Why Climate Models Don’t Work

Climate alarmism is not based on observation, it is based on predictions generated by climate models. This article by computer modeler Greg Chapman at Watts Up With That is a good primer on why those models are inherently unreliable, and in fact have been shown to be wrong:

The purpose of this article is to explain to the non-expert, how climate models work, rather than a focus on the issues underlying the actual climate science, since the models are the primary ‘evidence’ used by those claiming a climate crisis. The first problem, of course, is no model forecast is evidence of anything. It’s just a forecast, so it’s important to understand how the forecasts are made, the assumptions behind them and their reliability.

You really need to read the whole thing, but this is a summary of the chief points:

In summary:

* Climate models can’t be validated on initiatialisation due to lack of data and a chaotic initial state.

* Model resolutions are too low to represent many climate factors.

* Many of the forcing factors are parameterised as they can’t be calculated by the models.

* Uncertainties in the parameterisation process mean that there is no unique solution to the history matching.

* Numerical dispersion beyond the history matching phase results in a large divergence in the models.

* The IPCC refuses to discard models that don’t match the observed data in the prediction phase – which is almost all of them.

We now have enough years of temperature data, and enough experience with climate models, to know for sure that the models are wrong. A model that makes wrong predictions about the future has been falsified and is useless. This chart tells the story:

The next chart is one I hadn’t seen before. It represents the output of a model that does not use CO2 forcing as a factor in matching climate history:

Chapman writes:

[A]nalytic (as opposed to numeric) models have achieved matches without CO2 forcing. These are models, based purely on historic climate cycles that identify the harmonics using a mathematical technique of signal analysis, which deconstructs long and short term natural cycles of different periods and amplitudes without considering changes in CO2 concentration.

In Figure 6, a comparison is made between the IPCC predictions and a prediction from just one analytic harmonic model that doesn’t depend on CO2 warming. A match to history can be achieved through harmonic analysis and provides a much more conservative prediction that correctly forecasts the current pause in temperature increase, unlike the IPCC models. The purpose of this example isn’t to claim that this model is more accurate, it’s just another model, but to dispel the myth that there is no way history can be explained without anthropogenic CO2 forcing and to show that it’s possible to explain the changes in temperature with natural variation as the predominant driver.

The whole thing is worth your attention.

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