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

This paper reviews research approaches and open questions regarding data, statistical analyses, dynamics, modeling efforts, and trends in relation to temperature extremes. Our specific focus is upon extreme events of short duration (roughly less than 5 days) that affect parts of North America. These events are associated with large scale meteorological patterns (LSMPs). Methods used to define extreme events statistics and to identify and connect LSMPs to extreme temperatures are presented. Recent advances in statistical techniques can connect LSMPs to extreme temperatures through appropriately defined covariates that supplements more straightforward analyses. A wide array of LSMPs, ranging from synoptic to planetary scale phenomena, have been implicated as contributors to extreme temperature events. Current knowledge about the physical nature of these contributions and the dynamical mechanisms leading to the implicated LSMPs is incomplete. There is a pressing need for (a) systematic study of the physics of LSMPs life cycles and (b) comprehensive model assessment of LSMP-extreme temperature event linkages and LSMP behavior. Generally, climate models capture the observed heat waves and cold air outbreaks with some fidelity. However they overestimate warm wave frequency and underestimate cold air outbreaks frequency, and underestimate the collective influence of low-frequency modes on temperature extremes. Climatemore » models have been used to investigate past changes and project future trends in extreme temperatures. Overall, modeling studies have identified important mechanisms such as the effects of large-scale circulation anomalies and land-atmosphere interactions on changes in extreme temperatures. However, few studies have examined changes in LSMPs more specifically to 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 so more research is needed to understand the limitations of climate models and improve model skill in simulating extreme temperatures and their associated LSMPs. Furthermore, the paper concludes with unresolved issues and research questions.« less
 [1] ;  [2] ;  [3] ;  [4] ;  [5] ;  [6] ;  [7] ;  [8] ;  [9] ;  [10] ;  [1] ;  [11] ;  [4]
  1. Univ. of California Davis, Davis, CA (United States)
  2. Georgia Institute of Technology, Atlanta, GA (United States)
  3. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  4. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  5. Univ. of Massachusetts, Lowell, MA (United States)
  6. NASA GSFC Global Modeling and Assimilation Office, Greenbelt, MD (United States)
  7. Univ. of California San Diego, La Jolla, CA (United States)
  8. Iowa State Univ., Ames, IA (United States)
  9. McGill Univ., Montreal, QC (Canada)
  10. National Center for Atmospheric Research, Boulder, CO (United States)
  11. NASA Goddard Space Flight Center, Greenbelt, MD (United States)
Publication Date:
Report Number(s):
Journal ID: ISSN 0930-7575; KP1703010
Grant/Contract Number:
AC05-76RL01830; AC02-05CH11231; SC0004942; SC0012554; SC0006643
Accepted Manuscript
Journal Name:
Climate Dynamics
Additional Journal Information:
Journal Volume: 46; Journal Issue: 3; Journal ID: ISSN 0930-7575
Research Org:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Country of Publication:
United States
54 ENVIRONMENTAL SCIENCES; 97 MATHEMATICS AND COMPUTING; large scale meteorological patterns for temperature extremes; heat waves; hot spells; cold air outbreaks; cold spells; statistics of temperature extremes; dynamics of heat waves; dynamics of cold air outbreaks; dynamical modeling of temperature extremes; statistical modeling of extremes; Trends in temperature extremes
OSTI Identifier:
Alternate Identifier(s):
OSTI ID: 1252457