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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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GMD | Articles | Volume 11, issue 6
Geosci. Model Dev., 11, 2033–2048, 2018
https://doi.org/10.5194/gmd-11-2033-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 11, 2033–2048, 2018
https://doi.org/10.5194/gmd-11-2033-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Methods for assessment of models 04 Jun 2018

Methods for assessment of models | 04 Jun 2018

Cluster-based analysis of multi-model climate ensembles

Richard Hyde et al.

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Anna Wenzel on behalf of the Authors (09 May 2018)  Author's response
ED: Publish subject to technical corrections (10 May 2018) by Jeremy Fyke
Publications Copernicus
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Short summary
Clustering, the automated grouping of similar data, can provide powerful insight into large/complex data. We demonstrate the benefits of clustering applied to output from climate model inter-comparison initiatives. We focus on modelled tropospheric ozone from the ACCMIP project. Cluster-based subsampling of the model ensemble can (i) remove outlier data on a grid-cell basis, reducing model–observation bias and (ii) provide a useful framework in which to investigate and visualise model diversity.
Clustering, the automated grouping of similar data, can provide powerful insight into...
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