Comparing Methods to Constrain Future European Climate Projections Using a Consistent Framework

• Published in: Journal of climate 2020-10, Vol.33 (20), p.8671-8692
• Main Authors: Brunner, Lukas, McSweeney, Carol, Ballinger, Andrew P., Befort, Daniel J., Benassi, Marianna, Booth, Ben, Coppola, Erika, de Vries, Hylke, Harris, Glen, Hegerl, Gabriele C., Knutti, Reto, Lenderink, Geert, Lowe, Jason, Nogherotto, Rita, O’Reilly, Chris, Qasmi, Saïd, Ribes, Aurélien, Stocchi, Paolo, Undorf, Sabine
• Format: Article
• Language: English
• Subjects: Agreements; Climate change; Climate models; Constraints; Emissions; Future climates; Impact assessment; Methods; Physics; Precipitation; Risk aversion; Uncertainty; Variables
• ISSN: 0894-8755; 1520-0442
• DOI: 10.1175/JCLI-D-19-0953.1

Political decisions, adaptation planning, and impact assessments need reliable estimates of future climate change and related uncertainties. To provide these estimates, different approaches to constrain, filter, or weight climate model projections into probabilistic distributions have been proposed. However, an assessment of multiple such methods to, for example, expose cases of agreement or disagreement, is often hindered by a lack of coordination, with methods focusing on a variety of variables, time periods, regions, or model pools. Here, a consistent framework is developed to allow a quantitative comparison of eight different methods; focus is given to summer temperature and precipitation change in three spatial regimes in Europe in 2041–60 relative to 1995–2014. The analysis draws on projections from several large ensembles, the CMIP5 multimodel ensemble, and perturbed physics ensembles, all using the high-emission scenario RCP8.5. The methods’ key features are summarized, assumptions are discussed, and resulting constrained distributions are presented. Method agreement is found to be dependent on the investigated region but is generally higher for median changes than for the uncertainty ranges. This study, therefore, highlights the importance of providing clear context about how different methods affect the assessed uncertainty—in particular, the upper and lower percentiles that are of interest to risk-averse stakeholders. The comparison also exposes cases in which diverse lines of evidence lead to diverging constraints; additional work is needed to understand how the underlying differences between methods lead to such disagreements and to provide clear guidance to users.