Articles | Volume 15, issue 4
https://doi.org/10.5194/gmd-15-1803-2022
https://doi.org/10.5194/gmd-15-1803-2022
Model evaluation paper
 | 
03 Mar 2022
Model evaluation paper |  | 03 Mar 2022

Variability and extremes: statistical validation of the Alfred Wegener Institute Earth System Model (AWI-ESM)

Justus Contzen, Thorsten Dickhaus, and Gerrit Lohmann

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Cited articles

Acero, F. J., García, J. A., and Gallego, M. C.: Peaks-over-Threshold Study of Trends in Extreme Rainfall over the Iberian Peninsula, J. Climate, 24, 1089–1105, https://doi.org/10.1175/2010JCLI3627.1, 2011. a
Ackermann, L., Danek, C., Gierz, P., and Lohmann, G.: AMOC Recovery in a Multicentennial Scenario Using a Coupled Atmosphere-Ocean-Ice Sheet Model, Geophys. Res. Lett., 47, e2019GL086810, https://doi.org/10.1029/2019GL086810, 2020. a, b, c
Bador, M., Boé, J., Terray, L., Alexander, L. V., Baker, A., Bellucci, A., Haarsma, R., Koenigk, T., Moine, M.-P., Lohmann, K., Putrasahan, D. A., Roberts, C., Roberts, M., Scoccimarro, E., Schiemann, R., Seddon, J., Senan, R., Valcke, S., and Vanniere, B.: Impact of Higher Spatial Atmospheric Resolution on Precipitation Extremes Over Land in Global Climate Models, J. Geophys. Res.-Atmos., 125, e2019JD032184, https://doi.org/10.1029/2019JD032184, 2020a. a, b
Bargaoui, Z. and Bárdossy, A.: Modeling short duration extreme precipitation patterns using copula and generalized maximum pseudo-likelihood estimation with censoring, Adv. Water Resour., 84, 1–13, https://doi.org/10.1016/j.advwatres.2015.07.006, 2015. a
Bernard, E., Naveau, P., Vrac, M., and Mestre, O.: Clustering of Maxima: Spatial Dependencies among Heavy Rainfall in France, J. Climate, 26, 7929–7937, https://doi.org/10.1175/JCLI-D-12-00836.1, 2013. a, b
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Short summary
Climate models are of paramount importance to predict future climate changes. Since many severe consequences of climate change are due to extreme events, the accurate behaviour of models in terms of extremes needs to be validated thoroughly. We present a method for model validation in terms of climate extremes and an algorithm to detect regions in which extremes tend to occur at the same time. These methods are applied to data from different climate models and to observational data.