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

Abstract

[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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“…This exchange of information essentially has direction with no bearing on their common history or inputs, unlike cross correlation. Therefore, it can be utilized for determining the cause and effect relationship between two variables [ Das Sharma et al , ; Vichare et al , ]. Transfer entropy between two random variables or processes x and y is mathematically represented as where P(y(t + s), x(t)) is the joint probability of y(t + s) and x(t) and P(y(t + τ ), y(t), x(t)) is the joint probability of y (t + τ ), y (t) and x (t).…”

Quantitative assessment of drivers of recent global temperature variability: an information theoretic approach

“…To quantify actual information explicitly exchanged between these two variables x and y, an information measure known as Transfer Entropy was introduced by [34]. This overcomes the limitations posed by measures of correlation and other entropy metrics by enabling us to distinctly quantify actual information exchanged along with the directional flow of information between any two variables with no bearing on their common history or inputs [34,12,11,3,47]. The TE from a process x to another process y after a time lag τ is the quantity of information that the state of y has at a time t + τ based exclusively on the state of x at time t. This can be represented by the following expression [28]:…”

“…In the presence of multiple drivers, estimation of relative contribution of each variable/proxy to the observed response signal assumes importance. To enable this, [48] have introduced a measure known as normalized transfer entropy (NTE ) by accounting for the amount of information stored in x(t) and y(t + τ ) which is given as [11]:…”

A new method for forecasting the solar cycle descent time

“…This is accomplished by applying a moving average filter to the time series (Carbone et al 2004), which is akin to detrending the time series in order to extract statistically meaningful information. In particular, adequate caution should be exercised to avoid both under-and oversmoothing of data (Das Sharma et al 2012). Several moving average time window sizes (w s ) are examined.…”