Unfortunately, this is a “42” kind of article as in the answer to everything. Yes, I have the answer – but as yet it won’t mean much to anyone until I’ve found the right way to introduce it. But it is definitely the answer. A real Eureka moment – so I feel it is worth the post even if I’m going to be the only one celebrating.
For a while I have been trying to find the noise model used in climate simulations. I knew there was a problem , but all attempts to find such a model had failed until tonight. Then I read The ESSENCE project – signal to noise ratio in climate projections
Different ensemble members are generated by disturbing the initial state of the atmosphere. Gaussian noise with an amplitude of 0.1K is added to the initial temperature field. … The basic ensemble consists of 17 runs drive by a time varying forcing.
Eureka!!!
I now know exactly why all these climate academics think they understand the climate and don’t. From a purely physics perspective this is the end of the global warming scare. Obviously, there’s more to the scare than just physics, but physics is the foundation and I can now show where that foundation is wrong.
What it means is I know why natural variation has been mistaken for a man-made signal and I can prove it. It explains why e.g. in the 2011 paper “Separating signal and noise in atmospheric temperature changes: The importance of timescale” Santer et al. found that:
the decrease in noise amplitude with increasing trend length, so that any errors in model signal trends are less obscured by noise on longer time-scales.
This is not true. In fact the variance increases approximately proportional to the log of the time-scale. Unfortunately, just stating this won’t cut ice. Instead I had to show how their methods produced the wrong variance. I can now do that.
Till tonight, I’ve only mean able to say they were wrong without really knowing why. Now I am able to understand how they could come to such a counter intuitive conclusion as to suggest natural variation reduces over longer periods. Now I understand why the variance in their models reduces as time-scales increase. This allows me to explain some very peculiar things:
- the belief that longer time-scale projections are more accurate – which is akin to saying it is easier to predict the weather next week than this.
- the belief that even though they cannot predict the climate over 1 year, they can over longer periods. And indeed, the idea that even though they couldn’t predict the climate over the last 15+ years they can predict is over the next century.
- the reason why the patently massive noise to signal ratio (which makes it impossible to see any signal) is mistaken for a massive signal to noise ratio.
Now there is only the small issue of explaining this. That will take time.
But in the flicker of noise there is the answer.