| Autor: |
Freischem, Lilli J., Weiss, Philipp, Christensen, Hannah M., Stier, Philip |
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| Zdroj: |
Geophysical Research Letters; 10/28/2024, Vol. 51 Issue 20, p1-10, 10p |
| Abstrakt: |
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 modelsMultifractal analysis finds that deep convection in the ICON model is not organized enough leading to smaller fractal parametersThe model's bias toward smaller fractal parameters can be attributed to clouds simulated over the ocean [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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