APS and the Global Warming Scam


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Climate scientists most certainly do not presume the truth of AGW just because they demand a proof of an alternative hypothesis. On the contrary, the NIPCC people presume the truth of GW by entirely natural causes, and then try to argue that AGW is false simply because it doesn't explain everything.

Not true. The question is how much of the warming is due to natural drivers and how much to human activity. No one denies that CO2 has an effect and atmospheric temperature, but how much does CO2 concentration determine the average atmospheric temperature?

To answer questions like this we need a genuine -science of climate- which we do not have at present. Part of the difficulty is reckoning non-linear dynamics.

Ba'al Chatzaf

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I have no idea  who started the notion that global warming is a hoax.    Tyndall showed that CO2 retards the out-radiation in the IR bands back in 1880.  So we get a blanket effect. CO2 does not

Technically Lindzen is correct.  But blanket is a good analogy.  Blankets keep your body from losing heat quickly on a cold night.  The CO2, NH4 and H2O(g)  slow down the rate at which IR energy is ra

I am glad you posted that.  I was going to post Palmer's lecture.  it is excellent and it deals quite well the difficulties in making decent models of climate.  His discourse on the Navier Stokes equa

I think what we really need is an explanation of how hypothesis testing works in real science, because there are a lot of misconceptions out there due to oversimplified explanations of the scientific method.

You have no real-world knowledge of the scientific method or how "real science" works. As always, you're bluffing and blustering. You're making shit up.

Let's look at a simple made-up example to see how hypothesis testing actually works. Suppose that you have a model of the climate, and it predicts a temperature increase of about 3 degrees, while the observed temperature increase is 10 degrees. Is your model falsified? No.

Actually, your model IS falsified under those conditions.

Why is that? Because it successfully explains some of the variation of the temperature. What this means is that you're on the right track, but there are pieces missing in your puzzle, and you have to find them.

That's completely illogical. Being that far off in your predictions DOES NOT mean that "you're on the right track."

Let's say that after some amount of investigation and thinking, you propose a mechanism which explains an additional 4 degrees of the increase (it's important that this new mechanism not contradict any part of your original model, your combined hypothesis has to be logically consistent). Now, you can explain a total of 7 degrees of the increase. That's better. You now have a more accurate model.

The new model does not predict reality, and is also therefore falsified.

So the way to disprove AGW would not be to point out minor discrepancies between observations and model predictions.

"Minor" by what rational standard? How large of a discrepancy would it have to be before you would label it "major"?!!! Would ANY discrepancy, no matter how great, count as falsifying AGW?!!!

(nonetheless, these discrepancies need to be explained)

Indeed they need to be explained! And they need to be explained before people can legitimately claim that the "scientific consensus" is that it's a "settled matter, a fact of reality."

What one has to do, is to come up with an explanation that excludes anthropogenic factors and can explain the observed data even just a little bit better than the current theory can.

No, that's not how science works. You're just making shit up off of the top of your head. One needn't come up with a slightly better theory. The absence of a better theory doesn't make any theory true. If I claim that invisible pixies are the cause of climate change, and then my reporting of the pixies' predictions of what they're going to do to future temperatures turned out to be better than AGW theories' predictions, that wouldn't mean that I was "on the right track," and that the pixie theory could only be "falsified" if another theory made better predictions.

Thus far, no one has succeeded in proposing a theory that can explain the observed temperature trends using only natural factors.

No one has to. Scientifically, the absence of better theory doesn't make AGW correct.

In fact, natural factors explain virtually none of the recent temperature variations.

You're talking out of your ass. You cannot know, control for, or compute all natural factors. You're just making shit up.

J

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Jonathan,

Why don't you educate yourself on statistical analysis before you start spouting mere ignorant contradiction of everything I say?

Read chapter 10 section 10.2 of the full IPCC report, and maybe then you'll have some idea of how science is done by the grown-ups.

10.2 Evaluation of Detection and Attribution
Methodologies

Detection and attribution methods have been discussed in previous
assessment reports (Hegerl et al., 2007b) and the IPCC Good Practice
Guidance Paper (Hegerl et al., 2010), to which we refer. This section
reiterates key points and discusses new developments and challenges.

10.2.1 The Context of Detection and Attribution

In IPCC Assessments, detection and attribution involve quantifying the
evidence for a causal link between external drivers of climate change
and observed changes in climatic variables. It provides the central,
although not the only (see Section 1.2.3) line of evidence that has
supported statements such as ‘the balance of evidence suggests a discernible
human influence on global climate’ or ‘most of the observed
increase in global average temperatures since the mid-20th century is
very likely due to the observed increase in anthropogenic greenhouse
gas concentrations.’

The definition of detection and attribution used here follows the terminology
in the IPCC guidance paper (Hegerl et al., 2010). ‘Detection
of change is defined as the process of demonstrating that climate or
a system affected by climate has changed in some defined statistical
sense without providing a reason for that change. An identified change
is detected in observations if its likelihood of occurrence by chance
due to internal variability alone is determined to be small’ (Hegerl
et al., 2010). Attribution is defined as ‘the process of evaluating the
relative contributions of multiple causal factors to a change or event
with an assignment of statistical confidence’. As this wording implies,
attribution is more complex than detection, combining statistical analysis
with physical understanding (Allen et al., 2006; Hegerl and Zwiers,
2011). In general, a component of an observed change is attributed to
a specific causal factor if the observations can be shown to be consistent
with results from a process-based model that includes the causal
factor in question, and inconsistent with an alternate, otherwise identical,
model that excludes this factor. The evaluation of this consistency
in both of these cases takes into account internal chaotic variability
and known uncertainties in the observations and responses to external
causal factors.

Attribution does not require, and nor does it imply, that every aspect
of the response to the causal factor in question is simulated correctly.
Suppose, for example, the global cooling following a large volcano
matches the cooling simulated by a model, but the model underestimates
the magnitude of this cooling: the observed global cooling
can still be attributed to that volcano, although the error in magnitude
would suggest that details of the model response may be unreliable.
Physical understanding is required to assess what constitutes
a plausible discrepancy above that expected from internal variability.
Even with complete consistency between models and data, attribution
statements can never be made with 100% certainty because of the
presence of internal variability.

This definition of attribution can be extended to include antecedent
conditions and internal variability among the multiple causal factors
contributing to an observed change or event. Understanding the relative
importance of internal versus external factors is important in the
analysis of individual weather events (Section 10.6.2), but the primary
focus of this chapter will be on attribution to factors external to the
climate system, like rising GHG levels, solar variability and volcanic
activity.

There are four core elements to any detection and attribution study:

1. Observations of one or more climate variables, such as surface
temperature, that are understood, on physical grounds, to be relevant
to the process in question

2. An estimate of how external drivers of climate change have
evolved before and during the period under investigation, including
both the driver whose influence is being investigated (such as
rising GHG levels) and potential confounding influences (such as
solar activity)

3. A quantitative physically based understanding, normally encapsulated
in a model, of how these external drivers are thought to have
affected these observed climate variables

4. An estimate, often but not always derived from a physically
based model, of the characteristics of variability expected in these
observed climate variables due to random, quasi-periodic and chaotic
fluctuations generated in the climate system that are not due
to externally driven climate change

A climate model driven with external forcing alone is not expected to
replicate the observed evolution of internal variability, because of the
chaotic nature of the climate system, but it should be able to capture
the statistics of this variability (often referred to as ‘noise’). The reliability
of forecasts of short-term variability is also a useful test of the
representation of relevant processes in the models used for attribution,
but forecast skill is not necessary for attribution: attribution focuses on
changes in the underlying moments of the ‘weather attractor’, meaning
the expected weather and its variability, while prediction focuses
on the actual trajectory of the weather around this attractor.

In proposing that ‘the process of attribution requires the detection of a
change in the observed variable or closely associated variables’ (Hegerlet al., 2010), the new guidance recognized that it may be possible, in
some instances, to attribute a change in a particular variable to some
external factor before that change could actually be detected in the
variable itself, provided there is a strong body of knowledge that links
a change in that variable to some other variable in which a change can
be detected and attributed. For example, it is impossible in principle to
detect a trend in the frequency of 1-in-100-year events in a 100-year
record, yet if the probability of occurrence of these events is physically
related to large-scale temperature changes, and we detect and attribute
a large-scale warming, then the new guidance allows attribution
of a change in probability of occurrence before such a change can be
detected in observations of these events alone. This was introduced
to draw on the strength of attribution statements from, for example,
time-averaged temperatures, to attribute changes in closely related
variables.

Attribution of observed changes is not possible without some kind of
model of the relationship between external climate drivers and observable
variables. We cannot observe a world in which either anthropogenic
or natural forcing is absent, so some kind of model is needed
to set up and evaluate quantitative hypotheses: to provide estimates
of how we would expect such a world to behave and to respond to
anthropogenic and natural forcings (Hegerl and Zwiers, 2011). Models
may be very simple, just a set of statistical assumptions, or very complex,
complete global climate models: it is not necessary, or possible,
for them to be correct in all respects, but they must provide a physically
consistent representation of processes and scales relevant to the attribution
problem in question.

One of the simplest approaches to detection and attribution is to compare
observations with model simulations driven with natural forcings
alone, and with simulations driven with all relevant natural and
anthropogenic forcings. If observed changes are consistent with simulations
that include human influence, and inconsistent with those that
do not, this would be sufficient for attribution providing there were no
other confounding influences and it is assumed that models are simulating
the responses to all external forcings correctly. This is a strong
assumption, and most attribution studies avoid relying on it. Instead,
they typically assume that models simulate the shape of the response
to external forcings (meaning the large-scale pattern in space and/or
time) correctly, but do not assume that models simulate the magnitude
of the response correctly. This is justified by our fundamental understanding
of the origins of errors in climate modelling. Although there
is uncertainty in the size of key forcings and the climate response, the
overall shape of the response is better known: it is set in time by the
timing of emissions and set in space (in the case of surface temperatures)
by the geography of the continents and differential responses of
land and ocean (see Section 10.3.1.1.2).

So-called ‘fingerprint’ detection and attribution studies characterize
their results in terms of a best estimate and uncertainty range for ‘scaling
factors’ by which the model-simulated responses to individual forcings
can be scaled up or scaled down while still remaining consistent
with the observations, accounting for similarities between the patterns
of response to different forcings and uncertainty due to internal climate
variability. If a scaling factor is significantly larger than zero (at some
significance level), then the response to that forcing, as simulated by

that model and given that estimate of internal variability and other
potentially confounding responses, is detectable in these observations,
whereas if the scaling factor is consistent with unity, then that model-
simulated response is consistent with observed changes. Studies do
not require scaling factors to be consistent with unity for attribution,
but any discrepancy from unity should be understandable in terms of
known uncertainties in forcing or response: a scaling factor of 10, for
example, might suggest the presence of a confounding factor, calling
into question any attribution claim. Scaling factors are estimated by fitting
model-simulated responses to observations, so results are unaffected,
at least to first order, if the model has a transient climate response,
or aerosol forcing, that is too low or high. Conversely, if the spatial or
temporal pattern of forcing or response is wrong, results can be affected:
see Box 10.1 and further discussion in Section 10.3.1.1 and Hegerl
and Zwiers (2011) and Hegerl et al. (2011b). Sensitivity of results to the
pattern of forcing or response can be assessed by comparing results
across multiple models or by representing pattern uncertainty explicitly
(Huntingford et al., 2006), but errors that are common to all models
(through limited vertical resolution, for example) will not be addressed
in this way and are accounted for in this assessment by downgrading
overall assessed likelihoods to be generally more conservative than the
quantitative likelihoods provided by individual studies.

Attribution studies must compromise between estimating responses
to different forcings separately, which allows for the possibility of different
errors affecting different responses (errors in aerosol forcing
that do not affect the response to GHGs, for example), and estimating
responses to combined forcings, which typically gives smaller uncertainties
because it avoids the issue of ‘degeneracy’: if two responses
have very similar shapes in space and time, then it may be impossible
to estimate the magnitude of both from a single set of observations
because amplification of one may be almost exactly compensated for
by amplification or diminution of the other (Allen et al., 2006). Many
studies find it is possible to estimate the magnitude of the responses
to GHG and other anthropogenic forcings separately, particularly when
spatial information is included. This is important, because it means the
estimated response to GHG increase is not dependent on the uncertain
magnitude of forcing and response due to aerosols (Hegerl et al.,
2011b).

The simplest way of fitting model-simulated responses to observations
is to assume that the responses to different forcings add linearly, so
the response to any one forcing can be scaled up or down without
affecting any of the others and that internal climate variability is independent
of the response to external forcing. Under these conditions,
attribution can be expressed as a variant of linear regression (see Box
10.1). The additivity assumption has been tested and found to hold
for large-scale temperature changes (Meehl et al., 2003; Gillett et al.,
2004) but it might not hold for other variables like precipitation (Hegerl
et al., 2007b; Hegerl and Zwiers, 2011; Shiogama et al., 2012), nor for
regional temperature changes (Terray, 2012). In principle, additivity is
not required for detection and attribution, but to date non-additive
approaches have not been widely adopted.

The estimated properties of internal climate variability play a central
role in this assessment. These are either estimated empirically from
the observations (Section 10.2.2) or from paleoclimate reconstructions

(Section 10.7.1) (Esper et al., 2012) or derived from control simulations
of coupled models (Section 10.2.3). The majority of studies use
modelled variability and routinely check that the residual variability
from observations is consistent with modelled internal variability used
over time scales shorter than the length of the instrumental record
(Allen and Tett, 1999). Assessing the accuracy of model-simulated
variability on longer time scales using paleoclimate reconstructions is
complicated by the fact that some reconstructions may not capture
the full spectrum of variability because of limitations of proxies and
reconstruction methods, and by the unknown role of external forcing in
the pre-instrumental record. In general, however, paleoclimate reconstructions
provide no clear evidence either way whether models are
over- or underestimating internal variability on time scales relevant for
attribution (Esper et al., 2012; Schurer et al., 2013).

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Climate scientists most certainly do not presume the truth of AGW just because they demand a proof of an alternative hypothesis. On the contrary, the NIPCC people presume the truth of GW by entirely natural causes, and then try to argue that AGW is false simply because it doesn't explain everything.

Not true. The question is how much of the warming is due to natural drivers and how much to human activity. No one denies that CO2 has an effect and atmospheric temperature, but how much does CO2 concentration determine the average atmospheric temperature?

To answer questions like this we need a genuine -science of climate- which we do not have at present. Part of the difficulty is reckoning non-linear dynamics.

Ba'al Chatzaf

Well I guess if you repeat it often enough, it must be true... :rolleyes:

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That is incorrect. The null hypothesis is that temperature variations are explained by nothing at all, and are therefore completely unpredictable (that's why it's called a "null" hypothesis and not a natural hypothesis). That temperature variations are explained by natural causes, anthropogenic causes, or both, none of those are a null hypothesis.

Man I hate this gotcha stuff, but "explained by nothing at all" and "completely unpredictable" are not what a null hypothesis means. To quote from the paper I am reading:

In considering any such hypothesis, an alternative and null hypothesis must be entertained, which is the simplest hypothesis consistent with the known facts. Regarding global warming, the null hypothesis is that currently observed changes in global climate indices and the physical environment, as well as current changes in animal and plant characteristics, are the result of natural variability. To invalidate this null hypothesis requires, at a minimum, direct evidence of human causation of specified changes that lie outside usual, natural variability. Unless and until such evidence is adduced, the null hypothesis is assumed to be correct.

In contradiction of the scientific method, the IPCC assumes its implicit hypothesis is correct and that its only duty is to collect evidence and make plausible arguments in the hypothesis’s favor.

An "alternative and null hypothesis" as a standard to gage human causation of global warming has to be "the simplest hypothesis consistent with the known facts." Why? Because the hypothesis being tested needs to be gaged against something plausibly real, not something imaginary that could never exist.

The null hypothesis for AGW is that there is no HUMAN relationship between the different measurements, not that there is no causation at all and that matters are "completely unpredictable." Not unless you want to fall off into la-la land and use a hypothesis where the actions of physical phenomena don't have causes. Then you step outside of reality.

I'm just learning this stuff, but it's becoming obvious to me that you don't really know it.

You're faking knowledge and trying to teach others something you don't know.

Michael

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Why am I smelling a copy/paste dump of a whole lot of crap in the near future?

:smile:

Jonathan,

Why don't you educate yourself on statistical analysis before you start spouting mere ignorant contradiction of everything I say?

Read chapter 10 section 10.2 of the full IPCC report, and maybe then you'll have some idea of how science is done by the grown-ups.

10.2 Evaluation of Detection and Attribution

Methodologies

Detection and attribution methods ... (blah blah blah)...

Heh.

:smile:

I bet this is going to get worse...

:smile:

It's hard to discuss actual substance in your own words. It more fun to parrot the opinions of others, then do long copy/paste dumps for the substance part of your arguments.

Michael

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That is incorrect. The null hypothesis is that temperature variations are explained by nothing at all, and are therefore completely unpredictable (that's why it's called a "null" hypothesis and not a natural hypothesis). That temperature variations are explained by natural causes, anthropogenic causes, or both, none of those are a null hypothesis.

Man I hate this gotcha stuff, but "explained by nothing at all" and "completely unpredictable" are not what a null hypothesis means. To quote from the paper I am reading:

In considering any such hypothesis, an alternative and null hypothesis must be entertained, which is the simplest hypothesis consistent with the known facts. Regarding global warming, the null hypothesis is that currently observed changes in global climate indices and the physical environment, as well as current changes in animal and plant characteristics, are the result of natural variability. To invalidate this null hypothesis requires, at a minimum, direct evidence of human causation of specified changes that lie outside usual, natural variability. Unless and until such evidence is adduced, the null hypothesis is assumed to be correct.

In contradiction of the scientific method, the IPCC assumes its implicit hypothesis is correct and that its only duty is to collect evidence and make plausible arguments in the hypothesis’s favor.

An "alternative and null hypothesis" as a standard to gage human causation of global warming has to be "the simplest hypothesis consistent with the known facts." Why? Because the hypothesis being tested needs to be gaged against something plausibly real, not something imaginary that could never exist.

The null hypothesis for AGW is that there is no HUMAN relationship between the different measurements, not that there is no causation at all and that matters are "completely unpredictable." Not unless you want to fall off into la-la land and use a hypothesis where the actions of physical phenomena don't have causes. Then you step outside of reality.

I'm just learning this stuff, but it's becoming obvious to me that you don't really know it.

You're faking knowledge and trying to teach others something you don't know.

Michael

Thanks for proving that the NIPCC are full of it.

This is the standard usage of the term "null hypothesis" from wikipedia:

In statistical inference of observed data of a scientific experiment, the null hypothesis refers to a general statement or default position that there is no relationship between two measured phenomena,[1] or that a potential medical treatment has no effect.[2] Rejecting or disproving the null hypothesis – and thus concluding that there are grounds for believing that there is a relationship between two phenomena or that a potential treatment has a measurable effect – is a central task in the modern practice of science, and gives a precise sense in which a claim is capable of being proven false.

In statistical significance, the null hypothesis is often denoted H0 (read “H-nought” in Britain or "H-zero" in America[citation needed]), and is generally assumed true until evidence indicates otherwise (e.g., H0: μ = 500 hours). The concept of a null hypothesis is used differently in two approaches to statistical inference. In the significance testing approach of Ronald Fisher, a null hypothesis is potentially rejected or disproved on the basis of data that is significant under its assumption, but never accepted or proved. In the hypothesis testing approach of Jerzy Neyman and Egon Pearson, a null hypothesis is contrasted with an alternative hypothesis, and these are distinguished on the basis of data, with certain error rates. Proponents of these two approaches criticize each other, though today a hybrid approach is widely practiced and presented in textbooks. This hybrid is in turn criticized as incorrect and incoherent—see statistical hypothesis testing. Statistical significance plays a pivotal role in statistical hypothesis testing where it is used to determine if a null hypothesis can be rejected or retained.

The null hypothesis is most certainly not "the simplest hypothesis consistent with the known facts". It is simply the hypothesis which states that all outcomes of an experiment are equally likely.

What the NIPCC are doing is distorting the technical term "null hypothesis" so that their position is supported by default. They are hypocritically doing what they accuse, wrongly, mainstream scientists of doing.

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Climate scientists most certainly do not presume the truth of AGW just because they demand a proof of an alternative hypothesis. On the contrary, the NIPCC people presume the truth of GW by entirely natural causes, and then try to argue that AGW is false simply because it doesn't explain everything.

Not true. The question is how much of the warming is due to natural drivers and how much to human activity. No one denies that CO2 has an effect and atmospheric temperature, but how much does CO2 concentration determine the average atmospheric temperature?

To answer questions like this we need a genuine -science of climate- which we do not have at present. Part of the difficulty is reckoning non-linear dynamics.

Ba'al Chatzaf

Well I guess if you repeat it often enough, it must be true... :rolleyes:

aVZgT-120x91.gif

Well the gang's all here. This is going to be good.

5dZXT-120x153.gif

I am going to call my poem..."blood on your popcorn" ...

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Somad apparently has no grasp of how a scientific theory is falsified and how it is supported by evidrue ence.

Here is the bottom line: If a theory T under conditions C predicts X and observations shows not X and the conditions are met,

then theory T is false. End of story.

The way to falsify a theory is to test a prediction and if it is falsified empirically then the theory is wrong or incomplete.

Here is an interesting thing. There is no way to assure that a theory is true. The best that can be said of any scientific theory is True so Far.

However the IPCC does not have a climate theory (no one does right now). They have a model. If the predictions implied by the model are not the case then the model is defective. It has left out something or included something that is not the case.

Ba'al Chatzaf

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The null hypothesis is most certainly not "the simplest hypothesis consistent with the known facts". It is simply the hypothesis which states that all outcomes of an experiment are equally likely.

What the NIPCC are doing is distorting the technical term "null hypothesis" so that their position is supported by default. They are hypocritically doing what they accuse, wrongly, mainstream scientists of doing.

I read that Wikipedia article, too. Try linking to it next time, though. Here let me help you: Null hypothesis.

But I'm calling bullshit on you.

And this is the last I am going to discuss this particular point about null hypothesis with you because the level of the discussion has gone so far down to elementary errors on your end--and I'm the lay person, this is just not worth rebutting.

I'll let the reader decide after this.

Now, you dummy. Read correctly.

The paper is not defining what a null hypothesis is for the field of statistics. The wording is a bit clunky, but it is saying that a null hypothesis needs to be used and that the null hypothesis used needs to be "the simplest hypothesis consistent with the known facts."

Get it?

And why does a null hypothesis need to be used? Because an "alternative hypothesis" needs to be used for testing. Just because the writer said "alternative and null hypothesis" rather than "alternative hypothesis which is also a null hypothesis for testing the human input of climate change," that does not mean we are in kindergarten.

The "null" part refers to human causation of climate change, the hypothesis supposedly being tested by the wise ones lauded by the IPCC. The "null" part does not refer to random measurements of ducks or donuts or whatever the hell is in your head, nor does it refer to a "hypothesis which states that all outcomes of an experiment are equally likely," as you just just claimed. I agree with Jonathan. You're making shit up.

The alternative hypothesis of the NIPCC is there is no HUMAN relationship in the climate change measurements, only natural ones. (My wording.) The "alternative" part refers to natural causes and the "null" part refers to human causes. Another way to say it to align it with your precious Wikipedia article is the "alternative" part refers to natural relationships to climate change measurements and the "null" part refers to human relationships to climate change measurements.

That is not saying it is true or false. It is only a baseline hypothesis with specific characteristics for testing: the simplest as the alternative and null for human input.

Would you prefer the most complicated alternative and null hypothesis? That would make the experiments meaningless, especially since human input is being hellishly complicated to prove. Therefore, for the best testing, the best alternative hypothesis is "the simplest hypothesis consistent with the known facts."

The writer said "alternative hypothesis," not just "null hypothesis." So where's the damn Wikipedia article on "alternative hypothesis" so you can try to play gotcha with that one, too?

Is being a Wikipedia warrior the best you can do?

Jeez.

You're supposed to be the expert.

Some expert.

How did you make such a stupid mistake?

This is way too low a standard for intelligent discussion.

What a load of crap.

Michael

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Assuming the maths are somewhat predictive, though I would tend to make the assumption to the lower side of catastrophic, what then? How is energy production and human activity not a part of the natural system? What conceivable way is there to alter energy production that would not cause suffering on a global scale? Who is going to stop using fossil fuel to produce energy and why should "they"?

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The null hypothesis is most certainly not "the simplest hypothesis consistent with the known facts". It is simply the hypothesis which states that all outcomes of an experiment are equally likely.

What the NIPCC are doing is distorting the technical term "null hypothesis" so that their position is supported by default. They are hypocritically doing what they accuse, wrongly, mainstream scientists of doing.

I read that Wikipedia article, too. Try linking to it next time, though. Here let me help you: Null hypothesis.

But I'm calling bullshit on you.

And this is the last I am going to discuss this particular point about null hypothesis with you because the level of the discussion has gone so far down to elementary errors on your end--and I'm the lay person, this is just not worth rebutting.

I'll let the reader decide after this.

Now, you dummy. Read correctly.

The paper is not defining what a null hypothesis is for the field of statistics. The wording is a bit clunky, but it is saying that a null hypothesis needs to be used and that the null hypothesis used needs to be "the simplest hypothesis consistent with the known facts."

Get it?

And why does a null hypothesis need to be used? Because an "alternative hypothesis" needs to be used for testing. Just because the writer said "alternative and null hypothesis" rather than "alternative hypothesis which is also a null hypothesis for testing the human input of climate change," that does not mean we are in kindergarten.

The "null" part refers to human causation of climate change, the hypothesis supposedly being tested by the wise ones lauded by the IPCC. The "null" part does not refer to random measurements of ducks or donuts or whatever the hell is in your head, nor does it refer to a "hypothesis which states that all outcomes of an experiment are equally likely," as you just just claimed. I agree with Jonathan. You're making shit up.

The alternative hypothesis of the NIPCC is there is no HUMAN relationship in the climate change measurements, only natural ones. (My wording.) The "alternative" part refers to natural causes and the "null" part refers to human causes. Another way to say it to align it with your precious Wikipedia article is the "alternative" part refers to natural relationships to climate change measurements and the "null" part refers to human relationships to climate change measurements.

That is not saying it is true or false. It is only a baseline hypothesis with specific characteristics for testing: the simplest as the alternative and null for human input.

Would you prefer the most complicated alternative and null hypothesis? That would make the experiments meaningless, especially since human input is being hellishly complicated to prove. Therefore, for the best testing, the best alternative hypothesis is "the simplest hypothesis consistent with the known facts."

The writer said "alternative hypothesis," not just "null hypothesis." So where's the damn Wikipedia article on "alternative hypothesis" so you can try to play gotcha with that one, too?

Is being a Wikipedia warrior the best you can do?

Jeez.

You're supposed to be the expert.

Some expert.

How did you make such a stupid mistake?

This is way too low a standard for intelligent discussion.

What a load of crap.

Michael

Nope, you're still confused.

There are two kinds of variables in question, anthropogenic variables and natural variables. The null hypothesis is that there is no relationship between the anthropogenic and natural variables and temperatures.

The possible alternative hypotheses, state that there is some significant relationship between one or more of these variables with temperatures.

The null hypothesis has been rejected in favor of AGW, as the IPCC report shows.

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Nope, you're still confused.

There are two kinds of variables in question, anthropogenic variables and natural variables. The null hypothesis is that there is no relationship between the anthropogenic and natural variables and temperatures.

The possible alternative hypotheses, state that there is some significant relationship between one or more of these variables with temperatures.

The null hypothesis has been rejected in favor of AGW, as the IPCC report shows.

The IPCC rejected using a null hypothesis and thus abandoned falsifiability. They stepped outside the scientific method.

And you still got the meaning of the report wrong in your Wikipedia attack.

(That is you.)

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Hey let's play with some Circles -

Which circle would include the other circles?

Which circle would be within both of the other circles?

Circle A - reflects all of Earth's mass/atmosphere/electromagnetic fields and climate.

Circle B - reflects all humans in existence in 2014 including the Reds in the Space Station.

Circle C - reflects all that exists.

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Nope, you're still confused.

There are two kinds of variables in question, anthropogenic variables and natural variables. The null hypothesis is that there is no relationship between the anthropogenic and natural variables and temperatures.

The possible alternative hypotheses, state that there is some significant relationship between one or more of these variables with temperatures.

The null hypothesis has been rejected in favor of AGW, as the IPCC report shows.

The IPCC rejected using a null hypothesis and thus abandoned falsifiability. They stepped outside the scientific method.

(That is you.)

Dude, are you even serious right now? Do you even know what it means to reject a null hypothesis?

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Somad apparently has no grasp of how a scientific theory is falsified and how it is supported by evidrue ence.

Here is the bottom line: If a theory T under conditions C predicts X and observations shows not X and the conditions are met,

then theory T is false. End of story.

The way to falsify a theory is to test a prediction and if it is falsified empirically then the theory is wrong or incomplete.

Here is an interesting thing. There is no way to assure that a theory is true. The best that can be said of any scientific theory is True so Far.

However the IPCC does not have a climate theory (no one does right now). They have a model. If the predictions implied by the model are not the case then the model is defective. It has left out something or included something that is not the case.

Ba'al Chatzaf

What is "not-x"? If a theory predicts an observed value to be 45.365458985632 and the actual value is 45.365458985633, is it falsified? That kind of thinking would "falsify" everything in science. Literally, every last thing. That's why this naive view of the scientific method is wrong.

However the IPCC does not have a climate theory (no one does right now). They have a model. If the predictions implied by the model are not the case then the model is defective. It has left out something or included something that is not the case.

Yes, they most certainly do have a climate theory, and they use that theory to build models.

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I built a model of our solar system out of clay globes representing the planets and moons?

Let's have the IPCC use that one.

Just think of all the money that the US taxpayers could financing these International thieves...

(Reuters) - The United Nations General Assembly on Friday approved a $5.53 billion U.N. budget for 2014-2015, down 1 percent from the total spending during the previous two years.

The new biennial budget includes a 2 percent staffing cut, or some 221 posts, and a one year freeze in staff compensation.

The so-called core U.N. budget that was adopted does not include peacekeeping, currently running at over $7 billion a year and approved in separate negotiations, or the costs of several major U.N. agencies funded by voluntary contributions from member states.

As in past years, the biennial budget negotiations were marked by a tussle between poor countries seeking to raise U.N. development spending and major developed countries, which are the biggest budget contributors, trying to rein in the figures as they struggle to reduce expenditures in their own national budgets.

Hmm I think my model cost about ten bucks ($10.00) and it was completely recycled. I used the clay for small men BB targets...

A...

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I thought science was theory, experiment, results, compare results to theory to see if theory was right. The IPCC model ,heh, you say goes theory, experiment , results compare results to theory, then ignore results that are not what the theory said they would be(ocean levels, current non warming) keep theory, keep theory..?

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Here is the full section on scientific method from the Summary for Policymakers under discussion.

It's only four paragraphs and an introductory one. The reader may come to his or her own conclusions. (Go to the link if you want to see Figure 2 or the bibliographical references.)

For me, it accurately represents what I have read so far from AGW truthers and alarmists (over years).

The IPCC relies on three lines of reasoning: computer models that it asserts show CO2 to be responsible for most of the global warming in the twentieth century, a series of postulates that make a plausible case for its hypothesis, and circumstantial evidence that would be consistent with its hypothesis were it true. These IPCC arguments are summarized in Figure 2.

The Scientific Method

Although the IPCC’s reports are voluminous and their arguments impressively persistent, it is legitimate to ask whether that makes them good science. In order to conduct an investigation, scientists must first formulate a falsifiable hypothesis to test. The hypothesis implicit in all IPCC writings, though rarely explicitly stated, is that dangerous global warming is resulting, or will result, from human-related greenhouse gas emissions.

In considering any such hypothesis, an alternative and null hypothesis must be entertained, which is the simplest hypothesis consistent with the known facts. Regarding global warming, the null hypothesis is that currently observed changes in global climate indices and the physical environment, as well as current changes in animal and plant characteristics, are the result of natural variability. To invalidate this null hypothesis requires, at a minimum, direct evidence of human causation of specified changes that lie outside usual, natural variability. Unless and until such evidence is adduced, the null hypothesis is assumed to be correct.

In contradiction of the scientific method, the IPCC assumes its implicit hypothesis is correct and that its only duty is to collect evidence and make plausible arguments in the hypothesis’s favor. One probable reason for this behavior is that the United Nations protocol under which the IPCC operates defines climate change as “a change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods” (United Nations, 1994, Article 1.2). Not surprisingly, directing attention to only the effects of human greenhouse gas emissions has resulted in the IPCC failing to provide a thorough analysis of climate change in the round.

All three of the IPCC’s lines of reasoning, summarized in Figure 2, depart from proper scientific methodology. Global climate models produce meaningful results only if we assume we already know perfectly how the global climate works, and most climate scientists say we do not (Bray and von Storch, 2010). Moreover, it is widely recognized that climate models are not designed to produce predictions of future climate but rather what-if projections of many alternative possible futures (Trenberth, 2009). Postulates, commonly defined as “something suggested or assumed as true as the basis for reasoning, discussion, or belief,” can stimulate relevant observations or experiments but more often are merely assertions that are difficult or impossible to test (Kahneman, 2011). Observations in science are useful primarily to falsify hypotheses and cannot prove one is correct (Popper, 1965, p. vii).


Actually, regarding my rant on the gross elementary error of the AGW truther about alternative and null hypothesis, here is the null hypothesis I gave off the top of my head:

There is no HUMAN relationship in the climate change measurements, only natural ones. (My wording.)


Here is the alternative and null one from the "Summary for Policymakers" quote:

Regarding global warming, the null hypothesis is that currently observed changes in global climate indices and the physical environment, as well as current changes in animal and plant characteristics, are the result of natural variability.


For the record (and for those whose eyes glaze over with copy/paste technical stuff), here is the hypothesis that the NIPCC claims is "implicit in all IPCC writings, though rarely explicitly stated":

Dangerous global warming is resulting, or will result, from human-related greenhouse gas emissions.


I should have quoted the report, but my wording is not wrong in terms of meaning. It is just not as specific as the original.

Michael

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I thought science was theory, experiment, results, compare results to theory to see if theory was right. The IPCC model ,heh, you say goes theory, experiment , results compare results to theory, then ignore results that are not what the theory said they would be(ocean levels, current non warming) keep theory, keep theory..?

No, for any variation in the data which the models don't explain, one needs to either a) find an additional mechanism which explains the as of yet unexplained variation or b) find a new theory which explains both the already explained variation and the unexplained variation. (part b is where the theory would be falsified in favor of an alternative hypothesis).

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I want to reiterate the following statement from the "Summary for Policymakers" quote above for those who don't want to read the full thing:

"In contradiction of the scientific method, the IPCC assumes its implicit hypothesis is correct and that its only duty is to collect evidence and make plausible arguments in the hypothesis's favor."

That is the methodology skeptics are arguing against, that is until they are bullied into silence.

Actually, the skeptics are arguing against some other stuff, too, but that is terrible as it is.

Michael

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I want to reiterate the following statement from the "Summary for Policymakers" quote above for those who don't want to read the full thing:

"In contradiction of the scientific method, the IPCC assumes its implicit hypothesis is correct and that its only duty is to collect evidence and make plausible arguments in the hypothesis's favor."

That is the methodology skeptics are arguing against, that is until they are bullied into silence.

Actually, the skeptics are arguing against some other stuff, too, but that is terrible as it is.

Michael

There is absolutely nothing which corroborates this. Anybody can look at the methodology used in the IPCC's report and see that that assessment is based on nothing more than the authors' say so.

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WILL 97% OF SCIENTISTS LIKE THE COSTS OF CARBON RULES?

The Journal reports that next week the Obama administration will "unveil a cornerstone of its climate-change initiative with a proposed rule aimed at allowing states to use cap-and-trade systems, renewable energy and other measures to meet aggressive goals for reducing carbon emissions by existing power plants.

"Energy companies and others affected by the proposal will be watching for key details, including the percentage by which companies and states must reduce carbon emissions, which is expected to be proposed in a range instead of a single number. The baseline year against which those reductions are calculated will also be closely monitored."

As the price tag comes into view for measures aimed to prevent global warming, Americans may wish to reflect on whether the benefits really justify the costs. Joseph Bast and Roy Spencer write in today's Journal that "the assertion that 97% of scientists believe that climate change is a man-made, urgent problem is a fiction. The so-called consensus comes from a handful of surveys and abstract-counting exercises that have been contradicted by more reliable research."

Shh don't let her know about this...

A,,,

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There is absolutely nothing which corroborates this. Anybody can look at the methodology used in the IPCC's report and see that that assessment is based on nothing more than the authors' say so.

It is true that the NIPCC people are not being bullied into silence.

I should have been clearer. I had in mind the many scientists who are part of the normal scientific community. From the outside over the years, we have seen them speak out to express their skepticism, then suddenly back off.

One after another.

Many, many and many.

This reminds me so much of Scientology practices, it's not funny.

The public seems to think so, too. Just look at the drift...

Michael

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