CH2018 – National climate scenarios for Switzerland: How to construct consistent multi-model projections from ensembles of opportunity

• Main Authors: Silje Lund Sørland, Andreas M. Fischer, Sven Kotlarski, Hans R. Künsch, Mark A. Liniger, Jan Rajczak, Christoph Schär, Curdin Spirig, Kuno Strassmann, Reto Knutti
• Format: Article
• Language: English
• Published: Elsevier 2020-12-01
• Series: Climate Services
• Subjects: Regional climate change scenarios; Climate services; CORDEX; Ensembles of opportunities; Switzerland
• Online Access: http://www.sciencedirect.com/science/article/pii/S2405880720300480

The latest Swiss Climate Scenarios (CH2018), released in November 2018, consist of several datasets derived through various methods that provide robust and relevant information on climate change in Switzerland. The scenarios build upon the regional climate model projections for Europe produced through the internationally coordinated downscaling effort EURO-CORDEX. The simulations from EURO-CORDEX consist of simulations at two spatial horizontal resolutions, several global climate models, and three different emission scenarios. Even with this unique dataset of regional climate scenarios, a number of practical challenges regarding a consistent interpretation of the model ensemble arise. Here we present the methodological chain employed in CH2018 in order to generate a multi-model ensemble that is consistent across scenarios and is used as a basis for deriving the CH2018 products. The different steps involve a thorough evaluation of the full EURO-CORDEX model ensemble, the removal of doubtful and potentially erroneous simulations, a time-shift approach to account for an equal number of simulations for each emission scenario, and the multi-model combination of simulations with different spatial resolutions. Each component of this cascade of processing steps is associated with an uncertainty that eventually contributes to the overall scientific uncertainty of the derived scenario products. We present a comparison and an assessment of the uncertainties from these individual effects and relate them to probabilistic projections. It is shown that the CH2018 scenarios are generally supported by the results from other sources. Thus, the CH2018 scenarios currently provide the best available dataset of future climate change estimates in Switzerland.