Separating the roles of theory and direct empirical evidence in belief formation: the examples of minimum wage and anthropogenic global warming
I recently asked two questions on Quora with similar question structures, and the similarities and differences between the responses were interesting.
Question #1: Anthropogenic global warming, the greenhouse effect, and the historical weather record
I asked the question here. Question statement:
If you believe in Anthropogenic Global Warming (AGW), to what extent is your belief informed by the theory of the greenhouse effect, and to what extent is it informed by the historical temperature record?
In response to some comments, I added the following question details:
Due to length limitations, the main question is a bit simplistically framed. But what I’m really asking for is the relative importance of theoretical mechanisms and direct empirical evidence. Theoretical mechanisms are of course also empirically validated, but the empirical validation could occur in different settings.
For instance, the greenhouse effect is a mechanism, and one may get estimates of the strength of the greenhouse effect based on an understanding of the underlying physics or by doing laboratory experiments or simulations.
Direct empirical evidence is evidence that is as close to the situation we are trying to predict as possible. In this case, it would involve looking at the historical records of temperature and carbon dioxide concentrations, and perhaps some other confounding variables whose role needs to be controlled for (such as solar activity).
Saying that your belief is largely grounded in direct empirical evidence is basically saying that just looking at the time series of temperature, carbon dioxide concentrations and the other variables can allow one to say with fairly high confidence (starting from very weak priors) that increased carbon dioxide concentrations, due to human activity, are responsible for temperature increases. In other words, if you ran a regression and tried to do the usual tricks to infer causality, carbon dioxide would come out as the culprit.
Saying that your belief is largely grounded in theory is basically saying that the science of the greenhouse effect is sufficiently convincing that the historical temperature and weather record isn’t an important factor in influencing your belief: if it had come out differently, you’d probably just have thought the data was noisy or wrong and wouldn’t update away from believing in the AGW thesis.
I also posted to Facebook here asking my friends about the pushback to my use of the term “belief” in my question.
Question #2: Effect of increase in the minimum wage on unemployment
I asked the question here. Question statement:
If you believe that raising the minimum wage is likely to increase unemployment, to what extent is your belief informed by the theory of supply and demand and to what extent is it informed by direct empirical evidence?
I added the following question details:
By “direct empirical evidence” I am referring to empirical evidence that directly pertains to the relation between minimum wage raises and employment level changes, not empirical evidence that supports the theory of supply and demand in general (because transferring that to the minimum wage context would require one to believe the transferability of the theory).
Also, when I say “believe that raising the minimum wage is likely to increase unemployment” I am talking about minimum wage increases of the sort often considered in legislative measures, and by “likely” I just mean that it’s something that should always be seriously considered whenever a proposal to raise the minimum wage is made. The belief would be consistent with believing that in some cases minimum wage raises have no employment effects.
I also posted the question to Facebook here.
Similarities between the questions
The questions are structurally similar, and belong to a general question type of considerable interest to the LessWrong audience. The common features to the questions:
In both cases, there is a theory (the greenhouse effect for Question #1, and supply and demand for Question #2) that is foundational to the domain and is supported through a wide range of lines of evidence.
In both cases, the quantitative specifics of the extent to which the theory applies in the particular context are not clear. There are prima facie plausible arguments that other factors may cancel out the effect and there are arguments for many different effect sizes.
In both cases, people who study the broad subject (climate scientists for Question #1, economists for Question #2) are more favorably disposed to the belief than people who do not study the broad subject.
In both cases, a significant part of the strength of belief of subject matter experts seems to be their belief in the theory. The data, while consistent with the theory, does not seem to paint a strong picture in isolation. For the minimum wage, consider the Card and Krueger study. Bryan Caplan discusses how Bayesian reasoning with strong theoretical priors can lead one to continue believing that minimum wage increases cause unemployment to rise, without addressing Card and Krueger at the object level. For the case of anthropogenic global warming, consider the draft by Kesten C. Green (addressing whether a warming-based forecast has higher forecast accuracy than a no-change forecast) or the paper AGW doesn’t cointegrate by Beenstock, Reingewertz, and Paldor (addressing whether, looking at the data alone, we can get good evidence that carbon dioxide concentration increases are linked with temperature increases).
In both cases, outsiders to the domain, who nonetheless have expertise in other areas that one might expect gives them insight into the question, are often more skeptical of the belief. A number of weather forecasters, physicists, and forecasting experts are skeptical of long-range climate forecasting or confident assertions about anthropogenic global warming. A number of sociologists, lawyers, and politicians often are disparaging of the belief that minimum wage increases cause unemployment levels to rise. The criticism is similar: namely, that a basically correct theory is being overstretched or incorrectly applied to a situation that is too complex, is similar.
In both cases, the debate is somewhat politically charged, largely because one’s beliefs here affect one’s views of proposed legislation (climate change mitigation legislation and minimum wage increase legislation). The anthropogenic global warming belief is more commonly associated with environmentalists, social democrats, and progressives, and (in the United States) with Democrats, whereas opposition to it is more common among conservatives and libertarians. The minimum wage belief is more commonly associated with free market views and (in the United States) with conservatives and Republicans, and opposition to it is more common among progressives and social democrats.
Looking for help
I’m interested in thoughts from the people here on these questions:
Thoughts on the specifics of Question #1 and Question #2.
Other possible questions in the same reference class (where a belief arises from a mix of theory and data, and the theory plays a fairly big role in driving the belief, while the data on its own is very ambiguous).
Other similarities between Question #1 and Question #2.
Ways that Question #1 and Question #2 are disanalogous.
General thoughts on how this relates to Bayesian reasoning and other modes of belief formation based on a combination of theory and data.
- Downvote stalkers: Driving members away from the LessWrong community? by (2 Jul 2014 0:40 UTC; 58 points)
- Carbon dioxide, climate sensitivity, feedbacks, and the historical record: a cursory examination of the Anthropogenic Global Warming (AGW) hypothesis by (8 Jul 2014 1:58 UTC; 4 points)
Show me someone who makes predictions of the future by “just looking at the data,” and I’ll show you someone who’s using a theory but not admitting it.
Yeah, in the AGW case it sounds like the question’s more like “to what extent is your belief the result of climate models, and to what extent is it the result of a linear regression model?”
Theory also influences what data you consider in the first place. (Are you looking at your own local weather, global surface temperatures, stratospheric temperatures, ocean temperatures, extreme weather events, Martian climate, polar ice, or the beliefs and behavior of climatologists, and over what time scales and eras?) See also philosophy of science since at least Kuhn on theory-laden observation: http://plato.stanford.edu/entries/science-theory-observation/
The difference should be framed as: are you using a theory developed by fitting known data, or a theory developed from first principles?
I strongly disagree. “Fitting” data is not a theory-neutral process. As khafra points out, if you just have two time series, you can do linear regression to see if they seem correlated, and make predictions based off that. But for this to work requires lots of assumptions—one might even call it a ‘theory’ - about the world. For how this can go wrong, see the pirate theory of global warming.svg).
Conversely, “first principles” as they exist in reality are usually grounded in experiment. This is most glaring in the case of climate models. What does their code implement? Conservation of mass? Experimental result. Heat transfer? Experimental result. Cloud formation? Experiment. Optical properties of gases, experiment, solar spectrum, experiment, black-body radiation, experiment, Earth’s geography, experiment, seasonal cycles, experiment. This is all data! Using this data is just as much “just looking at the data” as linear regression.
Point taken, and I agree. I’ll try to better formulate what I meant:
Some theories are developed using data about the system you want to study. E.g., past climate data.
And some theories are developed using data about other systems. Either similar but causally unrelated ones (e.g., greenhouse effect in an actual greenhouse), or models which are so simplified that there’s a serious worry they may be too simplified to apply to the original system (e.g., black-body radiation). They also have the advantage that if they work on the system you want to study, then they let you explain it in terms of other things which you already understand.
On an abstract Bayesian level, they’re all the same; we don’t compartmentalize data about past climate from data about the optical properties of gasses. But for humans who work in different fields the difference matters.
In reference to your request for thoughts, It seems like in both cases you could have parties switch their professed beliefs about the systems, without actually switching their behavior. This kind of pivot can and does definitely happen among some politicians. Should a reference to it be included?
Here are some potential examples:
Parties currently opposed to taking action on Anthropogenic climate change:
“Anthropogenic climate change has models which explain it and data which confirm the models. And it is good as a practical matter, because it is predicted to cause some areas to become more temperate, which will increase the yields of particular crops, so unlike the opposing party, we don’t need to do anything to keep anthropogenic climate change from happening.”
Parties currently opposed to keeping minimum wages low:
“Minimum wage caused unemployment has models which explain it and data which confirm the models. And is is good as a practical matter, because it is predicted to increase automation in low skill fields, which will increase yields of particular services, so unlike the opposing party, we don’t need to keep the minimum wage low.”
That’s pretty much what Yvain said in “The death of wages is sin”.
Best suggestion I can think of: radiation hormesis in humans. Theoretically, as far as I know, one would expect radiation injury to increase about linearly with exposure for small exposures, but people have floated the idea that such radiation exposures might cause disproportionately less injury, or even prove beneficial. (I haven’t looked at the data on this and can’t offer a super-informed opinion.)
Noone can. LNT is the favored hypothesis, but the problem is that at levels in the vicinity of background radiation, the expected effect is so small that the necessary sample-sizes to confirm it empirically are entirely unreasonable. The people arguing hormesis sometimes point to the lack of any detectable cancer spikes in natural experiments (areas with higher background radiation) but that is at most mildly suggestive—to many confounding factors. In order to settle this, one would have to.. I dont know—build an automated high speed cancer detection aparatus for insects, breed and test a few million bugs in a salt mine for an ultra-low radiation baseline, then do it again after elevating the radiation levels in said salt mine in steps? And even if you did that, people would likely challenge the validity of the animal model.
Well, if LNT is valid for all radiation levels comparable to or greater than natural background levels, then it’s valid for (almost) all practical purposes.
No, knowing for sure would have practical implications. The world entire is radioactive, and LNT has had really major impact on the regulation of all things nuclear, so the small effects get multiplied by very large numbers of people affected. It LNT is wrong, not transitioning to an all fission grid ages ago was 100% certainly a dire, dire mistake, and radio-logical medicine can be used somewhat more aggressively. It thus actually matters that we don’t know the answer to this question for sure. It is just an obnoxious experiment to design.
I mean, what practical difference would it make whether the optimal level of radiation is exactly zero or non-zero but a couple orders of magnitude below the natural background?
(besides the health effects of working in places like LNGS—but then again people who work in such places take a larger-than-average number of flights (e.g. in order to attend conferences) and the cosmic ray exposure during flights would compensate for that)
We do not know that the optimal level isn’t 3 times average background. Or that the body does not adapt to constant low exposures in a way it does not to acute ones. Both of which are theoretical possibilities. - Earth has been getting less radioactive over geological time, and the mechanisms of cell repair are of very ancient origin. LNT isnt just an unconfirmed theory at levels below background. It is a line which is extrapolated from data derived from people who had doses much higher than that. “Hiroshima survivors”. “Ill advised handling of multiple naked near-critical masses”. sort of doses. The correlation is rock solid as we go from 90% mortality to 50, to 25, and it points at “Zero exposure, zero morbidity” more or less.. but once you get to doses within an order of magnitude of background, the expected morbidity is a very small percentage, and detecting it among all the other things that cause similar damage is just impractical. So we assume. It’s a solid assumption, but it is an assumption. It could be wrong.
OK, I think I misunderstood what you meant by “in the vicinity of” in the ancestor.
As to the first question, I think that you need to define “Anthropogenic Global Warming.” Does it simply mean that mankind’s activities are have or will lead to an increase in global surface temperatures? Does it mean that mankind’s CO2 emissions have or are likely to lead to an increase in global surface temperatures? Does it mean that mankind’s CO2 emissions are likely to cause warming which will then start a positive feedback loop, leading to dangerous levels of warming? Or does it mean something else entirely?
I realize that you want to keep the question brief, but these are extremely important distinctions.
By contrast, it’s much clearer what it means to increase the minimum wage and what unemployment means.
Responses to your questions, in forms like “I attribute most of my belief to the theory” or “70% data, 30% theory”, will be basically useless. There are too many variables for the reader to fill in. Using the AGW example: what alternative data set are we considering? One in which (A) average surface temperatures stayed flat despite the increase in CO2? Or one in which (B) temperatures rise over several decades, but within a range only slightly larger than the largest several-decade change in a few pre-industrial centuries?
If I’m pondering scenario A I’ll say that my belief is mostly data-based. After all, in that scenario I’d figure the theory doesn’t apply, in that (e.g.) there’s some negative feedback I’d overlooked. The data of A would sway me. On the other hand, the data of B wouldn’t, even if the effect size were smaller than expected. Thinking about scenario B, I’d say my view is mostly theory-based. So my response depends entirely on how I fill in the details of your question. I think similar points apply to the minimum wage question as well.
Historical example: exponential population growth.
Excellent post!
Regarding ways that the questions might be disanalogous: For temperature data, I don’t think that many people would question the data, average temperatures seem like good, hard facts to me. But some people might question unemployment data that they were presented with, stating that the measure of unemployment is flawed because it only measures people actively looking for work who are still eligible to receive unemployment. Some people ‘fall off’ and just become long term unemployed that no longer get counted in the statistics.
Perhaps you might note that some measure of ‘percentage of population that is employed (adjusting for demographics changes)’ would work better as the ‘data’ for some people?
Also, the post made me realize that in both of these two cases, the belief that I actually have (agree with both hypotheses), were formed due to the theory, and not due to looking at any empirical data. That is, when I hear empirical data in support of climate change, I think: ‘well, obviously!‘, not ‘here is the data that should be strengthening my belief in climate change’. I also realize that I haven’t investigated and seen any data either way regarding whether minimum wages really do increase unemployment or not, and maybe I should do that.
In my experience a non-negligible number of people do take issue with the relevant temperature data, although I have no good hard numbers on this. (Probably no one does, given the difficulty of taking a representative sample of people who dispute the occurrence/magnitude of AGW.)
I think it’s significant that of the well-known surface temperature indices, there is one—GISS—which has the highest recent temperatures. AFAIK, the GISS index is put together the authority of James Hansen who is pretty well known for his advocacy on the warmist side of the debate.
For the purpose of assessing the rate of global warming, whether a temperature index has the highest recent temperatures is less important than whether it has the highest difference between more recent temperatures and less recent temperatures.
The code for generating the GISS index is available online, as is a more user-friendly reimplementation of the GISS algorithm. So it should be possible to independently reproduce the GISS index oneself without relying on “the authority of James Hansen”.
[Edit, 27 hours later: not really sure why someone’s downvoted me for pointing these things out.]
I believe that GISS also wins by that standard.
Evidently, when creating a temperature index, judgments must be made about what data to use; how to crunch the numbers; and so on. Presumably that’s why the leading temperature indices don’t all agree. In the case of GISS, those judgments seem to have been made in such a way as to favor the warmist side of things. I strongly suspect this is the result of some kind of bias.
Not really. The problem is we don’t have uniformly spaced weather stations all over the earth. Furthermore the locations of the stations we do have tend to change over the time period of interest. (The various proxies suffer from similar problems.) Thus it’s necessary to apply weights to the data we do have to correct for this. Unfortunately, the weights are semi-arbitrary in practice and as we learned from the leaked climategate e-mails frequently have the warming built in.
What’s your reaction to the data that shows a lack of warming over the past 17-years?
Nothing, because you can make any trend in a noisy dataset vanish by looking at a carefully-chosen small slice. El Niño peaked in 1998, the subsequent temperatures look flatter in comparison. Yawn.
Zoom out, the trend is clear.
My main issue with it is that the people on the warmist side of the debate completely failed to predict it. Which is pretty good evidence that their thinking and their computer models are wrong. And yet, as far as they know, they continue to insist that their thinking and computer models are fundamentally sound. It seems to me like a class case of groupthink, self-serving bias, etc.
Then why do I see reddit links to NOAA articles, every single month, with titles like: “May 2014 the hottest May since 1880. Four of the five warmest Mays on record have occurred in the past five years. May 2014 marked the 39th consecutive May and 351st consecutive month (more than 29 years) with a global temperature above the 20th century average.”
Well, I constantly see headlines that say the opposite, i.e., about places with record cold.
Also, what do the headlines mean, do they mean hottest in some particular place, or hottest global average? If the former, statistically you’d always expect temperatures to hit a record somewhere; if the latter, see my remark in the parent about how hard it is to compute “average temperature”.
Also, on the occasions when global warming believers make independently verifiable predictions with definite dates they inevitably fail to occur as shown by the fact that Britain still has snow and Manhattan isn’t under water.
Yes, some global warming believers have made predictions that have been falsified, but “inevitably fail to occur” is wrong. Here’s a counterexample.
Julia Hargreaves does a lot of work evaluating predictive climate models, and her conclusion is that there are reliable models for predicting broad global climate response to anthropogenic forcing, but we don’t currently have trustworthy predictions at the sub-continental scale. So I think it is appropriate to be skeptical about confident and precise predictions about what will happen in particular parts of the world.
The only example of a successful prediction in your article is a rise in “mean surface temperature” which as I mentioned in the grand-parent is not hard to fudge, heck I also linked to data that gives opposite conclusions in the grand-parent. The rest of said article reads like an attempt to (preemptively?) explain away failed predictions.
And yet for some reason all said predictions fail in the same direction.
Your evidence that the weights used to calculate mean surface temperature are fudged in favor of global warming is a link to the “VERY ARTIFICIAL correction” in the CRU code. But that correction was not applied to global mean surface temperature data. It was applied to historical tree-ring data in order to account for the discrepancy between recent temperatures calculated using tree-ring data and recent temperatures calculated using other means known to be more reliable.
Uncorrected, the tree ring data suggests a decline in temperatures beginning around 1940 and continuing to the present. We have plenty of evidence that this is not in fact correct from actual thermometer-based records, so the correction was applied as a proxy for the unknown cause of this recent divergence. Now this does perhaps “hide” the fact that tree-ring records are not trustworthy (although CRU published papers explicitly mentioning this supposedly hidden fact), but it does not show that actual thermometer-based temperature records are being artificially tampered with to produce global warming.
It seems to me that ESR misrepresents this fact (although perhaps he was unaware of it) when he characterizes the “correction” as being applied to “Northern Hemisphere temperatures and reconstructions”, with no mention of tree rings.
And I am very skeptical that temperature records over a very recent decade (the basis for the article I linked) have had significant external weighting applied to them to “fudge the results”. The problem of changing station locations may necessitate differential weighting over longer time frames, but just from 2002 to 2011? I don’t believe you. If you have any evidence suggesting that this is what is going on, I’m interested to see it.
It doesn’t read that way to me.
Probably due to politically motivated reasoning. I’m not denying that climate change activists often make exaggerated and unwise predictions about the impact of climate change, especially in the popular media. I am denying your claim that the predictive record of climate science is entirely negative. There are climate models that have done pretty well, at least when it comes to global trends.
Here is the article I linked to above. Note that it implies a different conclusion about recent temperature trends. Do you have any evidence for preferring your letter to the editor over the article Eric discusses besides it confirming your pre-existing belief?
Have you even read the article you linked to? Here are the first four sentences:
Not sure what you mean by “different conclusion”. Both papers are based on the exact same data (the HadCRUT4 data set). There is no conflict between the articles that I can see. Curry’s paper is about discrepancies between the data and the CMIP5 model simulations. The paper I linked is about the success of the HadCM2 model. It also says some stuff about the CMIP5 model, but as far as I can tell it doesn’t say anything that is inconsistent with what Curry says.
So I don’t “prefer” one article to the other. It seems to me that both articles are making perfectly valid points. Are you sure you’re not falling for “arguments are soldiers” thinking? Just because I posted evidence that climate predictions don’t “inevitably” fail doesn’t mean I think that all climate model predictions are accurate at a 2% confidence level.
Again, not sure what you’re talking about. Why do those four sentences read like an attempt to explain away false predictions? The whole point that the authors are making is now that we do have independent observations taken over one and a half decades, we can evaluate the success or failure of models constructed in the mid-90s.
ETA: Double post. Retracting.
I honestly don’t know what you’re talking about. Both papers are based on the exact same data (the HadCRUT4 data set). There is no conflict between the articles that I can see. Curry’s paper is about discrepancies between the data and the CMIP5 model simulations. The paper I linked is about the success of the HadCM2 model. It also says some stuff about the CMIP5 model, but as far as I can tell it doesn’t say anything that is inconsistent with what Curry says. Is there some inconsistency you had in mind?
So I don’t “prefer” one article to the other. It seems to me that both articles are making perfectly valid points. Are you sure you’re not falling for “arguments are soldiers” thinking? Just because I posted evidence that climate predictions don’t “inevitably” fail doesn’t mean I think that all climate model predictions are accurate at a 2% confidence level.
Again, not sure what you’re talking about. Why do those four sentences read like an attempt to explain away false predictions? The whole point that the authors are making is now that we do have independent observations taken over one and a half decades, we can evaluate the success or failure of models constructed in the mid-90s.
http://xkcd.com/1321/ (SCNR).
More seriously, are you implying that any increase in the variance is irrelevant so long as the mean doesn’t change much?
Who predicted that Britain would no longer have snow or Manhattan would be under water by 2014?
I never said anything about an increase in variance, temperature records haven’t been around long enough for it to be hard to find record setting temperatures somewhere. Also, I notice you’re shifting your hypothesis from “temperatures are rising” to “variance is rising”.
As for the argument in the linked comic, when wine grapes can be grown in England and Newfoundland, as was the case during the medieval warm period I’ll start taking arguments of that type seriously.
The Climatic Research Unit for the no more snow in Britain. The Manhattan underwater one (or at least the West Side Highway) is Jim Hansen.
Regarding the wine point, it is doubtful if wine grapes ever grew in Newfoundland, as the Norse term “Vinland” may well refer to a larger area. From the Wikipedia article:
Also, wine grapes certainly do grow in England these days (not just in the Medieval period). There appear to be around 400 vineyards in England currently.
Reading your referenced article (Independent 2000):
Clearly the Climatic Research Unit was not predicting no more snow in Britain by 2014.
Regarding the alleged “West Side Highway underwater” prediction, see Skeptical Science. It appears Hansen’s original prediction timeframe was 40 years not 20 years, and conditional on a doubling of CO2 by then.
Yes, but some googling suggests that average snowfall in England hasn’t changed very much over the 2000s, which doesn’t seem consistent with the linked article.
“Over the 2000s” is certainly too short a period to reach significant conclusions. However the longer term trends are pretty clear. See this Met Office Report from 2006.
Figure 8 shows a big drop in the length of cold spells since the 1960s. Figure 13 shows the drop in annual days of snow cover. The trend looks consistent across the country.
I think the first question here is whether we have reached agreement on the forecasts being wrong, not what excuses should be made or conclusions drawn from said wrongness.
Yes, I’m sure they were, and that those were the basis for the mistaken prediction. Your point?
I think we have agreement that:
A) The newspaper headline “Snowfalls are now just a thing of the past” was incorrect
B) The Climatic Research Unit never actually made such a prediction
C) The only quoted statement with a timeline was for a period of 20 years, and spoke of heavy snow becoming rarer (rather than vanishing)
D) This was an extrapolation of a longer term trend, which continued into the early 2000s (using Met Office data published in 2006, of course after the Independent story)
E) It is impossible to use short periods (~10 years since 2006) to decide whether such a climatic trend has stopped or reversed.
I can’t see how that counts as a failed prediction by the CRU (rather than the Independent newspaper). If the CRU had said “there will be less snow in every subsequent year from now, for the next 20 years, in a declining monotonic trend” then that would indeed be a failed prediction. However, the CRU did not make such a prediction… no serious climate researcher would.
From the article:
Does heavy snow cause chaos in England now?
Is snow a ‘very rare and exciting event’ in England now?
If we asked them, would they not know first-hand what snow is, anymore than they know first-hand what wolves are?
You can’t?
What’s the date?
By your reaction, and the selective down votes, I have apparently fallen asleep, it is the 2020s already, and a 20-year prediction is already falsified.
But in answer to your questions:
A) Heavy snow does indeed already cause chaos in England when it happens (just google the last few years)
B) My kids do indeed find snow a rare and exciting event (in fact there were zero days of snow here last winter, and only a few days the winter before)
C) While my kids do have a bit of firsthand knowledge of snow, it is vastly less than my own experience at their age, which in turn was much less than my parents’ experience.
If you are a resident of England yourself, and have other experiences, then please let me know...
Well, all the quotes I gave were drawn from http://www.independent.co.uk/environment/snowfalls-are-now-just-a-thing-of-the-past-724017.html which was 14 years ago. That sounds like it’d cover ‘within a few years’. And as for the exact 20 year forecast of 2010, well, that’s just 6 years away. Not a lot of time to catch up.
Yes, looks like the usual chaos you could find in the ’80s and ‘90s to which the predicted ‘chaos’ was being compared as being greater.
And has your region changed much? And is your anecdote very trustworthy compared to the nation-wide changes in snowfall since 2000 (not much) when these predictions were made?
Sigh… The only dated prediction in the entire article related to 20 years, not 14 years, and the claim for 20 years was that snow would “probably” cause chaos then. Which you’ve just agreed is very likely to be true (based on some recent winters where some unexpected snow did cause chaos), but perhaps not that surprising (the quote did not in fact claim there would be more chaos than in the 1980s and 1990s).
All other claims had no specific dates, except to suggest generational changes (alluding to a coming generation of kids who would not have experienced snow themselves).
Regarding the evidence, I already gave you Met Office statistics, and explained why you can’t get reliable trend info on a shorter timescale. You then asked anecdotal questions (is snow “rare and exciting”, what would kids say if you asked them?) and I gave you anecdotal answers. But apparently that’s not good enough either! Is there any set of evidence that would satisfy you?
Still if you really want the statistics again, then the very latest published Met Office set runs up to 2009 if you really want to check, and the downward trend lines still continue all the way to the end of that data. See for instance this summary figures 2.32 and 2.35.
So if you want to claim that the trend in snow has recently stopped/reversed, then you are looking at a very short period (some cold winters in 2010-14). And over periods that short, it’s entirely possible we’ll have another shift and be back onto the historic trend for the next five year period. So “catch up in six years” doesn’t sound so implausible after all.
I’m sorry, I didn’t realize ‘within a few years’ was so vague in English that it could easily embrace decades and I’m being tendentious in thinking that after 14 years we can safely call that prediction failed.
So first, that’s ‘air frost’ (“usually defined as the air temperature being below freezing point of water at a height of at least one metre above the ground”), which is not what was in question. Second, looking at 2.32, the decline 2000-2007 (when the graph ends, so fully half the period in question when warming seems to have stopped) is far from impressive. Third, what’s with it being ‘filtered’? some sort of linear smoothing borrowing from the steeper-looking decline 1984-2000?
No, I’m fine with your chosen smoothed graphs indicating only a shallow decline at best 2000-2007. No need to look just at 2010-2014, although certainly more recent data would probably help here.
That sounds like wishful thinking. In those graphs, is there any 5-year period which if repeated would abruptly vindicate the confident predictions from 2000 that snow would soon be a thing of the past in England?
P.S. On the more technical points, the 2009 reports do not appear to plot the number of days of snow cover or cold spells (unlike the 2006 report) so I simply referred to the closest proxies which are plotted.
The “filtering” is indeed a form of local smoothing transform (other parts of the report refer to decadal smoothing) and this would explains why the graphs stop in 2007, rather than 2009: you really need a few years either side of the plotted year to do the smoothing. I can’t see any evidence that the decline in the 80s was somehow factored into the plot in the 2000s.
Seems like a bad proxy to me. Is snowfall really that hard a metric to find...?
If the window is a decade back then the ’90s will still be affecting the ’00s since it only goes up to 2007.
I think it may depend on how exactly the smoothing was being done. If it’s a smoothing like a LOESS then I’d expect the ’00s raw data to be pulled up to the somewhat higher ’90s data; but if the regression best-fit line is involved then I’d expect the other direction.
Presumably not, though since I’m not making up Met Office evidence (and don’t have time to do my own analysis) I can only comment on the graphs which they themselves chose to plot in 2009. Snowfall was not one of those graphs (whereas it was in 2006).
However, the graphs of mean winter temperature, maximum winter temperature, and minimum winter temperature all point to the same trend as the air frost and heating-degree-day graphs. It would be surprising if numbers of days of snowfall were moving against that trend.
Interesting. I wonder why they’re no longer plotting some trends. Maybe because it’s too hard to fit them into their preferred narrative.
Or moving from conspiracy land, big budget cuts to climate research starting in 2009 might have something to do with it.
P.S. Since you started this sub-thread and are clearly still following it, are you going to retract your claims that CRU predicted “no more snow in Britain” or that Hansen predicted Manhattan would be underwater by now? Or are you just going to re-introduce those snippets in a future conversation, and hope no-one checks?
I was going from memory, now that I’ve tracked down the actual links I’d modify the claims what was actually said, i.e., snowfalls becoming exceedingly rare and the West Side Highway being underwater.
Thanks.… Upvoted for honest admission of error.
Got it—so the semantics of “a few years” is what you are basing the “failed prediction” claim on. Fair enough.
I have to say though that I read the “few years” part as an imprecise period relating to an imprecise qualitative prediction (that snow would become “rare and exciting”). Which as far as my family is concerned has been true. Again in an imprecise and qualitative way. Also, climate scientists do tend to think over a longer term, so a “few years” to a climate scientist could easily mean a few decades.
And you’re right, no further 5 year period would make snow “a thing of the past” but we already agreed that was the Independent’s headline, and not CRU’s actual prediction. Rare snow in the 2020s is different from no snow in the 2020s.
No, that’s just one of the failed predictions I am pointing out, which you are weirdly carping on because it didn’t come with an exact number despite it being perfectly clear in ordinary language & every context that we are well past anything that could be called ‘a few years’.
Maybe your family should look at those Met charts you provided about ‘air frost’ and note how small the decline has been in the relevant period.
And ’20 years’ could be 200 years, because y’know, they think on such a long horizon. And maybe the ‘days’ in Genesis were actually billions of years and it’s an accurate description of the Big Bang!
So we are agreed that the 20 year prediction is going to be false just like the others and there was no point discussing how there’s still a chance.
I’m sorry, but you are still making inaccurate claims about what CRU predicted and over what timescales.
The 20 year prediction referred specifically to heavy snow becoming unexpected and causing chaos when it happens. I see no reason at all to believe that will be false, or that it will have only a slim chance of being true.
The vague “few year” claim referred to snow becoming “rare and exciting”. But arguably, that was already true in 2000 at the time of the article (which was indeed kind of the point of the article). So it’s not necessary to argue about whether snow became even rarer later in the 2000s (or is becoming rarer slower than it used to), when there’s really too little data to know over such a short period.
There was a totally undated claim referring to future children not seeing snow first-hand. You are clearly assuming that the “few year” time horizon also attached to that strong claim (and is therefore baloney); however, the article doesn’t actually say that, and I rather doubt if CRU themselves ever said that. It does seem very unlikely to me that a climate scientist would ever make such a claim attached to a timescale of less than decades. (Though if they’d really meant hundreds of years, or billions of years, they’d presumably have said that: these guys really aren’t like creationists).
Finally, the Independent put all of this under a truly lousy and misleading headline, when it is clear from what CRU actually said that snows were not and would not become a thing of the past (just rarer).
The general problem is that much of the newspaper article includes indirect speech, with only a few direct quotes, and the direct quotes aren’t bound to a timescale (except the specific 20-year quote mentioned above). So it’s hard to know exactly what CRU said.
I notice you’re shifting your hypothesis from ‘it’s not getting any warmer than in the 1990s’ to ‘it’s still not as warm as it was in the 1000s’. ;-)
Well, climate “scientists” are always warning that global warming will lead to disasters that didn’t happen during the medieval warm period, so presumably they’re predicting temperature increases larger than that. (Ok, they also like to pretend the medieval warm period didn’t exist.)
[citation needed] (preferably a peer-reviewed paper or a university-level textbook rather than an article in the popular press—everybody know people say all kinds of things in the latter).
No. The evidence for whether minimum wage laws produce unemployment is inconclusive. The recent studies showed no significant effect.
Being skeptical of long-range climate forecasting shouldn’t lead you to believe that CO2 has no effect. It should lead you to believe that we can’t quantify the effect. You widen your uncertainty interval instead of moving it’s center.
If you want to focus on Bayesianism don’t ask people whether they believe but ask them for a probability.
When I meant “supported through a wide range of lines of evidence” I was referring to the theory in its generality, not necessarily its application to the context at hand. So I meant that the theory of supply and demand on the whole is supported through several lines of evidence, not necessarily its application to the minimum wage issue (where the evidence alone does seem inconclusive). That was the point of the question.
Thanks for raising the issue, and sorry for the confusion I engendered.
The idea that supply and demand are a factor that can change some commercial interactions isn’t controversial. Human psychology on the other hand is quite complex. There is good evidence that most humans aren’t completely rational utility optimizers.
Widen?
Yes, I corrected the error.
Second proposition is not a good choice, because the emperical evidence pretty strongly suggests that the link.. Uhm, does not exist. Which has caused me to question the theory quite a lot. That is pretty basic- theories that don’t get support by the data are to be discarded.
Edit: Actually, let me put this in stronger terms. Looking into the literature behind this supposed link is one of the things that made me extremely skeptical about the discipline of economics as it is currently practiced in it’s entirety. Because most of the underlying studies focused on teens, a group that theory predicts wage increases should have vastly outsized impact on, and which has also had cultural pressures reducing employment. That reeks of fishing for results, and when despite this, the empirical data returned a null result, the theory was not tossed onto the rubble pile of history, and economists keep right on arguing against all minimum wage increases.
I mean, bravo for not lying about what the data says, but minus 9000 points for then not taking it onboard.