|
Post by FineWino on Apr 30, 2009 13:53:44 GMT
Like I said socold, what you have is belief, not understanding. And when you don't have the facts to support you, it's back to the dismissive arrogance toward the "skeptics." Spoken like a true zealot.
FWIW, I am a physicist. It's not that I don't understand the models, it's that I understand the limitations of them. I will admit I do not understand the details of a specific model (no one does except the modeler who wrote the code, and usually they only have a limited understanding of the pieces.)
Let's consider your nice pictures of the "model" of "average precipitation." It's relatively easy to "model" something that you already have the data for. It's called "fitting."
Did you know that for any bounded set of data, there exists an infinite number of solutions that will fit the data? That is a mathematical fact. The difference in the solutions is how they behave OUTSIDE the set of data they are designed to fit. What this means in the case of, say, the IPCC climate models is that they have only considered a few of the possible solutions, and they chose only those for which CO2 is the primary forcing agent.
They effectively fit the data to a bounded data set (recent past history) but they have failed to predict the future behavior. Their solutions "fit" the bounded set of data but outside that bounded set their solutions do not match the behavior of temperature. There is probably an infinite number of ways to say that their models fail to predict the behavior of the climate!
Getting back to your presentation of "average precipitation" data, ask for a map of what the precipitation will be in the period of 01JAN2014 through 31MAR2014, and if they are willing to do it, it will come with lots of disclaimers.
Monitor the 10 day weather forecast for your locale over a period of 2 weeks. Does it stay the same from day to day? I'd bet it doesn't; I live in an area with some of the mildest, most stable weather in the U.S. this time of year and the forecast is never that static.
There is a big difference between being able to model past or average behavior of climate, and being able to predict it, and therein lies the rub.
You believe in AGW because you have some need to believe it, just as others of your ilk.
|
|
|
Post by socold on Apr 30, 2009 15:25:42 GMT
Like I said socold, what you have is belief, not understanding. And when you don't have the facts to support you, it's back to the dismissive arrogance toward the "skeptics." Spoken like a true zealot. When people are right and they know they are right they often sound arrogant. It's a sad fact, but not one I am overly concerned about. I do not understand the detailed physics governing the atmosphere or ocean. True. But I do not see why that is relevant. It's complicated and takes years of study of that specific field to understand. What I disagree with is your argument that if someone doesn't understand the cutting edge math in a theory, therefore acceptance of such a theory is faith based. I disagree that my acceptance of General Relativity is faith based for example, despite the fact that I don't understand the detailed math underlying it. Am I wrong to accept general relativity when I haven't spent years studying it? Am I wrong to accept it when I only know the high level overview of why it's accepted by so many experts? Is that really faith? I would say it's more "trust based". I trust that there isn't a conspiracy of scientists internationally fabricating the evidence behind it. If someone were to accuse me of being a "general relativist" and demand me to provide "evidence of general relativity" and "equations that it's based on" and I linked to a website with equations, but one which I didn't understand, would it be acceptable for the challenger to then turn around and accuse me of having a faith based belief in general relativity? I don't think anyone on this board understands the maths involved. You first claimed climate models don't come close to duplicating the actual behavior of a system. Now you are claiming they do because scientists can easily fit them to do so. Which is it? Can models reproduce annual average rainfall patterns, seasonal temperature variations, daily temperature variations, etc fairly well or not? Obviously it isn't as clear cut as saying climate models are useless and can't get anything right. The constraints of physics narrow down the number of valid solutions enormously. They include the physics as best as it is understood. The strong warming from co2 is a result of current knowledge of physics. There have been decades to show a different result if one could be obtained. Noone has managed it. That remains to be seen obviously. And future observations are just one constraint of models, current observations are another. Predictions can be done futher out when the statistic being predicted is bigger both spacially and temporaly. For example it's a lot easier (ie to get it closer) to predict average global temperature next year than to predict average temperature in New York in 8 days time. I would have to dismiss current human understanding of physics to dismiss AGW.
|
|
|
Post by jimcripwell on Apr 30, 2009 15:47:48 GMT
There is another problem with Myhre et al, which struck me the first time I read it. What happens when you halve the concentration of CO2? Does the same numerical relationship hold? If it does not, why not? And if it does, when does the relationship break down, since such a realtionship cannot go down to a concentration of zero? What I would like to see is the greenhouse effectiveness of CO2, aka radiative forcing, plotted for the full range of CO2 values; from zero to the current levels. When that graph is available, maybe we can start to understand what the numerical values of RF mean.
|
|
|
Post by socold on Apr 30, 2009 16:18:37 GMT
As a rough estimate you can change the co2 value here and the difference in the "Iout, W / m2" figure is the radiaive forcing. geosci.uchicago.edu/~archer/cgimodels/radiation.html0ppm to 1ppm = 4.6wm-2 forcing 1ppm to 2ppm = 1.9wm-2 forcing 2ppm to 4ppm = 2.4wm-2 forcing 4ppm to 8ppm = 3wm-2 forcing 8ppm to 16ppm = 3.4wm-2 forcing 16ppm to 32ppm = 3.5wm-2 forcing 32ppm to 64ppm = 3.5wm-2 forcing 64ppm to 128ppm = 3.4wm-2 forcing 128ppm to 256ppm = 3.2wm-2 forcing 256ppm to 512ppm = 3.2wm-2 forcing
|
|
|
Post by jimcripwell on Apr 30, 2009 18:22:33 GMT
Thanks socold.
|
|
|
Post by steve on May 1, 2009 15:54:19 GMT
steve. Let me put our discussion into a wider historical perspective. I am, to some extent, speculating what happened. When the IPCC was formed, it was given a mandate to scientificly prove that CO2 was evil. They were faced with an insuperable problem, that they could never get any experimental data, because you cannot do experiments on the earth's atmosphere. They hunted around the literature, and found the concept of radiative forcing (RF) which was thought up over 100 years ago. The people who thought up RF almost immediatley abandoned the concept for two reasons. First it can never be measured, and second any numerical value can only be arrived at by so oversimplifying how the atmosphere works, that any numbers are completley meaningless. The IPCC could not, of course, do anything about experiements, but with the advent of high speed digital computers they could give the appearance that they had modelled the stmosphere well enough that numerical values of RF had a meaning. This is where Myhre came in. He found the radiative transfer models, and realized that he could pretend that they gave a proper value for RF. There were, at the time, no climate skeptics so the 1998 paper got into the literature. It has been quoted and requoted so many times that the numbers now appear to be written on tablets of stone. But any proper examination of the 1998 paper shows that it is basically scientific garbage. What I am trying to do is to turn a lot of scientific searchlights onto Myhre et al 1998 to show just how bad it is. Finally, after many years, people with appointments before their name, and initials after their name are starting to state the obvious in connection with AGW. I'm not sure I appreciate your historical context. Also, you are again referring to "smoke and mirrors". Were we not getting anywhere in tying down the fact that RF is separate from changes in convection etc? Myrhe's calculation is not a prerequisite for the projections of warming. Myrhe's calculation is in a 1998 paper - issued after the first two IPCC reports. Myhre's calculation is just a further refinement, and is a validation of three pre-existing radiation models. The radiative forcing is indeed just a metric. But the metric turns out to be a useful number to remember because along with the sensitivity number it reasonably accurately characterises the behaviour of the models to a range of different forcings. I would bet though that if you took at face value the forcing values for very low CO2 concentrations or very high CO2 concentrations, you'd eventually go way out of sample, and the model sensitivity figures (such as they are) would be radically different.
|
|
|
Post by donmartin on May 2, 2009 6:12:05 GMT
"The great tragedy of science: the slaying of a beautiful hypothesis by
an ugly fact."
---Thomas H. Huxley
|
|
van
Level 2 Rank
Posts: 59
|
Post by van on May 2, 2009 22:08:26 GMT
SOCOLD The model program you link to is just that a program model. I don't have any idea of how accurate the assumed forcings are for CO2 ( rough guess is an order of magnitude to high). However even with this program it shows a forcing for CO2 @1000 ppm and 0% humidity of 46.786 W/m2. However at 50% humidity its only 9.608 W/m2 over a 0% CO2 and 50% humidity atmosphere with a total forcing of 198.542 W/m2.
Oh yeh and the diff between 400 and 1000 ppm and 50% humidity is only .659 W/m2.
|
|
|
Post by socold on May 3, 2009 13:51:25 GMT
If you want 50% as much water vapor in the atmosphere then enter 0.5 in the water vapor box. Entering 50 will give you 5000%.
|
|
van
Level 2 Rank
Posts: 59
|
Post by van on May 3, 2009 16:55:56 GMT
SOCOLD
Then according to this model CO2 has between 2 and 20 times the forcing of water vapor depending on percent of water assumed at 100% humidity (1 to 4%).
|
|