It seems to me that there is also an issue of accounting for those deltas over the amount of time that they are in effect (the orbit time spent near perihelion (in days or months) is less than than the same number of days/months near aphelion).

… transform true anomaly to mean anomaly to have a parameter that does vary linearly in time… By re-reading the lecture notes and with little programming I got one example out:

Computed insolation per month / m2 for Trondheim, Norway, shifted one month (ad hoc, some delay is ok I guess) and then LS-fitted to observed temperature averages (Jones data). I did the same for circular orbit, and it seems that summer is too warm in that case:

The eccentricity effect is very weak, axial tilt of course dominates. And these are not additive but multiplicative factors, so it is not easy to extract the global eccentricity effect out.

However, because of Henry’s law, C02 is in a cycle. It is regularly absorbed by water in polar regions and also expelled from water in equatorial regions. It can’t really accumulate.

I also have a few points of contention with respect to the premises and axioms underlying these papers which seem to be in fundamental disagreement with the real system. Earth has a weather system. Our climate is a statistical summary of previously realized weather. How the statistical model gets specified is important.

We also live in and adapt to the atmospheric surface boundary layer and that’s where the weather action is. These papers being discussed mostly don’t seem to take that into account. Is it really being too purist to expect the ‘experts’ to have done the foundational work showing that their procedures are justified? Just who’s fooling whom here , really?

What Browning and Kreiss show mathematically is that it is physically impossible to extend the time one of these models are run before the exponentially increase in errors swamp the matrix. The amount of time depends on the grid size and step size. Where as Nick is calling it “purist”, it is a physical limitation inherrent to the too large grid size and too large time step.

In order that they get around this, modellers use hyperviscosity and adiabatic adjustment in order according to Dr. Gavin or his source to prevent negative mass and/or energy in a grid. This makes the models engineering applications more so than physical (physics) applications. However, note that there is just one run of the independent actual response instead of the 10′s of thousands that the engineering models require to validate the usefullness. Also note, extrapolation, which is what all the GCM’s do, is known to not neccessarily correct, and requires a separate validation. In other words as two modellers stated in the the peer reveiwed literature, it will take 130 or more years to show that a 100 year model was correct, Tebaldi and Knutti.

PS I am bad at spelling, my apologies if I have erred.

Now that we have the argument ‘Natural factors cannot explain G*M*D*observations’, it is interesting to see what we can say about the actual observations. G*M*D is not invertible, but some statistical statements *) can possibly be backtracked into the observation domain.

*) such as one in IPCC AR4WG1, “The Durbin Watson D-statistic (not shown) for the residuals, after allowing for first-order serial correlation, never indicates significant positive serial correlation.”

http://www.cru.uea.ac.uk/cru/data/temperature/absolute.nc

PS: It has been many years since I took physical chemistry, but I’d be willing to do some review in order to get up to speed on this.