Just three years ago, the U.N. Intergovernmental Panel on Climate Change predicted that, by the end of this century, sea-levels will rise somewhere between 1.7 and 3.22 feet. A new report has found, however, that prediction may be off by some 3 feet. “Study jolts sea-rise predictions” the Washington Post headlined its article on the research.
And what a jolt it is, a full-blown challenge to “current consensus predictions.” After all, if the existing models used by climate scientists put the worst-case scenario at 3 feet of sea-rise, and those models are off by 3 feet, then doesn’t that mean the oceans may just stay put?
Of course not. Because if someone had presented research showing that computer models overestimate the consequences of climate change they would have been denounced or ignored. The new research, by contrast, asserts that climate models have been underestimating the dire effects. The “startling findings,” you see, “paint a far grimmer picture” than that drawn by the current consensus. And so, naturally, it has been praised and highlighted.
Suggest that global warming climate models are possibly faulty predictions and you are lambasted as an antiscience, know-nothing no-goodnik. Even to question the believability of the computer models is to bring an immediate charge of denialism. That is, unless one is asserting that the models have it all wrong because they’ve been insufficiently apocalyptic.
Still, it’s remarkable to discover that the much-vaunted scientific consensus on the effects of global warming can indeed be wrong—and, what’s even more remarkable, that the error can be gladly and openly acknowledged by the very same scientific community that has been telling us that their computer models are unassailable.
The key, as any career-savvy scientist knows, is to find the models to be wrong in a politically correct way. Predict less doom and you are a potentially criminal skeptic putting the world at risk; predict more gloom and your research is fêted on the front page.
It doesn’t take a computer model to predict the sort of scientific climate those incentives create.
