A study published in Nature Climate Change purports to have found a link between daily weather patterns and climate change.
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 1,2,3,4,5,6,7.
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.
Science Daily is even more specific.
In plain English, this means that — despite global warming — there may well be a record low temperature in October in the US. If it is simultaneously warmer than average in other regions, however, this deviation is almost completely eliminated. “
Before we dismiss this study out of hand, it should be noted that this is a remarkable achievement. What the scientists from Norway and Switzerland accomplished is the Holy Grail of climate scientists: connecting the daily weather to climate change will significantly alter the debate and give new ammunition to climate change advocates.
If their methodology is sound and their conclusions are supportable.
The study concludes that patterns of global temperature and humidity have human factors and are distinct from natural variability. It also determines that the long-term rise in global average temperature can be predicted with one day’s weather information worldwide.
“We’ve always said when you look at weather, that’s not the same as climate,” study co-author Reto Knutti told The Washington Post. “That’s still true locally; if you are in one particular place and you only know the weather right now, right here, there isn’t much you can say.”
“Global mean temperature on a single day is already quite a bit shifted. You can see this human fingerprint in any single moment,” he added. “Weather is climate change if you look over the whole globe.”
Skepticism starts here — they used modeling and “machine learning” to “uncover the climate change signal” in daily weather.
In order to detect the climate signal in daily weather records, Sippel and his colleagues used statistical learning techniques to combine simulations with climate models and data from measuring stations.
Statistical learning techniques can extract a “fingerprint” of climate change from the combination of temperatures of various regions and the ratio of expected warming and variability.
By systematically evaluating the model simulations, they can identify the climate fingerprint in the global measurement data on any single day since spring 2012.
The study only came out a couple of days ago so skeptics haven’t had a lot of time to examine the methods used. What will they be looking for?
However, the study does contain uncertainties, including the accuracy of computer models in simulating various climate cycles and the use of machine learning techniques. It also does not incorporate other factors that influence the climate such as human-made and volcanic aerosols.
“Accuracy” of “models” has proven to be an oxymoron. And just what did the scientists teach the computers to do? They asked the machines to look for patterns in the chaos. I’m no statistical expert or computer geek, but isn’t that asking for trouble?
Lots of questions will be asked before this study is generally accepted.
Read more at PJ Media
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