by Judith Curry
“Letting go of the phantastic mathematical objects and achievables of model- land can lead to more relevant information on the real world and thus better-informed decision- making.” – Erica Thompson and Lenny Smith
The title and motivation for this post comes from a new paper by Erica Thompson and Lenny Smith, Escape from Model-Land. Excerpts from the paper:
“Model-land is a hypothetical world (Figure 1) in which mathematical simulations are evaluated against other mathematical simulations, mathematical models against other (or the same) mathematical model, everything is well-posed and models (and their imperfections) are known perfectly.”
“It also promotes a seductive, fairy-tale state of mind in which optimising a simulation invariably reflects desirable pathways in the real world. Decision-support in model-land implies taking the output of model simulations at face value (perhaps using some form of statistical processing to account for blatant inconsistencies), and then interpreting frequencies in model-land to represent probabilities in the real-world.”
“It is comfortable for researchers to remain in model-land as far as possible, since within model-land everything is well-defined, our statistical methods are all valid, and we can prove and utilise theorems. Exploring the furthest reaches of model-land in fact is a very productive career strategy, since it is limited only by the available computational resource.”
“For what we term “climate-like” tasks, the realms of sophisticated statistical processing which variously “identify the best model”, “calibrate the parameters of the model”, “form a probability distribution from the ensemble”, “calculate the size of the discrepancy” etc., are castles in the air built on a single assumption which is known to be incorrect: that the model is perfect. These mathematical “phantastic objects”, are great works of logic but their outcomes are relevant only in model-land until a direct assertion is made that their underlying assumptions hold “well enough”; that they are shown to be adequate for purpose, not merely today’s best available model. Until the outcome is known, the ultimate arbiter must be expert judgment, as a model is always blind to things it does not contain and thus may experience Big Surprises.”
The Hawkmoth Effect
The essential, and largely unrecognized, problem with global climate models is model structural uncertainty/error, which is referred to by Thompson and Smith as the Hawkmoth Effect. A poster by Thompson and Smith provides a concise description of the Hawkmoth effect:
“The term “butterfly effect”, coined by Ed Lorenz, has been surprisingly successful as a device for communication of one aspect of nonlinear dynamics, namely, sensitive dependence on initial conditions (dynamical instability), and has even made its way into popular culture. The problem is easily solved using probabilistic forecasts.
“A non-technical summary of the Hawkmoth Effect is that “you can be arbitrarily close to the correct equations, but still not be close to the correct solutions”.
“Due to the Hawkmoth Effect, it is possible that even a good approximation to the equations of the climate system may not give output which accurately reflects the future climate.”
From their (2019) paper:
“It is sometimes suggested that if a model is only slightly wrong, then its outputs will correspondingly be only slightly wrong. The Butterfly Effect revealed that in deterministic nonlinear dynamical systems, a “slightly wrong” initial condition can yield wildly wrong outputs. The Hawkmoth Effect implies that when the mathematical structure of the model is only “slightly wrong”, then even the best formulated probability forecasts will be wildly wrong in time. These results from pure mathematics hold consequences not only for the aims of prediction but also for model development and calibration, ensemble interpretation and for the formation of initial condition ensembles.”
“Naïvely, we might hope that by making incremental improvements to the “realism” of a model (more accurate representations, greater details of processes, finer spatial or temporal resolution, etc.) we would also see incremental improvement in the outputs. Regarding the realism of short- term trajectories, this may well be true. It is not expected to be true in terms of probability forecasts. The nonlinear compound effects of any given small tweak to the model structure are so great that calibration becomes a very computationally-intensive task and the marginal performance benefits of additional subroutines or processes may be zero or even negative. In plainer terms, adding detail to the model can make it less accurate, less useful.”
JC note: This effect relates to the controversy surrounding the very high values of ECS in the latest CMIP6 global model simulations (see section 5 in What’s the worst case?), which is largely related to incorporation of more sophisticated parameterizations of cloud-aerosol interactions.
Fitness for purpose
From the Thompson and Smith paper:
“How good is a model before it is good enough to support a particular decision – i.e., adequate for the intended purpose (Parker, 2009)? This of course depends on the decision as well as on the model, and is particularly relevant when the decision to take no action at this time could carry a very high cost. When the justification of the research is to inform some real-world time-sensitive decision, merely employing the best available model can undermine (and has undermined) the notion of the science-based support of decision making, when limitations like those above are not spelt out clearly.”
“Is the model used simply the “best available” at the present time, or is it arguably adequate for the specific purpose of interest? How would adequacy for purpose be assessed, and what would it look like? Are you working with a weather-like task, where adequacy for purpose can more or less be quantified, or a climate-like task, where relevant forecasts cannot be evaluated fully? How do we evaluate models: against real-world variables, or against a contrived index, or against other models? Or are they primarily evaluated by means of their epistemic or physical foundations? Or, one step further, are they primarily explanatory models for insight and understanding rather than quantitative forecast machines? Does the model in fact assist with human understanding of the system, or is it so complex that it becomes a prosthesis of understanding in itself?”
“Using expert judgment, informed by the realism of simulations of the past, to define the expected relationship of model with reality and critically, to be very clear on the known limitations of today’s models and the likelihood of solving them in the near term, for the questions of interest.”
My report Climate Models for Laypersons, addressed the issue of fitness for purpose of global climate models for attribution of 20th century global warming:
“Evidence that the climate models are not fit for the purpose of identifying with high confidence the relative proportions of natural and human causes to the 20th century warming is as follows:
- substantial uncertainties in equilibrium climate sensitivity (ECS)
- the inability of GCMs to simulate the magnitude and phasing of natural internal variability on decadal-to-century timescales
- the use of 20th century observations in calibrating/tuning the GCMs
- the failure of climate models to provide a consistent explanation of the early 20th century warming and the mid-century cooling.”
From my article in the CLIVAR Newsletter:
“Assessing the adequacy of climate models for the purpose of predicting future climate is particularly difficult and arguably impossible. It is often assumed that if climate models reproduce current and past climates reasonably well, then we can have confidence in future predictions. However, empirical accuracy, to a substantial degree, may be due to tuning rather than to the model structural form. Further, the model may lack representations of processes and feedbacks that would significantly influence future climate change. Therefore, reliably reproducing past and present climate is not a sufficient condition for a model to be adequate for long-term projections, particularly for high-forcing scenarios that are well outside those previously observed in the instrumental record.”
With regards to 21st century climate model projections, Thompson and Smith make the following statement:
“An example: the most recent IPCC climate change assessment uses an expert judgment that there is only approximately a 2/3 chance that the actual outcome of global average temperatures in 2100 will fall into the central 90% confidence interval generated by climate models. Again, this is precisely the information needed for high-quality decision support: a model-based forecast, completed by a statement of its own limitations (the Probability of a “Big Surprise”).”
While the above statement is mostly correct, the IPCC does not provide a model-based forecast, since they admittedly ignore future volcanic and solar variability.
Personally I think that the situation with regards to 21st century climate projections is much worse. From Climate Models for Laypersons:
“The IPCC’s projections of 21st century climate change explicitly assume that carbon dioxide is the control knob for global climate. The CMIP climate model projections of the 21st century climate used by the IPCC are not convincing as predictions because of:
- failure to predict the warming slowdown in the early 21st century
- inability to simulate the patterns and timing of multidecadal ocean oscillations
- lack of account for future solar variations and solar indirect effects on climate
- neglect of the possibility of volcanic eruptions that are more active than the relatively quiet 20th century
- apparent oversensitivity to increases in greenhouse gases”
With regards to fitness for purpose of global/regional climate models for climate adaptation decision making, there are two particularly relevant articles:
- The Myopia of Imperfect Climate Models, by Frigg, Smith and Stainforth
- On the use and misuse of climate change projections in international development by Nissan et al.
“When a long-term view genuinely is relevant to decision making, much of the information available is not fit for purpose. Climate model projections are able to capture many aspects of the climate system and so can be relied upon to guide mitigation plans and broad adaptation strategies, but the use of these models to guide local, practical adaptation actions is unwarranted. Climate models are unable to represent future conditions at the degree of spatial, temporal, and probabilistic precision with which projections are often provided which gives a false impression of confidence to users of climate change information.”
Pathways out of model land and back to reality
Thompson and Smith provide the following criteria for identifying whether you are stuck in model land with a model that is not adequate for purpose:
“You may be living in model-land if you…
- try to optimize anything regarding the future;
- believe that decision-relevant probabilities can be extracted from models;
- believe that there are precise parameter values to be found;
- refuse to believe in anything that has not been seen in the model;
- think that learning more will reduce the uncertainty in a forecast;
- explicitly or implicitly set the Probability of a Big Surprise to zero; that there is nothing your model cannot simulate;
- want “one model to rule them all”;
- treat any failure, no matter how large, as a call for further extension to the existing modelling strategy.”
“Where we rely more on expert judgment, it is likely that models with not-too-much complexity will be the most intuitive and informative, and reflect their own limitations most clearly.”
“In escaping from model-land do we discard models completely: rather, we aim to use them more effectively. The choice is not between model-land or nothing. Instead, models and simulations are used to the furthest extent that confidence in their utility can be established, either by quantitative out-of-sample performance assessment or by well-founded critical expert judgment.”
Thompson and Smith focus on the desire to provide probabilistic forecasts to support real-world decision making, while at the same time providing some sense of uncertainty/confidence about these probabilities. IMO once you start talking about the ‘probability of the probabilities,’ then you’ve lost the plot in terms of anything meaningful for decision making.
Academic climate economists seem to want probabilities (with or without any meaningful confidence in them), and also some who are in the insurance sector and the broader financial sector. Decision makers that I work with seem less interested in probabilities. Those in the financial sector want a very large number of scenarios (including plausible worst case) and are less interested in actual probabilities of weather/climate outcomes. In non financial sectors, they mostly want a ‘best guess’ with a range of uncertainty (nominally the ‘very likely’ range); this is to assess to what degree they should be concerned about local climate change relative to other concerns.
As argued in my paper Climate Change: What’s the Worst Case?, model inadequacy and an inadequate number of simulations in the ensemble preclude producing unique or meaningful probability distributions from the frequency of model outcomes of future climate. I further argued that statistical creation of ‘fat tails’ from limited information about a distribution can produce very misleading information. I argued for creating a possibility distribution of possible scenarios, that can be created in a variety of ways (including global climate models), with a ‘necessity’ function describing the level and type of justification for the scenario.
Expert judgment is unavoidable in dealing with projections of future climates, but expert judgment on model adequacy for purpose is arguably more associated with model ‘comfort’ than with any rigorous assessment (see my previous post Culture of building confidence in climate models .)
The ‘experts’ are currently stymied by the latest round of CMIP6 climate model simulations, where about half of them (so far) have equilibrium climate sensitivity values exceeding 4.7C – well outside the bounds of long-established likely range of 1.5-4.5C. It will be very interesting to see how this plays out – do you toss out the climate model simulations, or the long-standing range of ECS values that is supported by multiple lines of evidence?
Application of expert judgment to assess the plausibility of future scenario outcomes, rather than assessing the plausibility of climate model adequacy, is arguably more useful.
Alternative scenario generation methods
An earlier paper by Smith and Stern (2011) argues that there is value in scientific speculation on policy-relevant aspects of plausible, high-impact scenarios, even though we can neither model them realistically nor provide a precise estimate of their probability. A surprise occurs if a possibility that had not even been articulated becomes true. Efforts to avoid surprises begin with ensuring there has been a fully imaginative consideration of possible future outcomes.
For examples of alternative scenario generation that are of particular relevance to regional climatic change (which is exceptionally poorly simulated by climate models), see these previous posts:
Historical and paleoclimate data, statistical forecast models, climate dynamics considerations and simple climate models can provide the basis for alternative scenario generation.
Given the level and types of uncertainty, efforts to bound the plausible range of future scenarios makes more sense for decision making than assessing the probability of probabilities, and statistically manufacturing ‘fat tails.’
Further this approach is a heck of lot less expensive than endless enhancements to climate models to be run on the world’s most powerful supercomputers that don’t address the fundamental structural problems related to the nonlinear interactions of two chaotic fluids.
Kudos to Thompson and Smith for their insightful paper and drawing attention to this issue.
Judy, would you care to speculate on why the CMIP6 simulations have such a high ECS?
now thats funny.
…must step up and stop Scaring the Crap out of 8 year olds!!!
You Sir and Dr. Curry are Heros – the idea this poorly defined climate confusing mess is impacting children is an insult!
You are both so better than this — please consult and find the way to quiet the discord and inspire our children!
The average climate model today, based on a too high ECS, grossly over-predicts global warming, yet the government bureaucrats with science degrees do not care.
The 95% pro-leftist-biased mass media do not report the over-predictions
based on a too-high ECS.
When so-called climate scientists are committing science fraud, and they are, by claiming they can predict the future climate (with their predictions failing miserably for over 30 years), they have to RAMP UP the fraud every year to keep people scared, and following their climate proclamations (climate scaremongering) without question.
This year the science fraud theme was: “It’s going to be worse than we thought”.
Next year the science fraud theme will be: “It’s going to be worse than worse than we thought.”
A six year old child knows more about the actual ECS than 99.9% of the so called “climate scientists”.
I wrote an article explaining that bold statement today:
http://elonionbloggle.blogspot.com/2019/11/equilibrium-climate-sensitivity-ecs.html
I’m afraid that Ms. Curry is in the 99.9%, but we like her anyway.
Source: https://www.washingtonexaminer.com/opinion/this-greta-thunberg-thing-is-child-abuse
…
“The case of Thunberg is even more egregious. She began suffering from depression as a child, by her own admission, in part because she learned about climate change at age 8. She was later diagnosed with autism and obsessive compulsive disorder and gradually became despondent as she obsessed over her fear of climate change. She developed mutism and an eating disorder so severe that she once went two months without food, and she stopped going to school. Her only sibling, a sister named Beata, also suffers from Asperger’s and OCD, as well as ADHD. ”
“Now tell me, does it seem healthy to place a child with this many mental illnesses under the spotlight of public scrutiny, with a sole focus on the very phenomenon and associated alarmism that triggered her in the first place?”
This 17 year old child has been impacted by Climate Nonsense. Every child needs to know the limits of Climate Science as well as the obvious uncertainty in this “scientic” morass!
The Scientific Community must step up and stop Scaring the Crap out of 8 year olds!!!
As a retired financial economist, I know a something about “forecast failure.” Climate science has it IN SPADES! The anthropogenic Warmists have evolved/devolved into a cadre of True Believers who will stop at NOTHING to silence and discredit ANYONE who seeks to challenge them. However, from the sidelines, I sense the tide is turning. If the history of science is a guide, they will ultimately fade into irrelevance at worst, or a long footnote at best.
Dan, have you looked at Exxon’s projections for today’s CO2 and temperature, made in the late ’70s? They’re spot on.
https://www.theguardian.com/environment/climate-consensus-97-per-cent/2018/sep/19/shell-and-exxons-secret-1980s-climate-change-warnings#img-2
Appell
Who cares. We’re coming out of the Little Ice Age and on the warming side of the AMO. When the AMO flips the projections won’t look so prescient. Whether you want to face reality or not this is just moving toward a repeat of the Roman and Medieval Warm Periods with an extra boost from CO2.
The planet doesn’t just “come out of” a LIA like some zombie — climate changes if and only if it has a reason to change. AGHGs are that reason.
The AMO has a period of ~60 yrs, and has “flipped” four times since about 1900. (Viz. it’s only gone through two cycles.) The AMO only reshuffles heat, it does not add heat, and the oceans clearly show the planet is gaining heat.
Trying to explain 20th century warming via the AMO (and/or PDO, NAO, ENSO, etc) doesn’t measure up scientifically.
The RWP and MWP were regional phenomenon, not global. So they’re no big deal.
Dan Cogin,
Well said and dead right!
The climate alarmist movement is following a somewhat similar path to that of the anti-nuclear power protest movement, which began about 50 years ago. Here is an interesting summary of a chapter from a book written in 1980 by John Grover
The Struggle for Power
What we Haven’t Been Told and Why!
Read the on-line summary here: http://www.the-rathouse.com/2011/Grover-Power.html
Peter:
If you think something has caused modern warming besides AGHGs, show
your data and we’ll look at it.
Otherwise you have no reason to complain.
David
Appeal to Authority,
Warming is irrelevant. What is relevant is the impact of warming. Until you understand that your comments are irrelevant.
Bye!
I think I understand why Appell is having a problem. He doesn’t stay current with the literature. There are peer reviewed studies showing warming on all the continents. That dog doesn’t hunt anymore. Until we found out that the Medieval Warm Period had to disappear after IPCC1, the MWP existed. Its existence was too inconvenient for the establishment so just like everything else, it was banished to the junk heap of history. Almost every month new papers come out referencing the LIA and MWP.
Try to stay current will you. And no, Frank Sinatra isn’t that swell new crooner the Bobby soxers are raving about.
“Wait until the AMO flips negative.”
https://i.imgur.com/NvbiE5F.png
When the AMO flips negative, the this what is going to happen:
almost nothing
Why? Because almost nothing is what happened the last time it flipped negative. The North Atlantic is physically too small to wag the tail of the dog. Waiting for the AMO to flip negative is exactly the same thing as building a pseudo airport in an effort to get the cargo planes to come back:
mind-century cooling
The big changes in the direction of the GMST in the 20th and 21st century are all associated with the flip in the PDO. It can wag the tail. And it’s likely a cloud flip. The last time it happened it caused the PAWS, and the second the Eastern pacific clouds flipped back the GMST shot up like a rocket. Because the Eastern Pacific is physically large enough to wag the tail. Just not enough to make any real difference negative, and the place broils when it’s positive. And many in the climate science community still think this flip process – mid-century cooling – was caused by changes in aerosols.
The current literature on the MWP is that it was not a global event, and finding evidence of it on different continents is exactly why they do not think it was a global event.
JCH. I think I will trust the studies of glaciers to judge what is global. And not some “current literature on the MWP”, whatever it is.
https://images.app.goo.gl/6DmdFu4vUUzLucgJ6
The planet doesn’t just “come out of” a LIA like some zombie — climate changes if and only if it has a reason to change
And yet, climate models indicate that agw doesn’t take over until the mid 20th century. Well after the planet comes out of the LIA…
https://ossfoundation.us/projects/environment/global-warming/myths/images/mwp/ipcc_6_1_large.jpg
“The current literature on the MWP is that it was not a global event”
From IPCC AR5:
https://climate.mr-int.ch/images/graphs/ipcc_ar5_fig_5_7_temp_reconstruction_past_200_years.png
The argument started by Dan Coggins is intriguing; – in a particular way. Some comment:
A)-David Appell’s link re Shell/Exxon is of particular interest.
B)-cerescokid: “Whether you want to face reality or not this is just moving toward a repeat of the Roman and Medieval Warm Periods with an extra boost from CO2.” Two periods, that corresponded to two consecutive Eddy cycle peaks. But those peaks get more interesting if followed way back into the Holocene Max.
C)- David Appell: “The planet doesn’t just “come out of” a LIA like some zombie-“. No. It might come out with a complete Earth face-lift. Then: “The AMO only reshuffles heat, it does not add heat, and the oceans clearly show the planet is gaining heat.” Don’t really know. However there is evidence that the Med was substantially altered geologically in the Eddy peak of ~5700bce (evidence from man-made structures that are easy to interpret; from both Malta and Lampedusa). The next Eddy peak of ~3550bce left the Sahara a scorched land. Corroborating multiple proxies say the events were global.
D)-Peter Lang: “Warming is irrelevant. What is relevant is the impact of warming.” See point C)
E)- cerescokid: “There are peer reviewed studies showing warming on all the continents.” Yes; the proxies point to ‘global’.
F)-JCH: “The current literature on the MWP is that it was not a global event, and finding evidence of it on different continents is exactly why they do not think it was a global event.” ??? Because ‘finding evidence of it on different continents’ points to global.
G) nobodysknowledge: “-trust the studies of glaciers to judge what is global-“. One good proxy, but there are other corroborating proxies, like Med sea sediments, etc,.
Taking a long term view (with the help of this: https://melitamegalithic.wordpress.com/2019/03/15/searching-evidence-update-2/ ) pre 9500bce it looks like it was cold!!. During ~9500 to ~2000bce it was ‘eventful’. From 2000bce to 2000ce a period of moderate calm where Eddy peaks and roots were mild events comparatively. I suspect we don’t have a clue of the DNA of this elephant.
This is a list of authors who were involved in peer reviewed studies that found evidence of warming/cooling in locations across the globe during the MWP or LIA. The list is a small fraction of all such authors since it’s just off the top of my head:
Lia,Coffinet,Huguet,Bergonzinid,Pedentchouk,Williamson,Gafka,Kofacze,Majule,Wagner,Derrene,Castro,Vergara,Alvarez,Bernier,deVernal,ChartieGanzey,Bazarova,Arslanov,Mokkova,Belyanina,Lyaschevskaya,MazarellaMcGown,Callow,Soderham,McGrath,Zhao.Grove,Switsur,Cook,Palmer,D’Arrigo,Guglielmin,Convey,Malfasi,Cannone,Huang,Pollack,Shen,Smerdon,Wang,Cermak,Divine,Nilsen,Francus,Deng,Liu,Chen,Wei,,Zeng,Xie,Zhao,Rosenthal,Linsley,Oppo,Liu,Yan,Fei,Ma,Zhang,She,Dodson,An,Galka,Garcia,Rodriquez,Luning,Vahrenholt.Rouco,Zhao,Ivanov,Schneider,Talento,Wang,Yang,Lasher,Oxford,Zhu,Duan,Ge,Luterbacher
Or for those who have an affinity for just pretty pictures, there is this:
http://pages.science-skeptical.de/MWP/MedievalWarmPeriod1024x768.htmx
Oh, so where is Appell with his extensive knowledge of the non LIA and non MWP? Apparently, when confronted with facts his appetite for debate shrinks up.
The link below is a paper that has an assemblage of ~120 paleoclimatic proxies for the LIA and almost as many for the MWP. By reading Table 1 you can see the studies cover either singly or in unison a large part of the globe. The paper is nearly 17 years old. There have been many more published since, which has just added to the cumulative knowledge of past climates. I know that, because I’ve been reading them and collecting bookmarks.
I don’t know what really happened during those periods. But I know it’s a lot more complicated than some want us to believe. Maybe there was not synchronized warming. But a case can be made there is not now either. I don’t know if the amplitude is the same as today. But what difference does it make, since the natural and anthropogenic effects could be stacked. But I do know there is some evidence that warming of some kind occurred in more than just one region.
And, oh, save the derogatory crap about the authors of this study. I’ve heard it all before. The ad hominem attacks on skeptical scientists is the reason I started investigating the issue 10 years ago. I was firmly in the warmists camp in the late 1980s and debated with others for the precautionary principle. But then I noticed more recently that instead of attacking the science, the keepers of the treasure attacked the scientists. Why, I wondered. It became obvious. They couldn’t attack the science because they didn’t have the science on their side. So, instead, they leveled the most absurd, irrelevant broadsides against anyone or any line of reasoning that didn’t reinforce the most simpleton argument for CAGW. For the last decade every rathole I’ve gone down in doing intensive research has turned up plausible alternative explanations for the apocalyptic claim or evidence it was all just made up.
When I check out, I’m not worried the least about my grandkids or their great, great grandkids. They will be listening to the same blah, blah, blah. And nothing will be happening. Just more promises of the end of the world.
https://www.int-res.com/articles/cr2003/23/c023p089.pdf
Oh, so where is Appell with his extensive knowledge of the non LIA and non MWP? Apparently, when confronted with facts his appetite for debate shrinks up.
David posts sporadically, so it’s kind of hard to tell why he’s absent when he goes awol. Glad he’s posting though. Like him or not, the discussion does get livelier with his presence. (and that’s a good thing!) And as an added plus, for those of us with a left leaning bent he’s a pretty good guy to have around. You may agree or disagree with Big Appell, but you’ll always learn something in the process either way…
“the failure of climate models to provide a consistent explanation of the early 20th century warming and the mid-century cooling,” exposes ‘hockey stick’ science as an incredible belief in the failed AGW hypothesis.
The hockey stick isn’t a climate model, it’s a reconstruction of past temperatures.
BTW, the hockey stick has been verified many times by now, using many different mathematical techniques. See the PAGES 2k paper from this summer, which used 7 different statistical methodologies. The HS is also expected on simple theoretical grounds.
The Pages papers have been pretty thoroughly taken apart by work done by Steve McIntyre:
https://climateaudit.org/2019/07/31/cg2-and-ex-post-picking/
https://climateaudit.org/2019/02/01/pages2k-2017-antarctic-proxies/
https://climateaudit.org/2018/10/24/pages2k-north-american-tree-ring-proxies/
https://climateaudit.org/2018/10/07/pages2k-2017-south-america-revisited/
etc.
Sure. When his criticisms appear in a good peer reviewed journal, let me know.
Thing is, it’s easy to show the hockey stick is required by basic physics:
1) temperature change = (climate_sensitivity)*(change in forcing)
2) CO2 forcing = constant*log(CO2/initial_CO2)
3) Atmo CO2 has been increasing exponentially since the beginning of the industrial era.
So if CO2 isn’t changing, there is no temperature change — the flat handle of the hockey stick.
If CO2 is increasing exponentially, its forcing is changing linearly and hence so is the temperature – which is the blade of the hockey stick.
“When his criticisms appear in a good peer reviewed journal, let me know.”
Peer review does not prove that a paper is correct as has been proven by the many bogus papers that regularly get through peer review.
“Thing is, it’s easy to show the hockey stick is required by basic physics…”
The only problems with this analysis is it a linear analysis and, according to the IPCC AR3 “The climate system is a coupled non-linear chaotic system” so such a simple analysis is very unlikely to be correct and also the hockey stick claims that the Little Ice Age and the Medieval Warming Period did not happen when both Proxy Data and Historical Records show they did.
On the dark side of the comet Hale-Bopp, where the Heavens Gate cult resides, the LIA and MWP may have been erased; nevertheless, we still have a lot of historical information here on Earth that’s a lot more credible than the government-funded, global warming experts who can’t find the heat without playing with the data.
Wagathon wrote:
“On the dark side of the comet Hale-Bopp, where the Heavens Gate cult resides, the LIA and MWP may have been erased”
Where is the data that, before Mann et al, showed a globally synchronous MWP and LIA?
Of course a peer reviewed paper isn’t guaranteed to be right. But peer review does weed out the amateurs and glaring mistakes. You have to wonder why McIntyre never tried to publish if he found big mistakes in PAGES 2k. Scientists pay attention to and put stock in journals, not blogs.
Besides, there are even more papers and techniques that found a hockey stick, such as Tingley & Huybers, Marcott et al, and others.
My little model isn’t intended to be a full-on climate model — it’s just shows that in the big picture, a hockey stick is the expected result for last millenium’s temperature. The hockey stick is not a surprise.
“But peer review does weed out the amateurs and glaring mistakes.”
That is what it is supposed to do, but in fact it often doesn’t even accomplish that. What it does do is make sure no “unapproved” papers get published. That was clearly demonstrated by the contents of some of the ClimateGate e-mails including some of Dr. Mann’s e-mails.
“My little model isn’t intended to be a full-on climate model … hockey stick is the expected result for last millenium’s temperature.”
And there’s the problem, you expect a hockey stick so you create a model that that creates a hockey stick. Dr. Mann did the same thing. That only proves you can create hockey stick models, not that they have any relationship to the actual climate.
Good contrarian papers get published. Always have.
My little model makes no assumption about a hockey stick shape — the shape comes out of the physics. Can you refute the physics or my logic?
Just more, ‘globaly synchronous’ gobbledygook global warming pseudoscience.
“Good contrarian papers get published. Always have.”
True, unless of course, someone decides it is in his best interest to suppress the paper (see ClimateGate).
My little model makes no assumption about a hockey stick shape — the shape comes out of the physics. Can you refute the physics or my logic?
I never said it made any assumptions, I said it fulfills the purpose for which it was created. The physics, correct or not, are such an oversimplification of the actual climate system that it really doesn’t prove anything at all.
“But peer review does weed out the amateurs and glaring mistakes.”
No, the purpose of peer review is to weed out anything that disagrees with current consensus!
The Lancet found that 50% of peer reviewed studies could not be replicated. Bayer found the number to be 75%.
So which 75% of climate science would you like to consign to the trash?
We’re not ‘tricked’ — “It can’t be proven that he [Mann] intended to suppress or falsify inconvenient data. It’s entirely possible that he accidentally devised a method to suppress or falsify inconvenient data.” ~Middleton
DA, too bad what happened over at Spencer’s. (i think you will be missed over there) i don’t think he’s too well enamored toward the rough and tumble of the blogosphere — which means he’s just normal. Were he to adopt a moderation style like that of Climate, etc, then i think that he wouldn’t have such a big problem on his hands. He never* deletes comments (and that’s a recipe for disaster)…
*excepting d.c.’s afaik
In the climate emails, Phil Jones called it a “model” when explaining to Mann how he could hide is code from McIntyre and McKitrick. So, if the people who wrote it think it’s a model, …
David, “The hockey stick isn’t a climate model, it’s a reconstruction of past temperatures.” All reconstructions are made with models, at least the proxy selection. And all others: The HS stands for a low sensitivity! Learn to love it!
Mr Appeal to Authority Apple is back:
The Hockey Stick used minimal tree ring data, not suitable for temperature reconstructions … then secretly switched to surface thermometer data in roughly 1900, because the tree ring data did not show the “expected” global warming.
Surface temperature data are nearly worthless before 1920, due to very little global coverage, and are very rough before 1940, due to insufficient global coverage.
But early 1900s surface temperature numbers are fine for “goobermint” work, and for Mr. Appeal to Authority, who accepts ONLY the climate numbers he likes, rejects EVERYTHING else, and fears the future climate for no logical reason.
Everyone get out heir brooms and shovels, to clean up the mess made by the original Trained Parrot of Climate Change, Mr. Appeal to Authority Apple, and his many “accidents”, that he calls comments.
what do you mean by verified? what was verified?
frankclimate wrote:
“David, “The hockey stick isn’t a climate model, it’s a reconstruction of past temperatures.” All reconstructions are made with models”
Yes, but that doesn’t mean they’re *climate* models, that can project into the future.
mark4asp wrote:
“In the climate emails, Phil Jones called it a “model” when explaining to Mann how he could hide is code from McIntyre and McKitrick. So, if the people who wrote it think it’s a model,”
OF COURSE it’s a “model” — every calculation in physics is a model. But it’s not a “global climate model” in the sense of those that project the future.
The hockey stick doesn’t project the future.
Wagathon – where before MBH is the data showing the MWP and LIA were globally synchronous?
https://judithcurry.com/2019/10/29/escape-from-model-land/#comment-902132
“There were no globally synchronous multi-decadal warm or cold intervals that define a worldwide Medieval Warm Period or Little Ice Age….”
— “Continental-scale temperature variability during the past two millennia,” PAGES 2k Consortium, Nature Geosciences, April 21, 2013
http://www.nature.com/ngeo/journal/v6/n5/abs/ngeo1797.html
The hockey stick is so egregiously bad even the IPCC dropped it virtually immediately hey saw McIntyre’s papers.
> When [MsIntyre’s] riticisms appear in a good peer reviewed journal, let me know.
When government-funded pal-review journals start opening their minds and subscribing to the basic premises of science – as opposed to “redefining peer-review” – let me know.
BFJ
Sure, dropped like a hot potato. Not even mentioned. No sir.
Oh. Wait.
IPCC AR5 Chapter 5.
Fig 5.7:
https://www.ipcc.ch/site/assets/uploads/2018/02/Fig5-07-1024×639.jpg
BFJ wrote:
The hockey stick is so egregiously bad even the IPCC dropped it virtually immediately hey saw McIntyre’s papers.
Not true — it appears in the 4AR in Chapter 6, “Paleoclimate.” I found 4 citations of Mann et al 1998, 3 of Mann et al 1999, and 12 of Mann and Jones 2003. And a few graphs.
In the figure you posted it appears that the Medieval warm period has reappeared and it looks like a global event. This figure indeed contradicts Mann’s original flawed work. Defending Mann’s work is what nonscientists do.
> the hockey stick has been verified many times by now
Based on the same flawed data set.
And as I understand it Mann himself won’t properly defend it, having recently elected to accept a hefty penalty from the courts rather than have his work finally opened up for public audit, pursuant on his own libel action.
> it’s easy to show the hockey stick is required by basic physics:
And that basic (greenhouse) physics is enough to explain the actual climate system is it ?
dpy,
the comment was that the hockeystick was dropped by the IPCC. Now, is that a hockeystick or not? If in doubt I helpfully quoted the relevant analysis.
Smearing Mann is what [redacted] do.
Looking at the northern hemisphere, it sure looks like a global event.
VeryTallPerson, You are conflating what I said with what someone else said. Even Michael Tobis has admitted that quite quickly after the Hockey stick was published good people starting finding problems. Mann has a long history of flawed research, frivolous law suits, and is an incredibly abrasive personality. His fellow scientists in private say the same thing.
Mann has a long history of flawed research, frivolous law suits, and is an incredibly abrasive personality. His fellow scientists in private say the same thing.
dpy: what “flawed research” is Mann known for?
Are you aware of all the awards he’s won, including prestigious awards given by fellow scientists?
Abrasive personality? Way back I profiled Mann for Scientific American. He was quite pleasant and anything but abrasive. But he is a pugilist when it comes to defending his work from all the scoundrels who have attacked him over the years for political purposes. Few scientists in history have been as attacked as Mann has, and he’s come out with his analysis being replicated every which way, and is a noted spokesperson for the climate issue in media around the world. It’s his refusal to roll over that I think annoys people like you, who lie about him just as you did here.
dpy
Stones, glass houses etc. You really should try to stick to technical matters.
Back on topic, you’re acknowledging there’s still a hockeystick in the AR5, yes?
Verytallguy, even if that hockeystick was accurate, its blade starts much before any hypothesised AGW. The first principale is that you must not fool yourself.
D a m n autocorrect, principle of course!
Well, edim, let’s just celebrate the fact that we seem to agree the IPCC has not dropped the hockeystick.
‘cos examining who exactly is fooling themselves here could get ugly methinks.
Verytallguy,
https://judithcurry.com/2019/10/29/escape-from-model-land/#comment-902381
I note Mr Appell came out with the profound statement “Of course a peer reviewed paper isn’t guaranteed to be right. But peer review does weed out the amateurs and glaring mistakes. “. JC SNIP Why is Retraction Watch posting so many articles?
except they dont fail
“Evidence that the climate models are not fit for the purpose of identifying with
high confidence the relative proportions of natural and human causes to the 20th
century warming,” as Curry observed, does not stop anyone from indulging in religious beliefs about a coming doomsday caused by modernity. Undoubtedly, Dyson would say, a scientist should be skeptical.
Wagathon wrote:
“Evidence that the climate models are not fit for the purpose of identifying with high confidence the relative proportions of natural and human causes to the 20th century warming,” as Curry observed”
Where have climate models failed in this regard? There are no known natural causes for modern warming. If you think there are, show the evidence.
Climate Limits and Timelines
Solar indirect effects and multi-decadal oscillations of large scale ocean circulations have been effectively ignored in interpreting the causes of the recent warming.
(quote from a recent post by dr. c.)
Published Hockey Stick Critiques by McIntyre & McKitrick
It appears David Appell has mastered the art of the Argument from Ignorance in defending Mann’s hockey stick. Steve McIntyre’s critiques mathematically dismantaling Mann’s hockey stick claims were published with Ross McKitrick. See:
Ross McKitrick: Academic Papers.
Global Warming: Paleoclimate / Hockey Stick Academic Papers
https://www.rossmckitrick.com/paleoclimatehockey-stick.html
e.g.,
**McIntyre, Steven and Ross McKitrick, (2003). Corrections to the Mann et al. (1998) Proxy Data Base and Northern Hemisphere Average Temperature Series Environment and Energy 14(6) pp. 751-771.
http://www.uoguelph.ca/~rmckitri/research/MM03.pdf
*McIntyre, S. and R. McKitrick (2004). Materials Complaint Concerning MBH98 Nature 430 July 1, 2004, p. 105.
http://www.uoguelph.ca/~rmckitri/research/fallupdate04/MM.MC.Nov03.pdf
**McIntyre, Stephen and Ross McKitrick (2005) Reply to Comment by von Storch and Zorita on “Hockey Sticks, Principal Components and Spurious Significance” Geophysical Research Letters 32(20) L20714 10.1029/2005GL023089 21 October 2005
http://climateaudit.files.wordpress.com/2009/12/mcintyre-grl-2005.pdf
**McIntyre, Stephen and Ross McKitrick (2005) Reply to Comment by Huybers on “Hockey Sticks, Principal Components and Spurious Significance” Geophysical Research Letters 32(20) L20714 10.1029/2005GL023586 21 October 2005
http://climateaudit.org/2005/10/25/vz-and-huybers-comment-and-reply/
**McIntyre, Stephen and Ross McKitrick (2005) The M&M Critique of the MBH98 Northern Hemisphere Climate Index: Update and Implications Energy and Environment 16(1) pp. 69-100.
http://www.uoguelph.ca/~rmckitri/research/M&M.EE2005.pdf
**McIntyre, Stephen and Ross R. McKitrick (2009) Proxy inconsistency and other problems in millennial paleoclimate reconstructions Proceedings of the National Academy of Sciences February 2, 2009. 106:E10; doi:10.1073/pnas.0812509106
McKitrick, Ross R. (2014) “A Brief Retrospective on the Hockey Stick” forthcoming in Climate Change: The Facts 2014, Institute for Policy Analysis, Australia.
https://www.rossmckitrick.com/uploads/4/8/0/8/4808045/hockey-stick-retrospective.pdf
That’s what, two critiques of the hockey stick published in the peer reviewed literature? (Energy & Environment doesn’t count — its editor has admitted she’s biased.) Against a few dozen confirmations?
http://www.davidappell.com/hockeysticks.html
Responding to 100 Scientists Against Einstein, he retorted “Why 100. If I were wrong one would have been enough”. Mann’s principle component method creates “hockey sticks” out of random data. = Scientifically fails. End story. Consensus is not science.
David Appell “Consensus” is not Science. One article is sufficient to disprove a model. See Einstein responding to 100 Authors against Einstein.
Appell
I suggest you lead an effort to censure the countless authors who have referenced the MWP and LIA for having the temerity to mention nonexistent phenomena. Clearly they didn’t get the memo. Do you think they should be outed? Or better yet, how about the stockade?
The hockey stick can be discredited head on or piece by piece, one paper at a time. Evidence is accumulating every year, one heretical transgression at a time.
It is clear from this thread that those uncritical souls who prefer blog science to the real McCoy, because McIntyre told them so can’t tell a hawksmoth from a handsaw.
The recent fashion for pointing out the fact in philoosopy of science journals does not alter the fact that hard bitten analysts have known from the getgo that while models are not things, those capable of emulating the response of dynamic physical systems to physical forcings can , as Thompson & Smith hasten to point out, with due caution usefully inform the climate policy conversation.
Time, Moore’s Law , humility and heuristics all work on side of the climatemodelers, not those who want the problem ignored.
Russell Seitz Read McKitrick & McIntyre’s published papers. Try out the code posted. See medical researcher John Ioannidis, finding that ~ 90% of published medical research is WRONG – sample statistics fail to prove the argument. https://scholar.google.com/citations?user=JiiMY_wAAAAJ&hl=en&oi=ao
Mann’s principle component method creates “hockey sticks” out of random data
The PAGES 2k paper from this summer used seven different statistical techniques, and all gave a hockey stick.
It’d be surprising if there was NOT a hockey stick over the last few millennia. As I showed earlier, simple physics shows that a hockey stick is the expected result.
David Appell Mann’s statistical “analysis” that CREATES (“finds”) “hockey sticks” in random data is WRONG! Period! Ask any statistician, mathematician or physics or other hard science scientist.
Consensus is not science
Consensus plays a big role in science. There is consensus about everything except what’s at the cutting edge of science. In physics there is a consensus about Newtonian mechanics, thermodynamics, optics, electromagnetism, special and general relativity, and most of quantum mechanics. NASA used consensus science to go the the moon. You rely on consensus science every morning when you start your car or your computer or make a phone call. Lots of consensus.
The reason consensus is used in climate science is because it is not a science that will quickly tied down like some of those listed above. It’s a very difficult physics problem, full of approximations and uncertainties — a global climate model is the most difficult calculation humans have ever attempted. Because it will be many decades, perhaps centuries, before climate science gains the degree of certainty in the fields listed above, and because we can’t wait centuries to calculate ECS to three decimal places, the opinions of knowledgeable scientists matter. So does a consensus of those opinions. It matters because the problem is so pressing that we need to act now, despite having imperfect information. Acting when ECS is known to three decimal places will be far too late. This is why consensus is used and why it matters. If climate change wasn’t a large crucial social issue you’d never hear about it, just as you don’t in other fields. Physicists aren’t forever testing F=dp/dt (=ma usually) in their laboratories because “there is no consensus.” There is certainly a consensus that F = dp/dt.
David Appell wrote:
>Physicists aren’t forever testing F=dp/dt (=ma usually) in their laboratories because “there is no consensus.”
You’re not a physicist, I take it. I am — I studied under Dick Feynman, Kip Thorne, and Steve Weinberg, just to name the Nobelists I have taken classes from.
No, in modern physics, it is not quite true that F = ma: that is a non-relativistic approximation. Try using that in, say, elementary-particle physics, and you will get the wrong answer.
Of course it was indeed the “consensus” in 1900.
But it was wrong, at least at speeds close to light.
That’s how science works: no competent scientist simply accepts the consensus.
The search for “consensus” in climate science is a tell-tale that something is wrong: real science thrives on trying to prove the consensus is wrong.
A new analysis of radiosonde data shows there is no greenhouse effect in our atmosphere. See ( https://www.youtube.com/watch?v=XfRBr7PEawY ) at 1Hr-01 Min for their conclusions. These include that the IPCC was wrong to conclude that recent climate changes were due to greenhouse gasses and current climate model projections are worthless.
Model land must be willing to evaluate new findings and modify the models as necessary. The Connollys have done in depth analysis of 20 million data sets spanning 70 years and state “the data show categorically there is no greenhouse effect” the atmosphere is in thermodynamic equilibrium and more greenhouse gasses do not change temperature but act according to Einstein’s postulate.
I’m not a scientist, but after viewing the Connolly video I was hoping for some commentary here by those who are. It will be interesting to see if they can publish this, and what the criticism will be. Thanks for sharing it.
Bill, what they have published is here: http://oprj.net/articles/atmospheric-science/19
“It can be seen from the infra-red cooling model of Figure 19 that the greenhouse effect theory predicts a strong influence from the greenhouse gases on the barometric temperature profile. Moreover, the modeled net effect of the greenhouse gases on infra-red cooling varies substantially over the entire atmospheric profile.
However, when we analysed the barometric temperature profiles of the radiosondes in this paper, we were unable to detect any influence from greenhouse gases. Instead, the profiles were very well described by the thermodynamic properties of the main atmospheric gases, i.e., N 2 and O 2 , in a gravitational field.”
While water vapour is a greenhouse gas, the effects of water vapour on the temperature profile did not appear to be related to its radiative properties, but rather its different molecular structure and the latent heat released/gained by water in its gas/liquid/solid phase changes.
For this reason, our results suggest that the magnitude of the greenhouse effect is very small, perhaps negligible. At any rate, its magnitude appears to be too small to be detected from the archived radiosonde data.” Pg. 18 of referenced research paper.
https://rclutz.wordpress.com/2019/09/14/global-warming-theory-and-the-tests-it-fails/
Where in their paper do C&C consider the effects of radiative transfer? Nowhere that I can see — it’s all classical thermodynamics. Therefore it’s not surprising they don’t find a greenhouse effect — they didn’t look for one.
PS: This looks like a very amateurish journal and not something to be taken seriously.
Thanks, Ron.
Measurement of Earth’s outgoing spectrum at the top of the atmosphere makes it completely obvious there’s a greenhouse effect:
https://www.forum-ee9.eu/wp-content/uploads/2019/03/toa-clear-cirrus-cld-2-1024×341.png
This is not measured but calculated. SB says quite explicitly that the spectral gaps are filled in a warming planet. Now Harries 2001 is an empirical measure.
https://www.atmos.washington.edu/~dennis/321/Harries_Spectrum_2001.pdf
Ok, thanks. This page has some measured TOA outgoing spectra:
http://acmg.seas.harvard.edu/people/faculty/djj/book/bookchap7.html
The planet tends to energy equilibrium at TOA – albeit transient. The duration of energy imbalances is the question not satisfactorily answered.
Not sure the equipment exists – other than the Harries narrow aperture satellites – to unambiguously address this.
e.g. https://link.springer.com/article/10.1007/s40641-016-0039-5
DA
See Ron Clutz’s note above. I thought the hypothesized greenhouse effect was atmospheric warming from increased radiatively active gasses in the atmosphere not just that CO2 absorbs some LWIR.. I believe the Connollys are saying it is not there or very small in an ideal gas and their analysis says the troposphere and stratosphere act as ideal gasses.
The Economist took a recent look at climate models that I tried to summarize along with some quotations from Andrew Dessler and Gerald North of Texas A&M. https://www.masterresource.org/north-gerald-texas-am/climate-models-north-today/
In that link was written: (I finished the sentence for them) Building models is also made hard by lack of knowledge about Climate internal response to forcing.
Climate model projections are able to capture many aspects of the climate system
What a dumb lie! Whatever projections climate models capture should be put on the least likely list for our future.
Atmospheric and oceanic computational simulation models often successfully depict chaotic space–time patterns, flow phenomena, dynamical balances, and equilibrium distributions that mimic nature. This success is accomplished through necessary but nonunique choices for discrete algorithms, parameterizations, and coupled contributing processes that introduce structural instability into the model. Therefore, we should expect a degree of irreducible imprecision in quantitative correspondences with nature, even with plausibly formulated models and careful calibration (tuning) to several empirical measures. Where precision is an issue (e.g., in a climate forecast), only simulation ensembles made across systematically designed model families allow an estimate of the level of relevant irreducible imprecision…
Sensitive dependence and structural instability are humbling twin properties for chaotic dynamical systems, indicating limits about which kinds of questions are theoretically answerable. They echo other famous limitations on scientist’s expectations, namely the undecidability of some propositions within axiomatic mathematical systems (Gödel’s theorem) and the uncomputability of some algorithms due to excessive size of the calculation (see ref. 26).” https://www.pnas.org/content/104/21/8709
How are you going to think about future climate & climate change without a model? In science, *everything* is a model.
That is alarmist BS.
Climate changes in natural cycles that repeat. The future Climate Change is the continuation of the historic data for the most recent ten thousand years. Climate Models have been a total failure, rely on historic data and proxy data for ten thousand years, this is the new normal. One molecule of CO2 added to ten thousand molecules of atmosphere has not become the control knob of temperature. Water and Ice and Air and Water Vapor are many orders of magnitude more than CO2 change, Water vapor and precipitation and increase with temperature rise and decrease with temperature drop, immediately. CO2 changes lag temperature changes just like the CO2 in you carbonated drinks. Vapor pressure of CO2 depends, mostly, on the temperature of the carbonated drink, “our carbonated oceans”!
More CO2 makes green stuff grow better using water more efficiently, that can only result in good for life on earth for all life that depends on green things that grow.
You always harp on this “one molecule of CO2 added to 10,000, how can it make a difference?!!” It’s annoying and makes you sound disingenuous.
The relevance is that the bulk of the atmosphere, oxygen and nitrogen, are not GHGs. Estimates of CO2 proportion of the total GHG are on the order of ~15-20%. That means an increase from 300ppm to 400ppm of CO2 is significant. There is a lot of air stacked up in the atmosphere, so there is a lot of CO2.
I understand that predictions are fraught with difficulty but the basic idea that increasing CO2 in atmosphere means less energy escaping to space is sensible, as is the conclusion this will warm the planet.
Whatever you think your tea leaf readings of historical climate tell you about cyclical changes in climate is irrelevant to the discussions of the effects of rapid increase of CO2 in air due to anthropogenic forces, as the situation is unprecedented in recent geological history.
b1daly
“Estimates of CO2 proportion of the total GHG are on the order of ~15-20%”
Where does this estimate come from?
My understanding is that water vapour forms ~95% of all greenhouse gases and CO2 ~4%.
So we are down to 0.04 of the entire atmosphere, of which, mans contribution is ~3%.
When ranked by their direct contribution to the greenhouse effect, the most important are:[18][failed verification]
Compound
Formula
Concentration in
atmosphere[25] (ppm) Contribution
(%)
Water vapor and clouds H
2O 10–50,000(A) 36–72%
Carbon dioxide CO
2 ~400 9–26%
Methane CH
4 ~1.8 4–9%
Ozone O
3 2–8(B) 3–7%
notes:
(A) Water vapor strongly varies locally[26]
(B) The concentration in stratosphere. About 90% of the ozone in Earth’s atmosphere is contained in the stratosphere.
In addition to the main greenhouse gases listed above, other greenhouse gases include sulfur hexafluoride, hydrofluorocarbons and perfluorocarbons (see IPCC list of greenhouse gases). Some greenhouse gases are not often listed. For example, nitrogen trifluoride has a high global warming potential (GWP) but is only present in very small quantities.[27]
Proportion of direct effects at a given moment Edit
It is not possible to state that a certain gas causes an exact percentage of the greenhouse effect. This is because some of the gases absorb and emit radiation at the same frequencies as others, so that the total greenhouse effect is not simply the sum of the influence of each gas. The higher ends of the ranges quoted are for each gas alone; the lower ends account for overlaps with the other gases.[18][19] In addition, some gases, such as methane, are known to have large indirect effects that are still being quantified.[28]
Here you go, Scot. This is from wiki (assumed ipcc). i’ve never understood why the discrepancy with what you’ve stated. But, i can’t imagine that 1/10 matters a whole lot more than 1/20. So, i’ve never bothered to enquire further. Always nice to see you…
B1, good to see you over here. Looks like you represent the d’nier faction over there at attp. (i’m surprised that they even allow you to post over there at all) Must be tough holding hands with mosh & dikran and singing kumbaya (😖)
This study found that water vapor is only responsible for 50% of the greenhouse effect with CO2 responsible for 25% and clouds feedbacks for the rest. But water vapor is nearly constant except as it changes by climate change.
Lacis et al, Science 330, 2010, p 356-359, http://pubs.giss.nasa.gov/abs/la09300d.html
the post refers to general circulation global climate models, eg the CMIP series of simulations
By contrast, Apollo era NASA scientists and Engineers at The Right Climate Stuff developed the only validated policy grade climate model.
https://www.therightclimatestuff.com/
Accountants should wear green eyeshades and not use10-key calculators.
“In science, *everything* is a model.”
But, as George Box said, “All models are wrong, but some are useful”, so the real question is are the GCMs useful enough to bet the economy of the world on?
The economy of the world is being bet on whether climate models are good or not. Doing nothing has it owns significant risks.
“The economy of the world is being bet on whether climate models are good or not.”
And therein lies the problem; we are betting the world economy on GCMs that are not fit for that purpose.
“Doing nothing has it owns significant risks.”
If the GCMs are not fit for purpose, there is no basis for making that statement.
Do you have a better way of projecting climate change, without a model?
GCMs have done a fairly good job of projecting climate. Exxon’s 1979 model has been almost spot on, as was Hansen’s.
The simplest model is just that temperature change is 1.5 C per trillion tonnes of carbon emitted, with an error bar of about 0.5 C/TtC:
https://curryja.files.wordpress.com/2018/12/wgi_ar5_figspm-10.png?w=768&h=614
“Do you have a better way of projecting climate change, without a model?”
I didn’t say you needed something other than a model for projecting climate change, I said you need a model fit for the purpose of betting the world economy on, and GCMs are not fit for that purpose.
“GCMs have done a fairly good job of projecting climate”
GCMs are running significantly hotter than the actual temperature data and many of the new AR6 GCMs run even hotter than the earlier GCMs did so I guess you have to have a pretty loose definition of “a fairly good job”.
1.5 C of warming per Tt carbon emitted. +/- 1/3rd.
GCMs are the only game in town. We don’t need to know future warming to the nearest 0.1 C to know we have a problem. Besides the biggest uncertainty is how much carbon will be emitted, not GCM results.
We will have to make decisions about risk with less than perfect information. But we do that all the time.
“GCMs are the only game in town”
That is irrelevant if GCM are not fit for purpose.
“We don’t need to know future warming to the nearest 0.1 C to know we have a problem.”
We do need to know that GCMs are fit for purpose, and if not we have no way of knowing if there actually is a problem.
“Besides the biggest uncertainty is how much carbon will be emitted, not GCM results”
Why should we worry about how much Carbon is emitted as we have done a good job of removing Carbon from our emissions. CO2 emissions are only important if CO2 is the “Control Knob” for the climate, but that can’t be proven by GCMs as they assume it is true.
“We will have to make decisions about risk with less than perfect information”
True, but does it make sense to bet the world economy on information derived from GCMs that are not fit for that purpose?
“Do you have a better way of projecting climate change, without a model?”
YES! History and proxy data, especially the ice core data from Greenland and Antarctica. What has happened explains what is happening and what will happen. We really do not have anything else.
What has happened and the data that best describes it is the better way, and it does use a model, the climate system is the model and it has archived proxy data for us to use. The climate system is the perfect analog computer, it actually has run the simulation, real time, up to now and it will continue.
Climate models are useful, they are making a lot of alarmists extremely rich by using disaster stories to convince people to give up our wonderful life and live with little or no energy from fossil fuels or nuclear fuels.
The Antarctic Ice core is the true record of history. It shows the new Ice Age began about 18.000 years ago. The 250 meters of new ice, rise of CO2 in the beginning until green new green foliage was enough to overcome nature, then the arrival of the Industrial revolution, and common sense show this.
David Appell
“Doing nothing has it owns significant risks.
Can you be more specific, and precise please.
@David Appell
“GCMs have done a fairly good job of projecting climate.”
???? WHAT???
Not at all.
You CAGW guys always cite the “success” of uni-dimensional models run 35 years ago on pocket calculators about Tglobal, while forget to acknowledge the abysmal failure of modern days ‘ GCM-ESM 1+ million lines of code, running on supercomputers.
The latter don’t get it right for snow cover, sea ice, clouds, albedo, etc… and yet, for some unexplainable reason, they should be overall correct.
Fake science, plain and simple. – :(
robortoko6: What’s wrong with this model? Total warming = 1.5 degC/trillion tonnes of carbon emitted:
https://curryja.files.wordpress.com/2018/12/wgi_ar5_figspm-10.png
Most teenage boys are addicted to computer games as they appear more and more ‘real’. Adult climate scientists should know better about their computer models, but the emotional addiction stays…..
If your models are fundamentally crap, why bother to think about it? Chalk it up to an imponderable and just move on to something useful.
This Exxon projection from the last ’70s is almost spot-on:
https://www.theguardian.com/environment/climate-consensus-97-per-cent/2018/sep/19/shell-and-exxons-secret-1980s-climate-change-warnings#img-2
A model with a single point represents the entire planet for decades and decades? … are you kidding?
This Exxon projection from the last ’70s is almost spot-on:
DUH!, One can always go back and pick something similar to what has happened. How many other projections did they make that was not spot on? It is alleged that they had more than one forecast. That is being used in court against them.
On one hand, they say no one really understands why ice ages started and ended, then they say they have 97% understanding of climate. That is BS, not only do they not understand, they do not even have good clues.
“Chalk it up to an imponderable and just move on to something useful.”
Bingo. That is exactly what doubt-mongering is for. Instead of talking about how; without Earth2 to experiment on – models are all we have or how they could be improved or what we’ve learnt from them, we get destructive, anti-science doubt-mongering. Shame.
David Appell
This Exxon projection from the last ’70s is almost spot-on:
My understanding is the Exxon predictions were computer generated, just like the current ones.
The reality of observed temperatures are somewhat different.
Appell – A The exxon projection shows 2c warming from 1960-2020.
The reported warming for the same period is approx 1c. How is that spot on?
Loydo:
“Bingo. That is exactly what doubt-mongering is for.”
Instead of weather, how about modelling a lotto machine? The balls and barrel all would obey basic physics wouldn’t they? And you could incorporate all previous draws into a time series so your model could replicate them perfectly. Yet no sane person would expect such a model to get next week’s draw right.
What makes you so confident there are no factors in the weather as complicated as a lotto machine? What you term “doubt-mongering” seems like realism or prudence to me.
Appel appeals to the alleged climate modeling expertise of Big Oil. Very amusing.
joe- the non climate scientist wrote:
“The exxon projection shows 2c warming from 1960-2020.”
No, it shows 1 C. Look again.
I have a model that predicts mean temperatures will rise by 45 deg C in the next ten years…so yes everything is a model.
David, I used to think that models were necessary in science, but it doesn’t seem so. There are ways of thinking about causality that are entirely data-driven and don’t use models. https://www.researchgate.net/profile/Pablo_Verdes/publication/7567816_Assessing_causality_from_multivariate_time_series/links/0046351edbaf9aa2a3000000/Assessing-causality-from-multivariate-time-series.pdf
If you have model-free causality, you have a model-free basis for scenario planning. Sure, you can make the argument that that’s equivalent just to using a very fuzzy model. But at that point I think you would have to concede that the epistemological need for models still allows a great diversity of approaches to real-world problems.
HotScot wrote:
“So we are down to 0.04 of the entire atmosphere, of which, mans contribution is ~3%.”
That’s very disingenuous. Nature absorbs more CO2 than it emits. Man is responsible for CO2’s increase in the atmosphere, now up by 47% cp pre-industrial times.
Nature absorbs more CO2 than it emits.
Nature would also absorb more CO2 than it emits even if ACO2 were having no impact on CO2 levels. (iow, if the mass of ACO2 were sinking at the same rate as natural CO2, near 100%, then nature would still be a net sink for carbon dioxide)…
What’s the evidence?
Hi, David… i don’t think you’re getting my point. The skeptic claim is that aco2 sinks at a rate close to that of natural co2, near 100%. Even if that’s true, then nature would still be a net sink for co2. i’m not arguing here that it is true. (only that, whether or not it’s true, nature would be a net sink for carbon)…
post script~ not much fun over at spencer without you — nice to see you here.
Just penned the line that climate models are probabilistic and reality determinate. Strictly true mathematically.
Not to say that deep learning and pattern recognition on so many real time parameters these days can’t help.
https://www.mpimet.mpg.de/en/communication/news/single-news/news/kuenstliche-intelligenz-fuer-das-erdsystem/?tx_news_pi1%5Bcontroller%5D=News&tx_news_pi1%5Baction%5D=detail&cHash=bb13726de50f4130dad9a27104e747fe
Robert
Look forward to how hurricanes are naturally “determinate”. See the tracks of Atlantic Hurricanes.
https://twitter.com/philklotzbach/status/1189752641414299648/photo/1
Robert I Ellison: Just penned the line that climate models are probabilistic and reality determinate. Strictly true mathematically.
Someone has proven mathematically that reality is determinate?
I am very carefull with syntax out of love of a living lanhuage – and was in that instance as I quite clearly and concisely demonstrated. But instead of replying there you hold it back and constuct here a litany of disparaginmg nonsense loosely linked with with simple minded sophistry. Including the old jiggery-pokery of measurment error. Nothing can be proved. It may all be random. There you are at odds with Newton’s 4th rule of natural philosophy. We believe in what should be regarded as a truth that which is based on empiricism. Albeit with a little creative inference. And notwithstanding any hypothesis to the contrary.
I give you vortices in a fast flowing stream. They are always there and the size, number and location is determined by boundary condition and flow quantity. Throw in another rock and the vortices may change a little or a lot.
As I have said – approaching the real world imaginatively is the path to unsderstasnding geophysics. Seeing the jiggery-pokery before being able
to put it intio words and math. As both Einstein and Fegyman said. A lack of imagination limts the creative and fecund exercise of natural philosophy. Science is not just plodding sophisdtry.
Physical system with mass, momentum, viscosirty etc. Storm tracks may appear random but every turn has a physical cause.
Robert I Ellison: Storm tracks may appear random but every turn has a physical cause.
Is that strictly true mathematically?
You want mathematical proof that the motions of fluids are governed by laws of classical mechanics? It is there in the Navier-Stokes equation.
Robert I Ellison: You want mathematical proof that the motions of fluids are governed by laws of classical mechanics? It is there in the Navier-Stokes equation.
Is it your proposition that someone has proven strictly mathematically that something in the world is governed by the Navier-Stokes equation? Or are you once again using language loosely, words like “strictly”, “mathematically” and “proof”?
Nuts to you. You ask if it is mathematically proven that fluid flow is determinate yet the physical laws are there in the terms of the equation. Black and white. Then you quibble loosely about loose language. Yet again it is all just word salad with you. Throw in some words without reference to the real world and toss.
Everything in the physical system must have a cause – regardless of the complexity of the system or its unknowns. Or do you imagine that the world operates without cause and effect? By magic? It just decides to do something else? Such nonsense is just two steps from crazy town. But this seems more like empty sophistry than a search for truth and reality.
Robert I Ellison: You ask if it is mathematically proven that fluid flow is determinate yet the physical laws are there in the terms of the equation. Black and white. Then you quibble loosely about loose language.
One of the enjoyable aspects of interchanges with you is that you follow up absurdity and nonsense with more absurdity, nonsense, and non sequitur. It was you who announced your “fast and loose” use of language as a virtue, and implied that careful use of language was a vice. Strictly mathematical proofs can prove propositions as consequences of other propositions: for example, you can use Zermolo’s Axiom of Choice to prove the Heine-Borel theorem; but you can not use strictly mathematical proofs to prove anything about the real world.
Or do you imagine that the world operates without cause and effect? By magic? It just decides to do something else?
I do not imagine those things, except as entertaining mental exercises. Whether fluid flows are determinate can’t be proved; it can’t even be tested well because all research measurements are beset with random variation, making it difficult to assess whether the randomness is intrinsic or adventitious. This was thoroughly discussed in the early 20th century, and Einstein famously asserted without proof, that “God does not play at dice.” Others pointed out that Einstein did not know whether or not God did play at dice. For little things like photons and electrons the best results were obtained with stochastic models, treating God as though He did play at dice, but without deciding the issue. For big things, it is not yet determinable whether in the end we shall be stuck with irreducibly stochastic models.
Your proposition that fluid flows are determinate may be true, but it is an assumption, not the conclusion of a strictly mathematical proof.
I am very carefkul with syntax – as I clearly and concisely demonstrated. But instead of replying there he comes out very far from any context with his typically irrelevant and absurd llitany of disparagement. It is very tedious and I commonly ignore him and his lack of depth in physical sciences entirely.
“‘Perhaps we can visualize the day when all of the relevant physical principles will be perfectly known. It may then still not be possible to express these principles as mathematical equations which can be solved by digital computers. We may believe, for example, that the motion of the unsaturated portion of the atmosphere is governed by the Navier–Stokes equations, but to use these equations properly we should have to describe each turbulent eddy—a task far beyond the capacity of the largest computer. We must therefore express the pertinent statistical properties of turbulent eddies as functions of the larger-scale motions. We do not yet know how to do this, nor have we proven that the desired functions exist.” Edward Lorenz 1969
We believe for good reason that Navier-Stokes is the governing equation of fluid flow. But with Matthew yet again we never past a misgmash of terms and quotes not seemingly relevent – in this case – to the question of random or determinate in the Earth system. But it is a system that at its core is a fluid flow problem understood in terms of fundamental properties of matter and the calculus. What he would have to show is that the Navier-Stokes equation is not an analytical description of fluid flows derived useing fundamental properties of matter and calculus.
For big things, it is not yet determinable whether in the end we shall be stuck with irreducibly stochastic models.
Can you give even one example where this looks to be even a remote possibility for a macroscopic object?
PS: The Navier-Stokes equations are derived from Newton’s second law and some straightforward assumptions about the viscous nature of the fluid.
Was Einstein referring to the Schrodinger wave equation? It may be here it may be there where it is you cannot tell? I may have a problem with that as a model of reality. It seems more likely to me that we don’t much understand thew nature of wave/particle duality. But as Appel said – if you have an example of rasndomness in nature by all means. Vague ideas that cause and effect might not materialise every time in a physical system don’t count.
https://judithcurry.com/2019/10/29/escape-from-model-land/#comment-902364
David Appell: PS: The Navier-Stokes equations are derived from Newton’s second law and some straightforward assumptions about the viscous nature of the fluid.
So they are. They are also clearly first-order approximations (derivatives of flow rates as functions of derivatives of pressure). They are intentional simplifications to permit solutions that would be intractable with higher-order approximations. As I wrote, you can deduce propositions from other propositions via strictly mathematical proofs. Showing that the results are accurate representations of reality requires more than that.
Can you give even one example where this looks to be even a remote possibility for a macroscopic object?
Is there even one macroscopic flow that is predictable exactly? Consider David L Hagen’s presentation of the hurricane tracks from Phillip Klotzbach.
David Wojick assumes without much thought involved – and you know what that means – that cyclone tracks are random. They are not. Navier-Stokes solutions are a vector calculation for a point in any fluid giving both direction and velocity. The parameters – viscosity and momentum – are as precise as empirical science can be. These are not dry as bones philosophical or mathematical abstractions. but empirical science of natural systems – and you don’t seem to understand the difference.
And if you listen to Tim Palmer – the equation has an unparalleled elegance. They are unerringly precise on fundamental principles. They are not first approximations at all. They are a couple of problems in implementing the pure form in models.
Robert I Ellison: David Wojick assumes without much thought involved – and you know what that means – that cyclone tracks are random. They are not. Navier-Stokes solutions are a vector calculation for a point in any fluid giving both direction and velocity. The parameters – viscosity and momentum – are as precise as empirical science can be.
I like this part: as precise as empirical science can be.
That might be true. I would expect improved accuracy in the future, but they may already be as precise as empirical science can be.
But there is no strictly mathematical proof that the processes are determinate.
If one knows the position and momenta of all the (classical) particles in a system, then Newton’s laws allow, in principle, a calculation of their position and momenta for any point in the future.
In classical physics that’s what’s meant by “determinate.”
David Appell: In classical physics that’s what’s meant by “determinate.”
That is asserted, but there is no strictly mathematical proof, as asserted in the comment I responded to. And in real cases, it can not be known (or demonstrated) that the relevant quantities are known, so the proposition isn’t testable either.
It might be true, and I’m not claiming it isn’t (though it might not be), but there is no strictly mathematical proof.
It is not clear if he is looking for some axiomatic proof. Navier-Stokes is really an analytic math description of flow fields – as David Appell says in another way below. Though rather than particles I think of points in a flow. Navier-Stokes is true in the same way as E=mc2. Because physics says so. And let’s face it – all sorts of experiments are done daily both physical and numerical with flow fields. Often using simplified numeric versions. The full equation – or equations since it is solved in three dimensions – hence the partial differentials – is currently insoluble. But it is foreseeably determinate with precisely known initial conditions and quantum computing.
Contrast the probabilistic outcome of a single atmospheric and ocean model. Multiple runs diverge from positions proximate to a precision of measurement of inputs to give a probability distribution – on which statistics can then be done. Whether any modal probability turns out to match the evolution of the Earth system remains to be seen. I’d guess doubtful – and I’m an expert on everything.
Have you ever noticed that if you make fun of yourself – it always cames back twisted? I am a nonpracticing but still registered civil engineer. Doesn’t matter – a lot of my work in later decades was environmental science. Environmental scientists are meant to know everything. Undergraduate civil engineering was a great foundation – including much hydrodynamics over the years – and I have always been a greeny who insists that economic development in free markets is the solution to any grand human challenge. With governments big enough to provide for emergencies and disasters – and other things that governments should do in robust democracies. About 25% of GDP in the Austrian school. Politics, law, ecology, fluvial and coastal geomorphology, culture, economics…
It’s exhausting. Lucky I have always been a voracious reader. In the dark ages that meant ransacking libraries for references. It makes a lot more sense now that I have discovered my Viking cultural heritage. Now I am confined to a yellow racing wheelchair – Google has been a boon.
Robert I Ellison: It is not clear if he is looking for some axiomatic proof.
Robert I Ellison: Just penned the line that climate models are probabilistic and reality determinate. Strictly true mathematically.
You have now come full circle from claiming that it is strictly true mathematically that reality is determinant to wondering what “strictly true mathematically” might mean. Along the way you have separated out small things like electrons and photons from reality because they are possibly not determinate.
Climate models may be probabilistic (they are not always presented as such); whether hurricane tracks are determinate can not be demonstrated on present evidence.
That the Schrodinger wave
equation is just probability math is the widely held Copenhagen interpretation of quantum mechanics. And axiomatic proof in science is never possible is known about the scientific method.
It might be true, and I’m not claiming it isn’t (though it might not be), but there is no strictly mathematical proof.
The proof is that Newtonian mechanics has never failed to be deterministic, except in areas where special or general relativity takes over. And those are also both determinate theories.
Laplace put it this way:
“We ought to regard the present state of the universe as the effect of its antecedent state and as the cause of the state that is to follow. An intelligence knowing all the forces acting in nature at a given instant, as well as the momentary positions of all things in the universe, would be able to comprehend in one single formula the motions of the largest bodies as well as the lightest atoms in the world, provided that its intellect were sufficiently powerful to subject all data to analysis; to it nothing would be uncertain, the future as well as the past would be present to its eyes. The perfection that the human mind has been able to give to astronomy affords but a feeble outline of such an intelligence.” (1820)
David Appell: The proof is that Newtonian mechanics has never failed to be deterministic, except in areas where special or general relativity takes over.
That may be proof of a sort, aka “evidence”, or “passing severe tests”, but it is not “strictly mathematical”. The classical models break down with tiny things.
You are writing a lot of interesting stuff. but quoting Laplace as an authority does not establish that a “strictly mathematical” case can be made that “nature is determinate”. The relation of what is “strictly mathematical” to what is in nature is more heuristic and pragmatic (as you said of the success of Newton’s laws [except where they are not successful.]).
What makes you think *anything* about the real world can be modeled precisely?
The real world isn’t equations — it consists of real particles. And each of them can classically be modeled by their position, momentum and rate of change of momentum. With enough computing power we could predict the exact path of a hurricane.
You can’t point to a result of Newtonian mechanics that is not deterministic. Even quantum mechanics is strictly deterministic in some interpretations (like many worlds).
David Appell: What makes you think *anything* about the real world can be modeled precisely?
The real world isn’t equations — it consists of real particles. And each of them can classically be modeled by their position, momentum and rate of change of momentum. With enough computing power we could predict the exact path of a hurricane.
You can’t point to a result of Newtonian mechanics that is not deterministic. Even quantum mechanics is strictly deterministic in some interpretations (like many worlds).
You are writing about things other than my original question: Is there, as claimed by RIE, a strictly mathematical demonstration that nature is determinate?
I did not say that the real world is equations. I did not say that anything in the real world can be modeled “precisely” (all models contain at least some errors of approximation.) I did not write that results of Newtonian mechanics are not deterministic (though we know since the late 19th century of some systematic errors of approximation.) Whatever the value of the “many worlds” interpretation of stochastic models of small particle behavior may be, it does not follow from a “strictly mathematical” proof or any other kind of derivation.
“No”, is my answer. There is no “strictly mathematical” demonstration that the real world is determinate. Whatever his motive or intent may have been, what he wrote is a loose use of language, and vacuous..
“In experimental philosophy, propositions gathered from phenomena by induction should be considered either exactly or very nearly true notwithstanding any contrary hypotheses, until yet other phenomena make such propositions either more exact or liable to exceptions.
This rule should be followed so that arguments based on induction be not be nullified by hypotheses.” Newton’s 4th rule of natural philosophy.
Is Naver-Stokes the governing equation for fluid flow and is it mathematical? Well yes it is.
Robert I Ellison: Is Naver-Stokes the governing equation for fluid flow and is it mathematical? Well yes it is.
That’s your “strictly mathematical” claim that nature is determinate?
David Appell: The real world isn’t equations
Maybe RIE and DA should consult and see if they can agree on just where they think I went wrong.
The Gihl and Lucarini review linked here a shrt while ago does not lend itself well to short, punchy excerpts, but it does discuss the NSE and adjuncts, including stochastic PDEs, in good detail. This from the abstract: This paper reviews the observational evidence on climate phenomena and the governing equations of planetary-scale flow, as well as
presenting the key concept of a hierarchy of models as used in the climate sciences. Recent advances in the application of dynamical systems theory, on the one hand, and of nonequilibrium statistical physics, on the other, are brought together for the first time and shown to complement each other in helping understand and predict the system’s behavior.
And this from the text: Still, despite the fact that climate dynamics is governed by well-known evolution equations, it is way beyond our
scientic abilities to gain a complete picture of the mathematical
properties of the solutions. In fact, many fundamental questions are still open regarding the basic NSEs in a homogeneous fluid, without phase transitions and rotation (Temam, 1984, 1997). In particular, even for the basic NSEs, providing analytical, closed-form solutions is only possible in some highly simplied cases that are either linear or otherwise separable (Batchelor, 1974).
As already discussed above, Eqs. (1){(3) can simulate a range of phenomena that spans many orders of magnitude in spatial and temporal scales. Hence, it is virtually impossible to construct numerical codes able to represents explicitly all the ongoing processes, at the needed resolution in space and time. It is thus necessary to parametrize the processes that occur at subgrid scales and cannot, therewith, be directly represented. Among the most important processes of this type are cloud-radiation interactions and turbulent diffusion.
Climate-relevant flows may yet be determinate, but I don’t think you can show that “strictly mathematically” on present knowledge.
“According to the traditional notion of randomness and uncertainty, natural phenomena are separated into two mutually exclusive components, random (or stochastic) and deterministic…. Here I argue that such views should be reconsidered by admitting that uncertainty is an intrinsic property of nature, that causality implies dependence of natural processes in time, thus suggesting predictability, but even the tiniest uncertainty (e.g. in initial conditions) may result in unpredictability after a certain time horizon.” Dimiytis Koutsoyiannis 2010 – https://www.hydrol-earth-syst-sci.net/14/585/2010/
I don’t have to prove that fluid flow is physical and calculable. That would be bizarre. 🤣
Robert I Ellison: Just penned the line that climate models are probabilistic and reality determinate. Strictly true mathematically.
“According to the traditional notion of randomness and uncertainty, natural phenomena are separated into two mutually exclusive components, random (or stochastic) and deterministic…. Here I argue that such views should be reconsidered by admitting that uncertainty is an intrinsic property of nature, that causality implies dependence of natural processes in time, thus suggesting predictability, but even the tiniest uncertainty (e.g. in initial conditions) may result in unpredictability after a certain time horizon.” Dimiytis Koutsoyiannis 2010 – https://www.hydrol-earth-syst-sci.net/14/585/2010/
I don’t have to prove that fluid flow is physical and calculable. That would be bizarre. 🤣
Obviously a good quote from Kouytsoyiannis. How does it relate to your phrase “striclty true mathematically” as that phrase relates to ” climate models are probabilistic and reality determinate.”?
Judy,
Do you have any thoughts on how this analysis of model weakness ties into the bias/variance tradeoff?
the ultimate arbiter must be expert judgment, as a model is always blind to things it does not contain and thus may experience Big Surprises.”
All an “expert” has is a different conceptual model. Models can only be judged with respect to data, which unfortunately also depend on models of how the measurement processes work.
There is no reliable way to prevent surprises.
“The term ‘butterfly effect’, coined by Ed Lorenz, has been surprisingly successful as a device for communication of one aspect of nonlinear dynamics, namely, sensitive dependence on initial conditions (dynamical instability), and has even made its way into popular culture. The problem is easily solved using probabilistic forecasts.”
I have heard it asserted that “the problem [of sensitive dependence on initial conditions] is easily solved using probabilistic forecasts.” but I have never seen any proof that this true; has anyone see any proof that probabilistic forecasts can be used to overcome the problem of “sensitive dependence on initial conditions”?
If you hit something with a hammer, it vibrates at its internal natural frequencies. If you use a bigger hammer, it vibrates more, if you hit it with a butterfly, is does not even notice. Butterfly theory is for unstable systems that are near the point they could go any which way. Climate is a self correcting system that has regular bounded alternating cycles. The cycles have evolved but there was never any chaos or butterflies. When one bounded period of cycles ended, it evolved into another, never anything that resembled chaos. Chaos is another word that explains ignorance of a natural cause.
Yes there are natural trends, but all fluids are naturally chaotic – including Earth’s air, ocean, and molten core. Biosystem growth is also chaotic, (not crystal like).
I “like” Nassim Taleb for the depth of his thinking when analyzing various issues. On CC he leans toward the Precautionary Principle, something I have trouble agreeing with. In any case, so writes Nassim Taleb and co-authors on CC: we should ask “what would the correct policy be if we had no reliable models”?, further about uncertainty they write:
“Push a complex system too far and it will not come back. The popular belief that uncertainty undermines the case for taking seriously the “climate crisis” that scientists tell us we face is the opposite of the truth.
Properly understood, as driving the case for precaution, uncertainty radically underscores that case and may even constitute it”.
Further Taleb fully accepts that not only are climate models wrong, they MUST be wrong by definition…and suggests that nevertheless Climate change should be taken seriously.
No reliable model? Do no harm. Spending trillions of dollars based on a “feeling” does a lot of harm.
The Precautionary Principle, used properly, would look at history and data for the past ten thousand years and project that forward.
From 300 to 400 parts per million is adding :
ONE MOLECULE OF CO2 PER TEN THOUSAND MOLECULES!
IT IS REALLY NOT REASONABLE TO EXPECT MORE THAN ONE TEN THOUSANDTH OF A DIFFERENCE DUE TO THAT.
WHAT ARE PEOPLE EVEN THINKING?
CLEAR, THEY ARE NOT THINKING!
“Academic climate economists seem to want probabilities (with or without any meaningful confidence in them)”…
Academics in meteorology LOVE probabilities. Decision-makers hate them. The irony is the PoP probabilities in meteorology are pretty well calibrated.
wow
what a great post
thank you
i will read the cited paper as well
sincerely
chaamjamal from chaamjamal.com
chaamjamal.com
The problem is that “they” do not consider alternatives. This makes them religionists.
If Robert Heinlein was right, the slippery slope leading down to the sea of self delusion should turn out to be fairly oozing with oil of altruism.
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Off to the left, kind of out of the picture is, Political Land… and, what we’ve seen going on there the last 3 years should give us all a good insight into how deep fake science really works (behind the red curtain).
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Thank you Dr. Curry
Having just finished a game of Candy Land with a grandchild, I couldn’t help observe the similarity to Model Land.
In Candy Land: ” there is no strategy involved: players are never required to make choices, just follow directions. The winner is predetermined by the shuffle of the cards.” (wiki)
My interpretation of your comments it seems, as regards to models used for predictions, Dante’s Inferno “Abandon all Hope”
Instead; “Given the level and types of uncertainty, efforts to bound the plausible range of future scenarios makes more sense for decision making than assessing the probability of probabilities, and statistically manufacturing ‘fat tails.’”
We are left with expert opinion; realizing the fallibility of same, we proceed judiciously. Each of us has our own set point so that crowd sourcing maybe used to obtain a mean and then going a bit further by 10% or so seems how we do most things. The first element in all of this is getting the crowd to begin to think about the issue before seeking their crowd source opinion. The importance of having a large enough crowd, who has had a broad spectrum of information plus time to digest the pros & cons, is likely to yield a direction for human environmental stewardship that will be supported.
My trust in models and their output is limited, otherwise, I would be a wealthy man.
it’s about time to look to real discoveries on solar variability and the derived climate variability
https://www.researchgate.net/profile/Dimitris_Poulos
The models the world needs are of the costs and effectiveness of different alternatives to fossil fuels.
That’s been done* but the climate concerned don’t like it. So they glue themselves to subways in support of unicorns.
* http://energy.mit.edu/wp-content/uploads/2018/09/The-Future-of-Nuclear-Energy-in-a-Carbon-Constrained-World.pdf
Another year has gone by and it’s now the Fall of 2019. But winter will be here in another month. Having escaped the snow country of my youth for the dry boring flatlands of the US Northwest, I can’t say I miss it.
However, it’s time once again to put up ‘Beta Blocker’s Parallel Offset Universe Climate Model’, a graphical GMT prediction tool first posted on Climate Etc. and on WUWT in the summer of 2015.
Judith Curry’s blog post ‘Escape from model land’ seems like an appropriate place for my annual repost of this graph on Climate Etc. So here it is:
https://live.staticflickr.com/65535/48983339387_ce156f647c_o.png
Referring to the illustration, three alternative GMT prediction scenarios for the year 2100 are presented on the same graphic.
— Scenario #1 predicts a +3C rise in GMT by the year 2100 from the year 2015, roughly equivalent to a +4C rise from the year 1860, which should be considered the pre-industrial baseline year for this graphical analysis.
— Scenario #2 predicts a +2C rise from 2015, roughly equivalent to a +3C rise from 1860.
— Scenario #3 predicts a +1C rise from 2015, roughly equivalent to a +2C rise from 1860.
The above illustration is completely self-contained. Nothing is present which can’t be inferred or deduced from something else also contained in the illustration.
For example, for Beta Blocker’s Scenario #1, the rise in GMT of + 0.35 Degrees C / Decade is nothing more than a line which starts at 2016 and which is drawn graphically parallel to the rate of increase in CO2 which occurs in the post-2016 timeframe. Scenario #1’s basic assumption is that “GMT follows CO2 from Year 2016 forward.”
Beta Blocker’s Scenario #2 parallels Scenario #1 but delays the start of the strong upward rise in GMT through use of an intermediate slower rate of warming between 2025 and 2060 that is also common to Scenario #3. Scenario #2’s basic assumption is that “GMT follows CO2 but with occasional pauses.”
Beta Blocker’s Scenario #3 is simply the repeated pattern of the upward rise in GMT which occurred between 1860 and 2015. That pattern is reflected into the 2016–2100 timeframe, but with adjustments to account for an apparent small increase in the historical upward rise in GMT which occurred between 1970 and 2000.
Scenario #3’s basic assumption is that “Past patterns in the rise of GMT occurring prior to 2015 will repeat themselves from 2016 on through 2100, but with a slight upward turn as the 21st Century progresses.”
That’s it. That’s all there is to it. What could be more simple, eh?
All three Beta Blocker scenarios for Year 2100 lie within the IPCC AR5 model boundary range — which, it should also be noted, allows the trend in GMT in the 2000 – 2030 timeframe to stay essentially flat while still remaining within the error margins of the IPCC AR5 projections. (For all practical purposes, anyway.)
Scenario #3 should be considered as the bottom floor of the three scenarios, which is approximately a two degree C rise from pre-industrial CO2 concentration levels. It is also the scenario I suspect is most likely to occur.
The earth has been warming for more than 150 years. IMHO, the earth won’t stop warming just because some people think we are at or near the top of a long-term natural fluctuation cycle. The thirty-year running average of GMT must decline steadily for a period of thirty years or more before we can be reasonably certain that a long-term reversal of current global warming has actually occurred.
How did Beta Blockers Parallel Offset Universe Climate Model come about?
Back in 2015, I had been criticizing the IPCC’s climate models as being a messy hodge-podge of conflicting scientific assumptions and largely assumed physical parameterizations. Someone at work said to me, “If you don’t like the IPCC’s models, why don’t you write your own climate model?”
So I did. However, not having access to millions of dollars of government funding and a well-paid staff of climate scientists and computer programmers to write the modeling code, I decided to do the whole thing graphically. Back in 205, the illustration you see above took about thirty hours to produce. In October, 2019, I updated its verbiage to directly include the 1860 pre-industrial baseline datum.
If I’m still around in the year 2031, I will take some time to update the illustration to reflect the very latest HadCRUT numbers published through 2030, including whatever adjusted numbers the Hadley Centre might publish for the period of 1860 through 2015.
In the meantime, I’ll see you all next year in the fall of 2020 when the topic of ‘Are the IPCC’s models running too hot’ comes around once again.
And, given that the topic of climate change will be an important issue in the 2020 elections — unless it isn’t — then nothing in this world is more certain but that in another year’s time, the topic will in fact come around once again.
Interesting post, thanks.
Robbie, it’s been my view for some time now that as long as the thirty-year running average trend in GMT is above + 0.1 C / decade, then mainstream climate scientists will continue to claim that real-world temperature observations verify the IPCC models.
Over on WUWT, reader ‘stinkerp’ notes in response to my write-up about this graphical model that the longer we go at the current rate of warming, the higher the future acceleration in warming must be to match the IPCC’s predictions.
It seems to me that this characteristic of the IPCC’s models is a factor which ought to be addressed in evaluating the uncertainties of those models, and hence their value and credibility for use in public policy decision making.
Please note as well that the trend lines for each projected temperature rise beyond 2016 are based upon assumed trends of peak hottest years, and are either partially or wholly linearized across the 2016 – 2100 time span.
Where have we heard a lot of discussion recently about how trend linearization affects the uncertainties associated with a climate model? The whole topic seems to be a magnet for controversy.
Anyway, it’s been said that those who control the assumptions control the world.
Beta Blocker’s Parallel Offset Universe climate model is based 100% on assumptions. Change a few of its assumptions and the model changes accordingly, which is the means by which each of the three alternative scenarios are being produced.
And yet, the Parallel Offset Universe projections for the year 2100 lie within the boundaries of the IPCC model projections. Does this characteristic of the Beta Blocker model add to its credibility?
I suppose that depends on who is looking at the model, and for what reasons.
Beta,
Every rise in a time series is followed by a fall.
In forecasting climate, when will there be a case in the global warming trend of the past 3 decades?
Surely this turning point is one if the more fundamental parameters of interest to modellers.
Your own effort needs to put a date on the trend reversal.
Comparisons like CMIP would be more interesting of they required each model to estimate that date. Without it, the scenario of never-ending warming does indeed lead to catastrophe – but when? Geoff S
Geoff, that’s a good question. Why doesn’t my graphical climate model include the next major downturn in GMT, which would be an expected characteristic of a time series?
I’ll start by reposting a comment I made earlier this morning on the Cliff Mass Weather Blog in response to a suggestion from ‘Ali’ that my graphical model be subjected to formal peer review.
The Cliff Mass blog article, Extended forecasts are not reliable, is here:
https://cliffmass.blogspot.com/2019/10/extended-forecasts-are-not-reliable.html
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Ali, my primary interest lies in following the public policy debate concerning what, if anything, to do about climate change. My own perspectives concerning the validity of today’s mainstream climate science, and how it influences that public policy debate, are framed with this purpose in mind.
Formalized peer review doesn’t serve my purposes. Spawning further informed debate on the Internet through commentary made on climate blogs does.
With a weather forecasting model, the outcome of the model’s predictions are available within a week’s time or less. The data which feeds those models can be updated on a daily basis as a weather pattern evolves in real time.
Climate models are different. We won’t know with reasonably good certainty precisely how sensitive the earth’s climate system is to the addition of CO2 until some number of years have passed.
The uncertainties associated with the IPCC’s climate models are a continuing source of controversy between mainstream climate scientists and their knowledgeable critics. This is so because the physics of those particular climate system mechanisms thought to be most important in raising the earth’s global mean temperature over decadal periods of time are not well understood.
Cloud physics mechanisms, and the physics of the postulated water vapor feedback mechanism, are among the most influential of the factors which affect the predictions of the IPCC’s models and which determine the estimated sensitivity of global mean temperature to the addition of CO2.
At the current state of science, it isn’t possible to directly observe those particular mechanisms operating in real time. The nature of their physical operation must be inferred or deduced from other kinds of observations and measurements.
Because these physical mechanisms aren’t well understood, their influence must be estimated and parameterized within the climate models based on assumptions as to how they operate. Different models, and different runs of the same model, use different assumptions about cloud feedback physics and about water vapor feedback processes.
An often-discussed topic on the anti-AGW climate science blogs is the extent to which the IPCC’s models use assumed physical parameters as a replacement for direct knowledge of the actual physical processes that affect the earth’s climate system.
Those who follow the endless debates between mainstream climate scientists and their informed critics understand that Beta Blocker’s Parallel Offset Universe Climate Model is actually a commentary about the back-and-forth discourse and the polemics of these endless debates.
All of that commentary has been condensed into a single graphical illustration which covers these major topical points:
— The IPCC attributes most of the warming that occurred after 1950 to anthropogenic GHG emissions. And yet the rate of warming from 1900 through 1950 isn’t that much different than the rate from 1950 through 2014.
— Roughly two-thirds of the CO2 added to the atmosphere since the beginning of the industrial revolution was added post 1990. Shouldn’t the rate of GMT increase post 1990 be substantially larger than it actually is?
— If warming continues at the current rate over the next two decades, temperatures must greatly accelerate in the 2040’s and beyond in order to reach a 3C or a 4C rise above pre-industrial by 2100. What are the odds of this happening?
Here is the bottom line. My graphical climate model is based 100% on assumptions that are 100% transparent as to how they operate. Are the IPCC’s models any more reliable than my highly simplistic graphical model given that two of the most important physical processes affecting global mean temperature — cloud feedbacks and water vapor feedbacks — are covered with parameterized assumptions, not with data from direct observations made in the atmosphere?
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Geoff, the only GMT prediction based on cyclical pattern analysis that seems credible to me personally is the one Javier presented here on Climate Etc in January, 2018. His analysis places the next major downturn in GMT at about the year 2200.
Beta Blocker’s Parallel Offset Universe climate model is actually a commentary about the back-and-forth discourse and the polemics of the endless debates between mainstream climate scientists and their informed critics.
My simplified graphical model is a persuasion device meant for spawning further discussion among those who follow the scientific side of the global warming public policy debate. In short, it is an issues communication tool.
Why not extend the graphic out to 2200 and beyond, using Javier’s analysis as the assumption basis?
Including a major downturn in GMT that occurs well into the 22nd Century would bring in a host of complicated analytical details which cannot be clearly illustrated or inferred from the graphic itself — thus defeating its main purpose as a science issues communication tool.
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Judith,
Thank you for a neat essay and your comments about it.
From the start of my interest in global warming, about 1992, I have been puzzled why the important matter of climate sensitivity is conducted in models of “doubling”. Coming from early years in analytical chemistry, my instinct was to show concentrations of CO2 in the air compared with temperature changes. Later, I have come to realise that few of the modellers seem to know enough about the theory and measurement of the absorption of light of various wavelengths as it passes through various media. This accusation is supportable by the persistent failure of anyone to address and even answer a question of the following type:
The action of one molecule of CO2 cannot plausibly be able to change the temperature of the atmosphere. There would have to be too much heavy molecular lifting. What of a “doubling”? If one molecule is not enough, how about 2 molecules? Or 2 million? Or 2 billion? The fundamental question is, “At what concentration does CO2 in the air have to be before it imparts a measurable effect on air temperature?”
A rider question is “When a ‘doubling’ is invoked, what is the base level for doubling to be observed?” We measure CO2 in air today at some 400 ppm by volume. It was once plausibly 200 ppm, so that has been one doubling. But, as you use up more and more doublings, the postulated logarithmic response effect reduces the ability of each doubling to act as a greenhouse gas. The arcane question then arises, at what concentration of CO2 in the air can one start to invoke doublings and why? (To me, the first doubling is from one molecule to 2.)
There are about 4.2 x 10^37 molecules of CO2 in the air today.
Geoff S
Forget about the models and look at balloon observations over many decades.
The Connollys have done this and used millions of flights to draw their conclusion that increasing co2 levels are NOT THE control knob.
I hope Nic Lewis will look into their data and evidence, because everyone seems to be skirting around their findings.
“AOS models are members of the broader class of deterministic chaotic dynamical systems, which provides several expectations about their properties (Fig. 1). In the context of weather prediction, the generic property of sensitive dependence is well understood (4, 5). For a particular model, small differences in initial state (indistinguishable within the sampling uncertainty for atmospheric measurements) amplify with time at an exponential rate until saturating at a magnitude comparable to the range of intrinsic variability. ” https://www.pnas.org/content/104/21/8709
https://www.pnas.org/content/pnas/104/21/8709/F1.medium.gif
“Generic behaviors for chaotic dynamical systems with dependent variables ξ(t) and η(t). (Left) Sensitive dependence. Small changes in initial or boundary conditions imply limited predictability with (Lyapunov) exponential growth in phase differences. (Right) Structural instability. Small changes in model formulation alter the long-time probability distribution function (PDF) (i.e., the attractor).”
“Lorenz was able to show that even for a simple set of nonlinear equations (1.1), the evolution of the solution could be changed by minute perturbations to the initial conditions, in other words, beyond a certain forecast lead time, there is no longer a single, deterministic solution and hence all forecasts must be treated as probabilistic.”
https://royalsocietypublishing.org/cms/attachment/94141948-e1c2-4dc4-ac3e-82ca271c2911/rsta20110161f08.jpg
Solutions diverge from sensitively dpendent conditions for a single model shown at a conceptual decadal scale here. Panel b shows the Earth off doing its own spatio-temporally chaotic thing independent of temporal chaos of our mysterious set of nonlinear equations. Show me the million bucks Clay prize and I’ll agree it’s not mysterious.
https://watertechbyrie.files.wordpress.com/2018/05/rowlands-2012.jpg
https://www.nature.com/articles/ngeo1430
It is obviously potentially much worse with these perturbed physics ensembles than CMIP opportunistic ensembles. The former necessarily probabilistic and the other a bedraggled collection of ersatz deterministic solutions, As normally reserved and objective as I am – this is the scam of the millennium. They can’t not know it. But I suppose it came time to tell us something.
Here’s the deal… The age of instrumentation began when it was cooler because of widespread volcanism, The models are approximately correct, fiddled or not; it did warm up. The prime assumption that carbon dioxide is the cause is wrong. On top of that NOAA has apparently corrected the data to remove Volcanic and ENSO spikes. I used NOAA graphs. They claim these are lost.
https://www.dropbox.com/s/4473qqg9dw8lugm/Volcanoes%20ENSO%20and%20Carbon%20Dioxide.pdf?dl=0
Thank you for that!
Pass this around.
I did send send the link to several.
Glaciers have retreated as we warmed since the little ice age.
Less ice extent causes less cooling from warming and ice thawing.
That was cause and not result. It is warmer now, there is more open Arctic Ocean, there is more evaporation and snowfall, new ice is being added on top of old ice as the old ice continues to be lost at the tails of retreating glaciers and shrinking ice sheets. The new ice will replenish the ice sheets and glaciers and after a few hundred years of more snowfall, the ice will advance and cause cooling, the Arctic Ocean will form sea ice and the replenishing snowfall will cease. That next little ice age will last a few hundred years and then the depleted ice will retreat. Then we will have traveled a full cycle of the thousand year cycles with measurements.
We have that already, in the ice core proxy records, if anyone is interested to understand what has happened. Ice core records show warmer times with more ice accumulation alternating with colder times with less ice accumulation. The alternating ice extent causes the warming and cooling. The alternating ice extent is lead by hundreds of years by the alternating ice volume and weight cycles.
The age of instrumentation began when it was cooler because of more ice extent, the little ice age was colder because of more ice thawing and reflecting ice.
The widespread volcanism caused immediate correlations, the ice had already caused cooling over several hundred years, the snowfall that caused the little ice age, mostly fell during the Medieval Warm Period, check the ice core records, T
Taking climate model evaluation to the next level
Nature Climate Change volume 9, pages102–110 (2019)
https://doi.org/10.1038/s41558-018-0355-y
Earth system models are complex and represent a large number of processes, resulting in a persistent spread across climate projections for a given future scenario. Owing to different model performances against observations and the lack of independence among models, there is now evidence that giving equal weight to each available model projection is suboptimal.
Comparing model results to observations provides insight into the quality of model simulations and the way in which various processes are represented. Comparisons with observations can reveal short-comings in individual models and systematic errors in a large multi-model ensemble7’20. An example of a systematic error is the excessive simulated band of precipitation in the tropical Pacific south of the Equator, a feature not present in observations. Taken together with the usually correctly simulated climatological intertropical conver-gence zone (ITCZ) precipitation maximum that stretches across the tropical Pacific north of the Equator, this systematic splitting of tropical Pacific rainfall into two discrete branches is commonly referred to as the double ITCZ. Other examples of systematic errors include a dry Amazon bias, a warm bias in the eastern parts of tropical ocean basins, differences in the magnitude and frequency of El Nino and La Nina events, biases in sea surface temperatures (SSTs) in the Southern Ocean, a warm and dry bias of land surfaces during summer, and differences in the position of the Southern Hemisphere atmospheric jet.
Another thing we need to be doing is getting spatial CO2 data. We should be gathering station, balloon, and satellite data like we do for temperature.
There are dozens of CO2 monitoring stations around the world: https://is.gd/kMPLHQ
Dr. Curry ==> Thanks for this — covering this paper from LSE was on my to-do pile and now I can scratch it out — you’ve done a far better job at it than I would have.
I sent the following message to Roy Spencer, whom I consider to be a honest and knowledgable gentleman:
Dear Roy, Espoo, October 30, 2019
I would like to make you aware of a new approach that IPCC has very recently taken in their latest updated AR5 WGI science basis report. I have actually been for a longer time interested in the actual role of water vapor as a greenhouse gas in relation to CO2.
I happened by chance to note while once again glancing through the recently updated version and its Chapter 8: https://www.ipcc.ch/site/assets/uploads/2018/02/WG1AR5_Chapter08_FINAL.pdf , a mention which caught my attention. Among the FAQ’s there was FAQ 8.1 | with the title: How Important Is Water Vapor to Climate Change? The full address for Chapter 8.1 and especially pages 666 and 667 are worth reading>
The second paragraph in FAQ 8.1 reads: Water vapor is the primary greenhouse gas in the Earth’s atmosphere. The contribution of water vapor to the natural greenhouse effect relative to that of carbon dioxide (CO2) depends on the accounting method, but can be considered to be approximately two to three times greater (note that the “two to three times greater“ I consider to be a very conservative guess).
The IPCC continues to explain further: Additional water vapor is injected into the atmosphere from anthropogenic activities, mostly through increased evaporation from irrigated crops, but also through power plant cooling, and marginally through the combustion of fossil fuel. One may therefore question why there is so much focus on CO2, and not on water vapor which could as well be a forcing due to solar insolation, instead of “degrading” its role to a mere feedback in climate change, “sorry global warming”.
This IPCC statement astonished me although I was very much aware of the views expressed by e.g. Henrik Svensmark many years ago on the role of water vapor and clouds governing Earth’s climate.
Digging deeper into this issue I started wondering whether the IPCC is throwing CO2 out as the climate culprit, but NO! The CO2 is still the crucial radiative forcing and water vapor is degraded to merely the duty of feedback.
The explanation seems to go as follows: a small increase in atmospheric CO2 “loaded” with infrared radiative energy will spontaneously emit the absorbed IR energy and cause water to evaporate???; HEURECA! Causing increased evaporation and this increase in atmospheric water vapor is the real feedback warming the air and our planet. Simply a beautiful explanation, but? To keep the CO2 scare alive the IPCC does not forget CO2 but gives it the very important role, CALLED THE CONTROL KNOB – A minimal addition of CO2 that has absorbed IR-energy will spontaneously emit the absorbed IR energy and cause a substantive amount of evaporation.
This idea of a control knob was obviously coined a decade ago by Andrew Lacis et al.: https://www2.bc.edu/jeremy-shakun/Lacis%20et%20al.,%202010,%20Science.pdf
Unfortunately my knowledge in quantum physics is very limited, I am, however, under the impression that the infra red energy quanta, photons, emitted by the CO2 molecule will hardly be able to detach a surface water molecule due to strong surface tension between water molecules.
So, Roy, if you have somebody with proper knowledge who could tell me whether it is possible that CO2 with absorbed IR-energy quanta could by spontaneous emission actually detach water molecules despite the strong surface tension of water. I know that direct sunlight and especially when augmented by wind and waves with spray does speed up evaporation.
I should also add that having worked in tropical ocean environments, the maximum warming of open ocean surface waters will only in very special conditions exceeds anything above 32C. This is obviously the practical limit when the bulk of solar insolation energy has exhausted its ability to evaporate ocean water. Only surface winds can further enhance evaporation.
Tim Palmer – ultra authoritative climate modeller – say that emissions and land use are a wedge under an executive decision maker.
Sounds like rubbish!
The odd thing is it isn’t.
RIE, thank you for this Tim Palmer reference. His description of the Navier-Stokes equation as near asymptotic Russian Dolls provided a powerful visualiztion to understand the promise and challenges it represents.
At the end of this video, Palmer sums up his thoughts on the political nature of climate change. He suggests that buying insurance makes economic and moral sense even if we don’t think our house will be burgled – AS LONG AS – the insurance is not prohibitively expensive. I thought you would appreciate how the real-world problem of mitigation seems to be contained in that “as long as.”
Insurance makes sense for individuals with some assett at risk and limited cash. Most of us that is. I think for these grand challenges – food security, resilience in the wake of extreme events, environmental conservation, development – in terms of costs and benefits.
Clive Best is proposing icosahedral binning (repeating from a Nick Stokes posting) as a better way to grid the planet with equal size and shape voxels:
http://clivebest.com/blog/?p=9181
http://clivebest.com/blog/wp-content/uploads/2018/01/2017-Poles.png
The main datasets (e.g. Berkeley, GHCN) seem to be going that way.
The greatest blindness with the models, is that the so called internal variability at multidecadal scales, is really a negative feedback to solar variability and which controls low cloud cover also as a negative feedback. The Atlantic Multidecadal Oscillation is systematically warmer during centennial solar minima.
https://www.linkedin.com/pulse/association-between-sunspot-cycles-amo-ulric-lyons/
At the minimum, climate model output should be plotted against observed measurements and trends for comparison. After all, most scientists consider observed data a baseline. The RCP climate model projections are almost double century/decadal observed trends.
https://imgur.com/a/b7dUslY
In medicine, there are hierarchies of scientific evidence which look like this:
https://thelogicofscience.files.wordpress.com/2016/01/hierarchy-of-evidence2.png
Since climate is n=1 and not controlled, randomized, controlled experiments are not possible and the evidence wouldn’t appear to be strong.
On the other hand, model results for shorter terms still have some remarkable accurate qualitative predictions. In Theory of Climate – Carbon Dioxide and Climatic Change (Academic Press, 1983)p. 77, before many trends were apparent, Manabe predicted:
1. Warming troposphere, cooling stratosphere ( ✅, ✅ )
2. Polar warming 3x tropical warming ( Arctic:✅, Antarctic: ❌ )
3. Warming seasonality in Arctic, not in Tropics ( ✅, ✅ )
4. Global precipitation and evaporation increase ( no sig prcp, evap obs? )
5. Polar sea ice decreases ( Arctic:✅, Antarctic: ❌ )
6. Snow melt seasons occurs earlier ( ✅ )
7. Annual runoff increases at high latitudes ( insufficient obs )
8. Decreased mid-lat NH summer soil moisture ( not US long term )
The successes of the predictions are mostly for the directly thermal effects:
warming trop and cooling strat are direct effects of increased radiative forcing and predictions 2, 3, 5, and 6 directly follow from increased thermal content.
The unsuccessful outcomes of the Antarctic might be explained by the cooling effect of CO2 over the higher Antarctic terrain during winter ( when surface temperatures are lower than temperatures aloft and thus increased opacity means higher, not lower IR to space ).
Precipitation is a result of specific dynamics as well as the presumed increase of humidity. The variability of precipitation imposed by dynamics might explain why no trend is observed.
Since soil moisture depends on precipitation as well as evapo-transporation which in turn depends on not only temperature, but humidity, sunshine, wind, plant water capacity and retention, et. al., it would not be surprising if soil moisture were not very predictable.
So perhaps there is more strength for the modeled evidence of direct and simple processes such as radiative forcing, temperature and phase changes of water.
And perhaps there is lower strength for the modeled evidence of the indirect and multifactoral processes, especially those which depend upon specific motion of the atmosphere.
Russian researchers say warming due to cloud changes
http://joannenova.com.au/2019/11/new-study-settles-it-global-warming-and-the-pause-was-driven-by-changes-in-cloud-cover-not-co2/
This cloud result also explains why the surface statistics show steady surface warming before 2000 which the satellites do not find in the atmosphere. Cloud changes warm the surface, not the troposphere. Explaining this puzzling difference is serious evidence for the cloud change idea. It also strongly suggests that CO2 has nothing to do with it, as I pointed out two years ago. https://www.cfact.org/2018/01/02/no-co2-warming-for-the-last-40-years/
David, the southern ocean SSTs don’t show much in the way of warming before 2000 either. If the troposphere is influenced more by the sea surface than it is by land, that could be the reason. Spencer oft talks about how sea surface temps influence the lower troposphere with a lag of several months.
The problem here, of course, is that has not been a good old-fashioned cooling of the GMST since 1905:
https://i.imgur.com/LGcnVDv.png
Natural cycles do have a trend, so they cannot cause warming or cooling:
https://i.imgur.com/IFCLQh2.png
The PDO cooled from around 1983 until 2015. It has no trend. It can flash one briefly, but it always vanishes. Because it can’t create heat and it can’t uncreate it. Atmospheric CO2 is what is doing that. You will note that the blue trend on the PDO graph and blue trend on the Russian cloud graph is approximately the same. There is a reason for that:
https://i.imgur.com/TeSpcOb.png
SB – Natural cycles do not have a trend, so they cannot cause warming or cooling:
And here it is:
https://i.imgur.com/ZQU615P.png
It all went away, and it did not cool a darn thing. Knutson’s spring-back warming.
But please do pray for it come back. Cargo Cult.
JCH — “Natural cycles do not have a trend, so they cannot cause warming or cooling:”
Yerrbut, if people keep seeing a trend, it just shows how unfit for purpose the temperature record is.
https://i.imgur.com/hrW1Edw.png
The was another warming spike in 2015-2016. Caused by energy changes in the eastern Pacific mostly. Less low level marine strato-cumulus over warmer water – https://www.mdpi.com/2225-1154/6/3/62 – with less reflected light.
https://www.researchgate.net/profile/Ilan_Koren/publication/256119258/figure/fig2/AS:282563944960012@1444379902449/Open-and-closed-cell-formations-in-shallow-marine-clouds-over-the-South-Pacific-Ocean.png
The formation is as bistabnle convection cells where cells rain out from the centre leaving open cells at rates set by environmental conditions, Good luck modelling that.
Photon energy heats the oceans and is transformed and transported. The world warms or cools. Although this planetary energy store has seemed mostly to warm in the past century. The key to ENSO, PDO and the Pacific state generally is upwelling. I’m wondering if resurgence to historically more normal conditions of cold water upwelling increases low level marine stratocumulus cloud and cools the planet?
Is global warming all due to super El Niño’s?
By David Wojick
https://www.cfact.org/2019/11/02/is-global-warming-all-due-to-super-el-ninos/
In a recent CFACT article, climate expert Joe Bastardi says super El Niños have caused all the atmospheric warming since satellite measurements began in 1978. I suggested this two years ago in a CFACT article titled “No CO2 warming for the last 40 years?” Now Joe has confirmed it.
The focus of Joe’s long article is that these super El Niños are natural.
Most importantly, here is Joe’s picture of the 1998-2000 super El Niño step up in global temperatures, with nothing but 15 to 20 year pauses on either side:
My description of this big step up, posted two years ago, is here.
There is little to no CO2 warming in the entire satellite record! Just a step up warming due to the super El Niño 20 years ago. I told you so. We may now have a second super El Niño step warming but it is too soon to tell. In any case there will still be no evidence of any CO2 induced warming. The gradual increase in atmospheric CO2 has nothing to do with super El Niños. Joe explains this in great detail.
Regarding the supposed surface and ocean warming, while it may be real, to my knowledge it cannot be due to the CO2 increase. (I say may be real because I have serious doubts about the validity of the convoluted statistical methods used to estimate this warming.)
I know of no mechanism whereby steadily increasing CO2 in the atmosphere can cause steady surface warming without first causing steady atmospheric warming, which the satellite data say has not happened. The surface and ocean warming would require increased back radiation from the airborne CO2 molecules, which requires increased atmospheric temperature, which we do not see.
If the surface and ocean are in fact warming, then “why?” is a very big question, which ought to be the focus of research. A solar effect seems most likely. But whatever caused it, this warming is not evidence of AGW. Unfortunately the climate science community is so wedded to AGW that this research is still waiting to happen. Why the predicted CO2 warming has not occurred is another good question, a huge one.
The elegant thing about science, at least in principle, is that a single observation can falsify a popular hypothesis. But as Thomas Kuhn pointed out in his groundbreaking book — The Structure of Scientific Revolutions– this may not be true in practice when the hypothesis is deeply entrenched, due to what I call paradigm protection. The community of believers will resist what observation clearly says. So we get the argument that the data must be wrong. However, the satellite data is accurate enough to falsify AGW.
The great philosopher of science Karl Popper said that science was a process of elegant conjectures, followed by refutation by observation. The conjecture of AGW is largely refuted by observation.
David Wojick wrote:
“Is global warming all due to super El Niño’s?”
Where did all the added heat come from? The surface is warming, the lower troposphere is warming, the top half of the ocean is warming, the deep ocean is warming, ice is melting. The stratosphere is cooling (as predicted by greenhouse theory, after correcting for ozone loss).
So where has all the extra heat come from?
David Appell |
David Wojick wrote:“Is global warming all due to super El Niño’s?”
Where did all the added heat come from? So where has all the extra heat come from?
–
David, just a few clues
Heat (energy*)comes from the sun 99+% and a little from the cores down other sources.
Heat* is produced variably by the sun so answer 1 a little hotter sun in part.
Heat* does not always reach the earths surface and oceans due to albedo changes (clouds) so answer 2 a couple of decades or less of lower cloud formation gives more heat.
Heat retention is variable in the permeable layers of air, water and ice..
This results in the possible return of slightly warmer water to the ocean surface that has been taken lower for up to a thousand years ( sounds like a fairy story but…) . Obviously it could easily keep coming out for at least up to 500 years so answer 3 extra heat or added heat from the past.
Increased atmospheric heat retention answer 4 natural GHG mixture and GH clouds in the atmosphere variability.
Volcanoes and forest fire smoke presence or absence other causes etc
Did I leave one potentially very small cause out.
Hard to tell worth all that background noise.
Heat stored in the deep ocean is ice cold. So if it came out for 60 years this place would be like Chile man.
JCH | November 4, 2019 at 9:55 pm |
“Heat stored in the deep ocean is ice cold. So if it came out for 60 years this place would be like Chile man.”
I believe you JCH.
But thousands would not.
Note I did not say deep ocean. Freudian misunderstanding on your part..
I said, deliberately, taken lower. Not to the super deep and cold depths.
Being an El Niño fan you must enjoy the BOMs little treats such as anomalies 2 or 3 C warmer or colder than usual at 200 meters depth etc.
I believe when people like Spencer, Gavin, etc talk of heat coming to the surface they mean the temperature anomaly is 2 C or .2 C or .02 C warmer than normal for that water at that time of year.
Again as you know, even that small amount of extra heat in water makes a much larger change in atmospheric heat change.
Why if I did not know better I could enlist you as a fellow member of the anti Trembath brigade.
“JCH states there is no missing heat in the oceans”
JCH states it is impossible for heat to be stored in the oceans.”
JCH states it is impossible for heat to be retained at depth in the oceans.”
JCH states mystery of heat being trapped in the oceans is a lie.”
Can I quote you on this forever?
David Wojick: In a recent CFACT article, climate expert Joe Bastardi says super El Niños have caused all the atmospheric warming since satellite measurements began in 1978
Where did the extra heat that the super El Niños pumped into the atmosphere come from? The answer may not be known, but it is no explanation at all to claim that the super El Niños by themselves caused all the warming.
A decade of El Niños could have caused all the warming. It has only recently come to my notice that ENSO goes out of phase for about a decade at the end of Gleissberg cycles. An absence of La Nina is a way of warming the planet.
ENSO is the temperature control knob, operating on some kind of internal dynamic seeking equilibrium.
ENSO has furnace. What does it burn? Anchovies. What fed the Anchovies? Rays from Pluto.
ENSOs just redistribute heat — they don’t create or destroy it. They are not an external factor causing planetary warming or cooling, like CO2 or the sun. Over several decades their net influence is very close to zero.
Besides, the decade from 2001-2010 had an average MEI (v2) of -0.20, which is slightly La Nina-ish. This decade so far average is -0.09.
ENSO may have a furnace, with fixed non moving energy points, hydrothermal vents.
A perfect explanation. Belongs here. Bravo.
This is a fascinating discussion
I am by no means a professional modeler, except that I have had professional success in estimating which companies are likely to succeed or fail over time, and in estimating which new technologies are likely to grow or shrink.
For example, it was relatively easy to determine several decades ago that, of all the auto companies, Toyota and Honda were most likely to thrive, and most others were likely to shrink.
Why?
Because Toyota/Honda “mental models” of the future assumed that 90% of the future was unpredictable, so they reduced their fixed system investments to tiny fractions of the total enterprise (compared to GM, Chrysler, Ford, VW, Peugeot, etc)
Instead of building massive robotic factories, and then forcing workers to adapt to this a priori engineering, Toyota and Honda built very small bits of automation around the human actions of workers, and then gave workers the ability to adapt the automation to the tiny, unforcastable events that happened every day
GM, Ford and others would make 5 and 10 year strategic plans, and then buy $billion of capital equipment that would have to operate at high efficiency for 10 years to pay off the investment.
Because their stamping dies were so costly, and took weeks or months to change within the large stamping presses, the US and European makers would try to run those dies for months or years without changing them.
And, because consumer tastes change in 2-3 years, and GM stamping dies had to last for 10 years, the GM-style companies were constantly cutting prices on old models to try to keep obsolete factory equipment running long enough to justify their initial costs.
On the other hand, Toyota and Honda, like many Japanese companies would never make firm financial forecasts longer than 6 months.
Toyota and Honda used much smaller stamping machines and dies, and could change them in 30 minutes.
This meant Toyota and Honda were able to react to unpredictable changes in market demand within weeks, not years.
And the workers at the lowest levels could make these changes.
Toyota and Honda are the only long-term cash-flow positive auto companies at high volume. All the other high scale producers have had overt or hidden bankruptcies.
This is the world of Just-in-Time and it has proven to be much more successful than the world of “expert 10 year forecasts”…and massive investment in “expert-certified” capital equipment.
The process is also at work in global communications.
Google was Just-in-time packet-switching networking, versus slow-motion “circuit switching” networking at ATT.
The packet switching technology expanded rapidly to 7 billion humans.
The circuit switched network that ruled for 50 years is dead.
The point?
The large climate models are like the GM forecasts on steroids, with much less data.
Even if they prove accurate, they are useless for FORECASTING THE BEST IMPLEMENTATION OF TECHNOLOGY TO IMPROVE THE CLIMATE.
As we can already see with moderately large rollouts of LARGE SCALE, CENTRALIZED solar, wind, EV’s, etc – the systems have many unintended consequences and will most likely go the way of GM
Battery recycling in EV’s will be massive environmental challenge if EV’s reach intended volumes.
Large scale solar and wind in CA is being crippled by the same wildfire risk that cripples the traditional fossil fuel energy producers.
The CENTRAL GRID is the problem of implementation, even if the centralized solar and wind farms are cleaner.
Step back and look at the current “climate change industry” process
1. Massive forecasts have all the flaws discussed above, and the same flaws as the forecasts at GM, etc
2. Massive centralized investments in solar/wind etc, are subject to the same “stamping press changeover” problems that have shrunk all the traditional auto companies.
3. And because 1 and 2 require massive taxes on the “civilian” population, the desired goal of environmental improvement is meeting all kinds of decentralized, unpredictable resistance from USERS of the global energy system.
I am a devout environmentalist.
I know from traveling the world that we humans MUST stop our environmental damage.
But I also know from the objective experience of Toyota, Honda, Google, Alibaba, etc…
….that trying to forecast the future of “butterfly” systems will be mostly wrong all the time.
And that the decentralized, constant user experimentation, fast reaction systems of Toyota/Honda/Google are the best approach we can all take to solving an evidently unforecastable future of the Earth/cosmic ecosystem.
We need to find ways to make it FUN, and profitable, for 7 billion humans to improve the environment…just-in-time…every hour of every day….forever.
If we think the current massive GM-style investments in “green” infrastructure will be the right answer 20 years from now…..we are likely to be in for an expensive surprise.
For what it’s worth…
Just sayin’…..
Marty,
Your story is an example of a best practice taught and followed in the business world; that long term investment based on assumption should be avoided.
Also, I was surprised by what you said; I was an assembly engineer at Ford in the 70’s and they changed stamped parts every year. Back then cars would rust out every three years and would sell based on body style. When I left Ford the marketers were just beginning to realize the importance of quality.
Richard
Agreed. In the 1950’s and 1960’s perhaps the anticipated event in every town in America big enough to have car dealerships was the rollout of the new body styles. Kids. mostly boys, could ID a year, make, and model from a long distance.
ACH
So true. It was part life, part of American culture.
Richard
Oops “Part OF life”
“I am a devout environmentalist.
I know from traveling the world that we humans MUST stop our environmental damage”
Aren’t those two statements somewhat contradictory?
tonyb
Nope. Equating ‘environmentalism’ with CO2 emissions has caused us to neglect the actual environment. I like Marty’s essay
Judith
If you believe in a climate emergency caused by rampant co2 I can not see how ( presumably) flying round the world would be considered environmentally sound.
XR conflate the two and I agree that looking after the environment is key and it is unfortunate that co2 is put into the Same equation, but put into the equation it has been
Tonyb
Agreed, three separate equations needed: environment, CO2/climate, sea level rise. Some intersection, but mostly independent issues
Hi Tony If you meant ‘why would I fly if I believed it was bad for the climate?’…
…I understand.
My only defense is that for all my air travel I always bought an empty seat on an existing flight, so my personal contribution to atmospheric pollution was near zero at the margin. Perhaps 95 kilos of extra mass on those 747’s
:-)
I do believe that humans do not need to do the damage we are doing, and my professional approach has always been to execute, or help others execute large organizational changes – like ‘zero’ inventory practices – that scale beyond the ability of individuals to help cut waste and damage.
I have been instrumental in several very large corporate turnarounds that have done just that. I observed how Toyota/Honda did their JIT and helped many other organizations to do that around the world.
If the political environment were not currently so hostile, i would be much more vocal.
Am I an environmental threat?
Well …I do drive a larger vehicle than I need….and I relieve some of my guilt by traveling as much as I can on a bicycle.
:-)
I repeat my wish…
How can we all make it FUN and profitable to reduce our effect on the environment/climate/etc?
Marty
Tony – missed one of your comments above.
I do not personally believe that the data contained in the broad array of environmental, climate, and industry research support a statement so simple as a “climate emergency” caused by a single driver.
First, I have been learning from you folks on this blog, and elsewhere, how the various climate models are assembled and interpreted. This convinces me of the uncertainty involved in making simple “single variable” models of “the” climate.
Second, I helped create several of the federal fuel economy and pollution control cost-benefit models based on years of on-ground research into the manufacturing, deployment, and recycling of mobile and stationary sources of pollution.
I know how the sausage is made behind closed doors, and how the empirical and data-based research can be – honestly – ‘adjusted’, or “cleaned up”, at many levels of hierarchical organizations.
I spent weeks going through the hierarchy of IPCC papers that are narrowed down to the ‘report for policy makers’ on the most recent ‘consensus’ process.
Because I have spent years documenting supply-demand-use-recycling chains of autos, electronics, energy, etc, on the ground, in more than 25 nations, I can tell you that the recent mobile-source and technology parts of that summary consensus process are “interesting”.
Suffice it to say that there are meaningful variances between some of the data sets in those papers and the actual data of devices in use – especially with respect to how quickly vehicles/devices in use are replaced or upgraded..
I have seen the ‘single-variable’ noxious pollution targets change over 30 years from sulphur, to nitrogen, to carbon, etc. See historic models of “smog” etc.
It is my experience that single-point forecasts of even simple things – like how many cars will be sold next year – have wide error bands.
Building autos is one of the most concrete, finite, well-measured industrial systems humans manage.
Yet trying to forecast the profits from auto production three months from now has errors that almost always exceed 5% of forecast, and can be more than 20% of the forecast.
This makes me think twice about global, single-variable, consensus forecasts….of anything.
It is also clear from history, that many things that “suddenly appear by surprise” – can actually be traced back to visible events many years previous. (See the future of 5,000 cell EV batteries, for a possible example)
And as a follower of many wonderful scientific explorations, I have seen that science is so good it proves itself as much as 70% “wrong”
all the time.
Example. See that less than 20 years ago influential scientists said we had “cracked the human genome” – and all that debris left over was “junk DNA”
Today, that “junk” is seen as the door to amazing, accelerating discoveries in various “biomes”….that have even revealed that our human bodies are really dynamic parts of much larger ecosystems that are not bounded by our skin.
I see the world as a wondrous place, with huge opportunities for learning, experimenting, collaborating, enjoying.
I have also worked in some of the most terrible places on Earth, where warring factions have done horrible things.
The current hostility with respect to things that can be improved with collaboration makes me just plain sad.
So – I hope that my comments here are taken as an attempt to stimulate thinking beyond single disciplines…beyond factions…beyond single variable models….so that the great work done by you folks on this blog…and many others…can bring us all a better future.
(And – I’m really curious to see how recent “black hole” discoveries, which to this lay person relate to things like metrics of gravity, and therefore dimensions of parts of “Earth” – play out over the next decade.)
Again….just sayin’
Marty
Marty
I am not really disagreeing with anything you write (and I did enjoy your two long pieces)n
. The big problem is that ‘climate change’ and the environment have been inextricably linked by those as diverse as XR and David Attenborough.
There are many environmental issues I could get on board with, but the trouble is that I cant support them due to this link as I do not believe in this madness of co2 causing every ill in the world.
tonyb
I agree, Tony B.
Current temperature anomalies above the 60th parallel.
https://www.cpc.ncep.noaa.gov/products/stratosphere/strat-trop/gif_files/time_pres_TEMP_ANOM_OND_NH_2019.png
The ozone blockade over eastern Siberia is strengthening.
https://www.cpc.ncep.noaa.gov/products/stratosphere/strat_a_f/gif_files/gfs_t50_nh_f00.png
Jet streams in the lower stratosphere indicate the flow of Arctic air to the central US.
https://www.cpc.ncep.noaa.gov/products/stratosphere/strat_a_f/gif_files/gfs_z70_nh_f00.png
Very low solar magnetic activity.
http://www.n3kl.org/sun/images/noaa_satenv.gif?
Models have a use, but are only as good as the assumptions that go into them. If the assumptions are faulty, the conclusions will also likely be faulty.
The ice extent in Central Arctic has been growing the fastest in five years.
http://masie_web.apps.nsidc.org/pub/DATASETS/NOAA/G02186/plots/4km/r11_Central_Arctic_ts_4km.png?fbclid=IwAR3eunscDgw1xvOPBllfFJw52EDgKH3vdMKoHKK0G1B8rGRO1JGJCJisVSU
As the Ice breaks off in the Arctic and Antarctic there is less frozen land area for the new ice to land on. Therefore to keep the same amount of heat to mantain a constant surface temperature Mother Nature must move more watervapor to the poles.
When more ice is needed on land in polar regions, oceans warm, sea ice thaws and then evaporation and snowfall rebuild the ice.
When there is more than enough ice on land in polar regions, sea ice forms and stops evaporation and snowfall and the ice depletes until it gets warm again.
This happens in natural cycles and we do not cause them.
Tropical warm currents always flow from the tropics toward the poles in the upper ocean and cold currents always flow from the poles toward the tropics in the deep ocean. When sequestered ice runs low, sea ice is removed and the evaporation and snowfall is switched on to rebuild the ice. When there is more than enough sequestered ice, the sea ice returns to turn off toe evaporation and snowfall while the ice depletes. This is why there are warm and cold cycles and not an average equilibrium, “hockey stick handle” temperature.
When more ice is needed on land…
Define “needed.”
popesclimatetheory commented:
When there is more than enough ice
What specifies if there is “enough” ice?
It only says, that there was a big area of ice free water in September/October. And this ( of course) freezes fast if the insolation is near zero. What a surprise!!
See: https://i.imgur.com/4cNOQIu.jpg
Dr. Curry
This post reminds me that climate research continues waste money on dead end projects like trying find improved model parameters. And it ignores more potential projects like researching natural variability (for which it currently has no clue).
Richard
Models deny (natural) climate change altogether, except volcanic, which they use as a fudge factor, to simulate the well known AMO (a misnomer) pattern. They also take into account a bit of solar and maybe others (not sure), but they give it almost no significance. It’s a cargo cult.
Pray for the return of the divine wind – the equivalent of the allied cargo planes in Feynman’s Cargo Cult. It’s contrarians who are in a Cargo Cult. The stadium wave = Cargo Cult.
Rich – good point – As an alarmist would state – all the present day warming is due to Man – yet how would you know when zero research money is spent exploring natural variability.
Just as an example of how incredibly wrong you are, since Mantua’s original paper on the PDO, Pacific Decadal Oscillation, there are over 18,000 hits on Google Scholar, over 6,000 since 2015. Which is good since it’s a beast ocean cycle and needs to be better understood. Of special concern, is why is the PDO’d cool phase keeps getting clobbered by anthropogenic CO2.
ACH
Are you categorizing my statement as incredibly wrong because there have been thousands of hits on one research paper dealing with the PDO? Do “hits” = “research” in your mind? However, I admit that my evidence is also limited. My evidence is as follows:
1. Chapter 10 of AR5, which concludes that little is known regarding natural variability, but nevertheless reverts to meaningless assumptions to arrive at a near-zero number for the level of natural variability.
2. Dr. Curry has long stated that there needs to be more research into natural variability.
Richard
My comment above was intended to be a reply to JCH.
No, it’s not about one paper.
I was responding to this claim:
“…yet how would you know when zero research money is spent exploring natural variability.”
I would say the people who have no clue about natural variability are those who think it even begins to have the potential to derail ACO2 warming.
And it ignores more potential projects like researching natural variability (for which it currently has no clue).
They do not even suspect!
popesclimatetheory commented:
And it ignores more potential projects like researching natural variability….
Natural variability due to what?
https://m.imgur.com/yvrMXFy
THE SUN
Are you aware that the average solar output over a solar cycle has been slowly declining since the 1960s?
https://spot.colorado.edu/~koppg/TSI/Historical_TSI_Reconstruction_sm.png
High solar activity correlates with warming, low solar activity correlates with cooling. Although solar activity has been declining in recent decades, it has still been high. (hence warming)…
https://m.imgur.com/CErrHrT
DA – Are you aware the TSI remains/continues on an upward trend since the late 1880’s.
You know its not good science to cherrypick a starting point.
Check your chart again
“An example: the most recent IPCC climate change assessment uses an expert judgment that there is only approximately a 2/3 chance that the actual outcome of global average temperatures in 2100 will fall into the central 90% confidence interval generated by climate models. Again, this is precisely the information needed for high-quality decision support: a model-based forecast, completed by a statement of its own limitations (the Probability of a “Big Surprise”).
“While the above statement is mostly correct, the IPCC does not provide a model-based forecast, …”
In point of fact a confidence interval does not speak to the actual or true value in any way. Rather it is concerned with the estimation process used. Thus if the first sentence is an accurate representation of the IPCC’s assertion then the IPCC is wrong–in the sense that the statement conveys a common misunderstanding of confidence intervals. It follows that the second sentence then is wrong–an untrue statement [1st sentence] cloaked in sloppy statistical/science-ish language should not be considered to be high-quality decision-support. Also assuming that the Probability of a “Big Surprise” is referring to some ‘actual’ realized value…well that part is wrong too,i.e., Thompson and Smith miss on that too. Finally “While the above statement is mostly correct,…” is mostly incorrect. :)
Statistics is a much tougher and demanding subject where accuracy and clarity are required–much more so than most of us (including scientists and engineers) realize. I will note in passing that the same demands are placed on rational decision making–even when one has to rely on expert judgement. Certainly muddled statements such as:
” there is only approximately a 2/3 chance that the actual outcome of global average temperatures in 2100 will fall into the central 90% confidence interval generated by climate models”
are counterproductive.
Thompson and Smith attempted a good thing but produced a document that sounds more like a histrionic overtly-biased critique of the use of models than providing an actual structure to some important considerations when modeling and when using statistical statements.
MWGrant,
Thank you for this interesting and informative comment.
CI define a probability that the true value lies within a range. This is not relevant to CMIP ensembles. This is ensemble land.
1.You are wrong, Robert.
https://www.statisticssolutions.com/misconceptions-about-confidence-intervals/
2.) Hmmm… no mention of the use of ensembles. Could it be that was intentional because the use of ensembles Is completely irrelevant to the point being made?
“… in the sense that the statement conveys a common misunderstanding of confidence intervals.”
It is you who are incorrect . How well the sample represents an underlying population is commonly an issue. Not here. Opportunistic ensembles are what CMIP are.
Closer home for you see Chapter 3 of Helsel and Hirsch and in particular section 3.2 which discusses the interpretation of confidence intervals.
https://pubs.usgs.gov/twri/twri4a3/html/toc.html
You also might want to take a look at the Hocksta et al. paper cited in the ‘Statistics Solutions’ blog above.
The spread in these opportunistic ensembles – which is what is being discussed – are not confidence intervals.
How can a climatologist escape from model land? In doing so, a climatologist must allow for the possibility that the axiom of probability theory called “unit measure” is falsified by the evidence though being satisfied by the model. A finding of research that I and colleagues of mine have conducted over more than four decades is that for a researcher to allow for the possibility that “unit measure” is false is rare. With rare exceptions, researchers assume “unit measure” to be true. For a researcher to allow for the possibility that “unit measure” is false is for this researcher to label himself or herself as a heretic in his or her profession. To label oneself as a heretic is an altruistic rather than self-interested thing to do.
“Unit measure” is the proposition that “1 is the value of the measure of a sure event,” aka “event that is certain to occur.” Under the classical laws of thought of Aristotle and his followers, the value is not restricted to 1 but is restricted to 0, 1 or 2. That the value is 1 is assumed by mathematical
statistics but mathematical statistics is not reality itself but rather is only a model of reality. To escape from model land a climatologist must subscribe to the heresy that mathematical statistics may be wrong! In practice, vanishingly few climatologists elect have trod the altruistic rather than self-interested path.
That situations arise in practice under which “unit measure” is a false proposition is illustrated by the climate sensitivity argument of Svante Arrehnius. In his pioneering study of the effect of
carbon dioxide emissions on Earth’s global surface air temperature, Arrhenius advances the position that “unit measure” is unconditionally true. A consequence from this position is existence of the constant that today is called the “equilibrium climate sensitivity” (ECS). ECS is the ratio of the change in the global surface air temperature at steady state to the change in the logarithm of the atmospheric carbon dioxide concentration. According to the United Nations Intergovernmental Panel on Climate Change, the magnitude of ECS is about 3 Celsius degrees per doubling of the atmospheric carbon dioxide concentration. The existence of ECS as a physically meaningful concept is, however, dependent upon the false doctrine that “unit measure” is unconditionally true. Admission of
the possibility that this doctrine is conditionally false yields the finding that, rather than being a constant, ECS is generally a variable but this finding is heretical in modern day global warming climatology.
> How can a climatologist escape from model land?
“Climatologist” invokes a prototype, and is thus a model.
“Model land” works a bit differently, but it’s still an abstraction.
I erred. In the first of the above two paragraphs, please change “climatologists elect have trod” to “climatologists elected to have trod,:
The climate models have consistently demonstrated that they are not fit for purpose and cannot be validated and yet they persist long after failed models in other sciences would have been consigned to the bin.
Christy and Spencer have shown that they run too hot. Frank has shown that the propagation of uncertainty swamps any meaningful projection of CO2 induced warming. Yet still the models underpin a branch of climatology dominated by a trace gas. Mean free path and emissivity calculations show that unlike water vapour, carbon dioxide in the atmosphere has a negligible effect on warming.
This conclusion has been confirmed by studies involving data from millions of radiosondes as reported by Connolly. Other researchers have shown that reductions in cloud cover of around 4% can explain all the warming of the second half of last century. The data even explains the subsequent pause. Other papers show similar conclusions.
Reblogged this on I Didn't Ask To Be a Blog.
This is an ENSO subroutine linked to a climate model. I use it because examples of perturbed physics ensembles are rare. I can think of a couple in the lasr decade. The multiple solutions arise from differing initial conditions. The result is probasbilistic in that there is a mean, quartiles, deciles etc. All atmosperic and ocean simulations can produce 1000’s of divergent solutions that must be viewed as probabilities.
http://www.bom.gov.au/climate/enso/wrap-up/archive/20191029.sstOutlooks_nino34.png
CMIP ensembles are something else. They are collections of solutions from different models that each purport implicitly to be deterministic. All different of course. A step where there is a rigorous justification for selecting just one solution is missing. We may I suppose put the selection between different, divergent, feasible solutions down to expert judgement. But the basis of such judgements is uncertainty. Else why models? The circularity is dizzying.
I’m not celebrating, I feel disgusted at their behaviour/”science”. Of course the IPCC has not dropped the hockeystick, they will try to force it as much as they can till the bitter end. The latest (ar5) hockeystick looks different than the first one. The hockeystick resemblance is slowly fading – more mwp and lia, less unprecedency/blade. Not that the unprecedency was ever evidence for the hypothesised AGW because it appeared long before it.
Well, I shall celebrate our agreement alone regardless.
You do realise that the bigger the MWP and LIA were the higher climate sensitivity is likely to be, right?
No, I disagree. Factors causing climate change at these timescales are not exactly known, if at all. It doesn’t tell us anything about “climate sensitivity”.
Sure it does.
The more variability, the less negative feedbacks and more positive feedbacks must be operating.
If sensitivity were low, a global, significant MWP would be far less likely, and vice-versa.
Sure it doesn’t. The “forcings” are unknown. What caused these climate changes at multi-decadal/-centennial timescales?
The point I’m making is not what caused them. It’s that *any* perturbation to climate will be larger if climate sensitivity is high, and smaller if low.
That’s all. It’s a simple enough point.
If Mann or the IPCC were somehow trying to talk up climate sensitivity, they’d be enlarging the MCA and LIA, not airbrushing them.
I understand the point you’re making and I’ve seen it before. My reaction to it was a facepalm, everytime. Any perturbation to climate will be larger if forcing is high and smaller if forcing is law. Since the forcing is unknown, the perturbation does not tell us anything about the feedbacks or sensitivity.
Great, more agreement. Claims that the MWP was airbrushed out to accentuate AGW are nonsensical. How very agreeable things are!
Sorry, no agreement. Your point is obviously wrong and now you’re pretending we agree. There’s no need to add anything more.
I’m happy to accept your apology!
(I’m actually unsure what you disagree on, TBH, but you’re very sure about it, so I’ll leave it there)
If climate is sensitive to a radiative change, baby, it’s a snowflake in the big city. Mann’s hockey stick is the preferred stick of the most hallowed hockey team of all time, the Outback Lukewarmers:
https://i.imgur.com/CTFzgTi.png
(well, ain’t that a puck in the mouth)…
You do realize that the bigger the MWP and LIA were the higher climate sensitivity is likely to be, right?
The oceans are huge carbonated drinks! Vapor Pressure of CO2 naturally follows temperature up and down. The data shows it does follow, it does not lead. Now, when CO2 is going up, temperature is not following. There is no proof that the sensitivity of climate temperature to CO2 is anything more or less than ZERO!
Pope,
assuming that you’re real rather than a spoof, you wonderful theory has the minor disadvantage of being directly contradicted by the evidence
https://www.pmel.noaa.gov/co2/files/co2_time_series_aloha_06-11-2019.jpg
popesclimatetheory commented:
Vapor Pressure of CO2 naturally follows temperature up and down. The data shows it does follow, it does not lead.
Before you travel in your gasoline powered car, do you first make sure the temperature has increased?
Any perturbation to climate will be larger if forcing is high and smaller if forcing is law (change that to low)
NO. if a perturbation to climate is in proper phase with internal cycles it can cause larger forcing, if it is in proper phase it can cause smaller forcing. Climate is not a static system, it is a dynamic system. A tiny forcing can be used to drive a huge clock with a large weight swinging back and fourth. It would take a huge forcing to cause the clock to run at a different speed that was not the natural frequency.
Climate has evolved because the mass of ice and water and the spring rate of snowfall and thawing has changed the internal natural frequencies of the internal ice cycles.
Alex Pope
Have you seen the latest absurd climate models? They have an ECS of over 5C, way outside the IPCC range. And they fail to match 20th century temperature data. Worse still, the authors falsely claim that the models have “good agreement with observations”.
https://cliscep.com/2019/11/05/new-climate-models-even-more-wrong/
Two new accepted papers were published on Oct 30th. One from a UK group (mostly Met Office) and the other from GFDL (Princeton). It’s the UK one that makes the misleading claims about good agreement. Mainstream climate scientists including James Annan have criticised it (“UKESM1 does a great job at everything other than its primary function”).
The polar vortex pattern in the lower stratosphere does not change.
https://www.cpc.ncep.noaa.gov/products/stratosphere/strat_a_f/gif_files/gfs_z70_nh_f00.png
https://www.cpc.ncep.noaa.gov/products/stratosphere/strat_a_f/gif_files/gfs_t50_nh_f00.png
Based on results from ESA’s Swarm mission, the animation shows how the strength of Earth’s magnetic field has changed between 1999 and mid-2016. Blue depicts where the field is weak and red shows regions where the field is strong. The field has weakened by about 3.5% at high latitudes over North America, while it has grown about 2% stronger over Asia. The region where the field is at its weakest field – the South Atlantic Anomaly – has moved steadily westward and further weakened by about 2%. In addition, the magnetic north pole is wandering east.
https://dlmultimedia.esa.int/download/public/videos/2016/05/031/1605_031_AR_EN.mp4
Sorry.
https://dlmultimedia.esa.int/download/public/videos/2016/05/031/1605_031_AR_EN.mp4
Sorry.
http://www.esa.int/ESA_Multimedia/Videos
It will be this winter, which North America does not remember.
http://tropic.ssec.wisc.edu/real-time/mtpw2/webAnims/tpw_nrl_colors/namer/mimictpw_namer_latest.gif
Ireneusz Palmowski
It will be this winter, which North America does not remember.
I’m sorry if I missed “it”, but what is the “it” that will be this winter? (How could North America remember a winter that has not happened yet?)
Thank you for this and your other graphic displays.
(we have even shorter memories than previously thought… 😉)
Arctic air will hit November 10 in the east of the US at full power.
http://virga.sfsu.edu/gif/19110712_jetstream_h72.gif
You look at this as cold air comming from the Arctic. I look at it as cold dry air from the arctic after dropping the ice in the Arctic. It is comming down to gather more water vapor and take it back to the Arctic.
This is how Mother Nature keeps the average surface temperature of the earth constant.
Give it a rest ‘ren’. Had a cloud graphic for a comment about model scales that could be lost in clutter. Here’s a cloud hysterisis loop instead. Resoled by cloud scale simulation. Across the planet simulation at cloud scale requires millions of times more computing power.
https://climate-dynamics.org/people/tapio-schneider/
Both graphics are presented at Youtube by Tapio Schneider – https://climate-dynamics.org/people/tapio-schneider/
https://watertechbyrie.files.wordpress.com/2019/09/cloud-computation.png
A blow of the arctic air will end the growing season in the south of the US.
I have reread this essay, and I think it gets better with rereading. Thanks again Dr Curry!
Unusual freezing, like on November 9, on the East Coast.
https://files.tinypic.pl/i/00992/ms0y8mupdr91.png
I have just sent this to WUWT and thought although it is very simplistic your readers may wish to see it as I am looking more closely at a more sinusoidal variation in sea levels and not an accelerating scenario. Curve fits for quadratic v sinusoidal of Tidal Data shown a remarkable similarity (see the drive google link).
————————————————————————-
In my paper “Accelerating Sea Level Rise – Reconciliation of Tidal Gauge Readings with Satellite Data” I investigated other curve fitting alternatives. The 3 curves studied were quadratic (because most papers tended to use this as it represented a constant acceleration and is the easiest to use), exponential and sinusoidal. Other the range of data, 1880 to 2013, they all were very similar and subsequent work was based on the quadratic curve.
The sinusoidal curve fitted was eye-balled in and represented a long (770 year) curve. To try to improve this the following process was followed. Assume the curve is given by
y=A+B*sin((C+2x)/D)
Where y is the sea level in mm, A is an adjustment to allow the points of contraflexure not coinciding with y=0, B is the amplitude, C is a phase shift, x is time with 1800 being taken as x=0 and D is the period in years.
Three equidistant readings, each 60 years apart, are chosen to fit the curve through. The process then involves assuming values for C and D, solving for A and B using the outer 2 values, calculating the value at the middle position and checking the error between this calculated value and the actual value, then modifying the phase shift, C, until the error is nearly zero.
What value of D to choose? A value of 1000 years seemed a good start as that is roughly the perceived time gap between Warm Periods. The resulting equation, in EXCEL Format, is
y=250+415*SIN(((1420+2*x)/1000*PI())
This is a curve with an amplitude of 415mm (total range 830mm or 2.7 feet), peaking every 1000 years with the next peak occurring in about 2340. The link below shows a plot of both the quadratic and sinusoidal curves together with the annual averaged Tidal Readings. Other periods could have been chosen but the fit is so good it was considered no necessary.
https://drive.google.com/file/d/1gjBS78xYrVZ4U2RsZe-AhIF7v5hk4FUI/view?usp=sharing
A few comments I would like to make.
Although I have used extrapolation here, and in my paper, I still believe that is a dangerous technique and I think “Extrapolate and be damned” should apply in most situations.
The quadratic is that obtained from the Tidal Readings with an acceleration of 0.0126, about 1/7th of that obtained by Nerem et al from satellite readings. To extrapolate 80 years based on only 25 years set of results and to report the results in my mind is criminal. In my paper I also undermine Nerem’s methodology as if the approach is applied to sinusoidal set of data closely resembling the satellite readings it still predicts high acceleration levels, whereas no long-term acceleration should be involved.
A question – Is it known if peak sea levels and temperatures, should they both be sinusoidal, might display a pattern where the sea level peaks lag the temperature peaks by a few hundred years.
With respect to accelerations the level of accelerations involved from various decadal ocean oscillations greatly exceeds those seen in the Tidal and Satellite readings. They could influence, particularly the satellite set due to the short period involved and the fact that the coverage is not 100%.
Finally, may I list a link to my (corrected version) paper for anyone who missed it.
https://drive.google.com/file/d/17lXnNtsLSlzSOx7tRvbDPwYMDibpvXKG/view?usp=sharing
“We may regard the present state of the universe as the effect of its past and the cause of its future. An intellect which at a certain moment would know all forces that set nature in motion, and all positions of all items of which nature is composed, if this intellect were also vast enough to submit these data to analysis, it would embrace in a single formula the movements of the greatest bodies of the universe and those of the tiniest atom; for such an intellect nothing would be uncertain and the future just like the past would be present before its eyes.” Pierre Simon Laplace, A Philosophical Essay on Probabilities
Ironic then that F = ma was at hand to derive:
https://steemitimages.com/DQmR1QGXuufM9qxzAq3HCj58bypefGdXCu8d4tg4iWBmokW/ql_cabab8b787814b85ffa0071d15a3f2bf_l3.jpg
https://steemit.com/steemstem/@bikkichhantyal/introduction-to-navier-stokes-equation
Not that it’s calculable at a global scale with today’s technology. Millions of times more computing power needed to model cloud processes at global scale. The number is from Tapio Schneider – in case Mathew calls me loose. On the loose perhaps.
https://watertechbyrie.files.wordpress.com/2019/09/nested-models.png
I suspect the future is quantum computing, big data mining. pattern recognition and short to mid-term probabilistic forecasts.
Robert I Ellison, Pierre Simon Laplace: An intellect which at a certain moment would know all forces that set nature in motion, and all positions of all items of which nature is composed, if this intellect were also vast enough to submit these data to analysis, it would embrace in a single formula the movements of the greatest bodies of the universe and those of the tiniest atom;
Given the infinite suppositions, it is a wonder that anyone still takes this quote seriously. Does it have anything to do with scientific research or human knowledge?
In this whole off topic thread about mathemastical precision of my every utterance – your construct, divert, pile on infamy and neither or either Koutsoyinnis ot David can dissude you. I resile not. All fluid in flow is subject to classical mechanics so determinate to Laplace’s deman – who was mentioned above – if unpredictable to us.
Robert I Ellison: In this whole off topic thread about mathemastical precision of my every utterance
It’s so unfair! You write a measley orotund phrase and I point out that it’s without any basis.
Here’s a silly syllogism:
Major premise: Everything Pierre Simon Laplace wrote is true.
Minor premise: Pierre Simon Laplace wrote that nature is determinate.
Therefore, nature is determinate.
I don’t vouchsafe the major premise. Isn’t that peevish of me? I did not actually “complain” about Laplace; perhaps I “complained” about his believers. I “liked” Koutsoyiannis, though not in that exact wording.
See, I think the problem is that experimental philosophy – to paraphrase Sir Isaac – isn’t done that way at all. But in context a throwaway line about the strict mathematical determinacy of the Earth system that I don’t resile from at all. It is cause and effect in a physical system – to wit Earth’s flow field. Determinate in models from precise initial conditions and cloud scale grids. Probabilistic in today’s models. Or is he arguing that Navier-Stokes isn’t the governing fluid flow equation? 🤣
Robert I Ellison: But in context a throwaway line about the strict mathematical determinacy of the Earth system that I don’t resile from at all.
Indeed, it was a throwaway line.
Navier-Stokes is not strictly mathematical enough for Matthew. But it is something he chooses to argue about interminably at the shallow end of the intellectual pool. What he is arguing using pure rhetoric is a minor point I wouldn’t have given a second thought to. That the world of cause and effect is strictly determinate and computable using the laws of physics. Within the limits of scale, initial condition precision and computing power I have mentioned several times.
So here’s another lesson in reality. He would do better to ponder why I don’t resile on the strict mathematical truth of determinacy in climate than wasting our time yet again with silly little word games. His thought bubble that cyclone tracks are random is unlikely on first principles and he can’t show that it even might be so.
But it is something he chooses to argue about interminably at the shallow end of the intellectual pool.
Poetry, Robert, sheer poetry. Doesn’t get any better than that (👍).
(matthew, you can’t beat that)…
afonzerelli: (matthew, you can’t beat that)…
No, it was a pretty good line; I am glad you called it “poetry”.
Can you see where I “argued” anything other than that his earlier comment was orotund? What he now calls But in context a throwaway line about the strict mathematical determinacy of the Earth system that I don’t resile from at all.
Let me rephrase a question I asked above. How do you know that something untestable that was said by Laplace is true? Because Laplace was always right, as in the Silly Syllogism? Because you want it to be true? Because its falsity is “inconceivable”?
Another question: How do you know that hurricane tracks are more deterministic than Brownian motion? (which Einstein modeled about 100 years post Laplace). Do you deny outright the relevance or truth of the selection from Gihl and Lucarini that I quoted above?
He does know that Laplace’s demon is a metaphor and Brownian motion results from particles in a fluid colliding? Laplace’s thought experiment involved a creature who knew the position, mass and direction of every particle. Even in a jar of pollen ina fluid. What I discussed was Navier-Stokes. A governing equation of fluid flow derived from first physical principles. Now you and I have never had a great notion – and Matthew not even a passable one – but Navier-Stokes assuredly is a .great notion.
Still, despite the fact that climate dynamics is governed by well-known evolution equations, it is way beyond our scientific abilities to gain a complete picture of the mathematical properties of the solutions. In fact, many fundamental questions are still open regarding the basic NSEs in a homogeneous fluid, without phase transitions and rotation (Temam, 1984, 1997). In particular, even for the basic NSEs, providing analytical, closed-form solutions is only possible in some highly simplified cases that are either linear or otherwise separable (Batchelor, 1974).
Yes I know what this means and it isn’t what he imagines. It does not mean that nothing is known of the nature of this equation. And if he is going to quote Ghil – well he should get the name right for a start. Understanding its meaning may be a bridge too far.
He seems to have taken a liking to the word orotund.
1. (of the voice or speech) characterized by strength, fullness, richness, and clearness.
2. (of a style of speaking) pompous or bombastic.
I know what he is but what am I.
Robert I Ellison: He does know that Laplace’s demon is a metaphor and Brownian motion results from particles in a fluid colliding?
Yes it is a metaphor. And, yes Brownian motion results from collisions whose individual effects can’t be calculated.
It does not mean that nothing is known of the nature of this equation.
I didn’t say that it did.
And if he is going to quote Ghil – well he should get the name right for a start
Bummer. My fingers move the “h” around and I don’t always catch the mistakes. My bad.
Robert I Ellison: So here’s another lesson in reality. He would do better to ponder why I don’t resile on the strict mathematical truth of determinacy in climate than wasting our time yet again with silly little word games.
You have not made a direct argument, only indirect hints, such as Laplace’s metaphor. So I should have to speculate about your motives.
There I was as usual being precise in my use of language, comparing alternative usage, making a list, checking it twice…
lol
Neither Brownian motion or Laplace’s demon were discussed by me originally. I quoted Laplace as an introduction to other ideas. Because it was germane to other discussions – and because I have always had a fondness for the all seeing demon. It never occurred to me that despite quantum mechanics, relativity and chaos that it was not a clockmakers universe of cause and effect. The physical world is determinate cause and effect. Navier-Stokes is determinate given precisely known initial conditions and a fine grid. Otherwise, it is probabilistic.
“The fractionally dimensioned space occupied by the trajectories of the solutions of these nonlinear equations became known as the Lorenz attractor (figure 1), which suggests that nonlinear systems, such as the atmosphere, may exhibit regime-like structures that are, although fully deterministic, subject to abrupt and seemingly random change.” Julia Slingo and Tim Palmer, 2012
There are in fact a number of ways – including statistical – to calculate diffusion of pollen in a fluid. Not so with fluid flow at a global scale.
“We may believe, for example, that the motion of the unsaturated portion of the atmosphere is governed by the Navier–Stokes equations, but to use these equations properly we should have to describe each turbulent eddy—a task far beyond the capacity of the largest computer. We must therefore express the pertinent statistical properties of turbulent eddies as functions of the larger-scale motions. We do not yet know how to do this, nor have we proven that the desired functions exist’.” Edward Lorenz 1969
50 years later and the problem remains. But the equation is soluble at fine-scale and it derives from classical mechanics. The latter of which the demon is a past master. But then it doesn’t do to have a sense of humor or I might be cast as a demon’s apostle. 🤣
I have made arguments based on classical mechanics. Fo which I still do not need Laplaces’s demon – although he is an expert. You hand wave about God’s only knows what but it isn’t strictly mathematical. You were asked to give an example and all you can manage is cyclone tracks. That comes under the heading of fully determinant but seemingly random.
Robert I Ellison: I have made arguments based on classical mechanics.
No. What you might have written but didn’t is something like “Classical mechanics establishes, strictly mathematically, that nature is determinate.” Instead you wrote about particular equations, seeming to imply that they made the case, but leaving it as indirect as an innuendo or metaphor.
Put that way, the case is obviously deficient because of the known deficiencies of classical mechanics, some of which you have documented. And the case is circular: classical mechanics assumes determinacy. Consider again the NS equations: they may be the governing equations of fluid flow, but they assume determinacy, they do not prove determinacy
Neither Brownian motion or Laplace’s demon were discussed by me originally. .
So what? Brownian motion is a case where the classical mechanics is deficient (inadequate, or incomplete, or non-computably complex, or whatever). Sample paths are not continuous (they are “continuous in mean square” a concept introduced by Einstein), and they are not differentiable. In between two measured points, the location of the particle can’t be computed, only a probability distribution on the possibilities.
Nature might be determinate, but the quotes from Lorenz and Ghil and Lucarini show that it can’t be shown to be determinate on present evidence.
“An object at rest stays at rest and an object in motion stays in motion with the same speed and in the same direction unless acted upon by an unbalanced force.”
It works the same way for pollen in a jar, cyclones and planets What I said was climate is determinate and models probabilistic. I carefully explained the difference with reference to Naiier-Stokes and the laws of motion. I don’t think I said that there is a problem with classical mechanics. 🤣
Robert I Ellison: I don’t think I said that there is a problem with classical mechanics.
You did not. I introduced a couple situations where the classical mechanics was problematic. The Lorenz example you quoted and the Ghil and Lucarini quote I presented show that without precise knowledge of starting values and parameter values you can’t evaluate whether nature is determinate. With tiny particles, sufficiently precise knowledge is impossible. As a practical matter, the non-reproducibility and non-predictability of hurricane tracks shows that you can’t judge the claim that hurricane mechanics is deterministic.
That nature is determinate is sometimes an extremely useful heuristic, but it’s more of a world view or religious belief. It isn’t strictly true mathematically.
They found a bird as big as a car in New Zealand. They call it Herecles. Emerging as part of the great avian diaspora following the dinosaur disaster. Presumably Gondwanaland bred and drifting north on continents. But it gave me an idea. Laplace’s demon must have a name. So I Googled Asmodeus for his lust for knowledge.
“Put that way, the case is obviously deficient because of the known deficiencies of classical mechanics, some of which you have documented. And the case is circular: classical mechanics assumes determinacy. Consider again the NS equations: they may be the governing equations of fluid flow, but they assume determinacy, they do not prove determinacy.”
MM
Matthew has reversed the burden of proof from the laws of physics to vague notions of how random cyclone tracks or Browian motion looks. But in classical mechanics it is objects moving in space and time. In the 19th century Henri Poincaré pointed his Hamiltonian – think billiards in space – at orbital problems. A 2 body problem is perfectly periodic. Wrinkles creep in with 3 bodies – although there are still many periodic solutions. A wrinkle is a discontinuous function. It is solved assuming linearity over small increments. Mathematically this first hint that the universe is chaotic. But still determinate in some complex and puzzling way.
https://www.youtube.com/watch?v=8_RRZcqBEAc
The laws of motion there are the same laws for Navier-Stokes. And soluble. Contrary to Koutsoyiannis predictable and unpredictable rather than determinate and stochastic – the world is physical and determinate but is yet unpredictable. The world will keep on surprising us. But Navier-Stokes is calculable. You said it yourself – precise knowledge of initial conditions and millions of times more computing power to fundamentally model the atmosphere at cloud scale. As Tapio Schneider is doing at CalTec. Asmodeus knows but he’s a fiend and isn’t saying.
Although possible at cloud resolving scales as Tapio Schneider has shown.
https://watertechbyrie.files.wordpress.com/2019/09/nested-models.png
Sorry. He is on a different thread now with a complaint about Pierre Laplace that frankly seems a bit peevish. The point was that the equations are soluble at some finer scale than global. See the Tapio Schneider video. .
Only if you want to put fast and loose in context. There I was as usual being precise in my use of language, comparing alternative usage, making a list, checking it twice… Only to get pulled over by the grammar police on barely a credible technicality.
He follows me around dropping little snarks about loose lips. Invariably as words quoted out of context and with the sole intention of playing the troll. It’s all a bit cyber stalky isn’t it?
https://judithcurry.com/2019/10/26/week-in-review-science-edition-111/#comment-902666
At the top of the thread in question – https://judithcurry.com/2019/10/26/week-in-review-science-edition-111/#comment-902003
A few million hours of computing on a spot over a subtropical ocean. Watch what happens to cloud. Can weather and climate be fractal?
https://watertechbyrie.files.wordpress.com/2019/11/fine-scale-processes.png
https://watertechbyrie.files.wordpress.com/2019/09/cloud-computation.png
“In climate research and modelling, we should recognise that we are dealing with a coupled non-linear chaotic system, and therefore that the long-term prediction of future climate states is not possible.”
UN IPCC Third Assessment Report, pg. 774 https://www.ipcc.ch/site/assets/uploads/2018/03/WGI_TAR_full_report.pdf
Way back then they were arguing probabilistic families of solutions – starting from small initial differences. Make them decadal and spectacular. Depends what the planet does.
“Similarly, I think, it could be said: a calculation is not an experiment, for no experiment could have the peculiar consequences of a multiplication.”
Wittgenstein Philosophical Investigations p.218
More of a precisely defined hypothesis. Where it becomes a law of physics is with empirical evidence.
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Reblogged this on Climate- Science.press.
We may believe – as Edward Lorenz said – that Navier-Stokes equations govern flow in the atmosphere as laws of physical nature drive everything in the physical system. At the core of our belief is experimental philosophy as Newton put it in his 4th rule of natural philosophy. We regard as true these physical laws subject to further empirical test.
The NS equations can be solved but I can get gobbledegook out of a simple hyrodynamic model. Always a matter of scale. To model cloud. ice, desertification, clearing, fire… at global scale needs millions of
times more powerful computers. However. as observations of the system are imprecise – all it would yield is 1000’s of possibilities in a probability distribution. There are big data models incorporating surface and other observations. Deep learning in quantum computers? Possibly useful for probabilistic forecasting at seasonal to decadal scales. For now we have opportunistic ensembles of climate models that give a pineapple rough idea of a future range.
The best way to understand the futility of the GCMs is to realise the climate signal must propagate from step to step when the models run. So GCMs must resolve climate change to 20 min.
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