Assessing the Robustness of Climate Foundation Models under No-Analog Distribution Shifts

1. 1Identify no-analog distribution shifts in climate data
2. 2Develop climate foundation models using machine learning algorithms
3. 3Evaluate the robustness of these models under different climate scenarios
4. 4Analyze the results to understand the limitations of current models

Climate researchers and AI engineers on a team benefit from this research as it helps them understand the limitations of machine learning models in predicting future climate states, and why they need to develop more robust models

💡 Machine learning based climate emulators may not generalize well to future climate states due to non-stationarities

💡 Climate models face challenges under no-analog distribution shifts due to climate change #AIforClimate