Something like an echo from the past has come back from the grave of climate science, the dust bowl event that lay at the heart of Steinbeck’s best seller, ‘The Grapes of Wrath‘. I did read that book, a long time ago, as a youth, but can’t remember much about the details. I see there is a Wikipedia entry on the book. Basically, it is all about the 1930s great depression and the pollitical upheaval of the time, with a backcloth in a great drought. See https://phys.org/news/2022-11-1930s-bowl-extreme-northern-hemisphere.html … but it also involved a very hot period as far as temperatures were concerned. The heat is often ignored and the blame is cast on the sod farmers ploughing up the Great Plains. True or not but the heat was also important, as wel as lack of rainfall. Drought can take out farmers if it prevails for several years – as it did in the 1930s. Strange that the heat and drought coincided with the Great Depression. Is there a connection?
At the beginning of the global warming mantra the hottest years of the 20th century were clearly marked out as occurring in the 1930s. Obviously, this was inconvenient to the newly emerging alarmist songsheet. It was rectified with a clever algorythm inserted into the temperature data. Miraculously, the 1990s became the hottest years of the 20th century. The global warming/climate change hyperbole then went into full throttle.
Is this piece an own goal – not realised by the authors of the paper. Have they drawn attention to something climate scientists have been keen on eradicating from the temperature record? They quite openly say that ‘extreme weather’ conditions [heat, in this instance] extended far beyond the Great Plains to affect much of North America, northern Europe, and Siberia. However, they include the caveat – ‘the high temperatures of the 1930s are only now being exceed as temperature rises with climate change …’. Do they mean the temperatures within climate models, or the temperatures in the real world, as they appear to be contrary to that conciliatory statement see
https://wattsupwiththat.com/2022/11/30/worldwide-record-cold-challenges-climate-rhetoric-and-risks-lives-by-complacency/ … although it would be true to say that political administrations have their fingers crossed. The piece is written by Vijay Jayara who hails from Bengaluru in southern India, but has been to university in the UK and now works in the US. This month the city recorded the coldest temperatures in 10 years, this November. The same applies to New Delhi. We don’t hear about these things on mainstream media but Jajara continues by saying cold events have become common in recent years in India – anad around the world.. Since 2017 there have been many incidences of below average temperatures, in both winters and summers, but you would never know that as it is erased before the information is able to leak. The media appear to trumpet warm blips in temperature but overlook the cool weather blips. It doesn’t fit the agenda. That is the only answer and that is why one should not believe a dicky bird what the alarmists have to say. If there really was warming on a global scale – it would be everywhere. It isn’t, and the author provides various other examples from North America, China, and Greenland. Yes, in Greenland the ice mass of the ice sheet is in 2022 at one of its highest levels since 1981 and is set to increse further still during the coming winter. Greenland has in fact been registering an inconvenient growth in the ice sheet since 2016. Did the media report that? How long can the hedge fund managers and renewable energ magnates keep up the pretence of global warming? Will the collapse of western economies be the end result?
The third comment below the article has it nailed – ‘climate cooling – warming – change, a pot of gold at the end of the … every rainbow’.
Over at https://notrickszone.com/2022/11/30/a-look-at-climate-models-obviously-do-not-represent-the-physics-not-at-all-capable/ … is a look at climate models. It quotes an Oxford professor, ‘a highly non-linear system [the climate] where you have biases which are bigger than the signals you’re trying to predict is really a recipe for unrealiability .. ‘