Brain Storms: Exploring nextGEMS publications of the second half of 2023

– model improvements, fascinating insights and new data

The first half of 2023 spawned many new papers connected to the nextGEMS project, which you can read more about in our last blog post. In the second half, from July to December 2023, the nextGEMS community added another four publications to this list. These publications put forth new knowledge on the climate system and the improvement of climate models and decision-making processes. Members of the project also made available data generated in the third cycle of model development within nextGEMS. 

In one of the papers, Brunner and Sippel investigated how to enhance climate models using statistical and machine learning processes. Their insights are a vital step towards shortening the amount of time needed to evaluate the performance and independence of new climate models.

Climate model genealogy aims at understanding structural dependencies and sampling biases in multi-model ensembles. Dependencies and biases can, for example, occur between different model versions or models developed at the same institution as they partially share computer code, algorithms and parametrization schemes. Kuma, Bender and Jönsson looked into these structural similarities of models used in the Coupled Model Intercomparison Project (CIMP), identifying 12 different model families. Their findings suggest that using family and ancestry weighting for independent models in multi-model ensembles could improve data on model uncertainty and reduce bias originating from structural similarities between models of the same model family.

Moum et al. worked on understanding the influence of surface wind stress and shear on diurnal deep cycle turbulence at the equatorial cold tongues. Deep cycle turbulence describes the process of mixing the warmer ocean surface water with the colder water of deeper layers. This process plays an important role in climate regulation, effecting the ocean’s capacity to take up heat from the atmosphere.

The work of Baulenas and Bojovic highlights the potential of eliciting information from high-resolution Earth system models in a participatory process to support decision-making in complex matters. Especially tasks like the development of resilient renewable energy systems could benefit from this approach, as shown by the study conducted in Madrid in May 2023.

Finally, Koldunov et al. released a subset of data generated with the ICON and IFS models throughout the third model development cycle.

A comprehensive compilation of publications associated with the project can be found on the nextGEMS Publications page. 

July

• Brunner, L.And S. Sippel (2023). “Identifying Climate Models Based On Their Daily Output Using Machine Learning”. In: Environmental Data Science 2, E22. DOI: 10.1017/Eds.2023.23.

• Kuma, P., Bender, F. A.‐M., And Jönsson, A. R. (2023). „Climate Model Code Genealogy And Its Relation To Climate Feedbacks And Sensitivity“. In: Journal Of Advances In Modeling Earth Systems, 15, 7. Ems003588. Doi:10.1029/2022MS003588.

August

• Moum, J. N., W. D. Smyth, K. G. Hughes, D. Cherian, S. J. Warner, B. Bourlès, P. Brandt, And M. Dengler (2023). “Wind Dependencies Of Deep Cycle Turbulence In The Equatorial Cold Tongues”. In: Journal Of Physical Oceanography 53.8, Pp. 1979–1995. DOI: 10.1175/JPO-D-22-0203.1.

October

• Baulenas, E., And Bojovic, D. (2023). „Co-Producing Qualitative Storylines For Resilient Renewable Energy Scenarios Amid Climate Uncertainty“. In: Bulletin Of The American Meteorological Society, 104, 10. E1799-E1806. DOI: 10.1175/BAMS-D-23-0211.1.

• Koldunov, N., Kölling, T., Pedruzo-Bagazgoitia, X., Rackow, T., Redler, R., Sidorenko, D., Wieners, K.-H., And Ziemen, F. A. (2023). “Nextgems: Output Of The Model Development Cycle 3 Simulations For ICON And IFS”. World Data Center For Climate (WDCC) At DKRZ. DOI: 10.26050/WDCC/Nextgems_Cyc3.

If using our data in a publication, please make sure to use the following sentence in the acknowledgements:
„[XY] was supported by the nextGEMS project under the European Union’s Horizon 2020 research program (Grant No. 101003470).“