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Comments 41401 to 41450:
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StealthAircraftSoftwareModeler at 03:34 AM on 17 October 2013Why Curry, McIntyre, and Co. are Still Wrong about IPCC Climate Model Accuracy
I didn’t think 63 years – from 1950 until today – was short term at all. Perhaps it is, or we’re miscommunicating. I’d like to see model projections for air temp, sea temp, humidity, cloud cover, and precipitation, just as it has been shown for only air temperature. Take the IPCC’s chart, or better yet, Tom Curtis’ chart @159 and so me the model projections for air temp, sea temp, humidity, cloud cover, and precipitation. Is that an unreasonable request or do you think it will have meaningless implications?
Moderator Response:[JH] Your requests for the forecasts of the indicators that you have listed should be directed to the organizations who actually develop and maintain the GCMs. Please let us know if you need contact information for these organizations.
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StealthAircraftSoftwareModeler at 01:45 AM on 17 October 2013Why Curry, McIntyre, and Co. are Still Wrong about IPCC Climate Model Accuracy
Tom Curtis @168: “this illustrates the absurdity of SASM's approach of testing models against just one value”, you know, I had this very thought last night, and I agree with you totally.
The model ensembles show air temperature, but as this site has pointed out (presumably correctly), on the order of 90% of the energy goes into the oceans. Due to this, air temperature is almost irrelevant to the system. So, what would be a meaningful and informative test of climate models? How far back to look and what to examine or measure?
I do not think the models can properly represent the various components of the earth’s energy budget, all of which combine to produce an average air temperature. Air temperature is what everyone worries about the most, but that is just 1/3000th of the heat capacity of the earth, by volume. Since air is mostly warmed indirectly, it is important to correctly model the things that heat the air. In order for me to have a sliver of confidence in a climate model, I’d want to see that a model can reasonably** project changes in the energy budget, say: air temp, sea temp, humidity, cloud cover, and precipitation. If you have opinions about other or different drivers, then I’d like to hear about them.
I think these are the major components to the energy budget, and I’m only looking for global averages. I am not expecting climate models to able to predict when it will rain at some location. If climate scientists and modelers have a good handle on climate physics, then they should be able to do a reasonable** job at projecting the annual global average of these values. Let’s start from 1950, which is where the IPCC says our AGW effects start. Can anyone plot the model average and +/- 2.5%ile bounds for these values along with actual measured data? Or, can someone point me to a paper to covering this? I’d be stunned if someone has not already done this analysis.
**reasonable: let’s define “reasonable” as getting the trend direction correct. If a model projects one of these components to go up over the last 63 years, but it goes down, then the model is wrong for that component. If the model is wrong on several components, then how can anyone conclude they are correctly modeling the climate?
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Synapsid at 00:35 AM on 17 October 20132013 SkS Weekly News Roundup #42A
numerobis:
Pipelines are necessary to increase production if you listen to CEOs; there's money in pipelines. It's much faster to expand shipping by rail, though, than it is to build a pipeline; cheaper too, and rail allows great flexibility in supplying markets. Both are being used, and it's rapid growth in rail transport that is allowing rapid increase in export from Canada. Sideshow? I said that Keystone XL is a sideshow compared to coal, not that pipelines are a sideshow.
Nor did I say oil is less important to stop than coal is. Coal has much greater potential for future harm than oil has but, at least if you consider informing the public important, it gets far less attention than Keystone XL does and that imbalance should be fixed.
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funglestrumpet at 00:32 AM on 17 October 20132013 SkS Weekly News Roundup #42A
The link to the Coming Plague article appears to be broken, or my computer is having another one of its funny turns.
Moderator Response:[JH] The link has been fixed. Thank you for bringing this glitch to our attention.
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numerobis at 23:52 PM on 16 October 20132013 SkS Weekly News Roundup #42A
"In 2012 Canada exported more oil, crude plus concentrate in industry terms, than in any previous year; the total for 2013 will be higher" -- how does this make it *less* important to stop?
You seem to have bought the oil company claim that the pipelines are a sideshow; without pipelines, they'll just ship it by train. This is their message to those who want to limit production. They have another message to those who want to maximize short-term $$$, which is that the pipelines are absolutely crucial to increase production.
Moderator Response:[JH] To whom is your comment directed?
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Leto at 21:44 PM on 16 October 2013Why Curry, McIntyre, and Co. are Still Wrong about IPCC Climate Model Accuracy
Thanks Tom@168.
I was aware of Kosaka and Xie. Their approach is interesting, but potentially open to the charge (by motivated parties) that the close match they demonstrated merely reflects that the model was constrained by reality, and hence matched reality. (The model was not actually tightly constrained, but we all know contrarians have run with weaker material.)I was hoping that a few unconstrained model runs had chanced upon an ENSO profile similar to reality. For instance, if all model runs in the ensemble were ranked according to their mean squared SOI error with respect to the historical SOI, I would expect that the best-matched quartile (top 25% of runs in the ranking) would match reality more closely than other quartiles. After all, the models' developers never claimed to be able to predict ENSO conditions, so it would be nice to minimise the effects of ENSO mismatch. This would support the notion that the minor, statistically insignificant divergence between the ensemble mean and observational record (over some timeframes) is largely due to an el nino in 1998 followed by la nina and neutral conditions for many of the following years. It is a shame if this type of data is not available in the public domain.
Of course, if the science were being conducted in an ideologically neutral environment, such an obvious issue would not draw so much attention in the first place, and I would not propose such an analysis. For me, the Nielsen-Gammon plot, mentioned in several threads here at SkS, says it all.
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Tom Curtis at 21:03 PM on 16 October 2013Why Curry, McIntyre, and Co. are Still Wrong about IPCC Climate Model Accuracy
Leto @162, I am not sure that that can usefully be done. However, Kosaka and Xie have gone one better. They took a model, constrained the tropical pacific temperature values to historical values to force it to adopt the historical ENSO record, and compared that to observations:
Tamino's discussion (from which I draw the graph) is very informative.
To bring that back to the discussion on hand, I checked the 1990-2012 temperature trend of that model when run with the CMIP3 experiment. It was 0.317 C/decade. That puts it at the 80.8th percentile, and definitely one of the fastest warming models. Yet when constrained to historical ENSO fluctuations, it almost exactly matches the HadCRUT4 record.
This, I think, illustrates the absurdity of SASM's approach of testing models against just one value, and if they almost (but not quite) fail the test, rejecting them as completely irrelevant as a basis of information.
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Tom Curtis at 20:35 PM on 16 October 2013Why Curry, McIntyre, and Co. are Still Wrong about IPCC Climate Model Accuracy
SASM @165, unfortunately, I do not think you understand my position. In particular, I was not criticizing your choice of baseline on the basis that it makes the observations look cool. On the contrary, a 1990 baseline makes the observations look warm. The emphasis on that point is so that my allies pick up on the fact. Intuitively, we would expect a 1990 baseline to cause the observations to look cool, for 1990 is a local high point in the observations. However, that is not the case, for though the observations are warm, the ensemble mean is warmer still relative to adjacent years. Thus, if anything, a 1990 baseline is favourable to a defence of the validity of models.
But it is still wrong.
It is wrong, basically, because you have to analyze the data as you motivated me to do to know whether it is favourable, unfavourable or neutral with regard to any position. The only way to avoid that necessity is to use a long (thirty year) baseline so that the baseline is robust with respect to time period used. Ideally, we should use the long baseline that minimizes the Root Mean Squared Error (RMSE) between the observation and the ensemble mean. By doing so, we ensure that any discrepancy between observations and ensemble are real, ie, are not artifacts of an ill chosen baseline.
The impact of short baselines is shown by KR's graph @164. The UAH temperature record is far more sensitive to ENSO fluctations than any surface record. As a result, the inclusion of the strong El Nino of 1983 in a short baseline period artificially displaces the UAH record downwards with respect to HadCRUT4 (and both HadCRUT4 and UAH downward with respect to the ensemble mean).
The crux is this: Spencer (and you and McIntyre) have created graphs to illustrate the relationship between observations and models. Yet all of you have adopted non-standard conventions, the effect of which is to show a greater disparity than actually exists. This is achieved by three different methods in the three cases (and I have only discussed Spencer's method so far), but is the case none-the-less. Now, to the extent that you intend an honest comparison, you would avoid any method that might accidentally result in showing a greater disparity (if that is what you are trying to demonstrate). Short term baselining is a method that will have that effect. When it is adopted to compare data with known large differences in variance (as, for example, HadCRUT4 and UAH) it is scientific malpractice. It is, not to put to fine a point on it, the sort of thing I would expect from a person who uses a known faked Time magazine cover to establish a rhetorical point, and who refuses to take it down, issue a correction or acknowledge the fault when corrected by others.
I expect better of you than of Spencer. I expect you to at least acknowledge that the use of short baselines is bad practise, and should not be done for purposes of illustration nor in attempting to establish whether or not the observations have fallen outside the predicted range.
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Dikran Marsupial at 17:11 PM on 16 October 2013Why Curry, McIntyre, and Co. are Still Wrong about IPCC Climate Model Accuracy
SASM wrote "only that we should not rely upon model projections as a basis of making policy decisions."
O.K., so if we are not going to use models that embody what we know of climate physics, specifically what should we use as the basis for policy decisions?
"They have not passed enough testing and verification for them to have that kind of power."
Please specify how much testing and verification would be required for you to accept their use as a basis for policy making.
Lastly, please explain why you have not mentioned the occasions where models have under-predicted the effects of climate change.
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Synapsid at 16:24 PM on 16 October 20132013 SkS Weekly News Roundup #42A
We should get off fossil fuels, no argument. That won't happen overnight; where should effort to reduce their use be concentrated?
Consider two of the articles in this roundup:
Group blasts oil sands in US (refers to Canadian oil sands)
World coal consumption to pass oil by 2020 due to rising demand in China and India.
I'd say the target should be coal, to a much greater extent than we see presently in the various news sources. Producing and burning coal releases more CO2 than does production and burning of oil, even oil-sands oil, and heavy metals and other undesirables as well. And coal is widespread on the planet, and relatively easy to obtain compared to oil, especially the deep-water oil and the shale oil both of which are expensive to get, and which (together with Canadian oil sands) have provided most of the growth in oil production in the US and Canada--and that means in the world.
There has been pretty much a plateau in oil production worldwide for about eight years (the growth in Canadian and US production has been balanced by production decreases outside those two countries) but China and India are driving growth in coal production and use at a scary rate. Coal is the big challenge.
Keystone XL is a sideshow compared to coal. The White House has approval authority only over the part of the pipeline that crosses the US/Canada border, a matter of miles. Holding up approving or denying the permit has resulted in railways stepping in to transport crude oil from the oil sands (and the Bakken in the US), notably Canadian National, Canadian Pacific and Burlington Northern, to the Gulf Coast and to the eastern US especially, and to refineries on the West Coast too. In 2012 Canada exported more oil, crude plus concentrate in industry terms, than in any previous year; the total for 2013 will be higher. This is being done without White House approval because most of the four-part Keystone XL system is not subject to it.
Canadian crude was selling at a whopping discount for more than a year because there was a backup and thus a glut in the US Midwest, at Cushing, Oklahoma. That glut is being drained because of new pipeline capacity and reversal of pipelines that had carried oil to Cushing instead of south to the refineries at the Gulf Coast, and that reduction of glut will increase when the southern portion of Keystone XL becomes operational by the end of the year or soon after. Prices paid for Canadian crude have risen as a result, and that is incentive for oil-sands development. Canada is looking at shipping oil-sands crude from Alberta to refineries in eastern Canada which have been importing more expensive West African and other crudes, and that will boost the oil sands too.
There are already half a dozen pipelines carrying Canadian oil into the US and two of them, TransMountain from Alberta to Vancouver/Burnaby with a side branch to Washington state, and Enbridge's Midwest complex, are planned to be expanded along routes that already have the necessary approvals, and each of those expansions will result in capacity that will be greater than the planned capacity of Keystone XL.
The oil sands are booming because the demand is there, and the oil is flowing into the US in record volumes, without Keystone XL having had approval. This flow will increase. Shift the attention and publicity more to reducing coal use--and export: the US is the fourth largest exporter of coal in the world--and away from the small part of Keystone XL where so much of it has been focused.
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StealthAircraftSoftwareModeler at 13:20 PM on 16 October 2013Why Curry, McIntyre, and Co. are Still Wrong about IPCC Climate Model Accuracy
Dikran @161: I frequently use the quote, “all models are wrong, but some are more useful than others.” I have never said that climate models are not useful, only that we should not rely upon model projections as a basis of making policy decisions. They have not passed enough testing and verification for them to have that kind of power.
[text deleted here - reply to deleted comment (dogpiling)]
Tom Curits @164: I understand your position fully. I hate that you took so much time building charts to prove it. There are always problems on selecting the origin. I wasn’t trying to rig the chart to push the ensemble to the high side; I just used the starting date in the draft IPCC chart. I admit that 1990 +/- was a little bit of a warm year. So, let’s just focus on your chart @ 159. Can you compute the trend lines for the four anomaly curves without forcing all the lines to a common origin? In other words, compute the best fit line from 1983 to 2015 for each of the anomalies, or post the spreadsheet data somewhere and I’ll do it. The start point for each trend line should be on a vertical line at 1983. If you want to pick a different starting year that is fine too.
Moderator Response:[JH] You have made your points ad naseum and are now skating on the thin ice of excessive repetition. Please cease and desist or face the consequences.
[Rob P] Text removed from comment for the reason outlined above.
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Why Curry, McIntyre, and Co. are Still Wrong about IPCC Climate Model Accuracy
Regarding Spencer's chart, here are the HadCRUT4 (red) and UAH 5-year means - baselined to 1980-1999 (green) and Spencer and Christy's 1979-1983 extrema value (blue):
Note how the Spencer and Cristy running mean UAH temps remain entirely below HadCRUT4 for the entire period - a clear sign of incorrect baselining, as they do not have common grounds from which to judge departures.
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scaddenp at 12:36 PM on 16 October 2013A Rough Guide to the Jet Stream: what it is, how it works and how it is responding to enhanced Arctic warming
Gee, pseudoscience from people that cant comprehend arithmetic (like A>>b). If you think this is science, then perhaps you are in market for a DNA upgrade too.
(yes, I know this is going to be moderated out).
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chriskoz at 12:05 PM on 16 October 2013Two degrees: how we imagine climate change
JH@5,
I think panzerboy priviledges should be revoked at this point.
not only did he badly distort the meaning of my post in a very careless manner, but he also distort my name to cap it up. Not that I particularly care about the latter but others might do. SkS standards should not tolerate such poor quality comments.
Moderator Response:[JH] I suspect that panzerboy is nothing more than a drive-by denier drone. If I had the authority to do so, I would immediately revoke his posting privileges. Alas, i do not have such authority.
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Bert from Eltham at 10:31 AM on 16 October 2013Consensus study most downloaded paper in all Institute of Physics journals
What is abundantly clear, is that a refereed paper that measures or evaluates the reality of twenty years of thousands of refereed papers is a major counter to the false campaign of the deniers that attempt to throw doubt on real science with a pathetic litany of half truths and a very poorly argued inconsistent delusional world view. Bert
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Tom Curtis at 09:02 AM on 16 October 2013Why Curry, McIntyre, and Co. are Still Wrong about IPCC Climate Model Accuracy
SASM @160, I have to disagree with your claim that the charts discussed "are all basically the same". For a start, your chart, Spencer's chart, and the 2nd order draft chart from the IPCC all use single years, or very short periods to baseline observations relative to the model ensemble mean. In your, and Spencer's case, you baseline of a five year moving average, effectively making the baseline 1988-1992 (you) or 1979-1983 (Spencer). The 2nd Order draft, of course, baselines the observations to models of the single year of 1990.
The first, and most important, problem with such short period baselines is that they are eratic. If a 1979-1983 base line is acceptable, then a 1985-1989 base line should be equally acceptable. The later baseline, however, raises the observations relative to the ensemble mean by 0.11 C, or from the 1.5th to the 2.5th percentile. Should considerations of whether a model has been falsified or not depend on such erratic baselining decisions?
Use of a single year baseline means offsets can vary by 0.25 C over just a few years (CMIP3 comparison), while with the five year mean it can vary by 0.15 C (CMIP5 comparison). That is, choice of baselining interval with short baselines can make a difference equal to or greater than the projected decadal warming in the relative positions of observations to model ensemble. When you are only comparing trends or envelope over a decade or two, that is a large difference. It means the conclusion as to whether a model is falsified or not comes down largely to your choice of baseline, ie, a matter of convention.
Given this, the only acceptable baselines are ones encompensing multiple decades. Ideally that would be a thirty year period (or more), but the 1980-1999 baseline is certainly acceptable, and necessary if you wish to compare to satellite data.
Spencer's graph is worse than the others in this regard. It picks out as its baseline period the five year interval which generates the largest negative offset of the observations at the start of his interval of interest (1979-2012); and the third largest in the entire interval. Given the frequency with which his charts have this feature, it is as if he were intentionally biasing the display of data. In contrast, the 1990 baseline used by you (as the central value in a five year average) and the 2nd Order draft generates a postive offset relative to the 1961-1990 baseline, and near zero offset relative to the 1980-1999 baseline. Although 1990 is a locally high year in the observational record, it is not as high relative to the ensemble mean as its neighbours. Consequently the 2nd Order draft generates a more favourable comparison then the longer baselines. It is an error, never-the-less, because that is just happenstance.
More later.
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John Cook at 08:01 AM on 16 October 2013Consensus study most downloaded paper in all Institute of Physics journals
MichaelM, apologies for the video quality but as Kevin C points out, our resources are limited. However, at this blog post, I go into more detail, including making the Powerpoint slides fully available.
There are many different gauges of impact - downloads, tweets, citations, etc. I've read one fascinating paper that found that the number of initial tweets about a paper predict the eventual number of scholarly citations, so they provide an early indication of the long-term impact of a paper. There is similar research regarding the number of downloads of a paper and citations. The point is that the contribution that SkS readers made to make our consensus paper available in a high impact, open-access journal has had a strong, measureable effect by all available metrics.
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Leto at 07:14 AM on 16 October 2013Why Curry, McIntyre, and Co. are Still Wrong about IPCC Climate Model Accuracy
Tom, and others familiar with the data:
Is it possible to take the model ensemble and purge all those runs that are clearly ENSO-incompatible with the real-world sequence of el ninos and la ninas? (Obviously this begs the question of what counts as incompatible, but reasonable definitions could be devised.) In other words, do the runs have easily extractable SOI-vs-time data?
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Dikran Marsupial at 04:00 AM on 16 October 2013Why Curry, McIntyre, and Co. are Still Wrong about IPCC Climate Model Accuracy
SASM wrote "inaccurate and falsified"
As GEP Box (who knew a thing or to about models, to say the least) said "all models are wrong, but some are useful". We all know the models are wrong, just look at their projections of Arctic sea ice extent. The fact they get that obviously wrong doesn't mean that the models are not useful, even though they are wrong (as all models are). Of course the reason skeptics don't latch on to this is because it is an area where the models underpredict the warming, so they don't want to draw attention to that.
It seems to me that SASM could do with re-evaluating his/her skepticism, and perhaps ask why it is he/she is drawing attention to the fact that the observations are running close to the bottom of model projections now, but appears to have missed the fact that they exceeded the top of model projections in 1983 (see Toms nice diagram). Did that mean the models were falsified in that year and proven to be under-predicting global warming? Please, lets have some consistency! ;o)
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A Rough Guide to the Jet Stream: what it is, how it works and how it is responding to enhanced Arctic warming
Jubble - The paper is available at Barnes 2013 (h/t to Google Scholar).
It's only been out a month - it will be interesting to see if her analysis of atmospheric wave patterns holds up.
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Doug Bostrom at 03:11 AM on 16 October 2013Consensus study most downloaded paper in all Institute of Physics journals
Per Kevin, a casual check w/google scholar shows 20 cites as of today. Not bad, considering how slowly the publication sausage factor generally operates.
This item from the list of work citing Cook et al is particularly interesting for those of us who frequent climate science blogs (the somewhat awkward title isn't reflective of the paper's utility):
Mapping the climate skeptical blogosphere
Figure 1 is fascinating.
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Jubble at 01:47 AM on 16 October 2013A Rough Guide to the Jet Stream: what it is, how it works and how it is responding to enhanced Arctic warming
I read the article with interest, thank you.
I've just come across this recent paper (Revisiting the evidence linking Arctic Amplification to extreme weather in midlatitudes Elizabeth A. Barnes DOI: 10.1002/grl.50880) that states that "it is demonstrated that previously reported positive trends are an artifact of the methodology".
http://onlinelibrary.wiley.com/doi/10.1002/grl.50880/abstract
I don't have access to the full paper or the background to be able to verify the conclusion in the abstract. What do you think?
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StealthAircraftSoftwareModeler at 01:37 AM on 16 October 2013Why Curry, McIntyre, and Co. are Still Wrong about IPCC Climate Model Accuracy
Tom Curtis @159: Thanks for the quality posts. If we’re ever in the same town I’d like to buy you a beer or three. I might disagree with you on a few things, but I do appreciate your cogent and respectful posts.
You have drawn another nice chart. I think all of the charts discussed, including your current one, the one I’ve drawn @117, the one drawn by Spencer (http://www.drroyspencer.com/2013/10/maybe-that-ipcc-95-certainty-was-correct-after-all/), McIntyre’s (http://climateaudit.org/2013/10/08/fixing-the-facts-2/) and the two at the top of this page are all basically the same. They show model projections and the temperature data. Dana asserts that IPCC AR5 Figure 1.4 draft (his left chart at the top) was a mistake or has errors, but is now clear to me that it does not have any errors. The draft version (Dana’s left chart) from the IPCC matches the final draft version (Dana’s right chart), it’s just that the IPCC’s final version is zoomed out and harder to see.
This thread has discussed that initial conditions and boundary conditions are important, and using trend lines is important, but I disagree. Plotting model projection lines along with temperature data (not trend lines) much the way I have done, Spencer has done, McIntyre has done, is completely reasonable and meaningful. After all, this is the way the IPCC has drawn the chart in its final draft version (Dana’s right chart).
No matter how you slice it, the global temperature data is running at the very bottom of model projections. I think that falsification of model projections is near. If it temperature continues not warm for another 15 years as theorized by Wyatt’s and Curry’s Stadium Wave paper (http://www.news.gatech.edu/2013/10/10/%E2%80%98stadium-waves%E2%80%99-could-explain-lull-global-warming) then it will be obvious that climate models are inaccurate and falsified.
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panzerboy at 01:36 AM on 16 October 2013Two degrees: how we imagine climate change
Chriskov @ 3
The 'hundreds of years' and '10,000 times the rate' confused me.
Ten thousand times the rate of 200 years would be a little over a week.
So were going to have 2 degrees C higher average temperaures after a week?!
Perhaps its a typo, perhaps its pure alarmism.
Moderator Response:[JH] Lose the snark, or lose your posting privilege.
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PhilBMorris at 01:19 AM on 16 October 2013Two degrees: how we imagine climate change
Claude @ 1
2oC is a political target, not one based on science. Nor is it very realistic, given that there's little sign of anything other than BAU. I’d rather assume that Hansen is right – that 2oC is a prescription for disaster – than blindly accept a politically convenient target created simply so that all governments can ‘get on board’. But keeping below 2oC is going to be politically and economically very difficult. Although some people are encouraged by the increase in renewables, the impact of that increase is really very small and will not affect the path we're heading down. Decarbonizing the grid, in itself a major undertaking, will reduce CO2 emissions by just 30%, and would merely delay the inevitable by a few years. Entire manufacturing processes need to be changed to reduce industrial CO2 emissions. But, for example, how do you produce concrete without releasing CO2? And who's going to stop impoverished people in undeveloped nations from continuing to clear land for agriculture, a practice that not only releases CO2 but reduces the ability of the biomass to act as a global sink for CO2? The 2oC 'mantra' is almost as bad as the 'tokenism' individuals are asked to perform (changing out incandescent light bulbs for CFLs or LEDs; turn down the thermostat at night; drive less etc. etc.) as if any of that will make any real difference. Nothing short of massive and rapid investment in both non-fossil fuel sources such as Thorium based nuclear power, and in effective carbon sequestration, will undo what we have done over the past 100 years – and get us back to safe CO2 levels. i.e. 350 ppm.
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Paul D at 00:43 AM on 16 October 2013Consensus study most downloaded paper in all Institute of Physics journals
I think the morse telegraph was available in Tyndalls time, the original internet.
So it would have been possible to send messages about Tyndall.
Also 'Telegraph' shopping predates internet shopping. -
Tom Curtis at 23:01 PM on 15 October 2013Why Curry, McIntyre, and Co. are Still Wrong about IPCC Climate Model Accuracy
Further to my graph @126, here is a similar graph for CMIP5:
The most important difference is that I have shown trends from 1983, to match the chart shown by Roy Spencer in a recent blog.
There are some important differences between my chart, and that by Spencer. First, I have used the full ensemble for RCP 8.5 (80 realizations, including multiple realizations from some individual models); whereas Spencer has used 90 realizations with just one realization per model, but not all realizations from the same scenario. Second, I have used thirty years of annual average data. Spencer has used the five year running, terminal mean. That is, his data for 1983 is the mean of the values from 1979-1983, and so on.
Even with these differences, the results are very similar (unsurprisingly). Specifically, where he has the percentile rank of the HadCRUT4 trend vs the Ensemble as less than 3.3% of the ensemble realizations, I have it at 3.43 %ile of the ensemble realizations. For UAH, the 30 year trend is at the 3.32 %ile of ensemble realizations. Taking trends from 1979 for a more direct comparison, then we have:
UAH: 2.29 [0, 32.2] %ile
HadCRUT4: 3.34% [0.96, 23.16] %ile
GISS: 3.45 [0.92, 28.66] %ile.
For those unfamiliar with the convention, the numbers in the square brackets are the upper and lower confidence bounds of the observed trends.
Mention of confidence intervals raises interesting points. Despite all Spencer's song and dance, the 30 year trends for HadCRUT4 and UAH do not fall outside the 2.5 to 97.5 %ile range, ie, the confidence intervals as defined by the ensemble. More importantly, the confidence intervals of the observed trends encompas have upper bounds in the second quartile of ensemble trends. Consequently, observations do not falsify the models.
This is especially the case as there are known biases on the observed trends which Spencer knows about, but chooses not to mention. The first of these is the reduced coverage of HadCRUT4, which give is a cool bias relative to truly global surface temperature measures. The second is the ENSO record (see inset), which takes us from the strongest El Nino in the SOI record (which dates back to 1873) to the strongest La Nino in the SOI record. This strong cooling trend in the physical world is not reflected in the models, which may show ENSO fluctations, but do not coordinate those fluctuations with the observed record.
Indeed, not only does Spencer not mention the relevance of ENSO, he ensured that his average taken from 1979 included the 1983, ei, the record El Nino, thereby ensuring that his effective baseline introduced a bias between models and observations. Unsurprisingly (inevitably from past experience), it is a bias which lowers the observed data relative to the model data, ie, it makes the observations look cooler than they in fact were.
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Kevin C at 21:51 PM on 15 October 2013Consensus study most downloaded paper in all Institute of Physics journals
Actually, I'll agree with MartinG to the extent that I don't think downloads are a good measure of the contribution of the paper to human knowledge. For that the long term citation counts are a probably a better measure (but not much better).
However there is another way in which scientific work is guaged: Impact (apologies to those of you in UK academia, who will be cringing right now). Impact is the current fad among UK funding bodies (and largely hated by scientists), and measures the effect of a paper on public policy, culture or the economy. The download statistics may well be a better indicator of impact, and the media coverage certainly is.
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chriskoz at 21:28 PM on 15 October 2013Consensus study most downloaded paper in all Institute of Physics journals
MartinG@2,
Your conception that "most downloaded" can equal "most controversial, the most flawed, or the most criticised" aplies to thte world of sensationalism, false balance and loud contrarians.
In the world of science, the junk work is not simply not interesting: honestly, the sicentists simply don't have time to bother about it. The sensentionalism is not interesting to experts - check out e.g. what gavin has to say about it on RealClimate. So, if John is talking about this world, rather than the world of denialism you imply, then his "bragging" is fully justified: scientists usually download the stuff that is valuable and interesting: be it a valuable reference material, interesting new results or new, even controversial, skeptical (in a true sense) approach.
I'm confident (although I haven't measured), the scientific interest aound Cook 2013 work outweigh the interests of the contrarians who want to obfuscate its results.
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Kevin C at 21:20 PM on 15 October 2013Consensus study most downloaded paper in all Institute of Physics journals
MichealM: I'm going to let you into a secret. Almost all science is done on the cheap. I've had quite a few of my lectures webcast, including a number from major international meetings, and mostly they not much better than the one above.
Only one was edited as you suggest, and that was from a small workshop at a national centre which hosts several Nobel prizewinners.
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MichaelM at 20:33 PM on 15 October 2013Consensus study most downloaded paper in all Institute of Physics journals
That talk is a great example of cheap video production. Did somebody just plant a laptop and angle the screen camera upwards? The sound is mediocre (it did pick up some audience coughs perfectly), I can't hear any questions asked and I can't read what's on the screen. Would it have been so difficult to cut the slides into the video? I gave up after a few minutes.
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John Cook at 20:32 PM on 15 October 2013SkS social experiment: using comment ratings to help moderation
Pluvial, what you're describing sounds a lot like pagerank, the system that Google use to rank websites. How it works is each time a person gets a thumbs up, they gain some "reputation". A thumbs up from someone with higher reputation is worth more than a thumbs up from someone with low reputation. Reputation needs to be calculated iteratively - you work out an initial reputation based on simple thumbs up, then recalculate reputation with ratings weighed by the reputation of each rater, and repeat until the reputation values converge on final values.
I do plan to explore that as a possible feature to integrate into SkS but I need lots of data first. So everyone, do be sure to rate comments and blog posts in order to give me lots of data to examine.
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John Cook at 18:42 PM on 15 October 2013Consensus study most downloaded paper in all Institute of Physics journals
Plus the talk I gave at the University of Queensland where I address 5 of the major criticisms of our paper: http://sks.to/tcptalk
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ajki at 15:50 PM on 15 October 2013Temp record is unreliable
dvaytw@274:
Your opponent makes this extreme claim:
1. No average temperature of any part of the earth's surface, over any period, has ever been made. How can you derive a "global average" when you do not even have a single "local" average?
What they actually use is the procedure used from 1850, which is to make one measurement a day at the weather station from a maximum/minimum thermometer. The mean of these two is taken to be the average. No statistician could agree that a plausible average can be obtained this way. The potential bias is more than the claimed "global warming. [emphasis added]
This "argument" already breaks if it could be shown that there is at least one location where measurements over a long time span have been made with high quality. In fact many institutes or harbours all over the world are sentimentally proud (up and including irrationally proud) of having exactly those measurement series.
Invite your opponent to a search throughout the internet and s/he might find them. But you could even help her/him. For just one example, the "Long-Term Meteoroligical Station Potsdam Telegraphenberg" has a very nice and explaining website. And s/he could even find some very frustrating series to her/his worldview there. As anyone could clearly see by the "annual mean" graph there is a clear trend over the time and this trend is far atop the often cited 0.8/9°C worldwide. This difference is due to one of the predicted outcomes of the enhanced greenhouse effect - higher latitudes will warm up more than the global mean.
But do not hope to convince deniers by facts - here you'll encounter (perhaps) something like "but it is NOT since 1850!" or, if somebody else shows another time serie beginning at around 1840, the goalpost will shift. Or anything else ;-)
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gpwayne at 15:38 PM on 15 October 2013Consensus study most downloaded paper in all Institute of Physics journals
MartinG: to produce a logical rebuttal, a logical argument is required. Most of the arguments I've seen attempt to find error in the methodology where there was none, build straw-men only to set them on fire, or attack the authors in crude ad-hominems.
Outside of that, and with the patience of a saint, the team has responded repeatedly to criticisms, here and elsewhere. If you would really 'rather read some logical rebuttals', and given that it took me all of five minutes to find, list and link to the following, I'm moved to ask - what's stopping you?
Debunking 97% Climate Consensus Denial
Debunking New Myths about the 97% Expert Consensus on Human-Caused Global Warming
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MartinG at 15:23 PM on 15 October 2013Consensus study most downloaded paper in all Institute of Physics journals
Most downloaded? Hmmmm - I wouldnt brag about that - it may be the most downloaded because it is the most interesting, or most exciting, or most informative - or that it is the most controversial, the most flawed, or the most criticised. I think I would rather read some logical rebuttals to the massive criticism that it has induced please.
Moderator Response:[JH] Lose the snark, or lose your posting privilege.
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Consensus study most downloaded paper in all Institute of Physics journals
My compliments to all authors. IMO well written and well supported.
And considering the ongoing and multiple exploding heads over the paper in denier-ville, very effective at addressing the consensus gap.
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scaddenp at 12:40 PM on 15 October 2013Why Curry, McIntyre, and Co. are Still Wrong about IPCC Climate Model Accuracy
"have to respectfully disagree with you about testing climate model on past data. First, we really do not have enough solid and reliable measurements of the past to really determine the various components of the climate. "
Excuse me but have you read any paper on the topic at all? Looked at the paleoclimate section of WG1? You have measurements of various climatic parameters with obviously error bounds on them. You have have estimates of forcings with similar error bounds. You run the models and see whether range of results for forcings are within the error limits for forcings. Are the forcings well enough known to constrain the models for testing? Well no, in some cases, yes in others. You can certainly make the statement that observations of past climates are expliciable from modern climate theory.
Start at Chap 6 AR4. Read the papers.Moderator Response:(Rob P) - Please note that Stealth's last few comments have been deleted due to violation of the comments policy. Replies to these deleted comments have likewise been erased.
If you wish to continue, limit yourselves to either one or two commenters - in order to avoid 'dogpiling' on Stealth. And request that Stealth provide peer-reviewed literature to support his claims. Claims made without the backing of the peer-reviewed scientfic literature are nothing more than non-expert personal opinion.
Further 'dogpiling' and/or scientifically unsupported claims will result in comments being deleted.
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chriskoz at 12:29 PM on 15 October 2013Two degrees: how we imagine climate change
Third paragraph under the graphic says:
But these events (Laurentide icesheet) happened 18,000 years ago, over a timeframe of hundreds of years, as a result of changes in the earth’s orbit and other natural forces.
That does not make sense. If you're talking about Heinrich events, you may say they lasted for few hundred years. But if you are talking about Milankovic cycles, this is a typo, you probably meant "hundred thousands of years".
If my assumption about the typo is correct, then in next paragraph you state: "human-forced climate change is happening at 10,000 times the rate". That statement is not in the right ballpark (rate too fast), because in reality AGW's happening in hundreds of years, so ~1000 times faster that Milankovic forcings.
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Leto at 11:57 AM on 15 October 2013Why Curry, McIntyre, and Co. are Still Wrong about IPCC Climate Model Accuracy
Stealth wrote:
As a decent poker player myself, it is not a foregone conclusion that an expert will beat a complete novice.
I am afraid you appear determined to resist enlightenment. Try to understand the conceptual point behind the analogies, rather than getting side-tracked into a discussion of the analogy itself. The odds of my mother beating the world expert in poker over several hours of play are close to zero, despite the unpredictable short-term possibility of her getting an unbeatable hand at any one moment, but that is not the point. The point is, the long-term predictability of an expert-vs-novice poker session is obviously much higher than its short-term predictability. The long-term rise in a swimming pool's level during filling is much more predictable than the moment-to-moment splashes. The physics of heat accumulation on the planet are much more amenable to modelling than short-term cycles and weather.
Don't argue the analogies unless you put the effort into understanding the concept behind them. The analogies were raised because you seemed to take it as axiomatic that long-term predictability could never exceed short-term predictability, a view that is trivially easy to falsify.
I chose this name because it is what I do, and I think I know what I’m talking about when it comes to software and modeling. This doesn’t mean all modeling and all software, but it is easier for me to extrapolate my knowledge and experience from my domain than, say, someone in the mortuary business. (Emphasis added).
I think you have amply demonstrated that your experience in the aviation domain is actually preventing you from understanding. I see too much extrapolation from an entirely unrelated domain, which has a different dependence on small-scale and short-term phenomena completely unlike climatology, coupled with preconceived ideas about what conclusions you want to reach.
Perhaps this is what some of you were trying to explain, but it didn’t come across very clear.
See above. The problem is with the receiver. The points were very clear to anyone prepared to be educated.
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Tom Curtis at 11:41 AM on 15 October 2013Ocean In Critical State from Cumulative Impacts
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Why Curry, McIntyre, and Co. are Still Wrong about IPCC Climate Model Accuracy
Stealth - "...the fact remains that sea surface temps and global air temps have been very steady for a long time, contrary to the models projections."
Did you actually read the opening post??? It doesn't appear so... See the third figure; recent (and by that I mean the 15-17 years discussed by deniers) trends are but short term, statistically insignificant noise, cherry-picked from an extreme El Nino to a series of La Ninas. It's noteworthy that similar length trends (1992-2006) can be shown to have trends just as extreme in the other direction - a clear sign that you are not looking at enough data.
I've said this before, discussing recent trends and statistically significant trends. Examining any time-span starting in the instrumental record and ending in the present:
- Over no period is warming statistically excluded.
- Over no period is the hypothesis of "no warming" statistically supported WRT a null hypothesis of the longer term trends.
- And over any period with enough data to actually separate the two hypotheses – there is warming.
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bibasir at 10:05 AM on 15 October 2013Ocean In Critical State from Cumulative Impacts
The scariest piece of information to me was published in Nature July 29, 2010. I'm not good at links, but use Google Scholar and search "Boris Worm phytoplankton." His team reported a 40% drop in phytoplankton since 1950. Phytoplankton are crucial to much of life on Earth. They are the foundation of the bountiful marine food web, produce half the world's oxygen and suck up carbon dioxide. Half the world's oxygen! I don't know why that fact alone does not send shudders through the population. It may take a long time before oxygen levels are depleted enough to cause harm, but what then?
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scaddenp at 09:58 AM on 15 October 2013Why Curry, McIntyre, and Co. are Still Wrong about IPCC Climate Model Accuracy
Hmm, weather is chaotic. Whether climate is chaotic is an open question. Heat a large pan of water. The convection currents and surface temperatures are a challenge to model, but one thing for sure - while the heat at bottom exceeds heat loss from pan, then pan will keep on heating.
By the way, if you want to discuss modelling directly with the climate modellers then ask away at RealClimate (read their FAQ first) - or on Isaacc Held's blog when the US government reopens.
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Why Curry, McIntyre, and Co. are Still Wrong about IPCC Climate Model Accuracy
Stealth - Boundary value solutions can indeed deal with changes in forcings that were not in the original projection scenarios, by simply running on actual values. If the LA (or Detroit) freeway model accurately reflected those boundaries and limits, running it with a different set of economics would indeed give the average traffic for that scenario. That is the essence of running multiple projections with different emissions scenarios (RCPs) - projecting what might happen over a range of economic scenarios in order to understand the consequences of different actions.
And a projection is indeed testable - as Tom Curtis points out simply running a physically based boundary value model on past forcings should (if correct) reproduce historic temperatures, or perhaps using hold-out cross validation. And models can (and will) be tested against future developments, see the discussion of Hansens 1988 model and actual (as opposed to projected) emissions.
Finally, as to your statement "...in a chaotic and non linear system like the climate", I would have to completely disagree. Yes, there are aspects of chaos and non-linearity in the weather. But (and this is very important) even the most chaotic system orbits on an attractor, which center the long term averages, the climate. And the location of that attractor is driven by the boundary conditions, by thermodynamics and conservation of energy. Weather will vary around the boundary conditions, but the further it goes from balance the strong the correcting forcing.
Claiming that we cannot understand how the climate behaves under forcing changes is really an Appeal to Complexity fallacy - given that our models actually do very well.
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scaddenp at 09:45 AM on 15 October 2013Why climate change contrarians owe us a (scientific) explanation
I picked on Luxembourgh because they come in at a whooping 300kWh/p/d.
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MA Rodger at 09:20 AM on 15 October 2013Why climate change contrarians owe us a (scientific) explanation
scaddenp @78.
I'm not entirely sure why you pick on Luxembourg but they actually do have the largest per capita accumulative CO2 emissions (since 1750). There is a bit of geography here. Second on such a list is UK & Belgium is fourth (only receltly overhauled by USA) as this is the region where the industrial revolution first grew big. Luxembourg continues to have a big steel industry and is a very small country which is why it manages to remain out in front. (Thus didn't feel they should feature on this graphic of national & per capita accumulative emissions because I considered Luxembourg with its steel industry a sort of special case. And they are a small counrty.)
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Tom Curtis at 09:01 AM on 15 October 2013Why Curry, McIntyre, and Co. are Still Wrong about IPCC Climate Model Accuracy
SAM @152, I have to disagree with you on the falsifiability of models based on projections based on two points:
1) Although models can only make projections of future climate changes, they can be run with historical forcings to make retrodictions of past climate changes. Because the historical forcings are not the quantities being retrodicted, this represents a true test of the validity of the models. This has been extended to testing whether models reproduce reconstructed past climates (including transitions between glacials and interglacials) with appropriate changes in forcings.
2) Even projections are falifiable. To do so simply requires that the evolution of forcings over a given period be sufficiently close to match those of a model projection; and that the temperatures in the period of the projection run consistently outside the outer bounds of the projected temperature range. It should be noted that, because the outer bounds are large relative to the projected change in temperature in the short term, that is almost impossible in the short term. In the longer term (30+ years), however, the projected (and possible) temperature changes are large relative to the outer bounds and falsification becomes easy. In other words, the experiment takes a long time to run. There is nothing wrong with that. It is simply the case that some experiments do take a long time to run. That does not make them any less scientific.
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StealthAircraftSoftwareModeler at 08:29 AM on 15 October 2013Why Curry, McIntyre, and Co. are Still Wrong about IPCC Climate Model Accuracy
I’ve done a little more research, and I’ve stumbled upon something from the Met Office. This clears up some of the discussion, perhaps, and it is against some of the points I have made, so please give me credit for being honest and forthcoming in the discussion:
“We should not confuse climate prediction with climate change projection. Climate prediction is about saying what the state of the climate will be in the next few years, and it depends absolutely on knowing what the state of the climate is today. And that requires a vast number of high quality observations, of the atmosphere and especially of the ocean.”
“The IPCC model simulations are projections and not predictions; in other words the models do not start from the state of the climate system today or even 10 years ago. There is no mileage in a story about models being ‘flawed’ because they did not predict the pause; it’s merely a misunderstanding of the science and the difference between a prediction and a projection.”
I think this clears up some of the discussion, I think. Perhaps this is what some of you were trying to explain, but it didn’t come across very clear. I was thinking that GCMs are making predictions instead of projections. Nevertheless, I don’t like projections because they are not falsifiable. Without known inputs and projected outputs, and truth data to compare against, how are these models tested and validated? I can’t think of a single way to verify them, and only one way to falsify them, and that is for the climate to cool when it is projected to warm. So, if the current global temperature pause extends out further or turns into cooling, only then will we have a falsification of the models.
And here a few select replies to some points made above:
Tom Dayton @144: I’ll re-read the SkS post on “How Reliable are the Models” again. I had some specific issues with it, but I’ll keep them to that post. As for Steve Easterbrook’s explaination… did you read it and understand it? I don’t think so. One sentence there sums it all up: “This layered approach does not attempt to quantify model validity”. Nuf’said.
Leto @145: As for my username and background -- I am not pulling rank or appealing to authority as I understand those logical fallacies. I chose this name because it is what I do, and I think I know what I’m talking about when it comes to software and modeling. This doesn’t mean all modeling and all software, but it is easier for me to extrapolate my knowledge and experience from my domain than, say, someone in the mortuary business.
KR @146: yes, you are correct in that I was misunderstanding boundary and initial value problems. I’ve read up on them a little, and I am not sure how useful they in the longer term. In your traffic projection example, yes they work well, but only when all of your underlying assumptions are met. For example, 10 years ago Detroit assumed much about the economy and economic growth and built a lot of stuff. Then the broader economy turned down, people left, and the city has gone bankrupt. The future is really hard to predict.
I think models are at the very low end of their boundary conditions with the current pause. Whether all the heat is stealthily sinking into the deep ocean is a separate issue, but the fact remains that sea surface temps and global air temps have been very steady for a long time, contrary to the models projections. Even all the big climate houses are admitting the pause, so you guys might as well admit it too.
And for your statement of “as we have a pretty good handle on the physics”, I disagree. Sure we have a good understanding of a lot of physics, but in a chaotic and non linear system like the climate, I think there is much that is not known. Take ENSO, PDO, and ADO, no one has a good physics model describing these, and they are major drivers of the climate. Unless you believe there is almost nothing unknown in the climate, then your statement on having a good handle in physics is a red herring. Donald Rumsfeld said it best: “There are known knowns; there are things we know that we know. There are known unknowns; that is to say, there are things that we now know we don't know. But there are also unknown unknowns – there are things we do not know we don't know.” I am pretty confident that there will be some unknown unknowns that will turn current climate science on its head -- and the pause is starting to back it up. If Wyatt’s and Curry’s peer reviewed paper on “Stadium Waves” (see: http://www.news.gatech.edu/2013/10/10/%E2%80%98stadium-waves%E2%80%99-could-explain-lull-global-warming) published in Climate Dynamics is correct, the pause could last much longer and would clearly falsify the climate models and most of AGW. I think we have time to wait another decade or two before taking an drastic action on curbing CO2 emissions to see if the IPCC’s projections are correct or not.
Barry @149: coin flipping is a false analogy and not applicable forecasting climate or any chaotic system.
JasonB @150 (and Leto @145): As a decent poker player myself, it is not a foregone conclusion that an expert will beat a complete novice. On average it is much more likely an expert will win, but in any given instance the claim is not true at all. The entire casino industry is built upon the Central Limit Theorem, which states “the arithmetic mean of a sufficiently large number of iterates of independent random variables, each with a well-defined expected value and well-defined variance, will be approximately normally distributed.” If a roulette wheel pays 36-to-1 on selecting the correct number on the wheel but there are 38 numbers (counting 0 and 00), assuming a fair wheel that is random, then the house has a 5.3% advantage over the player. In the long run, they $0.053 for every dollar bet on the wheel. This gaming theory with a well defined mean does not apply to the climate – the climate is chaotic, non linear and does not have a well defined mean.
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scaddenp at 08:08 AM on 15 October 2013Why climate change contrarians owe us a (scientific) explanation
What I would really like to see is an American (or Luxembourg) version of the document. How do you actually manage to use 250kWh/p/d? That's more than double NZ or UK but I wouldnt have thought the lifestyle that much more energy intensive. I suspect the figure for consumer energy use have some issues.
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