Temperature emergence at decision-relevant scales

Abstract

Abstract Signal-to-noise (S/N) ratios are a useful method to assess the significance of future climate change relative to past experiences. Most assessments of climate change emergence have focused on S/N ratios of annual mean temperatures. However, averaging the daily experiences of weather across space or time removes the climate variability actually felt by individuals, and thus presents a less informative view of the speed of current climate change. For example, S/N ratios of annual-mean temperatures experienced by the global population after only 1 °C of warming are larger than emergent changes in daily temperatures after 3 °C of warming, and generally four times more significant when comparing the same warming threshold. Here, I examine the emergence of S/N ratios in temperature at decision-relevant scales, with a focus on daily temperatures where people live. I find that 2 °C of global warming will lead to between 30% and >90% of the global population experiencing the emergence of unusual daily temperatures (>1σ), while it is very unlikely (90% confidence) that more than 60% of the global population will also experience the emergence of unfamiliar daily temperatures (>2σ).