Research led by BC3 develops tool to standardise GCAM model results

To address the growing need to understand and manage uncertainty in climate change analysis, a BC3 research team has launched a new tool called gcamreport.

Published in the latest edition of The Journal of Open Source Software, the study led by  BC3 researcher Clàudia Rodés-Bachs and researchers Jon Sampedro, Russell Horowitz and Dirk-Jan Van de Ven explains the development of this open source tool specifically designed to process and standardize the results of the Global Change Analysis Model (GCAM), used to explore the impacts of climate policies in relevant publications such as the Intergovernmental Panel on Climate Change (IPCC) assessment reports.

“We hope that gcamreport will facilitate current research and foster collaboration and progress in climate change analysis. The tool is freely available and open source, inviting the scientific community to participate, contribute to and benefit from its continued development”. Clàudia Rodés-Bachs, BC3 researcher.

The gcamreport tool, developed in collaboration with UMD-CGS and PNNL, was created in response to several fundamental aspects. Firstly, the urgency of carrying out multi-model studies to adequately characterise the uncertainty in climate change analysis, considering the complexity inherent to the heterogeneity of the models. Secondly, to enable reproducibility of results, increasing transparency and reusability. Finally, to favour the visualisation of the results intuitively through an interface. Thus, the gcamreport tool could be considered as a crucial solution to the lack of a standardised way to process GCAM model output data in line with the Integrated Analysis Model Consortium (IAMC) standards, contributing to improve the comprehensibility and interoperability of the results.

“Our team will continue to work to keep track of gcamreport releases and its integration with other tools within the GCAM ecosystem, ensuring its long-term relevance and usefulness. Jon Sampedro, BC3 researcher.

You can access the publication at the following link:

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María de Maeztu Excellence Unit 2023-2027 Ref. CEX2021-001201-M, funded by MCIN/AEI /10.13039/501100011033

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