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Title: Assessing Regional Scale Variability in Extreme Value Statistics Under Altered Climate Scenarios

Abstract

Recent studies have suggested that low-frequency modes of climate variability can significantly influence regional climate. The climatology associated with extreme events has been shown to be particularly sensitive. This has profound implications for droughts, heat waves, and food production. We propose to examine regional climate simulations conducted over the continental United States by applying a recently developed technique which combines wavelet multi–resolution analysis with information theory metrics. This research is motivated by two fundamental questions concerning the spatial and temporal structure of extreme events. These questions are 1) what temporal scales of the extreme value distributions are most sensitive to alteration by low-frequency climate forcings and 2) what is the nature of the spatial structure of variation in these timescales? The primary objective is to assess to what extent information theory metrics can be useful in characterizing the nature of extreme weather phenomena. Specifically, we hypothesize that (1) changes in the nature of extreme events will impact the temporal probability density functions and that information theory metrics will be sensitive these changes and (2) via a wavelet multi–resolution analysis, we will be able to characterize the relative contribution of different timescales on the stochastic nature of extreme events. In ordermore » to address these hypotheses, we propose a unique combination of an established regional climate modeling approach and advanced statistical techniques to assess the effects of low-frequency modes on climate extremes over North America. The behavior of climate extremes in RCM simulations for the 20th century will be compared with statistics calculated from the United States Historical Climatology Network (USHCN) and simulations from the North American Regional Climate Change Assessment Program (NARCCAP). This effort will serve to establish the baseline behavior of climate extremes, the validity of an innovative multi–resolution information theory approach, and the ability of the RCM modeling framework to represent the low-frequency modulation of extreme climate events. Once the skill of the modeling and analysis methodology has been established, we will apply the same approach for the AR5 (IPCC Fifth Assessment Report) climate change scenarios in order to assess how climate extremes and the the influence of lowfrequency variability on climate extremes might vary under changing climate. The research specifically addresses the DOE focus area 2. Simulation of climate extremes under a changing climate. Specific results will include (1) a better understanding of the spatial and temporal structure of extreme events, (2) a thorough quantification of how extreme values are impacted by low-frequency climate teleconnections, (3) increased knowledge of current regional climate models ability to ascertain these influences, and (4) a detailed examination of the how the distribution of extreme events are likely to change under different climate change scenarios. In addition, this research will assess the ability of the innovative wavelet information theory approach to characterize extreme events. Any and all of these results will greatly enhance society’s ability to understand and mitigate the regional ramifications of future global climate change.« less

Authors:
 [1];  [1];  [2]
  1. Univ. of Kansas, Lawrence, KS (United States)
  2. Wichita State Univ., KS (United States)
Publication Date:
Research Org.:
Univ. of Kansas, Lawrence, KS (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1170412
Report Number(s):
DOE-KUCR-5359
DOE Contract Number:
SC0005359
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; 54 ENVIRONMENTAL SCIENCES

Citation Formats

Brunsell, Nathaniel, Mechem, David, and Ma, Chunsheng. Assessing Regional Scale Variability in Extreme Value Statistics Under Altered Climate Scenarios. United States: N. p., 2015. Web. doi:10.2172/1170412.
Brunsell, Nathaniel, Mechem, David, & Ma, Chunsheng. Assessing Regional Scale Variability in Extreme Value Statistics Under Altered Climate Scenarios. United States. doi:10.2172/1170412.
Brunsell, Nathaniel, Mechem, David, and Ma, Chunsheng. Fri . "Assessing Regional Scale Variability in Extreme Value Statistics Under Altered Climate Scenarios". United States. doi:10.2172/1170412. https://www.osti.gov/servlets/purl/1170412.
@article{osti_1170412,
title = {Assessing Regional Scale Variability in Extreme Value Statistics Under Altered Climate Scenarios},
author = {Brunsell, Nathaniel and Mechem, David and Ma, Chunsheng},
abstractNote = {Recent studies have suggested that low-frequency modes of climate variability can significantly influence regional climate. The climatology associated with extreme events has been shown to be particularly sensitive. This has profound implications for droughts, heat waves, and food production. We propose to examine regional climate simulations conducted over the continental United States by applying a recently developed technique which combines wavelet multi–resolution analysis with information theory metrics. This research is motivated by two fundamental questions concerning the spatial and temporal structure of extreme events. These questions are 1) what temporal scales of the extreme value distributions are most sensitive to alteration by low-frequency climate forcings and 2) what is the nature of the spatial structure of variation in these timescales? The primary objective is to assess to what extent information theory metrics can be useful in characterizing the nature of extreme weather phenomena. Specifically, we hypothesize that (1) changes in the nature of extreme events will impact the temporal probability density functions and that information theory metrics will be sensitive these changes and (2) via a wavelet multi–resolution analysis, we will be able to characterize the relative contribution of different timescales on the stochastic nature of extreme events. In order to address these hypotheses, we propose a unique combination of an established regional climate modeling approach and advanced statistical techniques to assess the effects of low-frequency modes on climate extremes over North America. The behavior of climate extremes in RCM simulations for the 20th century will be compared with statistics calculated from the United States Historical Climatology Network (USHCN) and simulations from the North American Regional Climate Change Assessment Program (NARCCAP). This effort will serve to establish the baseline behavior of climate extremes, the validity of an innovative multi–resolution information theory approach, and the ability of the RCM modeling framework to represent the low-frequency modulation of extreme climate events. Once the skill of the modeling and analysis methodology has been established, we will apply the same approach for the AR5 (IPCC Fifth Assessment Report) climate change scenarios in order to assess how climate extremes and the the influence of lowfrequency variability on climate extremes might vary under changing climate. The research specifically addresses the DOE focus area 2. Simulation of climate extremes under a changing climate. Specific results will include (1) a better understanding of the spatial and temporal structure of extreme events, (2) a thorough quantification of how extreme values are impacted by low-frequency climate teleconnections, (3) increased knowledge of current regional climate models ability to ascertain these influences, and (4) a detailed examination of the how the distribution of extreme events are likely to change under different climate change scenarios. In addition, this research will assess the ability of the innovative wavelet information theory approach to characterize extreme events. Any and all of these results will greatly enhance society’s ability to understand and mitigate the regional ramifications of future global climate change.},
doi = {10.2172/1170412},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Feb 20 00:00:00 EST 2015},
month = {Fri Feb 20 00:00:00 EST 2015}
}

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