Pretending it didn’t happen won’t make it go away. Global Cooling.

We could always use more guys like Rick Coddington around these days, he had a firm understanding of the Global Warming “theory” and definitely didn’t hold back his opinion on the subject… I like his delivery, straight to the point, hit them with facts!

For example, let’s travel back to 2007 and have a look at this article by Rick…

Mountain Mail – September- 27, 2007 – 30 Years Ago, Global Cooling Was The Scare.

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Now let’s step back even further to 1975 to see what Rick was talking about when scientists were singing a different tune. A snappy little number with a cool down beat!

The Cooling World – 1975 – Newsweek, April 28th.

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Like the titles says, pretending it didn’t happen won’t make it go away.

Forecasts of global cooling from the 70s are well documented. In fact, if you do a bit of research it’s easy to find predictions of climate catastrophe and weather doom going as far back as the printed word will take you. Today we have people in lab coats with vast amounts of government grant money continuing an age old ritual of forecasting the worst possible scenario for all mankind if we don’t do:____________(fill in the blank), now or it will be too late!


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Mother Nature sure can be a stubborn old girl. Now, if I were a betting man, and I’m not,  I’d lay odds on her over any scientists predictions more than 10 years down the road, any day of the week!

 

 

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One thought on “Pretending it didn’t happen won’t make it go away. Global Cooling.

  1. Greg,

    When you consider it, the taking of averages of any signal, even at sampling window rates, and then truncating or rounding the result devolves to a Nyquist window of a box plot best expressed as a normal distribution of that window value around the centre and the resolution that it was truncated/rounded to and steepness and storage of any filters/masses as the rising and falling edges.

    Thus using a Gaussian filter/producer to re-analogue that now digitised signal would mean that the digitisation error over the full A/D/A path would be reduced.

    This would then mean that the width of the averages window determines the amount of ‘spread’ as we both know with our previous work with filters.

    Like

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