
The sensitivity of a mathematical model is a measure of the effect on a certain model output from variation of certain model input data. The sensitivity to errors in data, modeling and computation directly connects to the accuracy of a model.
Climate sensitivity is primarily concerned with the effect on the global mean temperature from increasing the CO2 concentration in the atmosphere.
Concerning the climate sensitivity of current climate models, IPCC states:
- Spread in model climate sensitivity is a major factor contributing to the range in projections of future climate changes.
- Consequently, differences in climate sensitivity between models have received close scrutiny in all four IPCC reports.
- Climate sensitivity is largely determined by internal feedback processes that amplify or dampen the influence of radiative forcing on climate.
- (A) To assess the reliability of model estimates of climate sensitivity, the ability of climate models to reproduce different climate changes induced by specific forcings may be evaluated.
- (B) An alternative approach, which is followed here, is to assess the reliability of key climate feedback processes known to play a critical role in the models’ estimate of climate sensitivity.
Here (A) is a reasonable way of testing climate sensitivity, and gives a large spread shown in Fig 10.2, while (B) boils down to
- To assess the reliability of model estimates of climate sensitivity, we assess the reliability of key climate feedback processes known to play a critical role in the models’ estimate of climate sensitivity.
In other words, assessment of climate model sensitivities, is replaced by assessment of the feedback processes built into the model. But this is an internal check which appears to be circular: You build in a certain feedback process into the model and you then test model sensitivity by testing the validity of the feedback process you have put in. But in most cases you cannot isolate and experimentally test the validity of the feedback process you have put in: If you could directly observe climate sensitivity experimentally, then climate models would serve no purpose.
But some sensitivities can be observed experimentally, and thus can serve as reliability tests of climate models. This is done in a recent article by Richard Lindzen showing that the radiation sensitivity of current climate models with respect to surface temperature, does not fit with observations, as shown in the above figure with ERBE radiation measurements: Climate models show too small radiation. Something is apparently wrong with the climate models, and there are many things that could be wrong...
In our exploration of the secret of turbulence by computation, we have studied output sensitivity by duality techniques based on solving associated dual linearized problems, and we have found that local exponential turbulent perturbation growth is controled by effects of cancellation.
In turbulent flow, an important charcteristic of climate atmosphere/ocean circulation, cancellation means that the worst combination of effects does not occur: Increase in space-time is balanced by decrease in space-time so that the net effect is smaller than worst case. Duality techniques should be able to offer important information on sensitivity also in climate models, but current models lack this capability and there seems to be room for improvement...will cancellation and duality help save humanity?









