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 NonStationary Spatial Fields: Quantifying Uncertainty in the Pattern Scaling of Climate Models

Author(s):

Douglas William Nychka*

Companies:

NCAR

Keywords:

Gaussian Process;
Climate change;
Kriging

Abstract:

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 fixedrank 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.
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