And that's why we have more detailed models.

But it ends up being a no-win situation, when the objective is to criticize rather than to understand. If the model is simple, they say "that model's too simple! The real world is complicated! You need to include X Y and Z!". And if the model is modified to include X, Y, and Z, they say "The model is too complicated! You can't believe any complicated models like that!"

The actual answer is, you start simple, understand the simple models, and progressively add complexity. This is the way science is done. Planets don't move in uniform elliptical orbits. Nevertheless, starting with Kepler's laws and then adding peturbations is a good way to analyze planetary motions.

As for comparing models to reality, and asking what we know and how we know it, there isn't really time for me to go through this model by model since 1967 (since I do have other things to do). I'll again suggest as a start reading the WG-1 summary report, it goes into detail on this (and has references for more details). The one I'm more familiar with is the fourth: https://www.ipcc-wg1.unibe.ch/... (although there seems to be a more recent one, fifth, here:
http://www.climatechange2013.o... )

According to the link, this is from "The report by Japan Society of Energy and Resources (JSER) ... the academic society representing scientists from the energy and resource fields".

This is something I've noticed. While climate scientists mostly agree with the physics models, whenever you see a headline about a group of scientists who disagree, when you look at the details, you usually find it's commissioned by the energy industry. There was a headline article in Forbes a year or so back, similar: the headline was "here's a poll of hundreds of scientists who aren't sure about global warming," and when you looked at the details, it was a survey of the people working in the Alberta coal and petroleum extraction industry.

When you look at the details here, nothing seems to be new. People have been looking for a connection between solar activity and climate for a hundred years; this has been studied a lot, and as far as I know, nobody has found a correlation large enough to drive climate. At a top level, the issue is summarized in the IPCC WG-1 report: http://www.ipcc.ch/pdf/assessm... (There's a fifth assessment report out now, but the one I'm familiar with is the fourth, so that's what I link to.) A summary is in section 1.4.3, (Solar Variability and the Total Solar Irradiance); and the more detailed analysis is chapter 2.7, Natural Forcings, section 2.7.1 "Solar Variability."

(I'll also note that solar forcing tends to have a different signature from the greenhouse effect warming. Solar forcing tends to increase day/night temperature differences; the greenhouse effect tends to reduce them).

On the subject of the Japan Society for Energy and Resources critique, this is the page from the Japan Meteorological Agency: http://ds.data.jma.go.jp/tcc/t...
So the 2009 criticism by the Japanese Society for Energy and Resources doesn't seem to have made any influence to the actual people in Japan studying climate.

Right.

By present-day standards, the Manabe and Wetherald model is very simple. This was indeed the criticism at the time-- "but the model doesn't account for XXX effect"-- and all of the present-day models basically work on adding in the various feedback effects you mention.

The simplicity is both a flaw, but also a virtue. Basically, Manabe and Wetherald is a model of the greenhouse effect, and nothing but the greenhouse effect: while there are a hundred more sophisticated models these days, now all of the criticism is "but how do you know that you didn't get XXX feedback effect wrong?" Well, Manabe and Wetherald didn't have all the bells and whistles-- it was the first real greenhouse effect model that incorporated real-world, measured infrared absorptions, accurate radiative transfer, and convective equilibrium, but that's all.

This is typically the way science is done. First you make the back of the envelope models, then the simple models, then you progressively add more refinements.

Surprisingly, the other effects matter less than you might think. Clouds was the first criticism made (and all modern models have cloud effects)-- but clouds aren't actually a huge effect. If clouds blocked visible and infrared light equally well-- and to first order they do-- cloud cover would have little effect on average temperature: the infrared radiation scattered downward heats the planet, the albedo scattering cools the planet, and in the simplest model the two balance out. Of course, the real world isn't the simplest model, but in some places clouds can actually increase the temperature (you see this in models of carbon dioxide clouds on early Mars.) What clouds mostly do is tend to equalize the daytime and nighttime temperatures. This is actually a good way to separate cloud effects from infrared absorption effects. (Another way is to look at vertical profiles).

As for the constant relative humidity assumption-- well, what would you suggest would be a better input assumption? Again, it's a good simple assumption. It does implicitly include a feedback effect, but it's pretty much the most transparent way to incorporate it. Most importantly, note that by assuming constant humidity, there aren't any adjustable parameters. If you worry that models have been "tweaked" to make the output match the data, well, there isn't any feedback to tweak here.

With the advent of supercomputers in the 70s, models got more detailed. The next good summary of models would be the U.S. National Academy of Sciences report 1979, by which time the report could look at and compare several models. The '79 NAS report is a good go-to reference for models of the '70s; and is still a bit before the politically-motivated attacks started muddying up the conversation. That still gives 35 years of data that can be compared to prediction -- a long enough run to average out some of the year-to-year variation and compare the models to reality. I graphed (but haven't yet added the most recent data, 2014, yet) and, yes, the measured temperatures fit inside the error bars of the NAS models.

After that, models got much more sophisticated very quickly (and so did the attacks on the models, resulting in a fast evolution as ever more sophisticated models addressed ever more complicated critiques). Today there are hundreds, and probably thousands of models being run. Comparing them to measurements is more like looking at statistics than looking at an individual model. There's some good graphs comparing models to reality in the IPCC working group 1 report, if you're interested in tracking them down.

Manabe and Wetherald 1967.

So, basically, you consider it condescending that I insist that you should actually look at data. Real data. Not blog posts.

And you complain that I only gave you a link to one source of data. OK, here are data from four continents:

Berkeley Earth: http://berkeleyearth.org/
Hadley Center Climate Research Unit: http://www.cru.uea.ac.uk/cru/d...
Goddard Institute for Space Studies: http://data.giss.nasa.gov/gist...
Japanese Meteorological Agency: http://ds.data.jma.go.jp/tcc/t...
Australian Meteorological Agency: http://www.bom.gov.au/state-of...
NOAA: http://www.ncdc.noaa.gov/cag/t...

This is called a "global circulation model" (usually abbreviated GCM). There are dozens of GCMs around, being run by hundreds of institutions on five continents You want a GCM, pick one.

The models have error bars. The quoted error bars in the consensus model of global warming is plus or minus 50% on the sensitivity to carbon dioxide.

That's not the way it works. "Months" means weather, not climate. Now, there are also weather prediction models, and these also run using global circulation models-- but it's a different regime. Climate is what happens over decades.

And, global warming also is something that happens over decades. Longer time periods averages out the noise-- the random portion of the variation--so the longer the time period, the easier it is to pick out signal from noise.

Exactly! Now you've got it. Climate is long term; weather is short term. Climate predictions need to be evaluated over decades. So, your question needs to be: how accurate were the predictions of thirty to fifty years ago? I'll reference, yet again, the classic greenhouse calculation, Manabe and Wetherald 1967. Their model predictions are within the current estimated range of climate sensitivity, so it's a good test. Their model computes a warming of about 0.7C for the measured amount of carbon dioxide added to the atmosphere since 1967. The actual value is 0.6. The year to year variation about the regression line is 0.15. So, yes, so far the model looks good, well within error bars. Now, if there is the purported hiatus in warming, and it's not a statistical fluctuation (so far, it's within the historical fluctuations), and if the purported hiatus holds for roughly another decade, the prediction will move outside the measurement error. But that's equivalent to saying "although the present measurements fit the data, future measurements won't." Maybe the models will have to be reevaluated if future data shows they're wrong. But so far, the data hasn't.

Citation needed.

Repeat: Months are weather. Decades are climate. The longer the period, the easier it is to model.

So, you've basically said that anything that relies on observations of nature is not a science. You don't actually know what science is.

Astronomy is not a science, according to you; it's just comparing observations to models.

But, comparing observations to models is exactly what science is. Controlled experiments are nice. However, much of science is done on systems where controls are not possible-- In the absence of a second Earth, we don't have a second climate.

Which is not to say that climate science doesn't have any controlled experiments. You can measure the greenhouse effect by pointing an infrared radiometer at the sky. You can measure the absorption of carbon dioxide in a temperature controlled gas cell. The basic science behind the greenhouse effect is physics, and this is actually pretty well understood.

The way I distinguish skeptics from deniers is that skeptics are interested in the science and want to learn more, while deniers won't do one iota of work to analyze, or critically examine in any way, the conclusions that they have been carefully spoon-fed by the blogo machine.

It is amusing, in a bitter way, the lengths that deniers will go in their rationalization of why they avoid looking at data or doing any thinking of their own. But this is the signature of deniers: plenty of talk, but no critical thinking (of the opinions that support what they already believe).

I've given you links to the data. Your response is that you won't listen to others (that would be appeal to authority!) but you won't look at any data yourself either. The only remaining option is that you will just believe what you have been told.

Oh, and you say it's not about the science anyway. Well, since you have so eloquently said that you don't understand it, I will believe at least that much.

Guess what? We're already gambling the world economy. We're already placing bets: all that science has done is to begin the process of calculating the odds and the payout. And the best thing about it? For the most part, the people making the bets aren't the ones taking the risks. We're gambling with other people's money.

If you want to place a bet against global warming, my suggestion is to buy land in Kiribati. This is a nation of small islands south of Hawai'i. As long as sea levels don't rise-- its average height is two meters above sea level-- this is paradise. A wonderful retirement home.

Land there is going cheap. Real cheap.

Oddly, in the sciences (as opposed to in politics), acknowledging uncertainty and quoting error bars is considered to be a good thing. The fact that they acknowledge uncertainty is a useful indication that scientists are not "inept."

I'm sorry, but no. The null hypothesis is that carbon dioxide emitted by humans has no effect on climate. That hypothesis has been strongly ruled out by multiple measurements. It is simply not plausible.

The greenhouse effect model-- which, basically, is a model that says human-emitted carbon dioxide has the same effect on climate as natural carbon dioxide-- is at the moment the only model that fits known observations. There is no alternate model. People have been searching for one for a long time-- there are lots of people who very much want an alternative model, and have wanted one for years. The lack of an alternate model is not for lack of trying!

In addition to the obvious (has to come up with correct values for average temperature, day-night variation, variation with season, latitude, etc.) here are three additional things an alternate model has to explain:
1. It has to explain why carbon dioxide doesn't have the warming effect predicted from simple radiative transfer models.
2. It has to come up with an alternate explanation of the observed warming over the most recent period-- a period of time in which we have had good, and increasingly better and better, measurements of the planet. (We know it's not simply a change in the overall solar intensity, for example).
3. It has to come up with an explanation for why the amplifying effect postulated in part 2 (this has to have an amplifier, since we measure the input) doesn't also amplify the greenhouse warming.

This is very difficult. So far, nobody has come up with one that isn't easily shown to be faulty. And not for lack of trying-- the person who could come up with such a model that didn't fail would instantly become the most famous climate scientist in history.

And, in fact, the science is sound. You have just written three posts in this thread asserting that the science is wrong... and now you're telling me, that's not your point, you don't know or care if the science is sound, what you care about is the "alarmists" trying to "shape public and political opinion"?

OK. This is slashdot. Some of us do care about the science, and do care about people attacking science to make polical points.

The science is sound. If you don't like the alarmism, go challenge that (hopefully with actual facts) and quit making uninformed attacks on the science based on some random post you googled on some blog.

..and, by the way, what I mostly see here is you taking any possible excuse to avoid looking at the data and doing your own analysis, thinking about statistical analysis and confidence. Good job in avoiding dealing with actual science!

That's right: the current estimate for the climate sensitivity to carbon dioxide is 3C plus or minus 1.5C (that is: 1.5C to 4.5C). That's a big ballpark, but, indeed, that's the quoted uncertainty. Here's a graph of model simulations, showing this variability: http://earthobservatory.nasa.g...

But the interesting thing is, the deniers aren't jumping on that-- they are, instead, all about how confident the scientists are.

Perhaps you'd think that. But, instead, you will be hard pressed to find a denier website that even acknowledges this. Pointing out that the scientists acknowledge uncertainty in their models would destroy their argument that scientists are unwilling to acknowledge any uncertainty in their models.

Well, first I'd like to see a citation for that, since I have no idea what you're talking about. But you post as anonymous coward, so I doubt you have any references. Anonymous cowards should be assumed to be making stuff up unless proven otherwise.

But, in any case, that was just a convenient link to an essay from Robert Muller. Any other link would be just as good. If you want data, try http://berkeleyearth.org/

Basically, anonymous posters whose main argument is "It's all a leftist conspiracy" should be ignored, since you don't have any interest in actual science.

Can you substantiate that with statistics? Are they completely wrong? Analyze the statistics. What does a statistical analysis say?

I can't do this for you. Don't grab somebody else's opinion from the internet; look at the data. Analyze the statistics. What do the statistics say? Is it statistically significant?

ENSO is a driver of short term climate, not long-term climate.

It is, indeed, one of the large sources of natural variation. But let me repeat something I've said many many times, and will, I expect, will have to say many more times. Human-induced climate effects are not instead of natural variations; they are in addition to natural variations.

That's the problem, isn't it? None of the deniers make predictions. Because the deniers don't have any models. Not even one.

The argument seems to be "well, the measurements fit the data to within experimental error so far (which is accurate: do the analysis!) but in the future they won't. So, based on future measurements that haven't happened yet, global warming doesn't exist."

The easy answer: don't cherry pick. Use all the data.

So, don't cherry pick. Use all the data. Statistics tells us that a longer run of data will always have a better signal to noise ratio than a shorter run.

Interesting: you just changed the question. Now you are asking, "does the data show warming over a 15 year period ending in the present?" The answer to that question is the opposite of what you just said: The data shows that the climate warmed from 2000 to present. (I linked to you some sources of data: graph it yourself.) It warmed, oddly enough, just exactly according to prediction.

And if your response is "well, fifteen years is a cherry-picked number. If it warmed over that period, so what-- if you picked 18 years instead, it didn't warm!" Excellent. Exactly: if the calculation of slope depends on whether you add a single point (in this case the high point in 1998), that means that you're not analyzing a long enough run of data. A robust statistical analysis shouldn't be sensitive to any single point.

I don't blame every weather event on global warming. If you hear people who are blaming every single weather event on global warming, stop listening to them. Another thing I've said many many times, and will, I expect, will have to say many more times. Weather is not climate.

I am not interested in the doomsday scenarios, scare and fear mongering, alarmism. if there weren't so many people shouting so loudly that the science is wrong, I suppose I might spend some time debunking some of the worst of these. But there are always doomsday scenarios. People saying that scientists are frauds, and science is a hoax: that annoys me.

I never said that the weather is any worse then 10 to 18 years ago. Global warming is a long term effect. The change in 10 years is trivial. The effect on the weather of the change in 10 years is a trivial effect on a very small amount of warming; probably not observable.

Global warming is a long term effect, not a short term effect.