 0.8 < β < 1 on time scales from years to several centuries. This is somewhat stronger persistence than found in the reconstruction (β ≈ 0.7). The exception is the HadCM3 model, which exhibits β ≈ 0.6. We find no indication that LRM found in these model runs are induced by external forcing, which suggests that LRM on sub-decadal to century time scales in NH mean temperatures is a property of the internal dynamics of the climate system. Temperature records for a local site, Reykjanes Ridge, are also studied, showing that strong persistence is found also for local ocean temperature.

scholarly journals Long-range memory in internal and forced dynamics of millennium-long climate model simulations

2014 ◽  
Vol 5 (2) ◽  
pp. 295-308 ◽  
Author(s):  
L. Østvand ◽  
T. Nilsen ◽  
K. Rypdal ◽  
D. Divine ◽  
M. Rypdal
Keyword(s):  
Long Range ◽  
General Circulation ◽  
Climate Model ◽  
General Circulation Models ◽  
Climate System ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
External Forcing ◽  
Internal Dynamics ◽  
Temperature Records

Abstract. Northern Hemisphere (NH) temperature records from a palaeoclimate reconstruction and a number of millennium-long climate model experiments are investigated for long-range memory (LRM). The models are two Earth system models and two atmosphere–ocean general circulation models. The periodogram, detrended fluctuation analysis and wavelet variance analysis are applied to examine scaling properties and to estimate a scaling exponent of the temperature records. A simple linear model for the climate response to external forcing is also applied to the reconstruction and the forced climate model runs, and then compared to unforced control runs to extract the LRM generated by internal dynamics of the climate system. The climate models show strong persistent scaling with power spectral densities of the form S(f) ~ f −β with 0.8 < β < 1 on timescales from years to several centuries. This is somewhat stronger persistence than found in the reconstruction (β &amp;approx; 0.7). We find no indication that LRM found in these model runs is induced by external forcing, which suggests that LRM on sub-decadal to century time scales in NH mean temperatures is a property of the internal dynamics of the climate system. Reconstructed and instrumental sea surface temperature records for a local site, Reykjanes Ridge, are also studied, showing that strong persistence is found also for local ocean temperature.

scholarly journals Long-range persistence in sea surface temperature off the coast of central California

2019 ◽  
Vol 9 ◽  
pp. 175931311879111 ◽  
Author(s):  
Laurence C Breaker
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Long Range ◽  
Time Scales ◽  
Sea Surface ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
Scaling Exponents ◽  
Central California ◽  
Pacific Grove

We estimate long-range persistence in ocean surface temperature off the coast of central California, a region where similar observations have not been made. The database consists of 20-year records of daily sea surface temperature from three locations: Pacific Grove and Granite Canyon along the coast, and Southeast Farallon Island located 40 km off the coast and slightly further north. Long-range persistence is important for a number of reasons: on the negative side, it can have serious detrimental effects for statistical inference and on the positive side, it provides access to the ocean’s memory which can lead to a greater understanding of the processes involved and thus to better prediction. Long-range persistence also provides important insights into the relationship between the scaling that is obtained and the time scales employed. The first step in the analysis was to remove the annual cycle from the data at each location because of its detrimental effect on estimating long-range persistence. Then detrended fluctuation analysis was used to calculate long-range persistence where a single scaling exponent is obtained that relates the magnitudes of the fluctuations in the data to the time scales involved. Similar scaling exponents were obtained for Granite Canyon and Pacific Grove with values of 1.04 and 1.05, respectively. At Southeast Farallon Island, a value of 1.16 was obtained. The increase in the scaling exponent at Southeast Farallon Island is consistent with observations made elsewhere and model results, which indicate that as coastal influence decreases further offshore, the scaling exponents for sea surface temperature tend to increase. Because Southeast Farallon Island is exposed to subarctic waters offshore, whereas Pacific Grove and Granite Canyon are exposed to warmer waters from the California Undercurrent along the coast, these exposures to different water masses may contribute to the observed change in scaling behavior.

scholarly journals On the Difference of Scaling Properties for Temperature and Precipitation over China

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Lei Jiang ◽  
Liqing Zhao ◽  
Zihao Zhao
Keyword(s):  
Time Scales ◽  
Significance Test ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
Scaling Properties ◽  
Temperature And Precipitation ◽  
The Difference ◽  
Temperature Records ◽  
Precipitation Records ◽  
Short Time

The daily air temperature and precipitation records of four meteorological observation stations over China are used to investigate the differences of scaling property employing the detrended fluctuation analysis (DFA) method. The results show that the values in DFA-exponent for temperature are higher than those for precipitation compared by different orders DFA1–3. A 95% significance test is also applied to verify LRCs by resampling the temperature and precipitation records 10000 times in Beijing. The values of scaling exponent from original temperature and precipitation records are larger than the upper range value of the interval threshold after shuffling the data records, which implies there are positive LRCs. For temperature records, the value of scaling exponent calculated by FA is greater than those by DFA1–3 at all four stations. This indicates that the FA curve overestimates the scaling behavior due to the effect of trends. By contrast, the values of scaling exponent in precipitation are almost the same by using FA and DFA1–3 for all time scales, respectively. Furthermore, there are crossovers on short time scales in different orders DFA1–3 for the temperature records, while the slopes keep almost consistent on all time scales for the precipitation records.

scholarly journals Nonlinear temperature effects on multifractal complexity of metabolic rate of mice

2016 ◽  
Author(s):  
Fabio A Labra ◽  
Jose M Bogdanovich ◽  
Francisco Bozinovic
Keyword(s):  
Metabolic Rate ◽  
Long Range ◽  
Time Scales ◽  
Laboratory Mouse ◽  
Rate Of Change ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
Range Correlation ◽  
Environmental Temperatures ◽  
Do So

Complex physiological dynamics have been argued to be a signature of healthy physiological function. Here we test whether the complexity of metabolic rate fluctuations in small endotherms decreases with lower environmental temperatures. To do so we examine the multifractal temporal scaling properties of the rate of change in oxygen consumption r(VO2), in the laboratory mouse Mus musculus, assessing their long range correlation properties across 7 different environmental temperatures, ranging from 0°C to 30°C. To do so, we applied multifractal detrended fluctuation analysis (MF-DFA), finding that r(VO2) fluctuations show two scaling regimes. For small time scales below the crossover time (approximately 102 seconds), either monofractal or weak multifractal dynamics are observed depending on whether Ta<15°C or Ta > 15°C respectively. For larger time scales, r(VO2) fluctuations are characterized by an asymptotic scaling exponent that indicates multifractal anti-persistent or uncorrelated dynamics. For both scaling regimes, a generalization of the multiplicative cascade model provides very good fits for the Renyi exponents τ(q), showing that the infinite number of exponents h(q) can be described by only two independent parameters, a and b. We also show that the long-range correlation structure of r(VO2) time series differs from randomly shuffled series, and may not be explained as an artifact of stochastic sampling of a linear frequency spectrum. These results show that metabolic rate dynamics in a well studied micro-endotherm are consistent with a highly non-linear feedback control system.

scholarly journals Nonlinear temperature effects on multifractal complexity of metabolic rate of mice

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2607 ◽  
Author(s):  
Fabio A. Labra ◽  
Jose M. Bogdanovich ◽  
Francisco Bozinovic
Keyword(s):  
Metabolic Rate ◽  
Long Range ◽  
Time Scales ◽  
Laboratory Mouse ◽  
Rate Of Change ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
Range Correlation ◽  
Environmental Temperatures ◽  
Do So

Complex physiological dynamics have been argued to be a signature of healthy physiological function. Here we test whether the complexity of metabolic rate fluctuations in small endotherms decreases with lower environmental temperatures. To do so, we examine the multifractal temporal scaling properties of the rate of change in oxygen consumptionr(VO2), in the laboratory mouseMus musculus, assessing their long range correlation properties across seven different environmental temperatures, ranging from 0  °C to 30  °C. To do so, we applied multifractal detrended fluctuation analysis (MF-DFA), finding thatr(VO2)fluctuations show two scaling regimes. For small time scales below the crossover time (approximately 102s), either monofractal or weak multifractal dynamics are observed depending on whetherTa< 15  °C orTa> 15  °C respectively. For larger time scales,r(VO2)fluctuations are characterized by an asymptotic scaling exponent that indicates multifractal anti-persistent or uncorrelated dynamics. For both scaling regimes, a generalization of the multiplicative cascade model provides very good fits for the Renyi exponentsτ(q), showing that the infinite number of exponentsh(q)can be described by only two independent parameters,aandb. We also show that the long-range correlation structure ofr(VO2)time series differs from randomly shuffled series, and may not be explained as an artifact of stochastic sampling of a linear frequency spectrum. These results show that metabolic rate dynamics in a well studied micro-endotherm are consistent with a highly non-linear feedback control system.

scholarly journals Nonlinear temperature effects on multifractal complexity of metabolic rate of mice

2016 ◽  
Author(s):  
Fabio A Labra ◽  
Jose M Bogdanovich ◽  
Francisco Bozinovic
Keyword(s):  
Metabolic Rate ◽  
Long Range ◽  
Time Scales ◽  
Laboratory Mouse ◽  
Rate Of Change ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
Range Correlation ◽  
Environmental Temperatures ◽  
Do So

Complex physiological dynamics have been argued to be a signature of healthy physiological function. Here we test whether the complexity of metabolic rate fluctuations in small endotherms decreases with lower environmental temperatures. To do so we examine the multifractal temporal scaling properties of the rate of change in oxygen consumption r(VO2), in the laboratory mouse Mus musculus, assessing their long range correlation properties across 7 different environmental temperatures, ranging from 0°C to 30°C. To do so, we applied multifractal detrended fluctuation analysis (MF-DFA), finding that r(VO2) fluctuations show two scaling regimes. For small time scales below the crossover time (approximately 102 seconds), either monofractal or weak multifractal dynamics are observed depending on whether Ta<15°C or Ta > 15°C respectively. For larger time scales, r(VO2) fluctuations are characterized by an asymptotic scaling exponent that indicates multifractal anti-persistent or uncorrelated dynamics. For both scaling regimes, a generalization of the multiplicative cascade model provides very good fits for the Renyi exponents τ(q), showing that the infinite number of exponents h(q) can be described by only two independent parameters, a and b. We also show that the long-range correlation structure of r(VO2) time series differs from randomly shuffled series, and may not be explained as an artifact of stochastic sampling of a linear frequency spectrum. These results show that metabolic rate dynamics in a well studied micro-endotherm are consistent with a highly non-linear feedback control system.

Modeling Tropical Cyclones in a Changing Climate

2016 ◽  
Author(s):  
Enrico Scoccimarro
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
General Circulation Model ◽  
General Circulation ◽  
Climate Model ◽  
Circulation Model ◽  
Climate System ◽  
Ocean Dynamics ◽  
Small Scale ◽  
Computational Power

Tropical cyclones (TCs) in their most intense expression (hurricanes or typhoons) are the main natural hazards known to humankind. The impressive socioeconomic consequences for countries dealing with TCs make our ability to model these organized convective structures a key issue to better understanding their nature and their interaction with the climate system. The destructive effects of TCs are mainly caused by three factors: strong wind, storm surge, and extreme precipitation. These TC-induced effects contribute to the annual worldwide damage of the order of billions of dollars and a death toll of thousands of people. Together with the development of tools able to simulate TCs, an accurate estimate of the impact of global warming on TC activity is thus not only of academic interest but also has important implications from a societal and economic point of view. The aim of this article is to provide a description of the TC modeling implementations available to investigate present and future climate scenarios. The two main approaches to dynamically model TCs under a climate perspective are through hurricane models and climate models. Both classes of models evaluate the numerical equations governing the climate system. A hurricane model is an objective tool, designed to simulate the behavior of a tropical cyclone representing the detailed time evolution of the vortex. Considering the global scale, a climate model can be an atmosphere (or ocean)-only general circulation model (GCM) or a fully coupled general circulation model (CGCM). To improve the ability of a climate model in representing small-scale features, instead of a general circulation model, a regional model (RM) can be used: this approach makes it possible to increase the spatial resolution, reducing the extension of the domain considered. In order to be able to represent the tropical cyclone structure, a climate model needs a sufficiently high horizontal resolution (of the order of tens of kilometers) leading to the usage of a great deal of computational power. Both tools can be used to evaluate TC behavior under different climate conditions. The added value of a climate model is its ability to represent the interplay of TCs with the climate system, namely two-way relationships with both atmosphere and ocean dynamics and thermodynamics. In particular, CGCMs are able to take into account the well-known feedback between atmosphere and ocean components induced by TC activity and also the TC–related remote impacts on large-scale atmospheric circulation. The science surrounding TCs has developed in parallel with the increasing complexity of the mentioned tools, both in terms of progress in explaining the physical processes involved and the increased availability of computational power. Many climate research groups around the world, dealing with such numerical models, continuously provide data sets to the scientific community, feeding this branch of climate change science.

scholarly journals Early Warning Signals for Bearing Failure Using Detrended Fluctuation Analysis

2020 ◽  
Vol 10 (23) ◽  
pp. 8489
Author(s):  
Laith Shalalfeh ◽  
Ashraf AlShalalfeh
Keyword(s):  
Long Range ◽  
Early Warning ◽  
Detrended Fluctuation Analysis ◽  
Short Range ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
Bearing Failure ◽  
Kendall’S Tau ◽  
Vibration Data ◽  
Detrended Fluctuation

Prognostic techniques play a critical role in predicting upcoming faults and failures in machinery or a system by monitoring any deviation in the operation. This paper presents a novel method to analyze multidimensional sensory data and use its characteristics in bearing health prognostics. Firstly, detrended fluctuation analysis (DFA) is exploited to evaluate the long-range correlations in ball bearing vibration data. The results reveal the existence of the crossover phenomenon in vibration data with two scaling exponents at the short-range and long-range scales. Among several data sets, applying the DFA method to vibration signals shows a consistent increase in the short-range scaling exponent toward bearing failure. Finally, Kendall’s tau is used as a ranking coefficient to quantify the trend in the scaling exponent. It was found that the Kendall’s tau coefficient of the vibration scaling exponent could provide an early warning signal (EWS) for bearing failure.

scholarly journals Climate model attractors: chaos, quasi-regularity and sensitivity to small perturbations of external forcing

2001 ◽  
Vol 8 (4/5) ◽  
pp. 201-209 ◽  
Author(s):  
V. P. Dymnikov ◽  
A. S. Gritsoun
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Global Attractors ◽  
Atmospheric Model ◽  
Climate System ◽  
External Forcing ◽  
Attractor Dimension ◽  
Small Perturbations ◽  
System Sensitivity ◽  
Fluctuation Dissipation

Abstract. In this paper we discuss some theoretical results obtained for climate models (theorems for the existence of global attractors and inertial manifolds, estimates of attractor dimension and Lyapunov exponents, symmetry property of Lyapunov spectrum). We define the conditions for "quasi-regular behaviour" of a climate system. Under these conditions, the system behaviour is subject to the Kraichnan fluctuation-dissipation relation. This fact allows us to solve the problem of determining a system's sensitivity to small perturbations to an external forcing. The applicability of the above approach to the analysis of the climate system sensitivity is verified numerically with the example of the two-layer quasi-geostrophic atmospheric model.

Nonlinear analysis of pupillary dynamics

2016 ◽  
Vol 61 (1) ◽  
pp. 95-106 ◽  
Author(s):  
Francesco Onorati ◽  
Luca Tommaso Mainardi ◽  
Fabiola Sirca ◽  
Vincenzo Russo ◽  
Riccardo Barbieri
Keyword(s):  
Nonlinear Dynamics ◽  
Long Range ◽  
Time Scales ◽  
Pupil Size ◽  
Statistical Tests ◽  
Light Condition ◽  
Autonomic Response ◽  
Fluctuation Analysis ◽  
Fast Time ◽  
Experimental Conditions

Abstract Pupil size reflects autonomic response to different environmental and behavioral stimuli, and its dynamics have been linked to other autonomic correlates such as cardiac and respiratory rhythms. The aim of this study is to assess the nonlinear characteristics of pupil size of 25 normal subjects who participated in a psychophysiological experimental protocol with four experimental conditions, namely “baseline”, “anger”, “joy”, and “sadness”. Nonlinear measures, such as sample entropy, correlation dimension, and largest Lyapunov exponent, were computed on reconstructed signals of spontaneous fluctuations of pupil dilation. Nonparametric statistical tests were performed on surrogate data to verify that the nonlinear measures are an intrinsic characteristic of the signals. We then developed and applied a piecewise linear regression model to detrended fluctuation analysis (DFA). Two joinpoints and three scaling intervals were identified: slope α0, at slow time scales, represents a persistent nonstationary long-range correlation, whereas α1 and α2, at middle and fast time scales, respectively, represent long-range power-law correlations, similarly to DFA applied to heart rate variability signals. Of the computed complexity measures, α0 showed statistically significant differences among experimental conditions (p<0.001). Our results suggest that (a) pupil size at constant light condition is characterized by nonlinear dynamics, (b) three well-defined and distinct long-memory processes exist at different time scales, and (c) autonomic stimulation is partially reflected in nonlinear dynamics.

Sign in / Sign up

Export Citation Format

Share Document