The IPCC has some interesting things to say about water vapor and clouds:
Recent studies reaffirm that the spread of climate sensitivity estimates among models arises primarily from inter-model differences in cloud feedbacks. The shortwave impact of changes in boundary-layer clouds, and to a lesser extent midlevel clouds, constitutes the largest contributor to inter-model differences in global cloud feedbacks. The relatively poor simulation of these clouds in the present climate is a reason for some concern. The response to global warming of deep convective clouds is also a substantial source of uncertainty in projections since current models predict different responses of these clouds. Observationally based evaluation of cloud feedbacks indicates that climate models exhibit different strengths and weaknesses, and it is not yet possible to determine which estimates of the climate change cloud feedbacks are the most reliable.I'm not going to go into all the problems that are indicated by this IPCC explanation.
The ultimate source of most such errors is that many important small-scale processes cannot be represented explicitly in models, and so must be included in approximate form as they interact with larger-scale features. This is partly due to limitations in computing power, but also results from limitations in scientific understanding or in the availability of detailed observations of some physical processes. Significant uncertainties, in particular, are associated with the representation of clouds, and in the resulting cloud responses to climate change.
Many of the important processes that determine a model’s response to changes in radiative forcing are not resolved by the model’s grid. Instead, sub-grid scale parametrizations are used to parametrize the unresolved processes, such as cloud formation and the mixing due to oceanic eddies.
Cloud parametrizations are based on physical theories that aim to describe the statistics of the cloud field (e.g., the fractional cloudiness or the area averaged precipitation rate) without describing the individual cloud elements. In an increasing number of climate models, microphysical parametrizations that represent such processes as cloud particle and raindrop formation are used to predict the distributions of liquid and ice clouds.
But let me take up two. First: Electric motors are well understood. There are not 15 models of electric motors. There are not even two. There is one.
Second: The estimate of energy "forcing" from CO2 is 1.6W/m2. The estimate for cloud "forcing" is 30W/m2. An error of just 5% in how clouds are modeled will equal the CO2 "forcing". An error of 10% in the cloud models will dwarf any "forcing" from CO2. But of course given this uncertainty the politicians believe the modelers can tell us what the climate will be like in 100 years?
And don't forget the errors can accumulate. Especially if the feedback is assumed positive (as the models do). There is not (according to the modelers) any feedback that will tend to return the models to a given condition. The models show that deviations are increased and not reduced. So - off 5% for the first year could increase to 10%+ the second year and so on. Suppose the error is only 1%. It could lead to 100% or more errors 100 years out.
Why do I say could? Because the climate is a dynamic non-linear chaotic feedback system. What that means in practice is that a small error in the models could propagate or a large error could be damped out. And we can't predict in advance which is which. Nor can we tell (without comparing the results to reality) which is which.
So how do the results compare to reality? No model that I am aware of predicted in 2000 the flat lining of global temperature that has taken place since then. Have the models improved since then? To be sure. We should collect the latest predictions and see in ten years if they are reasonably correct. No way we should be committing ourselves to hundreds of trillions of expenditures globally until we know for sure we have something that reasonably compares to reality. And even then we can't be sure because climate is a dynamic non-linear chaotic feedback system.
Some people will bring up the precautionary principle: what if something goes wrong? Well what if. If it gets too hot we can cope. After all we already have crops that grow in hot climates. We just change where they are planted. However, as far as I can tell we do not have any crops adapted to grow under ice. And the last ice age lasted 100,000 years with huge glaciers covering North America significantly south of Chicago. (Yup. The glaciers are melting.) So how long do interglacials (like the one we are living in now) typically last? About 10,000 years. And how long has this one lasted? About 10,000 years. We are due.
So if you want to take precautions I'd say prepare for an ice age. In fact thinking about what we can do geoengineering wise to keep the planet warmer would be time well spent. The tipping point we have to worry about is the return of an ice age.
As snow and ice covers more of the land it reflects more energy into space cooling the planet which gives rise to more snow and ice further cooling the planet. And so on until the glaciers again cover much of the Earth.
Now I greatly admire Sarah Palin but there is no way I want to live in a Northern Illinois that resembles Alaska.
Cross Posted at Classical Values