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Time series derived from paleoclimate archives are often irregularly sampled in time and thus not analysable using standard statistical methods such as correlation analyses. Although measures for the similarity between time series have been proposed for irregular time series, they do not account for the time scale dependency of the relationship. Stochastically distributed temporal sampling irregularities act qualitatively as a low-pass filter reducing the influence of fast variations from frequencies higher than about 0.5 (Delta t(max))(-1) , where Delta t(max), is the maximum time interval between observations. This may lead to overestimated correlations if the true correlation increases with time scale. Typically, correlations are underestimated due to a non-simultaneous sampling of time series. Here, we investigated different techniques to estimate time scale dependent correlations of weakly irregularly sampled time series, with a particular focus on different resampling methods and filters of varying complexity. The methods were tested on ensembles of synthetic time series that mimic the characteristics of Holocene marine sediment temperature proxy records. We found that a linear interpolation of the irregular time series onto a regular grid, followed by a simple Gaussian filter was the best approach to deal with the irregularity and account for the time scale dependence. This approach had both, minimal filter artefacts, particularly on short time scales, and a minimal loss of information due to filter length.
Holocene temperature proxy records are commonly used in quantitative synthesis and model-data comparisons. However, comparing correlations between time series from records collected in proximity to one another with the expected correlations based on climate model simulations indicates either regional or noisy climate signals in Holocene temperature proxy records. In this study, we evaluate the consistency of spatial correlations present in Holocene proxy records with those found in data from the Last Glacial Maximum (LGM). Specifically, we predict correlations expected in LGM proxy records if the only difference to Holocene correlations would be due to more time uncertainty and more climate variability in the LGM. We compare this simple prediction to the actual correlation structure in the LGM proxy records. We found that time series data of ice-core stable isotope records and planktonic foraminifera Mg/Ca ratios were consistent between the Holocene and LGM periods, while time series of Uk'37 proxy records were not as we found no correlation between nearby LGM records. Our results support the finding of highly regional or noisy marine proxy records in the compilation analysed here and suggest the need for further studies on the role of climate proxies and the processes of climate signal recording and preservation.
Global patterns of declining temperature variability from the Last Glacial Maximum to the Holocene
(2018)
Changes in climate variability are as important for society to address as are changes in mean climate(1). Contrasting temperature variability during the Last Glacial Maximum and the Holocene can provide insights into the relationship between the mean state of the climate and its variability(2,3). However, although glacial-interglacial changes in variability have been quantified for Greenland(2), a global view remains elusive. Here we use a network of marine and terrestrial temperature proxies to show that temperature variability decreased globally by a factor of four as the climate warmed by 3-8 degrees Celsius from the Last Glacial Maximum (around 21,000 years ago) to the Holocene epoch (the past 11,500 years). This decrease had a clear zonal pattern, with little change in the tropics (by a factor of only 1.6-2.8) and greater change in the mid-latitudes of both hemispheres (by a factor of 3.3-14). By contrast, Greenland ice-core records show a reduction in temperature variability by a factor of 73, suggesting influences beyond local temperature or a decoupling of atmospheric and global surface temperature variability for Greenland. The overall pattern of reduced variability can be explained by changes in the meridional temperature gradient, a mechanism that points to further decreases in temperature variability in a warmer future.