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Response function models are often used to represent the behaviour of complex, high order global carbon cycle (GCC) and climate models in applications which require short model run times. Although apparently black-box, these response function models need not necessarily be entirely opaque, but instead may also convey useful insights into the properties of the parent model or process. By exploiting a transfer function (TF) framework to analyse the Lenton GCC model, this paper attempts to demonstrate that response function representations of GCC models can sometimes also provide structural information on the parent model from which they are identified and calibrated. We take a fifth-order TF identified from the impulse response of the Lenton model atmospheric burden, and decompose this to show how it can be re-expresses in a generic five-box form in sympathy with the structure of the parent model. [ABSTRACT FROM AUTHOR]

This paper formally introduces the concept of mitigation as a stochastic control problem. This is illustrated by applying a digital state variable feedback control approach known as Non-Minimum State Space (NMSS) control to the problem of specifying carbon emissions to control atmospheric CO2 concentrations in the presence of uncertainty. It is shown that the control approach naturally lends itself to integrating both anticipatory and reflexive mitigation strategies within a single unified framework. The framework explicitly considers the closed-loop nature of climate mitigation, and employs a policy orientated optimisation procedure to specify the properties of this closed-loop system. The product of this exercise is a control law that is suitably conditioned to regulate atmospheric CO2 concentrations through assimilating online information within a 25-year review cycle framework. It is shown that the optimal control law is also robust when faced with significant levels of uncertainty about the functioning of the global carbon cycle. [ABSTRACT FROM AUTHOR]