Try searching the headlines of Anthony Watts’ WUWT blog for the words “alarmist” or “alarmism” over the past year and you will get 17 hits. The words are never clearly defined but imply that climate scientist claims are exaggerated and thus unworthy of merit.
Restated, the question is whether the scientific community, for which the Intergovernmental Panel on Climate Change (IPCC) can be used as a proxy, has made exaggerated predictions. For a full answer to this question, please read the original article or the Skeptical Science post. In summary, the article’s conclusion is that the IPCC has been too cautious in its predictions with respect to sea level rise, Arctic sea ice extent retreat, snow cover reduction, the rise in carbon emissions and ice sheet melt. Hurricane intensity and global mean temperature rise have been in line with predictions.
The most interesting part of the article deals with why scientists have had a tendency to be conservative in their predictions—what the study calls to ‘err on the side of least drama (ESLD)’. In their words:
No doubt the issues addressed in these studies are complex, and there are several possible reasons that could explain the outcomes in any one particular case (e.g., the inherent nonlinearity of the climate system may cause current predictions to lag systematically behind the behavior of the continuously forced system). Still, it is striking that, from the available cases where scientists have done post hoc analyses of prior IPCC projections, a pattern emerges: one of under- rather than over- prediction.
James Hansen of NASA’s take on this topic in his book “Storms of My Grandchildren” is also referenced:
Scientists’ fear of ‘crying wolf’ is more immediate than their fear of ‘fiddling while Rome burns’ .
More formally, the Brysse paper suggests that the conservatism is a function of how scientists weight statistical errors:
A version of erring on the side of least drama can be found in what statisticians call Type 1 and Type 2 errors. As most scientists know, a Type 1 error involves thinking an effect is real when it is not; a Type 2 error means missing effects that are actually there. Making a Type 1 error can be thought of as being naıve, credulous, or gullible; making a Type 2 error can be interpreted as being excessively skeptical or overly cautious. Interestingly, conventional statistics is set up to be deeply skeptical and to avoid Type 1 errors, by placing a very high statistical bar on claims for statistical significance. The use of a 95% or even 99% confidence limit in many scientific experiments reflects a scientific worldview in which skepticism is a virtue and credulity is not. In fact, some statisticians claim that Type 2 errors aren’t really errors at all, just missed opportunities.
Note that in the financial markets the arbitrary adoption of 95% or 99% confidence limits would lead to guaranteed bankruptcy. Other investors prepared to accept lesser degrees of certainty would perpetually front run you. By the time you had secured sufficient information to give you that 95% plus confidence, the stock would have moved and the trade would have gone. In short, when you invest under uncertainty, the market equally weights Type 1 errors and Type 2 errors. Over the long haul, the penalty from the missed trade is the same as the penalty from the failed trade.
However, when it comes to climate change science, we are in a market for reputational risk. Type 2 errors—making claims that later turn out to be incorrect—are far the more costly. But in the wider world of decision-making under uncertainty this paradigm doesn’t hold. A failure to react at a lot lower level of probability will be punished.
Let’s take an example. Speculator A owns a beachfront property. She is quite sanguine over this investment as long as sea level rise is constrained to 3 mm per annum through 2040. Nonetheless, Speculator A would sell the property if sea level rise rose to 5 mm per annum as the beachfront would be under threat of inundation. Having a dislike for Type 2 errors, Speculator A decides only to sell should she be 95% confident that sea level rise has accelerated from 3 mm to 5 mm.
At the same time, Speculator B, being a more sophisticated student of uncertainty, is perfectly happy to make the sell decision at much lower levels of likelihood down to 60%.
A couple of years go by and the science progresses. A literature review suggests there is a 60% chance that sea level rise will jump to 5 mm per annum over the next two decades. While A holds on to the investment, B sells and the price of beachfront property falls. Much later, the probability of the higher rate of sea level rise has reached 95%, but by then it is too late for Speculator A. The market has already discounted the risk and beachfront property prices have collapsed. Of course, the bet could have gone Speculator A’s way, but—and this is key—the odds suggest it wouldn’t have.
In sum, the opportunity to adapt to climate change favours those who move early rather than those who wait. By the time the scientific community rings a bell saying it is time to act, it will be very costly to respond.