Abstract
Urban heat waves (UHWs) are strongly associated with socioeconomic impacts. Here, we use an urban climate emulator combined with large ensemble global climate simulations to show that, at the urban scale a large proportion of the variability results from the model structural uncertainty in projecting UHWs in the coming decades under climate change. Omission of this uncertainty would considerably underestimate the risk of UHW. Results show that, for cities in four high-stake regions – the Great Lakes of North America, Southern Europe, Central India, and North China – a virtually unlikely (0.01% probability) UHW projected by single-model ensembles is estimated by our model with probabilities of 23.73%, 4.24%, 1.56%, and 14.76% respectively in 2061–2070 under a high-emission scenario. Our findings suggest that for urban-scale extremes, policymakers and stakeholders will have to plan for larger uncertainties than what a single model predicts if decisions are informed based on urban climate simulations.
Highlights
Urban heat waves (UHWs) are strongly associated with socioeconomic impacts
The internal variability is assessed based on the Community Earth System Model (CESM) Large Ensemble (CESM-LE)[43] simulations, whereas the model structural uncertainty is characterized based on the emulated multimodel projections
We find that the model structural uncertainty contributes substantially to the variability of multi-model projections of localscale UHWs in the several decades under climate change
Summary
Urban heat waves (UHWs) are strongly associated with socioeconomic impacts. Here, we use an urban climate emulator combined with large ensemble global climate simulations to show that, at the urban scale a large proportion of the variability results from the model structural uncertainty in projecting UHWs in the coming decades under climate change. We use a newly-developed urban climate emulator framework[41] to assess the inter-model variability in projections of local urban heat waves (UHWs) for the global urban areas in a future high-emission scenario, and to quantify the relative contributions of uncertainties from internal variability and model structural variability associated with the projections.
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