Urban multi-model climate projections of intense heat in Switzerland

This paper introduces a straightforward approach to generate multi-model climate projections of intense urban heat, based on an ensemble of state-of-the-art global and regional climate model simulations from EURO-CORDEX. The employed technique entails the empirical-statistical downscaling method quantile mapping (QM), which is applied in two different settings, first for bias correction and downscaling of raw climate model data to rural stations with long-term measurements and second for spatial transfer of bias-corrected and downscaled climate model data to the respective urban target site. The resulting products are daily minimum and maximum temperatures at five urban sites in Switzerland until the end of the 21st century under three emission scenarios (RCP2.6, RCP4.5, RCP8.5). We test the second-step QM approach in an extensive evaluation framework, using long-term observational data of two exemplary weather stations in Zurich. Results indicate remarkably good skill of QM in present-day climate. Comparing the generated urban climate projections with existing climate scenarios of adjacent rural sites allows us to represent the urban heat island (UHI) effect in future temperature-based heat indices, namely tropical nights, summer days and hot days. Urban areas will be more strongly affected by rising temperatures than rural sites in terms of fixed threshold exceedances, especially during nighttime. Projections for the end of the century for Zurich, for instance, suggest more than double the number of tropical nights (Tmin above 20 ºC) at the urban site (45 nights per year, multi-model median) compared to the rural counterpart (20 nights) under RCP8.5.This research has been partly supported by the European Commission (HEAT-SHIELD 668786). EH is supported by the German Research Foundation under project number 40805747

Predictive models of distribution and abundance of a threatened mountain species show that impacts of climate change overrule those of land use change

Aim: Climate is often the sole focus of global change research in mountain ecosystems although concomitant changes in land use might represent an equally important threat. As mountain species typically depend on fine‐scale environmental characteristics, integrating land use change in predictive models is crucial to properly assess their vulnerability. Here, we present a modelling framework that aims at providing more comprehensive projections of both species’ distribution and abundance under realistic scenarios of land use and climate change, and at disentangling their relative effects. Location: Switzerland. Methods: We used the ring ouzel (Turdus torquatus), a red‐listed and declining mountain bird species, as a study model. Based on standardized monitoring data collected across the whole country, we fitted high‐resolution ensemble species distribution models to predict current occurrence probability, while spatially explicit density estimates were obtained from N‐mixture models. We then tested for the effects of realistic scenarios of land use (land abandonment versus farming intensification) and climate change on future species distribution and abundance. Results: Occurrence probability was mostly explained by climatic conditions, so that climate change was predicted to have larger impacts on distribution and abundance than any scenarios of land use change. In the mid‐term (2030–2050), predicted effects of environmental change show a high spatial heterogeneity due to regional differences in climate and dominant land use, with farming intensification identified as an important threat locally. In the long term (2080–2100), climate models forecast a marked upward range shift (up to +560 m) and further population decline (up to −35%). Main conclusions: Our innovative approach highlights the spatio‐temporal heterogeneity in the relative effects of different environmental drivers on species distribution and abundance. The proposed framework thus provides a useful tool not only for better assessing species’ vulnerability in the face of global change, but also for identifying key areas for conservation interventions at a meaningful scale