Segmented linear integral correlation Kernel ensemble reconstruction: A new method for climate reconstructions with applications to Holocene era proxies from an East Antarctic ice core.

Abstract

Understanding past climate is essential to our knowledge of how our current climate system operates, and how it might respond to future change. Techniques to reconstruct climate history are challenging, and both accuracy and certainty are hampered by the quality of the datasets used. Here we both develop a new reconstruction tool and apply it to four ice core proxy based multi-millennial Holocene climate reconstructions, chosen because of their potential influence on East Antarctic climate. The new multi-proxy reconstruction method is called Segmented Linear Integral Correlation Kernel Ensemble Reconstruction (SLICKER). This method employs a segmented linear rather than Gaussian correlation approach and builds an ensemble of reconstructions with a best fit and spread related to the best estimate of uncertainty. This method is robust for non-linear, uneven or differently sampled data and produces high-fidelity reconstructions and associated uncertainty estimates. This new method has the potential to produce more realistic reconstructions, with associated uncertainty estimates based on robust statistical measures that are insensitive to outliers. The main findings from these new reconstructions are: Antarctica temperature shows multi-decadal variability over the last twelve thousand years with increased frequency over the last two thousand years; Zonal Wave 3 index and the Southern Annular Mode both show limited trends over the last two thousand years, but an increase since the 1970s CE; and the Indian Ocean Dipole Moment index has a twentieth century CE upward trend, and a thirteenth to sixteenth century CE below average period which may be related to volcanic activity.