A Modest Cloud Cover Study (part 5)

Today I ramp up use of indicator variables to further improve my modelling of the ICOADS v3 monthly cloud cover dataset

Back in part 4 we discovered that a simple intervention approach to the wonky ICOADS data for 1945 – 1956 gave superior prediction results to a model based around the CRU TS4.08 dataset as an independent variable in an ARIMA time series tray bake. Today I’m going to take this approach a step further to see if it generates something even more superior.

I consider this fiddling to be important because the venerable ICOADS v3 dataset cannot be used for time series analysis as it stands owing to the peculiar bias observed during the post war era (1945 – 1956). Iron this wrinkle out and you suddenly get a juicy series dating all the way back to 1853 in order that you may say something about cloud cover. There is some evidence (see here, here and here) that changing cloud cover is affecting the Earth’s albedo and it is this that is altering the energy budget, resulting in recent periods of warming.

I can’t see this hypothesis going down well amongst alarmists, globalists, and those experts with a vested interest in promulgating the carbon dioxide story, but I’m one of those old school scientists who’d rather take a look see then have a think, a cuppa, and a chat (ideally with biscuits). Hence my interest in fixing the ICOADS v3 long-series dataset. I’m hoping everybody has been following this article series and understanding the methodology for I’m about to dive straight in to revealing my best ICOADS predictive model to date…

My Best Model To Date!

OK, so I’m trying to model the ICOADS v3 monthly cloud cover dataset for the UK for the period January 1855 to December 2024 using ARIMA. As stated before we can get a time series to predict future values of itself providing there is some sort of periodic structure to the data - we all know that this coming winter is going to be cooler than it is today because of the periodic nature of the seasons in the northern hemisphere; put some numbers to this and there you have it!

Up to now I have marked the entire wonky period 1945 – 1956 using a single binary (0,1) indicator variable and submitted this as a single independent (predictor) variable. Whilst this has yielded a half-decent result the method makes the rather bold assumption that all eleven years of 1945 – 1956 were equally as wonky. Chances are they were not, which is probably why we see a ramping of okta values over this period. We can do to refine this crude approach is to permit estimates of wonkiness for each year by submitting an array of eleven binary indicators and see what that brings.

Good Job, Good Job!

Let us wade into the deep end with the horrid tables that define all things…