A Modest Cloud Cover Study (part 8)
Today I boil the monthly values for the CRU TS4.08 and corrected ICOADS v3 datasets down into annual means and have a look-see at variance about those means
Here we are again come rain or shine, and today we’re getting both down here in the West Country, all pushed on by a stiff breeze. Mrs Dee tells me the changeable weather is conducive to growing lettuces, and since she’s planted up 86 lettuces then I guess she should know! I can’t help but think that down the lane somewhere, in a dark corner, is a poor soul believing climate change is going to kill us all because Leo DiCaprio says so. Dark times indeed.
And so, on with the story of clouds as seen through a dollop of data…
Back in June 2024 I started out on a 14-part series called ‘Climate Change & Climate Variability’. The notion behind this is quite simple: you can’t have one without the other! “One is both the same”, as my old school chum would mutter. Swap variability with variance and you approach this through the eyeballs of the statistician. Now statistical variance is a fine thing but it comes in squared lumps (sums of squares and all that), so if we want to step back toward the real world we need to take the square root and in doing so we arrive at standard deviation. There we have it; a thing of beauty!
It’s a rare thing to see slides of standard deviation in climate work, and especially standard deviation over time. I guess there are genuine reasons for this but, being a suspicious sort, I can’t help being suspicious. The trouble is, you see, standard deviations are likely to give the game away by telling us whether things are getting wilder (or not). This is important, for in a game where global warming is no longer a thing, then alarmists only have the getting wilder card left to play. And play it they do!
So let us take a look at the standard deviation of annual mean cloud cover for the UK study region for the three top notch datasets that I have been cooking these last few weeks. Kettle on and away we go…