Finding the Perfect Fit: Applying Regression Models to ClimateBench v1.0

TL;DR

This paper evaluates non-linear regression models, especially Gaussian Process Regression, for climate data emulation using ClimateBench v1.0, highlighting their performance and computational trade-offs to improve climate modeling efficiency.

Contribution

It compares the emulation capabilities of three non-linear regression models on ClimateBench, identifying Gaussian Process Regression as the top performer and exploring enhancements like composite kernels.

Findings

Gaussian Process Regressor outperforms others in emulation accuracy

Support Vector and Kernel Ridge models offer competitive results

Trade-offs exist between model performance and computational resources

Abstract

Climate projections using data driven machine learning models acting as emulators, is one of the prevailing areas of research to enable policy makers make informed decisions. Use of machine learning emulators as surrogates for computationally heavy GCM simulators reduces time and carbon footprints. In this direction, ClimateBench [1] is a recently curated benchmarking dataset for evaluating the performance of machine learning emulators designed for climate data. Recent studies have reported that despite being considered fundamental, regression models offer several advantages pertaining to climate emulations. In particular, by leveraging the kernel trick, regression models can capture complex relationships and improve their predictive capabilities. This study focuses on evaluating non-linear regression models using the aforementioned dataset. Specifically, we compare the emulation capabilities of three non-linear regression models. Among them, Gaussian Process Regressor demonstrates the best-in-class performance against standard evaluation metrics used for climate field emulation studies. However, Gaussian Process Regression suffers from being computational resource hungry in terms of space and time complexity. Alternatively, Support Vector and Kernel Ridge models also deliver competitive results and but there are certain trade-offs to be addressed. Additionally, we are actively investigating the performance of composite kernels and techniques such as variational inference to further enhance the performance of the regression models and effectively model complex non-linear patterns, including phenomena like precipitation.