论文标题
在一系列分辨率的气候建模的子网格过程的稳定机器学习参数化
Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
论文作者
论文摘要
全球气候模型代表了小规模的过程，例如云和使用的准经验模型，称为参数化，这些参数化是气候投影不确定性的主要原因。一种有希望的替代方法是使用机器学习直接从高分辨率模型输出中构建新的参数化。但是，从三维模型输出中学到的参数化尚未成功用于气候模拟。在这里，我们使用一个随机森林来学习从三维高分辨率大气模型的输出中学习亚网格过程的参数化。将此参数化整合到大气模型中会导致在粗分辨率下进行稳定的模拟，从而复制高分辨率模拟的气候。参数化遵守物理约束并捕获重要的统计数据，例如降水极端。从完全三维模拟中学习的能力为学习参数化的机会提供了从现在正在出现的各种全球高分辨率仿真中学习参数的机会。
Global climate models represent small-scale processes such as clouds and convection using quasi-empirical models known as parameterizations, and these parameterizations are a leading cause of uncertainty in climate projections. A promising alternative approach is to use machine learning to build new parameterizations directly from high-resolution model output. However, parameterizations learned from three-dimensional model output have not yet been successfully used for simulations of climate. Here we use a random forest to learn a parameterization of subgrid processes from output of a three-dimensional high-resolution atmospheric model. Integrating this parameterization into the atmospheric model leads to stable simulations at coarse resolution that replicate the climate of the high-resolution simulation. The parameterization obeys physical constraints and captures important statistics such as precipitation extremes. The ability to learn from a fully three-dimensional simulation presents an opportunity for learning parameterizations from the wide range of global high-resolution simulations that are now emerging.
