jshore, I appreciate your taking the time. I’ll answer your statements in parts, as the original post was getting long. Responding to brasil84 you said:
[QUOTE=jshore]
[QUOTE=brasil84]
It depends on what result they seek in their tuning. I gather that models are evaluated based on how well they match temperature.
[/QUOTE]
I am not sure how you gather that at all since that is not what they describe and, furthermore, as I noted, is not really something that the parameters are designed to tune to very effectively anyway since that is in large part determined by the time-dependence of the forcings. If I wanted to get a great fit to the historical temperature record, then I could easily create a purely phenomenological model with, say, 4 parameters that would do a bang-up job fitting the temperature record…while having no predictive value of course. (You know, that statement about fitting an elephant and wagging its tail.) However, I could also present you with a model with a thousand or more parameters that you could not for the life of you tune to fit the historical temperature record. Climate models are not particularly well designed to fit historical temperature records because that is not their purpose. Their purpose is to physically model the climate…and, particular, to do so in a way that allows one to predict the effect of a change in radiative forcing such as that due to increases in greenhouse gases.
[/QUOTE]
The models are assuredly tuned to match the 20th century global historical temperature record. Not only that, they make no bones about it. From Kiehl, as cited above:
[QUOTE=kiehl]
First, within the
range of uncertainty in aerosol forcing models have been
benchmarked against the 20th century as a way of establishing
a reasonable initial state for future predictions. The
analogy would be to weather forecasting where models
assimilate information to constrain the present state for
improved prediction purposes. Climate models are forced
within a range of uncertainty and yield a reasonable present
state, which improves the models predictive capabilities.
[/QUOTE]
and
[QUOTE=kiehl]
Methods of testing these models with
observations form an important part of model development
and application.
[/QUOTE]
Otherwise, I’d have to believe that all those 11 models were all able to reproduce the 20th century climate, and yet their sensitivity varies by a factor of 3, without their ever having been introduced to the 20th century dataset, and that just surpasses belief.
You say “However, I could also present you with a model with a thousand or more parameters that you could not for the life of you tune to fit the historical temperature record.”, which is true … but if I built the model, I guarantee I could tune at will, as could you.
Let me invite you to do a thought experiment. Imagine a planet just like the Earth, but where the sun is much weaker, just warm enough to keep the oceans unfrozen. How much cloud cover would it have?
Well, not much, because the temperature is so low there’s hardly any evaporation. The air would be dry, which means those cloud-free skies like we see in the dry desert air.
Now, suppose we turn up the heat on the sun just one notch. The earth starts to warm. A bit more water evaporates. A bit more clouds form. Immediately, the amount of incoming sunlight is cut down. But the earth is still warming, and more clouds form, and the process continues until equilibrium is reached. It occurs where the line of decreasing sunlight (from increasing clouds) meets the line of increasing temperature.
Note that the total temperature change will be smaller than would be predicted based on the change in solar output alone. The cloud feedback sets the final temperature, not the change in solar input.
Now, in the thought experiment, let’s turn the sun up a lot, let’s turn it up to our present heat. The world gets more warm and wet and tropical. From almost no cover the clouds increase to cover 70% of the globe. This jacks the albedo up to 30%, and cuts the sunlight down from 340 W/m2 outside the atmosphere to the 235 that is actually absorbed by the system.
And at that point, the warming stops. The lines cross, the temperature balances the cloud cover. The planet will not heat further despite the fact that significant additional energy is available.
Note that if some occurrence causes the temperature of the earth to drop (e.g. volcano), the cloud cover will drop as well and will hasten the return to equilibrium.
Remember that albedo feedback is not like any other feedback. The albedo is the throttle on the incoming energy, it is the gas pedal of the planetary heat engine. Clouds are the major determinant of the variable part of the albedo. Yes, changes in ice and snow play a part, but consider the Arctic winter, with all that polar ice. Does it change the albedo much? No, because mostly, the ice is where the sun isn’t …
In addition, snow and ice are up towards the poles. When you have low sun angles, the sun skips off most any surface. You can see it with glass, when you get past a certain angle you can no longer see through it, everything is reflected. So the albedo at low sun angles is high in general, with or without snow or ice.
So the cloud cover sets the amount of fuel entering the planetary heat engine. Where I live, I can see it acting as a limit on the temperature rise every day. In good tropical Pacific fashion, the morning is clear. But as the day warms, clouds start springing up. They reflect the sunlight and bring shade. If the sun is hot enough, thunderstorms form and bring shade and cooling rain. Both limit the heat buildup that would otherwise happen without this feedback.
In addition to reflecting sunlight back into space, thunderstorms are amazingly efficient at moving heat aloft. They function as a heat pipe, pumping moisture laden air aloft at vertical speeds up to a kilometre per minute. At the top of the pipe, the air and the moisture (ice) can radiate energy into space with little greenhouse effect. And of course, the more heat, the more thunderstorms.
And that’s why having 22 adjustable cloud parameters as I listed above still doesn’t do it. The temperature-cloud feedback is on the scale of minutes and hours, not months and years. Clouds are widely distributed, but in a blotchy, scattered manner. Each cloud both responds to and alters its own immediate environment. Clouds are born, have a life cycle, and die. There are many types of clouds, each with it’s own distinctive response to sunlight and longwave radiation. They are arranged in small and large scale patterns. And somewhere in all of that, no one knows where, the total albedo responds by increasing with temperature and limiting temperature rise.
All the best,
w.
PS – as a measure of how little we really know about clouds, it has been know for a long time that individual cloud droplets form around “cloud nuclei”, tiny bits of something solid. Dust and airborne sea salt are known to act as cloud nuclei. But a recent scientific paper I can’t lay my hands on right now showed that more than half of the cloud droplets they studied were formed around … wait for it …
bacteria.
Bacteria? Go figure.
And you think we can model this breathtakingly complex system that we are so very far from understanding? Yes, you are right, you can use approximations of all types in models. I have done so many times. But to do so, you have to first have a very good understanding of the system you are modeling. And understanding the climate system, to date, we are working on it but are far from “settling the science”. Bacteria as cloud nuclei? What happens if they all die off?
PPS - I am aware that as water vapor increases, as well as more clouds, you get a larger greenhouse effect. But the albedo is the throttle, and a tiny, 1% change in the albedo has the same effect as a doubling (or halving) of CO2.
w.
