Articles | Volume 17, issue 8
https://doi.org/10.5194/gmd-17-3321-2024
https://doi.org/10.5194/gmd-17-3321-2024
Methods for assessment of models
 | 
29 Apr 2024
Methods for assessment of models |  | 29 Apr 2024

Bergen metrics: composite error metrics for assessing performance of climate models using EURO-CORDEX simulations

Alok K. Samantaray, Priscilla A. Mooney, and Carla A. Vivacqua

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Manuscript not accepted for further review
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Cited articles

Aggarwal, C. C., Hinneburg, A., and Keim, D. A.: On the surprising behavior of distance metrics in high dimensional space, in: International conference on database theory, Springer, Berlin, Heidelberg, 420–434, https://doi.org/10.1007/3-540-44503-X_27, 2001. 
Ahmed, K., Sachindra, D. A., Shahid, S., Demirel, M. C., and Chung, E.-S.: Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics, Hydrol. Earth Syst. Sci., 23, 4803–4824, https://doi.org/10.5194/hess-23-4803-2019, 2019. 
Armstrong, J. S. and Collopy, F.: Error measures for generalizing about forecasting methods: Empirical comparisons, Int. J. Forecast., 8, 69–80, https://doi.org/10.1016/0169-2070(92)90008-W, 1992. 
Baker, N. C. and Taylor, P. C.: A framework for evaluating climate model performance metrics, J. Climate, 29, 1773–1782, https://doi.org/10.1175/JCLI-D-15-0114.1, 2016. 
Bell, B., Hersbach, H., Berrisford, P., Dahlgren, P., Horányi, A., Muñoz Sabater, J., Nicolas, J., Radu, R., Schepers, D., Simmons, A., Soci, C., and Thépaut, J.-N.: ERA5 monthly averaged data on pressure levels from 1950 to 1978 (preliminary version), Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://cds.climate.copernicus-climate.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels-monthly-means-preliminary-back-extension?tab=overview (last access: 16 April 2024), 2020. 
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Short summary
Any interpretation of climate model data requires a comprehensive evaluation of the model performance. Numerous error metrics exist for this purpose, and each focuses on a specific aspect of the relationship between reference and model data. Thus, a comprehensive evaluation demands the use of multiple error metrics. However, this can lead to confusion. We propose a clustering technique to reduce the number of error metrics needed and a composite error metric to simplify the interpretation.