Chapter 6: Abbot & Marohasy & Cat Astrology

In which this book completely loses its shit and a short digression into cat astrology.

Intro, Ch 1, Ch2, Ch3, An Aside, Ch4, Ch5, …

Michaels, Lindzen, Soon are close to being the ‘skeptic’ A-Team. The main character missing from this first ‘science’ section of the book is Roy Spencer – he of the UAH satellite temperatures. Plimer and Carter joined in to give this Australian book an Australian perspective but they too managed to project an air of the free-thinking scientist resolutely questioning the facts. Yes, these first five chapters meandered between disingenuous and misleading but watching the dance was fun.

But we have one chapter left in ‘The science of climate change’ section and it’s about time we got something a tad more entertaining.

Enter John Abbot & Jennifer Marohasy. I can’t say I know a lot about of either of them. The bio at the start of the book indicates that they were/are a senior research fellow and an adjunct research fellow at Central Queensland University. Which is nice.

The chapter starts in much the same way as the other chapters in this section: with a section on science as a discipline. This time a potted history of Copernicus and the heliocentric theory with a few stars at climate science and then a segue to Thomas Khun’s paradigm shift model again. So far the chapter is still on the rails.

“Prior to the establishment of the current Australian Bureau of Meteorology in 1909, Australian meteorologists had a keen knowledge of astronomy and considered solar, lunar and planetary cycles in their weather forecasting.”

Ah the good old days of weather forecasting in 1909! “There remained some interest in this approach, which was termed

“There remained some interest in this approach, which was termed solar terrestrial physics, at the bureau until the early 1950s.”I wonder what began to change in the 1950s that might have affected our capacity to predict

I wonder what began to change in the 1950s that might have affected our capacity to predict weather? Hands up anybody who knows the answer. Ok, ok, you can all put your hands down now.

“Since the 1950s the bureau, and other major climate research institutions around the world, have worked towards a global effort to simulate climate largely independently of extraterrestrial influences.”

It’s computers isn’t it? Computers have tricked us into ignoring the moon!
OK everybody – did you all spot the climate-change-debate-tactic elementary level dodge there? Did you all say ‘confusing climate and weather’? You did? Ten points.

Don’t worry, even though the whole chapter is going to be about weather forecasters that still isn’t the weakest argument in the chapter. It all gets wackier.

“Indeed, the idea that the moon influences the weather through its gravitational effect is generally scoffed at.”

There will be some scoffing but not quite yet. They don’t really clarify what they mean here and because the whole chapter is predicated on confusing weather forecasting with climate modelling, it isn’t clear what influences they mean. Tides? Well sure, tides are important and tidal forces on the Earth are important. Rather like the fact that the sun is important but also clearly not the cause of climate change. The moon is merrily doing its stuff – it’s a business as usual sort of thing.

But what are they trying to get at with this stuff about the moon and weather?

We diverge into a salutary tale of the hapless Professor Chris Turney. Turney was part of an expedition to the Antarctic in the southern hemisphere summer whose ship got stuck in sea ice. Which just goes to prove something and a big deal was made about this on climate change denials blogs in much the same way they make a big deal whenever it snows in the general vicinity of Al Gore.

But this is not just a generic anecdote of ironic weather. Nope. There is a more specific lesson:

“If, before setting out, he had consulted the long-range weather forecasters who operate independently of the established institutions, and without the aid of GCMs but with reference to patterns and phase changes associated with solar and lunar phenomena, he could have been forewarned of the unusually slow melt rate of Antarctic ice last austral summer.”

Oh yes! Forget climate change denial, we are setting sail straight into weather-forecast crankery! Joy!

So what’s the actual thrust of this chapter? Basically the claim is that the Australian Bureau of Metrology isn’t as good at weather forecasting as some heroic rugged individualist forecasters (who we will meet shortly), generally get the difference between weather and climate all confused, then basically assert that it all has to do with changes in government funding in the 1980s and maybe it’s all the moons fault or computers. Maybe its computers on the moon.

So who are these genius forecasters? The chapter cites three:

  • Kevin Long “a long-range weather forecaster based in Bendigo, Victoria”
  • Joseph D’Aleo based in the US
  • Ken Ring based in New ZealandOf these three Joe D’Aleo is the most notable and of sufficient stature that I’m surprised he didn’t get his own chapter in this book.

Of these three Joe D’Aleo is the most notable and of sufficient stature that I’m surprised he didn’t get his own chapter in this book. D’also has been predicting global cooling for some time now but unfortunately the world hasn’t cooperated.

Global cooling is a necessary implication of the its-all-just-some-sort-of-cycle category of climate change lets-pretend-it-isn’t happening. If temperatures rose just as part of some natural cycle then sooner or later they should fall again. With decades of warming the various cycles credited with global warming really should have produced some counter cooling by now. However, even the so called ‘pause’ has not led to significantly cooler temperatures.

Kevin Long is a mechanical engineer who also sells climate predictions to farmers from his website: He also expects global cooling sooner or later and both he and thinks sunspots are a big deal.

Ken Ring outdoes both D’Aleo and Long. While the other two merely try to predict weather based on ‘cycles’, Ring predicts earthquakes. In the tectonically frisky country of New Zealand this is a notable skill.

“Some claim Ken Ring is running a weather prediction scam because he uses the moon to inform his rainfall forecasts.”

Mmm, yes, I think some might well say that.

They go on to say:

“We have seen no independent assessment of the skill of Ring’s predictions, but he sells many hundreds of his weather almanacs to Australian farmers each year.”

Well there you go then! It couldn’t possibly be a scam if he makes money out of it!
There is a fun takedown of Ring’s methods here (from 2007).
And a different one here
Simply put it is crank nonsense and the earthquake stuff is particularly bad.

Ring also writes odd books about cats. Include a cat astrology/paw-reading book and also this one: Ken Ring is co-author of Pawmistry, the runaway best-seller that allowed the cat-owner for the first time to learn about their cats’ inner character by examining its paws. Here you will learn: * How you behave in relationships! * What you appear to be to others! * The extent of authority you really command! * Unconscious body language you are using!

But let’s move on. Does solar activity sort of cycle? Sure. Does that explain global warming?

  1. No
  2. If it somehow did then where did the warming from CO2 go?

Demonstrating 1 is not trivial because the theories of these ‘maverick’ forecasters are not well documented. In essence, it is an extended game of vague predictions plus variations on near future weather likely to be similar to present/recent past weather. Claiming ‘cycles’ can then become a game of epicycles – mashing patterns together until you get a short term match. The irony that this chapter starts with Copernican system v the Ptolemaic one as a scientific morality tale is huge, as the chapter essentially invites us to accept a Ptolemaic view of climate.

However, it isn’t that had to show that point 1 isn’t plausible. A neat tool, used online by people on multiple sides of these arguments is the Wood for Tree website. Essentially it is an online set of of major climate data sets with a neat graphing tool.

There are two solar activity data sets included:

  1. The SIDC monthly sunspot number (more sunspots = sun being more feisty)
  2. The PMOD composite total solar irradiance monthly average

By using the normalise function on the website you can plot either of these on the same graph with a temperature data set. I’m going to use HADCRUT4 because it has been the one discussed in the IPA’s book so far.

Here is HADCRUT4 with PMOD TSI normalised from 1980 (the PMOD data starts around then).

And here is HADCRUT venus sunspots count


There are stretches of both graphs where some sort of relation between the two is plausible but in either case the longer we go on the more warming independent of any ups and downs of the sun becomes clearer. Does this prove the cyclists wrong? No but here is one more graph.


This time the green data set is atmospheric CO2. Yeah.

Meanwhile Abbott and Marohasy move on to artificial intelligence or rather they move on from crank epicyclists to neural-network epicyclists. They explain:

“ANNs are massive, parallel-distributed, information-processing systems with characteristics resembling the biological neural networks of the human brain. They are a form of artificial intelligence and represent state of the art statistical modelling.”

There are indeed many amazing tools now available that can mine data and identify patterns and then make predictions based on those patterns. In the case of weather forecasting, I can well imagine that in the short term such tools can make improved forecasts in particular regions (although not being a meteorologist, I don’t know but it seems plausible). However, Abbot and Marohasy are back to the same problem: global warming is changing our climate globally. Any model based on finding patterns in the status quo will increasingly drift away from reality.

The point about building up climate models from empirical theories of how global climate works is to enable us to see what happens in situations that are not ‘business as usual’. This includes counterfactual questions such as scenarios based on different levels of CO2 emissions or different levels of industrial pollution. Remember if, as is rapidly becoming apparent, human activity is now becoming a major factor in shaping the climate, ‘predicting’ the climate becomes impossible without somehow predicting global trends in fossil use, industrialisation, atmospheric pollution etc. It is for this reason, among many, that it is better to talk in terms of projections and scenarios rather than prediction or forecast.