Multifractal Analysis for Evaluating the Representation of Clouds in Global Kilometre-Scale Models

Clouds are one of the largest sources of uncertainty in climate predictions. Global km-scale models need to simulate clouds and precipitation accurately to predict future climates. To isolate issues in their representation of clouds, models need to be thoroughly evaluated with observations. Here, we introduce multifractal analysis as a method for evaluating km-scale simulations. We apply it to outgoing longwave radiation fields to investigate structural differences between observed and simulated anvil clouds. We compute scaling parameters which compactly characterise the scaling behaviour of clouds and can be compared across simulations and observations. We use this method to evaluate the nextGEMS ICON simulations via comparison with observations from the geostationary satellite GOES-16. We find that multifractal scaling exponents in the ICON model are significantly lower than in observations. We conclude that too much variability is contained in the small scales (<100 km) leading to less organised convection and smaller, isolated anvils.