Climate Science is just not that hard

From Andy Lacis, this is motivation to keep on looking at what Isaac Held calls the “fruit-fly” models of climate science.

Rabbet Run blog — More from Andy Lacis

“It would seem more appropriate to assign “wickedness” to problems that are more specifically related to witches. The climate problem, while clearly complex and complicated, is not incomprehensible. Current climate models do a very credible job in simulating current climate variability and seasonal changes. Present-day weather models make credible weather forecasts – and there is a close relationship. Most of the cutting edge current climate modeling research is aimed at understanding the physics of ocean circulation and the natural variability of the climate system that this generates. While this may be the principal source of uncertainty in predicting regional climate change and weather extreme events, this uncertainty in modeling the climate system’s natural variability is clearly separate and unrelated to the radiative energy balance physics that characterize the global warming problem. The appropriate uncertainty that exists in one area of climate modeling doe not automatically translate to all other components of the climate system.”

I am continuing to work on the ENSO model, and as it gets simpler, the fit improves.  I started using an ENSO model that combines the SOI metric at 2/3 (Tahiti and Darwin) with the (negative to match SOI) NINO34 index at 1/3 (another interesting variation to consider is a median filter picking the middle value) This is a fit with a correlation coefficient of >0.80.

Fig 1: Recent SOIM fit. Yellow indicates regions of “poorer” fit

Another metric to consider is a variation of a binary classifier. The idea is simple. Since the model and data both show an oscillation about zero, then just by counting the number of times that both data and model agree with respect to positive or negative excursions, one can conveniently estimate fitness.  As it just so happens an 80% agreement in excursion classification also corresponds to around a 0.80 correlation coefficient.

Fig 2: Binary classifier for estimating correlation of model.

There is a limit to how high the CC can go, since the correlation between SOI and NINO34 tops off at about 0.86. This really has to do with nuisance noise in the system and the inability to identify the true oceanic dipole with confidence. So, for all practical purposes, a CC of 0.80 or identifying + or – excursions at 80% accuracy is quite good, with diminishing returns after that (since the fit is to the noise).

The other metric that I am exploring is an estimate of the agreement between two wavelet scalograms.   As one can see below, the fit in 2 dimensions appears quite good and the extra degrees of freedom provide better discrimination in identifying the better fit between two models.

Fig 3: Wavelet scalograms.

I am asking the participants at the Azimuth Forum as to how best to create an effective CC for comparing wavelet scalograms, but have had no response so far.

BTW, Archived discussions of the ENSO Revisited thread at the Azimuth Forum

Also a battle I had over at Real Climate  with a neo-denier. The moderators at RC dispatched the fella to the Bore Hole thread.