This article is published in Environmetrics as a special issue on Advances in Statistical Methods for Climate Analysis, edited by Peter Guttorp, University of Washington, Norwegian Computing Center, Stephan R. Sain, National Center for Atmospheric Research, Christopher K. Wikle, University of Missouri.
Special Issue Paper
An investigation of the pineapple express phenomenon via bivariate extreme value theory
Article first published online: 30 APR 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Special Issue: Advances in Statistical Methods for Climate Analysis
Volume 23, Issue 5, pages 420–439, August 2012
How to Cite
Weller, G. B., Cooley, D. S. and Sain, S. R. (2012), An investigation of the pineapple express phenomenon via bivariate extreme value theory. Environmetrics, 23: 420–439. doi: 10.1002/env.2143
Supporting information may be found in the online version of this article.
- Issue published online: 25 JUL 2012
- Article first published online: 30 APR 2012
- Manuscript Accepted: 20 MAR 2012
- Manuscript Revised: 15 MAR 2012
- Manuscript Received: 14 OCT 2011
- National Science Foundation (NSF). Grant Numbers: DMS-0905315, ATM-0502977, ATM-0534173
- extreme precipitation;
- atmospheric rivers;
- regional climate models;
- synoptic-scale patterns;
- precipitation generator
The pineapple express (PE) phenomenon is responsible for producing extreme winter precipitation events on the west coast of the United States and Canada. We study regional climate models’ ability to reproduce these events by defining a quantity that captures the spatial extent and intensity of PE events. We use bivariate extreme value theory to model the tail dependence of this quantity as seen in observational data and the Weather Research and Forecasting (WRF) regional climate model driven by reanalysis, and we find tail dependence between the two. To link to synoptic-scale processes, we use daily mean sea-level pressure fields from the reanalysis product to develop a daily “PE index” for extreme precipitation that exhibits tail dependence with our observational quantity. Other models from the North American Regional Climate Change Assessment Program ensemble are used to estimate the future marginal distributions of reanalysis-driven WRF output and observational precipitation. Finally, we employ the fitted tail dependence model to simulate observational precipitation measurements in the future, given output from a future run of WRF. We find evidence of a change in the tail behavior of precipitation from current to future climates, and examination of PE index values of simulated events suggests increases in frequency and intensity of PE precipitation in the future scenario. Copyright © 2012 John Wiley & Sons, Ltd.