Ian Enting: Research Interests
Lattice statistics, complex systems, global change modelling, carbon cycle
interpretation.
Career research achievements.
Lattice Statistical Physics
Stochastic cellular automata
Stochastic cellular automata are being investigated on the premise that
they can be analysed using the standard techniques of lattice statistical
physics and that the behaviour of determininistic cellular automata can
be better understood by regarding them as limits of the stochastic case.
(MORE).
Finite lattice method (FLM) of series expansion
The FLM has proved one of the most effective techniques
for calculating power series expansions
to investigate phase transitions in lattice statistics models,
particularly in two dimensions.
The use of the FLM has been an active area of research
in the Department of Mathematics and Statistics.
(MORE).
Student project
Yaoban Chan has recently completed a PhD,
supervised by Ian Enting, Andrew Rechnitzer and Tony Guttmann,
working on a range of projects in lattice statistical mechanics -
including corner transfer matrices, knotting of polygons and walks in strips.
Carbon Cycle Modelling
Carbon dioxide is the most important of the greenhouse gases that are causing
global climate change.
Two strands of carbon cycle modelling are predictive (assessing consequences
of future emissions) and interpretive (analysing the current behaviour
of the carbon cycle).
Predictive modelling of CO2 and global change
Predictive modelling of the carbon cycle aims to calculate the consequences
of choices of future emissions of carbon dioxide. Current research includes
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Analysis of consquences of alternative choices for future patterns
of greenhouse gas emission, including the so-called Brazilian
Proposal which, as an alternative to the Kyoto Protocol, proposed setting
targets for emission reductions that depend on the relative extent to which
individual nations are responsible for the greenhouse effect.
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Development of techniques for systematic analysis of uncertainties, using
computational approaches derived from statistical physics.
(MORE).
Intepretive modelling
A second strand of carbon cycle modelling, aims to interpret the current
state of the carbon cycle. The objectives are:
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more accurate assessments of future changes;
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detection of surprises.
One form of interpretive modelling involves deducing surface exchanges
on carbon dioxide from measurements of concentrations. This was the subject
of the book Inverse Problems in Atmospheric Constituent Transport,
by I.G. Enting, published by CUP, 2002. Much of this work is carried out
in collaboration with participants in the TransCom
intercomparison. A specific project involves developing statistical diagnostics
in order to improve the reliability of inversions. (MORE).
Algorithmic differentiation
This is a important tool for interpretive modelling, providing a
way of calculating sensitivities (through tangent linear models) and
gradients (through adjoint models). (MORE).
Algorithmic differentiation is also being applied for sensitivities in projections of
global change and analysis of policy options such as the Brazilian Proposal.
Complex Systems Science
The study of complex systems is of increasing importance, and it is, of
course, the aim of the Centre of Excellence in the Mathematics and Statistics
of Complex Systems. My research explores several strands of complex systems
science:
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Generic complex systems properties such as emergence, contingency and influence
of multiple scales, drawing on concepts derived from lattice statistical
physics in general and stochastic cellular automata in particular.
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The applicability of inversions, using observed boundary conditions as
an alternative to controlled experiment, when studying natural systems.
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Ian Enting: last update 28/11/05.