Weight assignment in regional climate models

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Standard

Weight assignment in regional climate models. / Christensen, Jens Hesselbjerg; Kjellström, Erik; Giorgi, Filippo; Lenderink, Geert; Rummukainen, Markku.

I: Climate Research, Bind 44, Nr. 2-3, 27.12.2010, s. 179-194.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Christensen, JH, Kjellström, E, Giorgi, F, Lenderink, G & Rummukainen, M 2010, 'Weight assignment in regional climate models', Climate Research, bind 44, nr. 2-3, s. 179-194. https://doi.org/10.3354/cr00916

APA

Christensen, J. H., Kjellström, E., Giorgi, F., Lenderink, G., & Rummukainen, M. (2010). Weight assignment in regional climate models. Climate Research, 44(2-3), 179-194. https://doi.org/10.3354/cr00916

Vancouver

Christensen JH, Kjellström E, Giorgi F, Lenderink G, Rummukainen M. Weight assignment in regional climate models. Climate Research. 2010 dec 27;44(2-3):179-194. https://doi.org/10.3354/cr00916

Author

Christensen, Jens Hesselbjerg ; Kjellström, Erik ; Giorgi, Filippo ; Lenderink, Geert ; Rummukainen, Markku. / Weight assignment in regional climate models. I: Climate Research. 2010 ; Bind 44, Nr. 2-3. s. 179-194.

Bibtex

@article{b0677362baa24edba47df96e3fee7413,
title = "Weight assignment in regional climate models",
abstract = "An important new development within the European ENSEMBLES project has been to explore performance-based weighting of regional climate models (RCMs). Until now, although no weighting has been applied in multi-RCM analyses, one could claim that an assumption of 'equal weight' was implicitly adopted. At the same time, different RCMs generate different results, e.g. for various types of extremes, and these results need to be combined when using the full RCM ensemble. The process of constructing, assigning and combining metrics of model performance is not straightforward. Rather, there is a considerable degree of subjectivity both in the choice of metrics and on how these may be combined into weights. We explore the applicability of combining a set of 6 specifically designed RCM performance metrics to produce one aggregated model weight with the purpose of combining climate change information from the range of RCMs used within ENSEMBLES. These metrics capture aspects of model performance in reproducing large-scale circulation patterns, meso-scale signals, daily temperature and precipitation distributions and extremes, trends and the annual cycle. We examine different aggregation procedures that generate different inter-model spreads of weights. The use of model weights is sensitive to the aggregation procedure and shows different sensitivities to the selected metrics. Generally, however, we do not find compelling evidence of an improved description of mean climate states using performance-based weights in comparison to the use of equal weights. We suggest that model weighting adds another level of uncertainty to the generation of ensemble-based climate projections, which should be suitably explored, although our results indicate that this uncertainty remains relatively small for the weighting procedures examined.",
keywords = "Climate projections, Ensemble forecast, RCM",
author = "Christensen, {Jens Hesselbjerg} and Erik Kjellstr{\"o}m and Filippo Giorgi and Geert Lenderink and Markku Rummukainen",
year = "2010",
month = dec,
day = "27",
doi = "10.3354/cr00916",
language = "English",
volume = "44",
pages = "179--194",
journal = "Climate Research Online",
issn = "1616-1572",
publisher = "Inter research",
number = "2-3",

}

RIS

TY - JOUR

T1 - Weight assignment in regional climate models

AU - Christensen, Jens Hesselbjerg

AU - Kjellström, Erik

AU - Giorgi, Filippo

AU - Lenderink, Geert

AU - Rummukainen, Markku

PY - 2010/12/27

Y1 - 2010/12/27

N2 - An important new development within the European ENSEMBLES project has been to explore performance-based weighting of regional climate models (RCMs). Until now, although no weighting has been applied in multi-RCM analyses, one could claim that an assumption of 'equal weight' was implicitly adopted. At the same time, different RCMs generate different results, e.g. for various types of extremes, and these results need to be combined when using the full RCM ensemble. The process of constructing, assigning and combining metrics of model performance is not straightforward. Rather, there is a considerable degree of subjectivity both in the choice of metrics and on how these may be combined into weights. We explore the applicability of combining a set of 6 specifically designed RCM performance metrics to produce one aggregated model weight with the purpose of combining climate change information from the range of RCMs used within ENSEMBLES. These metrics capture aspects of model performance in reproducing large-scale circulation patterns, meso-scale signals, daily temperature and precipitation distributions and extremes, trends and the annual cycle. We examine different aggregation procedures that generate different inter-model spreads of weights. The use of model weights is sensitive to the aggregation procedure and shows different sensitivities to the selected metrics. Generally, however, we do not find compelling evidence of an improved description of mean climate states using performance-based weights in comparison to the use of equal weights. We suggest that model weighting adds another level of uncertainty to the generation of ensemble-based climate projections, which should be suitably explored, although our results indicate that this uncertainty remains relatively small for the weighting procedures examined.

AB - An important new development within the European ENSEMBLES project has been to explore performance-based weighting of regional climate models (RCMs). Until now, although no weighting has been applied in multi-RCM analyses, one could claim that an assumption of 'equal weight' was implicitly adopted. At the same time, different RCMs generate different results, e.g. for various types of extremes, and these results need to be combined when using the full RCM ensemble. The process of constructing, assigning and combining metrics of model performance is not straightforward. Rather, there is a considerable degree of subjectivity both in the choice of metrics and on how these may be combined into weights. We explore the applicability of combining a set of 6 specifically designed RCM performance metrics to produce one aggregated model weight with the purpose of combining climate change information from the range of RCMs used within ENSEMBLES. These metrics capture aspects of model performance in reproducing large-scale circulation patterns, meso-scale signals, daily temperature and precipitation distributions and extremes, trends and the annual cycle. We examine different aggregation procedures that generate different inter-model spreads of weights. The use of model weights is sensitive to the aggregation procedure and shows different sensitivities to the selected metrics. Generally, however, we do not find compelling evidence of an improved description of mean climate states using performance-based weights in comparison to the use of equal weights. We suggest that model weighting adds another level of uncertainty to the generation of ensemble-based climate projections, which should be suitably explored, although our results indicate that this uncertainty remains relatively small for the weighting procedures examined.

KW - Climate projections

KW - Ensemble forecast

KW - RCM

UR - http://www.scopus.com/inward/record.url?scp=78650412727&partnerID=8YFLogxK

U2 - 10.3354/cr00916

DO - 10.3354/cr00916

M3 - Journal article

AN - SCOPUS:78650412727

VL - 44

SP - 179

EP - 194

JO - Climate Research Online

JF - Climate Research Online

SN - 1616-1572

IS - 2-3

ER -

ID: 186940913