Articles | Volume 11, issue 6
https://doi.org/10.5194/gmd-11-2033-2018
https://doi.org/10.5194/gmd-11-2033-2018
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, Ryan Hossaini, and Amber A. Leeson

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Latest update: 28 Mar 2024
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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.