Estimating Antarctic climate variability of the last millennium

Climate variability is determined by the climate system’s internal variability as well as its response to external forcing. A quantitative understanding of past Antarctic climate variability is therefore essential if we are to attribute and to detect anthropogenic influences on the current and future climate in Antarctica, and thus crucial for projecting the evolution of the Antarctic ice sheet. Analysis of stable water isotope data from ice cores in principle provides information on past temperature variability, but its quantitative interpretation is challenged by strong non-climate effects. So far, the magnitude and timescale dependency of both the climate signal and the noise in Antarctic isotope records remains largely unknown. Here, we present a new spectral method to separate climate signal and noise in a large collection of published and new annually-resolved ice core records from East Antarctic Dronning Maud Land and the West Antarctic Ice Sheet, spanning the last 200—1000 years. With this, we derive the first timescale-dependent estimate of Antarctic temperature variability and isotopic signal-to-noise ratio on decadal to centennial time scales. In contrast to the raw isotope data, we find a stronger increase in temperature variability on longer time scales, which is similar between the two study regions and to estimates from reanalysis and marine SST data. Spatial analysis of the estimated noise levels allows the separation of local stratigraphic noise from larger-scale noise due to precipitation intermittency. Signal-to-noise ratios only reach values above one for multi-centennial time scales. Our findings illustrate a consistent way of interpreting isotope records, but also highlight the remaining knowledge gaps in our understanding of Holocene climate and ice-core derived variability. We emphasize that our new method is applicable for distinguishing climate variability from local effects for any spatial, well-dated array of proxy temperature records

Reconciling discrepancies between Uk37 and Mg/Ca reconstructions of Holocene marine temperature variability

Significant discrepancies exist between the detrended variability of late-Holocene marine temperatures inferred from Mg/Ca and Uk37 proxies, with the former showing substantially more centennial-scale variation than the latter. Discrepancies exceed that attributable to differences in location and persist across various calibrations, indicating that they are intrinsic to the proxy measurement. We demonstrate that these discrepancies can be reconciled using a statistical model that accounts for the effects of bioturbation, sampling and measurement noise, and aliasing of seasonal variability. The smaller number of individual samples incorporated into Mg/Ca measurements relative to Uk37 measurements leads to greater aliasing and generally accounts for the differences in the magnitude and distribution of variability. An inverse application of the statistical model is also developed and applied in order to estimate the spectrum of marine temperature variability after correcting for proxy distortions. The correction method is tested on surrogate data and shown to reliably estimate the spectrum of temperature variance when using high-resolution records. Applying this inverse method to the actual Mg/Ca and Uk37 data results in estimates of the spectrum of temperature variance that are consistent. This approach provides a basis by which to accurately estimate the distribution of intrinsic marine temperature variability from marine proxy records

Comparing estimation techniques for temporal scaling in palaeoclimate time series

Characterizing the variability across timescales is important for understanding the underlying dynamics of the Earth system. It remains challenging to do so from palaeoclimate archives since they are more often than not irregular, and traditional methods for producing timescale-dependent estimates of variability, such as the classical periodogram and the multitaper spectrum, generally require regular time sampling. We have compared those traditional methods using interpolation with interpolation-free methods, namely the Lomb–Scargle periodogram and the first-order Haar structure function. The ability of those methods to produce timescale-dependent estimates of variability when applied to irregular data was evaluated in a comparative framework, using surrogate palaeo-proxy data generated with realistic sampling. The metric we chose to compare them is the scaling exponent, i.e. the linear slope in log-transformed coordinates, since it summarizes the behaviour of the variability across timescales. We found that, for scaling estimates in irregular time series, the interpolation-free methods are to be preferred over the methods requiring interpolation as they allow for the utilization of the information from shorter timescales which are particularly affected by the irregularity. In addition, our results suggest that the Haar structure function is the safer choice of interpolation-free method since the Lomb–Scargle periodogram is unreliable when the underlying process generating the time series is not stationary. Given that we cannot know a priori what kind of scaling behaviour is contained in a palaeoclimate time series, and that it is also possible that this changes as a function of timescale, it is a desirable characteristic for the method to handle both stationary and non-stationary cases alike