A data-driven approach for classifying GCM errors and understanding their impact on climate projections.

Tamzin Palmer; David Sexton; Anna-Louise Ellis; Douglas McNeall; Georgie Mercer

doi:https://doi.org/10.5194/egusphere-egu26-6734

[Back] [Session ESSI1.1]

EGU26-6734, updated on 13 Mar 2026

https://doi.org/10.5194/egusphere-egu26-6734

EGU General Assembly 2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

A data-driven approach for classifying GCM errors and understanding their impact on climate projections.

Tamzin Palmer¹, David Sexton¹, Anna-Louise Ellis¹, Douglas McNeall¹, and Georgie Mercer^1,2

Tamzin Palmer et al.

¹Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, United Kingdom (tamzin.palmer@metoffice.gov.uk)
²Department of Mathematics and Statistics, University of Exeter, Exeter, UK (gm616@exeter.ac.uk)

How to cite: Palmer, T., Sexton, D., Ellis, A.-L., McNeall, D., and Mercer, G.: A data-driven approach for classifying GCM errors and understanding their impact on climate projections., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6734, https://doi.org/10.5194/egusphere-egu26-6734, 2026.

This abstract has been withdrawn on 30 Mar 2026.