| Summary: | Climate sensitivity is the change of the global-mean surface temperature in response to a doubling of the CO2 concentration. It is typically used to describe climate change and to inform decision-making for mitigation and adaptation. Climate sensitivity is often estimated in numerical climate model experiments. A remarkable result from these experiments is that climate sensitivity changes over time. This has been explained by the so-called pattern effect: Surface-warming patterns shift over time and trigger different feedback processes, hereby changing climate sensitivity. The aim of this thesis is to study the time-dependence of climate sensitivity and the pattern effect in climate models. Publicly available model data are employed to investigate differences between climate sensitivity change and its dependence on feedback processes across models. Moreover, proprietary model simulations with prescribed surface heat transport changes are conduced to study the pattern effect. The lapse-rate and cloud feedbacks are found to be strongly influenced by surface-warming pattern changes and drive the climate sensitivity change over time. Warming in different geographical regions has different impacts: Warming in the Southern Ocean changes climate sensitivity more than warming in the North Atlantic. To provide more robust climate sensitivity estimates, the representation of surface-warming patterns in climate models should be improved.
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