Reconsidering the Quality and Utility of Downscaling

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Reconsidering the Quality and Utility of Downscaling. / Takayabu, I; Kanamaru, H; Dairaku, K; Benestad, R; von Storch, H; Christensen, JH.

I: Journal of the Meteorological Society of Japan, Bind 94A, 2016, s. 31-45.

Publikation: Bidrag til tidsskriftTidsskriftartikelfagfællebedømt

Harvard

Takayabu, I, Kanamaru, H, Dairaku, K, Benestad, R, von Storch, H & Christensen, JH 2016, 'Reconsidering the Quality and Utility of Downscaling', Journal of the Meteorological Society of Japan, bind 94A, s. 31-45. https://doi.org/10.2151/jmsj.2015-042

APA

Takayabu, I., Kanamaru, H., Dairaku, K., Benestad, R., von Storch, H., & Christensen, JH. (2016). Reconsidering the Quality and Utility of Downscaling. Journal of the Meteorological Society of Japan, 94A, 31-45. https://doi.org/10.2151/jmsj.2015-042

Vancouver

Takayabu I, Kanamaru H, Dairaku K, Benestad R, von Storch H, Christensen JH. Reconsidering the Quality and Utility of Downscaling. Journal of the Meteorological Society of Japan. 2016;94A:31-45. https://doi.org/10.2151/jmsj.2015-042

Author

Takayabu, I ; Kanamaru, H ; Dairaku, K ; Benestad, R ; von Storch, H ; Christensen, JH. / Reconsidering the Quality and Utility of Downscaling. I: Journal of the Meteorological Society of Japan. 2016 ; Bind 94A. s. 31-45.

Bibtex

@article{bdbe9124c83b4a9987943987b9116882,
title = "Reconsidering the Quality and Utility of Downscaling",
abstract = "Dynamical downscaling (DDS) is performed using regional climate models (RCMs) with global atmospheric states as the input, but there is no consensus among researchers on how to define and estimate the resolvable scale of the various climatic variables obtained by DDS. Sources of RCM uncertainties, including both internal model and intermodel variability, have been assessed by performing ensemble simulations and model intercomparisons, sometimes under the controversial assumption that model bias is independent of the climatic state. Compared with low-resolution global climate simulations, DDS can add value in several ways. For example, because they consider high-resolution topographic data, RCMs can often capture mesoscale phenomena and can better represent climate dynamics. Another downscaling method, empirical statistical downscaling (ESD), is complementary to DDS because it is based on a different philosophy (i.e., sources of information) and on a mostly different set of assumptions. More collaboration and communication should be encouraged among those who develop models, those who use models and perform downscaling, those who use downscaling data, and those who make decisions based on the scientific results provided by models. In addition, ensemble experiments should be devised that can more effectively benefit impact studies. Using DDS and ESD, separately or in combination, users can maximize the utility of local climate information.",
keywords = "dynamical downscaling empirical statistical downscaling ensemble experiments skillful scale uncertainty REGIONAL CLIMATE MODELS LIMITED-AREA MODEL GENERAL-CIRCULATION MODELS NORTHERN EUROPE SOLAR-RADIATION CHANGE IMPACTS UNITED-STATES POLAR LOWS SIMULATIONS PRECIPITATION Meteorology & Atmospheric Sciences Meteorology & Atmospheric Sciences",
author = "I Takayabu and H Kanamaru and K Dairaku and R Benestad and {von Storch}, H and JH Christensen",
note = "Cited References Count:82|DD6IV|METEOROLOGICAL SOC JAPAN|C/O JAPAN METEOROLOGICAL AGENCY 1-3-4 OTE-MACHI, CHIYODA-KU, TOKYO, 100-0004, JAPAN|Takayabu, Izuru|Kanamaru, Hideki|Dairaku, Koji|Benestad, Rasmus|von Storch, Hans|Christensen, Jens Hesselbjerg|ISI Document Delivery No.:DD6IV",
year = "2016",
doi = "10.2151/jmsj.2015-042",
language = "English",
volume = "94A",
pages = "31--45",
journal = "Journal of the Meteorological Society of Japan",
issn = "0026-1165",
publisher = "Meteorological Society of Japan",

}

RIS

TY - JOUR

T1 - Reconsidering the Quality and Utility of Downscaling

AU - Takayabu, I

AU - Kanamaru, H

AU - Dairaku, K

AU - Benestad, R

AU - von Storch, H

AU - Christensen, JH

N1 - Cited References Count:82|DD6IV|METEOROLOGICAL SOC JAPAN|C/O JAPAN METEOROLOGICAL AGENCY 1-3-4 OTE-MACHI, CHIYODA-KU, TOKYO, 100-0004, JAPAN|Takayabu, Izuru|Kanamaru, Hideki|Dairaku, Koji|Benestad, Rasmus|von Storch, Hans|Christensen, Jens Hesselbjerg|ISI Document Delivery No.:DD6IV

PY - 2016

Y1 - 2016

N2 - Dynamical downscaling (DDS) is performed using regional climate models (RCMs) with global atmospheric states as the input, but there is no consensus among researchers on how to define and estimate the resolvable scale of the various climatic variables obtained by DDS. Sources of RCM uncertainties, including both internal model and intermodel variability, have been assessed by performing ensemble simulations and model intercomparisons, sometimes under the controversial assumption that model bias is independent of the climatic state. Compared with low-resolution global climate simulations, DDS can add value in several ways. For example, because they consider high-resolution topographic data, RCMs can often capture mesoscale phenomena and can better represent climate dynamics. Another downscaling method, empirical statistical downscaling (ESD), is complementary to DDS because it is based on a different philosophy (i.e., sources of information) and on a mostly different set of assumptions. More collaboration and communication should be encouraged among those who develop models, those who use models and perform downscaling, those who use downscaling data, and those who make decisions based on the scientific results provided by models. In addition, ensemble experiments should be devised that can more effectively benefit impact studies. Using DDS and ESD, separately or in combination, users can maximize the utility of local climate information.

AB - Dynamical downscaling (DDS) is performed using regional climate models (RCMs) with global atmospheric states as the input, but there is no consensus among researchers on how to define and estimate the resolvable scale of the various climatic variables obtained by DDS. Sources of RCM uncertainties, including both internal model and intermodel variability, have been assessed by performing ensemble simulations and model intercomparisons, sometimes under the controversial assumption that model bias is independent of the climatic state. Compared with low-resolution global climate simulations, DDS can add value in several ways. For example, because they consider high-resolution topographic data, RCMs can often capture mesoscale phenomena and can better represent climate dynamics. Another downscaling method, empirical statistical downscaling (ESD), is complementary to DDS because it is based on a different philosophy (i.e., sources of information) and on a mostly different set of assumptions. More collaboration and communication should be encouraged among those who develop models, those who use models and perform downscaling, those who use downscaling data, and those who make decisions based on the scientific results provided by models. In addition, ensemble experiments should be devised that can more effectively benefit impact studies. Using DDS and ESD, separately or in combination, users can maximize the utility of local climate information.

KW - dynamical downscaling empirical statistical downscaling ensemble experiments skillful scale uncertainty REGIONAL CLIMATE MODELS LIMITED-AREA MODEL GENERAL-CIRCULATION MODELS NORTHERN EUROPE SOLAR-RADIATION CHANGE IMPACTS UNITED-STATES POLAR LOWS SIMULATIO

U2 - 10.2151/jmsj.2015-042

DO - 10.2151/jmsj.2015-042

M3 - Journal article

VL - 94A

SP - 31

EP - 45

JO - Journal of the Meteorological Society of Japan

JF - Journal of the Meteorological Society of Japan

SN - 0026-1165

ER -

ID: 186947507