It is almost the first lesson I was taught when I started doing “research”. Research 101. If the data does not fit the model, you change the model – not the data. The fundamental problem with climate models is that they are not falsifiable. And as long as “climate science” can not, or will not, put forward falsifiable hypotheses, it is not Science. The models all start with assumptions which are approved by the high-priests of the religion. The results are then “forced” to fit with past data and are then used to assert that the initial assumptions are correct. When they are then used for making forecasts they invariably fail. They then try to “adjust” the data (cooling the past) rather than change their religiously-held assumptions.
Five year running mean temperatures predicted by UN IPCC models and observations by weather balloons and satellites. University of Alabama’s John Christy presentation to the House Committee on Natural Resources on May 15, 2015.
Just the effect of carbon dioxide concentration on incoming and outgoing radiation is small, easy to include and not really an issue. The problem arises because of the assumptions made of the feedback loops and the subsequent “forcing” attributed to carbon dioxide concentration. It is politically incorrect and therefore no climate model is ever allowed to ignore carbon dioxide “forcings”. Even though the “forcings” are largely conjecture. The feedback loops due to changes in carbon dioxide concentration acting through consequent changes in water vapour concentration and cloud cover are not only not known – it is not even known if they are net positive or net negative on temperature. The unknown “forcings” are called “climate sensitivity”, just to make it sound better, but these “climate sensitivities” are little better than fudge factors used by each model. (Even more fudge factors are applied to assert how man-made carbon dioxide emissions affect the carbon dioxide concentration even though the long-term data show that carbon dioxide concentration lags temperature). What I note is that the error between the models and real data is of the same magnitude as ascribed to the effects – with “forcing” – of carbon dioxide concentration in the atmosphere. There is no evidence that the assumed “forcings” are valid. The obvious correction to be made in the model assumptions is that the “climate sensitivity” assumed for carbon dioxide concentration is too high and that any “forcing” effects must be scaled down. But that, of course, is politically incorrect. You cannot get funding for developing a model which does not pay homage to the orthodoxy.
A simple sanity check shows that every single climate model used by the UN’s IPCC would fit real data better if it used a much lower sensitivity to carbon dioxide concentration by using a lower level of assumed forcing.
The k2p blog 