An investigation of the pineapple express phenomenon via bivariate extreme value theory

Abstract

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.

Evaluation of extreme rainfall and temperature over North America in CanRCM4 and CRCM5

“…A study region called Western North America (WNA, 30°-65°N, 220°-260°E) was defined for the RCMs, NARR and ERA-Interim. Similar regions have been used previously to study ARs (Weller et al 2012). In this region, we evaluate the ability of the RCM to reproduce the location of AR landfall, the intensity of the event and the percentage of total winter rainfall that is attributed to AR days.…”

“…ARs are an important source of rainfall, particularly in mountainous regions and are also associated with extreme rainfall that can cause flooding, landslides and loss of property and life. The impact of ARs on extreme rainfall has been well established on the west coast from the observational record (Dettinger 2013;Dettinger et al 2011;Weller et al 2012Weller et al , 2013. Differences between the simulations of extreme rainfall in models may be associated with the high uncertainty in the sign and magnitude of rainfall projections (Pierce et al 2013).…”

Characterization of extreme precipitation within atmospheric river events over California

“…In another study by Bernard et al (2013), heavy rainfall in France was spatially clustered based on the measurement of spatial dependence. Weller et al (2012) investigated the tail dependence in daily precipitation between reanalysis output and observations of pineapple express events using bivariate extreme value theory.…”

A hierarchicalBayesian regional model for nonstationary precipitation extremes inNorthernCalifornia conditioned on tropical moisture exports

“…Dettinger et al [2011] explored the PE under an A2 greenhouse-gas emission scenario and found that average statistics for ARs over the Pacific did not change significantly, although there were shifts in their extreme characteristics and seasonality. Weller et al [2012] employed bivariate extreme value theory and found an increase in the Figure 12. The distribution of the logarithm of DQ during years in which the frequency of rainfall extremes are overpredicted (1981 and 1983), as well other TME years.…”