Journal Article

Multifractal Analysis for Evaluating the Representation of Clouds in Global Kilometer‐Scale Models

Weiss, Philipp, Freischem, Lilli J., Christensen, Hannah M., Stier, Philip

2024

Overview

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 fractal parameters which compactly characterize the scaling behavior 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 (<100km)$(< 100\\ \\mathrm{k}\\mathrm{m})$leading to less organized convection and smaller, isolated anvils. Plain Language Summary In this paper, we present a new approach to evaluating state‐of‐the‐art high‐resolution climate models. We use a type of analysis that captures how a field like outgoing radiation varies between two points in space; it is called multifractal analysis. We apply multifractal analysis to snapshots of climate model simulations and satellite observations, and compare the results to evaluate the model. In contrast to traditional evaluation approaches, our method focuses on the evaluation of the spatio‐temporal structure of cloud fields, exploiting previously untapped information content. Hence, it can take into account the fine details in time and space that high‐resolution climate models provide. We use our method to evaluate the ICON atmospheric model. We find that the simulations does not contain enough large clusters of clouds, as found in big thunderstorms, but instead clouds are randomly distributed in space: the simulated clouds are not organized enough. Key Points Quantifiable, structural evaluation metrics such as multifractal analysis should be used to evaluate and improve km‐scale models Multifractal analysis finds that deep convection in the ICON model is not organized enough leading to smaller fractal parameters The model's bias toward smaller fractal parameters can be attributed to clouds simulated over the ocean

• Publisher: John Wiley & Sons, Inc,Wiley
• Subject: Anvil clouds / Atmospheric models / Climate / Climate models / Climate prediction / Climatic analysis / Clouds / Convection / convective organization / deep convection / Fractal analysis / fractals / Future climates / Geostationary satellites / global km‐scale models / Long wave radiation / model evaluation / Radiation / Representations / Satellite observation / Satellites / Scale models / Scaling / Simulation / Spacecraft recovery / Synchronous satellites / Thunderstorms / Uncertainty analysis