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Title: New Methods for Inferring Past Climatic Changes from Underground Temperatures
Author: Hopcroft, Peter Orlando
ISNI:       0000 0004 2681 5224
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2009
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In this thesis new methods have been developed for the recovery of past surface temperaturevariations from underground temperature-depth profiles. This has been undertakenfrom a Bayesian standpoint with an emphasis on model comparison, which allowsdifferently parameterised inverse models (inferred past temperature histories) to be automaticallyconstructed and compared in the light of the data and the prescribed priorinformation. In the first contribution a new method for inverting temperature-depth profiles ispresented which relies on trans-dimensional Bayesian sampling. The temperature historiesare parameterised in terms of a variable number of linear segments over time. Relying onthe natural parsimony of Bayesian inference, whereby simpler models which can adequatelyexplain the data are preferred, the complexity or roughness of the temperature historiescan be determined without the need for explicit a priori smoothing. This method thereforeallows a more objective inference of the past temperature changes. These concepts are extended to the spatial domain in the following chapter using themethod of Bayesian partition modelling. This seeks to find the posterior distribution ofthe number and spatial distribution of independent temperature histories given a spatiallydistributed ensemble of temperature-depth profiles. The results from applicationto 23 real boreholes in the UK are discussed in detail and show a clear preference for8 or 9 independent (and mostly contrasting) temperature histories. It is thus concludedthat the majority of these data cannot be considered as reliable sources of palaeoclimatereconstruction. A 3D finite element heat transfer forward model is developed in the latter part of thethesis, 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 reductionin average ground surface temperature with altitude is included in the forward model andinversion of the resultant profiles using a 1D forward model is shown to give significantdiscrepancies in the inferred temperature histories. Finally the inversion results fromthe Bayesian formulation are compared with those using a conventional gradient descentmethod. The thesis concludes with some possibilities for future research in this field which buildsupon the work presented herein.
Supervisor: Gallagher, Kerry ; Pain, Christopher Sponsor: NERC ; EPSRC
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral