3.3. Do Climate Models Account for Observations? «First, the computer models are very good at solving the equations of fluid dynamics but very bad at describing the real world. The real world is full of things like clouds and vegetation and soil and dust which the models describe very poorly. Second, we do not know whether the recent changes in climate are on balance doing more harm than good. The strongest warming is in cold places like Greenland. More people die from cold in winter than die from heat in summer. Third, there are many other causes of climate change besides human activities, as we know from studying the past. Fourth, the carbon dioxide in the atmosphere is strongly coupled with other carbon reservoirs in the biosphere, vegetation and top-soil, which are as large or larger. It is misleading to consider only the atmosphere and ocean, as the climate models do, and ignore the other reservoirs. Fifth, the biological effects of CO 2 in the atmosphere are beneficial, both to food crops and to natural vegetation. The biological effects are better known and probably more important than the climatic effects.» Freeman Dyson As Lindzen stated (1997) «The more serious question then is do we expect increasing CO 2 to produce sufficiently large changes in climate so as to be clearly discernible and of consequence for the affairs of humans and the ecosystem of which we are part. This is the question I propose to approach in this paper. I will first consider the question of whether current model predictions are likely to be credible. We will see why this is unlikely at best» Models must be subordinated to the observations, not the other way round. This is the way science has always proceeded, for example when you compute the orbit of a double star (Poyet, 2017a; 2017b) if it does not match the observations you just try to recompute a better orbit. And every astronomer, given the method you have stated that you use, can have access to the observations, reproduce the work that you have done and check that it was correct. This is the very basics of science, the theory or the model should match the observations and science should be reproducible. As long as the theory or the model is able to make decent forecasts (i.e. an ephemeris in the previous example), it is considered appropriate, as soon as it fails, everything must be reconsidered. It seems that climate tinkerers have completely forgotten the basics and the observations must be wrong as 95% of the models fail to reproduce them, even on extremely short timescales as it is displayed in the next figure 99!
Figure 99. >95% of the models have over-forecast the warming trend since 1979, whether use is made of their own surface temperature dataset, i.e. HadCRUT4 (Morice et al., 2012), or of UAH satellite dataset of lower tropospheric temperatures. After Spencer (2014).
«Unfortunately, no model can, in the current state of the art, faithfully represent the totality of the physical processes at stake and, consequently, no model is based directly on the basic mechanical, physical or geochemical sciences. On the contrary, these models are fundamentally empirical and necessarily call on arbitrary parameters which must be
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