final_program_abstracts[1]

11 IMSC Session Program
A likelihood-based scoring method for the evaluation of
climate model predictions
Wednesday - Parallel Session 2
Amy Braverman 1 , Noel Cressie 2 , and Joao Teixeira 1
1
Jet Propulsion Laboratory, California Institute of Technology, USA
2 Department of Statistics, The Ohio State University, USA
Like other scientific and engineering problems that involve physical modeling of
complex systems, climate models can be evaluated and diagnosed by comparing their
output to observations of similar quantities. Though the global remote sensing data
record is relatively short by climate research standards, these data offer opportunities
to evaluate model predictions in new ways. For example, remote sensing data are
spatially and temporally dense enough to provide distributional information that goes
beyond simple moments. For time periods during which remote sensing data exist,
comparisons against multiple models can provide useful information about which
models, and therefore which physical parameterizations and assumptions, most
closely match reality. In this talk, we propose a method for scoring climate models
according to the relative likelihood that a model's time series of predictions arises
from the true process generating the observations. Our approach is based on a given
summary statistic, computed for the observations and for resamples from the output of
each of the models being compared. To respect temporal-dependence characteristics,
the resamples are based on a moving-block bootstrap method. Relative scores are
formed as the ratio of each candidate model's likelihood to the largest likelihood. We
demonstrate our methodology by scoring several models' predictions of the pressure
at the planetary boundary layer, with reanalysis output standing in for observations.
Abstracts 181