Why are Climate Models so Estranged from Reality

Update: Blockbuster report on how temperature data has been altered to create a faked warming trend. What if it has all been one big lie?

Why do climate models predict apocalyptic levels of warming that do not match up with reality? It comes down to assumptions being made about numerous unknown dynamic quantities that are included in — and excluded from — the models. Here is a list of some of these unknown dynamic parameters that are “guessed at” (or conveniently omitted) when computer modelers of climate generate their apocalyptic predictions:

Ocean Oscillations : What about the larger and longer ocean effects like the AMO (Atlantic Multidecadal Oscillation), PDO (Pacific Decadal Oscillation), IOD (Indian Ocean Dipole), etc. Understood? No. Contribution in the models : 0%.

Ocean Currents : Are the major ocean currents, such as the THC (Thermohaline Circulation), understood? Well we do know a lot about them – we know where they go and how big they are, and what is in them (including heat), and we know much about how they affect climate – but we know very little about what changes them and by how much or over what time scale. In summary – Understood? No. Contribution in the models : 0%.

Volcanoes : Understood? No. Contribution in the models : 0%.

Wind : Understood? No. Contribution in the models : 0%.

Water cycle (ocean evaporation, precipitation) : Understood? Partly. Contribution in the models : the contribution in the climate models is actually slightly negative, but it is built into a larger total which I address later.

The Sun : Understood? No. Contribution in the models : 0%. Now this may come as a surprise to some people, because the Sun has been studied for centuries, we know that it is the source of virtually all the surface and atmospheric heat on Earth, and we do know quite a lot about it. Details of the 11(ish) year sunspot cycle, for example, have been recorded for centuries. But we don’t know what causes sunspots and we can’t predict even one sunspot cycle ahead. Various longer cycles in solar activity have been proposed, but we don’t even know for sure what those longer cycles are or have been, we don’t know what causes them, and we can’t predict them. On top of that, we don’t know what the sun’s effect on climate is – yes we can see big climate changes in the past and we are pretty sure that the sun played a major role (if it wasn’t the sun then what on Earth was it?) but we don’t know how the sun did it and in any case we don’t know what the sun will do next. So the assessment for the sun in climate models is : Understood? No. Contribution in the models : 0%. [Reminder : this is the contribution to predicted future warming]

Galactic Cosmic Rays (GCRs) : GCRs come mainly from supernovae remnants (SNRs). We know from laboratory experiment and real-world observation (eg. of Forbush decreases) that GCRs create aerosols that play a role in cloud formation. We know that solar activity affects the level of GCRs. But we can’t predict solar activity (and of course we can’t predict supernova activity either), so no matter how much more we learn about the effect of GCRs on climate, we can’t predict them and therefore we can’t predict their effect on climate. And by the way, we can’t predict aerosols from other causes either. In summary for GCRs : Understood? No. Contribution in the models : 0%.

Milankovich Cycles : Milankovich cycles are all to do with variations in Earth’s orbit around the sun, and can be quite accurately predicted. But we just don’t know how they affect climate. The most important-looking cycles don’t show up in the climate, and for the one that does seem to show up in the climate (orbital inclination) we just don’t know how or even whether it affects climate. In any case, its time-scale (tens of thousands of years) is too long for the climate models so it is ignored. In summary for Milankovich cycles : Understood? No. Contribution in the models : 0%. (Reminder : “Understood” is used in the context of predicting climate).

Carbon Dioxide (CO2) : At last we come to something which is quite well understood. The ability of CO2 to absorb and re-emit a specific part of the light spectrum is well understood and well quantified, supported by a multitude of laboratory experiments. [NB. I do not claim that we have perfect understanding, only that we have good understanding]. In summary – Understood? Yes. Contribution in the models : about 37%.

Water vapour : we know that water vapour is a powerful greenhouse gas, and that in total it has more effect than CO2 on global temperature. We know something about what causes it to change, for example the Clausius-Clapeyron equation is well accepted and states that water vapour increases by about 7% for each 1 deg C increase in atmospheric temperature. But we don’t know how it affects clouds (looked at next) and while we have reasonable evidence that the water cycle changes in line with water vapour, the climate models only allow for about a third to a quarter of that amount. Since the water cycle has a cooling effect, this gives the climate models a warming bias. In summary for water vapour – Understood? Partly. Contribution in the models : 22%, but suspect because of the missing water cycle.

Clouds : We don’t know what causes Earth’s cloud cover to change. Some kinds of cloud have a net warming effect and some have a net cooling effect, but we don’t know what the cloud mix will be in future years. Overall, we do know with some confidence that clouds at present have a net cooling effect, but because we don’t know what causes them to change we can’t know how they will affect climate in future. In particular, we don’t know whether clouds would cool or warm in reaction to an atmospheric temperature increase. In summary, for clouds : Understood? No. Contribution in the models : 41%, all of which is highly suspect

ENSO (El Nino Southern Oscillation) : The regrettable fact is that we do not understand El Nino at all well, or at least, not in the sense that we can predict it years ahead. Here we are, only a month or so before it is due to cut in, and we still aren’t absolutely sure that it will happen, we don’t know how strong it will be, and we don’t know how long it will last. Only a few months ago we had no idea at all whether there would be one this year. Last year an El Nino was predicted and didn’t happen. In summary : Do we understand ENSO (in the sense that we can predict El Ninos and La Ninas years ahead)? No. How much does ENSO contribute, on average, to the climate models’ predicted future warming? 0%.

The following table summarises all of the above:

Factor Understood? Contribution to models’ predicted future warming
ENSO No 0%
Ocean Oscillations No 0%
Ocean Currents No 0%
Volcanoes No 0%
Wind No 0%
Water Cycle Partly (built into Water Vapour, below)
The Sun No 0%
Galactic Cosmic Rays (and aerosols) No 0%
Milankovich cycles No 0%
Carbon Dioxide Yes 37%
Water Vapour Partly 22% but suspect
Clouds No 41%, all highly suspect
Other (in case I have missed anything) 0%

__ Source

Much more at the article linked above

Sunspot Cycle Hamshack files wordpress

Sunspot Cycle
Hamshack files wordpress

What we seem to be left with, is that computer climate models exclude many of the most important parameters contributing to real world climate. Of the parameters that are included, several of them appear to be based upon sloppy — even faith-based — assumptions. No wonder they fail the test of observation. They are no better than the predictions of psychic grifters.

Thus, it looks as if the great green climate apocalypse movement and associated green energy scam, are nothing more than a money-grab by political cronies and a power grab by political insiders — of the governmental, the inter-governmental, and the non-governmental varieties . . .

A Few Cycles Bearing on Climate

A Few Cycles Bearing on Climate


… When we talk about the lefty-Luddite green dieoff.orgiasts, we are referring to the groupthinking echo choirs pulling the strings of governments in Europe and the Anglosphere. These miscreants also control much of media, academia, foundations, many religious entities, and several other cultural institutions.

What kind of society and people would stand back and allow these abominations to destroy their lives, livelihoods, and their futures — and the minds of their children?

We’re going to need a lot more guillotines! 😉

More:
Solar and wind energy technologies are not suited for modern industrial and post-industrial economies. They are unreliable, intermittent, and provide poor quality electricity that pollutes and degrades the power grid, and makes it more liable to brownouts and blackouts. They cannot be tapped “on demand” or in a form appropriate to maintain a steady voltage or frequency. Thus, they would have been discarded long ago by any intelligent and informed government or policy group.

Bill Gates, who is no fossil fuel apologist, recently concluded, that the “current renewables are dead-end technologies” and that (t)he cost of decarbonization using today’s technologies is “beyond astronomical”. Google, which has invested over $1 billion in alternative energy, created at project several years ago—RE<C- to find a way to produce alternative energy more cheaply than coal. After four years, Google abandoned the project concluding that “renewables will never permit the human race to cut CO2 emissions to the levels demanded by climate activists.”

Solar power is not new technology. In the 1970s, when there was a belief that the world was running out of oil, energy companies like Arco made major investments in solar power. By the late 1980s, they had concluded that low cost solar energy was the like the horizon, it recedes as you approach it. Today, solar provides less than 1% of our power and is kept viable by generous subsidies that allow promoters to get rich on taxpayer dollars. If the claims of proponents were true, they would not be lobbying to keep their subsidies.

Professor Bruce Yandle in his book Bootleggers and Baptists, describes how political entrepreneurs wrap themselves in environmental aspirations—save the planet—as a way to enrich themselves at the expense of taxpayers and consumers.

Environmental advocates have been promoting a special interest agenda for decades by claiming that they were trying to save “our environment, economy, and future generations” from the ravages of global warming. This is pure advocacy. Well established facts demonstrate that global temperatures are not soaring and that there has been no, repeat no, increase in extreme weather events.
__ http://fuelfix.com/blog/2015/09/18/solar-flim-flam-and-more-snake-oil/

The fictitious crises of climate apocalypse, resource scarcity, and CO2 poisoning are feeding the desperate rush into “green energy.” Obama — the worst US president to this point — has danced to the same band of hysteria that governments in Europe have used for their own policy fetes.

Global Temperatures over Time via Proxy Climatologist Cliff Harris

Global Temperatures over Time via Proxy
Climatologist Cliff Harris

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6 Responses to Why are Climate Models so Estranged from Reality

  1. bob sykes says:

    If you look at the spaghetti plot of the hundred or so models in use, a few of them do track the temperature times series. I can find any information regarding which models they are. Do you know?

  2. Pingback: Outside in - Involvements with reality » Blog Archive » Chaos Patch (#80)

  3. jccarlton says:

    Reblogged this on The Arts Mechanical and commented:
    This post point out just how many oscillators there are in the climate system. I don’t think that the post got all of them as there are some local ones and the ocean oscillators such as the PDO. The real problem is that for a computer model to take all these into account you would first have to know some baseline temp and inputs to get the coefficients to get updated temps to get new coefficients and so on and so on. If you have large temp differences you can wing the approximations and keep the computer time from being completely insane. When you are dealing with the atmosphere and low delta T’s and energy transfers you are sitting right in the margins of errors. Use bad approximations and coefficients and you model is going to be garbage.

  4. Ralph Gizzip says:

    If your model needs significant modification as more and more data become available you didn’t have a very good model in the first place.

  5. Pingback: And you question our questioning? | Because, Science!

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