A promising approach to improve cloud parameterizations within climate models and thus climate projections is to use deep learning in combination with training data from storm-resolving model (SRM) simulations. The ICOsahedral Non-hydrostatic (ICON) modeling framework permits simulations ranging from numerical weather prediction to climate projections, making it an ideal target to develop neural network (NN) based parameterizations for sub-grid scale processes. Within the ICON framework, we train NN based cloud cover parameterizations with coarse-grained data based on realistic regional and global ICON SRM simulations. We set up three different types of NNs that differ in the degree of vertical locality they assume for diagnosing cloud cover from coarse-grained atmospheric state variables. The NNs accurately estimate sub-grid scale cloud cover from coarse-grained data that has similar geographical characteristics as their training data. Additionally, globally trained NNs can reproduce sub-grid scale cloud cover of the regional SRM simulation. Using the game-theory based interpretability library SHapley Additive exPlanations, we identify an overemphasis on specific humidity and cloud ice as the reason why our column-based NN cannot perfectly generalize from the global to the regional coarse-grained SRM data. The interpretability tool also helps visualize similarities and differences in feature importance between regionally and globally trained column-based NNs, and reveals a local relationship between their cloud cover predictions and the thermodynamic environment. Our results show the potential of deep learning to derive accurate yet interpretable cloud cover parameterizations from global SRMs, and suggest that neighborhood-based models may be a good compromise between accuracy and generalizability.
翻译:改善气候模型内云度参数化,从而进行气候预测的一个有希望的方法是利用深度学习,结合来自风暴溶解模型模拟的培训数据,利用来自风暴溶解模型(SRM)的准确度数据。ICOSahedal None-hystistic(ICON)模型框架允许模拟从数字天气预测到气候预测等各种范围,使基于神经网络的普通电网参数化成为开发与培训数据相似的普通电网系统规模化进程的理想目标。此外,在ICON框架内,我们培训基于NN的云度参数化与基于现实的区域和全球ICON SRM模拟的粗度级云度云度数据化相结合。我们设置了三种不同的NNF,这三种类型在它们假设的垂直位置上不同,它们所假设的云度不同,从它们假定的粗度大气状态变量的云度覆盖范围范围,我们准确地估计了以粗度为基础的云度云度覆盖范围覆盖范围,同时我们所训练的SRMM模型和直观区域结构之间的精确度变化,我们所了解的云度和直观的云度结构结构结构中,我们所了解的云度解释的准确度关系也表明了全球范围的深度环境的准确度和精确度变化。