(Updated August 11, 2010 to include added sources; for further updates and related material, see Publications, particularly #s1 and 4, and this 2015 post.)
In a previous post I explained why I believe that the United Nations GHG hypothesis that significant global warming will occur as a result of increasing greenhouse gas (such as CO2) levels is implausible. In this post I will explain why I believe that the best available evidence indicates that the hypothesis is not just implausible but rather should and can be rejected on scientific grounds.
For a broader view of how science progresses see here. Clearly I am in the Popper camp in this regard. Kuhn’s view may more accurately describe how science has unfortunately sometimes been historically conducted, but certainly not how it should be.
Before going further, it is important to explain that the important word in the definition of the UN GHG hypothesis is “significant.” There is little doubt that higher levels of greenhouse gases are likely to lead to slightly higher global temperatures since that is why they are called greenhouse gases. The United Nations, however, claims that increases in the levels of these gases in the atmosphere are the predominant influence on global temperatures. Hence the qualification “significant” in order to include the UN claims while excluding the minor warming that has probably been caused by increasing levels of greenhouse gases in the atmosphere.
As explained in my Comments, the models relied on so heavily by the UN do not prove anything scientifically one way or the other. They simply show what the model builders believe would happen if the hypothesis and all their other assumptions were correct. The model results are interesting, perhaps even useful, but irrelevant in deciding whether their AGW hypothesis should be accepted or rejected. This is because they do not compare the implications of the hypothesis with real world data other than past temperature data which the models have been modified to emulate.
The Critical Role Played by the Scientific Method
According to the scientific method, a scientific hypothesis must be tested by comparing real world data with the implications of the hypothesis. This is how Albert Einstein was able to persuade the world that his ideas on relativity had merit. Scientists kept proposing real world tests of his hypothesis but each test confirmed its validity. After a number of these tests, the opposition conceded that his hypothesis was valid. (For a description of this extended process see, for example, Jeffrey Crelinsten’s Einstein’s Jury: The Race to Test Relativity). A similar process resulted in the acceptance of Newtonian mechanics and other hypotheses which gradually assumed the status of theories.
If the comparison with real world data does not confirm the hypothesis, the hypothesis should be rejected. There are only two alternatives from a scientific viewpoint when this happens: Discard or at least modify the hypothesis or discover an error in the data used to reject it. From a scientific viewpoint, it is totally irrelevant how many public officials or scientific organizations–or how prominent they may be–support a particular hypothesis. A hypothesis has scientific validity only by comparison with real world data. Joanne Nova has expressed this very well in her Handbook downloaded here.
There are numerous inconsistencies between the UN CO2 hypothesis and observed data. Gregory explicitly compares the explanatory power of the UN hypothesis with the competing Svensmark hypothesis and finds the UN hypothesis wanting. Idso and Singer provide extensive scientific evidence against the UN hypothesis.
But perhaps the most fundamental comparisons are between the major physical effects of the UN hypothesis and available real world data. There are four particularly telling physically-based basic comparisons in this regard. According to the scientific method an inconsistency even in one of these comparisons means that the hypothesis should be rejected from a scientific viewpoint. It is important to deal with the uncertainty introduced by the word “significant,” however. This uncertainty increases the likelihood that a few of the comparisons may prove positive. Hence it increases the strength of any negative finding. In fact, if a number of tests should prove negative it makes the tests very powerful evidence against the hypothesis.
Four Critical Comparisons with Real World Data
Acceptance of the hypothesis requires that each of the following four observations are present:
- There is a hot spot in the upper troposphere in the tropics as predicted by the UN. If greenhouse gases are significantly warming the Earth the first signs of it are supposed to appear about 10 kilometers above the tropics. The lack of such a hotspot is discussed in my Comments in Section 2.9 as well as by Joanne Nova downloaded here. She discusses the major objections that have been raised to this comparison and why she believes they are not credible. For a further update see here. For more detailed information see here.
- There is heating of the oceans. The added heat generated by increasing greenhouse gas levels in the atmosphere must be stored somewhere. It has not been showing up in the atmosphere in the last decade, so if the hypothesis is valid it must be going into the oceans. But in the last few years this has not been the case. An extensive discussion of the evidence can be found here. The bottom line is that the AGW hypothesis fails this test as well.
- The observed outgoing radiation fluxes from the Earth decrease with increases in sea surface temperatures. Satellite data, however, shows an increase, which is inconsistent with the high climate sensitivities to increases in CO2 and positive feedback so crucial to the UN’s case. A new peer reviewed paper accepted for publication on this subject can be found here. For a video of Christopher Monckton’s presentation on this study on the Glenn Beck program see here. For a more up-to-date but considerably more technical presentation, see starting at slide 28 here or in the corresponding Richard Lidzen video found here.
- The atmospheric response times for volcanic sequences would be longer than they would be without the UN hypothesis. If climate sensitivity is as high as the UN claims, it should show up in the atmosphere’s response time from volcanic eruptions. The reason for this is that climate sensitivity is also a measure of how tightly air and sea temperatures are coupled. High sensitivity is associated with weak coupling, allowing the establishment of significant disequilibration of the sea surface temperature. A discussion of this can be found in a 1997 report from the National Academy of Sciences here. The discussion may be a little technical, but the conclusion that the data “is consistent with low [climate] sensitivity,” which is inconsistent with one of the UN’s crucial conclusions, is clear.
The conclusions are the same in each of these four cases: The UN hypothesis is not supported or even partially supported by these comparisons with real world data. As Professor Richard Lindzen of MIT has recently observed with regard to his findings on comparison 3 above, “In a normal field, these results would pretty much wrap things up, but global warming/climate change has developed so much momentum that it has a life of its own – quite removed from science.”
The data are far from perfect, of course, perhaps in part because of a lack of effort to gather it. But they all tell the same story. This means that the hypothesis should be rejected scientifically based on current information. Future testing could lead to other conclusions, of course, but for now rejection is the rational course of action.
Implications
Accordingly, using this hypothesis has no scientific basis based on current knowledge concerning these four comparisons. Attempts to argue that it is anything more than a religious or superstitious belief must show that the data used in each and every one of these tests (as well as others that may be proposed in the future) is wrong.
Accordingly, using the UN hypothesis as a basis for formulating policy is not useful or relevant from a scientific viewpoint. Attempts to do so are likely to lead to scientifically unsound policy. Given that the current proposed “solution”–radically reducing CO2 emissions–would cost many tens of trillions of dollars, it is particularly incumbent on those advocating this very large expenditure (for which there are many other uses if it should actually become available) to show that their solution should not also be rejected since it is based on a hypothesis that should be rejected.
The UN reports issued to date do not show that the data used in these four important comparisons is incorrect, and therefore the reports should not be used as a basis for policy in my view. Reports substantially based on the UN reports, such as the draft EPA Endangerment Technical Support Document reviewed in my Comments, should also not be used for policy purposes for the same reason in my view.
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Why should the UN GHG hypothesis be rejected on scientific grounds? Because this hypothesis is not falsifiable. As it is not falsifiable, it is not “scientific,” as “scientific” is described by the philosopher of science Karl Popper.
The UN’s hypothesis references a set of climate models. Each such model outputs a mathematical function which maps the time to the global average temperature. The UN’s Intergovernmental Panel on Climate Change (IPCC) calls such a function a “projection.”
A projection may be compared to the associated instrument readings. However, such a comparison does not render a projection or any subset of the elements of this projection true or false. Something is missing in a projection that is needed for determination of whether the associated model is a valid or invalid one.
The missing ingredient is the set of predictions that are made by the model. A prediction is a proposition which states the projected outcome of a statistical event. In such an event, a prediction is made, a specified period of time elapses and the outcome occurs. If the real outcome differs from the predicted one and the real outcome is observed, the associated model is falsified. That none of the IPCC’s models issue predictions (as the IPCC itself states in its 2007 report) signifies that none of these models are falsifiable. Thus, according to Popper, none of these models are scientific models.
As a basis for policy making, a model that is “scientific” under Popper’s definition of the term, has the merit of providing information about its own reliability. A non-scientific model has the demerit of providing none of this information.
In its “endangerment” finding of today (Dec. 7, 2009), the Obama administration reveals that the reliability of the models underlying the case for regulation is of no interest to it. Though it may lead to the loss of hundreds of trillions of dollars to the citizens of the U.S., this apparently is of no interest to Obama and his cabinet.
Alan
Your article should be required reading for all in Congress. Nicely done.
Now that Climategate has pulled back the curtain, we are grateful that there is growing recognition that AGW is unproven.
[…] UN hypothesis that increases in GHGs/CO2 will result in significant increases in global temperatures has not been confirmed by comparisons with real world data. Unless it is, attempts to decrease GHG/CO2 emissions in order to significantly change global […]
After years of studying climate science and lately delving into highly technical blogs that debate back and forth each paper on global warming that comes out (who I call the “Bicker Brigade”), I have finally come to a conclusion and have created a “worth a thousand words” picture to fully express it. I present The Central England Don’t Panic Yet Chart!
http://i37.tinypic.com/14t0abr.jpg
[…] the alleged adverse effects of the resulting increased emissions of CO2. As explained in an earlier post, the hypothesis that there is a significant relationship is not supported by current observations. […]
The biggest concern of all is that it is assumed against all historical evidence that global warming is bad for humanity. The little ice age led to famine, plague, and civil unrest. In comparison the Medieval Warm Period was a time of peace and prosperity.
The climate models have been falsified, yet many such as Vic above are ready to destroy the economy based on a dis-proven theories. Already children are dying of malnutrition in poor areas due to the biofuels projects raising the cost of food. One only needs to look at the sad recent history of Zimbabwe to see how bad government policy can destroy a people.
The worst evidence of all is how the Obama administration has stopped work on the nuclear waste site so that we cannot develop nuclear power that emits zero CO2. This shows that the current administration has no real concern about CO2, but is using it as an excuse to take over the economy.
Alan,
I have tested the AGW hypothis against the data that has been used to promote it and found it wanting in several critical areas. First, the changes in SST and and CO2 are closely correlated and is a natural process related to the differences in partial pressures in different regions of the ocean that function as sources and sinks. When this factor is included along with changing rates of anthropogenic emissions in a regression, the anthropogenics are not statistically significant. Second, the global uniformity of background levels of CO2 strongly suggests that the half-life of CO2 as a gas in the atmosphere is a matter of days rather than many years. The “smoking gun” of CO2-13 depletion based on burning fossil fuel is a toy compared to natural emmisions of organic sources in the oceans. Third, and most important, although CO2 levels have increased significantly in the last 50 years along with an increase in atmospheric moisture, only moisture has had a statistically significant affect on outbound longwave radiation. Yes, the globe is presently in a warming phase of a long term natural cycle that is not AGW. Read my presentation and draw your own conclusions. http://www.kidswincom.net/climate.pdf.
Fred
Vic,
it is admitted by the modelers that they do not “DO” clouds and precip well. In fact, their “projections” of precip are not useful except as further steps in learning HOW to model the climate. In other words, your comment misses the boat based on poor assumptions. The models do not match enough data points to be useful for what they are being forced to do, and that is PREDICTING climate.
As for “data points”, what if the models matched the temperature of the last 100 years EXACTLY but were almost exactly WRONG about precip, circulation, and ocean temps. Would you trust them going into the future?? This is close to reality.
Now, since the temp DATA POINTS have been adjusted to match the models, do you still think the models have any robust results??
Dan,
in regards to minor anomalies in Newtonian Physics, we have an increasing number including the decelleration of the 2 probes leaving the solar system and several of the probes that were put into slingshot orbits to reach the Jovians. The better we can measure, the more we get “out”, the more anomalies we will have to deal with. It would appear that CONSENSUS science has screwed the pooch again!!!!
Vic,
Your rather snarky response has accidentally hit upon a somewhat interesting question: In what situations are models appropriate instruments of scientific prediction?
Certainly we have no problems modelling the motions of the planets through the solar system. Newtonian physics even works pretty well for that, aside from a few anomalies like the orbit of Mercury. And if we want a greater level of precision, we can adjust our program to model relativistic effects.
Now, how do we know we can trust these models? Mainly, because we’ve been using the same one for decades without needing to make any modifications as a result of new data or observations. Show me a model that you haven’t had to adjust recently because of new observations, and I’ll show you a worthwhile model. But if you’re adjusting your model every year/month/day/hour to account for new observations, then maybe your model isn’t quite ready for prime time yet. And as to climate models, hardly a year goes by that we don’t uncover some kind of new data that requires further refinement of the model.
The point I’m using too many words to make: All else equal, the more frequently you have to tinker with your model to fit new facts, the less reliable it is. The longer the intervals between tinkerings, the more reliable it is.
I leave it to you to answer for yourself how reliable climate models are in light of the above.
“They simply show what the model builders believe would happen if the hypothesis and all their other assumptions were correct. […] they do not compare the implications of the hypothesis with real world data other than past temperature data which the models have been modified to emulate. ”
Ok, let me get this straight. The model incorporates the laws of nature as best we understand them, and any tuning parameters (diffusivities, material constants) have been tuned to make the model be correct for any observation available to us.
And how is this not science, in a Popperian sense or otherwise? It explains the past and predicts the future. Unfortunately, we can not wait for the future to see if missed something and the model is falsified. Given that the model is the best we can do, maybe we should gamble on its correctness for the future and act on that.
If you have a better model that is equally accurate for all data points seen so far show us. Oh wait, you’re an economist. You can’t even model the stuff that you’re supposed to be an expert on. Silly me, asking you to be correct about a different science.
Note by Alan Carlin: Economists build lots of models too, but do not claim that they represent scientific reality.