Re: Denier no more?
Ok, buckle up, because this is a long one. Let us start with the basics then. Methodology:
1. The principles of the IPCC state that: “The role of the IPCC is … understanding the scientific basis of risk of human-induced climate change”. So, from the outset in 1998, the IPCC assumes there is climate change and that it is caused by humans. This reversal of the null hypothesis is a big scientific no-no. This starting point biases everything that comes after it. The proper null-hypothesis is to start from the point that there is no climate change and then assess whether there is evidence to disprove this.
2. The main KPI for climate policy is to keep the average deviation of the baseline temperature below 1.5 °C. Apart from that it is a gross simplification to characterise the change in the thirty types of climate our planet has in a single variable, it also hides a lot of information. A serious positive temperature excursion one way can be masked with an excursion in another direction. On average everything looks good while we in fact have a major problem. A better measure would be to look at the standard deviation, however, this is something most policy makers and the general public would not be able to wrap there head around. It also does not work because of the next point.
3. Temperatures measured with thermometers represent the temperature for a certain volume of air around them. The main worry of rising atmospheric temperatures is that it might negatively impact natural processes on Earth. It is possible to take the numerical temperature values and perform statistics on them, but the result is unphysical.
Case in point: if I have two volumes of dry air at 1000 mbar, one at 0 °C and one at 20 °C and I allow these volumes to mix without external influences, what do you think the final temperature is? Many people would say 10 °C and they would be wrong. The actual value is 9.65 °C. A significant difference.
The proper way to calculate this is to determine the enthalpy of each volume, add them together and from that calculate the final temperature. The enthalpy of air depends on temperature, pressure and humidity and this dependency is non-linear. Any HVAC engineer with knowledge of psychrometrics can tell you this. The outcome of 9.65 °C becomes easier to understand once you realise that air at 0 °C has 7.3% more specific weight than air at 20 °C.
In the 17 years of tracking climate science, only once have I come across a paper that takes air pressure and humidity into account. Blindly averaging temperatures biases the result to warm values, although there are instances where averaging yields a value that is too low (high humidity warm air at 1040 mbar mixed with dry cold air at 980 mbar for example). Anyhow the method is fundamentally flawed.
And yes, temperatures are also measured with satellites, but they too do not measure air pressure and humidity.
4. Next plot hole: models. Ever wonder why tens of models are used to make climate projections? Because after decades of research not a single one has been found fit for purpose. So it was decided to bunch the results from many models up in the hopes that the outcome would be more accurate. The necessary prerequisites for this to work is that: a) the models are independent and b) the errors have a gaussian distribution. Both requirements are not met. Many models share parts of the same core source code and are therefore not independent. The errors in the models have far from a gaussian distribution, they are systematically wrong. The researchers working on the most recent CMIP-6 project themselves have said that their models run too hot. The average of a bunch of outcomes wrong in the same direction, is a wrong outcome.
Not only that, the models were all taken into account to avoid having to select the best one because all other research groups would be clamouring about this as they would lose their funding. It would also spark much debate that was deemed to draw energy away from progressing climate science. This was a political decision, not a scientific one.
Further consider this: if you feed climate models pink noise, i.e. zero trend slightly autocorrelated time series as temperatures, you would expect climate models to sometimes predict warming climates and sometimes colder climates. In practice they all predict warming, it is built in to their algorithms.
5. Another nail in the coffin: more not following the scientific method. Quote from Phil Jones, Director of Climate Research Unit, UEA, UK: "Why should I make the data available to you, when your aim is to try and find something wrong with it?". The whole point of the scientific method is show your work and make sure it is reproducible. There are many instances of institutions and researches declining to show their data and explain their methods. Older temperature records are mysteriously cooler over time and newer ones warmer.
The above just scratches the surface of what is wrong with climate science, there are many more topics that I not have touch upon here. My conclusion is that climate science is unfit as an input for policy making.
Biting the hand that feeds IT
