Understanding regional discrepancies using the climate model hierarchy

As Earth warms, regional climate signals are accumulating. Some signals, for example, land warming more than the ocean and the Arctic warming the most, were expected and successfully predicted. Underlying this success was the application of physical laws across a climate model hierarchy under the assumption that large and small spatial scales are well separated. With additional warming, however, discrepancies between real-world signals and model predictions are accumulating, especially at regional scales. In this talk, we will highlight the emerging list of model-observation discrepancies in historical trends. We demonstrate how the climate model hierarchy can be used to understand the physical processes underlying these discrepancies. We argue that progress can be made by filling gaps in the hierarchy and making more process-informed observations.