ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning

TL;DR

ClimateSet is a comprehensive, large-scale climate model dataset designed to support machine learning tasks like emulation and prediction, enabling scalable climate analysis and policy support.

Contribution

We introduce ClimateSet, a large, ML-ready climate dataset with a modular pipeline, and demonstrate its use as a benchmark for climate model emulation.

Findings

ML models' performance varies across different climate models

A super emulator trained on multiple models can efficiently project new scenarios

ClimateSet facilitates scalable ML applications in climate science

Abstract

Climate models have been key for assessing the impact of climate change and simulating future climate scenarios. The machine learning (ML) community has taken an increased interest in supporting climate scientists' efforts on various tasks such as climate model emulation, downscaling, and prediction tasks. Many of those tasks have been addressed on datasets created with single climate models. However, both the climate science and ML communities have suggested that to address those tasks at scale, we need large, consistent, and ML-ready climate model datasets. Here, we introduce ClimateSet, a dataset containing the inputs and outputs of 36 climate models from the Input4MIPs and CMIP6 archives. In addition, we provide a modular dataset pipeline for retrieving and preprocessing additional climate models and scenarios. We showcase the potential of our dataset by using it as a benchmark for ML-based climate model emulation. We gain new insights about the performance and generalization capabilities of the different ML models by analyzing their performance across different climate models. Furthermore, the dataset can be used to train an ML emulator on several climate models instead of just one. Such a "super emulator" can quickly project new climate change scenarios, complementing existing scenarios already provided to policymakers. We believe ClimateSet will create the basis needed for the ML community to tackle climate-related tasks at scale.