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

Journal Article · 20 September 2019 · Climate Dynamics

DOI: https://doi.org/10.1007/s00382-019-04958-z · OSTI ID:1580989

Barlow, Mathew  ^[1]; Gutowski, Jr., William J. ^[2]; Gyakum, John R. ^[3]; Katz, Richard W. ^[4]; Lim, Young-Kwon ^[5]; Schumacher, Russ S. ^[6]; Wehner, Michael F. ^[7]; Agel, Laurie ^[1]; Bosilovich, Michael ^[8]; Collow, Allison ^[9]; Gershunov, Alexander ^[10]; Grotjahn, Richard ^[11]; Leung, Ruby ^[12]; Milrad, Shawn ^[13]; Min, Seung-Ki ^[14]

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1. Univ. of Massachusetts, Lowell, MA (United States)
2. Iowa State Univ., Ames, IA (United States)
3. McGill Univ., Montreal, QC (Canada)
4. National Center for Atmospheric Research, Boulder, CO (United States)
5. NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States)
6. Colorado State Univ., Fort Collins, CO (United States)
7. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
8. NASA GSFC Global Modeling and Assimilation Office, Greenbelt, MD (United States)
9. Universities Space Research Association, Greenbelt, MD (United States); NASA GSFC Global Modeling and Assimilation Office, Greenbelt, MD (United States)
10. Univ. of California San Diego, La Jolla, CA (United States)
11. Univ. of California, Davis, CA (United States)
12. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
13. Embry-Riddle Aeronautical University, Daytona Beach, FL (United States)
14. Pohang Univ. of Science and Technology (POSTECH) (Korea, Republic of)

This paper surveys the current state of knowledge regarding large-scale meteorological patterns (LSMPs) associated with short-duration (less than 1 week) extreme precipitation events over North America. In contrast to teleconnections, which are typically defined based on the characteristic spatial variations of a meteorological field or on the remote circulation response to a known forcing, LSMPs are defined relative to the occurrence of a specific phenomenon—here, extreme precipitation—and with an emphasis on the synoptic scales that have a primary influence in individual events, have medium-range weather predictability, and are well-resolved in both weather and climate models. For the LSMP relationship with extreme precipitation, we consider the previous literature with respect to definitions and data, dynamical mechanisms, model representation, and climate change trends. There is considerable uncertainty in identifying extremes based on existing observational precipitation data and some limitations in analyzing the associated LSMPs in reanalysis data. Many different definitions of “extreme” are in use, making it difficult to directly compare different studies. Dynamically, several types of meteorological systems—extratropical cyclones, tropical cyclones, mesoscale convective systems, and mesohighs—and several mechanisms—fronts, atmospheric rivers, and orographic ascent—have been shown to be important aspects of extreme precipitation LSMPs. The extreme precipitation is often realized through mesoscale processes organized, enhanced, or triggered by the LSMP. Understanding of model representation, trends, and projections for LSMPs is at an early stage, although some promising analysis techniques have been identified

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC); National Science Foundation (NSF); Korea Meteorological Administration Research and Development Program
• Grant/Contract Number: AC02-05CH11231; KMI2018-03610
• OSTI ID: 1580989
• Journal Information: Climate Dynamics, Vol. 53, Issue 11; ISSN 0930-7575
• Publisher: Springer-VerlagCopyright Statement
• Country of Publication: United States
• Language: English