An Improved Dynamical Downscaling Method with GCM Bias Corrections and Its Validation with 30 Years of Climate Simulations

Handle URI:
http://hdl.handle.net/10754/597528
Title:
An Improved Dynamical Downscaling Method with GCM Bias Corrections and Its Validation with 30 Years of Climate Simulations
Authors:
Xu, Zhongfeng; Yang, Zong-Liang
Abstract:
An improved dynamical downscaling method (IDD) with general circulation model (GCM) bias corrections is developed and assessed over North America. A set of regional climate simulations is performed with the Weather Research and Forecasting Model (WRF) version 3.3 embedded in the National Center for Atmospheric Research's (NCAR's) Community Atmosphere Model (CAM). The GCM climatological means and the amplitudes of interannual variations are adjusted based on the National Centers for Environmental Prediction (NCEP)-NCAR global reanalysis products (NNRP) before using them to drive WRF. In this study, the WRF downscaling experiments are identical except the initial and lateral boundary conditions derived from the NNRP, original GCM output, and bias-corrected GCM output, respectively. The analysis finds that the IDD greatly improves the downscaled climate in both climatological means and extreme events relative to the traditional dynamical downscaling approach (TDD). The errors of downscaled climatological mean air temperature, geopotential height, wind vector, moisture, and precipitation are greatly reduced when the GCM bias corrections are applied. In the meantime, IDD also improves the downscaled extreme events characterized by the reduced errors in 2-yr return levels of surface air temperature and precipitation. In comparison with TDD, IDD is also able to produce a more realistic probability distribution in summer daily maximum temperature over the central U.S.-Canada region as well as in summer and winter daily precipitation over the middle and eastern United States. © 2012 American Meteorological Society.
Citation:
Xu Z, Yang Z-L (2012) An Improved Dynamical Downscaling Method with GCM Bias Corrections and Its Validation with 30 Years of Climate Simulations. J Climate 25: 6271–6286. Available: http://dx.doi.org/10.1175/JCLI-D-12-00005.1.
Publisher:
American Meteorological Society
Journal:
Journal of Climate
Issue Date:
Sep-2012
DOI:
10.1175/JCLI-D-12-00005.1
Type:
Article
ISSN:
0894-8755; 1520-0442
Sponsors:
The authors thank the Texas Advanced Computing Center for providing computer resources and technical support. NCEP-NCAR reanalysis data were obtained from the Research Data Archive (http://dss.ucar.edu/datasets/ds090.0/), which is maintained by the Computational and Information Systems Laboratory (CISL) at NCAR. Financial support was provided by NASA (Grant NNX11AE42G), "National Basic Research Program of China" Project 2011CB952004, National Natural Science Foundation of China General Program (Grant 40905042), and KAUST.
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Full metadata record

DC FieldValue Language
dc.contributor.authorXu, Zhongfengen
dc.contributor.authorYang, Zong-Liangen
dc.date.accessioned2016-02-25T12:41:29Zen
dc.date.available2016-02-25T12:41:29Zen
dc.date.issued2012-09en
dc.identifier.citationXu Z, Yang Z-L (2012) An Improved Dynamical Downscaling Method with GCM Bias Corrections and Its Validation with 30 Years of Climate Simulations. J Climate 25: 6271–6286. Available: http://dx.doi.org/10.1175/JCLI-D-12-00005.1.en
dc.identifier.issn0894-8755en
dc.identifier.issn1520-0442en
dc.identifier.doi10.1175/JCLI-D-12-00005.1en
dc.identifier.urihttp://hdl.handle.net/10754/597528en
dc.description.abstractAn improved dynamical downscaling method (IDD) with general circulation model (GCM) bias corrections is developed and assessed over North America. A set of regional climate simulations is performed with the Weather Research and Forecasting Model (WRF) version 3.3 embedded in the National Center for Atmospheric Research's (NCAR's) Community Atmosphere Model (CAM). The GCM climatological means and the amplitudes of interannual variations are adjusted based on the National Centers for Environmental Prediction (NCEP)-NCAR global reanalysis products (NNRP) before using them to drive WRF. In this study, the WRF downscaling experiments are identical except the initial and lateral boundary conditions derived from the NNRP, original GCM output, and bias-corrected GCM output, respectively. The analysis finds that the IDD greatly improves the downscaled climate in both climatological means and extreme events relative to the traditional dynamical downscaling approach (TDD). The errors of downscaled climatological mean air temperature, geopotential height, wind vector, moisture, and precipitation are greatly reduced when the GCM bias corrections are applied. In the meantime, IDD also improves the downscaled extreme events characterized by the reduced errors in 2-yr return levels of surface air temperature and precipitation. In comparison with TDD, IDD is also able to produce a more realistic probability distribution in summer daily maximum temperature over the central U.S.-Canada region as well as in summer and winter daily precipitation over the middle and eastern United States. © 2012 American Meteorological Society.en
dc.description.sponsorshipThe authors thank the Texas Advanced Computing Center for providing computer resources and technical support. NCEP-NCAR reanalysis data were obtained from the Research Data Archive (http://dss.ucar.edu/datasets/ds090.0/), which is maintained by the Computational and Information Systems Laboratory (CISL) at NCAR. Financial support was provided by NASA (Grant NNX11AE42G), "National Basic Research Program of China" Project 2011CB952004, National Natural Science Foundation of China General Program (Grant 40905042), and KAUST.en
dc.publisherAmerican Meteorological Societyen
dc.subjectClimate modelsen
dc.subjectError analysisen
dc.subjectExtreme eventsen
dc.subjectModel output statisticsen
dc.subjectNorth Americaen
dc.subjectRainfallen
dc.titleAn Improved Dynamical Downscaling Method with GCM Bias Corrections and Its Validation with 30 Years of Climate Simulationsen
dc.typeArticleen
dc.identifier.journalJournal of Climateen
dc.contributor.institutionUniversity of Texas at Austin, Austin, United Statesen
dc.contributor.institutionInstitute of Atmospheric Physics Chinese Academy of Sciences, Beijing, Chinaen
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