One thing which struck me about the recent climate science hearing is how little attention was paid to Dr. John Christy’s demonstration of a flawed climate model prediction – the missing Tropospheric hotspot.
A flawed prediction does not automatically mean the models are totally wrong – but it is a strong indicator that something isn’t right.
Consider the primary observation. The world has warmed since the mid 1850s, and for the sake of argument lets assume that the world has warmed since the mid 1930s.
Given that warming, you could propose a number of different theories for the cause of that warming, for example;
1. Chaotic shifts in ocean currents or solar influences have influenced global temperature.
2. Anthropogenic CO2 emissions have caused global temperature to rise
3. Gnomes are lighting fires under the polar icecaps.
All three of the theories proposed above can potentially explain the primary observation – the world is warming, and heating is more pronounced in polar regions.
How do you eliminate the incorrect theories?
The way you eliminate incorrect theories is to test other non-trivial secondary predictions. It is easy to create a theory which explains global warming – even my Gnome theory does that. What is more difficult is to create a theory which coherently explains other observable phenomena, or better still predicts observations which haven’t been attempted yet.
For example, there are simple tests for the presence of Gnomes lighting fires under the polar icecaps. You could dig holes and try to find the Gnomes. If you don’t find any Gnomes, you cannot conclusively prove they don’t exist – the Gnomes might be very good at evading attempts at discovery. But failure to find Gnomes, or failure to find evidence of extensive efforts to light fires under the polar icecaps, should allow you to conclude that the Gnome theory is very unlikely to be correct.
How do you test the Anthropogenic CO2 theory? Just as the Gnomes lighting fires theory predicts the existence of Gnomes and extensive fire pits under the polar ice caps, so the Anthropogenic CO2 theory predicts various observations.
We could simply wait 50 years and see if global temperatures go crazy, but it would be nice to know whether the theory is correct before we all cook. So we need a non trivial secondary observation which we can test here and now.
One of the key predicted observations of anthropogenic CO2 climate theory is the existence of an equatorial tropospheric hotspot.
The hotspot prediction is easy to understand. The atmosphere is thicker, reaches higher into space over the equator than the poles, due to centrifugal force of the Earth’s spin. Centrifugal force is greater at the equator than the poles, so air, including CO2, tends to pile up higher into space over the equator.
The equator also receives more sunlight.
If the buildup of greenhouse gasses is trapping significantly more heat, the effect on the atmosphere should be most pronounced where the sunlight is strongest and the greenhouse blanket is thickest.
But nobody has yet managed to unequivocally detect that predicted hotspot.
Various theories have been advanced to explain the missing hotspot.
For example one theory is the balloon measurements are not being analysed correctly, so the hotspot is there, but it is evading detection unless you properly homogenise the data.
In my opinion this theory is undermined by satellite measurements which confirm the un-homogenised balloon measurements. This confirmation of un-homogenised balloon measurements casts doubt on the data homogenisation process which led to the alleged detection of the hotspot.
Another theory I have seen mentioned is that the hotspot is there, but the effect is not pronounced enough to be detectable as yet. More plausible in my opinion than the instrument anomaly theory, but this proposition verges intriguingly close to an admission that anthropogenic global warming is not a big deal.
Whatever the reason, the absence of a pronounced hotspot is or should be as much of an embarrassment to the Anthropogenic CO2 theory, as the absence of fire pits and captured Gnomes is an embarrassment to the Gnome theory.
Does the absence of a tropospheric equatorial hotspot mean anthropogenic climate models are unequivocally wrong?
The answer is no.
There are plenty of examples of scientific theories which were slightly wrong, which didn’t fully explain observations, which were later found to be mostly right.
Newtonian gravity mostly explains the orbit of the planets, but some observations don’t match the theory. For example, Newtonian predictions of the orbit of Mercury do not match observations. Mercury is very close to the sun, much closer than the Earth. That close to a massive body like the Sun, Einstein’s General Relativity becomes important. Relativistic effects cause Mercury’s orbit to diverge from Newtonian predictions of what its orbit should be.
This deviation from theoretical predictions does not mean Newtonian theory is broken, in this case it simply means the Newtonian theory is incomplete. Unless you need extreme precision, for example when creating a global positioning satellite system, the tiny perturbations introduced by Einstein’s theory are not significant enough to worry about.
But a flawed prediction is not something which should be ignored. Sometimes when you don’t find any gnomes at the bottom of the garden, you should stop digging holes.
As for the theory that chaotic shifts in ocean currents or solar influences control the climate, the evidence for this seems to be a mixed bag.
Suggestions that the eleven year solar cycle affects climate are convincingly disputed by Willis. If the powerful eleven year solar cycle doesn’t do anything to the climate, why would longer solar cycles have any effect?
On the other hand, there appears to be growing evidence solar modulation of cosmic rays may have a significant effect on atmospheric chemistry.
In my opinion, the short answer is we simply don’t know what drives the climate. More research is required, without premature efforts to formulate policy around theories which clearly do not explain all the key observations.