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

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
On the atmospheric budget of ethylene dichloride and its impact on stratospheric chlorine and ozone (2002–2020)
Ryan Hossaini, David Sherry, Zihao Wang, Martyn Chipperfield, Wuhu Feng, David Oram, Karina Adcock, Stephen Montzka, Isobel Simpson, Andrea Mazzeo, Amber Leeson, Elliot Atlas, and Charles C.-K. Chou
EGUsphere, https://doi.org/10.5194/egusphere-2024-560,https://doi.org/10.5194/egusphere-2024-560, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
A comparison of supraglacial meltwater features throughout contrasting melt seasons: Southwest Greenland
Emily Glen, Amber A. Leeson, Alison F. Banwell, Jennifer Maddalena, Diarmuid Corr, Brice Noël, and Malcolm McMillan
EGUsphere, https://doi.org/10.5194/egusphere-2024-23,https://doi.org/10.5194/egusphere-2024-23, 2024
Short summary
Evaluation of satellite methods for estimating supraglacial lake depth in southwest Greenland
Laura Melling, Amber Leeson, Malcolm McMillan, Jennifer Maddalena, Jade Bowling, Emily Glen, Louise Sandberg Sørensen, Mai Winstrup, and Rasmus Lørup Arildsen
The Cryosphere, 18, 543–558, https://doi.org/10.5194/tc-18-543-2024,https://doi.org/10.5194/tc-18-543-2024, 2024
Short summary
Improved monitoring of subglacial lake activity in Greenland
Louise Sandberg Sørensen, Rasmus Bahbah, Sebastian B. Simonsen, Natalia Havelund Andersen, Jade Bowling, Noel Gourmelen, Alex Horton, Nanna B. Karlsson, Amber Leeson, Jennifer Maddalena, Malcolm McMillan, Anne Solgaard, and Birgit Wessel
The Cryosphere, 18, 505–523, https://doi.org/10.5194/tc-18-505-2024,https://doi.org/10.5194/tc-18-505-2024, 2024
Short summary

Related subject area

Atmospheric sciences
A machine learning approach for evaluating Southern Ocean cloud radiative biases in a global atmosphere model
Sonya L. Fiddes, Marc D. Mallet, Alain Protat, Matthew T. Woodhouse, Simon P. Alexander, and Kalli Furtado
Geosci. Model Dev., 17, 2641–2662, https://doi.org/10.5194/gmd-17-2641-2024,https://doi.org/10.5194/gmd-17-2641-2024, 2024
Short summary
Decision Support System version 1.0 (DSS v1.0) for air quality management in Delhi, India
Gaurav Govardhan, Sachin D. Ghude, Rajesh Kumar, Sumit Sharma, Preeti Gunwani, Chinmay Jena, Prafull Yadav, Shubhangi Ingle, Sreyashi Debnath, Pooja Pawar, Prodip Acharja, Rajmal Jat, Gayatry Kalita, Rupal Ambulkar, Santosh Kulkarni, Akshara Kaginalkar, Vijay K. Soni, Ravi S. Nanjundiah, and Madhavan Rajeevan
Geosci. Model Dev., 17, 2617–2640, https://doi.org/10.5194/gmd-17-2617-2024,https://doi.org/10.5194/gmd-17-2617-2024, 2024
Short summary
How non-equilibrium aerosol chemistry impacts particle acidity: the GMXe AERosol CHEMistry (GMXe–AERCHEM, v1.0) sub-submodel of MESSy
Simon Rosanka, Holger Tost, Rolf Sander, Patrick Jöckel, Astrid Kerkweg, and Domenico Taraborrelli
Geosci. Model Dev., 17, 2597–2615, https://doi.org/10.5194/gmd-17-2597-2024,https://doi.org/10.5194/gmd-17-2597-2024, 2024
Short summary
A grid model for vertical correction of precipitable water vapor over the Chinese mainland and surrounding areas using random forest
Junyu Li, Yuxin Wang, Lilong Liu, Yibin Yao, Liangke Huang, and Feijuan Li
Geosci. Model Dev., 17, 2569–2581, https://doi.org/10.5194/gmd-17-2569-2024,https://doi.org/10.5194/gmd-17-2569-2024, 2024
Short summary
MEXPLORER 1.0.0 – a mechanism explorer for analysis and visualization of chemical reaction pathways based on graph theory
Rolf Sander
Geosci. Model Dev., 17, 2419–2425, https://doi.org/10.5194/gmd-17-2419-2024,https://doi.org/10.5194/gmd-17-2419-2024, 2024
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.