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New Methods for Inferring Past Climatic Changes from Underground Temperatures

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Title: New Methods for Inferring Past Climatic Changes from Underground Temperatures
Authors: Hopcroft, Peter Orlando
Item Type: Thesis or dissertation
Abstract: In this thesis new methods have been developed for the recovery of past surface temperature variations from underground temperature-depth profiles. This has been undertaken from a Bayesian standpoint with an emphasis on model comparison, which allows differently parameterised inverse models (inferred past temperature histories) to be automatically constructed and compared in the light of the data and the prescribed prior information. In the first contribution a new method for inverting temperature-depth profiles is presented which relies on trans-dimensional Bayesian sampling. The temperature histories are parameterised in terms of a variable number of linear segments over time. Relying on the natural parsimony of Bayesian inference, whereby simpler models which can adequately explain the data are preferred, the complexity or roughness of the temperature histories can be determined without the need for explicit a priori smoothing. This method therefore allows a more objective inference of the past temperature changes. These concepts are extended to the spatial domain in the following chapter using the method of Bayesian partition modelling. This seeks to find the posterior distribution of the number and spatial distribution of independent temperature histories given a spatially distributed ensemble of temperature-depth profiles. The results from application to 23 real boreholes in the UK are discussed in detail and show a clear preference for 8 or 9 independent (and mostly contrasting) temperature histories. It is thus concluded that the majority of these data cannot be considered as reliable sources of palaeoclimate reconstruction. A 3D finite element heat transfer forward model is developed in the latter part of the thesis, and is used to simulate underground temperatures. This forward model is linked to the first of the two Bayesian inverse methods described above. The effect of the reduction in average ground surface temperature with altitude is included in the forward model and inversion of the resultant profiles using a 1D forward model is shown to give significant discrepancies in the inferred temperature histories. Finally the inversion results from the Bayesian formulation are compared with those using a conventional gradient descent method. The thesis concludes with some possibilities for future research in this field which builds upon the work presented herein.
Issue Date: Aug-2008
Date Awarded: Mar-2009
URI: http://hdl.handle.net/10044/1/4377
DOI: https://doi.org/10.25560/4377
Supervisor: Gallagher, Kerry
Pain, Christopher
Sponsor/Funder: NERC and EPSRC
Author: Hopcroft, Peter Orlando
Department: Earth Science and Engineering
Publisher: Imperial College London
Qualification Level: Doctoral
Qualification Name: Doctor of Philosophy (PhD)
Appears in Collections:Earth Science and Engineering PhD theses

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