Abstract
Climate change can cause property damage and deaths in cities. City-scale climate projections are essential for making informed decisions towards climate change mitigation and adaptation at city levels. This study aims at developing ensemble projections of temperature extremes at the city-level and quantifying the contributions of various factors to the resulting uncertainty of the ensemble projections. The city of Toronto will be used here as an example to demonstrate the effectiveness of the proposed research framework. In particular, the stepwise cluster analysis (SCA) model will be used to perform climate downscaling to three GCM datasets (GFDL, IPSL, and MPI) under three emission scenarios (RCP2.6, RCP4.5, and RCP8.5) in order to generate city-level climate projections for the city of Toronto. The SCA model is demonstrated to be capable of capturing the inter- and intra-annual variations of the daily maximum, mean, and minimum temperatures in the studied city. The results suggest that mean temperatures in Toronto are projected to increase at the rate of 0.15 and 0.5 °C/decade under RCP4.5 and RCP8.5, respectively, while no significant warming trend is detected for RCP2.6. In terms of temperature extremes, extreme warm events are projected to increase while extreme cold events decrease under all emission scenarios. The decrease in the heating demand is two to four times larger than the increase in the cooling demand, indicating a decrease in the city’s total energy use. The projected warming might be beneficial for the urban growers because of the significant increases in the growing season length and growing degree days; however, the residents of the city of Toronto are likely to experience simultaneous increases in the intensity, duration, and frequency of heatwave events in future summers. Because of the warming, coldwave events in winters are likely to become less frequent and be shorter in duration, but their intensity is expected to increase significantly. Through decomposition of the resulting uncertainty of the ensemble projections, emission scenario is found to be the dominant factor for the uncertainty associated with urban climate projection.
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This research was supported by Canada Research Chair Program, Natural Science and Engineering Research Council of Canada, Western Economic Diversification (15269), and MITACS. We are also very grateful for the helpful inputs from the Editor and anonymous reviewers.
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Lu, C., Huang, G., Wang, X. et al. Ensemble projection of city-level temperature extremes with stepwise cluster analysis. Clim Dyn 56, 3313–3335 (2021). https://doi.org/10.1007/s00382-021-05644-9
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DOI: https://doi.org/10.1007/s00382-021-05644-9