Water vapor is not tracked in this indicator, as it is generally accepted that human activities have not increased the concentration of water vapor in the atmosphere.
Are we talking consensus here?!?! I thought that was a bad word! If it gets hotter, there WILL BE more water vapor in the atmosphere. That's simple physics. The fact that it's not tracked makes it a tough thing to hang your hat on. I thought you were opposed to data being ignored. It's a red herring anyway, since it's the other ADDED GHGs since the advent of the Industrial Revolution which would drive temps up. The rise in water vapor is a consequence of that. Therefore, the reason it isn't tracked like GHGs is because, it's a result not a cause of AGW and it's good scientific practice to measure root causes instead of results, if you can.
Water Vapor is THE DOMINANT GREENHOUSE GAS. Climatologists though don't know enough about it so they ignore it. Look at any climate prediction model and water vapor is not included as a variable.
What I find amusing is the scientific method requires the creation of a hypothesis, the demonstration of said hypothesis through experimentation and the presentation of results to the scientific community to determine if those results can be reproduced by anybody.
The climatologists computer models are the hypothesis. They can not predict what will happen in a month much less ten years. They can not reproduce what we know has happened in the past.
Below is Gavin Schmidts revelation about the climate model problems (once again highlighted in blue) and this from one of the lead alrmists.
The only consensus is they havn't a clue how the climate works. That is a fact. All the rest is the waving of hands to confuse you.
THE PHYSICS THAT WE KNOW
[GAVIN SCHMIDT:] The key environmental question facing us now (and for at least the next century) is to what extent are the changes we are making to the atmosphere, to the oceans, to the composition of the air, going to impact sea levels, temperature, and rainfall and hydrological resources?
Societies make decisions based on certain expectations for their climates. How far away from the shore should we build? How do we design our agriculture? What kind of air conditioning system do we put in a building? All of these decisions depend on the expectations we have for the summer temperature, or the intensity of the ocean surge during a Northeasterly storm. Our expectations in such matters have been built over hundreds of years of experience. But things have changed.
How do we come up with new expectations when our models are no longer valid? We need new information on which to base decisions being made now that will affect how we deal with the climate in 10, 20, 30, 50 years time because we are now building infrastructure for with these kinds of timeframes.
We have to ask questions about what expectations we may have for the future based on the physics that we presently know, on processes we can measure, and that resonate with our ability to understand the current climate. Among the questions we need to ask are: Why are there seasonal cycles? Why are there storms? What controls the frequency of such events over a winter, over a longer period? What controls the frequency of, say, El Nino events in the tropical Pacific that impacts on rainfall in California or in Peru or in Indonesia?
We have decided, as a scientific endeavor, to extrapolate as much as we can from our knowledge of the individual processes that we can measure: evaporation from the ocean, the formation of a cloud, rainfall coming from a cloud, changes in wind patterns as a function of the pressure field, changes in the jet stream. What we have tried to do is encapsulate those small-scale processes, put them together, and see if we can predict the emerging properties of that fundamental complex system.
This is very ambitious because there is a lot of complexity, a lot of structure in the climate that is not a priori predictable from any small scale process. The wet and dry seasons in the tropics come about because of the combination of the seasonal cycle of the orbit around the earth, changes in evaporation, changes in moist convection (the process that creates the big cumulus towers and thunderstorms), water vapor transports because of the moist convection, because of the Hadley Cell that gets set up as a function of all those things. It is a very complex environment.
We have been quite successful at building these models on the basis of small-scale processes to produce large-scale simulation of the emerging properties of the climate system. We understand why we have a seasonal cycle; we understand why we have storms in the mid-latitudes; we understand what controls the ebb and flow of the seasonal sea ice distribution in the Arctic. We have good estimates in this regard.
But we don't have perfect estimates. Instead, there are about twenty groups around the world that have reported on such processes, which ones are important, which ones are not. Each group has produced a separate digital world, a digital climate, and they each differ as a whole and in terms of their levels of sensitivity. For example, if I change an element in one of the models, each of the other models reacts in a slightly different manner.
In some respects they all act in very similar ways — for instance, when you put in more carbon dioxide, which is a greenhouse gas, it increases the opacity of the atmosphere and it warms up the surface. That is a universal feature of these models and it is universal because it is based on very fundamental physics that you don't need a climate model to work out. But when it comes to aspects that are slightly more relevant — I mean, nobody lives in the global mean atmosphere, nobody makes the global mean temperature an important part of his expectations — things change. When it comes to something like rainfall in the American Southwest or rainfall in the Sahel or the monsoon system in India, it turns out that the assumptions we make in building the models (the slightly different decisions about what is important and what isn't important) have an important effect on the sensitivity of very complex elements of the climate.
Some models strongly suggest that the American Southwest will dry in a warming world; some models suggest that the Sahel will dry in a warming world. But other models suggest the opposite. Let's imagine that the models have an equal pedigree in terms of the scientists who have worked on them and in terms of the papers that have been published — it's not quite the case but it's a good working assumption. With these two models, you have two estimates — one says the area will get wetter and one says it will get drier. What do you do? Is there anything you can say at all? It is a really difficult question.
There are a couple of other issues that come up. It turns out that the average of these twenty models is a better model than any one of the twenty models. It better predicts the seasonal cycle of rainfall; it better predicts surface air temperatures; it better predicts cloudiness. This is odd because these aren't random models. You can't rely on the central limit theorem to demonstrate that their average must be the best predictor, because these are not twenty random samples of all possible climate models.
Rather, they have been tuned and they have been calibrated and they have been worked on for many years in trying to get the right answer. In the same way that you can't make an average arithmetic be more accurate than the correct arithmetic, it is not obvious that the average climate model should be better than all of the other climate models. For example, if I wanted to know what 2+2 was and I picked a set of random numbers, averaging all those random numbers is unlikely to give me four.
Yet in the case of climate models, this is kind of what you get. You take all the climate models, which give you numbers between three and five, and you get a result that is very close to four. Obviously, it's not pure mathematics. It's physics, it's approximations, it involves empirical estimates.. But it's very odd that the average of all the models is better than any one individual model.
Does that mean that the average of all the models' predictions is better than the prediction of any individual model? This doesn't follow, because it may be that all of the models contain errors, which for today's climate average out when you bring them together. Who is to say what controls their sensitivity since we know that in each model the sensitivity is being controlled by slightly different elements?
You need to have some kind of evaluation. I don't like to use the word validation because it implies a kind of binary true-false set up. But you need tests of the model's sensitivity compared to something in the real world that can confirm that model has the right sensitivity. This is very difficult.
For instance, let's imagine that the models I want to pay attention to are the ones that get the best seasonal cycle of rainfall. I rank the models, give them a score, and take the top 10 scoring models for that metric. Then somebody else says, no, I think it's more important that they get the annual mean right or they get the inter-annual variability (the variability from one year to another). Well, I can do that same ranking. It turns out that if I do the ranking for three different metrics — there is nothing that says that one metric is better than the other — I end up with ten completely different rankings.
Not only are the rankings uncorrelated to one another, but depending on the metric, the projections, the estimates that you get going into the future turn out to be uncorrelated to the score, as well. I get the same spread if I take the top ten models over here as I had for the whole set. There will still be some positive ones and there will still be some negative ones when, for instance, I look at projected rainfall in the American Southwest.
This is a real problem. How do you deal with these models in an intelligent way? Which information from the observational record, either over the 20th century or longer, can you use to test whether the models have any skill in their predictions? This is what I spent all of my time on: trying to find ways to constrain the models to improve the Bayesian subjective probability that they are telling you anything of use. It's not that we have been working in a complete vacuum for the last 30 years — these models are relatively mature and people have been thinking about these issues ever since the beginning.
There are lots of examples in the current climate with which you can demonstrate that the models have skill. Say, the response to the eruption of Mount Pinatubo in 1991 in the Philippines. The volcano released a huge amount of sulfur dioxide and sulfate aerosols into the atmosphere, which spread around the stratosphere and stayed for about two to three years. These aerosols are reflective; they are white.
So the sun gets reflected out. These aerosols acted as a kind of sunshade over the planet that caused the planet to cool. Our group (though this is before my time), before this cooling happened, did the calculations with their model, and predicted that the cooling would reach a maximum of about half a degree in about two years time. Lo and behold, such a thing happened.
If you go back, and we had lots of information about what happened over that period — what happened to radiation at the top of the atmosphere, what happened to the winds that changes the function of the temperature gradients in the lower stratosphere, what happened to water vapor — we can see whether the models got the right answer for the right reason, and for the most part they did. So that was a good real prediction in real time that could be tested in a short amount of time.
The problem with climate prediction and projections going out to 2030 and 2050 is that we don't anticipate that they can be tested in the way you can test a weather forecast.
It takes about 20 years to evaluate because there is so much unforced variability in the system — the chaotic component of the climate system — that is not predictable beyond two weeks, even theoretically. This is something we can't really get a handle on. We can only look at the climate problem once we have had a long enough time for that chaotic noise to be washed out, so that we can see that there is a full signal that is significantly larger than the inter-annual or the inter-decadal variability. This is a real problem because society wants answers from us and won't wait 20 years.
Edge: THE PHYSICS THAT WE KNOW: A Conversation With Gavin Schmidt
