Efficient surrogate modeling methods for large-scale Earth system models based on machine learning techniques

Understand

Improving predictive understanding of Earth system variability and change requires data-model integration.

• Efficient data-model integration for complex models requires surrogate modeling to reduce model evaluation time.
• However, building a surrogate of a large-scale Earth system model (ESM) with many output variables is computationally intensive because it involves a large number of expensive ESM simulations.
• In this effort, we propose an efficient surrogate method capable of using a few ESM runs to build an accurate and fast-to-evaluate surrogate system of model outputs over large spatial and temporal domains.