› Research : IMAGe :: Statistical Emulator
›› Statistical Emulator for Global Climate Models
To understand future climate change, different Earth system models from groups worldwide simulate projections of future climates. However, results from these simulations are computationally very expensive, often requiring several months on a supercomputer. In this work, we provide a new statistical emulation method that may allow a realization of future climate projections within a day rather than several months. Specifically, we analyze the structure of several existing outputs from various climate models on a manifold of covariance matrices. The manifold covariance structure provides a method to compare existing climate model outputs, as well as to sample a new realization of future climate projections. We validated our climate model output comparison method using known dependencies between various climate models. Additionally, we showed, using semi-variogram plots, that the distribution of our realizations lie within the distribution of existing climate model outputs. The proposed statistical emulator could find its use in future climate impact assessment.