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

Related authors

Evaluating tropospheric nitrogen dioxide in UKCA using OMI satellite retrievals over south and east Asia
Alok K. Pandey, David S. Stevenson, Alcide Zhao, Richard J. Pope, Ryan Hossaini, Krishan Kumar, and Martyn P. Chipperfield
Atmos. Chem. Phys., 25, 4785–4802, https://doi.org/10.5194/acp-25-4785-2025,https://doi.org/10.5194/acp-25-4785-2025, 2025
Short summary
A comparison of supraglacial meltwater features throughout contrasting melt seasons: southwest Greenland
Emily Glen, Amber Leeson, Alison F. Banwell, Jennifer Maddalena, Diarmuid Corr, Olivia Atkins, Brice Noël, and Malcolm McMillan
The Cryosphere, 19, 1047–1066, https://doi.org/10.5194/tc-19-1047-2025,https://doi.org/10.5194/tc-19-1047-2025, 2025
Short summary
On the atmospheric budget of 1,2-dichloroethane and its impact on stratospheric chlorine and ozone (2002–2020)
Ryan Hossaini, David Sherry, Zihao Wang, Martyn P. Chipperfield, Wuhu Feng, David E. Oram, Karina E. Adcock, Stephen A. Montzka, Isobel J. Simpson, Andrea Mazzeo, Amber A. Leeson, Elliot Atlas, and Charles C.-K. Chou
Atmos. Chem. Phys., 24, 13457–13475, https://doi.org/10.5194/acp-24-13457-2024,https://doi.org/10.5194/acp-24-13457-2024, 2024
Short summary
Bayesian hierarchical model for bias-correcting climate models
Jeremy Carter, Erick A. Chacón-Montalván, and Amber Leeson
Geosci. Model Dev., 17, 5733–5757, https://doi.org/10.5194/gmd-17-5733-2024,https://doi.org/10.5194/gmd-17-5733-2024, 2024
Short summary
A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends
Lily Gouldsbrough, Ryan Hossaini, Emma Eastoe, Paul J. Young, and Massimo Vieno
Atmos. Chem. Phys., 24, 3163–3196, https://doi.org/10.5194/acp-24-3163-2024,https://doi.org/10.5194/acp-24-3163-2024, 2024
Short summary

Related subject area

Atmospheric sciences
Evaluation of dust emission and land surface schemes in predicting a mega Asian dust storm over South Korea using WRF-Chem
Ji Won Yoon, Seungyeon Lee, Ebony Lee, and Seon Ki Park
Geosci. Model Dev., 18, 2303–2328, https://doi.org/10.5194/gmd-18-2303-2025,https://doi.org/10.5194/gmd-18-2303-2025, 2025
Short summary
Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy
Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
Geosci. Model Dev., 18, 2231–2248, https://doi.org/10.5194/gmd-18-2231-2025,https://doi.org/10.5194/gmd-18-2231-2025, 2025
Short summary
A Bayesian method for predicting background radiation at environmental monitoring stations in local-scale networks
Jens Peter Karolus Wenceslaus Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
Geosci. Model Dev., 18, 1989–2003, https://doi.org/10.5194/gmd-18-1989-2025,https://doi.org/10.5194/gmd-18-1989-2025, 2025
Short summary
Inclusion of the ECMWF ecRad radiation scheme (v1.5.0) in the MAR (v3.14), regional evaluation for Belgium, and assessment of surface shortwave spectral fluxes at Uccle
Jean-François Grailet, Robin J. Hogan, Nicolas Ghilain, David Bolsée, Xavier Fettweis, and Marilaure Grégoire
Geosci. Model Dev., 18, 1965–1988, https://doi.org/10.5194/gmd-18-1965-2025,https://doi.org/10.5194/gmd-18-1965-2025, 2025
Short summary
Development of a fast radiative transfer model for ground-based microwave radiometers (ARMS-gb v1.0): validation and comparison to RTTOV-gb
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
Geosci. Model Dev., 18, 1947–1964, https://doi.org/10.5194/gmd-18-1947-2025,https://doi.org/10.5194/gmd-18-1947-2025, 2025
Short summary

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. 
Download
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.
Share