Anthopogenic Global Warming Myth or Real ?

Uh? What are you talking about?

I never said accurate regional predictions weren't important. In fact, they are nothing short of vital if we are to make the kind of forecasts needed to aid adaptation. But if the average global temperature is what you're interested in (like it would be if you're asking 'is GW real'?), you can get a more than reasonable answer without being bang-on accurate on local scales.

The GCMs have matured to the point where we can be reasonably certain about predicting overall, long-term warming. But they have a way to go before they'll have the same confidence when it comes to predicting things like the impacts on regional hydrology. Mind you, they're improving all the time.

Is it too much to ask for evidence? I suppose it is. What does "reasonably certain" mean?

Scrapping results from earlier failed model predictions and replacing them with 'new and improved' ones forced to match reality then claiming they are validated is GIGO.
 
Uh? What are you talking about?

I never said accurate regional predictions weren't important. In fact, they are nothing short of vital if we are to make the kind of forecasts needed to aid adaptation. But if the average global temperature is what you're interested in (like it would be if you're asking 'is GW real'?), you can get a more than reasonable answer without being bang-on accurate on local scales.

The GCMs have matured to the point where we can be reasonably certain about predicting overall, long-term warming. But they have a way to go before they'll have the same confidence when it comes to predicting things like the impacts on regional hydrology. Mind you, they're improving all the time.


Slap that Milkshake around Frylock!
 
Paleobotany shows an overall ecosystem that is adapted for cool and wet conditions during a glaciation. :)

Fair enough if evidence like that is there. My point is that simply trying to invoke very rudimentary thermodynamics is not sufficient when it comes to making sweeping statements like that.
 
Is it too much to ask for evidence? I suppose it is. What does "reasonably certain" mean?

According to the last IPCC report, 90%. Their exact phrase is 'very likely', but the point remains.

Scrapping results from earlier failed model predictions and replacing them with 'new and improved' ones forced to match reality then claiming they are validated is GIGO.

They early models didn't fail. Even back in the seventies they were predicting overall warming and in that respect they were right. Also, for more recent models, you can take this from AR4:
Since IPCC’s first report in 1990, assessed projections have suggested global average temperature increases between about 0.15°C and 0.3°C per decade for 1990 to 2005. This can now be compared with observed values of about 0.2°C per decade, strengthening confidence in near-term projections.

So, from a perspective of global mean temperatures, how does that constitute a 'failure'?

And incidentally, model development doesn't simply constitute forcing them to match the more recent data; the level of mechanistic understanding of the in the atmosphere has come on massively in recent years. As has the detail in the data that is available, particularly those from satellites. And let's not forget that computing power has grown by a staggering amount too, allowing for higher resolutions and more couplings in the models, which in themselves have lead to massive improvements.

Bottom line is that the models are improving all the time and with that comes better predictions of the regional effects of climate change. The fact that the answers to the questions 'is the earth warming up?' and 'is CO2 mainly responsible?' just keep getting more certain is little more than a byproduct nowadays.
 
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Hahahaha

that is Mhaze for you, the fact that precipitation goes up around the globe in a glaciatiion never prevents the simple minded....

The LIA in Europe also saw wetter conditions, on average. (More specifically, incidents of several wet summers in succession were more common.)That's actually more damaging to European agriculture than cold conditions, or even droughts. Away from the Mediterranean we have more than enough rain, thank you very much :mad:.

In the LIA the damage was exacerbated by primitive road and the lack of drying facilites for the grain that was harvested. Damp grain delayed by mud-filled roads and interrupted river transport was pretty degraded by the time it hit the towns and cities (and army supply-depots).

(As an aside, grain production in Europe provides most of the best time-series from Medieval Europe. It was documented for tax, rent and tithe purposes, and by the late Medieval period Netherland and Hanseatic trading cities were researching and recording market stocks and prices. Loads of this stuff has survived.)
 
And incidentally, model development doesn't simply constitute forcing them to match the more recent data; the level of mechanistic understanding of the in the atmosphere has come on massively in recent years.

This is the benefit of modelling complex systems. There comes a point where you have to put all the mechanics you think you've identified (in, say, protein-folding or whatever happens to be your bag) together and try it out. Where the model doesn't match up to reality is where to look for errors or interactions previously missed. After some work on that comes a new model, and so on.

And let's not forget that computing power has grown by a staggering amount too, allowing for higher resolutions and more couplings in the models, which in themselves have lead to massive improvements.

I first discussed climate (and weather) modelling in the mid-70's, when I was studying Computer Science at UEA. But we were just building castles in the air over pub-tables. There's way more processing power in a cellphone than there was on campus at the time. Orders of magnitude.

Bottom line is that the models are improving all the time and with that comes better predictions of the regional effects of climate change. The fact that the answers to the questions 'is the earth warming up?' and 'is CO2 mainly responsible?' just keep getting more certain is little more than a byproduct nowadays.

Regional models are surely the current and future focus. What's it going to mean on our ground? A communal Mediterranean modelling project, for instance, would be a great help to all concerned.

Brazenly cut-and-pasting from a comment by Lazar on Open Mind (Open Thread #6) :

Rauscher, S. A., J. S. Pal, N. S. Diffenbaugh, and
M. M. Benedetti (2008),
Future changes in snowmelt-driven runoff timing over the western US,
Geophys. Res. Lett., 35, L16703,
doi:10.1029/2008GL034424.

"We use a high-resolution nested climate model to investigate future changes in snowmelt-driven runoff (SDR) over the western US. Comparison of modeled and observed daily runoff data reveals that the regional model captures the present-day timing and trends of SDR. Results from an A2 scenario simulation indicate that increases in seasonal temperature of approximately 3 deg C to 5 deg C resulting from increasing greenhouse gas concentrations could cause SDR to occur as much as two months earlier than present. These large changes result from an amplified snow-albedo feedback driven by the topographic complexity of the region, which is more accurately resolved in a high-resolution nested climate model."

A high-resolution model to answer useful questions. If intelligence gives our species any real edge then by the 21stCE we should be able to predict and pre-adapt to what's coming. Rather than having to adapt during and after the event, like all the other species.
 
Oh my god. Are you sure that doesn't break Henry's Law? :eek:

mhaze may not have been introduced to the concepts of relative and absolute humidity. I've been over the ground quite extensively with him, regarding the "more water in the atmosphere means more clouds" fallacy, but it may not have sunk in. The phrase "water off a duck's back" does come to mind.
 
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They early models didn't fail. Even back in the seventies they were predicting overall warming and in that respect they were right. Also, for more recent models, you can take this from AR4:

Quote:
Since IPCC’s first report in 1990, assessed projections have suggested global average temperature increases between about 0.15°C and 0.3°C per decade for 1990 to 2005. This can now be compared with observed values of about 0.2°C per decade, strengthening confidence in near-term projections.

To illustrate this point, nothing like Rahmstorf et al (2007):

 
It is indeed nonpareil.

Another excellent graph is that decade-wide one you made superimposing 80's, 90's and oughties temperatures. I'd like to see that one again, if you have it to hand.

Not updated, I'm afraid, but here it is... the anomalies of the later months have been in the highs 0.4s, IIRC, but the 00's average is still 0.52.

 
Is it too much to ask for evidence? I suppose it is. What does "reasonably certain" mean?

The term used in AR4 is "very likely", which is defined in the same report as >90% certainty. Which would include, for instance, 98% certainty, or even more. IPCC reports are very conservative. The last thing the IPCC wants to do is risk its credibility by taking a radical position, especially when the papers and observations IPCC reports are based on are about two years old by the time they're published.

Scrapping results from earlier failed model predictions and replacing them with 'new and improved' ones forced to match reality then claiming they are validated is GIGO.

Climate models have, of course, proved to be commendably accurate, even from twenty years ago. Your claim that they have failed continues to match unreality, and has done consistently for years. I think it's more than very likely that you'll just keep on doing it, whatever happens in the real world (the big bad analogue model).
 
This is the benefit of modelling complex systems. There comes a point where you have to put all the mechanics you think you've identified (in, say, protein-folding or whatever happens to be your bag) together and try it out. Where the model doesn't match up to reality is where to look for errors or interactions previously missed. After some work on that comes a new model, and so on.

The work I'm involved in is more from a 'bottom up' approach, where the motivation is to identify the things that aren't understood or treated properly and to deal with the fundamentals. Slotting them into the larger-scale models comes later. Not saying either approach is philosophically better; sometimes it can result in you spending ages working on a detail that turns out to be inconsequential but at the same time, it can lead you to things you might not have spotted by simply comparing model outputs with reality.

ETA: Of course, we're always more likely to get work funded if an important area of uncertainty is identified using both top-down and bottom-up approaches.

There was a golden quote heard from a high-profile figure in my field at a conference dinner recently. It was along the lines of, "models are definitely the way of the future, but it is the job of the experimentalist to keep them honest."
 
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There was a golden quote heard from a high-profile figure in my field at a conference dinner recently. It was along the lines of, "models are definitely the way of the future, but it is the job of the experimentalist to keep them honest."

Hasn’t the process has always been for a theorist to develop a model and the experimentalist to try and break it? IMO the only thing that’s changed is we have the computing power to resolve more complex models then we could 40 years ago.
 
Hasn’t the process has always been for a theorist to develop a model and the experimentalist to try and break it?

That's the 'top-down' approach I was talking about. The 'bottom-up' approach is for the experimentalist to figure out something interesting first and the modeller to take it and work out the implications.

IMO the only thing that’s changed is we have the computing power to resolve more complex models then we could 40 years ago.

That's not quite the whole story; in a lot of cases with the atmosphere, we just simply didn't used to have the data to be able to validate some of the fiddlier aspects. For instance, a lot of the recent advances in understanding the effects of aerosols and clouds has been a direct result of the new instrumentation that has come available in the last 15 years or so. This in turn makes for better climate models. Mind you, given the field I work in, I could be biased on that one. ;)

But there are other good examples of improvements in the mechanistic understandings producing big improvements in the model outputs. The current one grabbing all the attention is atmospheric couplings to ocean currents.
 
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That's the 'top-down' approach I was talking about. The 'bottom-up' approach is for the experimentalist to figure out something interesting first and the modeller to take it and work out the implications....

But there are other good examples of improvements in the mechanistic understandings producing big improvements in the model outputs. The current one grabbing all the attention is atmospheric couplings to ocean currents.
Hey, here is all you need to do to get credible models.

Separate those who hold the data from those who do modeling, institute double blind test runs, and have an indendent group with no vested interests evaluate results.

As is, your modelers have no credibility. That is of their own doing, isn't it? It is unfortunate that effectively this area of science acts to shore up propaganda about climate and taxation schemes.

Wait...I explained a simple and honest way to earn credibility ....

Oh, they don't like it?
 
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Hey, here is all you need to do to get credible models.

Separate those who hold the data from those who do modeling, institude double blind test runs, and have an indendant group with no vested interest evaluate results.

As is, your modelers have no credibility.

Watch it. Some of my friends are modellers.

All this stuff is available to the community (the funding agencies normally insist), so people are more than free to evaluate whatever they want independently. And it's not like there is an issue with 'vested interests'; finding a bona fide shortcoming in a model (of any description) is most definitely noteworthy and papers on exactly that topic crop up all the time. But the idea that you can completely separate the data from the models is crazy; as a bare minimum, the models still need data for stuff like initialisation conditions, emissions inventories and such like. But beyond that, separating the two sides would almost completely stifle development; without knowing where the shortfalls are, how would the modellers ever know what needs working on?

At a guess, you really have been spending way too much time on Climateaudit. It isn't a simple case of models 'working' or 'not working'; models are tools that are used to address specific scientific questions and they each have their own set of strengths and limitations. Whether they do the job or not is dependent on the right tool being applied in the right way to the specific problem you want it to solve. Wanting to know how much the planet as a whole will warm up over the course of decades is one thing. Wanting to know how much it will rain in Greece is another.
 
Watch it. Some of my friends are modellers....

At a guess, you really have been spending way too much time on Climateaudit. ....

I only indicated a path toward credibility. And noted they wouldn't like it.

Watch it, yourself. What makes you think I have not written computer models ?
 
I only indicated a path toward credibility. And noted they wouldn't like it.

The scientific process is entirely credible, just not for you, McIntyre, et al. It has worked very well for a good long while, and will continue to do so.

The blunt truth is that nobody in the real world cares how credible McIntyre finds them. They are not going to reorder standard scientific practice to win the wee squit over.

Watch it, yourself. What makes you think I have not written computer models ?

Are you claiming some level of expertise by that? (I modelled stud-poker strategies for my final-year project; at school we modelled betting strategies to beat the bookies.)
 
It is unfortunate that effectively this area of science acts to shore up propaganda about climate and taxation schemes.

Taxation. When cornered you always come back to that.

Wait...I explained a simple and honest way to earn credibility ....

You might gain some credibility when you stop regarding climate modelling as a world-wide scientific conspiracy to impose taxes.

Oh, they don't like it?

They don't hear you, and if they did they wouldn't care. You (and McIntyre) are to the real world as a tiny bloody dot on the windscreen is to a car-journey.

In the meantime, the Arctic melts and McIntyre still hasn't been invited to speak at next year's Copenhagen Conference. Scandalous, isn't it? irony
 

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