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.
As an engineering student back in the late 60’s, I was taught “if the results from your model don’t match observations of the responses of your system, you must change your MODEL”! IPCC, Mann, Hansen and the rest of the AGW crowd believe you change the data to make your model’s predictions fit. But they have still been wrong for the past 15+ years. When will they go to jail for their fraudulent behavior?
Bill
There is no more common error than to assume that, because prolonged and accurate mathematical calculations have been made, the application of the result to some fact of nature is absolutely certain.
– Alfred North Whitehead (1861-1947)
GARBAGE in, Gospel out.
Despite costing many $millions each, not one GCM was able to predict the most significant event of the past 30 years: the fact that global warming has stopped.
They all predicted that global warming would continue apace, or that it would accelerate. Neither event happened. So as a taxpayer, I have a question:
When do we get a refund?
Global warming many not have stopped. What seems to have flattened out is the surface temperature increase. However, there´s indications ocean water temperature may have increased a tiny amount. My guess is this may be happening because sea level is rising a little bit. But I´m not sure.
no, it is not:
http://wattsupwiththat.com/2014/10/06/the-heat-went-to-the-oceans-excuse-and-trenberths-missing-heat-is-awol-deep-ocean-has-not-warmed-since-2005/
Fernando Leanme
October 17, 2014 at 3:02 am
“What seems to have flattened out is the surface temperature increase.”
Well, that’s a falsification of the theory, and therefore of the models, so we agree about that, and the models should now be scrapped.
Lars, that post gives us these possible exits:
1. The ocean temperature is increasing between 700 and 2000 meters…Below 2000 meters it´s cooling. Above 700 meters it´s not doing much. The net result is a slight water expansion which raises sea level a teensy amount.
2. The data isn´t worth much because the temperature changes are very subtle and we don´t have enough buoys and elephant seals. Plus the reanalysis fails to account for geothermal heat. So the whole thing may be a bit off.
3. Maybe sea level rise is all caused by glacier melt and sea floor shape changes….but I tend to be skeptical.
4. Maybe the sun and the clouds took over for a while and the energy level isn´t changing at all.
I don´t think this climate zingy is measured or understood well enough to be able to say “I´m pretty sure”. As I stated before, “I´m not sure”.
DirkH: I’ve been saying this for some time. The models have been falsified. Global warming may be real, it may be man-made, it may be worse than we thought, but THE MODELS HAVE BEEN FALSIFIED. They are wrong. They don’t work.
I’ve even tried to tell this to warmists. Call me King Canute…
Uncle Gus:
The models have not been falsified.
A model is falsified when the predicted relative frequencies of the outcomes of events are compared to the observed relative frequencies and there is not a match. For the models that were used in making policy on CO2 emissions there are no events or relative frequencies hence is no opportunity to make this comparison.
IPCC AR4 and prior assessment reports replace this comparison with one in which an observed global temperature is compared to various projections. Though projections can exhibit error they do not possess the property of a proposition that is called its “truth-value.” Thus, they cannot exhibit falsity. The first assessment report to broach the topic of events and relative frequencies is AR5.
And neither is anybody else, but if you need help deciding i can give you a dart board and you can paste different climate change ideas on it and chuck a couple of darts and go with what ever the gods of fate say. thats better then going with something that we know doesn’t work.
Fernando Leanme October 17, 2014 at 7:28 am
To your list you should add:
* The huge amounts of water being extracted from underground aquifers, measured in thousands of cubic kilometers of water,
* The amounts of silt and other runoff from rivers into the sea
The ocean environment is not static
Fernando, the warming of the oceans through backradiation directly is not possible, as backradiation’s IR cannot penetrate deeper then a couple of microns in water.
The oceans do have a cool skin due to evaporation at the urface. This skin is several orders of magnitude wider then the couple of microns, which ensures that net heat transfer goes from below to the surface for the last centimeter of water.
The warming comes from sunrays:
http://wattsupwiththat.files.wordpress.com/2013/10/ocean-penetration-by-solar-spectrum1.png
Which leaves us with some limited options how the ocean water could be warmed up over the time by backradiation: it would need to increase the surface temperature (everything else remaining equal). But we do have better observations of the temperature of the surface. Much better then the ocean heat content data.
http://bobtisdale.wordpress.com/2014/10/12/september-2014-sea-surface-temperature-sst-anomaly-update/
And the surface does not really show warming.
Therefore I am not surprised when data does not show warming for the ocean heat content. (not to talk about the precision needed to measure variations in the value of a 1/100 of a degree, we do not have that data quality and accuracy when we measure the ocean heat content.)
Where does the sea level increase come from?
Here again we have 2 type of measurements, the satellites and the former tide-gauges.
The satellite have been calibrated and show less then 3 mm/year increase (ignoring GIA adjustment), tide-gauge show about 1 mm.
Interesting to see is that tide-gauge measurements – if one takes longer period of time – will not show sudden acceleration for the last 30 years in comparison with the previous 30-50 years.
http://www.psmsl.org/data/obtaining/
http://www.burtonsys.com/climategate/global_msl_trend_analysis.html
There are a lot of questions about the way the satellite sea level has been calibrated, however what I think is important is that it shows about constant values, no acceleration, rather deceleration for the last decade.
http://joannenova.com.au/2012/05/man-made-sea-level-rises-are-due-to-global-adjustments/
http://hockeyschtick.blogspot.co.uk/2013/07/new-paper-finds-global-sea-levels.html
Why is the sea level rising? There is also a human contribution, surprisingly, I think the whole pumping of freatic waters is actually the highest human contribution to sea level rise. I remember having seen numbers between 0.4 or 0.7 mm/year, but cannot find the links to the papers now.
So yes, as you say “I´m not sure”, but in the end Claes Johnson may be right, the effect from more CO2 to climate may be so that we are not even able to measure it:
http://claesjohnson.blogspot.co.at/search/label/greenhouse%20effect
These my 2 cents…
Fernando Leanme says:
1. The ocean temperature is increasing between 700 and 2000 meters… Above 700 meters it´s not doing much.
Convince me, Fernando. Explain how the temperature of a huge layer of ocean water like that can be increasing, while cooler water sits on top of it.
You are saying the laws of thermodynamics don’t apply to the oceans. Otherwise, the warmer water would rise, no?
Also, there is no indication that a warmer water layer is sitting there uder a cooler layer. That is a measurement-free conjecture, made in desperation to try and explain why global warming has stopped.
You need evidence, my friend. Measurements. A conjecture like that by itself is just not enough.
dbs
“Otherwise, the warmer water would rise, no?”
No, the water is warmer than it was. It is not warmer than the water above. There is still a gradient.
The water below can warm. Just ask how it remains cool. There is a supply of cool water from deep currents which balances the downward diffusion of heat. If that cool supply becomes slower or warmer, then the deep will warm without heat having come from above.
The water which is warmed by the sun can be pushed down through ENSO processes. So the warm water is relocated by prevailing westerlies during ENSO neutral and progressively more during La Nina. This does not require an end run around Thermodynamics.
Terry Oldberg: What is your point?
You are using a very complex and formal definition of “falsification”. I am using the simplest. The models do not successfully predict. Their predictions are false. They are falsified. They are therefore of no practical use.
You know what I mean. What do you mean?
Uncle Gus:
Perhaps you would gain an understanding of what I mean by reading peer-reviewed article at http://wmbriggs.com/blog/?p=7923 .
Nick Stokes,
If, as you claim, the deep ocean is warming, why is it that the ARGO buoy array shows cooling?
The only warming shown was after they ‘adjusted’ ARGO. You need to produce verifiable, real world measurements that indicate ocean warming at those depths. The 2000 meter measurements show that generally, the ocean is cooling. Where are your measurements showing that the deep ocean is warming? The only data I can find shows that generally, the oceans are cooling.
Trenberth’s claim that there is heat hiding in the dep oceans looks increasingly preposterous. The simplest explanation is that global warming has stopped.
Terry Oldberg: Uh huh. That’s what I thought.
I do understand what you mean – the models and their conclusions are not well enough defined to be falsified. (In the old cant phrase, they’re “not even wrong!”) There is even a certain deliberateness about that that makes it almost fraudulent in some cases.
But the policy makers, let alone the MSM, don’t understand “not even wrong”. They see the predicted warming hasn’t happened. They’re oblivious.
There’s an old saying about not taking a knife to a gunfight. It seems to me that you’re turning up to a knifefight with… a laptop. Or a slide-rule. Something, anyway, that doesn’t cut.
First, you’re referring to past tense where it’s present. ie. They PREDICT that global warming is continuing, they continue to claim that it IS accelerating.
Also, it’s more likely a peak than a pause or stop. Things go downward from here. Cycles.
No refunds, sorry. You knew what you were getting into when you had income that got taxed.
For all the claims that global warming has stopped, the actual facts are that the linear trend from 1975 to the present is essentially indistinguishable to the trend from 1975 to 1997: http://www.woodfortrees.org/plot/hadcrut4gl/from:1970/plot/hadcrut4gl/from:1975/to:1997/trend/plot/hadcrut4gl/from:1975/trend (In this example, the trend is actually a bit steeper for 1975 to present, although that exact detail is sensitive to exactly when in and around 1997 you choose as the end point..)
And, GISS Temp tells a similar story:
http://www.woodfortrees.org/plot/gistemp/from:1970/plot/gistemp/from:1975/to:1997/trend/plot/gistemp/from:1975/trend
while the two satellite products actually show a much higher trend from the start of their records (~1979) to the present than from 1979 to 1997:
http://www.woodfortrees.org/plot/uah/from:1970/plot/uah/from:1975/to:1997/trend/plot/uah/from:1975/trend
http://www.woodfortrees.org/plot/rss/from:1970/plot/rss/from:1975/to:1997/trend/plot/rss/from:1975/trend
The fact is that since c. 1996 (depending upon data set & degree of “adjustment”) there has been no statistically significant warming & indeed from some more recent date cooling.
You might as well point out that the warming trends from c. 1700 & c. 1850 is also still intact. The further facts are that CO2 started rising monotonically c. 1944, but the world cooled until c. 1977, when the PDO shifted, & that it has now stopped warming (& more recently cooled) despite continued rise in CO2.
Thus there is no observed correlation between the alleged sharp increase in CO2 for ~70 years & temperature, as GASTA (as measured) has fallen, risen & stayed flat all the while that CO2 has steadily climbed.
It is always possible to say “there has been no statistically significant warming for the last N years” where the value of N varies simply because it takes a while to establish a statistically-significant trend.
There is in fact quite a good correlation between CO2 rise and warming during the historical temperature record, as can be seen by plotting both on the same graph and scaling accordingly. Is the correlation perfect, indicating that CO2 is the ONLY factor affecting the temperature? No…but nobody has claimed that it is. The claim is that it became the dominant driver over multidecadal timescales toward the end of the 20th century. (Over shorter time scales, internal variability dominates, just like it does for the seasonal cycle where the temperature here in Rochester don’t monotonically go between high in summer and low in winter or vice versa, despite the fact that we have a very strong seasonal cycle.)
There are a host of barriers of a logical nature to the conclusion that the rise in the CO2 concentration caused the rise in the warming. One of these barriers is ambiguity of reference by the term “warming” to the associated idea. It isn’t the change in the global temperature but what is it?
joeldshore October 18, 2014 at 3:04 pm
It is always possible to say “there has been no statistically significant warming for the last N years” where the value of N varies simply because it takes a while to establish a statistically-significant trend.
There is in fact quite a good correlation between CO2 rise and warming during the historical temperature record, as can be seen by plotting both on the same graph and scaling accordingly. Is the correlation perfect, indicating that CO2 is the ONLY factor affecting the temperature? No…but nobody has claimed that it is. The claim is that it became the dominant driver over multidecadal timescales toward the end of the 20th century. (Over shorter time scales, internal variability dominates, just like it does for the seasonal cycle where the temperature here in Rochester don’t monotonically go between high in summer and low in winter or vice versa, despite the fact that we have a very strong seasonal cycle.)
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No you are not correct. N = now, and looking back for at least half the record it’s not been warming.
No you are not correct where you state “No…but nobody has claimed that it is.” In fact, the IPCC and Gore and others have claimed that it is indisputable, that virtually all of the warming is due to CO2.
And virtually all ice core samples show CO2 follows, not leads temperature change.
If you look at short time periods, people like you get confused when they see correlation and want there to be causation.
Mario,
My statement does not contradict what the IPCC said. The point is not that there are no other effects but that these other effects have not caused significant net warming, at least since the middle of the 20th century. Internal variability and natural factors like solar and volcanic aerosols are probably pretty much a wash…and manmade aerosols have causes some cooling (particularly notable in the mid-20th century).
The ice cores seem to show that temperature starts to change before CO2, which is compatible with the idea that CO2 plays the role of a feedback in those cases. (There were not humans around to emit prodigious quantities of CO2 by rapidly putting carbon stored in fossil fuels back into the atmosphere.) The CO2 is the most likely explanation of how the warming in the two hemispheres becomes synchronized (since the Milankovitch oscillations would tend to make them out of phase). And, since we know quite accurately the radiative forcing due to CO2 and can estimate the radiative forcing due to other factors (albedo changes mainly due to ice sheets and to a lesser extent vegetation changes and changes in aerosol loading), it can be determined that CO2 was probably responsible for about 1/3 of the warming/cooling in the ice age – interglacial cycles.
More importantly, these estimates allow us to estimate the warming that occurs with each W/m^2 of forcing….with that value somewhere around 0.75 C per (W/m^2). And, this, along with the universally agreed-upon forcing of ~4 W/m^2 per CO2 doubling leads to the conclusion that the climate sensitivity is somewhere around 3 C per doubling. In fact, as Hansen points out, this is the forcing one obtains if one assumes that albedo changes due to ice changes are a forcing rather than a feedback. Since they play the role of a feedback in our current climate “experiment”, the actual sensitivity could be higher…Hansen thought perhaps even about double that…although hopefully that is not true in our current climate state when there is not that much ice to melt.
Hey Genius: Try the same experiment from 1900 to 1940 and tell me what you find? Or was your post supposed to have a /sarc
Thank you for this post. It is not an easy subject, however I think it is one of the main pillars of the current climatology and it clearly shows why this “science” stands on shacky grounds:
“… computer models, but Lamb knew they were only as good as the data used for their construction. Lamb is still correct. ”
With my limited knowledge in computer climate models I think I have spotted one additional problem not mentioned above:
computer models do not generate data
When colected temperature data is passed through a computer model to generate the “data” for future modelling, as I understand GISS does, this only ensures that “data” already contains the gospel input preparing the gospel output.
With each new iteration and each new data version GISS is reworking and altering the data making the past cooler and taking out any variances from the natural variations that do not fit to the model – a fact many skeptics have highlighted on and on again.
This is why we can see with our eyes how past data transforms (do not trust your lying eyes, trust “the science” kind of thing), cooling becomes non-cooling, the 70s ice age scare dissapears in Nirvana and “the pause” will also dissapear retroactively given enough iterations.
Everything will end to be just a computer generated input/output gospel that will perfectly fit in an ideal GISS world.
The average fingerprint of ten individuals in a room wouldn’t identify anybody.
Exactly
Back some 30 years ago while taking some elementary meteorological courses at my state’s largest university and fledgling Meteorology department with Dr. Howie Bluestein( what a great guy!) of tornado research fame as my instructor, we talked about how coarse the grids were and inaccurate the measurements used by models to predict the weather. The longer out you went and the more calculations performed using the less than accurate data, the results became less and less reliable – and that was for 7-10 day periods! I’m not claiming Dr. Bluestein as a skeptic in today’s argument, btw, at least publicly.
The computers today are better by orders of magnitude, but are the grids and weather measurements improved enough to match that new calculating power – especially as the periods of time get longer?
I still haven’t forgotten what a math professor told out class – when error ridden data is plugged into long complex calculations, the errors are magnified, inevitably.
That was the seed of my initial suspicions about climate models. and why I was instantaneously a skeptic. Especially when a mathematician friend with a statistical and computer background pointed out all the errors in the whole hockey stick presentation. And not to mention the scale of the temperature differences upon which they were laying claim to all sorts of dramatic conclusions about our world to come.
My doubts were strengthened further as I saw cadres of alarmists laying down in devotion to their computer model Gods and painting their results as evidence, even over hard data that should be the real test of any hypothesis.
Now all I can do is hide and watch and hope that the truth about how little we REALLY know can over come the certainty upon which the alarmist base their catastrophic forecasts, so we don’t end up betting the economic farm and the well being of millions who possess no blame nor guilt in the situation, but will be hurt by ill conceived and premature public policy actions,
While current models have clearly failed, it is a really unfortunate title that tops this article. Models absolutely can work and can work reasonably well with simple factors as long as the factors are set to the right levels.
That is an oversimplification of the problem. It is not an accurate statement about a complex, non-linear, chaotic system. Chaotic systems cannot be modeled by reduction of the system to ‘simple factors’ and I’m not sure what you mean by ‘reasonably well’.
@Jeff Id October 17, 2014 at 3:41 am
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True – However (and this is a significant point) we’re talking about Climate models which only seek to prove CO2 is causing virtually all of the warming in what is claimed as an otherwise static climate. The models in climate science deny that climate changes naturally. These models cannot be useful because their purpose is fatally flawed.
That is the whole problem with data that is seen as information. Information has its roots in the exformation of a shared story. Data has no roots at all and can thus be interpreted as needed … and can thus tell a story of its own.
Dr. Ball is correct as it applies to gridded GCMs: initial values for grid cells can never be right. And it’s far worse than he states. He is talking two-dimensional, the surface station values. Initial values must be 3D, including cells at high altitude.
The Nudge Models, as I call them, can’t succeed, regardless of granularity. Rounding errors alone would destroy results. Gridding the atmosphere is the wrong approach. The belief is that gridding can overcome the lack of knowledge of how the atmosphere operates. Bigger computers and more granularity can’t overcome the basic lack of understanding.
As a computer jock, I was responsible for a set of models for 20 years. They worked fine, because the software correctly codified the behaviors in the system. Our knowledge of global atmospheric behaviors is far too limited to model the overall system. You can’t model what you don’t know. In that sense, I say software is the big problem, not lack of data. Adequate models might work with far less data. Bad models won’t work with a lot more data.
I agree. For example, one of the most critical processes is cloud formation. Obviously, the extent, placement, and timing of cloud formation makes a big difference. Yet the physics is not adequately understood. If my sources are correct, the way they deal with this is to “parameterize” cloud formation. Now, I am not familiar with the details of the models, but I think this means that they reduce the process of cloud formation to some mathematical expressions that can be used to describe it (but that no one claims actually models it). Then they make guesses about the values of the parameters in the expressions, and provide that to the model so that it can calculate the extent, placement, and timing of cloud formation. [Question for those more knowledgeable — am I correct that they parameterize cloud formation, and is my characterization of parameterization correct?] It would seem that if you get to pick the parameter values, you can get any result you want.
Sorry, but I’ve heard “Hang down your head, Tom Wigley” too many times by now. Someone switch off the gramophone please?
believe it he was Tom Dooley.
So tell us, please. Dr Tim, the way forward. All climate models should be scraped? Climate modelling forbidden? Efforts to understand the planet’s geo-physics abandoned? Or do you have a sensible suggestion for what should replace climate models?
Return the money, you scoundrels.
In the field Observational data and a great deal of hard work.
Bingo! Until we actually understand natural cycles the chances of modeling outside changes is impossible.
Yes, abandon climate modelling. Shut off the funding to any “climate change” study. Spend the money on new generation nuclear reactors.
I don’t think Dr Ball is suggesting any of those wild leaps.
I think his ‘suggestion’ would be that we should not base global energy and ‘climate change’ policy on the results of current models – as it seems we are currently doing now.
That has been my fundamental point all along about climate models. Given the known inadequacies of such models, both in what data may be missing AND in what is assumed by those modelers (whether they have an agenda or not), no one can adequately predict what has been predicted about the behavior of the climate. As such, if I am a policy maker, I cannot base any reasonable policy of any kind upon the dicey nature of these models, and the fact that their predictions have proven to be unfulfilled makes especially draconian policies as have been proposed both undesirable and unjustified.
I make that a
yes
yes
no
Village Idiot For the way forward I repeat the conclusion of my earlier comment at 16/10:08pm
“During the last few years I have laid out in a series of posts an analysis of the basic climate data and of the methods used in climate prediction. From these I have developed a simple, rational and transparent forecast of the possible timing and extent of probable future cooling by considering the recent temperature peak as a nearly synchronous peak in both the 60- and 1000-year cycles and by using the neutron count and AP Index as supporting evidence that we are just past the peak of the controlling millennial cycle and beginning a cooling trend which will last several hundred years.
For the forecasts and supporting evidence go to the link at the beginning of this comment.”
see http://climatesense-norpag.blogspot.com
Try replacing it with a model that works and can be established as working over a long period of time, until then how about not trying to change the whole worlds socicio/economic structure.
As someone here has said, there ARE no climate models, simply circulation models. That don’t work.
How about replacing them with different models (maybe of something a little simpler?) that do work a little bit. Then we can build from there.
Village,
Regarding the way forward, this gem from Steven Mosher is helpful.
http://wattsupwiththat.com/2014/09/25/quote-of-the-week-models-climate-sensitivity-the-pause-and-psychology/
“All models are wrong. Some are useful”
-George Box
Would that be a one-box model?
Outside of which thinking was employed?
I take exception to the statement:
The simpler hypothesis is not more likely to be correct; it is easier to work with (see Robert Grosseteste’s Commentarius in Posteriorum Analyticorum Libros).
I agree with the exception you took, Akatsukami, but disagree with your correction. Simpler models are preferred because more complex models are necessarily superfluous.
First, let me say that I don’t believe in global warming. I’ve read enough articles here and in other places to be convinced that there has been no significant warming for almost two decades. So, having said that, can someone please explain an article like this one: http://www.slate.com/blogs/future_tense/2014/10/13/nasa_earth_just_experienced_the_warmest_six_month_stretch_ever.html?utm_content=bufferac518&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer
Is NASA or the National Climatic Data Center just fabricating these numbers?
No. The “pausers” will soon have to shift their emphasis elsewhere, like pointing out that there is limited data and all models are wrong. They will also say that global warming impacts are not that bad — hey, who doesn’t want a warmer winter?
Seriously? “Pausers”? Are you actually going to put people in a little labeled box because they have the gall to point out a painfully obvious fact? Really obvious fact, on the scale of an elephant in a room or an Emperor with no clothes…
Or are you being sarcastic? See, it’s hard to tell here, there are so many whack-job warmists posting that you can rarely tell when someone is genuinely ill or just mocking them.
Barry says…”They will also say that global warming impacts are not that bad — hey, who doesn’t want a warmer winter?”
==================
Barry, please pay better attention. What we say, at least I say, is that the benefits of CO2 are KNOWN (In thousands of experiments, both laboratory and real world,) and they are very positive, the harms are “ever projected and ever failing to materialize. As to the “Pause” well it certainly is, but many do not ASSUME it will be broken by warming. In fact if the AMO turns it will most likely cool. Then the “alarmists” will what. likely blame some anthropogenic source and still want to change the world to their statist vision. I foresee no pause in their efforts.
Fabricating? Too strong a word I think. It implies an intent to deceive, which I am not willing to accept. They are ‘fabricating’ the numbers in the sense that, due to the sparse nature of the stations and the amount of missing data from stations, they are forced to use ‘mathematics’ to project what temperatures may have been 1200 km from stations that do exist. Do you think that a temperature in Washington, DC can be mathematically ‘manipulated’ to give an accurate temperature in Jacksonville, FL? The issue is far more complex than that tho.
dccowboy
October 17, 2014 at 5:48 am
“Fabricating? Too strong a word I think. It implies an intent to deceive, which I am not willing to accept.”
Assuming that the climate scientists do not want to deceive is a preposterous notion at thiss point in time.
http://notrickszone.com/2014/10/15/giss-targeted-data-truncation-and-tricks-alone-produce-half-of-the-warming-trend-since-1880/
The Null hypothesis must be that they are lying scoundrels with the intent of making off with billions of tax payer Dollars. I am perfectly serious.
It’s science, Jim, but not as we know it!
(Sorry, couldn’t resist!)
Basically, it’s spin. I seriously doubt that the Earth has experienced its warmest six months ever by any reasonable definition. But they’re not using reasonable definitions. Neither of “ever” nor of “warmest”. Probably not of “six months” either.
You’ll generally see a story like this turning up every few months. It usually means that some particularly damning evidence has just cast doubt on the “consensus” view of global warming.
Only GISS shows that. (Surface stations) Neither satellite data set shows anything that warm. http://stevengoddard.wordpress.com/2014/10/15/polar-amplification-6/
I’m looking at figure 3. There is no dot for the weather station at Summit Greenland (Summit Camp weather station, in the center of Greenland, was established in 1989):
http://summitcamp.org/status/weather/index?period=month
and
http://www.wunderground.com/weather-forecast/GL/Summit.html
(It is -40 degrees C at the moment)
I would characterize modelling and climate-modelling and much of what passes for popular science as ad hockery by those afeared of error.
Thank you, Dr. Tim Ball, for a really great commentary …. and one that truly exposes the lunacy involved with the calculations of Regional and/or Global Average Temperatures.
And as a side note, to wit:
Dr. Tim Ball said:
“Society is still enamored of computers, so they attain an aura of accuracy and truth that is unjustified”.
———–
Right you are. Many years ago, early 70’s, I was telling my co-workers they should be using the “text editor” program for our mini-computer to key-enter all of their “income & expense” line items to a mag-tape data file so that they could then “sort” them by date and type and then “print” them out on fan-fold computer paper.
When asked why I would tell them because …. iffen their yearly Income Tax Returns were ever audited that the IRS auditor would more likely believe the “truth and accuracy” of the computer print-out than he/she would any verbal or hand-written figures.
There are two ways to estimate the impacts of global warming:
1. Build the best computer model that can be made: or,
2. Observe the real Earth and see how increased GHGs are actually impacting the climate, use those observations to extrapolate the future impacts.
I’ve always understood that 2. was the way to go because of the extreme complexity of what we are talking about here – trillions upon trillions of energy/photon packets moving through trillions upon trillions of molecules every picosecond.
The physics involved in this system cannot really be described a few dozen dynamic equations nor downscaled to 10 km by 10 km by 10 km grids – at least not to the level of accuracy that one should rely on.
We need to be empiricists, not modellers.
Bill, atmospheric temperatures are at an all-time high (2014 may be the warmest year on record), oceans continue to warm, and land ice continues to decrease. Is that enough observation for you?
Even if what you said were true, what would it tell you about the relative impacts of all the various forces that impact climate? What is the relative impact of the AMO on Greenland ice melt? What is the effect to geothermal forces on land ice over time? How much of the warming was induced by changes in the THC speed? How does the PDO affect ocean temperatures? What about land use changes? etc. etc. All of these need to be understood before we could have any clue as to the impact of increased GHGs and/or aerosols.
“atmospheric temperatures are at an all-time high”
**snigger**
Funny, land ice only seemed to start decreasing once the public began to twig that melting sea ice could have no effect on sea levels.
Before that, it was “Lowest Arctic ice cover in history! We’re all going to drown!” And of course it was all (seasonal) pack ice. Which is now increasing again year by year…
Barry October 17, 2014 at 5:41 am
Bill, atmospheric temperatures are at an all-time high (2014 may be the warmest year on record.
—————————
The atmosphere records are recorded by RSS and UAH and they are very low right now compared to the historic highs (which means you are listening to propaganda, not being an empiricist).
And at the rate the adjusted land surface temperatures and the not-seasonally-adjusted-properly sea surface temperatures are actually increasing at, warming will not be a problem. You have to look at the actual data here and not read propaganda again.
Wrong, only GISS shows anything like that. The satellite datasets cover far more area and atmosphere depth, and show nothing like that. BTW, a little warmth is a good thing. http://stevengoddard.wordpress.com/2014/10/15/polar-amplification-6/
This is an example of what I was talking about – water vapor – is it really operating the way global warming theory predicts it should be? Water vapor, as a feedback to CO2 warming, is only responsible for about half of the total warming of 3.0C predicted for a doubling of CO2.
But this is what it is “actually” doing over the past 67 years – as in 67 years is a long enough time to see how the real Earth tm responds.
http://s15.postimg.org/6omwa7r9n/Hadcrut4_vs_PCWV.png
And then just a little further description about why the theory “assumes” CO2 is the control knob. Water vapor as a feedback at 7.0%/K with varying CO2 producing the initial temperature response, would control almost all of the greenhouse effect. Its just that that is not what is really happening. (Of course, now clouds enter the equation and how can there be any clouds if there is almost no water vapor in the atmosphere – the theory actually predicts a massive “increase” in clouds at these impossibly low values of water vapor – its just a theory that is not based on real physical parametres).
http://s9.postimg.org/ekm5lx7nz/Hadcrut4_PCWV_Theory.png
I worked out that Wet Bulb temperature (Tw) = dry bulb temperature (T) x (0.45 + 0.006 x h x sq. root of p/1060 where T & Tw are degrees Celsius, h is the relative humidity in % and p normal pressure in mb. Tw = C x sq. root of W where in W is the precipitable water vapour gm/cm2 and C is constant varies with season.Net radiation intensity (Rn, cal/cm2/day) is = bl Tw where bl are constants related to length of the day, latitude, station level pressure in mb.
Dr. S. Jeevananda Reddy
“All models are wrong; some are useful.” So then it would be easy to point out how models are wrong, and cannot perfectly predict the future (even meteorologists are not 100% accurate even 1 day in advance). But then what do we do to plan for the future? Your supposition seems to be that models are biased towards warming, but in fact, if the models are completely wrong, they could be biased towards cooling (less warming), and the future may be even worse than predicted. The solution supported here seems to be to bury our heads in the sand.
I don’t think ‘bias’ has anything to do with the inaccuracy of GCMs. They simply do not (and some claim that that they cannot) model the ocean-atmosphere system that would allow them to be used as the basis of energy and ‘climate’ policy. Should we abandon them? No, but we should not use their current output to ‘plan for the future’ either.
;but in fact, if the models are completely wrong, they could be biased towards cooling ‘ Your amusing that there ‘wrongness ‘ is an accident which not be the case , if they ‘want’ the models to tell a certain story then all they have to do is front load them to make sure they do. You ‘need warmth ‘ to keep the money flowing in you make sure your models produce warmth reality has no part in that process . Such ‘front loading ‘ is why models are often not trusted in any area , in climate ‘science’ its highly political and self serving nature , no AGW an lot less climate ‘science’, suggest font loading is very likley to be seen and its one reason they are so wrong so often in pratice when this approach fails match events.
George Box’s aphorism that “All models are wrong; some are useful” is incorrect. Wrongness is a consequence of the use of heuristics in selection of the inferences that are made by a model. It can be avoided through replacement of heuristics by optimization.
I told this story a few years ago on this blog but I will tell it again as it is very relevant. In 1968 I was a senior in high school in Hamden CT. The school had invested in a computer for a class on programming and understanding computers. Now, at that time, this very limited computer was the size of a small refrigerator and used a punched hole tape with a language called Focal (pre-Fortran). It was a simple, English based language that used if and then boxes. We were all assigned to design a program and run it to see if it worked. I designed a very simple program I called the Personality Analyzer. My intent was to play a joke on one of my friends. The program asked for all kinds of data; hair color, eye color, height, weight and even ethnic background. Now the only thing that mattered was the height because I wrote it so that when you put in the height of the friend I was playing the joke on, 5’9”, it always spit out the same answer which was not very flattering to my friend. For comparison and knowing the heights of two other friends I did the same thing but with different results, both very flattering. It worked and the joke went over very well. Since that day I have always been skeptical of computer results because one can always produce the desired outcome simply by controlling the input.
[Rather, “…by controlling what is done with the input.” ? .mod]
re: Mod. The program itself will control what is done with the input, as it did in this case. But I was referring to the fact that I knew what response I wanted and controlled the input to achieve that goal.
I can’t tolerate the way averages are used sometimes
The earth is considered to have an average temperature of 15C.
Ignoring albedo and the fact that the earth is not a blackbody, I have noticed this:
15C= 390 w/m2
Average of -10c and +40C = 15C
Blackbody radiation:
-10 = 271 w/m2
+40C = 544 w/m2
Midpoint is 407.5 w/m2
So what figure are these climate scientists working on? 390 or 407
http://www.spectralcalc.com/blackbody_calculator/blackbody.php
Now change the humidity in both data sets and see what happens to your w/m2!
Now change the atmospheric density in both data sets, and see what happens to your w/m2!
Perhaps I am alone amongst your readers of having had the honour and privilege of meeting Professor Lamb back in 1977 by which time he was semi retired. I commissioned the CRU to create a model relating sugar beet havests to weather conditions. This was after 3 catastrophic years. They produced a model that predicted the past wonderfully well. It was the future that the model had difficulty with…..
From memory, Hans Morth took over from Professor Lamb if only for a short time. He had a theory about the impact of Saturn and Jupiter when they were with the Earth at 90 degrees. Something to with the conservation of angular momentum – a bit beyond me.
“Two states differing by imperceptible amounts may eventually evolve into two considerably different states … If, then, there is any error whatever in observing the present state — and in any real system such errors seem inevitable — an acceptable prediction of an instantaneous state in the distant future may well be impossible….In view of the inevitable inaccuracy and incompleteness of weather observations, precise very-long-range forecasting would seem to be nonexistent.” – Deterministic Nonperiodic Flow, in Journal of Atmospheric Sciences, Lorenz E. ’63
Saying climate models don’t work is fine, they don’t. Saying they cannot work is myopic. We’ll never break the sound barrier, never get to the moon, oh wait…
Climate models can never tell us anything useful.
Still waiting for those predicted moon colonies to develop, and who imagined average people carrying a computer that fits in their shirt pocket and allows them to take pictures, video, and communicate with people across the planet while having access to a literally infinite trove of information instantly.
In other words the future as always remains unpredictable and often un-imaginable. What is certain is climate models cannot have worked yesterday, last month, last year, or 10 years ago and cannot work today. It’s not that they “do not” work, it is they “cannot” due to the mulitple reasons the article explains.
Anyways I am contacting Las Vegas and betting on lunar colonies developing before climate models becoming robust enough to forecast global climate over decades. Always wanted a condo on the moon.
We’ll never have a pocket sized fusion generator either, or unicorn feathers or leprechaun gold.
Climate models will NEVER work as long as “an increase in CO2 = higher temps” is a factor in them
Jeff,
In my post immediately prior to yours, I referenced the work of Edward Lorenz, who showed in the ’60s that it is mathematically impossible to make accurate long- term weather predictions, when all inputs to the system are not precisely known and added to the calculations. What is a climate model other than a long- term weather- prediction model on a grand scale? Not only are none of the climate models accurate, they can not be and never will be accurate. That isn’t a myopic statement, it’s reality.
Your statement is a “red herring”. Climate models demonstrate what Lorenz talked about every day: Started from slightly perturbed initial conditions, they do indeed give very different predictions for the weather and even the up and down jiggles in global temperature. However, they give the same result for the trend over, say, a century due to increases in greenhouse gases.
Look, if we take your argument at face value, nobody could ever predict that the climate here in Rochester is cooler in the winter than in the summer by some 25-30 C. Yet, such predictions can be made confidently. (What can’t be made as confidently are predictions of whether this winter will be warmer or colder than average because such predictions are sensitive to the initial conditions.)
So, here’s the point: Some predictions are very sensitive to the initial conditions and some depend more on what are called the boundary conditions. Predicting the change in climate due to a significant perturbation in the Earth’s radiative budget is a boundary conditions problem, not an initial conditions problem. Just like predicting the seasonal cycle is a boundary conditions problem.
The myopia was of those people who made such statements as “we’ll never break the sound barrier”. At the time the sound barrier had been broken for several hundreds or thousands of years. The crack of a whip is due to the tip of the lash going supersonic. Most rifle bullets were supersonic, i.e. had broken the sound barrier, and had been since the late 1800’s.
Your statements are a mere distraction.
Of course “climate modeling” computer programs work, all of them do, …. but the quality of their “work” output is totally FUBAR and of no practical value whatsoever.
One can not “model” a chaotic process that is “driven by” thousands of different randomly occurring input variables which never “repeat” themselves from one day to the next.
That model building work conducted thus far is of no practical value can be expressed in information theoretic and control theoretic terms by pointing out that: a) The mutual information of the models is nil and b) If the mutual information is nil then the climate system is uncontrollable.