Fractal analysis of long-range paleoclimatic data: Oxygen isotope record of pacific core V28-239

Fluegeman, Richard H.; Snow, R. Scott

doi:10.1007/bf00874493

Cited by 30 publications

(13 citation statements)

References 9 publications

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“…The obtained H values from the gravity data are in agreement with the H values found from other geophysical data, e.g. H = 0.78 for global climate change (Fluegeman & Snow 1989) and the H values of 0.71 to 0.91 for density and sonic porosity logs for a sandstone reservoir (Emanuel et al 1989). The H values for the bathymetry data correlate well with the values of 0.5 found from the worldwide data of bathymetry/topography (Malinverno 1995), i.e.…”

Section: Application To Free Air Gravity and Bathymetry Data Of Thesupporting

confidence: 88%

Self-affine gravity covariance model for the Bay of Bengal

Bansal

Dimri

2005

Geophysical Journal International

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S U M M A R YAn anisotropic covariance model embedded with self-affine characteristics of gravity and bathymetry anomalies is proposed. High-resolution free air gravity data obtained from the high-density satellite altimetry data have been used to study the self-affinity of the gravity measurements. The digital terrain model-5 (DTM-5) is used for the bathymetry data. The Hurst coefficients (H) are calculated using 2-D power spectral density of the free air gravity and bathymetry data from 140 blocks of 1 • × 1 • . The Hurst coefficients (H) are also determined for free air gravity data along 24 orthogonal profiles using rescaled range (R/S) analysis. Further, the estimated H values are used in order to find the appropriate gravity and bathymetry covariance model for the area. The average value of H is more for 2-D free air gravity data as compared with the bathymetry data. The H values from the 1-D free air gravity profiles are found to be 0.75 and 0.77 along the N-S and E-W directions, respectively. The Von Karman covariance model with different correlation lengths is found to be suitable for 2-D and 1-D free air gravity and 2-D bathymetry data.

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Section: Application To Free Air Gravity and Bathymetry Data Of Thesupporting

confidence: 88%

Self-affine gravity covariance model for the Bay of Bengal

Bansal

Dimri

2005

Geophysical Journal International

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“…Time series for the three variables (temperature, pressure and precipitation) have already been shown [Hurst et. al., 1965;Mandelbrot and Wallis, 1969;Fluegeman and Snow, 1989;Hsui et. al., 1993; Turcotte, 1992] to correspond to a fractional Brownian motion [Mandelbrot, 1983].…”

mentioning

confidence: 99%

“…However, we will be concerned only with persistence behaviour since all geophysical time records analysed till date [Hurst et. al., 1965;Mandelbrot and Wallis, 1969;Fluegeman and Snow, 1989;Hsui et. al., 1993;Turcotte, 1992]…”

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confidence: 99%

A climate predictability index and its applications

Rangarajan¹,

Sant²

1997

Geophysical Research Letters

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Abstract. In this letter, a climate predictability index is proposed. It quantifies the predictability of three major components comprising the climate temperature, pressure and precipitation. The quantification is done using a fractal dimensional analysis of the corresponding time series. The climate predictability index is calculated for 25 stations spread throughout India using the Global Historical Climatology Network dataset. Change in the index with the seasons suggests a strong influence of more than one climatic dynamics. In such cases, calculations done using mean yearly data are suspect. The index is shown to be useful in studying the interplay between various climatic components (viz. temperature, pressure and precipitation). Changes in predictability indices for temperature and pressure are seen to affect the index for precipitation. The index can be used as a discriminant for determining which stations are selected for use in developing regional climatic models.

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“…Using different proxy records, adopting climatic time series analyses, several researchers have attempted in this direction. Among the various approaches adopted, spectral and fractal analyses are perhaps the most widely used tools [e.g., Ghil et al , 2002; Fluegeman and Snow , 1989; Schmitt et al , 1995; Yiou et al , 1997]. The former involves detection of periodicity while the latter is content with extraction of self‐similarity.…”

Section: Introductionmentioning

confidence: 99%

Sea surface temperatures in cooler climate stages bear more similarity with atmospheric CO₂ forcing

et al. 2012

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[1] The interglacial Marine Isotope Stage (MIS) 11 received special attention due to its remarkable resemblance with present-day climate. Based on synchronicity of marine, ice sheet and terrestrial proxy responses, warm episodes with intervening cool phase(s) at MIS 11 are qualitatively established. Here we quantitatively evaluate 15 climate proxies during 368-552 kyr intervals adopting a novel long-range cross-correlation approach and information theory based similarity measures. We also estimate the information flow rate and dominant flow direction between these climate variables using transfer entropy and the related directionality index. Our results unequivocally establish that atmospheric CO 2 (p CO2 ) is the driving signal while all other proxies used in this study are the responses. The climate forcing greenhouse gas, the atmospheric CO 2 (p CO2 ) and the response signals like sea surface temperature (SST) and carbon isotope composition of total organic carbon (d 13 C TOC ) proxies are strongly correlated ($1 or À1) without significant observable time lag (less than 1 kyr). Various substages of MIS 11 are recognizable in the SST data alone based on normalized similarity measures. Additionally, eight more proxies from lacustrine sediments are identified as primary. During the cooler substages these proxies bear more similarity with ambient atmospheric p CO2 . Thus, the information theory-based similarity measures suggest that atmospheric CO 2 fluctuations are best captured by at least 9 climate proxies during cooler interglacial events. Based on the results related to interglacial MIS 11 and 13 obtained in this study, an important implication relevant to anthropogenic CO 2 input to the present-day atmosphere can be distilled. It is that sensitive and better-coupled response proxies such as SST and MAT, which already show an increasing trend, are likely to behave in a more dissimilar manner in future. That is, they tend to behave more independently in the near future ($0.75 kyr).Citation: Das Sharma, S., D. S. Ramesh, C. Bapanayya, and P. A. Raju (2012), Sea surface temperatures in cooler climate stages bear more similarity with atmospheric CO 2 forcing,

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Fractal analysis of long-range paleoclimatic data: Oxygen isotope record of pacific core V28-239

Cited by 30 publications

References 9 publications

Self-affine gravity covariance model for the Bay of Bengal

Self-affine gravity covariance model for the Bay of Bengal

A climate predictability index and its applications

Sea surface temperatures in cooler climate stages bear more similarity with atmospheric CO₂ forcing

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Fractal analysis of long-range paleoclimatic data: Oxygen isotope record of pacific core V28-239

Cited by 30 publications

References 9 publications

Self-affine gravity covariance model for the Bay of Bengal

Self-affine gravity covariance model for the Bay of Bengal

A climate predictability index and its applications

Sea surface temperatures in cooler climate stages bear more similarity with atmospheric CO2 forcing

Contact Info

Product

Resources

About

Sea surface temperatures in cooler climate stages bear more similarity with atmospheric CO₂ forcing