| Summary: | Polar amplification is a prominent feature of recent and projected climate change. The Arctic region shows some of the strongest signs of climate change, including sea-ice retreat and temperatures increasing at twice the rate averaged over the northern hemisphere. A major concern for humanity is the sea-level rise associated with the melting of the ice-sheets and glaciers due to climate change. The atmospheric circulation transports an amount of energy into to the Arctic equivalent that received by the Arctic from the Sun. Thus, the atmospheric energy transport is an important subject to study in the light of Arctic climate change. The atmospheric energy transport may be decomposed into contributions by planetary-scale waves such as Rossby waves and small-scale waves such as cyclones. The energy transport contributions by the different length-scale separated systems are shown to affect the Arctic differently. The meridional energy transport is separated into length-scale contributions using a Fourier-series-based approach. Here we evaluate this approach by comparing it to a novel wavelet-based length-scale decomposition, developed as a part of this project. Further a machine-learning-based length-scale decomposition approximator is developed. The approximator may be applied to climate model output to investigate future changes in the length-scale decomposed energy transport. From the comparisons it is apparent that both the Fourier and wavelet-based length-scale decompositions are skilled approaches, which produce physically meaningful decompositions. Additionally, the Fourier-based decomposition is further developed to yield a length-scale decomposition on a latitude-longitude grid. Once evaluated the Fourier and wavelet-based decompositions are applied to investigate the effects of recent climate change on the atmospheric energy transport, and how these changes affect the Arctic and the Greenland ice-sheet. Through these studies it is conspicuous that shifts of energy transport between length-scale components ...
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