Guest essay by Dr. Tim Ball
I have no data yet. It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts. Arthur Conan Doyle. (Sherlock Holmes)
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. A.N.Whitehead
The recent article by Nancy Green at WUWT is an interesting esoteric discussion about models. Realities about climate models are much more prosaic. They don’t and can’t work because data, knowledge of atmospheric, oceanographic, and extraterrestrial mechanisms, and computer capacity are all totally inadequate. Computer climate models are a waste of time and money.
Inadequacies are confirmed by the complete failure of all forecasts, predictions, projections, prognostications, or whatever they call them. It is one thing to waste time and money playing with climate models in a laboratory, where they don’t meet minimum scientific standards, it is another to use their results as the basis for public policies where the economic and social ramifications are devastating. Equally disturbing and unconscionable is the silence of scientists involved in the IPCC who know the vast difference between the scientific limitations and uncertainties and the certainties produced in the Summary for Policymakers (SPM).
IPCC scientists knew of the inadequacies from the start. Kevin Trenberth’s response to a report on inadequacies of weather data by the US National Research Council said
“It’s very clear we do not have a climate observing system…” “This may come as a shock to many people who assume that we do know adequately what’s going on with the climate, but we don’t.”
This was in response to the February 3, 1999 Report that said,
“Deficiencies in the accuracy, quality and continuity of the records place serious limitations on the confidence that can be placed in the research results.
Remember this is 11 years after Hansen’s comments of certainty to the Senate and five years after the 1995 IPCC Report. It is worse now with fewer weather stations and less data than in 1990.
Before leaked emails exposed its climate science manipulations, the Climatic Research Unit (CRU) issued a statement that said,
“GCMs are complex, three dimensional computer-based models of the atmospheric circulation. Uncertainties in our understanding of climate processes, the natural variability of the climate, and limitations of the GCMs mean that their results are not definite predictions of climate.”
Phil Jones, Director of the CRU at the time of the leaked emails and former director Tom Wigley, both IPCC members, said,
“Many of the uncertainties surrounding the causes of climate change will never be resolved because the necessary data are lacking.“
Stephen Schneider, prominent part of the IPCC from the start said,
“Uncertainty about feedback mechanisms is one reason why the ultimate goal of climate modeling – forecasting reliably the future of key variables such as temperature and rainfall patterns – is not realizable.”
Schneider also set the tone and raised eyebrows when he said in Discover magazine.
Scientists need to get some broader based support, to capture the public’s imagination…that, of course, entails getting loads of media coverage. So we have to offer up scary scenarios, make simplified dramatic statements, and make little mention of any doubts we may have…each of us has to decide what the right balance is between being effective and being honest.
The IPCC achieved his objective with devastating effect, because they chose effective over honest.
A major piece of evidence is the disparity between the Working Group I (WGI) (Physical Science Basis) Report, particularly the Chapter on computer models and the claims in the Summary for Policymakers (SPM) Report. Why did the scientists who participated in the WGI Report remain so silent about the disparity?
Here is the IPCC procedure:
Changes (other than grammatical or minor editorial changes) made after acceptance by the Working Group or the Panel shall be those necessary to ensure consistency with the Summary for Policymakers (SPM) or the Overview Chapter.
The Summary is written then the WGI is adjusted. It is like an executive publishing findings then asking employees to produce material to justify them. The purpose is to present a completely different reality to the press and the public.
This is to ensure people, especially the media, read the SPM first. It is released well before the WGI Report, which they knew few would ever read. There is only one explanation for producing it first. David Wojick, an IPCC expert reviewer, explained:
Glaring omissions are only glaring to experts, so the “policymakers”—including the press and the public—who read the SPM will not realize they are being told only one side of a story. But the scientists who drafted the SPM know the truth, as revealed by the sometimes artful way they conceal it
What is systematically omitted from the SPM are precisely the uncertainties and positive counter evidence that might negate the human interference theory. Instead of assessing these objections, the Summary confidently asserts just those findings that support its case. In short, this is advocacy, not assessment.
The Physical Basis of the Models
Here is a simple diagram of how the atmosphere is divided to create climate models.
Figure 1: Schematic of General Circulation Model (GCM).
The surface is covered with a grid and the atmosphere divided into layers. Computer models vary in the size of the grids and the number of layers. They claim a smaller grid provides better results. It doesn’t! If there is no data a finer grid adds nothing. The model needs more real data for each cube and it simply isn’t available. There are no weather stations for at least 70% of the surface and virtually no data above the surface. There are few records of any length anywhere; the models are built on virtually nothing. The grid is so large and crude they can’t include major weather features like thunderstorms, tornados, or even small cyclonic storm systems. The IPCC 2007 Report notes,
Despite the many improvements, numerous issues remain. Many of the important processes that determine a model’s response to changes in radiative forcing are not resolved by the model’s grid. Instead, sub-grid scale parameterizations are used to parametrize the unresolved processes, such as cloud formation and the mixing due to oceanic eddies.
O’Keefe and Kueter explain how a model works: “
The climate model is run, using standard numerical modeling techniques, by calculating the changes indicated by the model’s equations over a short increment of time—20 minutes in the most advanced GCMs—for one cell, then using the output of that cell as inputs for its neighboring cells. The process is repeated until the change in each cell around the globe has been calculated.”
Interconnections mean errors are spread and amplified. Imagine the number of calculations necessary that even at computer speed take a long time. The run time is a major limitation.
All of this takes huge amounts of computer capacity; running a full-scale GCM for a 100-year projection of future climate requires many months of time on the most advanced supercomputer. As a result, very few full-scale GCM projections are made.
A comment at Steve McIntyre’s site, Climateaudit, illustrates the problem.
Caspar Ammann said that GCMs (General Circulation Models) took about 1 day of machine time to cover 25 years. On this basis, it is obviously impossible to model the Pliocene-Pleistocene transition (say the last 2 million years) using a GCM as this would take about 219 years of computer time.
So you can only run the models if you reduce the number of variables. O’Keefe and Kueter explain.
As a result, very few full-scale GCM projections are made. Modelers have developed a variety of short cut techniques to allow them to generate more results. Since the accuracy of full GCM runs is unknown, it is not possible to estimate what impact the use of these short cuts has on the quality of model outputs.
Omission of variables allows short runs, but allows manipulation and moves the model further from reality. Which variables do you include? For the IPCC only those that create the results they want. Besides, because climate is constantly and widely varying so a variable may become more or less important over time as thresholds change.
By selectively leaving out important components of the climate system, likelihood of a human signal being the cause of change is guaranteed. As William Kinninmonth, meteorologist and former head of Australia’s National Climate Centre explains,
… current climate modeling is essentially to answer one question: how will increased atmospheric concentrations of CO2 (generated from human activity) change earth’s temperature and other climatological statistics? Neither cosmology nor vulcanology enter the equations. It should also be noted that observations related to sub-surface ocean circulation (oceanology), the prime source of internal variability, have only recently commenced on a consistent global scale. The bottom line is that IPCC’s view of climate has been through a narrow prism. It is heroic to assume that such a view is sufficient basis on which to predict future ‘climate’.
Static Climate Models In A Virtually Unknown Dynamic Atmosphere.
“Heroic” is polite. I suggest it is deliberately wrong. Lack of data alone justifies that position, lack of knowledge about atmospheric circulation is another. The atmosphere is three-dimensional and dynamic, so to build a computer model that even approximates reality requires far more data than exists, much greater understanding of an extremely turbulent and complex system, and computer capacity that is unavailable for the foreseeable future. As the IPCC note,
Consequently, for models to predict future climatic conditions reliably, they must simulate the current climatic state with some as yet unknown degree of fidelity. Poor model skill in simulating present climate could indicate that certain physical or dynamical processes have been misrepresented.
The history of understanding the atmosphere leaps 2000 years from Aristotle who knew there were three distinct climate zones to George Hadley in the 18th century. The word climate comes from the Greek word klima for slope referring to the angle of the sun and the climate zones it creates. Aristotle’s views dominated western science until the 16th century, but it wasn’t until the 18th century wider, but still narrow, understanding began.
In 1735 George Hadley used the wind patterns, recorded by English sailing ships, to create the first 3D diagram of circulation.
Figure 1. Hadley Cell (Northern Hemisphere)
Restricted only to the tropics, it became known as the Hadley Cell. Sadly, today we know little more than Hadley although Willis Eschenbach has worked hard to identify its role in transfer of heat energy. The Intergovernmental Panel on Climate Change (IPCC) illustrates the point in Chapter 8 of the 2007 Report.
The spatial resolution of the coupled ocean-atmosphere models used in the IPCC assessment is generally not high enough to resolve tropical cyclones, and especially to simulate their intensity.
The problem for climate science and modelers is the Earth is spherical and it rotates. Rotation around the sun creates the seasons, but the rotation around the axes creates even bigger geophysical dynamic problems. Because of it, a simple single cell system (Figure 2) with heated air rising at the Equator moving to the Poles, sinking and returning to the Equator, breaks up. The Coriolis Effect is the single biggest influence on the atmosphere caused by rotation. It dictates that anything moving across the surface appears to be deflected to the right in the Northern Hemisphere and to the left in the Southern Hemisphere. It appears that a force is pushing from the side so people incorrectly refer to the Coriolis Force. There is no Force.
Figure 2: A Simple Single Cell.
Figure 3 shows a more recent attempt to approximate what is going on.
Figure 3: A more recent model of a cross-section through the Northern Hemisphere.
Now it is the “Indirect Ferrell Cell”. Notice the discontinuities in the Tropopause and the “Stratospheric – Tropospheric Mixing”. This is important, because the IPCC doesn’t deal with the critical interface between the stratosphere and a major mechanism in the upper Troposphere in their models.
Due to the computational cost associated with the requirement of a well-resolved stratosphere, the models employed for the current assessment do not generally include the QBO.
This is just one example of model inadequacies provided by the IPCC.
What the IPCC Working Group I, (The Physical Science Basis Report) Says About the Models.
The following quotes (Italic and inset) are under their original headlines from Chapter 8 of the 2007 IPCC AR4 Report. Comments are in regular type.
There is currently no consensus on the optimal way to divide computer resources among finer numerical grids, which allow for better simulations; greater numbers of ensemble members, which allow for better statistical estimates of uncertainty; and inclusion of a more complete set of processes (e.g., carbon feedbacks, atmospheric chemistry interactions).
Most don’t understand models or the mathematics on which they are built, a fact exploited by promoters of human caused climate change. They are also a major part of the IPCC work not yet investigated by people who work outside climate science. Whenever outsiders investigate, as with statistics and the hockey stick, the gross and inappropriate misuses are exposed. The Wegman Report investigated the Hockey Stick fiasco, but also concluded,
We believe that there has not been a serious investigation to model the underlying process structures nor to model the present instrumented temperature record with sophisticated process models.
FAQ 8.1: How Reliable Are the Models Used to Make Projections of Future Climate Change?
Nevertheless, models still show significant errors. Although these are generally greater at smaller scales, important large-scale problems also remain. For example, deficiencies remain in the simulation of tropical precipitation, the El Niño- Southern Oscillation and the Madden-Julian Oscillation (an observed variation in tropical winds and rainfall with a time scale of 30 to 90 days).
Models continue to have significant limitations, such as in their representation of clouds, which lead to uncertainties in the magnitude and timing, as well as regional details, of predicted climate change. Nevertheless, over several decades of model development, they have consistently provided a robust and unambiguous picture of significant climate warming in response to increasing greenhouse gases.
Of course they do, because that is how they are programmed.
8.2.1.1 Numerics
In this report, various models use spectral, semi-Lagrangian, and Eulerian finite-volume and finite-difference advection schemes, although there is still no consensus on which type of scheme is best.
But how different are the results and why don’t they know which is best?
8.2.1.3 Parameterizations
The climate system includes a variety of physical processes, such as cloud processes, radiative processes and boundary-layer processes, which interact with each other on many temporal and spatial scales. Due to the limited resolutions of the models, many of these processes are not resolved adequately by the model grid and must therefore be parametrized. The differences between parametrizations are an important reason why climate model results differ.
How can parameterizations vary? The variance is evidence they are simply guessing at the conditions in each grid and likely choosing the one that accentuates their bias.
8.2.2.1 Numerics
Issues remain over the proper treatment of thermobaricity (nonlinear relationship of temperature, salinity and pressure to density), which means that in some isopycnic coordinate models the relative densities of, for example, Mediterranean and Antarctic Bottom Water masses are distorted. The merits of these vertical coordinate systems are still being established.
8.2.3.2 Soil Moisture Feedbacks in Climate Models
Since the TAR, there have been few assessments of the capacity of climate models to simulate observed soil moisture. Despite the tremendous effort to collect and homogenise soil moisture measurements at global scales (Robock et al., 2000), discrepancies between large-scale estimates of observed soil moisture remain. The challenge of modelling soil moisture, which naturally varies at small scales, linked to landscape characteristics, soil processes, groundwater recharge, vegetation type, etc., within climate models in a way that facilitates comparison with observed data is considerable. It is not clear how to compare climate-model simulated soil moisture with point-based or remotely sensed soil moisture. This makes assessing how well climate models simulate soil moisture, or the change in soil moisture, difficult.
Evaporation is a major transfer of long-wave energy from the surface to the atmosphere. This inadequacy alone likely more than equals the change created by human addition of CO2.
8.2.4.1 Terrestrial Cryosphere
Glaciers and ice caps, due to their relatively small scales and low likelihood of significant climate feedback at large scales, are not currently included interactively in any AOGCMs.
How big does an ice cap have to be to influence the parameterization in a grid? Greenland is an ice cap.
8.2.5 Aerosol Modelling and Atmospheric Chemistry
The global Aerosol Model Intercomparison project, AeroCom, has also been initiated in order to improve understanding of uncertainties of model estimates, and to reduce them (Kinne et al., 2003).
Interactive atmospheric chemistry components are not generally included in the models used in this report.
8.3 Evaluation of Contemporary Climate as Simulated by Coupled Global Models
Due to nonlinearities in the processes governing climate, the climate system response to perturbations depends to some extent on its basic state (Spelman and Manabe, 1984). Consequently, for models to predict future climatic conditions reliably, they must simulate the current climatic state with some as yet unknown degree of fidelity. Poor model skill in simulating present climate could indicate that certain physical or dynamical processes have been misrepresented.
They don’t even know which ones are misrepresented?
8.3.1.2 Moisture and Precipitation
For models to simulate accurately the seasonally varying pattern of precipitation, they must correctly simulate a number of processes (e.g., evapotranspiration, condensation, transport) that are difficult to evaluate at a global scale.
Precipitation forecasts (projections?) are worse than their temperature projections (forecasts).
8.3.1.3 Extratropical Storms
Our assessment is that although problems remain, climate models are improving in their simulation of extratropical cyclones.
This is their self-serving assessment. How much are they improving and from what baseline?
8.3.2 Ocean
Comparisons of the type performed here need to be made with an appreciation of the uncertainties in the historical estimates of radiative forcing and various sampling issues in the observations.
8.3.2.1 Simulation of Mean Temperature and Salinity Structure
Unfortunately, the total surface heat and water fluxes (see Supplementary Material, Figure S8.14) are not well observed.
8.3.2.2 Simulation of Circulation Features Important for Climate Response
The MOC (meridional overturning circulation) is an important component of present-day climate and many models indicate that it will change in the future (Chapter 10). Unfortunately, many aspects of this circulation are not well observed.
8.3.2.3 Summary of Oceanic Component Simulation
The temperature and salinity errors in the thermocline, while still large, have been reduced in many models.
How much reduction and why in only some models?
8.3.3 Sea Ice
The magnitude and spatial distribution of the high-latitude climate changes can be strongly affected by sea ice characteristics, but evaluation of sea ice in models is hampered by insufficient observations of some key variables (e.g., ice thickness) (see Section 4.4). Even when sea ice errors can be quantified, it is difficult to isolate their causes, which might arise from deficiencies in the representation of sea ice itself, but could also be due to flawed simulation of the atmospheric and oceanic fields at high latitudes that drive ice movement (see Sections 8.3.1, 8.3.2 and 11.3.8).
8.3.4 Land Surface
Vast areas of the land surface have little or no current data and even less historic data. These include 19 percent deserts, 20 percent mountains, 20 percent grasslands, 33 percent combined tropical and boreal forests and almost the entire Arctic and Antarctic regions.
8.3.4.1 Snow Cover
Evaluation of the land surface component in coupled models is severely limited by the lack of suitable observations.
Why? In 1971-2 George Kukla was producing estimates of varying snow cover as a factor in climate change. Satellite data is readily available for simple assessment of the changes through time.
8.3.4.2 Land Hydrology
The evaluation of the hydrological component of climate models has mainly been conducted uncoupled from AOGCMs (Bowling et al., 2003; Nijssen et al., 2003; Boone et al., 2004). This is due in part to the difficulties of evaluating runoff simulations across a range of climate models due to variations in rainfall, snowmelt and net radiation.
8.3.4.4 Carbon
Despite considerable effort since the TAR, uncertainties remain in the representation of solar radiation in climate models (Potter and Cess, 2004).
8.4.5 Atmospheric Regimes and Blocking
Blocking events are an important class of sectoral weather regimes (see Chapter 3), associated with local reversals of the mid-latitude westerlies.
There is also evidence of connections between North and South Pacific blocking and ENSO variability (e.g., Renwick, 1998; Chen and Yoon, 2002), and between North Atlantic blocks and sudden stratospheric warmings (e.g., Kodera and Chiba, 1995; Monahan et al., 2003) but these connections have not been systematically explored in AOGCMs.
Blocking was a significant phenomenon in the weather patterns as the Circumpolar flow changed from Zonal to Meridional in 2013-14.
8.4.6 Atlantic Multi-decadal Variability
The mechanisms, however, that control the variations in the MOC are fairly different across the ensemble of AOGCMs. In most AOGCMs, the variability can be understood as a damped oceanic eigenmode that is stochastically excited by the atmosphere. In a few other AOGCMs, however, coupled interactions between the ocean and the atmosphere appear to be more important.
Translation; We don’t know.
8.4.7 El Niño-Southern Oscillation
Despite this progress, serious systematic errors in both the simulated mean climate and the natural variability persist. For example, the so-called ‘double ITCZ’ problem noted by Mechoso et al. (1995; see Section 8.3.1) remains a major source of error in simulating the annual cycle in the tropics in most AOGCMs, which ultimately affects the fidelity of the simulated ENSO.
8.4.8 Madden-Julian Oscillation
The MJO (Madden and Julian, 1971) refers to the dominant mode of intra-seasonal variability in the tropical troposphere. Thus, while a model may simulate some gross characteristics of the MJO, the simulation may be deemed unsuccessful when the detailed structure of the surface fluxes is examined (e.g., Hendon, 2000).
8.4.9 Quasi-Biennial Oscillation
The Quasi-Biennial Oscillation (QBO; see Chapter 3) is a quasi-periodic wave-driven zonal mean wind reversal that dominates the low-frequency variability of the lower equatorial stratosphere (3 to 100 hPa) and affects a variety of extratropical phenomena including the strength and stability of the winter polar vortex (e.g., Baldwin et al., 2001).. Due to the computational cost associated with the requirement of a well-resolved stratosphere, the models employed for the current assessment do not generally include the QBO.
8.4.10 Monsoon Variability
In short, most AOGCMs do not simulate the spatial or intra-seasonal variation of monsoon precipitation accurately.
Monsoons are defined by extreme seasonality of rainfall. They occur in many regions around the word, though most only associate them with Southern Asia. It is not clear what the IPCC mean. Regardless, these are massive systems of energy transfer from the region of energy surplus to the deficit region.
8.4.11 Shorter-Term Predictions Using Climate Models
This suggests that ongoing improvements in model formulation driven primarily by the needs of weather forecasting may lead also to more reliable climate predictions.
This appears to contradict the claim that weather and climate forecasts are different. As Norm Kalmonavitch notes,
The GCM models referred to as climate models are actually weather models only capable of predicting weather about two weeks into the future and as we are aware from our weather forecasts temperature predictions…
In 2008 Tim Palmer, a leading climate modeller at the European Centre for Medium-Range Weather Forecasts in Reading England said in the New Scientist.
I don’t want to undermine the IPCC, but the forecasts, especially for regional climate change, are immensely uncertain.
8.5.2 Extreme Precipitation
Sun et al. (2006) investigated the intensity of daily precipitation simulated by 18 AOGCMs, including several used in this report. They found that most of the models produce light precipitation (<10 mm day–1) more often than observed, too few heavy precipitation events and too little precipitation in heavy events (>10 mm day–1). The errors tend to cancel, so that the seasonal mean precipitation is fairly realistic (see Section 8.3).
Incredible, the errors cancel and since the results appear to match reality they must be correctly derived.
8.5.3 Tropical Cyclones
The spatial resolution of the coupled ocean-atmosphere models used in the IPCC assessment is generally not high enough to resolve tropical cyclones, and especially to simulate their intensity.
8.6.2 Interpreting the Range of Climate Sensitivity Estimates Among General Circulation Models
The climate sensitivity depends on the type of forcing agents applied to the climate system and on their geographical and vertical distributions (Allen and Ingram, 2002; Sausen et al., 2002; Joshi et al., 2003). As it is influenced by the nature and the magnitude of the feedbacks at work in the climate response, it also depends on the mean climate state (Boer and Yu, 2003). Some differences in climate sensitivity will also result simply from differences in the particular radiative forcing calculated by different radiation codes (see Sections 10.2.1 and 8.6.2.3).
Climate sensitivity has consistently declined and did so further in IPCC AR5. In fact, in the SPM for AR5 the sensitivity declined in the few weeks from the first draft to the final report.
8.6.2.2 Why Have the Model Estimates Changed Since the TAR?
The current generation of GCMs[5] covers a range of equilibrium climate sensitivity from 2.1°C to 4.4°C (with a mean value of 3.2°C; see Table 8.2 and Box 10.2), which is quite similar to the TAR. Yet most climate models have undergone substantial developments since the TAR (probably more than between the Second Assessment Report and the TAR) that generally involve improved parametrizations of specific processes such as clouds, boundary layer or convection (see Section 8.2). In some cases, developments have also concerned numerics, dynamical cores or the coupling to new components (ocean, carbon cycle, etc.). Developing new versions of a model to improve the physical basis of parametrizations or the simulation of the current climate is at the heart of modelling group activities. The rationale for these changes is generally based upon a combination of process-level tests against observations or against cloud-resolving or large-eddy simulation models (see Section 8.2), and on the overall quality of the model simulation (see Sections 8.3 and 8.4). These developments can, and do, affect the climate sensitivity of models.
All this says is that climate models are a work in progress. However, it also acknowledges that they can only hope to improve parameterization. In reality they need more and better data, but that is not possible for current or historic data. Even if they started an adequate data collection system today it would be thirty years before it would be statistically significant.
8.6.2.3 What Explains the Current Spread in Models’ Climate Sensitivity Estimates?
The large spread in cloud radiative feedbacks leads to the conclusion that differences in cloud response are the primary source of inter-model differences in climate sensitivity (see discussion in Section 8.6.3.2.2). However, the contributions of water vapour/lapse rate and surface albedo feedbacks to sensitivity spread are non-negligible, particularly since their impact is reinforced by the mean model cloud feedback being positive and quite strong.
What does “non-negligible “ mean? Is it a double negative? Apparently. Why don’t they use the term significant? They assume their inability to produce accurate results is because of clouds and water vapor. As this review shows there are countless other factors and especially those they ignore like the Sun. The 2001 TAR Report included a table of the forcings with a column labeled Level of Scientific Understanding (LOSU). Of the nine forcings only two have a ”high” rating, although that is their assessment, one is medium and the other six are “low”. The only difference in the 2007 FAR Report is the LOSU column is gone.
8.6.3.2 Clouds
Despite some advances in the understanding of the physical processes that control the cloud response to climate change and in the evaluation of some components of cloud feedbacks in current models, it is not yet possible to assess which of the model estimates of cloud feedback is the most reliable.
The cloud problem is far more complicated than this summary implies. For example, clouds function differently depending on type, thickness, percentage of water vapor, water droplets, ice crystals or snowflakes and altitude.
8.6.3.3 Cryosphere Feedbacks
A number of processes, other than surface albedo feedback, have been shown to also contribute to the polar amplification of warming in models (Alexeev, 2003, 2005; Holland and Bitz, 2003; Vavrus, 2004; Cai, 2005; Winton, 2006b). An important one is additional poleward energy transport, but contributions from local high-latitude water vapour, cloud and temperature feedbacks have also been found. The processes and their interactions are complex, however, with substantial variation between models (Winton, 2006b), and their relative importance contributing to or dampening high-latitude amplification has not yet been properly resolved.
You can’t know how much energy is transported to polar regions if you can’t determine how much is moving out of the tropics. The complete lack of data for the entire Arctic Ocean and most of the surrounding land is a major limitation.
8.6.4 How to Assess Our Relative Confidence in Feedback to controls Simulated by Different Models?
A number of diagnostic tests have been proposed since the TAR (see Section 8.6.3), but few of them have been applied to a majority of the models currently in use. Moreover, it is not yet clear which tests are critical for constraining future projections. Consequently, a set of model metrics that might be used to narrow the range of plausible climate change feedbacks and climate sensitivity has yet to be developed.
The IPCC chapter on climate models appears to justify use of the models by saying they show an increase in temperature when CO2 is increased. Of course they do, that is how they’re programmed. Almost every individual component of the model has, by their admission, problems ranging from lack of data, lack of understanding of the mechanisms, and important ones are omitted because of inadequate computer capacity or priorities. The only possible conclusion is that the models were designed to prove the political position that human CO2 was a problem.
Scientists involved with producing this result knew the limitations were so severe they precluded the possibility of proving the result. This is clearly set out in the their earlier comments and the IPCC Science Report they produced. They remained silent when the SPM claimed, with high certainty, they knew what was going on with the climate. They had to know this was wrong. They may not have known about the political agenda when they were inveigled into participating, but they had to know when the 1995 SPM was published because Benjamin Santer exploited the SPM bias by rewriting Chapter 8 of the 1995 Report in contradiction to what the members of his chapter team had agreed. The gap widened in subsequent SPMs but they remained silent and therefore complicit.
As an engineer with experience using CFD modelling software and using all sorts of other analytical tools from simple formulae to one page spreadsheets to large databases it is a no brainer that the analysis one is doing is based on the mathematics of a particular mechanism that is understood to be applicable and whose limitations are understood. It is also implicit that the scale at which one works is a scale at which the models being used are applicable and appropriate and the aggregate result is properly thus determined by summation of all the local outcomes.
The very idea that one would have a model which has a cellular scale which is too large to apply to so much of the actual mechanisms known to be in play is so ludicrous as to be laughable in a very nervous way. Nervous because I wonder what kind of science/engineering nut job would even do such a thing? What possible use would the output be?
Apart from perpetrating a fraud that is.
this was the IPCC video i watched. the caveats on the models are at the end [bit mumbled]
http://www.rmets.org/events/climate-change-2013-physical-science-basis-working-group-1-contribution-fifth-assessment
compare that to what the public gets. Notice the constant images of implied exactness. The term ‘inter glacial warming period’ is not mentioned once. The warming is decontextualised from the ice age cycle which allows for ‘something else’ to be the cause.
Wow, what an article. Thanks for all the work Dr. Ball and WUWT for posting this. This only confirms what climate realists have known since 1998 or before, that the cult of warm, is not about science, but about egos, money, getting published, and falsifying the real climate record. There are about 1 million or so variables in climate, as an IT professional for a long time, I can confirm that you cannot model, nor simulate many-to-many relationship with so many variables and unknowns. It is simply impossible.
So – the chicanery, muddled thinking, lack of data, assumptions, predictions and conclusions – are actually FAR worse than any of us thought..!
yes there are a lot of variables but people were predicting the uk winter storms back in oct 2013 with normal meteorological reasoning while those with supercomputers predicted a drier than av winter and actually can’t forecast past 2 days. So which group of people have the better understanding of energy transference processes?
just because the co2ers models can’t do anything doesn’t mean the processes cannot be predicted. One doesn’t have to model everything. Just the essential mechanisms. ie understanding the hierarchy. Putting co2 at the top is why they get nonsense.
“[Computer models] don’t and can’t work because data, knowledge of atmospheric, oceanographic, and extraterrestrial mechanisms, and computer capacity are all totally inadequate. Computer climate models are a waste of time and money.”
Their *advertised* function of modeling earth’s climate is unachievable.
However, their *designed* function – to provide numerical tables in bulk and persuasive numbers for the rhetoric of grant applications – has been an unqualified success.
With so many involved, and each being able to claim “but I was only involved in this small part”, I guess frogmarching to the Hague is probably not in their cards, though it should be.
Cagw is about power and money. Science has little to do
With it. These cowards are alarmed at losing their money.
If the models could just predict one growing season they would be worth a lot.
The Summary is written then the WGI is adjusted. It is like an executive publishing findings then asking employees to produce material to justify them. The purpose is to present a completely different reality to the press and the public.
I’d say it’s closer to an executive of a publicly held company signing off on the annual report, when the executive is aware of significant misrepresentations in the report. The DOJ/SEC send people to prison for that crime.
In the IPCC case, scientists are signing off on documents with significant misrepresentations. Public policy is based on these misrepresentations. These “scientists” are no different than the corporate executive who misrepresents material facts. The scientists should be prosecuted, or at least held accountable in some manner.
Here is an excellent essay from Dr. John Christy.
Excellent essay.
We knew the models weren’t giving good results, but:
they are worse than we though.
(Someone had to say it. )
“IPCC Scientists Knew Data and Science Inadequacies Contradicted Certainties Presented to Media, Public and Politicians, But Remained Silent”
For the most part, aren’t the IPCC scientists still remaining silent?
Shouldn’t most, if not all, of them be shouting “no, you are misrepresenting my/our work!”
steveta_uk says:
March 21, 2014 at 3:39 am
There’s some awefully old stuff referenced here. Does it really matter that in 1997 they couldn’t accurately desribe the climate? The is pre-argo, for example.
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Surely the importance of the old models is that serious policy decisions were taken on demonstrably faulty grounds then. And they continue to be now.
The problem is that is not in the policy-makers interest to question the gift of a global disaster on which they can build power and raise taxes.
<Watch out! The blue car is going to run us over!
-That is not a car, it is a van.
<Come on it is car.
-It is a van and is not blue is grey but it depends on the angle of the light from where you see it.
<Are you saying I am blind and can not think by myself? It is a car and it is blue cause I know what I know and when I tell you … … …
Science is as much about developing suitable methodologies to give answers as about finding and addressing the "appropriate questions". When there is a mismatch between "the question" and "the methodology" both should be reassessed. I see much debate about data and its manipulation. I don´t see much debate about the suitability of the questions. If the technical development to obtain data is limited the questions are the ones to be re-framed. And from my point of view, sooner or later there are some questions that need to be addressed. They might not be the ones you agree on but these are the ones I haven´t seen a consensuated answer yet.
Could human development have an impact in the ecosystem at global scale? What would have to do humans to alter the ecosystem at global scale? Which part of the ecosystem (soil, atmosphere, light and heat (from our sun), water or living organisms) would reflect primary the impact from human perturbation? In case the answer is “yes” to the first question, how much of the answer for the second and third questions matches with actual facts?
“Computer climate models are a waste of time and money.”
But not for those elites who use them for political fun and profit. They have the time and they have the money. Prejudgment in, prejudgment out.
Thank you Dr Ball.
As Dr Ball points out, on many levels the climate models do not reflect reality. These failings are not simply minor. They are major failings over a wide range of issues. This is well known in the scientific community but the community remains largely silent out of fear. To speak out risks the loss of funding and the end of your scientific career. The scientific search for truth has been replaced by the search for fame and funding.
In my small work I pointed out that no matter how much we spend on Climate Models, there is a fundamental problem in predicting the future. Some very simple experiments in physics have demonstrated that our common sense understanding of the future is fundamentally wrong. These experiments gave rise to quantum mechanics, the single most successful description of reality in science.
Asking a computer to predict the future is asking a computer to solve the impossible. There is no specific future to be predicted; only a probability out of an infinity of possible futures. Adding CO2 may increase the odds of a warmer future, but it in no way determines that the future will be warmer. The future can remain stubbornly colder, no matter how much CO2 we add.
Some futures are more likely, but that is simply God is playing dice. We are not guaranteed to arrive at any specific future, thus there is nothing for the climate models to solve. They are being asked to deliver an impossible result and like Hal in 2001 they have gone insane. They are killing people by cutting life support via energy poverty.
HAL: “The 9000 series is the most reliable computer ever made. No 9000 computer has ever made a mistake or distorted information. We are all, by any practical definition of the words, foolproof and incapable of error”.
The models are massively deficient; many of them have been “parameterized” differently, and some have been corrected for this and that and some not. So let’s take an average and pretend that it’s accurate to within a few 10ths of a degree at 100 years? Can they really say that with a straight face?
What does an “average” of the models actually mean? Does anyone really argue that they are samples from the same population? Is the same weight applied to each model? Why or why not? Does this procedure imply that they can’t distinguish among models by their predictive skill?
Whatever it is their doing, it’s not science as we know it, Jim.
Modeling is likely less expensive than measuring. Plus it allows authors to set and get instead of walk and measure. Setting while waiting for a computer to output its output gives them time to twiddle their fingers or teach a class (what IS the difference?). Walk and measure is field research, which is very expensive and requires far more skill that many scientists no longer have or were never trained in how to do it. So I imagine there is pressure to write grants that revolve around set and get. More papers and researchers. IE more bang for the buck. Which would explain the explosion of climate science papers..
As a corporate computer jock for over 30 years, I supported models. The models were accurate and useful to the businesses.
Models are the codification of the interaction between different parameters. A model run consists of inputting data (actual or hypothetical) for the parameters, and the output, a forecast, is the result of the interaction of the data.
The problem with climate “models” is not that they don’t have enough data.
The problem with climate “models” is not that they don’t have enough computing power.
The problem is that they don’t understand the interaction of all the parameters, or even what all the parameters are.
If you don’t know how things interact, more data gives you nothing. Climate models have zero predictive capability, and will continue so until we know how the atmosphere works in sufficient detail to codify it into the models. Even then, we won’t know what Ol’ Sol will decide to do (unless we can then adequately model the sun, too!).
My bias: My first computer system (1978) was a Dec PDP-11/45. The degreed computer scientists I worked with had a saying, “If you can’t get it done in 128k, it’s not worth doing.” Hence, all my career, I was suspect of the value of more computing power. I considered it a crutch for those without sufficient intellect to figure out how to get it done in 128k. In my not so humble opinion, more computing power or more data is not going to help climate models. Only more understanding of the atmosphere will, and reaching a useful level is decades off. Progress on that has been dead for over 20 years, as scientists try to force fit CO2 into the equations. They are stuck on stupid.
Tim Ball’s post is a very detailed explanation the argument which I have been making for some years now that the model outputs are useless as a basis for climate and energy policy and that it is time to move to a different method of climate forecasting. In a series of posts at
http://climatesense-norpag.blogspot.com
I have presented forecasts of a likely coming cooling based on using the 60 and 1000 year periodicities in the temperature data and the neutron count (and10Be) as the best proxy for solar activity.Here for convenience are the conclusions of the latest post on my blog
“With that in mind it is reasonable to correlate the cycle 22 low in the neutron count (high solar activity and SSN) with the peak in the SST trend in about 2003 and project forward the possible general temperature decline in the coming decades in step with the decline in solar activity in cycles 23 and 24.
In earlier posts on this site http://climatesense-norpag.blogspot.com at 4/02/13 and 1/22/13
I have combined the PDO, ,Millennial cycle and neutron trends to estimate the timing and extent of the coming cooling in both the Northern Hemisphere and Globally.
Here are the conclusions of those posts.
1/22/13 (NH)
1) The millennial peak is sharp – perhaps 18 years +/-. We have now had 16 years since 1997 with no net warming – and so might expect a sharp drop in a year or two – 2014/16 -with a net cooling by 2035 of about 0.35.Within that time frame however there could well be some exceptional years with NH temperatures +/- 0.25 degrees colder than that.
2) The cooling gradient might be fairly steep down to the Oort minimum equivalent which would occur about 2100. (about 1100 on Fig 5) ( Fig 3 here) with a total cooling in 2100 from the present estimated at about 1.2 +/-
3) From 2100 on through the Wolf and Sporer minima equivalents with intervening highs to the Maunder Minimum equivalent which could occur from about 2600 – 2700 a further net cooling of about 0.7 degrees could occur for a total drop of 1.9 +/- degrees
4)The time frame for the significant cooling in 2014 – 16 is strengthened by recent developments already seen in solar activity. With a time lag of about 12 years between the solar driver proxy and climate we should see the effects of the sharp drop in the Ap Index which took place in 2004/5 in 2016-17.
4/02/13 ( Global)
1 Significant temperature drop at about 2016-17
2 Possible unusual cold snap 2021-22
3 Built in cooling trend until at least 2024
4 Temperature Hadsst3 moving average anomaly 2035 – 0.15
5 Temperature Hadsst3 moving average anomaly 2100 – 0.5
6 General Conclusion – by 2100 all the 20th century temperature rise will have been reversed,
7 By 2650 earth could possibly be back to the depths of the little ice age.
8 The effect of increasing CO2 emissions will be minor but beneficial – they may slightly ameliorate the forecast cooling and help maintain crop yields .
9 Warning !! There are some signs in the Livingston and Penn Solar data that a sudden drop to the Maunder Minimum Little Ice Age temperatures could be imminent – with a much more rapid and economically disruptive cooling than that forecast above which may turn out to be a best case scenario.
How confident should one be in these above predictions? The pattern method doesn’t lend itself easily to statistical measures. However statistical calculations only provide an apparent rigor for the uninitiated and in relation to the IPCC climate models are entirely misleading because they make no allowance for the structural uncertainties in the model set up. This is where scientific judgment comes in – some people are better at pattern recognition and meaningful correlation than others. A past record of successful forecasting such as indicated above is a useful but not infallible measure. In this case I am reasonably sure – say 65/35 for about 20 years ahead. Beyond that certainty drops rapidly. I am sure, however, that it will prove closer to reality than anything put out by the IPCC, Met Office or the NASA group. In any case this is a Bayesian type forecast- in that it can easily be amended on an ongoing basis as the Temperature and Solar data accumulate. If there is not a 0.15 – 0.20. drop in Global SSTs by 2018 -20 I would need to re-evaluate
Sheep entrails and “Turtles all the way down”.
Dr Tim Ball: They don’t and can’t work because data, knowledge of atmospheric, oceanographic, and extraterrestrial mechanisms, and computer capacity are all totally inadequate. Computer climate models are a waste of time and money.
I found one ambiguity in your presentation. It isn’t clear which of the problems you address are simply problems with current models, and which are problems you think will never be solved. A computer model that could accurately forecast the mean, s.d., quartiles, and extremals of rain and temperature for a bunch of regions over spans of 2 decades would be quite valuable, even if it could not forecast the mean temp and rainfall on Oct 11 of any year in Columbus OH.
Gamecock: Hence, all my career, I was suspect of the value of more computing power. I considered it a crutch for those without sufficient intellect to figure out how to get it done in 128k.
Does it bother you how much computing power is built into modern commercial aircraft? The facilities that design and build such aircraft? Or cell phones? CAT-scans and fMRI? Were you appalled by the waste of computing resources in mapping and sequencing the genomes of humans and rice?
“The only possible conclusion is that the models were designed to prove the political position that human CO2 was a problem.”
Hell, it took some time to get there, but you make the case is a very detailed and irrefutable way. Well done.
It’s all one massive game of computerised smoke and mirrors. Climate models start out with a CO2 driven rise then decades of research and billions of dollars putting a few “climate-like” wiggles onto it. ( Which may or may not coincide with the wiggles in the “corrected and adjusted” datasets ).
One massive SCAM masquerading as “the” science.
Great article, Dr. Tim Ball
CO2 sensitivity theory is like non-stick coating or fabric protector or the miracle knife you never sharpen. In testing and demonstration it all works fine, and there’s an answer to every possible FAQ. Professor so-and-so agrees. An audience of stunned professionals can’t believe their eyes. Unsolicited testimonials from ecstatic customers. Celebrities will vouch. And so on.
In the vast and messy place called the real world none of it works. Not the non-stick coating, not the fabric protector, not the miracle knife…and not the CO2 theory.