There's also the fact that we have had a pretty solid grasp about the chemical reactions of greenhouse gases since long before computers, both theoretically and empirically, and we know roughly how much is put out in the atmosphere.
Where the models diverge is on far finer points than what is needed to make the basic policy changes that seems to be where we are stuck right now.
Entertaining[0] badpun’s skepticism, it is not necessary they have identical bugs, only that their bugs yield similarly biased results.
For example, if a significant number of bugs are identified by their affect on the results, then bugs contributing to “wrong” results might be more likely to be identified and fixed.
[0] In the Aristotelian sense.
If global warming is actually wrong, it's most likely because of bad/corrupt data in the datasets used.
http://www.realclimate.org/index.php/archives/2009/11/the-cr... http://www.realclimate.org/index.php/archives/2009/11/the-cr...
Since it was sound science in 2009, why would it not be now?
this will be the least of our problems. we understand very little about nature to have any reasonable prediction model. we don't know the inflection point which will cause massive collapse of major ecosystems we depend on.
all we know is that things are changing fast. faster than many non-human organisms are able to adapt.
Are you talking about predictions (and not measurements, you don't need a model to measure temperature)? Assuming you are, there's unfortunately a huge problem with modeling (and heavy math and stats-based science in general), in that researchers tend to stop looking for bugs in the model when it returns the results that they expect. In other words, if a bug in the model tells the researcher that Earth's temperature will decrease by 4 C by 2100, he will look over the model until he finds the bug, but if the model tells him that the temperature will increase by 2 C, thus confirming his inner bias, he'll declare it correct and move on to writing a paper based on the "finding".
Alternatively, as a thought experiment, imagine if math research were done in the way climate science is done. We would have proofs that are millions of pages long and were never verified by anyone. We would trust in them only because the author says that they are correct. Is this science?
>>Researchers tend to stop looking for bugs in the model when it returns the results that they expect.
Again... they are _ALL_ biaised ?
Also, how many independent, comprehensive models (with codebases) for global climate change are there in the science world?
False. Lots of past climate changes were due to changes on Earth and its atmosphere (and sometimes, specifically, life on Earth), not changes in solar output (e.g., notably, the Huronian glaciation believed to have resulted from the Great Oxygenation Event, which resulted from the exponential growth of photosynthetic life.)
That the post-industrial rise in atmospheric CO2 is of anthropogenic origin is hopefully not a point of dispute, but it is demonstrable if necessary. Thus it remains to show that this must raise the equilibrium temperature. So, as you say, CO2 selectively absorbs outgoing IR. In the lower atmosphere, this actually does not have as much of an effect as you might think. Water vapor blocks quite a bit of the absorption spectrum, and the effect of CO2 is more-or-less saturated already.
The mean free path of an outgoing IR photon in the lower atmosphere is quite short. Absorbed photons are re-emitted in a random direction, but take an overall upward course, the mean free path rising with altitude. At the (radiative) top-of-atmosphere, the mean free path is infinite: the photon is more likely to leave Earth. At the edge of space, there is essentially no water vapor, so the action of CO2 is greater. The effect of increasing the amount of CO2 in the atmosphere is to push the CO2-dense region of the atmosphere further out into space. Photons must take a longer path out of the atmosphere, and this must raise the overall temperature of the Earth proportionally, specifically by 3.7 W/m^2 per doubling of CO2, which is commonly held to be equivalent to 1 degree C of global temperature. This must be the case unless our understanding of thermodynamics is very wrong (and if you have an issue with thermodynamics then you have some pretty serious issues).
So, one degree C ain't so bad, right? Well, it wouldn't be if that were it. However, there are several problematic feedbacks. One is that melting a lot of ice lowers the Earth's albedo, which causes it to absorb more heat. Another issue is that there is a lot of this "water" stuff around, which is very readily absorbed by the atmosphere, in a manner that increases very sharply with temperature. Water vapor is a much better greenhouse gas than CO2 by all accounts.
Climate science is not an extrapolation from the temperature record. There is a solid minimum bound on the temperature effects of doubling atmospheric CO2, and a variety of amplifying positive feedback effects. So far, in the last twelve decades, we have not managed to find anything which would reduce those effects to something manageable. At this point, the effect would need to be both very large, in order to offset the strong H2O feedbacks, and very small, to not have been noticed. The most plausible option would be "something poorly understood about the H2O feedbacks". I believe the most successful of such theories would be Dr. Richard Lindzen's Iris Hypothesis, which has generally failed to find support. At this point, there are no particularly plausible mechanisms which would transfer this extra energy to space, and if those did exist, then they would not necessarily be a non-issue: even if thermodynamics and optics are entirely wrong, the planet is warming, and we will have to deal with that even if it can't be prevented.
If you have any other questions, or would like citations for any of the above, do feel free to ask.
- https://en.wikipedia.org/wiki/Milankovitch_cycles
- http://www.indiana.edu/~geol105/images/gaia_chapter_4/milankovitch.htm
Interestingly, the original paper proposing AGW (in 1896) was actually intended to explain Ice Ages:
- http://www.rsc.org/images/Arrhenius1896_tcm18-173546.pdfThere could be lots of reasons for that. Anyway, the temperatures were not always rising, clear temperatures rise was observed in 1930-40s and in 1980-90s. Cooling in 1960-1970s. And yes, prediction has to be precise. If a model predicts rise of 3K in 100years, and you measure 1.3K - your model is wrong. It's even more wrong if you don't take into account any of the natural cycles, even if prediction is accidentally correct.
I think at this point the group think is so strong that if anyone came out with evidence against global warming they would commit career suicide by publishing it
Oh good. So you'd rather see millions of people be displaced. We're fucked as a species when "I'm a contrarian" is a valid reason to disagree.
> they would commit career suicide by publishing it
Meanwhile some researches create their career by publishing this sort of stuff, even when it isn't well researched
The bugs, if serious enough, can make it not scientific, in the same way that a paper that has grave errors in it is wrong and thus obviously not scientfic. So, before we do a thorough review of the code, we should treat it with due scepticism.
EDIT: this can also be expressed in terms of risk analysis. For typical software, the consequence of a bug is low - most software is commercial, so the impact of the bug will be limited to that company's bottom line (with exceptions of software that can kill, but these people already are serious about bugs). Also, most bugs in most software are either highly evident (the button does not work, you get random segfaults etc.) or have limited impact.
On the other hand, the bugs in climate change models, given their "pipeline" nature (wrong result from one module is propagated downstream all the way to the final prediction of expected temperature change), can quite often have severe impact. They can also be not evident at all - they can for example change the final outcome predicition by 1 C.
Compound that with the fact these predictions are used to make trillion dollar decisions on global policies, and you can see that the actual damage done by bugs is not unlikely to be in the trillions. And that's why I say it's probably wise to subject the models to extreme scrutiny.