Daron, Joseph David
(2012)
Examining the decision-relevance of climate model information for the insurance industry.
PhD thesis, London School of Economics and Political Science.
Abstract
The insurance industry is becoming increasingly exposed to the adverse impacts of climate
variability and climate change. In developing policies and adapting strategies to better manage
climate risk, insurers and reinsurers are therefore engaging directly with the climate modelling
community to further understand the predictive capabilities of climate models and to develop
techniques to utilise climate model output. With an inherent interest in the present and future
frequency and magnitude of extreme climate-related loss events, insurers rely on the climate
modelling community to provide informative model projections at the relevant spatial and
temporal scales for insurance decisions. Furthermore, given the high economic stakes associated
with enacting strategies to address climate change, it is essential that climate model experiments
are designed to thoroughly explore the multiple sources of uncertainty.
Determining the reliability of model based projections is a precursor to examining their relevance
to the insurance industry and more widely to the climate change adaptation community.
Designing experiments which adequately account for uncertainty therefore requires careful
consideration of the nonlinear and chaotic properties of the climate system. Using the
well developed concepts of dynamical systems theory, simple nonlinear chaotic systems are
investigated to further understand what is meant by climate under climate change. The thesis
questions the conventional paradigm in which long-term climate prediction is treated purely as
a boundary value problem (predictability of the second kind). Using simple climate-like models
to draw analogies to the climate system, results are presented which support the emerging view
that climate prediction ought to be treated as both an initial value problem and a boundary
condition problem on all time scales. The research also examines the application of the ergodic
assumption in climate modelling and climate change adaptation decisions. By using idealised
model experiments, situations in which the ergodic assumption breaks down are illustrated.
Consideration is given to alternative model experimental designs which do not rely on the
assumption of ergodicity. Experimental results are presented which support the view that large
initial condition ensembles are required to detail the changing distribution of climate under
altered forcing conditions. It is argued that the role of chaos and nonlinear dynamic behaviour
ought to have more prominence in the discussion of the forecasting capabilities in climate
prediction.
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