Contributions of different bias-correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes
Abstract
The use of different bias-correction methods and global retrospective meteorological forcing data sets as the reference climatology in the bias correction of general circulation model (GCM) daily data is a known source of uncertainty in projected climate extremes and their impacts. Despite their importance, limited attention has been given to these uncertainty sources. We compare 27 projected temperature and precipitation indices over 22 regions of the world (including the global land area) in the near (2021–2060) and distant future (2061–2100), calculated using four Representative Concentration Pathways (RCPs), five GCMs, two bias-correction methods, and three reference forcing data sets. To widen the variety of forcing data sets, we developed a new forcing data set, S14FD, and incorporated it into this study. The results show that S14FD is more accurate than other forcing data sets in representing the observed temperature and precipitation extremes in recent decades (1961–2000 and 1979–2008). The use of different bias-correction methods and forcing data sets contributes more to the total uncertainty in the projected precipitation index values in both the near and distant future than the use of different GCMs and RCPs. However, GCM appears to be the most dominant uncertainty source for projected temperature index values inmore »
- Authors:
-
[1];
[3];
[4];
- Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization, Tsukuba Japan
- KOZO KEIKAKU ENGINEERING Inc., Tokyo Japan
- Institute of Industrial Science, University of Tokyo, Tokyo Japan
- National Institute for Environmental Studies, Tsukuba Japan
- Publication Date:
- Research Org.:
- Oregon State Univ., Corvallis, OR (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1374078
- Alternate Identifier(s):
- OSTI ID: 1374079; OSTI ID: 1533007
- Grant/Contract Number:
- FG02-04ER63917; FG02-04ER63911
- Resource Type:
- Published Article
- Journal Name:
- Journal of Geophysical Research: Atmospheres
- Additional Journal Information:
- Journal Name: Journal of Geophysical Research: Atmospheres; Journal ID: ISSN 2169-897X
- Publisher:
- American Geophysical Union
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; Meteorology & Atmospheric Sciences
Citation Formats
Iizumi, Toshichika, Takikawa, Hiroki, Hirabayashi, Yukiko, Hanasaki, Naota, and Nishimori, Motoki. Contributions of different bias-correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes. United States: N. p., 2017.
Web. doi:10.1002/2017JD026613.
Iizumi, Toshichika, Takikawa, Hiroki, Hirabayashi, Yukiko, Hanasaki, Naota, & Nishimori, Motoki. Contributions of different bias-correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes. United States. https://doi.org/10.1002/2017JD026613
Iizumi, Toshichika, Takikawa, Hiroki, Hirabayashi, Yukiko, Hanasaki, Naota, and Nishimori, Motoki. Sat .
"Contributions of different bias-correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes". United States. https://doi.org/10.1002/2017JD026613.
@article{osti_1374078,
title = {Contributions of different bias-correction methods and reference meteorological forcing data sets to uncertainty in projected temperature and precipitation extremes},
author = {Iizumi, Toshichika and Takikawa, Hiroki and Hirabayashi, Yukiko and Hanasaki, Naota and Nishimori, Motoki},
abstractNote = {The use of different bias-correction methods and global retrospective meteorological forcing data sets as the reference climatology in the bias correction of general circulation model (GCM) daily data is a known source of uncertainty in projected climate extremes and their impacts. Despite their importance, limited attention has been given to these uncertainty sources. We compare 27 projected temperature and precipitation indices over 22 regions of the world (including the global land area) in the near (2021–2060) and distant future (2061–2100), calculated using four Representative Concentration Pathways (RCPs), five GCMs, two bias-correction methods, and three reference forcing data sets. To widen the variety of forcing data sets, we developed a new forcing data set, S14FD, and incorporated it into this study. The results show that S14FD is more accurate than other forcing data sets in representing the observed temperature and precipitation extremes in recent decades (1961–2000 and 1979–2008). The use of different bias-correction methods and forcing data sets contributes more to the total uncertainty in the projected precipitation index values in both the near and distant future than the use of different GCMs and RCPs. However, GCM appears to be the most dominant uncertainty source for projected temperature index values in the near future, and RCP is the most dominant source in the distant future. Our findings encourage climate risk assessments, especially those related to precipitation extremes, to employ multiple bias-correction methods and forcing data sets in addition to using different GCMs and RCPs.},
doi = {10.1002/2017JD026613},
journal = {Journal of Geophysical Research: Atmospheres},
number = ,
volume = ,
place = {United States},
year = {Sat Aug 05 00:00:00 EDT 2017},
month = {Sat Aug 05 00:00:00 EDT 2017}
}
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