These findings, published Thursday in Nature Climate Change, could toss out the long-established narrative that daily weather is distinct from long-term climate change. Kind of buries Inhofe’s snowball under a still weightier avalanche of pesky ‘science’ and ‘data’
“The new study, ... uses statistical techniques and climate model simulations to evaluate how daily temperatures and humidity vary around the world. Scientists compared the spatial patterns of these variables with what physical science shows is expected because of climate change.”www.washingtonpost.com/…
Yes it comes with some caveats and yes it needs future , ongoing modeling as a verification — but the use of historical data as part of “detection and attribution” technique gives them a lot of confidence...
The authors, from research institutions in Switzerland and Norway, use machine learning to estimate how the patterns of temperature and moisture at daily, monthly and annual time scales relate to two important climate change metrics: global average surface temperatures and the energy imbalance of the planet. Increasing amounts of greenhouse gases in the atmosphere are causing Earth to hold in more of the sun’s energy, leading to an energy surplus.
The researchers then utilized machine learning techniques to detect a global fingerprint of human-caused climate change from the relationships between the weather and global warming metrics, and compare it with historical weather data.
By doing this, scientists were able to tease out the signal of human-caused global warming from any single day of global weather observations since 2012. When looking at annual data, the human-caused climate signal emerged in 1999, the study found.