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.
The CAGW advocates know nothing of science, except in so much as what is required to manipulate and deceive the masses. This is nothing new. Sadly, anyone with any sense has known this for a long long time. Nice write up and kudos to Anthony, Dr. Ball and those who continue the fight for the truth being let loose.
“Rotation around the sun creates the seasons…”
In Astronomy, we refer to rotation about an axis, revolution about another body.
I think all is not lost yet, but current reductionist computational modelling paradigm needs to be profoundly reconsidered. See exposition of the issue at Judith Curry’s site.
Journal of Climate, Volume 26, Issue 2 (January 2013)
doi: 10.1175/JCLI-D-12-00132.1
The Observed Hemispheric Symmetry in Reflected Shortwave Irradiance
Aiko Voigt, Bjorn Stevens, Jürgen Bader and Thorsten Mauritsen
One may also want to do actual experiments on irreproducible quasi stationary non equilibrium thermodynamic systems other than climate.
jorgekafkazar says:
March 21, 2014 at 10:11 am
“Rotation around the sun creates the seasons…”
In Astronomy, we refer to rotation about an axis, revolution about another body.
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Oh you are such a GOOD boy. You caught a booboo. Do you want a gold star?
Matthew R Marler says:
March 21, 2014 at 7:04 am
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?
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You conflate results with methods. I have no idea how efficient their programming is, nor do you.
A site search of the IPCC website for the word ‘uncertainties’ yields 2,560 results in Google.
A site search of the IPCC website for the word ‘poorly understood’ yields 206 results in Google.
site:www.ipcc.ch uncertainties
site:www.ipcc.ch “poorly understood”
KevinK says:
March 21, 2014 at 8:04 am
You had 128k ?, boy back in my day all we had was ones and zeros, and sometimes we ran out of zeros and we had to use o’s.
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Heh, heh.
My father was a computing pioneer. His first system (early 1950s) was a room full of mechanical relays. Bugs were roaches stuck in a relay. Really. Mechanics worked two shifts a day to keep it working. No output was trusted; all results were subjected to a reasonableness test.
“It is heroic to assume that such a view is sufficient basis on which to predict future ‘climate’.” — Kinninmonth
“’Heroic’ is polite. I suggest it is deliberately wrong.” — Dr. Ball
Yes, wrong, in a sense, and certainly deliberate. More precisely, I’d call it a transparent euphemism for ‘quixotic,’ i.e., “caught up in the romance of noble deeds and the pursuit of unreachable goals; idealistic without regard to practicality.”*
In other words, barmy in the crumpet.
* The American Heritage® Dictionary of the English Language.
CJOrach at 8.42am
The ‘fight’ against catastrophic man made global warming/climate change is a self serving indulgence by well fed, well housed, liberal Western nations in the face of their inability to reduce the bloodshed and human misery that is occuring in so many places in the world on a daily basis.
Because of their impotence in solving current problems, Western leaders have convinced themselves that saving the world from a long term hypothetical catastrophe should be their key role. This is akin to the spreading of Christianity in past centuries to save the world from going to hell in a handbasket, and is likely to be to be just as pointless.
Meanwhile, the real, day to day, agony continues for many millions of the world’s inhabitants, whilst the West obsesses over ‘green’ policies.
Climate scientists should not have allowed themselves to become pawns in this game.
dbakerber says:
March 21, 2014 at 7:35 am
This is a good article, but Dr. Ball is wrong on one point. A computer model cannot prove a theory. At best a computer model can provide evidence for or against a theory.
It cannot provide evidence either. Certainly not good quality evidence.
Dr Brown, Duke. Be kind to Tim and say did not address rather than “failed”. please
Love your posts by the way. Remind me of my misspent youth in libraries studying physics.
DesertYote says:
March 21, 2014 at 10:25 am
jorgekafkazar says:
March 21, 2014 at 10:11 am
“Rotation around the sun creates the seasons…”
In Astronomy, we refer to rotation about an axis, revolution about another body.
###
It isn’t rotation that causes the seasons. It’s the TILT.
“It is worse now with fewer weather stations and less data than in 1990.”
huh? earth to ball!!
The amount of data measured any way you like has increased.
1. The number of stations is over 40,000 and climbing.
2. The number of station months is increasing.
Things you dont know.
1. GHCM is not the only repository. It’s one of the smaller ones.
2. Data recovery of previously archived records proceeds. This is recovery and digitization
of records that previously only existed on paper. I know of 13 projects in different countries
that are doing data recovery projects.
izen says:
March 21, 2014 at 10:05 am
Bollocks. We know how much a 777 weighs, we know how much fuel it can carry, we know how far it can fly on a given amount of fuel, we know how far it can glide, we have a fair approximation of a last known position, we know the general state of the winds in the area. We have a few decades of experience & literally hundreds of millions of miles of real world verification of these few parameters & we still have no idea what happened, & we can’t even begin to predict anything about the future state or disposition of the aircraft with any more certainty than Don Lemon.
By contrast, with the CO2 -> temperature model we know how much CO2 is in the air near Hawaii for the last few decades & we know about a few percentages of the land temperatures for a very short period of human history. By analogy, we know the diameter of the fuselage of the plane & we know how many packets of crisps the fellow in the window seat of the third aisle had nicked off the drinks cart & that’s it: we don’t know with any precision the last location of your hypothetical climate model plane, we don’t know if it’s a particular type of plane (or even if there’s more than one type), we can’t accurately guess how much fuel your plane was carrying, we don’t know the glide ratio of your plane, we can’t be sure the engines consume the type of fuel we assume, nor how many there are, nor how many are functional, for all we know your hypothetical climate model plane is actually a trolly bus in London that’s fallen on its side.
The CO2 -> temperature model has managed to demonstrate one thing though, & that is that the CO2 -> temperature model is utterly wrong.
Oh dear, not this old chestnut again.
As it was in the beginning is now and ever shall be.
‘In sum, a strategy must recognise what is possible. In climate research and modelling, we should recognise that we are dealing with a coupled non-linear chaotic system, and therefore that the long-term prediction of future climate states is not possible.’
The IPCC report of 2001
Anyone who claims that such systems are amenable to projection/prediction over any but very short time scales is either deluded or a computer salesman. All a faster computer does is give you the wrong answer quicker.
Ironically, it was a climatologist, Ed Lorenz, who originally pointed this out.
>>> The vastness and complexity of Creation should awe and humble us.
Not formally religious, but that’s possibly the most profound thing I’ve read in this thread today. The more you dig into any part of nature and natural processes, the more complex it appears, yet we never seem humble enough to admit our limitations The ironic thing is that this complex system has been essentially stable for millenia without any input from humanity at all.
ps: My first computer was a $100 Kim 1 with 1k (1024 bytes) of ram. It was much later that I became aquainted and worked with pdp, vax, Sun and others. A long strange and wonderous trip indeed …
This link, http://www.rmets.org/events/climate-change-2013-physical-science-basis-working-group-1-contribution-fifth-assessment, does not contain a video. I watched the video below that link, and it appears that the caveats have been removed from it.
JohnWho says:
March 21, 2014 at 6:07 am
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!”
————————————————————
Most of them would never, ever be reading this “misrepresentation” of their work. My academic friends regard WUWT as evil.
SO, let me get this right, ALL the Climate Models have agreed on one basic fact….They Have all failed. They have all NOT predicted the dead in its tracks halt to warming for 17.5 years and thus we can extrapolate that they will continue to fail. And Thank You Dr. Ball for a very informative essay!!!!
I think ‘computer games’ would probably be a better description than ‘computer models’. They seem to have the required basic grasp on the real world to fit that first description quite well.
Dr. Ball’s analysis of model construction is simply devastating. He uses the very words of the IPCC authors to skewer the validity of the models used by the IPCC to come up with their vacuous “95% certainty” claims. I think he shows great restraint in not naming names and pointing fingers at specific “guilty” originators of the scientific nonsense he exposes.
Indeed, rather than measuring the “climate change scientists” for their striped prison suits, his posting may be showing the beginnings of a pathway to a much brighter future for models. Many of the neglected or glossed over climate parameters (clouds, soil moisture, terrestrial cryosphere, etc.) are actually amenable to the measurement of – gasp! – actual data. As Dr. Ball points out, a lot of ground needs to be covered to develop supportable hypotheses for the behavior of these parameters, although I sincerely hope is wrong about his 30-year estimate (mentioned by Dr. Ball in coverage of 8.6.2.2).
One may hope that someone (perhaps Dr. Ball, perhaps Dr. Brown of Duke University, perhaps Willis Essenbach, probably not I) might take a look at fifteen or twenty of these parameters, one by one, and sketch out a set of research projects to address each one. Busily scribbling on the back of a convenient envelope, I’m reckoning that about $15 billion per year, for about 15 years, would bring the United States (and perhaps the rest of the world along with it) to an actionable level of understanding of climate physics and geophysics. The framework for such a project already exists in the form of the NASA Center for Climate Simulation, although it may need to be extracted from the blinkered Goddard alarmists.
At that point, in 2029, it may be reasonable to restart the IPCC (which we may hope has been in uncomfortable hibernation for the duration) and provide actual sensible guidance to political figures about what (if any) action to take with regard to future climate trends. In addition to much better theories, data and (I hope) models, we’ll have experienced another fifteen years of weather and we may be worrying (again) about global cooling.
It’s not that I’m against computer modeling and simulations. Indeed, for my sins, I at one time managed a large-scale environmental computer simulation project (two dozen programmers and engineers, plus one hapless documentation expert). I actually think a properly-developed model, with the right inputs, can be created and will give useful outputs. And I hope that climate scientists 15 years hence will be smart enough, and honest enough, never to claim that their science is “settled”.
Steven Mosher says:
March 21, 2014 at 10:46 am
“1. The number of stations is over 40,000 and climbing.”
But the number of 1*1 degree cells with coverage will be increasing to what? From what? At 200, 150 and 100 years ago as well as today. Mind you we do have satellites with a better area coverage all round for today.
” 2. The number of station months is increasing.”
Well as we only have 15 contiguous thermometers in the BEST database that are longer than 200 years right now I would certainly hope so.
“1. GHCM is not the only repository. It’s one of the smaller ones.”
There are VERY few temperature records of any sort that are >200 years.
” 2. Data recovery of previously archived records proceeds. This is recovery and digitization
of records that previously only existed on paper. I know of 13 projects in different countries
that are doing data recovery projects.”
And valuable work that is. May help to reduce some of the wilder claims.
Between Dr. Ball’s essay and Dr. Brown’s post (March 21, 2014 at 9:54 am, above), it is a wonder anyone says “Here, look at what the models prove.”
Still, it would be nice if the political forcings were removed and these models were refined to a useful state. It would probably take decades, but they could turn into something helpful.
Pizen!
What brings such a devious troll as you back? Dr. Ball’s short simple revelation of the darkness and foolishness behind the blind beliefs and obeisance to ‘climate modeling’ brings you back from the slimy side of the model?
Interesting piece of dodge bluff smoke and nonsense you spouted. Why don’t you break it down to specifics in a direct one on one comparison to Dr. Ball’s or Dr. Robert G Brown’s identified issues?
Actually, I don’t believe you are capable of responding to specifics. You’re preference if for straw men and obfuscation, but it would be nice if you could surprise us.
Meanwhile:
Really? Perhaps you could explain these ‘models’ in better detail.
Models intended to simulate a physical process, e.g. ‘process control’ models are actually quite common and very capable for their specific roles and are found throughout industry, science and many other disciplines. There again, whenever, let’s emphatically state that word, whenever a model fails to match the process that model is pulled apart and all lines of code along with their steps and results are compared to reality until the reason for the divergence is found!
Only in ‘climate science’ are ‘models’ used without verification nor certification. They are run as they are and when their ‘envelope’s of behavior’ fail to match reality the ‘climate teams and trolls’ run out the spinners and dancers to ‘distract’ the masses while the modelers announce new ‘disaster is upon us’ results.
Now that is interesting.
Elucidate!
In detail, giving examples, model run meta data, insights, envelope behaviors, explicit analysis with relevant meta data about the analysis and results all specific to that exact model run.
Interesting conclusion or statement. So these models do not give specific predictions of final states?
I suppose your slipping in that ‘predictions’ gives you a slippery answer that can mean almost anything.
Now let’s get into detail about the ‘final states’ that are not predictions? What constitutes a ‘final state’? The computer burps and stops? A run of data is spit out and all of the modelers don their head wraps and light incense before seeking guidance from the ‘final state’?
Give examples of ‘final states’ and how your model does not leave people with something they write up as a ‘prediction’.
Hint, if a number or envelope is purported to represent anything beyond ‘now’ it is technically a prediction even if extremely unlikely. As the warmistas have discovered, ‘predicting’ tomorrow’s climate is not easy nor is it valid till the models get it ‘exact’.
One may also want to do actual experiments on irreproducible quasi stationary non equilibrium thermodynamic systems other than climate.
Yes, that does seem sensible, doesn’t it?
I visited one of your links and looked over the discussion on hemispheric symmetry, and would add something remarkable to that already remarkable observation. That is that the average annual temperature variation countervaries with the actual TOA insolation. People have been making noises about the importance of the order unity W/m^2 variation associated with CO_2, land use changes, or albedo, but do not forget that the Earth is in a rather eccentric orbit such that TOA insolation varies by 91 W/m^2 — a number that dwarfs all other variations in the system put together by more than an order of magnitude — from perigee to apogee.
In spite of this huge number variation literally at the top of the mechanism for energy delivery into the open system, the coldest annual average temperatures occur at or near perigee, and the warmest at apogee, the exact opposite of what one would expect for a simple uniform spherical ball. Normally, one attempts to explain this by arguing that the average albedo of the northern vs southern hemisphere are quite different, but the data you present seems not to support that.
To be honest, I don’t know what to make of the albedo data you present. If the NH and SH albedos are empirically a close match, one is right back to the drawing board in any attempt to explain the temperature countervariation by albedo alone. It has be a synchronized albedo variation — one that averages out the same but somehow manages to countervary in alignment with the NH and SH temperature asymmetry out of phase with the 45 W/m^2 insolation variation amplitude around its annual mean.
In the end, I agree with your assertion that there is some point to trying to build climate models, just as there is a point to many “grand challenge” activities such as trying to prove the Goldbach conjecture, or the Riemann hypothesis or that P = NP or whatever, even if some of them never succeed. However, there is no point in claiming that they work when they not only don’t work, but at a time when climate modeling is in its infancy, probably decades away from where it has any significant predictive skill at all.
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