Quote of the week – Nature on the failure of climate models

Your assessment leaves out a very key passage, which gives the article a very different complection than your interpretation:
“…In one such analysis4, Doblas-Reyes and his colleagues say that their model anticipated the slowdown in global warming up to five years in advance. Their paper also bolstered the theory that the deep oceans, notably the Atlantic and tropical Pacific, had stalled atmospheric warming by absorbing much of the heat being trapped by rising concentrations of greenhouse-gas concentrations in the air (see ‘Lost heat’).”
In short, the heat is still there; rather than being expressed in more volatile changes in atmospheric temperature, energy is being picked up in the thousands-fold greater reservoir of the ocean.
The limitations of models to provide exacting predictions has actually been discussed for a long time. That does not detract from their utility as analysis and forecasting tools, nor allow us to dismiss empirical observation which demonstrate very clear human-driven changes occurring in our climate. *Even if* data proved change was taking place more slowly (which it does not), all that does is give us more time. It does not give us a “pass” to continue business as usual…..

“Kevin Trenberth, a climate scientist at the National Center for Atmospheric Research in Boulder, Colorado, says that it could be a decade or more before this research really begins to pay off in terms of predictive power, and even then climate scientists will be limited in what they can say about the future.”
Trenberth must have about 10 years until retirement – gotta keep that money pumping!

Frank K. says:
July 11, 2013 at 8:41 am
‘For example, why would anyone in their right mind maintain a very expensive branch of NASA GSFC in downtown New York City???’
Because the restaurants are good?
(Seriously, I’ll bet that’s the reason. They’d probably prefer Marrakech, Morocco; Durban, So. Africa; Cancun, Mexico; and others but that would be too obvious.)

I’m having a hard time reconciling the following statement in the article with “the science is settled”.
“It’s fair to say that the real world warmed even less than our forecast suggested,” Smith says. “We don’t really understand at the moment why that is.”

@Rod Everson:

It (the model) begins to drift back to its preferred climate? Does that mean what it sounds like it means? Because it sure sounds like it means that regardless of the initial inputs, the model is going to end up “drifting” to a preordained (“preferred”) climate prediction. Preferred by the modeler, no doubt.

It’s essentially saying that each model has its estimate of climate sensitivity and a few other parameters. Simplistically speaking, these models model how energy from the sun flows into various earth systems and back out again to produce an average of the global temperature. You start with a temperature estimated from the instrumental record for a given time where you also have decent estimates for all of the inputs (CO2 level, volcanic activity, aerosol patterns, methane level…) and jiggle the parameters around until the modelled temperature matches the estimate from the instrumental record. Given no change in the inputs, that model will then keep on producing the same temperature – it’s at equilibrium (if you’ve done it right).
Then you disturb the model by changing one of the input parameters. its temperature estimate will begin to change and (assuming you’ve made a stable model) it will settle at a new equilibrium. But each model will settle at a different equilibrium because each one used a different combination of parameters to match that original temperature estimate. Model A might have a very high CO2 sensitivity but a very low methane sensitivity, for instance, while model B has a very low CO2 sensitivity but a very high methane sensitivity. They will both match the instrumental temperature estimate for a given time, but in model A the heat is mostly trapped by CO2 while in model B it is trapped mostly by methane. But if you change the CO2-concentration input to both models, model A will produce a much higher temperature than model B.
Whether those different responses to input reflect reality or the researchers biases depends very much on the process they used to tune the model. If you base them on theory-based estimates of model processes and parameters then you’d probably say they reflect reality, while if they are just adjusted to arbitrarily match the instrumental estimate then they reflect either the modeller’s prejudices or are entirely random.
The problem is that using the best theoretical estimates of model parameters never produces a model that matches reality even at one point in time, let alone through time. This simply reflects the fundamental defect of climate modelling: We don’t know enough about the system to model it accurately. On some aspects we understand the processes but don’t know enough to estimate the parameters with any certainty. On others we’re entirely ignorant what drives the processes (such as the PDO, AMO etc). No doubt there are entire processes that we don’t know about, or which are still controversial (cosmic ray cloud seeding, for instance). So every climate model, to a greater or lesser degree, is either a tedious curve-fitting exercise, only valid for the range the curve was fitted over, or reflects the prejudices of the modeller. It’s no real surprise (or shouldn’t be) that they spit out garbage.

““Kevin Trenberth, a climate scientist at the National Center for Atmospheric Research in Boulder, Colorado, says that it could be a decade or more before this research really begins to pay off in terms of predictive power, and even then climate scientists will be limited in what they can say about the future.”
This is one of the most profoundly stupid things I have ever read.
Translation: Our previous and current predictions are wrong. You need to wait ten years to see see that we were right, but don’t count on it.
Awesome. Give that man some more money.