Implementation and validation of a supermodeling framework  into Community Earth System Model version 2.1.5

Chapman, William E.; Schevenhoven, Francine; Berner, Judith; Keenlyside, Noel; Bethke, Ingo; Chiu, Ping-Gin; Gupta, Alok; Nusbaumer, Jesse

doi:https://doi.org/10.5194/gmd-18-5451-2025

Articles | Volume 18, issue 17

https://doi.org/10.5194/gmd-18-5451-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Theoretical and computational aspects of ensemble design,...

https://doi.org/10.5194/gmd-18-5451-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 17

Development and technical paper

|

01 Sep 2025

Development and technical paper |

| 01 Sep 2025

Implementation and validation of a supermodeling framework into Community Earth System Model version 2.1.5

William E. Chapman, Francine Schevenhoven, Judith Berner, Noel Keenlyside, Ingo Bethke, Ping-Gin Chiu, Alok Gupta, and Jesse Nusbaumer

Supplement

https://doi.org/10.5194/gmd-18-5451-2025-supplement

Data sets

ERA5 Reanalysis (0.25 Degree Latitude-Longitude Grid) (Updated monthly) European Centre for Medium-Range Weather Forecasts https://doi.org/10.5065/BH6N-5N20

The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present) (https://psl.noaa.gov/data/gridded/data.gpcp.html) R. F. Adler et al. https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2

Model code and software

SuperModel_CAM: PauseResume_v1.1.0 W. Chapman et al. https://doi.org/10.5281/zenodo.14983620

Chapman_2024_GMD: Figure Release V1 W. Chapman et al. https://doi.org/10.5281/zenodo.14983576

Short summary

We introduce the first state-of-the-art atmosphere-connected supermodel, where two advanced atmospheric models share information in real time to form a new dynamical system. By synchronizing the models, particularly in storm track regions, we achieve better predictions without losing variability. This approach maintains key climate patterns and reduces bias in some variables compared to traditional models, demonstrating a useful technique for improving atmospheric simulations.