Representative impact scenarios from weather and climate ensembles

Ensembles have revolutionized weather forecasts, by providing a probabilistic view of future weather. However, ensembles contain a large amount of information, and using this to understand the potential impacts of the forecasted weather requires a skilled user with extensive computational resources. This challenge is particularly acute for users considering the weather conditions over a geographical region, rather than at a single location. In these cases, considering every single ensemble member and its impacts may be practically unfeasible. Many such users thus simply consider the ensemble mean and some measure of ensemble spread, such as the ensemble’s standard deviation. While this facilitates the use of ensemble forecasts, it does not explore the range of possible impacts of the forecasted weather. Here, we propose a framework facilitating the use of ensemble forecasts for weather impacts. We specifically represent the ensemble by the mean and a single deviation from the mean. This deviation is defined so as to both be representative of the variability in the ensemble, and have a significant impact according to some impact metric. We determine such a deviation using a statistical method known as Directional Component Analysis, which is based on linearizing an impact metric around the ensemble mean. We provide a concrete example using 2-m temperature forecasts for continental Europe from ECMWF, and show that this approach is more robust than considering the single worst (in terms of impacts) ensemble member. This same approach can be applied to ensembles of projections of future climates. We illustrate this by deriving representative deviations for the UKCP18 and EURO-CORDEX projections of future precipitation in Europe. We conclude that the mean and representative deviation method we propose may both contribute to automated early warnings for weather impacts and support users who wish to explore the implications of longer-term climate impacts in a resource-effective fashion.