Non-Linear Dimensionality Reduction With a Variational Encoder Decoder to Understand Convective Processes in Climate Models.
Non-Linear Dimensionality Reduction With a Variational Encoder Decoder to Understand Convective Processes in Climate Models.
复制标题
DOI：
10.1029/2022ms003130
复制
发表时间：
2022-08
影响因子：
6.8
通讯作者：
Eyring, Veronika
中科院分区：
文献类型：
--
作者：
Behrens, Gunnar;Beucler, Tom;Gentine, Pierre;Iglesias-Suarez, Fernando;Pritchard, Michael;Eyring, Veronika
关键词：
深度学习可以准确地表示气候模型中的次网格尺度对流过程，从高分辨率模拟中学习。然而，深度学习方法通常由于内部维度大而缺乏可解释性，导致这些方法的可信度降低。在这里，我们使用变分编码器解码器结构（VED），一种非线性降维技术，来学习和理解水行星超参数化气候模型模拟中的对流过程，其中显式模拟了深对流过程。我们表明，与以前基于前馈神经网络的深度学习研究类似，VED能够学习和准确地再现对流过程。在过去的工作相比，我们表明，这可以通过压缩到只有五个潜在的节点的原始信息。因此，VED可以用来理解对流过程，并通过探索其潜在的维度来描绘对流模式。对潜空间的仔细研究使得能够识别不同的对流系统：（a）稳定条件与具有低输出长波辐射和强降水的深对流明显不同;（B）高光学薄卷云与低光学厚积云分离;（c）浅对流过程与大尺度水分含量和地面非绝热加热有关。我们的研究结果表明，VED可以准确地表示气候模型中的对流过程，同时能够解释和更好地理解亚网格尺度的物理过程，为越来越多的可解释的机器学习参数化铺平了道路。变分编码解码器（VED）可以从粗尺度气候状态预测亚网格尺度热力学VED的潜空间可以区分对流系统，包括浅/深/无对流VED的潜空间揭示了不同纬度对流可预报性的主要来源
Deep learning can accurately represent sub‐grid‐scale convective processes in climate models, learning from high resolution simulations. However, deep learning methods usually lack interpretability due to large internal dimensionality, resulting in reduced trustworthiness in these methods. Here, we use Variational Encoder Decoder structures (VED), a non‐linear dimensionality reduction technique, to learn and understand convective processes in an aquaplanet superparameterized climate model simulation, where deep convective processes are simulated explicitly. We show that similar to previous deep learning studies based on feed‐forward neural nets, the VED is capable of learning and accurately reproducing convective processes. In contrast to past work, we show this can be achieved by compressing the original information into only five latent nodes. As a result, the VED can be used to understand convective processes and delineate modes of convection through the exploration of its latent dimensions. A close investigation of the latent space enables the identification of different convective regimes: (a) stable conditions are clearly distinguished from deep convection with low outgoing longwave radiation and strong precipitation; (b) high optically thin cirrus‐like clouds are separated from low optically thick cumulus clouds; and (c) shallow convective processes are associated with large‐scale moisture content and surface diabatic heating. Our results demonstrate that VEDs can accurately represent convective processes in climate models, while enabling interpretability and better understanding of sub‐grid‐scale physical processes, paving the way to increasingly interpretable machine learning parameterizations with promising generative properties. A Variational Encoder Decoder (VED) can predict sub‐grid‐scale thermodynamics from the coarse‐scale climate state The VED's latent space can distinguish convective regimes, including shallow/deep/no convection The VED's latent space reveals the main sources of convective predictability at different latitudes