North American extreme temperature events and related large scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends

Richard Grotjahn,John R Gyakum,Ruby Leung,William J Gutowski,Mathew Barlow,Alexander Gershunov,Mike Bosilovich,Young-Kwon Lim,Prabhat Prabhat,Richard W Katz,Michael F Wehner,Yun-Young Lee,Robert Black

doi:10.1007/s00382-015-2638-6

Abstract

The objective of this paper is to review statistical methods, dynamics, modeling efforts, and trends related to temperature extremes, with a focus upon extreme events of short duration that affect parts of North America. These events are associated with large scale meteorological patterns (LSMPs). The statistics, dynamics, and modeling sections of this paper are written to be autonomous and so can be read separately. Methods to define extreme events statistics and to identify and connect LSMPs to extreme temperature events are presented. Recent advances in statistical techniques connect LSMPs to extreme temperatures through appropriately defined covariates that supplement more straightforward analyses. Various LSMPs, ranging from synoptic to planetary scale structures, are associated with extreme temperature events. Current knowledge about the synoptics and the dynamical mechanisms leading to the associated LSMPs is incomplete. Systematic studies of: the physics of LSMP life cycles, comprehensive model assessment of LSMP-extreme temperature event linkages, and LSMP properties are needed. Generally, climate models capture observed properties of heat waves and cold air outbreaks with some fidelity. However they overestimate warm wave frequency and underestimate cold air outbreak frequency, and underestimate the collective influence of low-frequency modes on temperature extremes. Modeling studies have identified the impact of large-scale circulation anomalies and land–atmosphere interactions on changes in extreme temperatures. However, few studies have examined changes in LSMPs to more specifically understand the role of LSMPs on past and future extreme temperature changes. Even though LSMPs are resolvable by global and regional climate models, they are not necessarily well simulated. The paper concludes with unresolved issues and research questions.

Highlights

Introduction to temperature extremesTemperature extremes have large societal and economic consequences
Classify events and find The number K of large scale meteorological patterns (LSMPs) clusters can Significance of classification stability relating LSMPs that provide physi- be somewhat arbitrary since K is can be obtained from Monte Carlo cal insight pre-specified, but the ‘dissimilarity test by rejecting the null hypothesis
Understanding extreme events ranges from how events are defined and measured, to how extreme events are studied statistically, theoretically, and with models

Summary

Introduction

Introduction to temperature extremesTemperature extremes have large societal and economic consequences. While many heat waves are short-lived, longer events can have a large economic cost. Cold air outbreaks (CAOs) tend to be short-lived but carry large economic losses. Timing of the CAOs can be more important than the minimum temperatures of the freeze; during 4–10 April 2007 low temperatures across the South caused $2B in agricultural losses since many crops were in bloom or had frost sensitive buds or nascent fruit (Gu et al 2008). The event exemplifies how monthly means can be misleading: April 2007 average temperatures were near normal. Both hot spells (HSs) and CAOs have great societal importance and they are short-term events that do not necessarily appear in monthly mean data

Objectives

Results

Conclusion