skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: A Dynamical and Statistical Characterization of U.S. Extreme Precipitation Events and Their Associated Large-Scale Meteorological Patterns

Authors:
 [1];  [1];  [1]
  1. School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1342286
Grant/Contract Number:
SC0012554
Resource Type:
Journal Article: Published Article
Journal Name:
Journal of Climate
Additional Journal Information:
Journal Volume: 30; Journal Issue: 4; Related Information: CHORUS Timestamp: 2017-04-18 16:32:20; Journal ID: ISSN 0894-8755
Publisher:
American Meteorological Society
Country of Publication:
United States
Language:
English

Citation Formats

Zhao, Siyu, Deng, Yi, and Black, Robert X. A Dynamical and Statistical Characterization of U.S. Extreme Precipitation Events and Their Associated Large-Scale Meteorological Patterns. United States: N. p., 2017. Web. doi:10.1175/JCLI-D-15-0910.1.
Zhao, Siyu, Deng, Yi, & Black, Robert X. A Dynamical and Statistical Characterization of U.S. Extreme Precipitation Events and Their Associated Large-Scale Meteorological Patterns. United States. doi:10.1175/JCLI-D-15-0910.1.
Zhao, Siyu, Deng, Yi, and Black, Robert X. Wed . "A Dynamical and Statistical Characterization of U.S. Extreme Precipitation Events and Their Associated Large-Scale Meteorological Patterns". United States. doi:10.1175/JCLI-D-15-0910.1.
@article{osti_1342286,
title = {A Dynamical and Statistical Characterization of U.S. Extreme Precipitation Events and Their Associated Large-Scale Meteorological Patterns},
author = {Zhao, Siyu and Deng, Yi and Black, Robert X.},
abstractNote = {},
doi = {10.1175/JCLI-D-15-0910.1},
journal = {Journal of Climate},
number = 4,
volume = 30,
place = {United States},
year = {Wed Feb 01 00:00:00 EST 2017},
month = {Wed Feb 01 00:00:00 EST 2017}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1175/JCLI-D-15-0910.1

Citation Metrics:
Cited by: 2works
Citation information provided by
Web of Science

Save / Share:
  • 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 tomore » 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. Climate 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
  • We 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 synopticmore » 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. We note that even though LSMPs are resolvable by global and regional climate models, they are not necessarily well simulated.« less
  • This paper presents a comprehensive analysis of interannual and interdecadal variations of summer precipitation and precipitation-related extreme events in China associated with variations of the East Asian summer monsoon (EASM) from 1979-2012. A high-quality daily precipitation dataset covering 2287 weather stations in China is analyzed. Based on the precipitation pattern analysis using empirical orthogonal functions, three sub-periods of 1979-1992 (period I), 1993-1999 (period II) and 2000-2012 (period III) are identified to be representative of the precipitation variability. Similar significant variability of the extreme precipitation indices is found across four sub-regions in eastern China. The spatial patterns of summer mean precipitation,more » the number of days with daily rainfall exceeding 95th percentile precipitation (R95p) and the maximum number of consecutive wet days (CWD) anomalies are consistent, but opposite to that of maximum consecutive dry days (CDD) anomalies during the three sub-periods. However, the spatial patterns of hydroclimatic intensity (HY-INT) are notably different from that of the other three extreme indices, but highly correlated to the dry events. The changes of precipitation anomaly patterns are accompanied by the change of the EASM regime and the abrupt shift of the position of the west Pacific subtropical high around 1992/1993 and 1999/2000, respectively, which influence the moisture transport that contributes most to the precipitation anomalies. Lastly, the EASM intensity is linked to sea surface temperature anomaly over the tropical Indian and Pacific Ocean that influences deep convection over the oceans.« less
  • In this study, an atmospheric river (AR) detection algorithm is developed to investigate the downstream modulation of the eastern North Pacific ARs by another weather extreme, known as the East Asian cold surge (EACS), in both reanalysis data and high-resolution global model simulations. It is shown that following the peak of an EACS, atmospheric disturbances of intermediate frequency (IF; 10 30 day period) are excited downstream. This leads to the formation of a persistent cyclonic circulation anomaly over the eastern North Pacific that dramatically enhances the AR occurrence probability and the surface precipitation over the western U.S. between 30 Nmore » and 50 N. A diagnosis of the local geopotential height tendency further confirms the essential role of IF disturbances in establishing the observed persistent anomaly. This downstream modulation effect is then examined in the two simulations of the National Center for Atmospheric Research Community Climate System Model version 4 with different horizontal resolutions (T85 and T341) for the same period (1979 2005). The connection between EACS and AR is much better captured by the T341 version of the model, mainly due to a better representation of the scale interaction and the characteristics of IF atmospheric disturbances in the higher-resolution model. The findings here suggest that faithful representations of scale interaction in a global model are critical for modeling and predicting the occurrences of hydrological extremes in the western U.S. and for understanding their potential future changes.« less
  • Atmospheric rivers (ARs) are large, spatially coherent weather systems with high concentrations of elevated water vapor. These systems often cause severe downpours and flooding over the western coastal United States – and with the availability of more atmospheric moisture in the future under global warming we expect ARs to play an important role as potential causes of extreme precipitation changes. Therefore, we aim to investigate changes in extreme precipitation properties correlated with AR events in a warmer climate, which are large-scale meteorological patterns affecting the weather and climate of California. We have recently developed the TECA (Toolkit for Extreme Climatemore » Analysis) software for automatically identifying and tracking features in climate data sets. Specifically, we can now identify ARs that make landfall on the western coast of North America. Based on this detection procedure, we can investigate the impact of ARs by exploring the spatial extent of AR precipitation using climate model (CMIP5) simulations and characterize spatial patterns of dependence for future projections between AR precipitation extremes under climate change within the statistical framework. Our results show that AR events in the future RCP (Representative Concentration Pathway)8.5 scenario (2076–2100) tend to produce heavier rainfall with higher frequency and longer days than events from the historical run (1981–2005). We also find that the dependence between extreme precipitation events has a shorter spatial range, within localized areas in California, under the high future emissions scenario than under the historical run.« less