Online Program Home
My Program

Abstract Details

Activity Number: 244
Type: Contributed
Date/Time: Monday, August 1, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #321461 View Presentation
Title: Downscaling of Climate Model Projections of Temperature and Related Uncertainty Quantification
Author(s): Ernst Linder and Yiming Liu and Meng Zhao*
Companies: University of New Hampshire and University of New Hampshire and University of New Hampshire
Keywords: quantile translation ; climate change ; impact assessment ; infrastructure design ; time series ; regression splines
Abstract:

Quantile translation, or, quantile matching, is a popular method for downscaling between a variable of a climate model output and the corresponding weather variable at a monitoring station. The method assumes asynchronicity of climate and weather and boils down to translating the cdf's of the two variables. For purely random series, such as extremes, this reduces to applying a relationship between the quantile pairs to future model outputs, and obtaining prediction intervals. We extend this method to time-dependent variables. For daily temperature averages, we use low-order regression splines for trend and cycle fitting, and low-order autoregressive model for dependence. The cdf translation is defined for trend and cycle, and non-parametric quantile matching is applied for the remainder. For uncertainty quantification, we propose a parametric bootstrap. We present an application of infrastructure design and adaptation to climate change that examines the timing of freezing and thawing of soils and road beds. Our model predicts spring thaw dates and provides prediction intervals.


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

Back to the full JSM 2016 program

 
 
Copyright © American Statistical Association