Guest opinion by Dr. Tim Ball –
Ockham’s Razor says, “Entities are not to be multiplied beyond necessity.” Usually applied in making a decision between two competing possibilities, it suggests the simplest is most likely correct. It can be applied in the debate about climate and the viability of computer climate models. An old joke about economists’ claims they try to predict the tide by measuring one wave. Is that carrying simplification too far? It parallels the Intergovernmental Panel on Climate Change (IPCC) objective of trying to predict the climate by measuring one variable, CO2. Conversely, people trying to determine what is wrong with the IPCC climate models consider a multitude of factors, when the failure is completely explained by one thing, insufficient data to construct a model.
IPCC computer climate models are the vehicles of deception for the anthropogenic global warming (AGW) claim that human CO2 is causing global warming. They create the results they are designed to produce.
The acronym GIGO, (Garbage In, Garbage Out) reflects that most working around computer models knew the problem. Some suggest that in climate science, it actually stands for Gospel In, Gospel Out. This is an interesting observation, but underscores a serious conundrum. The Gospel Out results are the IPCC predictions, (projections), and they are consistently wrong. This is no surprise to me, because I have spoken out from the start about the inadequacy of the models. I watched modelers take over and dominate climate conferences as keynote presenters. It was modelers who dominated the Climatic Research Unit (CRU), and through them, the IPCC. Society is still enamored of computers, so they attain an aura of accuracy and truth that is unjustified. Pierre Gallois explains,
If you put tomfoolery into a computer, nothing comes out but tomfoolery. But this tomfoolery, having passed through a very expensive machine, is somehow ennobled and no-one dares criticize it.
Michael Hammer summarizes it as follows,
It is important to remember that the model output is completely and exclusively determined by the information encapsulated in the input equations. The computer contributes no checking, no additional information and no greater certainty in the output. It only contributes computational speed.
It is a good article, but misses the most important point of all, namely that a model is only as good as the structure on which it is built, the weather records.
The IPCC Gap Between Data and Models Begins
This omission is not surprising. Hubert Lamb, founder of the CRU, defined the basic problem and his successor, Tom Wigley, orchestrated the transition to the bigger problem of politically directed climate models.
Figure 2: Wigley and H.H.Lamb, founder of the CRU.
Lamb’s reason for establishing the CRU appears on page 203 of his autobiography, “Through all the Changing Scenes of Life: A Meteorologists Tale”
“…it was clear that the first and greatest need was to establish the facts of the past record of the natural climate in times before any side effects of human activities could well be important.”
Lamb knew what was going on because he cryptically writes,
“My immediate successor, Professor Tom Wigley, was chiefly interested in the prospects of world climates being changed as a result of human activities, primarily through the burning up of wood, coal, oil and gas reserves…” “After only a few years almost all the work on historical reconstruction of past climate and weather situations, which first made the Unit well known, was abandoned.”
Lamb further explained how a grant from the Rockefeller Foundation came to grief because of,
“…an understandable difference of scientific judgment between me and the scientist, Dr. Tom Wigley, whom we have appointed to take charge of the research.”
Wigley promoted application of computer models, but Lamb knew they were only as good as the data used for their construction. Lamb is still correct. The models are built on data, which either doesn’t exist, or is by all measures inadequate.
Figure 2
Climate Models Construct.
Models range from simple scaled down replicas with recognizable individual components, to abstractions, such as math formula, that are far removed from reality, with symbols representing individual components. Figure 2 is a simple schematic model of divisions necessary for a computer model. Grid spacing (3° by 3° shown) varies, and reduction is claimed as a goal for improved accuracy. It doesn’t matter, because there are so few stations of adequate length or reliability. The mathematical formula for each grid cannot be accurate.
Figure 3 show the number of stations according to NASA GISS.
Figure 3.
It is deceiving, because each dot represents a single weather station, but covers a few hundred square kilometers at scale on the map. Regardless, the reality is vast areas of the world have no weather stations at all. Probably 85+ percent of the grids have no data. The actual problem is even greater as NASA GISS, apparently unknowingly, illustrated in Figure 4.
Figure 4.
4(a) shows length of record. Only 1000 stations have records of 100 years and almost all of them are in heavily populated areas of northeastern US or Western Europe and subject to urban heat island effect (UHIE) 4(b) shows the decline in stations around 1960. This was partly related to the anticipated increased coverage of satellites. This didn’t happen effectively until 2003-04. The surface record remained the standard for the IPCC Reports. Figure 5 shows a CRU produced map for the Arctic Climate Impact Assessment (ACIA) report.
Figure 5.
It is a polar projection for the period from 1954 to 2003and shows “No Data” for the Arctic Ocean (14 million km2), almost the size of Russia. Despite the significant decline in stations in 4(b), graph 4(c) shows only a slight decline in area covered. This is because they assume each station represents, “the percent of hemispheric area located within 1200km of a reporting station.” This is absurd. Draw a 1200km circle around any land-based station and see what is included. The claim is even sillier if a portion includes water.
Figure 6 a, shows the direct distance between Calgary and Vancouver at 670 km and they are close to the same latitude.
Figure 6 a
Figure 6 b, London to Bologna, distance 1154 km.
Figure 6 b
Figure 6 c, Trondheim to Rome, distance 2403 km. Notice this 2400 km circle includes most of Europe.
Figure 6 c
An example of problems of the 1200 km claim occurred in Saskatchewan a few years ago. The Provincial Ombudsman consulted me about frost insurance claims that made no sense. The government agricultural insurance decided to offer frost coverage. Each farmer was required to pick the nearest weather station as the base for decisions. The very first year they had a frost at the end of August. Using weather station records, about half of the farmers received no coverage because their station showed 0.5°C, yet all of them had “black frost”, so-called because green leaves turn black from cellular damage. The other half got paid, even though they had no physical evidence of frost, but their station showed -0.5°C. The Ombudsman could not believe the inadequacies and inaccuracies of the temperature record and this in essentially an isotropic plain. Especially after I pointed out that they were temperatures from a Stevenson Screen, for the most part at 1.25 to 2 m above ground and thus above the crop. Temperatures below that level are markedly different.
Empirical Test Of Temperature Data.
A group carrying out a mapping project, trying to use data for practical application, confronted the inadequacy of the temperature record.
The story of this project begins with coffee, we wanted to make maps that showed where in the world coffee grows best, and where it goes after it has been harvested. We explored worldwide coffee production data and discussed how to map the optimal growing regions based on the key environmental conditions: temperature, precipitation, altitude, sunlight, wind, and soil quality.
The first extensive dataset we could find contained temperature data from NOAA’s National Climatic Data Center. So we set out to draw a map of the earth based on historical monthly temperature. The dataset includes measurements as far back as the year 1701 from over 7,200 weather stations around the world.
Each climate station could be placed at a specific point on the globe by their geospatial coordinates. North America and Europe were densely packed with points, while South America, Africa, and East Asia were rather sparsely covered. The list of stations varied from year to year, with some stations coming online and others disappearing. That meant that you couldn’t simply plot the temperature for a specific location over time.
Figure 7
The map they produced illustrates the gaps even more starkly, but that was not the only issue.
At this point, we had a passable approximation of a global temperature map, (Figure 7) but we couldn’t easily find other data relating to precipitation, altitude, sunlight, wind, and soil quality. The temperature data on its own didn’t tell a compelling story to us.
The UK may have accurate temperature measures, but it is a small area. Most larger countries have inadequate instrumentation and measures. The US is probably the best, certainly most expensive, network. Anthony Watts research showed that the US record has only 7.9 percent of weather stations with a less than 1°C accuracy.
Precipitation Data A Bigger Problem
Water, in all its phases, is critical to movement of energy through the atmosphere. Transfer of surplus energy from the Tropics to offset deficits in Polar Regions (Figure 8) is largely in the form of latent heat. Precipitation is just one measure of this crucial variable.
Figure 8
It is a very difficult variable to measure accurately, and records are completely inadequate in space and time. An example of the problem was exposed in attempts to use computer models to predict the African monsoon. (Science, 4 August 2006,)
Alessandra Giannini, a climate scientist at Columbia University. Some models predict a wetter future; others, a drier one. “They cannot all be right.”
One culprit identified was the inadequacy of data.
One obvious problem is a lack of data. Africa’s network of 1152 weather watch stations, which provide real-time data and supply international climate archives, is just one-eighth the minimum density recommended by the World Meteorological Organization (WMO). Furthermore, the stations that do exist often fail to report.
It is likely very few regions meet the WMO recommended density. The problem is more complex, because temperature changes are relatively uniform, although certainly not over 1200km. However, precipitation amounts vary in a matter of meters. Much precipitation comes from showers that develop from cumulus clouds that develop during the day. Most farmers in North America are familiar with one section of land getting rain while another is missed.
Temperature and precipitation, the two most important variables, are completely inadequate to create the conditions, and therefore the formula for any surface grid of the model. As the latest IPCC Report, AR5, notes in two vague under-statements,
The ability of climate models to simulate surface temperature has improved in many, though not all, important aspects relative to the generation of models assessed in the AR4.
The simulation of large-scale patterns of precipitation has improved somewhat since the AR4, although models continue to perform less well for precipitation than for surface temperature.
But the atmosphere is three-dimensional and the amount of data above the surface is almost non-existent. Just one example illustrates the problems. We had instruments every 60 m on a 304 m tower outside the heat island effect of the City of Winnipeg. The changes in that short distance were remarkable, with many more inversions than we expected.
Some think parametrization is used to substitute for basic data like temperature and precipitation. It is not. It is a,
method of replacing processes that are too small-scale or complex to be physically represented in the model by a simplified process.
Even then, IPCC acknowledge limits and variances
The differences between parameterizations are an important reason why climate model results differ.
Data Even More Inadequate For Dynamic Atmosphere.
They “fill in” the gaps with the 1200 km claim, which shows how meaningless it all is. They have little or no data in any of the cubes, yet they are the mathematical building blocks of the computer models. It is likely that between the surface and atmosphere there is data for about 10 percent of the total atmospheric volume. These comments apply to a static situation, but the volumes are constantly changing daily, monthly, seasonally and annually in a dynamic atmosphere and these all change with climate change.
Ockham’s Razor indicates that any discussion about the complexities of climate models including methods, processes and procedures are irrelevant. They cannot work because the simple truth is the data, the basic building blocks of the model, are completely inadequate. Here is Tolstoi’s comment about a simple truth.
“I know that most men, including those at ease with problems of the greatest complexity, can seldom accept even the simplest and most obvious truth if it be such as would oblige them to admit the falsity of conclusions which they delighted in explaining to colleagues, which they have proudly taught to others, and which they have woven, thread by thread, into the fabric of their lives.”
Another simple truth is the model output should never be used as the basis for anything let alone global energy policy.
The role of Ockham’s razor is to select which among the many inferences that are candidates for being made by a generalization is the one that will be made by it. Ockham’s razor suffers from lack of uniqueness of the inference that is selected by it with consequential violation of the law of non-contradiction. The rule called “entropy minimax” provides the uniqueness that Ockham’s razor lacks thusly satisfying non-contradiction.
Where warmth runs away?
http://arctic.atmos.uiuc.edu/cryosphere/NEWIMAGES/arctic.seaice.color.002.png
Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences
Naomi Oreskes,* Kristin Shrader-Frechette, Kenneth Belitz
SCIENCE * VOL. 263 * 4 FEBRUARY 1994
Abstract: Verification and validation of numerical models of natural systems is impossible. This is because natural systems are never closed and because model results are always non-unique. Models can be confirmed by the demonstration of agreement between observation and prediction, but confirmation is inherently partial. Complete confirmation is logically precluded by the fallacy of affirming the consequent and by incomplete access to natural phenomena. Models can only be evaluated in relative terms, and their predictive value is always open to question. The primary value of models is heuristic.
http://courses.washington.edu/ess408/OreskesetalModels.pdf
The paper of Oreskes et al is incorrect. As the information about a natural system is incomplete it is impossible to verify a model of such a system. However, using available technology it is possible to build and validate such a model.
“The primary value of models is heuristic.”
Kind of like rule of thumb, an educated guess, intuitive guess, close enough for government work, the piece fits just have to pound it in, the piece fits just add extra filler, stereotyping; Joe had good luck with his 2003 Honda, loosey goosey, etc.
Ok heuristic, fine a shortcut method of some kinds of problem solving, but not a good approach for example for brain surgery. In terms of climate science and global politics, I think it should only be used for entertainment purposes.
In the world of model building a “heuristic” is an intuitive rule of thumb that is used in selecting the inferences which will be made by the induced model. Climatologists commonly employ heuristics which select those inferences that are most likely to make it possible to pay off the mortgage and send the kids through college!
TITO
Tomfoolery In Tomfoolery Out
MIMO
Mendacity In Mendacity Out
NINO
Nick In Nick Out
All the same thing.
This is the money quote
I can’t say it more succinctly than that.
Unfortunately, this is all old hat to those dwindling numbers of genuine scientists that are aware of their limitations. See the paper titled, A First Look at the Second Metamorphosis of Science, by Attlee Jackson. Modern science is all about dogma, and very little about data.
When this whole Globel Warming thing first came about, I wondered” how could you tell if the earth was warming or not”. You can’t tell by the earth based temperatures because we abviously don’t have enough of them for decent data. Plus you don’t have any real history data to go by. You could use satallite phots of the polar ice caps but that is a very slow process and again, we have no real history. I guess the answer is that even to this day, you can’t really tell if the earth is getting warmer or colder. And oviously you cant tell if CO2 is causing anything. Now if an idiot like me can easily figure this out, why is it that these brillient scientist can’t see this?
Is not very complicated really….for the last century till around year 2000 there has been a global temperature rise at about 0.7C to 0.8C…that actually is a big anomaly according to climate data, is a huge variation in such a short period of time…all known natural variations can not explain it… besides is worse when you consider that to be a warming in the top of another prior warming of 0.2C and while most probably there should have been a cooling expected during the same period instead of so much warming…The anomaly is shown clearly by the temperature records, I don’t think there is or ever was any disputes about it.
Either AGW a wrong or a right-correct hipothesis to explain the anomaly the anomaly still stand and remains to be explained…and as time goes it shows clearly that is not something to be ignored lightly.
cheers
Oh…….. some clarification:
The +10 below @earwing42 is meant for the comment above I replied to @BeegO.
CHEERS
It’s a case of them being too effing smart…
+ 10
Ockham’s Razor is basically a provable theorem of Bayesian inference. The “causes” being multiplied are fundamentally Bayesian priors. We never know that the priors are true, we can at best assign them a probability of being true, one strictly in the range
where 
is the index of the probability of the ith cause being true. I include 0 only because some causes are contradictory — if we insist on logically consistent clauses the range is 
and we are never certain that a prior is true or false.
. The second theory has three priors, including 
from the first theory.
. In order to be correct all three of its priors have to independently be true, compared to only one for the first theory. The overlap illustrates the danger of needless multiplication of causes — if two theories rely on a large common base of assumptions, but one of them requires a lot more assumptions than the other but gets no better agreement with the result, one should assign a strictly higher probability to belief in the first theory compared to the second.
, 
, 
, $p_d = 0.001$ and conclude that the probability that all of these are true is roughly one in a billion (or likely less, especially 
is probably a vast overestimation for the stupid, party animal, forgetful store owner portrayed). Indeed, an empirical estimate of the probability would be much lower — we’ve never even heard of jewelry store owner who smashed his own glass and took all of his own jewels at all, let alone on the way to a party after failing to set an alarm and forgetting his keys. As far as we know for sure, this has never before happened in the entire history of the world.
Predictions of the theory are then strictly conditioned by the probability that the the priors of the theory are true.
Suppose we have two theories that are both in equally good agreement with the empirical data. One of the two theories has only a single prior with a probability of being correct
It is now formally true that the second theory is less likely to be correct because
There is a second case where a more complex theory is likely to be formally less probably true even if the second theory does better at explaining the data. The proof is much more difficult — it has to compare the information entropy of the two theories relative to the results. The classic limit here is the invisible fairy class of theories. An invisible fairy theory can explain absolutely everything by stating fairies did it, only the fairies are invisible. If you drop a penny and it falls, it is because invisible fairies grab it and flap their teeny tiny wings and carry it towards the ground exactly as if Newtonian gravity were true. Why? Because they feel like it, that’s why! This theory can do a better job than Newtonian gravity in explaining the data, because the fairies feel like making the perihelion of the orbit of Mercury precess as if general relativity were at least approximately true. It also explains the data associated with Dark Matter and Dark Energy — there is no such thing in either case, it is just that the fairies who move stars around are too lazy to keep up with Newtonian gravity. The fairy theory, however, has a truly staggering amount of information entropy compared to Newtonian gravity, or Einsteinian gravity, and is at least comparable to Dark Matter theories because they are invisible fairy theories, they just give special names to the invisible fairies in question.
Most of this is worked through in Cox’s monograph:
http://www.amazon.com/Algebra-Probable-Inference-Richard-Cox/dp/080186982X
(arguably the most important work in the history of philosophy) or in more detail with more/better examples in Jayne’s lovely book:
http://www.amazon.com/gp/product/0521592712/
Jaynes has a lovely example that he works through of how inference really works. One is walking down the street and observes a jewelry store with the glass smashed and all of the display jewelry missing. We would all infer that the store was recently robbed. Why?
It isn’t that we cannot come up with alternative explanations. For example, the window could have been smashed by the store’s owner, because he was on his way to a party and was going by the store and realized that he’d forgotten to turn on the alarm but didn’t have the key and so he decided it was less expensive to smash the glass himself and just take the window display jewelry with him to the party than to leave it there unprotected and risk somebody else smashing the glass and taking it while he was partying with his store basically unprotected. We can probably make up dozens of stories capable of explaining the observation.
But each of these stories involves a comparatively unlikely set of prior assumptions. How probable is it that the store’s owner a) was going to a party by a path that took him by the locked store; b) forgot to turn on the alarm earlier that day; c) forgot the key; d) would decide to smash the glass and take the jewelry with him rather than return home, get the key, unlock the store, set the alarm, lock the store, and go on to the party? We could estimate
On the other hand, we might conclude that the store was robbed by an ordinary miscreant. Our computation there is much simpler. Maybe 1 person in 20 in the city is poor and willing to break the law in order to obtain money for drugs, for food, for whatever. We know this because we read about them in the newspapers every day and have experience with being robbed ourselves perhaps once every decade. The jewelry store with its easily smashed window and expensive display is an attractive target, and we are pretty certain that people walk by every day and look at it and wish they could just take it without paying for it. There is no question that jewelry stores in general are robbed in just this way every day (somewhere) — it requires little imagination to conclude that this is by far the most likely explanation for the observation of a broken window and missing jewels.
It is, in other words, simple common sense. Bayesian reasoning generally is.
It’s why Bayesians do not, or at least should not, believe in God(s). Complex, “psychological” explanations involving invisible fairies of all sorts, while usually capable of being made plausible as in not overtly contradictory, are almost never the best (most probable) explanation given the data and the rest of our evidence-supported probable beliefs.
The most parsimonious (or more generally information entropically optimized between explanatory power and the probable truth of the required assumptions) theory is not necessarily the correct one, but it often is, provably the most probable one, given the data and set of theories that can explain it equally well so far.
rgb
Generally Bayesian inference suffers from the non-uniqueness of uninformative priors with consequential violation of the law of noncontradiction. An exception to the rule is that the uniform prior over the limiting relative frequency is uniquely uninformative.
I couldn’t have said that better myself ??
Hello rgb.
If you allow me to make a simple question.
After all you say above (I confess that I have not read it further than the third line) can you tell me please what is your take on the projections of GCMs, ACCORDING TO YOU do these projections prove or disprove AGW or what else?
Maybe if I could have managed to read all of the above you say, maybe I could have had an answer to my question.
Your answer will be appreciated, a simple one will be great…thanks.
cheers
IMO it is very probable that the increase in CO_2 concentration from whatever source can reasonably be causally linked to some fraction of the recently observed (say, post 1950) warming. The data is most consistent with the simplest null hypothesis of warming produced only by the direct forcing from the CO_2 increase itself with net neutral feedbacks from all sources, and if one fits the entire range of CO_2 increase from 300 to 400 ppm to the temperature increase observed across the same period, one gets excellent agreement from a simple logarithmic model with a TCS of around 1.25 C (fitting HADCRUT4 — GISS LOTI would require a slightly higher TCS because it shows slightly greater warming, go figure). That is, the fit agrees to within around 0.1C across the entire range and is obviously of the same order as the uncertainty in the data being fit (given that GISS and HADCRUT4 differ by at least this much, outside of their individual claims).
That is, we might expect a total anthropogenic warming of 1.25 C by 2100 if CO_2 increases to 600 ppm due to human burning of fossil fuels, where we’ve already realized over 1/3 of this (so we have less than 0.8C to go from present temperatures). However, this is also of the close order of observed natural variation and so it is difficult to place a lot of confidence in the predictivity of the model. Basically, we have no good way to tease natural variation out of the total temperature change, nor any chaotic contribution, nor any particular feedback positive or negative, nor any effects due to other variables not being stationary. The null hypothesis is sufficient to explain the data and hence is not falsified, but that doesn’t mean it is correct.
Hope this helps. Oh — this means that no, the GCMs neither prove nor disprove AGW. Indeed, it works the other way around. Agreement between the GCMs and observations of the climate might help increase our degree of belief in the GCMs. Cart = GCM. Horse = Actual climate. Let’s keep the horse in front of the cart.
rgb
@ur momisugly rgbatduke
October 17, 2014 at 12:18 pm
Thank you for the reply.
“Hope this helps. Oh — this means that no, the GCMs neither prove nor disprove AGW. Indeed, it works the other way around. Agreement between the GCMs and observations of the climate might help increase our degree of belief in the GCMs. Cart = GCM. Horse = Actual climate. Let’s keep the horse in front of the cart.”
——————————-
Sorry but the above to me reads like ” unless GCMs prove the AGW by the agreement with the real climate observetions then they never to be “believed” even while the very same GCMs may just project and agree with reality but not under the AGW”
You say:
“TCS of around 1.25 C (fitting HADCRUT4 — GISS LOTI would require a slightly higher TCS because it shows slightly greater warming, go figure).”
Now I have got this impression that you are a proferssor, so let me try this and hope you understand it at least in principle.
You see, we talk and use concepts like CS, CR, TCS, TCR. All these concepts nedeed in the case of an AGW with CS of 2C to 4.5C, especially in any case of CS TO BE SEEN OR CONSIDERED ABOVE 2.4C, because in this cases the CR value is too insignificant to help and determine where and when climate equilibrium reached, and so on you have the ECS. All these concepts portray and lead to conclusion of considering the CS metric as a variable.
AGW in principle means a new climate equilibrium when the CS changes the value. So in principle the ECS at any value above 2C is a new one…… a AGW CS. And that is one of the most significant problems of AGW in principle.
So, officially in the climate science orthodoxy I can’t complain any further about this concepts, but while getting below 1.5C there is no need anymore of the TCS TCR…. simply the CS and CR will do on pointing out the where and when about the Climate equilibrium or the Transient climate, CS becomes (atually is as it should be) a constant, more or less, and the CR is very detectable as will be bigger… and no chance at all for new ECS, as the very concept of ECS means nothing at all in the case of a constant natural CS,, CR=~0,1C =ECR (equilibrium climate response). CR depending in the value of CS (0.6C 1.2C) and the climate’s initial state could varie from a range somewhere of 0.2C to 0.8C, I expect….
CR is so significant at any CS value below 1.5 as there no chance of any new climate equilibrium, but only the chance of the next normal equilibrium of climate.
I think you need to revisit your models with a bit more care because all projections for 0.6C to 1.2C are quasy similar, as far as I can tell, all will show a cooling of ~0.8C to ~1C at about the middle of next century from now, with a bigger anomaly of CR for the value of 0.6C and 1.2C CS, and a small anomaly for CS=~ 0.9C….where CR anomaly means any value above 0.8C of the CR.
If you do compensate with the right CR value for the corresponding CS value and keeping in mind the initial state of 0.8 C warming and compensating the value of the left natural warming in accordance with the CS value used you may just get the same results as above.
Ah one more thing about CR in the case of CS 0.6C to 1.2C…. it seems to be above 0.4 C only in cases of excess and anomaly with CO2 emissions…at about 80-100ppm excess, and depending in the CS the warming in excess will be a value from 0.2 – 0.35C…….go figure….. a new need for a new kind of CR now. something like HCR
( Hyper CR) .:-)
Please do your self a favor and start to understand that there is not the slightest chance of any AGW below 1.5C CS, it hardly can hold true in principle for 2C CS…… and I am sure that you are not really contemplating any possibility of 2.5C CS and above.
Also, while any CS above 2.5C holds ok with the concept of AGW and any climate data prior to 2000 and somehow with a push and shoveling is made to look like it fitts with paleaoclimate trends and data BY TURNING THE DEFINITION OF A METRIC TO A VARIABLE, the 1.5 to 1.0C CS have a significant problem with the paleoclimate estimated trends….
According to climate data (paleo and modern up to date) , 1.5C to 1.3C CS are too meaningless and as same as the 0C to 0.4C CS.
Anyway, a natural climate with a 0.6C CS up to a 1.2C CS as it stands will not miss but show that any short burst warming ( like due to El Ninos etc..) will be followed by a stronger cooling of the same short term at nearly double streangth, That what the CR under these conditions means, a double response to any further short term warming.
If that becomes a regular serial then in not a long time the hiatus will turn to a cooling trend….but anyway we wait and see…:-)
Thanks for your interest and hope this does not bother you much..
cheers
rgbatduke
October 17, 2014 at 12:18 pm
Thanks for yet another lucid statement of your conclusions regarding man-made global warming.
IMO the null hypothesis is that the fluctuations of global average temperature in the 19th, 20th & 21st centuries are natural, as they were during the 16th, 17th, 17th & earlier centuries, ie there is no discernible human signal in the record.
While a 1.2 degree C per doubling curve may well be fitted to the warming allegedly observed from c. 1977 to ’96, the fact remains that the slope of that line is indistinguishable from that of the early 20th century warming (1920s-40s) & at most not much different from the mid-19th century (post-Dalton Minimum) & late 18th century warmings, while definitely lower in amplitude & shorter in duration than the early 18th century warming, coming off the depths of the LIA during the Maunder Minimum. These earlier warming intervals of course occurred with CO2 at “pre-industrial” levels.
Thus IMO there is no reason to reject the null hypothesis of natural fluctuation for the late 20th century warming, without even calling into question the validity of the surface station “data”. Any human signal is negligible, ie within measurement error.
This doesn’t mean that human activities necessarily had no effect during the post-war rise in CO2 levels, but does suggest that negative feedbacks outweigh positive, or that they roughly cancel each other out, while other man-made perturbations, such as aerosol inputs, cool the planet, thus balancing the insignificant warming from anthropogenic GHGs.
Also, IMO more than a third of the warming effect from an increase in CO2 from 300 to 600 ppm should arise from the first hundred ppm (300 to 400) already experienced, since the function is logarithmic. Against this is that the effect might still be in transient rather than equilibrium mode.
So, IMO, there is as yet no evidence of any detectable human “fingerprint” in the temperature data, such as they are, let alone a catastrophic effect. The null hypothesis of natural fluctuation has not been rejected.
This terminology is not standard to probability, where there can only be one prior (probability distribution) in a Bayesian analysis. Presumably, one theory depends on just one proposition (event), while the other theory depends upon the validity of 3 propositions (intersection of three events). Assuming the latter events are independent, this replicates the stated probabilities as the respective prior probabilities – p(1) versus p(1) x p(2) x p(3) – for each theory. As you say, the posterior probability of the more complicated theory being true conditional on the same empirical fit is then likely much less.
Dodging Kant, are we? The trouble with probability on its own is that all events have consequences. It is expected value, and not exclusively probability, that is used for speculation. So if we’re going to play fairly, we should at least have the honesty to multiply the lowest probability bound for a deity’s existence by the maximum numerical bound of the consequence for that event. Of course, it should be no surprise then that when we do so, we end up with an indeterminate form without resolution for the expected value, and right straight back to Kant’s Antimony 4. Attempts at squaring circles leads to either poorly constructed squares or damaged circles.
The cavalier domain jumping I see here is indistinguishable from the examples provided by climate modelers and their non-redeemable claims. This serves only to dissipate the credibility of modern science. Frankly, it’s turning into an embarrassing hack, and already on several fronts is no better at explaining reality than your fairies. Scientists have got to reacquaint themselves with their limitations, and quickly.
http://thepointman.wordpress.com/2011/01/21/the-seductiveness-of-models/#comment-16954
Professor Brown,
Coincidence, then, that so so many physical constants are the only value that permits life, while in a random Universe they could have been anything? For instance why is ice lighter than liquid water? Why do we have a large Moon, making ours a very unusual binary planet, with tides without which life is even more unlikely? Where are all the intelligent aliens if we are not alone in the Universe ( of course Fermi said it best, “Where is everybody?”)?
God is a Bayesian, and believes in Bayesians…
We can count the logical fallacies in this, although there is little point. From the strict point of view or probability, though, the God you imagine existed in order to design a complex Universe is necessarily still more complex. So however unlikely you think it might be to pull a Universe out of a hat containing uniformly distributed “random” Universes that can support life (which is seriously self-contradictory, given the meaning of the word Universe as everything that objectively exists and hence the union of all spacetime continua you might pull out of a hat and the hat besides and the hand that did the pulling), it is almost infinitely more unlikely that a God exists to engineer all that complexity and tuning.
Again, a religious person really needs to learn a wee bit of information theory. Just for God to be “omniscient” in any reasonable sense of the term is already impossible. However large the information content of the dualistic Universe you envision, God’s information encoding and storage has to be larger still, and furthermore has to include the ability to encode itself or God does not know God, any more than my brain is capable of encoding and storing the state of every molecule in brain as higher order information. So if you think yourself unlikely simply because you are complex, imagine how much less likely it is that something even more complex organized and defined you right down to the last photon, across all of space-time.
Not so likely, hmmmm.
There is no other Universe than this one. God existed, and exists, because He does. The Universe follows His rules, because He likes it that way. It’s a mystery, and a beautiful one. Why do you say the Universe is complex? It is just a bunch of matter/energy, doing what matter/energy does, such as to permit my parents to meet and make me. I think the fact that aliens have not made themselves known to us is strong evidence that God made the Universe to make us, and to challenge us to be our best. There is no proof of this, but it is the simplest explanation, and hence the most likely to be true…
Excuse me? And what about Roswell?
, relativity theory states that nothing can go faster than light, and practical economics states that the cost of transportation between stars is enormous, given everything we know about physics right now.
Just kidding. How about “aliens haven’t made themselves known to us because the nearest star is roughly 25 trillion miles away, it takes light 4 and a quarter years to cross the distance in between, light intensity drops off like
Then there is the possibility that the evolution of human-scale intelligence and the ability to generate energy by burning stuff (almost) inevitably causes a greenhouse extinction event or maybe just a simple ecological collapse, so that only a bare handful of “lucky” civilizations scrape by to “adulthood” and by then they are too wise to burn the enormous amounts of stuff that must be burned to get into a planetary orbit. Perhaps this isn’t terribly probable, but I know a lot of True Believers in CAGW believe precisely this.
Note well: We’ve only been able to determine that extrasolar planets exist within the last 25 years or so, because it is actually very, very difficult to see stuff smaller than million-kilometer balls of fusion-heated gas over distances of 25 trillion miles and up, and only a few thousand of those are visible at all to the naked eye, and you are surprised that aliens “have not made themselves known to us”?
And why, exactly, did God make the roughly trillion-trillion stars we can either see directly or infer the existence of without using arguments from homogeneity to multiply that by another factor of a hundred million or so, given that a large fraction of the stars that we can see with the Hubble have already burned out, exploded, moved on? The lifetime of stars larger than red dwarfs is typically a few billion years, and most of the volume of a sphere is near its surface. Then there are all of those extrasolar planets, most of the ones we have found (so far) being completely unsuitable for human life or exploitation even if we somehow beat the enormous economic and physical challenges of interstellar travel to reach them. God may have made the Universe “just” to make us, but boy did he waste a lot of material, at least if he thought we would find most of it to be accessible or useful!
And why did God “challenge us to be our best?” What does this even mean? How does existence itself challenge anything to be its best, because that is what you are asserting, that the Universe (with us in it) by existing, is itself a challenge to be our best, one left there to be so interpreted by God.
I have a different interpretation, one based on observation.
What I can directly observe is that God created a Universe that is the proximate cause of dying children. I fact, dozens of children have died from utterly random causes in the limited time I have been typing this reply, and I type like the wind. I’m not talking about being murdered, or dying because of human evil — I’m talking about dying of cancer, dying of diseases, dying from silly accidents. God created a Universe that is full of casual sources of enormous amounts of pain and suffering, suffering experienced by humans and animals alike. You have all of this suffering to explain, and yet it is the absence of space aliens that convinces you that a benevolent God made the Universe?
What I can directly observe is that God created a Universe that shows not the slightest actual trace of the existence of God, no matter where and how you look. Indeed, the God of the Gaps that we began with in ancient mythology has steadily shrunk along as our knowledge has grown. Since we now have physical laws that pretty much explain anything we can see with remarkably few gaps, teleological arguments are reduced to asserting that it is the existence of physical law itself that proves God. No, it doesn’t. At least not for any meaning of the word “proof” that means, well, actual proof.
And finally, God as an explanation is not simple. It is both complex and useless. It is complex because you have to assert all sorts of things about God’s psychology, intent, nature, that we cannot directly observe (and that often contradict things that we can directly observe). You have to invent an entire alternate physics to support God’s existence and mentation process and experience of time (although no one ever does, of course, preferring the “mystery”). It is useless because you cannot use it to predict one single thing about the actual Universe we live in.
rgb
Michael Moon writes (October 17, 2014 at 9:53 am): “Coincidence, then, that so so many physical constants are the only value that permits life…”
Anthropic Principle, anyone?
“Where are all the intelligent aliens…”
Steering clear of a species that largely believes in invisible fairies? 🙂
life exists, there for the systems to allow life must exist, random or not. If god exists what are the conditions that allow for him/her, random or something even more powerful?
I am really puzzled…
What else or who else appart from the very excellent AGW projections of GCMs can prove so clearly the fallacy and the shallowness of AGW thesis?
Please anyone who can tell?
So I “hear” so many complaining about the GCMs because their projections can not be disputed as the best possible there under the cicumstances, while in the same time the projections are the only and the most clear evidence showing and proving clearly how wrong the hipothesis of AGW is.
I can’t make my mind yet in the point of understanding this….is it because of the jealousy or because of the mental conditioning to AGW dependence…..”can’t do without it anymore”… the AGW addiction, …..or because of both?
I am sure some of you can help me with the answer to the above question!
cheers
Models at least have the utility of demonstrating that CO2 is not the control knob on climate which their programmers imagine, since the results of their assumptions are at such wide variance from observed reality.
But, as above, GCMs are worse than worthless as a basis for making public policy decisions.
Premises are at the heart of applied reasoning. Where do we get the premises from? That is the key fundamental question. Premises dependent on faith in the existence of supernatural, omnipresent and omnipotent beings demarcate the arguments using them as irrelevant to humans focused on using their natural capacity for reasoning to naturally understand nature.
John
What?
Mods. My comment disappeared. Did I cross a line?
– – – – – – –
Bob Boder,
Premises irrelevant to a focus on understanding nature by natural means and tools.
John
One of us is really confused? I am just trying to figure out which one
Bob Boder,
We should then revisit our premises.
John
See how simple that is
It is indeed. Because, as Hume noted, given that premises are by their nature unprovable assumptions from which a contingent derivable theory proceeds, all philosophical theories are basically one large exercise in question-begging, no matter how pretty they are or how pristine their logical arguments. If one begins one’s derivation with the premise “God exists”, it is going to be unsurprising that you find lots of ways to prove that this is true within your theory, but they are all basically tautology. The same is true when you make assertions of contingent necessity, e.g. if we observe complexity in the Universe, then it is necessarily the case that God exists. This statement cannot possibly be proven, because it is rather obviously not necessarily the case that God exists at all, independent of whether or not one observes complexity. It merely states the opinion of the individual making the statement, and gives a quasi-logical feel to the teleological argument for God in any of its many forms, in spite of the fact that we can demonstrate awe inspiring complexity in absolutely trivial mathematical iterated maps that I would assert depends in no way at all on the existence of God any more than ordinary arithmetic depends on the existence of God.
In the end, lacking any of the sort of straightforward, direct evidence for God that one would (frankly) expect if God existed, faced with the rather crushing problem of theodicy and the existence of evil and an omnipotent omnibenevolent deity, one is forced to generate indirect arguments that turn everyday stuff into evidence that God exists in spite of the fact that it is no such thing.
Perhaps it is “surprising” that something exists. It is no less surprising that something exists if you name part or all of that something God. You cannot gain anything explanatory from the hypothesis, when you put it that way, not without begging all questions and generating the mother of all Ockham’s Razor violations and asserting that it is somehow logically necessary for God to exist, when of course it isn’t. That’s the problem. It isn’t logically necessary, and while one cannot really falsify the proposition empirically because believers will always point out that the God they believe in is a deliberately deceptive invisible fairy who cannot be expected to simply directly communicate with humans even though It deeply cares about whether or not we “believe” in It or expose a female nipple in a public place, neither can one provide the slightest reliable evidence for the proposition, one can only engage in endless rounds of question-begging argument.
rgb
IMO trying to argue for the existence of a Creator from observations of the physical world is both bad science & bad religion. There is no need for faith if you’re convinced that the existence of the universe offers conclusive evidence for the existence of a Supreme Being.
As Martin Luther said, “Die Vernunft…ist die höchste Hur, die der Teufel hat” (Reason is the highest whore the devil has).
And “Wer…ein Christ sein will, der…steche seiner Vernunft die Augen aus” (Whoever would be a Christian must tear the eyes out of his reason).
As the Early Church Father Tertullian wrote of the Christian story, “Prorsus credibile est, quia ineptum est” (It is to be believed precisely because it is absurd).
Efforts by the Scholastics & subsequent apologists to “prove” the existence of God IMO fundamentally miss the point, at least that of Paul, that the believer is saved by faith alone.
OTOH even militant atheist Richard Dawkins admits that lack of faith is also a choice, since the existence of God can’t be conclusively shown false, although Stephen Hawking has recently tried to do so. However it’s an easier chice given the lack of convincing evidence for a Creator, let alone a Sustainer grading human performances on earth & counting hairs on heads & falling sparrows.
Though philosophy might be organized around unprovable premises it is more fruitful to organize it around observable states of nature. Under this form of organization a state is a proposition that is “true” when “observed.” Thus in a coin flip the state and proposition “heads” is “true” when “observed.”
This isn’t necessarily a reply to rgbatduke (hah, gotham may have a batMAN, but we have a batDUKE (royal sibling?)), it’s just the lowest “reply” link in this chain.
Arguing a scientific rationale for the existence/non-existence of a creator is somewhat like trying to drive from New York to Paris. Yes, there is a way to get from NY to Paris, but it’s not by driving.
In my experience, those who have spent time considering why they believe in existence of God come to the conclusion that the universe makes no sense without a creator who is outside the bounds of it. That is, the explanation for anything is not to be found within that thing. Thus, “why the universe” is answered by something outside it. (This leads me to a point/question I’d like to make/ask about the GCM’s…to follow below.) And therein lies the futility of using scientific rationale to discuss the existence of God. God answers the question of “why”, not of “how”. Furthermore, although this is really off-topic, but maybe relevant, if you really probe, I suspect that most people who believe in the existence of God cannot point to a logical, stepwise decision tree that leads to their conclusion. Their conclusion is a matter of faith which, by definition, is a belief in something not seen or observed. It is interesting to note, however, that though mankind has formulated incredibly elegant, complex, and even fantastic scientific theories to explain the existence of the universe/life, and have argued that a creator is not, therefore, necessary, it’s precisely the set of issues pointed out by rgb that is necessarily answered by an appeal to a creator/God. That is, the argument for a moral absolute…the existence of which is absolutely and inescapably intertwined in the human experience. Without the qualitative guidelines of a moral absolute, the angst described by rgb (and, truly, felt by all of humanity) wouldn’t even make sense. For, why else would the death of a child be cause for lament? Why does suffering matter? And furthermore, what is suffering? Isn’t it just electro-chemical impulses interpreted by a bio-chemical computer?
Sorry, looks like I’ve meandered off track here. I really just meant to expound upon the idea that science isn’t at all trying to answer the same question as faith. That’s really why I find it somewhat absurd to suggest that the increase of knowledge (reduction of gaps) somehow reduces the need for God as an explanation. As if “God” was ever just a mechanism to help humans understand the how. This is a meaningless argument predicated upon a complete lack of understanding as to what question “God” really answers.
Back to AGW and GCM’s, as alluded to above. Isn’t there a sense in which trying to determine the average global temperature by examining some few thousands of discrete points around the globe sort of silly? It seems sort of like trying to understand the average body temperature of a person by measuring the temperatures of a few cells. Extrapolate this out (maybe it’s not meaningful as an analogy, but it seems to mirror the gist of the problem) to a climate model, and you’re trying to understand and predict how a person’s temperature might rise by modeling at a cellular level. I imagine it going something like this:
GCM Researcher: Periodic distribution of averaged cellular response to decreasing amounts of available sucrose, combined with an increased level of saturation of hydroxyl compounds should result in a decrease in energetic output and lowered average temperature. Ahhh…if this continues the body will freeze to death. We must do something immediately…give us money for research!
Step back, though, and you’ll see: Hmm…spring break, Florida, sand-volleyball in the hot sun, scantily dressed members of both sexes, lots of beer…yep, that’s person’s heart rate and temperature are both up. Good thing the body has natural cooling mechanisms…
On another note, to summarize what I think Nick Stokes was asserting elsewhere, the assumption seems to be that initial conditions are irrelevant because the models can start from anywhere and naturally resolve into meaningful simulations of the climatic processes. But, correct me if I’m wrong, isn’t this in direct contradiction to the observed chaotic nature of the climate? If the climate truly exhibits sensitive dependance upon initial conditions, and has non-linear instability, how could it randomly resolve into anything meaningful? Seems like the models, if they were accurately capturing the climate mechanisms, will just wildly oscillate all over the place until they’ve been precisely tuned-in to an actual starting point.
This does bring up another question, though. That is, I understand that many people (at least those who habit the halls of this website) believe the climate is naturally governed by negative feedbacks…which keep it from oscillating too wildly. Does this then negate, or diminish, the true chaotic-ness of the climate? If it’s bound by internal feedbacks, maybe Nick Stokes is correct, and you can “start anywhere” as long as you get the mechanisms correct…I’d love to understand this further…
rip
ripshin: “I’d love to understand this further”
The climate system and GCMs are two different things. CGMs can be programmed to show warming regardless of the inputs. Like Mann’s statistical procedures.
Oceans may establish a thermostat that make Earth and other planets habitable. Weather is the just a chaotic behavior around the melting point of ice. We know that ice ages do exists, but our existence tells that Earth has quite much tolerance to internal and external shocks.
Try as I may, I cannot get past your diatribe.
You can measure the ave temperature of a human with a few temperature readings. And where is their sucrose in the human body? Glucose I can see, but sucrose? Not for long. Sucrose breaks up into Glucose and Fructose immediately and fructose goes through the liver to be turned into triglycerides and glucose depending on how much fructose is left over.
PS – my rant was in response to ripshin October 20, 2014 at 2:34 pm
Mario, thank you for your clarification regarding the correct word for sugar in the human body. Glucose was definitely the word I was looking for.
Also, I didn’t realize I was writing a diatribe, and certainly didn’t mean to. I was merely attempting to point out a couple things that, I feel, are commonly misunderstood, or mistakenly conflated. (Or, are you referring to my completely light-hearted joke about rgb’s screen handle…which was meant as a humorous compliment…)
Regarding temperature, my point stands. Avg body temperature, as measured by modern instruments, certainly aren’t taking readings at the cellular level. If you were to measure individual cells, I’m certain you would find a disparity between different ones based on their respective levels of activity (at the time of measurement). Those individual measurements wouldn’t necessarily be indicative of the avg body temp, though, which is my point. [My silly analogy of the spring break thing, including oblique (and apparently incorrect) references to low blood sugar and elevated BAC, was more a function of end-of-the-day-punchiness than serious scientific analysis.]
In the same way, I assume that taking a few thousands of spot measurements around the globe don’t necessarily indicate anything meaningful about avg global temp. Didn’t someone above, sorry can’t remember who, mention the wide range of measured temp deltas in a single column of air, mere meters apart? Based on this, it would seem logical to conclude that you’d have to figure out a way to get a much broader global temperature reading in order to make valid determinations of avg global temp trends.
Finally, my question about negative feedbacks is really whether or not they reduce the chaotic-ness of the system…and following that to it’s apparent conclusion, can we then say that initial starting points (of GCMs) are not important? (Which, I think, was Nick Stokes’ contention.)
Hope this helps.
rip
The system is incompletely or insufficiently characterized, and unwieldy. This necessarily limits our forecasts and predictions to the scientific domain. A domain which is constrained in time and space, with accuracy inversely proportional to the product of time and space offsets from an established frame of reference.
Models or estimates can be accurate, but their value is marginalized by mortal limitations. We are limited to sub-universal observations and speculation. Shifting to universal or extra-universal domains violates scientific integrity. At present, we don’t even have comprehensive knowledge or skill in our immediate neighborhood, Earth.
Systemic changes are fundamentally a risk management problem, with limits on actions inherent to this set. The credibility of an Anthropogenic Global Cooling or AGW(arming) or AGC(limate)C(hange) problem was undermined by people declaring an affirmative skill and knowledge outside of the scientific domain and a resolution incoherent with risk management best practices.
This is a cogent and compact mathematical argument that reveals climatology for what it is, a hoax, a clearly dishonest use of mathematics. This is straightforward scientific knowledge that should have stopped these policy pursuits in their tracks. Surely with all the self-professed skills and abilities at the disposal of agitprop science, these obstacles are understood there as well. So ignorance can be no defense. The question is, why its practitioners refuse to admit as much. Perhaps the only form of stupidity really is knowing so much that just isn’t so.
Sorry. I haven’t read everything here.
But in this layman’s opinion instead of “climate models” the effort put into them should have instead been put into cataloging past known (not ‘proxied’) weather conditions. Then a computer program designed to to match present conditions with past conditions to produce a better forecast.
If a “match” doesn’t produce the expected result, then explore the “why not?”, “what else is going on?” rather than assuming a cause that hasn’t effected anything for 17 or 18 years.
Dr Ball:
I invite you to re-read these paragraphs extracted from
Journal of Oceanography
, Vol. 57, pp. 207 to 234, 2001.
Heat and Freshwater Budgets and Pathways in the
Arctic Mediterranean in a Coupled Ocean/Sea-ice Model
XIANGDONG ZHANG
JING ZHANG
2.2 Forcing data
The climate monthly windstress, 2 m air tempera-
ture, 2 m air specific humidity, surface pressure and 10 m
windspeed are prepared from NCEP/NCAR (National
Centers for Environmental Prediction/National Center for
Atmospheric Research) reanalysis data from 1958–1997
(Kalnay et al
., 1996). Precipitation is constructed from
the 0.5 ° × 0.5 ° Corrected Monthly Precipitation Dataset
(Legates and Willmott, 1990) and re-interpolation is made
by the Cressman method to remove unreasonable values
north of Spitzbergen, caused by different data sources
when the precipitation dataset was compiled (Legates,
personal communication, 1998). Hibler and Bryan (1987)
and Zhang et al . (1998) used climate river runoff data
from eight major rivers. In our modeling, thirteen major
rivers along the Eurasian and the North American conti-
nents are included, as shown in Fig. 1. The river runoff
data is from long-term observations (from 1950s to 1980s)
supplied by NSIDC and Becker (1995).
Usually, climate drift could not be completely pro-
hibited in the state-of-the-art coupled models. To limit
drift and measure model fidelity, we include surface level
restoring of temperature and salinity on time scale of 50
days to Levitus (1982) data. As Zhang et al
. (1998) sum-marized, other models used 11 or 30 days for their sur-
face level restoring. The restoring time scale is longer
than others, putting weaker constraint on modeling. Di-
agnosed heat flux and FW flux from restoring terms help
to understand uncertainties in modeling. As for effects of
restoring conditions on Arctic ocean/sea-ice modeling, see
discussions by Zhang
et al
. (1998).
Inflowing water properties from the Bering Strait and
portions of the GIN Sea are given from Levitus (1982)
data. Volume transports through open boundaries are from
Nazarenko
et al
. (1998), with inflow at the Bering Strait
fixed at 0.85 Sv (with no annual cycle) after Coachman
and Aagaard (1988) who estimated long term mean 0.85
Sv. Outflow through the Canadian Archipelago is set at
1.7 Sv after Fissel et al . (1988). Volume conservation for
this rigid-lid model implies net GIN Sea inflow of 0.85
Sv with spatial distribution.
The model was integrated over 120 years with the
asynchronous strategy after Bryan (1984) under repre-
senting annual cycle of forcing from initial temperature
and salinity from Levitus (1982) without initial sea-ice.
The model achieved an approximately stable state in its
sea-ice and upper ocean properties. Last 5 years mean is
used as model climate.
“Surface level restoring” ?
“climate drift could not be prohibited” ??
Their whole program read data, ran their model equations, then changed the output to get what they thought they were going to get from the output.
These models overfit to historical data. They are optimized to match the past but are poor at predicting the future. I work as a programmer for a hedge find. Have done so for 15 years, you would not believe how many phds have come to me with models that make large amounts of money when back tested but when put into real system make no money.
Is there somewhere listed all the people who run these computer models? Who are they? Seeing they are the people who are causing me to have increased electricity bills, from the use of their statistics to claim global warming by the greens, which leads to increased use of expensive and unreliable so called “renewables” in the electricity grid system of most countries, I should know their names. It is about time they were held accountable to the consumer.
I enjoyed Cristopher Essex’s story of how a modeller informed him that he “Solved the Navier-Stokes equations for policymakers.” Essex then pointed out to his audience that there is still a straight $1Million available, waiting to be claimed by the first mathematician who can actually do that.
He might also have pointed out that it isn’t the $1M prize for proving that it can be solved that matters, it is the fact that so far, no one has showed that one can solve it numerically using an integration gridding/stepsize some 30 orders of magnitude in all four dimensions too coarse to represent the Kolmogorov scale of around 1 mm, the smallest eddies know to be important in the turbulent/viscous motion of air.
Oh, and then there is the practical omission of the entire ocean, a second, coupled Navier-Stokes equation with the double bonus of complex chemistry, a complex surface interface, complex density, and latent heat and heat capacity galore.
rgb
spelling: Christopher Essex.
Reblogged this on Flying Tiger Comics and commented:
Ockham’s Razor says, “Entities are not to be multiplied beyond necessity.”
3 X 3 degrees is 333 X 333 km. I couldn’t begin to list all the obvious climate differences between where I am and other places, much less than 1/10th of that cell distance.
Nyquist came up with a theorem, about believing the results of such sampling.
My position about moving forward with climate research is, as it was from the start, and expressed in the last sentence of the article.
There are two major responsibilities. One is to science, when working with climatology and the models. The second is to society, when you take the output of your models and convince society to base policy on them. In my opinion, the IPCC failed in both instances. Worse, they set out to bypass scientific repsonsibilities in order to pre-determine the policy message.