We present a new additive method, nicknamed sage for Simplified Additive Gaussian processes Emulator, to emulate climate model Perturbed Parameter Ensembles (PPEs). It estimates the value of a climate model output as the sum of additive terms. Each additive term is the mean of a Gaussian Process, and corresponds to the impact of a parameter or parameter group on the variable of interest. This design caters to the sparsity of PPEs which are characterized by limited ensemble members and high dimensionality of the parameter space. sage quantifies the variability explained by different parameters and parameter groups, providing additional insights on the parameter-climate model output relationship. We apply the method to two climate model PPEs and compare it to a fully connected Neural Network. The two methods have comparable performance with both PPEs, but sage provides insights on parameter and parameter group importance as well as diagnostics useful for optimizing PPE design. Insights gained are valid regardless of the emulator method used, and have not been previously addressed. Our work highlights that analyzing the PPE used to train an emulator is different from analyzing data generated from an emulator trained on the PPE, as the former provides more insights on the data structure in the PPE which could help inform the emulator design.

翻译：本文提出了一种新的加法方法，称为简化加法高斯过程仿真器（sage），用于仿真气候模型的扰动参数集合。该方法将气候模型输出值估计为多个加法项之和。每个加法项对应一个高斯过程的均值，代表单个参数或参数组对目标变量的影响。这种设计专门针对扰动参数集合的稀疏性特点——即集合成员有限而参数空间维度较高。sage能够量化不同参数及参数组解释的变异性，从而为参数与气候模型输出之间的关系提供新的见解。我们将该方法应用于两个气候模型扰动参数集合，并与全连接神经网络进行比较。两种方法在两个扰动参数集合上表现出相当的性能，但sage能够提供关于参数及参数组重要性的见解，以及有助于优化扰动参数集合设计的诊断信息。这些见解不依赖于所使用的仿真器方法，且此前尚未被探讨。我们的研究强调：分析用于训练仿真器的扰动参数集合，与分析基于该集合训练的仿真器所生成的数据存在本质区别——前者能更深入地揭示扰动参数集合的数据结构特征，从而为仿真器设计提供重要参考。