Posted on 04/04/2002 9:46:28 AM PST by Smile-n-Win
These changes are highly significant. The measured thermal radiative energy loss at the top of the tropical atmosphere, for example, is of the same magnitude as the thermal radiative energy gain that is generally predicted for an instantaneous doubling of the air's CO2 content. Yet as Hartman correctly notes, "only very small changes in average tropical surface temperature were observed during this time." So what went wrong? Or as we should probably more correctly phrase the question, what went right?
One thing, of course, was the competing change in solar radiation reception that was driven by changes in cloud cover, which allowed more solar radiation to reach the surface of the earth's tropical region and warm it. These changes were produced by what Chen et al. determined to be "a decadal-time-scale strengthening of the tropical Hadley and Walker circulations." Another helping-hand was likely provided by the past quarter-century's slowdown in the meridional overturning circulation of the upper 100 to 400 meters of the tropical Pacific Ocean, which was recently reported by McPhaden and Zhang (2002). This circulation slowdown also promotes tropical sea surface warming, by reducing the rate-of-supply of relatively colder water to the region of equatorial upwelling.
So what do all of these observations have to do with evaluating the ability of climate models to correctly predict the future? For one thing - and one very important thing - they provide several new phenomena for the models to replicate as a test of their ability to properly represent the real-world. In the words of McPhaden and Zhang, for example, the time-varying meridional overturning circulation of the upper Pacific Ocean provides "an important dynamical constraint for model studies that attempt to simulate recent observed decadal changes in the Pacific." If the climate models can't reconstruct this simple wind-driven circulation, in other words, why should we believe anything else they tell us.
In an eye-opening application of this principle, Wielicki et al. tested the ability of four state-of-the-art climate models and one weather assimilation model to reproduce the observed decadal changes in top-of-the-atmosphere thermal and solar radiative energy fluxes that occurred over the past two decades. And how did the models do?
The results were truly pathetic. No significant decadal variability was exhibited by any of the models; and they all failed to reproduce even the cyclical seasonal change in tropical albedo. The administrators of the test thus kindly concluded that "the missing variability in the models highlights the critical need to improve cloud modeling in the tropics so that prediction of tropical climate on interannual and decadal time scales can be improved."
Hartmann was a little more candid in his scoring of the test, saying it indicated "the models are deficient." Amplifying this assessment, he noted that "if the energy budget can vary substantially in the absence of obvious forcing," as it well did over the past two decades, "then the climate of earth has modes of variability that are not yet fully understood and cannot yet be accurately represented in climate models."
In conclusion, doesn't it seem strange that if (1) the energy budget of the planet can vary substantially in the absence of any obvious forcing, and if (2) earth's climate has modes of variability that are (a) not yet fully understood and (b) cannot yet be accurately represented in climate models - as these studies demonstrate is truly the case - the global political power brokers would not at least consider the possibility that the models upon which the Kyoto Protocol is based might not be painting an accurate picture of the future? It sure seems so to us. But, hey, that's politics, not science. And that about tells you all you need to know about the issue.
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The understatement of the decade. The models are lies to further their propaganda. The models are spun up to show outrageous amounts of warming when they know its not realistic. The whole basis of the global warming scare are these models, which assume the worst of the worst case and then assumes an exponential relationship to boot. Using exponential relationships is an easy way to scare people and has been done for hundreds of years.
I'd hazard a guess that a lot of Hartmann's research (if it's Dennis Hartmann of the University of Washington) is government-funded. A quick Web search indicates that he's been the recipient of research funds from both NOAA and NASA.
Baloney. John Christy of the University of Alabama - Huntsville, a noted skeptic and one of the best climate change skeptical scientists working today, has received DOE funding. And I offer him as but one example. His colleague, Roy Spencer, is employed by NASA and has received several NASA research awards.
And yesterday you sang their praises.
I looked around on their website, and it seems they're funded like FR: by donations from individuals and corporations.
Granted, this is still not entirely convincing; once it is established that the organization will not bend over backwards to prove the global warming hoax correct, the donors are likely to be ones who stand to profit from a refutation of that hoax. They might even threaten to withhold donations if the organization releases pro-warming data.
The only research I would believe without reservations would be research done by me, or by people I know well and trust.
That may be true, but he doesn't depend on government grants; the Center for the Study of Carbon Dioxide and Global Change is privately funded. He will still make a living if the government withholds its grants.
That doesn't mean I believe him blindly; see my post #13.
If I were a government-funded scientist and I saw in the course of my research that there was no human-induced global warming, I would not say there was any human-induced global warming. In other words, I would not lie.
The same may be true for a number of actual scientists; it may not be true for some others.
In terms of predictions? I don't remember that. I remember replying to someone where I said that they're better than they used to be (actually I said this to you yesterday regarding inclusion of the ocean, and to someone else today) which is an excellent example of "damning with faint praise". I do remember pointing out that one of the best climate scientists working, James Hansen, recently published predictions (based both on climate models and emissions scenarios) that reduced his estimate of global temperature increase for the next century. Hansen is also someone (as we discussed) that has indicated that the predictive ability of climate models is really poor.
The context of the question you asked (if I remember correctly now) was the ability of models to simulate past climate patterns using proper input data. I know that I provided one of the GISS "Science Brief" Web sites, "Forcing and Chaos in Interannual to Decadal Climate Change" to show that this ability is much better than you indicated. That doesn't mean the models are any better at predicting the future.
I just made a model that perfectly simulates this years NCAA basketball tournment. It will work about as well as the climate models do when it comes time to predicting next years results.
Chuckle. The difference between modeling the past and predicting the future is that you know the magnitude of the inputs for the past. All of these models are GCMs (global circulation models). The inputs are things like a certain amount of solar energy, the concentration of greenhouse gases in the atmosphere, the ocean surface temperature, etc. The problem with predictions is that you can't accurately specify the inputs. Tomorrow the sun could start a gradual increase in radiation amounting to a 2% increase over 20 years, and it would be very difficult to detect it. But it would have a significant global effect.
So if you have a model that does a good job of simulating the past, it might be able to do a good job of predicting the future provided that the necessary guesses about the future value of input variables are close to what actually ends up happening. That's why the people who know about climate models say that they are so incapable of predicting the future -- the number of variables is too great to allow sufficient accuracy in the estimation of their future value.
With respect to your NCAA tournament model, you could "predict" that a team with a solid inside man, a star scorer who gets better during the tournament than his season average, a defensive specialist, and a bench with scoring from the 6-7-8 men would likely do well. You could even predict what teams appear to have those "parts". But you can't predict things like injuries, off nights, and a star player that can hit a 3-pointer but can't make a free throw in a clutch situation. (No, I didn't have money on Duke, but if I did...) So your model would have some predictive value, but probably not enough to risk big money on it. Likewise for climate models!
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