Generation and transfer of internal variability in a regional climate model

There is a strong need for tools allowing the comparison between the performance of a regional climate model (RCM) and the corresponding model providing lateral boundary conditions (LBC) for the RCM, which is a global general circulation model (GCM) in most cases. A method is presented to investigat...

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Main Authors: Thorsten Simon, Dinan Wang, Andreas Hense, Clemens Simmer, Christian Ohlwein
Format: Article
Language:English
Published: Stockholm University Press 2013-12-01
Series:Tellus: Series A, Dynamic Meteorology and Oceanography
Subjects:
Online Access:http://www.tellusa.net/index.php/tellusa/article/download/22485/pdf_1
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author Thorsten Simon
Dinan Wang
Andreas Hense
Clemens Simmer
Christian Ohlwein
author_facet Thorsten Simon
Dinan Wang
Andreas Hense
Clemens Simmer
Christian Ohlwein
author_sort Thorsten Simon
collection DOAJ
description There is a strong need for tools allowing the comparison between the performance of a regional climate model (RCM) and the corresponding model providing lateral boundary conditions (LBC) for the RCM, which is a global general circulation model (GCM) in most cases. A method is presented to investigate the temporal scales on which a RCM is able to generate internal variability on its own and on which variability is copied from the driving model. This is implemented by a cross-spectral analysis between the RCM output and a bi-linearly interpolated version of the driving model, leading to an estimate of the coherence spectrum. Applying the aforementioned technique to surface temperature and temperature and specific humidity at 850 hPa from the RCM COSMO-CLM East Asia with a horizontal resolution of 50 km and its driving model ECHAM5, it was found that features in the spatial distribution of coherence are related to atmospheric dynamics in East Asia, e.g. monsoons and inter-tropical convergence zone (ITCZ). A further application to a double-nesting approach, where COSMO-CLM East Asia is the driving model for two domains – namely the Haihe catchment and the Poyang catchment – each with a horizontal resolution of 7 km, shows that the frequencies on which internal variability is generated by the driven model are much higher compared to the first nesting step. Concluding RCMs can produce a considerable variability on the respective temporal scales. This implies that a dynamical downscaling with a re-analysis as LBC is conceptually different to a regional re-analysis, i.e. data assimilation on the regional scale.
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spelling doaj.art-4bc22aec5ef44e3fb1daac7389d9add02022-12-22T02:56:48ZengStockholm University PressTellus: Series A, Dynamic Meteorology and Oceanography0280-64951600-08702013-12-0165011410.3402/tellusa.v65i0.2248522485Generation and transfer of internal variability in a regional climate modelThorsten Simon0Dinan Wang1Andreas Hense2Clemens Simmer3Christian Ohlwein4Meteorological Institute, University Bonn, Bonn, GermanyInstitute for Energy Systems and Energy Business, Hochschule Ruhr West, Mülheim, GermanyMeteorological Institute, University Bonn, Bonn, GermanyMeteorological Institute, University Bonn, Bonn, GermanyMeteorological Institute, University Bonn, Bonn, GermanyThere is a strong need for tools allowing the comparison between the performance of a regional climate model (RCM) and the corresponding model providing lateral boundary conditions (LBC) for the RCM, which is a global general circulation model (GCM) in most cases. A method is presented to investigate the temporal scales on which a RCM is able to generate internal variability on its own and on which variability is copied from the driving model. This is implemented by a cross-spectral analysis between the RCM output and a bi-linearly interpolated version of the driving model, leading to an estimate of the coherence spectrum. Applying the aforementioned technique to surface temperature and temperature and specific humidity at 850 hPa from the RCM COSMO-CLM East Asia with a horizontal resolution of 50 km and its driving model ECHAM5, it was found that features in the spatial distribution of coherence are related to atmospheric dynamics in East Asia, e.g. monsoons and inter-tropical convergence zone (ITCZ). A further application to a double-nesting approach, where COSMO-CLM East Asia is the driving model for two domains – namely the Haihe catchment and the Poyang catchment – each with a horizontal resolution of 7 km, shows that the frequencies on which internal variability is generated by the driven model are much higher compared to the first nesting step. Concluding RCMs can produce a considerable variability on the respective temporal scales. This implies that a dynamical downscaling with a re-analysis as LBC is conceptually different to a regional re-analysis, i.e. data assimilation on the regional scale.www.tellusa.net/index.php/tellusa/article/download/22485/pdf_1East AsiaCOSMOcross-spectruminternal variabilitydynamical downscalingnestingdouble-nesting
spellingShingle Thorsten Simon
Dinan Wang
Andreas Hense
Clemens Simmer
Christian Ohlwein
Generation and transfer of internal variability in a regional climate model
Tellus: Series A, Dynamic Meteorology and Oceanography
East Asia
COSMO
cross-spectrum
internal variability
dynamical downscaling
nesting
double-nesting
title Generation and transfer of internal variability in a regional climate model
title_full Generation and transfer of internal variability in a regional climate model
title_fullStr Generation and transfer of internal variability in a regional climate model
title_full_unstemmed Generation and transfer of internal variability in a regional climate model
title_short Generation and transfer of internal variability in a regional climate model
title_sort generation and transfer of internal variability in a regional climate model
topic East Asia
COSMO
cross-spectrum
internal variability
dynamical downscaling
nesting
double-nesting
url http://www.tellusa.net/index.php/tellusa/article/download/22485/pdf_1
work_keys_str_mv AT thorstensimon generationandtransferofinternalvariabilityinaregionalclimatemodel
AT dinanwang generationandtransferofinternalvariabilityinaregionalclimatemodel
AT andreashense generationandtransferofinternalvariabilityinaregionalclimatemodel
AT clemenssimmer generationandtransferofinternalvariabilityinaregionalclimatemodel
AT christianohlwein generationandtransferofinternalvariabilityinaregionalclimatemodel