JoNova

A science presenter, writer, speaker & former TV host; author of The Skeptic's Handbook (over 200,000 copies distributed & available in 15 languages).


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Australian Environment Conference Oct 20 2012


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Debunking every IPCC climate prophesy of war, pestilence, famine, drought, impacts in one line

We could spend hours analyzing the new IPCC report about the impacts of climate change. Or we could just point out:

Everything in the Working Group II report depends entirely on Working Group I.

( see footnote 1 SPM, page 3).

Working Group I depends entirely on climate models and 98% of them didn’t predict the pause.

The models are broken. They are based on flawed assumptions about water vapor.

Working Group I, remember, was supposed to tell us the scientific case for man-made global warming. If our emissions aren’t driving the climate towards a catastrophe, then we don’t need to analyze what happens during the catastrophe we probably won’t get. This applies equally to War, Pestilence, Famine, Drought, Floods, Storms, and Shrinking Fish (which, keep in mind, could have led to the ultimate disaster: shrinking fish and chips).

To cut a long story short, the 95% certainty of Working Group I boils down to climate models and 98% of them didn’t predict the pause in surface temperature trends (von Storch 2013) . Even under the most generous interpretation, models are proven failures,  100% right except for rain, [...]

Kill the IPCC says Judith Curry. After decades and billions there is nothing to show for it.

And the public conversation finally starts to move on to discussing not whether the IPCC is wrong, but why it was wrong, and what we need to do about it. Credit to Judith Curry and the Financial Post. I’ve posted a few paragraphs here. The whole story is in the link at the top. – Jo

Judith A. Curry, Special to Financial Post

Kill the IPCC: After decades and billions spent, the climate body still fails to prove humans behind warming

 The IPCC is in a state of permanent paradigm paralysis. It is the problem, not the solution

The IPCC has given us a diagnosis of a planetary fever and a prescription for planet Earth. In this article, I provide a diagnosis and prescription for the IPCC: paradigm paralysis, caused by motivated reasoning, oversimplification, and consensus seeking; worsened and made permanent by a vicious positive feedback effect at the climate science-policy interface.

In its latest report released Friday, after several decades and expenditures in the bazillions, the IPCC still has not provided a convincing argument for how much warming in the 20th century has been caused by humans.

We tried a simple solution for a [...]

Climate Models cannot explain why global warming has slowed

Finally climate scientists are starting to ask how the models need to change in order to fit the data. Hans von Storch, Eduardo Zorita and authors in Germany pointedly acknowledge that even at the 2% confidence level the model predictions don’t match reality. The fact is, the model simulations predicted it would get warmer than it has from 1998-2012. Now some climate scientists admit that there is less than a 2% chance that the models are compatible with the 15-year warming pause, according to the assumptions in the models.

In a brief paper they go on to suggest three ways the models could be failing, but draw no conclusions. For the first time I can recall, the possibility that the data might be wrong is not even mentioned. It has been the excuse du jour for years.

Note in the chart that while the 10 year “pause” passed the basic 5% test of statistical significance, by 13 years, the pause was so long that only 2% of CMIP5 or CMIP3 models simulations could be said to agree with reality. By 16 years that will be 1% of simulations. If the pause continues for 20 years, there would be “zero” [...]

WARNING: Using a different computer could change the climate catastrophe

How bad are these global forecast models?

When the same model code with the same data is run in a different computing environment (hardware, operating system, compiler, libraries, optimizer), the results can differ significantly. So even if reviewers or critics obtained a climate model, they could not replicate the results without knowing exactly what computing environment the model was originally run in.

This raises that telling question: What kind of planet do we live on? Do we have a Intel Earth or an IBM one? It matters. They get different weather, apparently.

There is a chaotic element (or two) involved, and the famous random butterfly effect on the planet’s surface is also mirrored in the way the code is handled. There is a binary butterfly effect. But don’t for a moment think that this “mirroring” is useful: these are different butterflies, and two random events don’t produce order, they produce chaos squared.

How important are these numerical discrepancies? Obviously it undermines our confidence in climate models even further. We can never be sure how much of the rising temperature in a model forecasts might change if we moved to a different computer. (Though, since we already know the models [...]

Even with the best models, warmest decades, most CO2: Models are proven failures

This beautiful graph was posted at Roy Spencer’s and WattsUp, and no skeptic should miss it. I’m not sure if everyone appreciates just how piquant, complete and utter the failure is here. There are no excuses left. This is as good as it gets for climate modelers in 2013.

John Christy used the best and latest models, he used all the models available, he has graphed the period of the fastest warming and during the times humans have emitted the most CO2. This is also the best data we have. If ever any model was to show the smallest skill, this would be it. None do.

Scores of models, millions of data-points, more CO2 emitted than ever before, and the models crash and burn. | Graph: John Christy. Data: KMNI.

Don’t underestimate the importance of the blue-green circles and squares that mark the “observations”. These are millions of radiosondes, and two independent satellite records. They agree. There is no wiggle room, no overlap.

Any sane modeler can only ask: “But how can the climate modelers pretend their models are working?” Afterall, predicting the known past with a model is not-too-hard; the modeler tweaks the assumptions, fiddles with the fudge [...]

Do forests drive wind and bring rain? Is there a major man-made climate driver the models miss?

Clouds over Amazon forest (Rio Negro). Image NASA Earth Observatory.

What if winds were mainly driven by changes in water vapor, and those changes occurred commonly in air over forests? Forests would be the pumps that draw in moist air from over the oceans. Rather than assuming that forests grow where the rain falls, it would be more a case of rain falling where forests grow. When water vapor condenses it reduces the air pressure, which pulls in more dense air from over the ocean.

A new paper is causing a major stir. The paper is so controversial that many reviewers and editors said it should not be published.  After two years of deliberations,  Atmospheric Chemistry and Physics decided it was too important not to discuss.

The physics is apparently quite convincing, the question is not whether it happens, but how strong the effect is. Climate models assume it is a small or non-existent factor. Graham Lloyd has done a good job describing both the paper and the reaction to it in The Australian.

Sheil says the key finding is that atmospheric pressure changes from moisture condensation are orders of magnitude greater than previously recognised. The paper concludes “condensation [...]

Climate Models: 100% right except for rain, drought, storms, humidity and everything else

Yet more observations from the planet show that modelers misunderstand the water based part of the climate – on our water based planet.

Modelers thought that dry ground would decrease afternoon storms and rainfall over those frazzled parched lands (though I don’t remember many headlines predicting “More Drought means Fewer Storms” ). But observations show that storms are more likely to rain over dry soil. Why? Probably the dry soil heats up faster than moist areas thanks to the cooling effect of evaporation, and that in turn creates stronger thermals over dry land. Modelers assumed that wetter soils means more evaporation and thus more rain, but the moisture laden air is evidently coming from further away.

It’s another example of a point where climate modelers assume a positive feedback, yet the evidence suggests the feedback is negative. Once again water appears to be the dominant force with feedbacks (it does cover 70% of the surface). In a natural stable system the net feedbacks are likely to be negative. Positive feedbacks make the system less stable (and more scary and harder to predict.)

Climate change models misjudge drought: “A four-nation team led by Chris Taylor from Britain’s Centre for Ecology and [...]

Models get cloud feedback wrong, but *only* by 70W/m2 (that’s 19 times larger than the CO2 effect)

Yet another paper shows that the climate models have flaws, described as “gross” “severe” and “disturbing”. The direct effect of doubling CO2 is theoretically 3.7W per square meter. The feedbacks supposedly are 2 -3 times as strong (according to the IPCC). But some scientists are trying to figure out those feedbacks with models which have flaws in the order of 70W per square meter. (How do we find that signal in noise that’s up to 19 times larger?)

Remember climate science is settled:  like gravity and a round earth. (Really?)

Miller et al 2012 [abstract] [PDF] find that some models predict clouds to have a net shortwave radiative effect near zero, but observations show it is 70W per square meter. Presumably, cloud shortwave radiative effect means the sunlight bounced upwards off the surface of the clouds and out into space.

What’s especially interesting about this paper is the level of detail. They test shortwave and longwave radiation, precipitation flux, integrated water vapor, liquid water path, cloud fraction, and they have observations from the top of the atmosphere and the surface. With so much information they can test models against short wave and long wave radiation, to see [...]

David Evans in the Fairfax press: Climate change science is a load of hot air and warmists are wrong

Today in the Sydney Morning Herald and The Age, for the first time, David Evans has been published in the Op-Ed section. Something is going on in those newsrooms…? This article, below, simply makes the point that the models amplify the direct effect of CO2 by a factor of three and that is where the most important uncertainties lie. This key factor in the debate — which we cover repeatedly on this blog– has virtually never been made before in these newspapers which are the major dailies for Australia’s two largest cities. Any debate about the effects of CO2 needs to start with the fact that most of the warming in the models comes from amplification of humidity and clouds. If the models were right about water vapor, we would have found that missing hot spot.  –   Jo  PS: The SMH and The AGE have both closed comments already! Have they run out of electrons? Oh my? Or were they afraid the comments looked like a debate?

UPDATE: I’ve just posted that these major dailies have “disappeared” the Muller conversion article too!

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Dr David M.W. Evans

31 Jul 2012

Climate scientists’ theories, flawed as they are, ignore [...]

We can’t predict the climate on a local, regional, or continental scale

This is part of a series that Tony Cox and I are doing that drills down to the most important points and papers, with proper references, as a definitive resource.The models are wrong: not just “unverified”, not just “uncertain”, but proven to have failed. — Jo

Joint Post: Tony Cox and Jo Nova

Across different regions, and different time-spans over the last century, the models fail.

Koutsoyiannis and Anagnostopolous  et al show those models can’t model the recent century, and because the models fail to predict regional and smaller scale effects it’s impossible that they could predict longer and global values.[i]

On 30 year time frames, the original observations are nothing like the models projections on a local scale. (Click to enlarge).

The models should retrospectively match the actual temperature over the past 100 years. This test of retrospectivity is called hindcasting. If a model has valid assumptions about the climatic effect of variables such as greenhouse gases, particularly CO2, then the model should be able to match past known data.

“…all the models were “irrelevant with reality” at the 30 year climate scale…”

When tested, the global climate models failed to [...]

Way back when climate scientists were scientists: Chapter 8, FAR, circa 1990

You’ll find this hard to believe but I get excited about the 1990 First Assessment Report (FAR). It’s very different from wading through the later ones, because it’s remarkably honest, and things are not hidden in double-speak (well, not so much). Scientists behave like scientists and talk of null hypothesis, and even of validating models. Indeed they had a whole chapter back then called “validation”. How times have changed.

This is the short summary of Chapter 8 “Attribution”

Thanks to Alan for sending me this link today (Chapter 8, IPCC FAR).

The “Attribution” Chapter is the part where they try to figure out what “caused” the warming. Chapter 8 says, essentially, “we don’t know, we might never know, our models don’t work, and we can conclude it might all be natural, but then again, it might not.” Got it?

This is in the same era that Al Gore was saying “the science is settled” and “there is no debate”.

What’s clear in 1990 from the FAR was that it was widely admitted that the models were bodgy, and that figuring out exactly what caused the recent warming was very difficult, indeed impossible at the time. There were too many variables, [...]

The debate continues: Dr Glikson v Joanne Nova

Dr Andrew Glikson (an Earth and paleoclimate scientist, at the Australian National University) contacted Quadrant offering to write about the evidence for man-made global warming. Quadrant approached me asking for my response. Dr Glikson replied to my reply, and I replied again to him (copied below). No money exchanged hands, but Dr Glikson is, I presume, writing in an employed capacity, while I write pro bono. Why is it that the unpaid self taught commentator needs to point out the evidence he doesn’t seem to be aware of? Why does a PhD need to be reminded of basic scientific principles (like, don’t argue from authority). Such is the vacuum of funding for other theories that a debate that ought to happen inside the university obviously hasn’t occurred. Such is the decrepit, anaemic state of university science that even a doctorate doesn’t guarantee a scientist can reason. Where is the rigor in the training, and the discipline in the analysis?

Credibility lies on evidence

by Joanne Nova

April 29, 2010

Reply to Andrew Glikson

Dr Andrew Glikson still misses the point, and backs his arguments with weak evidence and logical errors. Instead of empirical evidence, often [...]

How to create a crisis graph in 6 simple steps

One of the main arguments from the IPCC is that essentially, we can’t explain temperature changes any other way than with carbon forcings. This is matched with impressive pink and blue graphs that pose as evidence that carbon is responsible for all the recent warming.

This is argumentum ad ignorantiam — essentially they say: we don’t know what else could have caused that warming, so it must be carbon. It’s a flawed assumption.

It’s easy to create impressive graphs, especially if you actively ignore other possible causes, like for example, changes in cloud cover and solar magnetic effects.

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The one flaw that wipes out the crisis

Carbon dioxide only causes 1.1°C of warming if it doubles. That’s according to the IPCC. Did you know?

The real game is water.

Researchers made guesses about humidity and clouds in the early 1980s and they built these guesses into their models. We now know they were wrong, not about carbon, but about water in the form of humidity and clouds. Here’s how the models can be right about carbon and wrong about the climate.

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CRU data-cooking: recipe exposed!

Thanks to Eric Raymond, famous computer guru and leader of the open-source movement, at ESR, we can see what those sophisticated climate modelers were doing. They’ve found the code from the leaked files, and Eric’s comment is:

This isn’t just a smoking gun, it’s a siege cannon with the barrel still hot.

Here’s the code. The programmer has written in helpful notes that us non-programmers can understand, like this one:  “Apply a very artificial correction for decline”. You get the feeling this climate programmer didn’t like pushing the data around so blatantly. Note the technical comment:  “fudge factor”.

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Missing Climate Headlines from May 2009

Undoubtedly the best summary of the current state of affairs is the SPPI monthly CO2 report. The April report contains news that—if there was a free and high quality media—would have generated headlines like these (well, sort of—you get the idea).

Any investigative journalist who was doing their job only had to Google for the other side of the story. I’m not saying those journalists have to agree with us, just that, at the moment most environmental writers think ‘balanced’ means saying, “The world will cook: the question is, lightly toasted OR totally pan-fried’.

Here’s the counter summary of the headlines we didn’t see, accompanied by an analysis you probably won’t see anywhere else.

Planet Unmoved by IPCC Forecast

Despite the power of the authority vested in the International Panel on Climate Change (IPCC), The Planet appears to be unswayed by the large well funded international bureaucracy, and is similarly immune to following the collected wisdom of the software engineers who compress it’s 1100 billion cubic kilometers of complexity into a PC.

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