A simple emulator that enables interpretation of parameter-output relationships, applied to two climate model PPEs

TL;DR

This paper introduces Sage, a simple additive Gaussian process emulator for climate model PPEs, which effectively captures parameter-output relationships, offers interpretability, and compares favorably with neural networks.

Contribution

The paper presents Sage, a novel additive Gaussian process emulator tailored for high-dimensional, sparse climate PPEs, providing interpretability and insights into parameter importance.

Findings

Sage performs comparably to neural networks on climate PPEs.

Sage offers valuable insights into parameter and parameter group importance.

The method enhances understanding of PPE data structure and emulator design.

Abstract

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.