Articles | Volume 11, issue 6
Geosci. Model Dev., 11, 2033–2048, 2018
Geosci. Model Dev., 11, 2033–2048, 2018

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.


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

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
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.