Online Program Home
My Program

Abstract Details

Activity Number: 391 - Statistical Advancements in Forestry, Ecology and Climate Modeling
Type: Invited
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: International Indian Statistical Association
Abstract #326865
Title: Large and Non-Stationary Spatial Fields: Quantifying Uncertainty in the Pattern Scaling of Climate Models
Author(s): Douglas William Nychka*
Companies: NCAR
Keywords: Gaussian Process; Climate change; Kriging

Pattern scaling has proved to be a useful way to extend and interpret Earth system model (i.e. climate) simulations. This work explores methodologies using spatial statistics to quantify how the pattern varies across an ensemble of model runs. The key is to represent the pattern uncertainty as a Gaussian process with a spatially varying covariance function. When applied to the NCAR/DOE CESM1 large ensemble experiment this approach can reproduce the heterogenous variation of the pattern among ensemble members. The climate model output at one degree resolution has more than 50,000 spatial locations. The size of these "big data" break conventional spatial methods and so motivates the development of approximate methods that are computationally feasible. A fixed-rank Kriging model (LatticeKrig) exploiting Markov random fields is presented that gives a global representation of tthe covariance function on the sphere and provides a route to quantifying the uncertainty in the pattern. Much of the local statistical computations are embarrassingly parallel and the analysis can be greatly accelerated by parallel tools using the R statistical environment and on a supercomputer.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2018 program