Deep Kernel Learning for Multi-model Ensemble Analysis

Oregon Convention Center 

Multi-model ensemble analysis combines output from multiple climate models into a single projection. Recent work has shown that Gaussian processes (GPs) are effective tools for multi-model analysis, but have rapidly increasing error rates as the test distribution diverges from the train distribution. To combat this, we propose a Deep kernel learning model that combines a deep convolutional neural network (CNN) with a neural network Gaussian process (NNGP) to produce accurate, high resolution projections even when the train and test distributions are highly dissimilar. To quantify the projection uncertainty we develop a conformal prediction method, based on data depth, to generate prediction ensembles with exact coverage. We evaluate our method on monthly surface temperature data and show that it outperforms GP approaches, in terms of spatial prediction accuracy and uncertainty quantification, without a commensurate increase in computational cost. Moreover, we show that the growth rate of the prediction errors and UQ errors are much slower than GP approaches leading to reduced uncertainty in far future projections.