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Climate change projections for building energy simulation studies: a CORDEX-based methodological approach to manage uncertainties

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Abstract

We propose a comprehensive methodological approach to address uncertainties in building energy simulation (BES) studies within a climate change context. Drawing upon expertise from the climate community, our approach aims to improve the reliability of climate-dependent BES for sustainable building design studies. The methodology focuses on creating weather files that accurately retain the climate variability from CORDEX high-frequency climate data, and performing multiple BES (conducted with climatologies from various climate models and emissions scenarios) while removing the climate models biases. The robustness of the results is assessed through statistical analysis, and an uncertainty range is attributed to future energy demand estimations. This approach is illustrated using a representative prototype of a social house located in central-eastern Argentina. The evaluation specifically focuses on assessing the influence of climate change projections on cooling and heating energy demand. We systematically assessed uncertainties related to climate scenarios, seasonality, and building design sensitivity. Our exercise highlight that uncertainty levels rise with higher emissions scenarios. Within our case study, the cooling (heating) energy demand exhibits substantial variations, ranging from 27-37 (303-330) MJ/m² in a moderate emissions context to 51-70 (266-326) MJ/m² in a high emissions scenario. Notably, improvements in building efficiency correlate with reduced uncertainty and, in the context of higher emissions, the projected energy demand can range between 24-37 (201-243) MJ/m². Finally, a discussion is provided on the added value of the proposed methodology compared to solely utilizing a single climate projection file in BES, when uncertainties within climate projections remain unassessed.

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Data availability

The weather files generated during the current study are available from the corresponding author on request.

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Acknowledgements

The authors would like to thank A.R.C. Remedio, G. Nikulin, J. Fernández and R.P. da Rocha for providing the high frequency outputs of climate models. We also wish to thank the CPA staff from CIMA Institute for their generous support and technical assistance. We especially thank Rodrigo Marquez, Claudio Mattera, Paula Richter, Alfredo Rolla, Pablo Roselli, and Gabriel Vieytes.

Funding

This work was partially supported by Consejo Nacional de Investigaciones Científicas y Técnicas [Project PIP-112-2020-0102141-CO]; and Universidad Nacional de Rosario [Project PID-UNR SECYT 80020190100069UR]; and Agencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la Innovación [Project PICT-2021-I-A-01097].

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Authors and Affiliations

  1. CONICET - UNR, Instituto de Física Rosario (IFIR), Rosario, Argentina

    Tanea Coronato & Rita Abalone

  2. UNR, Facultad de Ciencias Exactas, Ingeniería y Agrimensura (FCEIA), Rosario, Argentina

    Tanea Coronato & Rita Abalone

  3. CNRS – IRD – CONICET – UBA, Institut Franco-Argentin d’Estudes sur le Climat et ses Impacts (IRL IFAECI), Buenos Aires, Argentina

    Tanea Coronato, Pablo G. Zaninelli & Andrea F. Carril

  4. IGEO, CSIC-Universidad Complutense de Madrid, Madrid, España

    Pablo G. Zaninelli

  5. UNLP, Facultad de Ciencias Astronómicas y Geofísicas, La Plata, Argentina

    Pablo G. Zaninelli

  6. UBA - CONICET, Centro de Investigaciones del Mar y la Atmósfera (CIMA), Buenos Aires, Argentina

    Andrea F. Carril

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  1. Tanea Coronato
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  2. Pablo G. Zaninelli
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  3. Rita Abalone
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Tanea Coronato. The first draft of the manuscript was written by Tanea Coronato and all authors commented on previous versions of the manuscript. The funding acquisition was obtained by Andrea F. Carril and Rita Abalone. The study was supervised by Andrea F. Carril. All authors read and approved the final manuscript.

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Correspondence to Tanea Coronato.

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Coronato, T., Zaninelli, P.G., Abalone, R. et al. Climate change projections for building energy simulation studies: a CORDEX-based methodological approach to manage uncertainties. Climatic Change 177, 43 (2024). https://doi.org/10.1007/s10584-024-03710-9

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