Abstract
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 °C) at the urban site (45 nights per year, multi-model median) compared to the rural counterpart (20 nights) under RCP8.5.
Highlights
People living in urban environments tend to be more exposed to heat stress and the resulting health risks than people living in non-urban regions (Gabriel and Endlicher, 2011; Kjellstrom and Weaver, 2009; Kovats and Hajat, 2008; Scherer et al, 2013), because air temperatures at urban sites are often higher than temperatures in nearby rural sur roundings
The Coordinated Downscaling Experiment for the European domain (EURO-CORDEX; Jacob et al, 2014; Kotlarski et al, 2014) initiative provides the largest and state-of-the-art ensemble of climate change projections based on global (GCM) and regional climate model (RCM) simulations, but their spatial resolution of approximately 12 km (EUR11) and 50 km (EUR-44) is usually too coarse to account for the urban heat island (UHI)
Median biases of Tmin approach the bias of Split sample approach (SSA)(WC) with increasing number of years considered for calibration, whereas skills of Tmax behave similar to the results of SSA(CW)
Summary
People living in urban environments tend to be more exposed to heat stress and the resulting health risks than people living in non-urban regions (Gabriel and Endlicher, 2011; Kjellstrom and Weaver, 2009; Kovats and Hajat, 2008; Scherer et al, 2013), because air temperatures at urban sites are often higher than temperatures in nearby rural sur roundings. Ensemble from EURO-CORDEX to study future temporal UHI trends by contrasting simulated air temperatures of an urban and a rural station in Athens (Greece) They bias correct the simulated data against 20-year observational records using a quantile mapping (QM) technique. Due to the relatively short observational record for urban areas, ranging between 7 and 28 years, we employ QM in a two-step manner: first, bias correcting and downscaling regional climate models (RCMs) to the rural site scale (done within CH2018) and second, spatially transferring scenario data from rural to urban locations (done in this paper), resulting in climate scenarios for urban sites. Applying the soestablished correction function to the entire simulated period 1981–2099 results in the CH2018 product DAILY-LOCAL, which is available for various (rural) stations in Switzerland and used in the present study for further analyses. To ac count for model uncertainty, we consider the 5th–95th percentiles of the multi-model ensemble
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