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Abstract:
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Global climate model validation is integral for ensuring climate models produce realistic climatologies. However, many post-hoc statistical evaluation methods rely on simplifying models that discard information and fail to distinguish between different sources of variability. Here, we introduce a functional data analysis approach for computing sliced amplitude and phase distances between spatiotemporal processes, analogous to the sliced Wasserstein distance. Because our method uses time-warping, which respects temporal ordering, we can more precisely quantify differences between climate models than the previous Wasserstein-based approach. Finally, we apply our method to compare the performance of CMIP5 vs. CMIP6 models in representing historical surface temperature and precipitation from 1979-2005.
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