SoAMadDeathWish
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Posts posted by SoAMadDeathWish
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Participating in this thread stopped being fun or interesting a while ago.
I'm tired of it.
That is what you cause others to feel in your posting behavior. In addition to irritation.
Now do I have your attention?
Or do you have some more games to play?
Michael
Perhaps I do play too roughly with the other children.
I guess I'll try to be more.....uhh... what's the word?..... n...... n... ni--... nice....
even though I find such nonsense absolutely disgusting...
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I have no interest in being banned.
Participating in this thread stopped being fun or interesting a while ago.
I'm tired of it.
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You adopt the least charitable interpretation of whatever I say just to get a dig at me.
I don't agree with the rest of your characterizations, but I do agree with this.
I'm glad you noticed because, after watching you do this over and over to others, I told you this is exactly what I was going to do. I even had to repeat it a few times for you to understand it.
And I'm doing it.
Does that irritate your highness?
Michael
Really, when have I ever done that except in your imagination?
Before you said you got tired of these types of discussions because they degenerated into mudslinging, and that you would prefer for the forum to not degenerate into "kindergarten level" discussion. There's a lot of good arguments made by both sides here, even if the discussion gets a little pointed at times, but what you're doing is counterproductive, not only to others, but to everything you said you wanted.
If you want to express your opinion on how terrible and stupid and deluded "AGW truthers" are, why not make your own thread about it? This is the Science and Mathematics subforum, I assume it's called that because it's for people who want to discuss math and science. If you wanna piss on people you don't like, then the politics subforum is for you.
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I have not changed my position, just made it clearer. My position has always been that the mainstream climate scientists are right about AGW, any differences in wording merely reflect my own understanding and knowledge of that position (which definitely isn't perfect). That's why I made post #258.
THIS is so illustrative of the AGW truther mentality.
When called on specifics and contradictions in the AGW truther's own statements, they finally admit they believe whatever the "mainstream climate scientists" say (whatever the hell a mainstream climate scientist is), AND CALL THAT CLARITY.
They go from specific measurements and definitions to general mush for scientific reasoning.
"Whatever those dudes say is what I believe."
Is that science or faith?
Michael
Michael,
You don't understand even the basics of the science involved. You don't even show any attempt to understand it. You don't contribute positively to the discussion in the slightest. You openly admit that you're doing nothing but trolling this thread. You adopt the least charitable interpretation of whatever I say just to get a dig at me. You plug your fingers into your ears and go "the reader can judge for himself!" when your misunderstandings are pointed out to you, and then you grasp at whatever even seems to remotely kind of sort of look like it supports your preconceived beliefs.
Honestly, you are the last person here who should be opining on whether or not something is science or faith.
PS: I never said that I believe whatever the "mainstream climate scientists" say, just that they're right about AGW.
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I defer to Judith Curry at Georgia Tech
Well then where's her data?
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This has already been addressed in the thread earlier. The Medieval Warm Period was restricted to parts of the north hemisphere, while the global temperature overall was lower than today.
You know this for a fact how? - does the Maunder Minimum mean anything to you?
From reconstructions of global surface temperatures here.
Pretty sad. Relied on East Anglia. Data caveats. Interpretation caveats.
Worse: they concede "Peak multidecadal warmth centered at A.D. 960 (representing average conditions over A.D. 940–980) in this case corresponds approximately to 1980 levels (representing average conditions over 1960–2000)."
Busted flush, honey.
I suppose you have better data then?
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I don't see why any of this is relevant. post #258 makes my position clearer than I ever could have by myself. If you disagree with any of the claims made there, then say which ones and why, and we can have a debate.
You've twice changed what your "position" supposedly is, the second time to endorsement of material you hadn't read when you confidently claimed that "AGW" has been proven, is subscribed to by every "reputable" climate scientist, is supported by thousands of peer-reviewed articles and countered by none. Do you even acknowledge yet that, no, your "position" - in any of its sequential versions - isn't subscribed to by every "reputable" climate scientist?
As to the IPCC Summary Report - I'll have to look through it again to double check, but I think there isn't anything in it with which I agree.
I have no interest in a debate with you, however, just in correcting some of the things you say.
I want to address a particular statement you made in the beginning part of post #258, the part which you wrote yourself, and I might address some other issues later. Meanwhile, I have a quarrel with something Mr. Kolker said.
Back in a bit.
Ellen
I have not changed my position, just made it clearer. My position has always been that the mainstream climate scientists are right about AGW, any differences in wording merely reflect my own understanding and knowledge of that position (which definitely isn't perfect). That's why I made post #258.
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It's difficult to answer your questions because answering them would require me to give you an entire course on statistics, specifically with regard to regression analysis, and on top of that I'm using a simplified example to explain very complicated and subtle concepts. But I guess I can try anyway:
When climate scientists report that "greenhouse gasses explain at least 50% of the variation in temperatures with 95% certainty", what that means is that, we can expect that 95% of the time, if a model (that accounts for only ghgs) predicts that the temperature will go up by 1 degree, then the observed temperature increase will fall within at most a 50% range of that prediction , i.e. between 0.5 and 1.5 degrees.
If the observed temperatures in reality don't fall within that range, are you saying that the model which predicted temperatures within that range is therefore falsified, or is it still just a "minor discrepancy"?
See, it would be nice if you would actually answer my questions, rather than bluffing and blustering while avoiding them. I have to wonder why you refuse to answer the questions. Is it because you know that once you actually identify precisely what you mean by "minor discrepancies" versus observations which would falsify a given model, then we can begin to apply your own stated standards to the "consensus scientists'" models, at which point you'd have to try to explain why so many of them are outside of your own stated acceptable range?
So, once again, the unanswered questions are: What standards are you using to judge a "discrepancy" to be "minor" versus "major," and, more importantly, how large can a "discrepancy" be before it would count as falsifying an AGW model. How far off from reality could AGW models be in their predictions before you would classify the models as falsified? Yes or no, if a model fails to "explain at least 50% of the variation in temperatures with 95% certainty," has that model therefore been falsified? If your answer is "no," then which observations in reality would falsify the model?
J
Yes, at the 95% level.
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This has already been addressed in the thread earlier. The Medieval Warm Period was restricted to parts of the north hemisphere, while the global temperature overall was lower than today.
You know this for a fact how? - does the Maunder Minimum mean anything to you?
From reconstructions of global surface temperatures here.
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I don't see why any of this is relevant.
That's for sure.
Total brain shut-down.
Rand called it a blank-out.
Gotta like this discussion style:
We can debate anything you agree with me on--and it doesn't have to make sense. But anything you disagree with me about doesn't count. And by the way, don't ask me for a fucking yes or no!
I've never seen a clearer case of this.
Michael
Have you stopped beating your wife yet?
It's a simple "yes or no" question.
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So is anyone actually prepared to criticize the IPCC position on climate change described in my big post?
Sure. It fails to explain the Medieval Warm Period when Greenland was green. Personally I'm in favor of global warming, which is much preferable to global cooling. I'm also in favor of industrial production, mechanized farming, and kicking the U.N. out of New York. For a good laugh, check out the East Anglia Climate Centre's history. It was founded by oil companies to gin up the global cooling scare of the 1970s. East Anglia University doesn't even dent the Top 10 UK research ranking. Until recently, it was a little community college known for bookbinding, American media studies, and Victorian architecture hooey.
This has already been addressed in the thread earlier. The Medieval Warm Period was restricted to parts of the north hemisphere, while the global temperature overall was lower than today.
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#1 is a statement with which most of those often called "denialists" would agree.
#3 isn't a more detailed version of #2. For one thing, it changes the verb tense from present to future. For another, #2 does not specify warming, just a vague "'disruptive' to 'catastrophic' effect on climate dynamics." I deliberately used the non-specific wording when I stated what I surmised you meant by "AGW," since that wording has become a common fall-back among alarmists confronted with no "global mean temperature" increase (according to the data bases used by the IPCC itself) for the last 15-17 years.
You first acceded to #2 as your meaning, but then a bit later (see the links) changed to #3 as expressing what you're saying. #3 is a prediction that we will get warming ahead due to positive feedbacks set off by human-produced CO2.
#4 is the position apparently held by the person, Dennis Hartmann, to whose book you referred readers of the thread as the source of your claims.
You then say that your post #258 "is supposed to be a direct answer to: 'Which of these claims is the one which you assert has been proven, is subscribed to by every "reputable" climate scientist, is supported by thousands of peer-reviewed articles and countered by none?"
However, what you posted, after attempting to explain how atmospheric temperature regulation works (including a number of assumptions and errors in your explanation) is material from the Fifth IPCC Summary Report - material which you had never read when you made your earlier assertions. So how can that material have been what you meant?
Furthermore, what the summary material gives is partly outright false (with its use of wording like "unequivocal" and "clear"), partly questionable statements of fact (with confidence levels assigned) and partly prognostications, again with confidence levels assigned. Prognostications haven't been proven. Nor is any of the report "subscribed to by every 'reputable' climate scientist," not by a long way - unless you define "reputable" as subscribing to the report. Nor is the material in the Summary Report "supported by thousands of peer-reviewed articles and countered by none." It isn't even supported by all the articles in the body of the report itself.
Ellen
I don't see why any of this is relevant. post #258 makes my position clearer than I ever could have by myself. If you disagree with any of the claims made there, then say which ones and why, and we can have a debate.
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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...
Frantic Tard,
Why don't you start answering direct questions with direct answers? I'm not interested in wasting my time digging through one of your document dumps and trying to guess what you think it means, and how you think that it answers the questions that you're being asked.
In post 302, I responded to your false assertions about results that would not count as falsifying a hypothetical scenario that you proposed. You didn't respond to my question about what standards you are using to judge a "discrepancy" to be "minor" versus "major," and, more importantly, you didn't answer my question about how large a "discrepancy" would have to be before it would count as falsifying AGW. How far off from reality could AGW models be in their predictions before you would classify the models as falsified?
J
It's difficult to answer your questions because answering them would require me to give you an entire course on statistics, specifically with regard to regression analysis, and on top of that I'm using a simplified example to explain very complicated and subtle concepts. But I guess I can try anyway:
When climate scientists report that "greenhouse gasses explain at least 50% of the variation in temperatures with 95% certainty", what that means is that, we can expect that 95% of the time, if a model (that accounts for only ghgs) predicts that the temperature will go up by 1 degree, then the observed temperature increase will fall within at most a 50% range of that prediction , i.e. between 0.5 and 1.5 degrees.
The null hypothesis is that, at the 95% level, the models using only ghgs will be able to explain less than 50% of the variance in temperature. So that, if we collected the data and found that, more than 5% of the time, a predicted increase of 1 degree matched with an observed increase that fell outside of the range between 0.5 and 1.5 degrees, then we cannot reject the null hypothesis, and the model's predictions would not be statistically significant at the 95% level. That is, any fit between the model and the data would more likely be due to mere chance than anything else.
EDIT: It's actually a lot more complicated than this, but this is the basic gist of it.
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Blah blah blah...
Let the reader look and judge for himself.
When people consistently run from clarity, my view is that they are generally trying to deceive you.
Michael
Run from clarity? Post #258 couldn't be clearer if you melted it into glass.
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Here, let me help.
We'll go slow so it won't be difficult to understand what answering a question means.
Let's start with one, shall we?
First question: Do you recognize that the four statements above are different claims?
Anybody who refuses to answer a question like that, but instead furnishes a long copy/paste dump, is trying to deceive people.
"Yes" and "no" are words we all learned when children. They shouldn't be difficult to deploy for alleged experts...
Michael
I'm not sure where she's getting 1. 4 is out of context. 3 is a more detailed version of 2. My post after hers is supposed to be a direct answer to: "Which of these claims is the one which you assert has been proven, is subscribed to by every "reputable" climate scientist, is supported by thousands of peer-reviewed articles and countered by none?"
Happy?
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Earlier in this thread, our AGW truther yapped on and on and on that nobody could prove this or that.
Well, I dug around and came up with something. I'm still reading it (and it looks like this is going to take some time), but it is based on serious work by reputable scientists. Let's see what they've got to say. (Our AGW truther has already condemned it without even looking based on an elementary error.)
But when called on to clarify her own views and get specific, she was oddly silent.
Look here:
Any answer?
Nope.
Crickets chirping on that score.
But lots of noise about everything else.
Then the comedy.
Where's Ellen?
The reason our AGW truther did not answer Ellen's questions about what AGW means is not because she has no answers. It's because--using the crusade of the faithful method mentioned in my quote from the NIPCC--the correct answer is it doesn't matter what AGW means just so long as humans are to blame. And that sounds really ugly and boneheaded if one is pretending to talk about proven science.
Michael
Well actually I did answer her questions, right in the post after hers:
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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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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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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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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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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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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...
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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
MethodologiesDetection 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 question2. 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 variables4. 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 changeA 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 bythat 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).
Highly Advanced General Relativity for the Layman 101
in Science & Mathematics
Posted
One very important thing, no observer can ever travel at the speed of light.
We can, however, consider the same scenario if both observers are traveling at very nearly the speed of light.
First of all, we always have to define the reference frames. One possible frame here is the Mars-Neptune frame, in which Mars and Neptune are at rest. The other two frames are that of each of the racers. Supposing that they start off an equal distance from Neptune, and leave at the same time and place in the Mars-Neptune frame, and so long as they travel at the same near-light speed speed, they will finish at the same time in the Mars-Neptune frame. Since they are traveling with the same speed, the two racers are at rest relative to each other in their frame, while Neptune is approaching them at very nearly the speed of light. In that frame, they still both observe that Neptune reaches them at the same time.
However, your thought experiment is a clever demonstration of why it is impossible to have observers at light speed.