The Error Propagation is Defined in a Time-Stepped Model

There is some controversy on how errors are realized in a time-stepped model.   In such a model that results for one time step then become the starting conditions for the next time step.   Dr Frank at Stanford set out to determine how this error could propagate given how poorly clouds are analyzed in the GCM models.   See this link for his results.  What he did not take into consideration is all of the other sources of errors possible in other categories. 


Since the GCM models are low resolution the models cannot accurately estimate the effects of convection.   So the errors are large as they combine in ways that have not been characterized. 


 Dr Frank's work has come under criticism and the refutation by several AGW scientists like a scientist who states that the overall estimate for error is the classic square root of the sum of the squares.    This is not fitting a time step model of the kind used in climate.   The errors accumulated in any single time step have several components to them.  There could be a bias as we have seen in the GCM results that gives a push.   If one imagines the square of the error at the first time step being added to the square of the errors at subsequent time steps, how would this relate to the error at the end of the time steps?


That error cannot be the squareroot of these cum of squares, as Prof Brown of Stanford claims.   There is no way to support that thesis.  The error in the first time step adds to the error at the second time step given the nature of estimating an average global temperature.  This should not require a mathematical representation, but stands on its own observational merits.  If the error does occur and it will, it is impossible to predict its nature.  What Prof Frank's estimates is the maximum error or uncertainty due to just one cause.  One would have to do explicit error analysis over a long enough time and compare that against the measurements.    One could do this in theory in comparing the past with the measured actuals, but alas the tuning of the GCM models precludes this comparison with any rigor.


What also amazes is the fact that the IPCC claims this to be accurate to predict with enough accuracy to determine broad and deeply disruptive policies to remediate the effects of climate change they cannot justify. 



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