资源环境科技发展态势分析平台

For generations, climate scientists have educated the public that 'weather is not climate', and climate change has been framed as the change in the distribution of weather that slowly emerges from large variability over decades(1-7). However, weather when considered globally is now in uncharted territory. Here we show that on the basis of a single day of globally observed temperature and moisture, we detect the fingerprint of externally driven climate change, and conclude that Earth as a whole is warming. Our detection approach invokes statistical learning and climate model simulations to encapsulate the relationship between spatial patterns of daily temperature and humidity, and key climate change metrics such as annual global mean temperature or Earth's energy imbalance. Observations are projected onto this relationship to detect climate change. The fingerprint of climate change is detected from any single day in the observed global record since early 2012, and since 1999 on the basis of a year of data. Detection is robust even when ignoring the long-term global warming trend. This complements traditional climate change detection, but also opens broader perspectives for the communication of regional weather events, modifying the climate change narrative: while changes in weather locally are emerging over decades, global climate change is now detected instantaneously.

Detection and attribution typically aims to find long-term climate signals in internal, often short-term variability. Here, common methods are extended to high-frequency temperature and humidity data, detecting instantaneous, global-scale climate change since 1999 for any year and 2012 for any day.