AWI-CM3 coupled climate model: description and evaluation experiments for a prototype post-CMIP6 model

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<jats:p>Abstract. We developed a new version of the Alfred Wegener Institute Climate Model (AWI-CM3), which has higher skills in representing the observed climatology and better computational efficiency than its predecessors. Its ocean component FESOM2 (Finite-volumE Sea ice–Ocean Model) has the multi-resolution functionality typical of unstructured-mesh models while still featuring a scalability and efficiency similar to regular-grid models. The atmospheric component OpenIFS (CY43R3) enables the use of the latest developments in the numerical-weather-prediction community in climate sciences. In this paper we describe the coupling of the model components and evaluate the model performance on a variable-resolution (25–125 km) ocean mesh and a 61 km atmosphere grid, which serves as a reference and starting point for other ongoing research activities with AWI-CM3. This includes the exploration of high and variable resolution and the development of a full Earth system model as well as the creation of a new sea ice prediction system. At this early development stage and with the given coarse to medium resolutions, the model already features above-CMIP6-average skills (where CMIP6 denotes Coupled Model Intercomparison Project phase 6) in representing the climatology and competitive model throughput. Finally we identify remaining biases and suggest further improvements to be made to the model. </jats:p>

Item Type
Publication Status
Published online
Eprint ID
DOI 10.5194/gmd-15-6399-2022

Cite as
Streffing, J. , Sidorenko, D. , Semmler, T. , Zampieri, L. , Scholz, P. , Andrés-Martínez, M. , Koldunov, N. , Rackow, T. , Kjellsson, J. , Goessling, H. , Athanase, M. , Wang, Q. , Hegewald, J. , Sein, D. V. , Mu, L. , Fladrich, U. , Barbi, D. , Gierz, P. , Danilov, S. , Juricke, S. , Lohmann, G. and Jung, T. (2022): AWI-CM3 coupled climate model: description and evaluation experiments for a prototype post-CMIP6 model , Geoscientific Model Development, 15 (16), pp. 6399-6427 . doi: 10.5194/gmd-15-6399-2022

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