Articles | Volume 16, issue 13
https://doi.org/10.5194/gmd-16-3927-2023
https://doi.org/10.5194/gmd-16-3927-2023
Methods for assessment of models
 | 
13 Jul 2023
Methods for assessment of models |  | 13 Jul 2023

Evaluating precipitation distributions at regional scales: a benchmarking framework and application to CMIP5 and 6 models

Min-Seop Ahn, Paul A. Ullrich, Peter J. Gleckler, Jiwoo Lee, Ana C. Ordonez, and Angeline G. Pendergrass

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Cited articles

Abramowitz, G.: Towards a public, standardized, diagnostic benchmarking system for land surface models, Geosci. Model Dev., 5, 819–827, https://doi.org/10.5194/gmd-5-819-2012, 2012. 
Ahn, M. and Kang, I.: A practical approach to scale-adaptive deep convection in a GCM by controlling the cumulus base mass flux, npj Clim. Atmos. Sci., 1, 13, https://doi.org/10.1038/s41612-018-0021-0, 2018. 
Ahn, M.-S., Gleckler, P. J., Lee, J., Pendergrass, A. G., and Jakob, C.: Benchmarking Simulated Precipitation Variability Amplitude across Time Scales, J. Climate, 35, 3173–3196, https://doi.org/10.1175/JCLI-D-21-0542.1, 2022. 
Ahn, M.-S., Ullrich, P. A., Lee, J., Gleckler, P. J., Ma, H.-Y., Terai, C. R., Bogenschutz, P. A., and Ordonez, A. C.: Bimodality in Simulated Precipitation Frequency Distributions and Its Relationship with Convective Parameterizations, npj Clim. Atmos. Sci., submitted, https://doi.org/10.21203/rs.3.rs-2874349/v1, 2023. 
Ashouri, H., Hsu, K. L., Sorooshian, S., Braithwaite, D. K., Knapp, K. R., Cecil, L. D., Nelson, B. R., and Prat, O. P.: PERSIANN-CDR: Daily precipitation climate data record from multisatellite observations for hydrological and climate studies, B. Am. Meteorol. Soc., 96, 69–83, https://doi.org/10.1175/BAMS-D-13-00068.1, 2015. 
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Short summary
We introduce a framework for regional-scale evaluation of simulated precipitation distributions with 62 climate reference regions and 10 metrics and apply it to evaluate CMIP5 and CMIP6 models against multiple satellite-based precipitation products. The common model biases identified in this study are mainly associated with the overestimated light precipitation and underestimated heavy precipitation. These biases persist from earlier-generation models and have been slightly improved in CMIP6.