University of Illinois Urbana-Champaign

Weighted ensemble analysis of extreme precipitation under climate change

Zhang, Chen

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ZHANG-THESIS-2016.pdf

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https://hdl.handle.net/2142/90646

Description

Title
Weighted ensemble analysis of extreme precipitation under climate change
Author(s)
Zhang, Chen
Issue Date
2016-04-28
Director of Research (if dissertation) or Advisor (if thesis)
Cai, Ximing
Department of Study
Civil & Environmental Eng
Discipline
Civil Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2016-07-07T19:57:59Z
Keyword(s)
  • Ensemble analysis
  • Extreme precipitation event
  • Climate change
Abstract
The frequency or intensity of heavy precipitation has likely increased in North America since 1950s. In order to analyze climate change impacts on extreme precipitation events in Chicago area, historical (1961-2000) and projected (2046-2065, 2081-2100) daily precipitation data are calculated from 13 statistical downscaling general circulation models under 3 CMIP3 emission scenarios: A1B, A2 and B1, as well as from 17 stations in NCDC and CCPN rain gage network. Then precipitation events of different recurrence intervals are calculated through regional frequency analysis and based on average deviation of climate model estimates from observation estimates, tricube weight function is used to assign weights to climate model ensemble. This weight result is further applied to projected quantile estimates to derive weighted expected values and confidence intervals of future extreme precipitation events under different emission scenarios, these results are further compared with current available estimates from NOAA Atlas 14. Finally, maximum entropy method (MEM) is applied to assign weights and the results are compared with those from weighted ensemble method (WEM). It is found that intensity and the confidence intervals of heavy precipitation is likely to increase significantly for about 20% from now to 2050s under all emission scenarios (A1B>A2>B1), afterwards, this increase trend will slow down (B1>A1B>A2). As for the performance of expected value projection based on MEM, it can also provide accurate estimates with high computational efficiency.
Graduation Semester
2016-05
Type of Resource
text
Permalink
http://hdl.handle.net/2142/90646
Copyright and License Information
Copyright 2016 Chen Zhang

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