Shallow Ice Borehole Thermometry: Bayesian inversion techniques for reconstructing the Antarctic temperature evolution of the last centuries

Reconstructing paleo-climate information using subsurface temperature data from ice borehole thermometry has proven to be a promising approach in climate reconstruction. Using Bayesian inversion, we aim to reconstruct the temperature evolution of Antarctica over the last centuries. A forward heat transfer model is implemented that simulates borehole temperature profiles for time-dependent surface temperatures. We invert this forward model using Bayesian inference by modeling known uncertainties as priors and obtain the reconstructed surface temperatures with associated uncertainty information. Through synthetic examples, we highlight the extent to which signals can be retrieved from a 200-meter borehole along with its uncertainty information. We further apply this to measured data for surface temperature reconstruction from EPICA Dronning Maud Land and analyze the results.