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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Cited articles

Aggarwal, C. C. and Reddy, C. K. (Eds.): DATA Clustering Algorithms and Applications, CRC Press, Boca Raton, available at: https://www.crcpress.com/Data-Clustering-Algorithms-and-Applications/Aggarwal-Reddy/p/book/9781466558212 (last access: 28 May 2018), 2014. 
Arroyo, A., Tricio, V., Herrero, A., and Corchado, E.: Time Analysis of Air Pollution in a Spanish Region Through k-means, in: International Joint Conference SOCO'16- CISIS'16-ICEUTE'16, edited by: Grana, M., Lopez Guede, J. M., Etxaniz, O., Herrero, A., Quintian, H., and Corchado, E., Advances in Intelligent Systems and Computing, 527 63–72, https://doi.org/10.1007/978-3-319-47364-2, 2017. 
Austin, E., Coull, B. A., Zanobetti, A., and Koutrakis, P.: A framework to spatially cluster air pollution monitoring sites in US based on the PM2.5 composition, Environ. Int., 59, 244–254, https://doi.org/10.1016/j.envint.2013.06.003, 2013. 
Bador, M., Naveau, P., Gilleland, E., Castellà, M., and Arivelo, T.: Spatial clustering of summer temperature maxima from the CNRM-CM5 climate model ensembles and E-OBS over Europe, Weather Clim. Extrem., 9, 17–24, 2015. 
Braconnot, P., Harrison, S. P., Kageyama, M., Bartlein, P. J., Masson-Delmotte, V., Abe-Ouchi, A., Otto-Bliesner, B., and Zhao, Y.: Evaluation of climate models using palaeoclimatic data, Nat. Clim. Change, 2, 417–424, https://doi.org/10.1038/NCLIMATE1456, 2012. 
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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.
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