Fast Dimension-Reduced Climate Model Calibration

• Activity Number: 555
• Type: Topic Contributed
• Date/Time: Wednesday, August 6, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Won Chang*+ and Murali Haran and Klaus Keller and Roman Olson
• Companies: Penn State and Penn State and Penn State and University of New South Wales
• Keywords: Climate Model Calibration ; Computer Model Emulation ; Gaussian Processes ; Principal Components ; Kernel Convolution ; Large Spatial Data

Abstract

How will the climate system respond to anthropogenic forcings? One approach to this question relies on climate model projections. Current climate projections are considerably uncertain. Characterizing and, if possible, reducing this uncertainty is an area of ongoing research. We consider the problem of making projections of the North Atlantic meridional overturning circulation (AMOC). Uncertainties about climate model parameters play a key role in uncertainties in AMOC projections. When the observational data and the climate model output are high-dimensional spatial data sets, the data are typically aggregated due to computational constraints. The effects of aggregation are unclear because statistically rigorous approaches for model parameter inference have been infeasible for high-resolution data. Here we develop a flexible and computationally efficient approach using principal components and basis expansions to study the effect of spatial data aggregation on parametric and projection uncertainties. Considering high-dimensional spatial observations reduces the effect of deep uncertainty associated with different priors and results in sharper projections based on our climate model.