Posted by: Grant | February 22, 2015

Perspective On Modelling.

A computer model is just a model. It does not predict anything. It just models what might happen. It is purely specious to gather like minded models and present them as credible prediction. You can average data and get a useful result, if you average models you just get another model.

This expert delivers an expose’ on UNIPCC claims which are based on modelling.
Of course, this doesn’t matter, incorrect claims are just shrugged off. “Climate Change” is a belief system and disbelievers are “deniers”.
You can watch the lecture here – http://wattsupwiththat.com/2015/02/20/believing-in-six-impossible-things-before-breakfast-and-climate-models/if you want some science.

I just liked these images that he uses – this one shows the global temperature on the full range of temperature found on the planet. You will need to click on it to notice the tiny variations.
1988 was when it started warming, 1998 was when it stopped warming and 2014 was supposed to be the “hottest year ever”.
real temperature hotest yr

 

and this is why the UNIPCC climate models cannot be predictive and are just illustrative of their doomsday theory.

“…The climate system is a coupled non-linear chaotic system, and therefore the long-term prediction of future climate states is not possible…”
So states the IPCC’s Working Group I: The Scientific Basis, Third Assessment Report (TAR), Chapter 14 (final para., 14.2.2.2), p774.
http://www.ipcc.ch/ipccreports/tar/wg1/501.htm ipcc-models-predict-future
full paragraph –
” Further work is needed in eight broad areas:-
• Improve methods to quantify uncertainties of climate projections and scenarios, including development and exploration of long-term ensemble simulations using complex models. The climate system is a coupled non-linear chaotic system, and therefore the long-term prediction of future climate states is not possible. Rather the focus must be upon the prediction of the probability distribution of the system�s future possible states by the generation of ensembles of model solutions. Addressing adequately the statistical nature of climate is computationally intensive and requires the application of new methods of model diagnosis, but such statistical information is essential.”

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