Department of Statistics Seminar
North Carolina State University


Doug Nychka

FROM National Center for Atmospheric Research 

Formerly member of our Department

Large and non-stationary spatial fields: Quantifying uncertainty in
the pattern scaling of climate models


This work is a substantive application of data science to the analysis of climate model experiments. Pattern scaling has proved to be a
useful way to extend and interpret Earth system model (i.e. climate) simulations. In the simplest case the response of local temperatures
is assumed to be a linear function of the global temperature. This relationship makes it possible to consider many different scenarios of
warming by using a simpler, global climate model and combining them with the scaling pattern from a more complex model. 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 the 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 accelerated by parallel tools within the R statistical environment.


Friday, 20 October, 2017

Refreshments will be served at 10:00am, 5104 SAS Hall (The Solomon Commons) prior to the talk.