Developing Representative Impact Scenarios From Climate Projection Ensembles, With Application to UKCP18 and EURO‐CORDEX Precipitation

Abstract

Abstract Calculating impacts from climate projection ensembles can be challenging. A simple approach might consider just the ensemble mean, but this ignores much of the information in the ensemble and does not explore the range of possible impacts. A more thorough approach would consider every ensemble member, but may be computationally unfeasible for many impact models. We investigate the compromise in which we represent the ensemble by the mean and a single deviation from the mean. The deviation from the mean would ideally be representative both of variability in the ensemble, and have a significant impact, according to some impact metric. We compare methods for calculating the deviation from the mean, based on traditional compositing and a statistical method known as Directional Component Analysis (DCA). DCA is based on linearizing the impact metric around the ensemble mean. We illustrate the methods with synthetic examples, and derive new mathematical results that clarify the interpretation of DCA. We then use the methods to derive scenarios from the UKCP18 and EURO‐CORDEX projections of future precipitation in Europe. We find that the worst ensemble member is not robust, but that deviations from the ensemble mean calculated using compositing and DCA are robust. They thus give robust insight into the patterns of change in the ensemble. We conclude that mean and representative deviation methods may be suitable for climate projection users who wish to explore the implications of the uncertainty around the ensemble mean without having to calculate the impacts of every ensemble member.

Keywords

• climate projection
• ensemble mean
• worst‐case
• DCA
• EURO‐CORDEX
• precipitation