Comparison of GCM- and RCM-simulated precipitation following stochastic postprocessing

Jonathan M. Eden, Martin Widmann, Douglas Maraun, Mathieu Vrac

Research output: Contribution to journalArticle

30 Citations (Scopus)
25 Downloads (Pure)

Abstract

In order to assess to what extent regional climate models (RCMs) yield better representations of climatic states than general circulation models (GCMs), the output of each is usually directly compared with observations. RCM output is often bias corrected, and in some cases correction methods can also be applied to GCMs. This leads to the question of whether bias-corrected RCMs perform better than bias-corrected GCMs. Here the first results from such a comparison are presented, followed by discussion of the value added by RCMs in this setup. Stochastic postprocessing, based on Model Output Statistics (MOS), is used to estimate daily precipitation at 465 stations across the United Kingdom between 1961 and 2000 using simulated precipitation from two RCMs (RACMO2 and CCLM) and, for the first time, a GCM (ECHAM5) as predictors. The large-scale weather states in each simulation are forced toward observations. The MOS method uses logistic regression to model precipitation occurrence and a Gamma distribution for the wet day distribution, and is cross validated based on Brier and quantile skill scores. A major outcome of the study is that the corrected GCM-simulated precipitation yields consistently higher validation scores than the corrected RCM-simulated precipitation. This seems to suggest that, in a setup with postprocessing, there is no clear added value by RCMs with respect to downscaling individual weather states. However, due to the different ways of controlling the atmospheric circulation in the RCM and the GCM simulations, such a strong conclusion cannot be drawn. Yet the study demonstrates how challenging it is to demonstrate the value added by RCMs in this setup.

Original languageEnglish
Pages (from-to)11040-11053
Number of pages14
JournalJournal of Geophysical Research
Volume119
Issue number19
Early online date6 Oct 2014
DOIs
Publication statusPublished - 16 Oct 2014
Externally publishedYes

Fingerprint

Climate models
General Circulation Models
climate models
regional climate
general circulation model
climate modeling
value added
output
weather
statistics
quantiles
Statistics
comparison
atmospheric circulation
United Kingdom
logistics
downscaling
simulation
Logistics
regression analysis

Bibliographical note

Publisher Statement: This is the peer reviewed version of the following article: Eden, JM, Widmann, M, Maraun, D & Vrac, M 2014, 'Comparison of GCM- and RCM-simulated precipitation following stochastic post processing' Journal of Geophysical Research, vol 119, no. 19, pp. 11040-11053, which has been published in final form at https://dx.doi.org/10.1002/2014JD021732This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.

ASJC Scopus subject areas

  • Geophysics
  • Oceanography
  • Forestry
  • Ecology
  • Aquatic Science
  • Water Science and Technology
  • Soil Science
  • Geochemistry and Petrology
  • Earth-Surface Processes
  • Atmospheric Science
  • Earth and Planetary Sciences (miscellaneous)
  • Space and Planetary Science
  • Palaeontology

Cite this

Comparison of GCM- and RCM-simulated precipitation following stochastic postprocessing. / Eden, Jonathan M.; Widmann, Martin; Maraun, Douglas; Vrac, Mathieu.

In: Journal of Geophysical Research, Vol. 119, No. 19, 16.10.2014, p. 11040-11053.

Research output: Contribution to journalArticle

Eden, Jonathan M. ; Widmann, Martin ; Maraun, Douglas ; Vrac, Mathieu. / Comparison of GCM- and RCM-simulated precipitation following stochastic postprocessing. In: Journal of Geophysical Research. 2014 ; Vol. 119, No. 19. pp. 11040-11053.
@article{8961c6a27aaf42649753aa6b9c4c0319,
title = "Comparison of GCM- and RCM-simulated precipitation following stochastic postprocessing",
abstract = "In order to assess to what extent regional climate models (RCMs) yield better representations of climatic states than general circulation models (GCMs), the output of each is usually directly compared with observations. RCM output is often bias corrected, and in some cases correction methods can also be applied to GCMs. This leads to the question of whether bias-corrected RCMs perform better than bias-corrected GCMs. Here the first results from such a comparison are presented, followed by discussion of the value added by RCMs in this setup. Stochastic postprocessing, based on Model Output Statistics (MOS), is used to estimate daily precipitation at 465 stations across the United Kingdom between 1961 and 2000 using simulated precipitation from two RCMs (RACMO2 and CCLM) and, for the first time, a GCM (ECHAM5) as predictors. The large-scale weather states in each simulation are forced toward observations. The MOS method uses logistic regression to model precipitation occurrence and a Gamma distribution for the wet day distribution, and is cross validated based on Brier and quantile skill scores. A major outcome of the study is that the corrected GCM-simulated precipitation yields consistently higher validation scores than the corrected RCM-simulated precipitation. This seems to suggest that, in a setup with postprocessing, there is no clear added value by RCMs with respect to downscaling individual weather states. However, due to the different ways of controlling the atmospheric circulation in the RCM and the GCM simulations, such a strong conclusion cannot be drawn. Yet the study demonstrates how challenging it is to demonstrate the value added by RCMs in this setup.",
author = "Eden, {Jonathan M.} and Martin Widmann and Douglas Maraun and Mathieu Vrac",
note = "Publisher Statement: This is the peer reviewed version of the following article: Eden, JM, Widmann, M, Maraun, D & Vrac, M 2014, 'Comparison of GCM- and RCM-simulated precipitation following stochastic post processing' Journal of Geophysical Research, vol 119, no. 19, pp. 11040-11053, which has been published in final form at https://dx.doi.org/10.1002/2014JD021732This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.",
year = "2014",
month = "10",
day = "16",
doi = "10.1002/2014JD021732",
language = "English",
volume = "119",
pages = "11040--11053",
journal = "Journal of Geophysical Research",
issn = "0148-0227",
publisher = "American Geophysical Union",
number = "19",

}

TY - JOUR

T1 - Comparison of GCM- and RCM-simulated precipitation following stochastic postprocessing

AU - Eden, Jonathan M.

AU - Widmann, Martin

AU - Maraun, Douglas

AU - Vrac, Mathieu

N1 - Publisher Statement: This is the peer reviewed version of the following article: Eden, JM, Widmann, M, Maraun, D & Vrac, M 2014, 'Comparison of GCM- and RCM-simulated precipitation following stochastic post processing' Journal of Geophysical Research, vol 119, no. 19, pp. 11040-11053, which has been published in final form at https://dx.doi.org/10.1002/2014JD021732This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.

PY - 2014/10/16

Y1 - 2014/10/16

N2 - In order to assess to what extent regional climate models (RCMs) yield better representations of climatic states than general circulation models (GCMs), the output of each is usually directly compared with observations. RCM output is often bias corrected, and in some cases correction methods can also be applied to GCMs. This leads to the question of whether bias-corrected RCMs perform better than bias-corrected GCMs. Here the first results from such a comparison are presented, followed by discussion of the value added by RCMs in this setup. Stochastic postprocessing, based on Model Output Statistics (MOS), is used to estimate daily precipitation at 465 stations across the United Kingdom between 1961 and 2000 using simulated precipitation from two RCMs (RACMO2 and CCLM) and, for the first time, a GCM (ECHAM5) as predictors. The large-scale weather states in each simulation are forced toward observations. The MOS method uses logistic regression to model precipitation occurrence and a Gamma distribution for the wet day distribution, and is cross validated based on Brier and quantile skill scores. A major outcome of the study is that the corrected GCM-simulated precipitation yields consistently higher validation scores than the corrected RCM-simulated precipitation. This seems to suggest that, in a setup with postprocessing, there is no clear added value by RCMs with respect to downscaling individual weather states. However, due to the different ways of controlling the atmospheric circulation in the RCM and the GCM simulations, such a strong conclusion cannot be drawn. Yet the study demonstrates how challenging it is to demonstrate the value added by RCMs in this setup.

AB - In order to assess to what extent regional climate models (RCMs) yield better representations of climatic states than general circulation models (GCMs), the output of each is usually directly compared with observations. RCM output is often bias corrected, and in some cases correction methods can also be applied to GCMs. This leads to the question of whether bias-corrected RCMs perform better than bias-corrected GCMs. Here the first results from such a comparison are presented, followed by discussion of the value added by RCMs in this setup. Stochastic postprocessing, based on Model Output Statistics (MOS), is used to estimate daily precipitation at 465 stations across the United Kingdom between 1961 and 2000 using simulated precipitation from two RCMs (RACMO2 and CCLM) and, for the first time, a GCM (ECHAM5) as predictors. The large-scale weather states in each simulation are forced toward observations. The MOS method uses logistic regression to model precipitation occurrence and a Gamma distribution for the wet day distribution, and is cross validated based on Brier and quantile skill scores. A major outcome of the study is that the corrected GCM-simulated precipitation yields consistently higher validation scores than the corrected RCM-simulated precipitation. This seems to suggest that, in a setup with postprocessing, there is no clear added value by RCMs with respect to downscaling individual weather states. However, due to the different ways of controlling the atmospheric circulation in the RCM and the GCM simulations, such a strong conclusion cannot be drawn. Yet the study demonstrates how challenging it is to demonstrate the value added by RCMs in this setup.

U2 - 10.1002/2014JD021732

DO - 10.1002/2014JD021732

M3 - Article

VL - 119

SP - 11040

EP - 11053

JO - Journal of Geophysical Research

JF - Journal of Geophysical Research

SN - 0148-0227

IS - 19

ER -