North Carolina State University

presents

**Doug Nychka**

Formerly member of our Department

Large and non-stationary spatial fields: Quantifying uncertainty in

the pattern scaling of climate models

**Abstract**

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.

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.

{Abstract}

- G20 Kamphoefner Hall

**Friday, 20 October, 201710:30-11:30
**