How To Discover Any Trend You Want In Climate Time Series

Excellent! I have always asked why climate change cannot be measured by simply looking at the data. Why must we torture (i.e. homogenize) and twist the data into something foreign, then draw a Straight line (only skeptics and obscure peer-reviewed papers use anything other than a straight line—ask the global warming advocates. They’ll tell you and mock you for asking!) through the tortured mess? Why can’t we just plot the temperatures and look?
(I do know the answer, of course. Data doesn’t always conform to the desired end. Tortured data does. Plus you can argue that only smart, highly educated people can understand what you’re doing. Yeah…..)

LOESS? Ugh. First, what’s the point of scatterplot smoothing in this context? The data are already there! Also, LOESS is just a whole bunch of really small regressions over a couple of points in the time series. It’s your Figure 6 repeated over and over for small chunks of data. I wish I came up with FOR-loop of linear regressions and published it, imagine the citations!

LOESS can’t even give you coefficients to reify and apply causal significance to! You also can’t extrapolate, so there’s really no way to get interesting predictive information out of it. Ok, someone could extrapolate, but that would be a miserable idea.

Lesson 0:

Never connect the data points by drawing straight lines between them (or any other kinds of lines). To do so is to fabricate facts.

“He created “anomalies”, which is to say, he replaced the data with something that isn’t the data. Everybody does this.”

Climate researchers love anomalies for 2 very important reasons.

1. They make the trends look bigger than they are. How can you see a trend of a small fraction of a unit per decade on a scale that covers a range of 25 or more units over 10 decades of data?
2. Anomalies remove seasonal variability that is orders of magnitude larger than the alleged trends. This much larger seasonal variability just confuses lay-people, making them think the alleged trends are much less significant (importance, not statistical significance) than the researchers think they are.

Throw in a bit of philosophical musing and this post would make an nice chapter for your book.

A couple of questions:
On the graph of the red and blue regression lines for the anomalies, does the fact that they all converge around 2011-13 say anything meaningful about the linear trend(s)? Or about the quality of the linear model here?

Thank you for this insight into “statistics” for me as a layman.

In an observational science like climate science, you rarely get the data you’d like to have; you get the data that exists — with time gaps, station relocations, instrument failures and other imperfections. Yes, there are questions about the length of trends and homogenized data and such. But it’s the only data out there so scientists do the best they can with it. The question of AGW is too important not to; too important to just throw up your hands and say, the data isn’t perfect so we don’t know anything.

BTW, long-term satellite trends (since 1979) are the same as the surface trends. And the satellite temperature data also uses a complicated model to turn microwave measurements into temperatures.

“Without models, there are no data.”
– Paul N Edwards, “A Vast Machine”

David Appel, your comments reveal techniques for necromancy, but not science. Science is prediction, from theory to new data. If prediction falters, then theory (or more realistically models) are useless.

PS–the importance of the topic does not justify non-applicable investigation. The question of God’s existence is supremely important, but science has nothing to say about this.

“Without models, there are no data.”
– Paul N Edwards, “A Vast Machine”

Without models, there are no jobs in global warming research or propaganda (you left that important point out). I would love to know how zeros and ones became reality. It is, of course, far easier to manipulate models than data. There is no reason a model is required. We can look at the data. It’s just that you cannot produce whatever results you want without models. The data is sufficient. Funny, thousands of years of science went by without models. Only quantum mechanics requires a model and that is only because there are no direct measurements. In quantum mechanics, if the model fails as often as global warming models do, the physicist would be fired, rather than people claiming the particles are just not behaving as we know they must and if we wait just a bit longer, we KNOW we are right.

The full name of the book appears to be: A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming

“Science is prediction, from theory to new data. If prediction falters, then theory (or more realistically models) are useless.”

In climate science you can’t “predict” anything — climate models aren’t capable of predicting, because no one can read the future. Climate models “project,” based on a set of assumptions. Since the modelers can’t know the El Ninos and La Ninas that will occur over the next ~2 decades, they can’t project the temperature in 20 years, which in that time frame is influenced heavily by ENSOs, volcanic eruptions, solar changes, air pollution, the ozone hole, etc. But over the long-term all of those average out to zero, and that’s when climate models best project temperatures. But it’s still a projection — they still must make assumptions about GHG emissions and aerosol emissions.

Sheri wrote:
“Without models, there are no jobs in global warming research or propaganda (you left that important point out).”

Without models there is no science at all, anywhere, period. ALL branches of science consists of models.

If you want to know how the 1s and 0s get turned into climate projections, go read some books and learn something — there are many good ones out there, such as Trenberth’s “Climate System Modeling.”
http://www.amazon.com/Climate-System-Modeling-Kevin-Trenberth/dp/0521128374

Or read the 1967 paper by Manabe and Wetherald — it is an excellent introduction to climate modeling:

“The Effects of Doubling the CO2 Concentration on the Climate of a General Circulation Model,” Syukuro Manabe and Richard T. Wetherald, Journal of the Atmospheric Sciences, vol 32 no 1 pp 3-15 (1975).
https://courses.seas.harvard.edu/climate/eli/Courses/EPS281r/Sources/Greenhouse-effect/more/Manabe-Wetherald-1975.pdf

Sheri wrote:
“Funny, thousands of years of science went by without models.”

This shows you don’t understand the first thing about science.

Science is nothing but models.

Well then, David, if your climate models are valueless as predictions, please don’t try to dignify them as science. They are no more scientific than economics, or other disciplines included in that oxymoronic class, “social science”.
If you would like to know what science is about please follow Richard Feynman’s discussion:
http://www.youtube.com/watch?v=EYPapE-3FRw

“Science is nothing but models”
but models which are predictive, as in the phenomological equations for fluid flow,
Snell’s Law, quantum mechanics, etc.
I do believe David, speaking as a physicist with 50 years behind me of papers, refereeing, committees, and readings in the philosophy of science that it is you who know nothing of science.

MattS,

You left out that the usual definition of anomaly is a departure from the norm implying anyone could know what is normal for temperature.

David Appell,

The question of AGW is too important not to [mess around with homogenization and trend finding]; too important to just throw up your hands and say, the data isn’t perfect so we don’t know anything.

Which makes one wonder how any of the homogenization or trend finding has anything to do with the “A” part of “AGW”. The “A” part is assigning a cause to the “GW” part. Even without the assignment of cause, the “G” part is questionable because of the messing around with homogenization and trend finding. How does doing this answer any questions about– or even indicate t– he “A” part of “AGW”.

In climate science you can’t “predict” anything — climate models aren’t capable of predicting, because no one can read the future.

One can only wonder what the models are for then if they are useless for prediction. And if they are useless for prediction how can they be considered to have any validity?

“You left out that the usual definition of anomaly is a departure from the norm implying anyone could know what is normal for temperature.”

It doesn’t matter what the “normal” temperature is — anomalies calculated with respect to any chosen baseline give the same trends.

It doesn’t matter what the “normal” temperature is — anomalies

I was referring to the use of the word which implies there is a normal temperature and we are experiencing a departure from it. Why not call the values “differences” or “deltas” ?

“…but models which are predictive, as in the phenomological equations for fluid flow,
Snell’s Law, quantum mechanics, etc.”

All models. All simplified versions of reality. All make assumptions so the analysis is tractable.

Quantum mechanics is just a model; it replaced the Bohr model. When QM was found to fail, Dirac modified it to give more accurate results. The Dirac model failed too, so nuclear physics became important and QED was invented. Both eventually failed, requiring the development of QCD and then the Standard Model. And that was modified over the years, and no doubt something like string theory or loop quantum gravity or something not yet known will replace it. They are all models.

“I was referring to the use of the word which implies there is a normal temperature and we are experiencing a departure from it. Why not call the values “differences” or “deltas” ?”

I don’t know anyone who uses the word “normal” to mean the baseline. People say and write, “baseline.”

@David Appell

You are saying that current climate theories (the models) are not *good* enough to make reliable predictions. Which is not quite the same as the climate being inherently unpredictable.

David, you didn’t reply to the statement that models in science are predictive, as in QED, etc. Models which fail of prediction are cast aside. Reply to that point please.

“Which makes one wonder how any of the homogenization or trend finding has anything to do with the “A” part of “AGW”.”

Of course that’s a legitimate thing to investigate. But don’t pretend you are the first person to wonder about this, or that a lot of thought and work hasn’t gone into developing and verifying homogenization techniques.

” The “A” part is assigning a cause to the “GW” part. Even without the assignment of cause, the “G” part is questionable because of the messing around with homogenization and trend finding.”

No, G is obvious — ice is melting and the ocean is rising. These are macro indicators that are easily observed.

Besides, CO2 absorbs infrared radiation, and the Earth emits it. (Tyndall measured the absorption properties of CO2 in 1859.) Given that, it’s just a matter of working out how much it absorbs and what that implies for the atmosphere. This is actually where the science is strongest, because it’s based on well-tested physics like quantum mechanics where you can really calculate something in the physics-traditional sense.

It’s the rest of the picture — the feedbacks — were the uncertainties lie, and even some of these are definitely going to occur, even if we can’t predict them exactly how or how much, such as the water vapor feedback and the ice-albedo feedback.

All models. All simplified versions of reality. All make assumptions so the analysis is tractable.

But if the models have no predictive power they can’t be shown to have any connection to reality as even simplified general rules and thus are no better than curve fits which tell us nothing.

When QM was found to fail, …

How could QM fail if it has no predictive power? You mean it didn’t fit known data? I would think what is more important if it continues to fit future data (i.e., can successfully predict them).

That is what science is — an attempt to establish rules by which we can predict. You have stated that climate models can’t predict future data — and thus can’t be verified as general rules so what possible use can they have?

Mr. Appell: Once again, the “you disagree with me so you must be stupid” fallacy (which I created especially for the followers of the global warming type). If I tell you I did take a course, I do know how this works, then you ignore my comment or change the subject. Same way people who shout “TAKE A PHYSICS CLASS” do and then find out the person has a masters or PhD in physics. Never acknowledge that your comment was made based on ignorance. (Thankfully you did not tell Bob to take a physics class.)

AND the “You know nothing about science” all in one. WOW. You must have studied the global warming attack process thoroughly. Again, you ASSUME if someone does not agree with you, they know nothing about science, even though you have no idea who they are, what college degrees they may or may not have, what jobs they do. It’s simple for you–anyone who disagrees with you is obviously stupid and needs to take a class. No matter how many classes they have taken. Perfect delivery though. And so early in the discussion, too. I am proud of you.

You are correct that the “normal” temperature does not matter when you use anomalies. That’s the beauty of them. I do have a favorite graph where the Y axis is labeled: “Temperature variation from an Arbitrary Baseline”. This is very, very honest. I’d like to see more of it.

“David, you didn’t reply to the statement that models in science are predictive, as in QED, etc. Models which fail of prediction are cast aside.”

Climate models can’t *possibly* predict, because to do so they’d have to know the future.

There are lots of reasons why climate models haven’t “predicted” the last 20 years, but none of them have much to do with CO2., but instead with natural variability as the added heat goes into different Earth systems. Especially the ocean, which are definitely warming and which show the Earth still has an energy imbalance that’s causing it to warm:
http://davidappell.blogspot.com/2014/11/third-quarter-ocean-warming-is-200.html

In the 1980s and 1990s surface warming was ABOVE projections — call it the anti-pause. Did contrarians dismiss climate models because they predicted too little surface warming? Of course not — they claimed the temperature data was wrong (all that surfacestations.org business). Now that the surface temperatures are increasing a little slower than expected, they are all happy to make claims using that data. When it warms again — and it’s starting to now — you’ll see more of them again questioning the data — or, like now, simply refusing to acknowledge that the UAH satellite data for the lower troposphere shows something very different than the RSS satellite data. Someone’s temperature model is wrong, and even Cowtan and Way found a better surface temperature model, and their model shows +0.22 C of warming in the last 18 years.

“But if the models have no predictive power they can’t be shown to have any connection to reality.”

They have projective power, but they can’t predict. Unless you can read the future, in which case you’d have a blockbuster career in climate science.

“How could QM fail if it has no predictive power?”

I never said QM has no predictive power. I said those predictions eventually failed, and new and better models were then developed.

Sheri: Actually I do have a PhD in physics.

And if you don’t recognize that all science is based on models that simplify reality, then you don’t know much about science. Sorry, but that’s the way it is.

Sander,

Which is not quite the same as the climate being inherently unpredictable.

Much depends on how wrong a model can be and still be useful. Folk in this forum apparently want long term weather forecasting models, which ain’t never gonna happen.

Of course that’s a legitimate thing to investigate. But don’t pretend you are the first person to wonder about this, or that a lot of thought and work hasn’t gone into developing and verifying homogenization techniques.

Ignoring the “first person to wonder” part which arose from nowhere, how exactly was the verifying of homogenization techniques accomplished? On of the assumptions in getting a “global” temperature average is that widely separated an clumped thermometers can represent temperature values in areas of the globe where no thermometers are present. One of these assumptions is that a thermometer can represent temperatures as far away as 700 miles or more; implying a thermometer in London can tell you what the temperature is in Rome.

So, no, the “G” part — particularly the value of the “G” part — is far from obvious.

It’s the rest of the picture — the feedbacks — were the uncertainties lie

Well, for one, it is pure arrogance to assume all of the causes have been identified and the only remaining question is how much of an effect each has. It is also a mistake to assume the mechanisms in a simple system (Tyndall, et al.) can be carried directly into a complex system.

Climate models can’t *possibly* predict,

Which, again, means they are worthless. The only purpose behind a model is to make predictions otherwise it is a pretty toy.

Is geology a science? If so, why can’t they predict earthquakes and volcanic eruptions?

Why can’t solar physicists predict the sunspot number for a year from today?

Why can’t biologists predict how any given human will react to any medication. whether it’s effective or not, what its side effects are for that person, etc.

Why didn’t chemists predict the ozone hole?

A body of knowledge is a science if it’s based on the scientific method. If that includes good predictive power, great. But it need not be able to make a prediction about any given relevant question to be a science.

“The only purpose behind a model is to make predictions otherwise it is a pretty toy.”

To make a prediction climate models would need to know the upcoming ENSOs, changes in solar irradiance, the world’s population up to 2100, and the spectrum of energy sources people have been using from now to then.

When you can supply these, let me know.

“And if you don’t recognize that all science is based on models that simplify reality, then you don’t know much about science. Sorry, but that’s the way it is.”
That, david, is a matter of philosophic interpretation of what science is all about.
You are assuming an anti-realist position (as would Nancy Cartwright or Bas van Fraassen). There are realists who would disagree with you. And since space is limited here for a tutorial on the philosophy of science I suggest you look at
“Tipping the Sacred Cow of Science–Confessions of a Science Agnostic”
http://rationalcatholic.blogspot.com/2014/06/confessions-of-science-agnostic.html
and references contained therein.
By the way you still have not replied to my point that models that are not predictive (in the context of the experiment) are cast aside in TRUE science–e.g. phlogiston as heat, the ether as a medium for vibration of electromagnetic waves, or Glashow’s and Georgi’s GUT theory (1974). Or are you simply evading that issue?

David Appell and Sheri,

It seems that there is some talking past each other that is happening. Brigg’s references (and also Sheri’s) to models is not as broad as David’s. David, you appear to be talking about things as generic as mental conception and paradigms about how the world works. For example “the acceleration due to gravity increases with the amount of stuff something has” is a simplified mental model. So is “CO2 makes the earth warmer”.

It’s the mathematical (and computer/statistical) instantiations of mental models that Sheri and others of us take issue with, especially in contrast to data. It’s clear to me that Sheri was to computational and statistical models in the history of science, relative to today.

Besides, the notion that science is nothing but models that simplifies reality is so basic that I think no one will or has ever tried to dispute it.

Thanks to Bob, I now know that apparently people do dispute that point.

At any rate, I think the distinction between mental models (ideas) and computer/statistical models is clear.

James, there are lots of philosophers and scientists who would dispute
“Besides, the notion that science is nothing but models that simplifies reality is so basic that I think no one will or has ever tried to dispute it.”
See
http://plato.stanford.edu/entries/scientific-realism/
and this quote:
“Behind the tireless efforts of the investigator there lurks a stronger, more mysterious drive: it is existence and reality that one wishes to comprehend.” Albert Einstein, Essays in Science.

Bob,

Or are you simply evading that issue?

Not by my reading. He’s told you and others that he doesn’t agree with your narrow definition of science. Not agreeing is not evasion.

Brandon,

What people want are a testable and falsifiable hypothesis. What people want are metrics for a model that can measure its efficacy and usefulness.

What we are getting from many amounts to “it doesn’t matter”, “the model is still accurate”, “we never said the model would be accurate”, or mostly explanations that require the Jedi mind trick “these are the results that we were looking for”.

A simple “hmmmmm….this plateau might represent a possible deficiency in our models” would be nice. The models running hot for an extended period of time suggests that higher climate sensitivities are less likely. Take a deep breath, was that really so hard to say?

A huge question is how large is the internal variability in the climate? Given the apparent chaotic nature and the non-linear relationships, how long a period is necessary for the internal variability to zero out? 20 years? 60 years? 200 years? They aren’t even really sure if we are in a positive phase or negative phase. Statistics on chaotic system are impossible to derive from short term observational records. How long is long enough? What we do know is that the current models don’t model this very well. What does this imply about their long term forecasts? It sure doesn’t improve confidence.

Sheri,

You are correct that the “normal” temperature does not matter when you use anomalies. That’s the beauty of them.

Does it make sense to average the absolute temperature in Nome, AK with that of Quito, Ecuador?

y = mx + b. If I arbitrarily change b, does it have any effect on m?

Yes, David, I know you have a PhD in physics. And I also know not all science is based on models. Sorry, that’s the way it is. (The sad thing here is you could engage in dialogue yet you insult instead. Your response is very telling–I said nothing about your degree yet it immediately became about you. Very sad. And you are still insulting people who you know nothing about.)

Geologists don’t claim to predict. Chemists don’t either. Not do many biologists. You’re deliberately obfuscating here. Global warming was all predicated on those models with hockey sticks and we were all going to die from heat or drowning. (Yes, I exaggerate, but not much.) It was a PREDICTION. Therefore, the science itself claimed to make the prediction. Your other examples did not.

From the IPCC:
Projection
The term “projection” is used in two senses in the climate change literature. In general usage, a projection can be regarded as any description of the future and the pathway leading to it. However, a more specific interpretation has been attached to the term “climate projection” by the IPCC when referring to model-derived estimates of future climate.

Forecast/Prediction
When a projection is branded “most likely” it becomes a forecast or prediction. A forecast is often obtained using deterministic models, possibly a set of these, outputs of which can enable some level of confidence to be attached to projections.

All of this says “foretelling future events” so far as I can see.

Brandon: I really don’t think most here want long term weather forecasting. Most of us understand that three days out is the best that can be done–something about chaos theory? Most want to understand why there is a claim that climate models can forecast when they cannot.
Bob’s question was why isn’t the model discarded, I believe. So in are you saying, or David saying, that science keeps models that do not work and ignore reality? If the model fails, just keep making excuses? When did that change occur? (Let me guess, when the models failed and global warming HAD to be true, so ignore the data.)

Excepting the misuse of the term “science” It is accurate to state that “In climate science you can’t ‘predict’ anything.” It inaccurate to state that “no one can read the future.” An ability to predict the outcomes of its events is essential to controlling a system. In fields outside global warming climatology we often do that successfully.

David Appell:

To make perfect predictions one would need to know details such as upcoming ENSOs but imperfect predictions are often adequate for the control of a system. For control of a system one needs information (even if incomplete) about the outcomes of the events of the future given the actions that are taken. Models that make projections supply no information. Models that make predictions supply information.

“Actually, I predict it will be a miracle if even 10% read as far as the end.”

In 978 tests so far, I have read to the end 97.8 times. So your prediction holds so far.

Regarding the meaning of “science,” in the English vernacular this word is polysemic. For it to have multiple meanings works for some purposes. It does not work when an argument is being made for when a word changes meaning in the midst of an argument a logical conclusion cannot be drawn from this argument. Thus, it would be well if scientists were to settle on a single meaning for “science.” The best of the alternatives is to make “science” synonymous with the information theoretic idea of “mutual information.” The mutual information is a property of a scientific theory and is the information which this theory makes available for the control of a system per event.

David Appell:

In generalizing there is no alternative to simplifying reality. There is a logical way in which reality may be simplified in making a generalization and an illogical way. Climatologists have tended to employ the latter. This is one of the mistakes that have led climatologists to provide models to policy makers that provide them with no information.

Tom,

What people want are a testable and falsifiable hypothesis. What people want are metrics for a model that can measure its efficacy and usefulness.

None more so than I.

What we are getting from many amounts to “it doesn’t matter”, “the model is still accurate”, “we never said the model would be accurate”, or mostly explanations that require the Jedi mind trick “these are the results that we were looking for”.

What many climate contrarians say is that many consensus climatologists aren’t addressing the actual issues. Many contrarians are wrong.

A simple “hmmmmm….this plateau might represent a possible deficiency in our models” would be nice.

Paper after paper discusses GCM deficiencies, and have been doing so since well before The Great Millennial Pause.

The models running hot for an extended period of time suggests that higher climate sensitivities are less likely. Take a deep breath, was that really so hard to say?

It’s not so hard to say, and those better qualified than I have been saying it for some time. But not just based on the past 20 years. Two decades ago the published range was virtually the same as today: 1.5-4.5 °C/2xCo2. Part of that two decades old analysis likely included looking at another “pause” in the instrumental record: the period between 1940 and 1970. Not to oversimplify an already oversimplified analysis, but note that the trend during those 30 years was clearly negative. 20 years on, this hiatus is somewhat flatter.

A huge question is how large is the internal variability in the climate?

Over the instrumental record, +/- 0.25 °C deviations from multi-decadal means are the rule, not the exception. Longer term than that, say hundreds of thousands of years, 12°C from trough to peak over the glacial/interglacial cycle.

Given the apparent chaotic nature and the non-linear relationships, how long a period is necessary for the internal variability to zero out? 20 years? 60 years? 200 years?

Your question depends on the planning window. 100 years is on the outer edge of being meaningful for purposes of policy, so I pick 60 years, which is roughly one complete AMO cycle. About as short as I’d go is 10 years, which is enough time for ENSO to do its up/down thing two or three times depending on its mood.

From a geologic perspective, a century is a picosecond, so your point is not lost on me. I’m not seeing that you understand the implication of the paleo record, however.

They aren’t even really sure if we are in a positive phase or negative phase.

Of what? On a 10K year perspective we’re long past the critical 65°N summer insolation peak shown to be most directly responsible for the ice age cycles, and temps were trending down accordingly.

Statistics on chaotic system are impossible to derive from short term observational records. How long is long enough? What we do know is that the current models don’t model this very well.

Just a few sentences ago, 20 years was an “extended” period of time. Now you’re complaining about short term observation records. Well, we’ve got nearly a million years of observations showing ~120Kyr ice age cycles to use as a basis for comparison.

What does this imply about their long term forecasts?

It’s not a relevant question until you decide whether or not 20 years is sufficient time to “falsify” the GCMs or not. If that’s enough time for you to say that GCMs are bollocks, well I’d say that 200 years of instrumental data are certainly comprehensive enough to demonstrate that the theory behind them isn’t utter rubbish. But that’s just me being consistent.

Tom, errata: “Longer term than that, say hundreds of thousands of years, 12°C from trough to peak over the glacial/interglacial cycle.”

I’m being sloppy with terminology here. That 12 degree swing is a natural variability caused by external forcings … not quite the question you were asking. Apologies for the inaccuracy.

Taking a linear fit to data that has strong periodicity and not truncating to an integer number of cycles is also cheating.
Going from January to January instead of January to December is a common mistake so I don’t call that cheating.

Dav,

“You left out that the usual definition of anomaly is a departure from the norm implying anyone could know what is normal for temperature.”

I left it out, because it isn’t relevant to my point about why climate researchers are so in love with anomalies.

Sheri,

I really don’t think most here want long term weather forecasting.

I know you don’t, but I’ve got my broad brush out today.

Most of us understand that three days out is the best that can be done–something about chaos theory?

One key component of chaos theory is that of the attractor.

Most want to understand why there is a claim that climate models can forecast when they cannot.

What I think is that the IPCC is very clear that what it is doing is making projections based on scenarios, which are a set of assumptions that they readily admit cannot be known in advance. There is a movement toward producing decadal forecasts, but it’s contentious in the modeling community because it’s antithetical to their traditional approach to the problem — which explicitly ignores initial values and treats climate as a more chaos-friendly boundary condition problem. Or less cryptically, as a “the wiggles average out over time” or “internal variability eventually reverts to a mean”.

Bob’s question was why isn’t the model discarded, I believe.

To be replaced with what?

So in are you saying, or David saying, that science keeps models that do not work and ignore reality?

No, that’s what you are saying. David is being precise, as I have also been in the past, about where the unpredictabilities lie, and how it’s not at present feasible to make predictions at the resolution and fidelity desired. Y’all slough that off as lazy and/or irresponsible, so this rapidly turns into feeling like one is trying to reason with a brick wall.

If the model fails, just keep making excuses? When did that change occur? (Let me guess, when the models failed and global warming HAD to be true, so ignore the data.)

Now you’re being silly, if not disingenuous. There was no magic, clearly defined demarcation line. According to your standard, the models have been failing since they were first written, and much primary literature has been devoted to discussing where, when how and why. They have been incrementally changed as their failure modes have become known since contrary to your wishful thinking, the people who develop them obviously do care very much about getting the right answer — otherwise I don’t think they’d spend so much ink on publicly discussing their failures in the first place.

Dav,

“One can only wonder what the models are for then if they are useless for prediction. And if they are useless for prediction how can they be considered to have any validity?”

The function of the models is to distract the rubes from the fact that there is not, never has been and never will be any empirical evidence to support either the C part or the A part of CAGW.

“It doesn’t matter what the “normal” temperature is — anomalies calculated with respect to any chosen baseline give the same trends.”

No, they don’t give any trends at all, they exaggerate meaninglessly small trends that already exist in the real data to the point that the miniscule trend looks unprecedented and catastrophic.

What the heck is a ‘projection’ that makes it different from a prediction? You’re making the projection and advocating a specific response to prevent it from happening based entirely upon the projection, and this is somehow different from a prediction? Sophistry at it’s finest.

David Appell:

While there is the need to make assumptions about GHG emissions and aerosol emissions when the model is of the type currently existing, a model that was the algorithm for a filter would not present this need. Implementation of this idea might yield a predictive model and thus an ability to control the climate. I’ve built models having this characteristic. Email me at terry@knowledgetothemax.com for details if interested .

David Appell, your examples to show that predictability is not a necessary attribute of a scientific theory/model are ill chosen. They all refer to individual events. One cannot predict the screen location of an individual particle in a double-slit experiment even though one can predict, via probability considerations (pace Matt Briggs), the overall pattern. One cannot predict the trajectory of an individual radioactive particle in a liquid or gas, even though using statistics (statistical mechanics–again, pace Matt) one can predict the bulk properties of the fluid. One can predict cycles for sunspots, and regions and frequencies for earthquakes, with appropriate margins of error.
One is not asking that climatologists predict the temperature at a specific time and place but that they predict average trends, and in this they / their models fail.

Brandon: my definition of what constitutes TRUE science is indeed narrow, but it is this narrowness, limiting science to quantifiable/verifiable consequences that has made it so useful. Unfortunately the cloak of science is assumed by disciplines in which the discipline of quantification/verifiability is tossed into the ashcan.

MattS,

No, they don’t give any trends at all, they exaggerate meaninglessly small trends that already exist in the real data to the point that the miniscule trend looks unprecedented and catastrophic.

Explain how anomaly calculations do this as opposed to some other method. It would help if you first describe how anomalies are calculated to begin with, and then describe how some other (as yet unspecified) method would avoid introducing the error(s) you allege.

Spellbound:
To distinguish between the meanings of “projection” and “prediction” is not sophistry but rather is a necessity for avoidance of applications of the equivocation fallacy in making climatological arguments. A “prediction” is an extrapolation from an observed to an inferred state of an event. A “projection” is a function that maps the time to the computed global temperature. Until recently the events of global warming climatology were undefined making it impossible for climate models to make predictions but leaving it possible for them to make projections.

Brandon,

“The Great Millennial Pause” – ha ha.

There is of course no black/white line that models move from being right to wrong. It’s not clear to me that there are not longer term internal variabilities beyond an apparent 60 year cycle. Looking at the science of doom’s recent posts on chaotic statistics with the pendulum was a revealing experience. There is a lot of non-Gaussian randomness, but it is typically a bounded magnitude for many systems. How does this apply to climate? Well heat isn’t mystically created and destroyed by chaos I assume. However heat is moved around between the oceans / atmosphere in a chaotic fashion. Clouds. If we had sensors everywhere we could get a better handle on this. We should concentrate on better and more sensors I think.

And yes, I do discount paleo, a lot, maybe totally. This is a personal failing from having investigated the HS a while back and determining tree rings aren’t very good thermometers and certain scientists have character flaws. Not worth debating. Maybe ice cores or ocean sediments are better, but for now, I don’t give them the benefit of the doubt. I may look at them again later. Paleo is kind of guilty until proven innocent for me. I stipulate this may not be fair, but may possibly be justified.

“Successful Predictions of Climate Science,” Raymond Pierrehumbert, Dec 2012
https://www.youtube.com/watch?v=RICBu_P8JWI&feature=player_embedded

For a list and discussion, see:

http://www.easterbrook.ca/steve/2012/12/successful-predictions-agu-2012-tyndall-lecture/

Really, Brandon, you are consistent–in defending the indefensible. 🙂
Models: To be replaced with what? Do we keep garbage models because we have nothing better?
Actually, you do seem to be saying to keep the garbage models. I haven’t yet seen you vote to throw them out.
Yes, I know there is no magic demarcation line–or at least no one I have clearly identified. No, I am not saying the models were failing since first written. Up to the temperatures failing to follow the famous hockey stick, it looked pretty good. I’m not sure where the “public discussion of failures” are found. The major discussions appear to have been in secret emails. Otherwise, until badgered by those who are so sure of the accuracy, these scientists were silent. Now, they seem to be throwing out all kinds of ideas in an effort to save face. There’s really no “gosh, we were wrong and we are rethinking this” responses.

Terry: I can’t see the difference between your projections and predictions. Both seem to involve the ability to say what will happen in the future based on the past.

Terry is also peddling a magic model if you give him your email. I’ll wait for someone else to respond.

David Appell:

Pierrehumbert’s use of “prediction” makes it something other than an extrapolation from an observed state of an event to an inferred state of the same event. The idea of an “event” seems absent from his thinking about a “prediction.”

Hi!

Funny to see my own work criticised on a blog that I occasionally read. Maybe a little e-mail before this item was published could have cleared up the very imperfect reading of my Dutch. My pdf was (and it says so) a replication of Dijkstra’s plots with some added explorations. It is supplementary material, the critique itself is on Marcel’s Blog.

The whole point that started this discussion is that Dijkstra should have said: “nothing much to see”, but he didn’t. With that it would however never have been published. It got a more catchy title from the editors and Dijkstra wrote: “it is not getting warmer and this graph shows it” (he claimed he is a statistician, adding authority instead of proof).

I literally said on Marcel’s blog: for a statistician there is nothing here to see with this approach, no cooling, no warming, nothing. I refered to Mudelsee who wrote that climate data estimates without error bars are useless. Dijkstra never reacted to that so I thought, let’s just give an example of how to do this properly. I even showed him an example of subtracting ENSO etc. from the signal,

When I refered to the R^2 in the plot, which is a 100% replication of Dijkstra’s first plot, it was to say that it is useless to look at things like R^2 or p-values etc. in this and similar cases, and that a bootstrap might give Dijkstra an idea how wild that data really is. I never said the R^2 is too small – I called it useless. Maybe the bootstrap is not the best way, but it is quick and gives some idea.

Dijkstra never mentioned the uncertainties, which was the essence of my critique. He just performed an exploratory analysis, but never went any further.

–/–

PS re Homework [..] van Rongen additionally used a technique called loess to supply another modeled line.

No I didn’t. Dijkstra used the loess in his second plot, called it moving average. This was another replication. I rejected his use of a smoother as a model for inference in the tail of the time series.

Spellbound:

I’m peddling a scientific model. What’s a “magic model?”

And here are some uncessful predictions of climate science.
Dr. Douglas Hoyt (Phd physics) kept a scorecard.
http://www.warwickhughes.com/hoyt/scorecard.htm

“This is a personal failing from having investigated the HS a while back and determining tree rings aren’t very good thermometers and certain scientists have character flaws. ”

Mann et al’s “hockey stick” work has been confirmed and replicated by many different groups, some using independent mathematical techniques:

http://www.ncdc.noaa.gov/paleo/pubs/mann2008/mann2008.html

“A Reconstruction of Regional and Global Temperature for the Past 11,300 Years,” Marcott et al, Science v339 n6124 pp 1198-1201, March 8, 2013
http://www.sciencemag.org/content/339/6124/1198.abstract

“Continental-scale temperature variability during the past two millennia,” PAGES 2k Consortium, Nature Geosciences, April 21, 2013
http://www.nature.com/ngeo/journal/v6/n5/abs/ngeo1797.html

Coverage of Tingley and Huybers, who used independent mathematical techniques:

“Novel Analysis Confirms Climate “Hockey Stick” Graph,” Scientific American, November 2009, pp 21-22.
http://www.scientificamerican.com/article.cfm?id=still-hotter-than-ever

Sander van der Wal wrote:
“You are saying that current climate theories (the models) are not *good* enough to make reliable predictions. Which is not quite the same as the climate being inherently unpredictable.”

That’s NOT what I’m saying. I’m saying climate models do not and cannot make predictions. Period.

This is so obvious I can’t see how anyone can think otherwise.

Terry: You can find my email on my blog which will now come up if you click on my name, I hope! I’ll bite!

David: Your article on “correct predictions”: Only a non-scientist would use predictions that are still under debate and deliberately divide predictions so skeptics are wrong and warmists are right. We call that propaganda. Had you actually included warmists who were wrong and skeptics who were right, I might have cared. However, I really have no respect for this kind of behaviour.

Marcott said the hockey stick portion of his paper was not statistically robust—and should not be considered as such.

David Appel, I’ve gone to your link and the list of “successful” predictions are inconsequential, pale compared to the list of failed ones: rising average global temperature in the past two decades; decreasing polar ice caps; rise in sea level.
Models that predict correctly only 50% (or less) of the time aren’t scientific, but like the reading of entrails or casting lots.
Here’s a better picture to be explained by AGW theorists:
http://www.youtube.com/watch?v=5CzsDByT1Lw
and since individual events aren’t relevant to this issue, as I’ve said, here’s an average data for NE US:
http://www.weatherworksinc.com/winter-statistics-2013-2014
Where, or where is AGW when we need it?

Appell, you can believe in tree rings all you want and put Mann up for Sainthood if you wish, in fact I suggest that you do so, he can add it to his shelf with the Nobel prize.

“The idea of an “event” seems absent from his thinking about a “prediction.””

An “event” would be what? If it’s the emission of CO2 from burning fossil fuels, AGW has different predictions than solar warming, One is that the stratosphere should cool. Solar warming predicts it would warm. It’s cooling. Another is that the Northern Hemisphere should warm more than the SH. Also observed.

This page has others:
http://climatechangenationalforum.org/teaching-climate-change-through-six-questions/

Sheri:

The definitions of “projection” and “prediction” are commonly accepted. As you suggest, both involve the ability to say what will happen in the future based upon the past. However, the definition of each term differs. The significance of this difference lies in the fact that a model which makes projections conveys no information to a policy maker about the outcomes from his/her policy decisions. A model which makes predictions conveys information. Thus, a model that makes projections is unsuited to the task of controlling the climate. A model that made predictions might be suited to this task. The models underlying the EPA’s “endangerment” finding conveyed no information to policy makers but were assumed by them to convey information. They proceeded to try to control the climate under circumstances in which control was impossible This is one of the prices we have paid for failing to distinguish between “projection” and “prediction.” According to one estimate, the price is going to come to about one trillion US$ per year over the next century.

“Explain how anomaly calculations do this as opposed to some other method. ”

They do this by compressing the vertical scale of the data to the point where a trend that would be visually indistinguishable from no trend at all when plotted on a 30 unit vertical scale to appearing exponential because the trend is displayed on a half unit vertical scale (-0.25 units to + 0.25 units).

You want proof of this, take the temperature anomaly series (global or any single station), add the baseline value used for calculating the anomalies back in and plot the data with a vertical scale with a 25.0 or 30.0 degree Celsius range and see if you can see any trend to the data.

“and then describe how some other (as yet unspecified) method would avoid introducing the error(s) you allege”

The other method is not unspecified, Briggs has already specified it, you look at the real data with no anomalies on a meaningful vertical scale that represents the full natural seasonal variability of the data.

Sharf: Can’t stick to the science?

“Models that predict correctly only 50% (or less) of the time aren’t scientific, but like the reading of entrails or casting lots.”

Does that number come from God, or did you make it up?

“You want proof of this, take the temperature anomaly series (global or any single station), add the baseline value used for calculating the anomalies back in and plot the data with a vertical scale with a 25.0 or 30.0 degree Celsius range and see if you can see any trend to the data.”

The linear trend would be the same as if you used anomalies.

And deciding to include 10 units on your scale or 100 doesn’t change the result.

“The significance of this difference lies in the fact that a model which makes projections conveys no information to a policy maker about the outcomes from his/her policy decisions. A model which makes predictions conveys information. ”

False. These projections show that emitting carbon on the scale that the world is emitting it, and that it is expected to emit in the future if business is as usual, will cause significant warming and ocean acidification.

That is _extremely_ useful knowledge.

Appell: 50% represents the cut-off for chance. Models that are right half the time are no better than chance. It’s probably not statistically correct and I’d demand a much higher level (down, Brandon!) but that’s just me.
Isn’t your complaint about Scarf a bit hypocritical, since your original response to me was to insult me and tell me to learn about science? Why yes, it is.

Terry: I understand now. Thank you for the explanation.

(If this posts twice, please ignore the second one!)

Appell: Then the models are supposed to be predicting the future.

Tom,

“The Great Millennial Pause” – ha ha.

I find that a robust sense of humor is a necessity when (C)AGW/CC is the topic.

There is of course no black/white line that models move from being right to wrong.

Thank you.

It’s not clear to me that there are not longer term internal variabilities beyond an apparent 60 year cycle.

I would not be surprised if there are, or that some have actually been identified. AMO is the longest relevant one — that fits within the policy horizon — I know of. Some fluctuations I do know of — Younger Dryas, MWP, LIA, etc. could be “flukes” of chaos, or they could be part of some cycle. The stadium wave hypothesis purports to have characterized an oscillation that would explain at least part of the past 150 year rising trend, but AFAIK does not propose a causal mechanism such has been done for the CO2 mode of causality.

Well heat isn’t mystically created and destroyed by chaos I assume.

You know what they say about assumptions … [grin]

However heat is moved around between the oceans / atmosphere in a chaotic fashion. Clouds.

Ice sheets are an even larger uncertainty IMO. “The models” have been really wrong about melt rate, on the conservative side. I can’t recall ever reading a climate contrarian that discussed the potential implications of GCMs overstating surface warming and understating ice sheet melt.

If we had sensors everywhere we could get a better handle on this. We should concentrate on better and more sensors I think.

Won’t come from the US so long as Inhofe is the Senate Environment and Public Works Committee chair … but yes, I wholeheartedly agree with you.

Paleo is kind of guilty until proven innocent for me. I stipulate this may not be fair, but may possibly be justified.

Oh I don’t know. I think all empirical claims are guilty until proven innocent. The (Mike’s Nature) trick is knowing when to accept the preponderance of evidence and understand the warts for what they are. The nice thing about treemometers is the clearly defined annual resolution. The not so nice thing about them is that many of them go the wrong direction in modern times. It really is reasonable to be skeptical of their veracity prior to then, but that’s exactly where looking at other proxy data — from researchers who are not Jones, Briffa and/or Mann if you insist — and finding convergence is key. Which I have. I think it’s quite remarkable that we’ve figured out as much as we have. Shame that such a fascinating and triumphant science has been sullied by spleen-rupturing politics.

Brandon Gates wrote:
“The nice thing about treemometers is the clearly defined annual resolution. The not so nice thing about them is that many of them go the wrong direction in modern times. ”

Which is why no one (including Mann et al) used them at proxies past 1970.

“Appell: Then the models are supposed to be predicting the future.”

If you want a prediction, please specify:
1) what is the rate of all GHG emissions from going forward? You can supply each year’s annual total, if that’s easier. Be sure to include these values for each of the 18 GHGs on this list:
http://en.wikipedia.org/wiki/IPCC_list_of_greenhouse_gases
2) what will the Sun’s average radiance be for each year going forward?
3) What aerosols will be emitted by humans from now until forever? Annual data is OK, but you also have to specify their locations, because that matters.

And if you want a short-range prediction (<~30 yrs):
4) what ENSOs will occur between now and then, and what will their time series of Nino 3.4 values be for each?
5) What volcanoes will erupt between now and then? Please also specify the amount of aerosols each will emit.
6) When will the PDO flip phase? The AMO?
7) What will happen with the ozone hole between now and then?

These will be a very good start. When can we expect them?

David Appel,
“Does that number come from God, or did you make it up?”
The 50% comes from the Principle of Indifference or as it is otherwise known, the Principle of Insufficient Reason…Google that if you’re ignorant of this foundational notion in probability.

To all–
It strikes me that the warmists, the proponent of AGW, have found a new religion.
And as with most religions, “To those who have faith, no proof is necessary; to those who have not faith, no proof is sufficient.”
I wonder what the correlation between belief in AGW and lack of belief in religion might be and whether there is a similar (i.e. anti-correlation) between skepticism about AGW and religious faith.
Anyone know of studies about this?

MattS, Appell

When speaking of anomalies and baseline, shouldn’t one add 290 or 300 to make the scale an absolute temperature scale? Real scientists know that Kelvin rules.

David Appell:

So far as I know, the set of events that is referenced by the study of global warming climatology has yet to be identified. The following hypothetical might be helpful.

In the hypothetical, each event starts at noon local time and ends 24 hours later. At the start of each event, a local meteorologist looks up in the sky and observes whether it is cloudy or not cloudy. Based upon his observation, this meteorologist infers whether there will be rain in the next 24 hours or no rain in the next 24 hours. Each event can be described in one of four ways. They are: { cloudy, rain in the next 24 hours }, { cloudy, no rain in the next 24 hours }, { not cloudy, rain in the next 24 hours }, {not cloudy, no rain in the next 24 hours }.

Sheri,

(down, Brandon!)

I’m doing my level best to stay out of it because it’s such a rare treat to watch him field the same arguments from the same crowd. But be being me, I just can’t resist this niggle: climate is not a coin toss.

[skips away whistling]

Really, Brandon, you are consistent–in defending the indefensible. 🙂

Well I did recently attend a stage production of Man of La Mancha …

Do we keep garbage models because we have nothing better?

No. But one man’s garbage is another woman’s treasure. Seriously though, when are you going to actually investigate why the models are off and figure out that radiative forcing isn’t the major issue? Heck, since looking at the graph is the meme du jour, look at the graph and find any place in the past 150 years that doesn’t have flat spots in it … or even periods of downward trends over decades long. It’s bizarre that you seem to think tens of thousands of scientists have missed something so basic and are not actively investigating why those deviations occur. Even more surreal that you think the past 20 years is CO2-debunking unusual — the charts I’ve memorized from having stared at them often enough tell me that 1998 to present is nothing to write home about in terms of precedence. 1850 to now … different conclusion.

Actually, you do seem to be saying to keep the garbage models. I haven’t yet seen you vote to throw them out.

I don’t live in your all or nothing world. Where I come from, science is the practice of incremental changes in understanding … IOW building upon successes (and mistakes!) of the past and applying improvements as more observations and development of theory are published. You still haven’t answered: replace them with what? What else explains upward trending temperatures since the dawn of industrial age when all indications are that we should have been in a regime of gradual cooling?

No, I am not saying the models were failing since first written.

But they have been. If they were right in the past, we’d be using them, not the latest ones. And all models are always wrong, hey? Just don’t forget the rest of the maxim.

Up to the temperatures failing to follow the famous hockey stick, it looked pretty good.

1940-1970? What on God’s green earth was going on there? 1980-2000? Remember this pretty graph? https://drive.google.com/file/d/0B1C2T0pQeiaSOXFxZ0ZnczFjc2c

I’m not sure where the “public discussion of failures” are found.

Print out 10 papers explicitly discussing GCMs and post them on a cork board. Put on a blindfold and throw five darts. Take off the blindfold and read the paragraphs penetrated by a dart.

The major discussions appear to have been in secret emails.

You mean like this one:

To: Michael Mann
From: Al Gore
CC: George Soros

Best regards,
Big Al

I guess I missed that one.

Otherwise, until badgered by those who are so sure of the accuracy, these scientists were silent. Now, they seem to be throwing out all kinds of ideas in an effort to save face. There’s really no “gosh, we were wrong and we are rethinking this” responses.

And now we’ve devolved to the mantra which flies in the face of reality of what it is scientists are supposed to do, which is throw out ideas in the face of things which are not well understood. And then go to work testing those ideas. Nothing has changed on that score. Not for the first time: go read John Tyndall circa 1860 and play it forward.

Bob,

And as with most religions, “To those who have faith, no proof is necessary; to those who have not faith, no proof is sufficient.”

Every time I think my irony meters are up to resisting you arguing this point, I find I am wrong. Here’s a hint: if you limit the evidence to forward-looking GCM output, you will win the argument every time. I rather suspect that’s the point framing the discussion on such terms.

David,

Which is why no one (including Mann et al) used them at proxies past 1970.

We don’t throw away data on this blog, not even clearly wrong data.

Brandon,
“Every time I think my irony meters are up to resisting you arguing this point, I find I am wrong. Here’s a hint: if you limit the evidence to forward-looking GCM output, you will win the argument every time. I rather suspect that’s the point framing the discussion on such terms.”
Huh??? Please explain to this person of very little brain (I understand only simple things in which the reasoning goes in a straight line) what that was all about. And what is GCM? I don’t keep up with warming acronyms and abbreviations, other than AGW.

Seriously, Brandon, I have. What I don’t understand is why you seem to be so in love with the models. I HAVE answered–replace them with a big blank spot. It is NOT required to have a replacement when discarding a theory.
(Maybe you shouldn’t stare at the graphs so much–maybe it’s affecting your outlook. :))
IF this were not so political, perhaps I would be okay with redoing the theories and models. But clinging to them reinforces the nonsense that Obama and others preach about doomsday. From a purely scientific viewpoint, it’s okay to rework. We both know this is not purely scientific. So, if you want to cobble together the models, revise and revamp, fine. Just realize you are probably adding to the very politics you dislike. I’m okay with it if you are.
What else explains upward trending–about the end of a Little Ice Age? Or should I expect the temperatures to go down at the end of said period? Not saying that accounts for all of this, just it is one very real possibility and that was what you asked.
Now go check out that Richard Feynman video on how science works until you understand that one. 🙂

David Appell:

You claim the projections show emitting carbon on the current scale will cause significant warming yet projections are subject neither to falsification nor to validation. Isn’t that so?

Re the Pages2k model mentioned earlier. The model has been revised, and the MWP is back, at least mostly. http://www.nature.com/articles/sdata201426
There are still issues with proxy orientation. The implications of the changes in re-establishing the MWP are discussed at CA. http://climateaudit.org/2014/10/28/warmest-since-uh-the-medieval-warm-period/

Bob,

GCM = General Circulation Model, here used as a catch-all to describe the suite of climate models used making projections based on various emissions scenarios. An inaccurate shorthand on my part, but a widely used one so go with it. For reasons amply discussed upthread, GCMs are, and will continue to be, lousy prediction tools on decadal timescales for the foreseeable future, if not forever — there are simply too many variables in the system which cannot be known beforehand.

However, based on empirical observation, there is a clear relationship between temperature and CO2 concentration on this rock, a correlation backed by textbook physics principles which have been known since the mid 19th century. There are no articles of faith at play here. Clearly there is some chance that we’re completely wrong, but drawing conclusions based on overwhelming evidence conforming with pre-determined theory is about as far from religion as it gets.

Which I think is supremely ironic. I’m not surprised that you don’t, but I’ll be darned if I understand why. If I believed in God perhaps I’d be singing a different tune, but I don’t. I don’t know what that’s like, so your argument does not make sense to me. Not one bit.

Sheri,

What I don’t understand is why you seem to be so in love with the models.

Oh well, that’s easy: I’m a techno-geek. I especially appreciate physical simulations and have been fascinated with them since well before I knew such a thing as global warming was a concept.

It is NOT required to have a replacement when discarding a theory.

Then you resign yourself to ignorance. Those of us who wish to understand temperature trends since the age of CO2 happened upon us, and would like to have some idea of what temperature will do in the next 50-100 years whether CO2 is doing it or not, are not satisfied with simply throwing away theories and shrugging our shoulders.

(Maybe you shouldn’t stare at the graphs so much–maybe it’s affecting your outlook. :))

Show me a more credible version of reality and I’ll go crosseyed staring at that instead.

IF this were not so political, perhaps I would be okay with redoing the theories and models. But clinging to them reinforces the nonsense that Obama and others preach about doomsday.

Nothing I can ever say to you will change your political views, Sheri. If we really are all going to die (we aren’t) because of what we’re doing to the planet, it will not give two turds and a whiz who’s in charge when it cooks (or freeze dries) us and starts over. Out of sheer frustration, I almost wish I could be alive to see it when Miami is under three meters of sea water because I know for a surety Republicans will blame Democrats for not stopping it when they had the chance. Because that’s how politics works, and the Democrats are just as guilty of it as their friends on the other side of the aisle. It’s a zero-sum argument, and I don’t think it’s either interesting or useful when it comes to answering the question: what the heck are we or are we not doing to the environment we depend on for our lives and well-being?

What else explains upward trending–about the end of a Little Ice Age?

Ok, so all of a sudden we do trust tree ring proxies. One thing I can guarantee, temperature doesn’t just wander around randomly. This is “there is no such thing as random” central. If we can figure out temperature fluctuations during the LIA (or even know they occurred) it stands to reason that with better instrumentation we have a heck of a lot better idea what’s going on in the here and now. Doesn’t it? And isn’t that what really matters?

Not saying that accounts for all of this, just it is one very real possibility and that was what you asked.

It’s very easy to propose possibilities. Yet somehow when you do it it’s ok, but when published climate researchers do it, then it’s face-saving and politics. Why the double-standard? That rant aside, “we’re just recovering from the LIA” is not a causal explanation. There is no mechanism, just an observation, and not even a very complete one. Saying that the excess heat is hiding in the oceans is qualitatively better, not because it’s the answer I want, but because those explanations are specific about where the heat might be, how much might be there, how it might have gotten there, and what we might expect to happen in the future.

Could be all wrong, of course, but to me looks a lot less like handwaving than invoking vague references to a recovery from the LIA.

Now go check out that Richard Feynman video on how science works until you understand that one. 🙂

Feynman was nothing if not curious about how things worked. He would tell an eminent physicist (before he was himself eminent) why he thought their theory was wrong, but he’d also talk with them about what he thought was going on, or at the very least what he’d do to find out. He was prized for his bluntness in the Los Alamos crowd for this — but only by honest scientists who understand proper skeptical thinking, something that amounts to far more than, “NO you’re wrong because Al Gore is a buffoon, thow out your theory” and walking away self-satisfied. My favorite Feynman video is the one of him at the NASA press conference wherein he fished a piece of O-ring out of a glass of ice water and snapped it. I get it that gummint administrators do not much love such antics, but you tread on dangerous territory trying to sell me on the argument that working climatologists and NASA bureaucrats are all cut from the same cloth.

Sheri:

It sounds as though you’d like to hear more about the possibility of a climate model that predicts. This can be accomplished by building a model that is the algorithm for an optimal filter.

One of the places in which filters are used is in navigation systems. Suppose a ship’s captain wishes to know the longitude of his ship. He cannot observe the longitude. What he can observe is the reading of the Greenwich mean time on his chronometer plus the angle to the North star on his sextant. Using the observed values of these two quantities he can infer the value of the longitude through a straightforward application of statistical pattern recognition.

In my example, the Greenwich mean time is an example of a “feature” and the sextant reading is another such feature. Ideally a “pattern” is an information theoretically optimal condition on the Cartesian product of the values that are taken on by the two features. (The “Cartesian product” is the complete set of pairings of the values taken on by the two features.)

A pattern is a state of nature and description of an event. The same event is associated with a state of nature that is called the “outcome” of this event. The outcomes are conditions on the Cartesian product of the values that are taken on by the longitude. In a prediction, the pattern is observed and the outcome is inferred from it. As the information about the outcome is incomplete this results in assignment of values to the probabilities of the various possible outcomes.

For the climate, a model that is the algorithm for an optimal filter cannot currently be built for details on the events are unavailable. Were they to be made available it would be found that predictions could be made over no more than about a year. This conclusion follows from experience suggesting that the number of events for construction of a predictive model of modest capabilities is no less than about 150 and from the length of the longest of the various global temperature time series.

For the climate, 100 year predictions are not in the cards. On the other hand, 100 year projections require only enough money to pay for the energy that runs the supercomputers. However, while predictions provide us with information of potential use in controlling the climate projections provide us with no such thing. For climatologists to have spent 200 billion dollars on the construction of models that make only projections is indicative of managerial incompetency of the highest order.

Brandon: Ignorance is better than misinformed. Any day.
I was funning you with the graph remark.
You might have to live for a very, very long time before Miami goes underwater, assuming that ever happens. You have a lot of faith in models and predictions.

I have no idea what you mean with proxies and the Little Ice Age. None.

You asked for an idea on what else would cause a rise in temperatures. Then you beat me up for answering your question? How does that work???? Seems like a conversation ender to me. (Besides, you’re again ascribing ideas to me based on what you think skeptics believe, which I clearly have stated are not my views. Enough.)

Terry: Will read through your comment tomorrow. Out of time now, sadly.

Briggs,
Do I get any brownie points if I quote you about removing outliers?

@P Molitor,

You make an excellent point, plot the temperature data with a vertical scale of 0 – 350 Kelvin. No trend visible here.

David Appeal, you are still molesting the truth…

Lumberjack can tell you that: two trees 6 feet apart, have different thickness rings – because many different factors influence the prosperity of a tree. Therefore: tree-rings cannot tell the past temp on that hill where are grown – but, for the shonks; a tree in California can tell the past temp in Siberia, Oceania, Latin america, Asia and Africa…?!!!

David; those ”tree-rings theories” of yours, you should shove them up your own ring!

However, based on empirical observation, there is a clear relationship between temperature and CO2 concentration on this rock, a correlation backed by textbook physics principles

The textbook principle has CO2 as a cause of warming which means CO2 rise in concentration must lead the rise in temperature always. The observed relationship is CO2 concentration lagging temperature rise more often than not. If anything, the causal chain must lead from temperature to CO2 concentration and not vice versa unless you are proposing effect leading cause. This lead/lag, relationship reversing even once, implies a hidden driver of both rises. NOTE: the converse of this, i.e. no reversal in lead/lag, does NOT imply the absence of a hidden driver.

It is an error to assume effects observed in simple systems can be carried directly over to a complex system. You continuously make this error.

For the climate, 100 year predictions are not in the cards. On the other hand, 100 year projections require only enough money to pay for the energy that runs the supercomputers

1) The difference between “prediction” and “projection” is purely semantic saying a projection is not a prediction is purely foolish. It’s a lot like saying a sled is not a vehicle because it isn’t built like a car. All projections are predictions.

2) No money is required at all for a projection. All that is needed is a pencil and straightedge.

If the “projections” can’t be relied upon then they have no purpose except possibly for obfuscation with irrelevancy otherwise colloquially expressed as “smoke and mirrors”.

Matt, a good post except for this:

Lesson 1 Never homogenize.

In the notes to the data it said in 1950 there was “relocation combined with a transition of the hut”. Know what that means? It means that the data before 1950 is not to be married to the data after that date. Every time you move a thermometer, or make adjustments to its workings, you start a new series. The old one dies, a new one begins.

The problem with this one is best illustrated by what happens when we have let’s say a routine painting of the Stevenson Screen every ten years. Now, this is “making adjustments to its workings”, so by your idea we should start a new series each time it’s repainted.

Here’s the problem. In between paintings, the temperature gradually rises, due to increased absorption as the paint gets old, darkens, and gets dirty. So let’s assume that there is absolutely no temperature change over the period. We end up with a sawtoothed wave, where the measured temperature gradually rises for ten years, and then drops back down when the Screen is painted.

But if you follow your method and start a new series, every series will show rising temperatures, because you’ve removed the adjustment which returned the temperature to the original value.

This is a common situation in climate science due to the urban heat island. We have a thermometer near town. Let’s assume the temperature is steady for a long time. The town gets built up over the years, and the measured temperature rises due to UHI. Finally someone says hey, the temperature is reading way high, there’s pavement all around, let’s move it to the airport. Then, of course, over the years the area surrounding the airport is built up and once again the temperature gradually rises … so they move the thermometer out of the airport and to some more rural area.

Now, if you consider the town record and the airport record as separate records, all you are doing is baking in a non-existent trend. Remember that the temperature is stable … but the two individual records both show trends. So what you’ve done is removed the important information, the part where the temperature drops precipitously back down to or near the real value.

The Berkeley Earth Surface Temperature folks use your method … and I used to be an enthusiastic supporter of this method, until the penny finally dropped. So I asked Zeke Hausfather and Steven Mosher how the Berkeley Earth folks deal with that … dead silence. I’ve asked them several times, but no answer … which I suppose is an answer in itself.

Other than that, however, everything else you say is completely correct, which is to say of course that I agree with it …

All the best,

w.

David. You might be a PhD but your arrogance is that of an MD.

Did anybody even notice that Jan van Rongen responded in the comments? Everybody’s goring their oxen, that they all seemed to have missed his comment. He even did the homework assignment and Briggs didn’t even acknowledge.

JohnB: Yes, I noticed. Did not have a comment for the comment, however. I should have just said thank you to Jan van Rongen for the clarifications.

Terry: Maybe what I really want is no projections or predictions on climate. I do understand that the possibility of an accurate climate prediction is virtually nil, especially years out. On the other hand, a projection does not seem to be useful for much other. They seem to resemble the scenarios we would construct in philosophy that had no useful purpose if applied to reality.

Willis: Each data set should still be separate. Including all of the pertinent data, such as why the new set began, should provide a way to understand why the new set had to be made. Much of this reminds me of construction, where one person uses a 12 inch ruler, one “paces it off”, one uses a tape measure and a fourth uses a laser then everyone’s measurements are homogenized and the building is built–crooked and badly, but it is built. Had each worker been confined to one section of the building, the results would have been very different.

You’ve written about this many times before of course, but I think this is your best ever.

What is really depressing about the so-called climate debate is that so many blog discussions, regardless of the initial topic, obeys a kind of Godwin’s law in that it usually descends to pointless bickering about what “the trend” is and whether it is “significant” or not (Judith Curry’s blog seems particularly susceptible).

The golden rule I think is
“Don’t use statistics unless you have to.”
To put it another way, just look at the data.

A good example of what happens when people don’t present the data but just present a statistic that they don’t understand is the Lewandowsky nonsense

https://ipccreport.wordpress.com/2014/11/07/lewandowskys-loopy-logic/

Matt, is there a term for the Lewandowsky error? (Ask 1000 people to answer 2 questions on a scale 1-5, “is your name Stephan Lewandowsky” and “do you think you have been abducted by Martians”, find a positive correlation and conclude…)

I read the post (interesting) and every comment to the end (106 at my writing). I am what Briggs calls a civilian – not a scientist, no PhD – but with adequate university training to sustain 44 years of professional engineering practice. Few civil engineers practice in an environment free of politicians. Politicians don’t care about the dictates of professional practice. They have power. They apply power to influence outcomes. That is my world. The Intergovernmental Panel on Climate Change is a marriage of a lot of politicians with scientists. My skepticism about scientific pronouncements on Anthropogenic Global Warming arises from my knowledge of politicians not science. Climate scientists do not have the leverage of withholding a PE stamp to balance power.

Sheri:

In thinking about global warming climatology, to think strictly in terms of the accuracies of models tends to be a trap for projections have a degree of accuracy even if it is not great. The way in which projections fail us is by providing us with no information about the outcomes of the events of the future. This information is essential to controlling the climate yet projections do not provide it.

You can experience a complete lack of information by turning on your HDTV and tuning it to a channel that is providing content. If the signal is stong enough you’re receiving information from the content provider. Now turn your TV off. You’re receiving no information.

The electromagnetic signal that brings you this information is corrupted by noise as it travels to you, steadily decreasing its information content. Engineers cope with this phenomenon by sending more information than is necessary and by using error correcting codes to reverse the errors that are produced by the information loss. Use of both ideas facilitates receipt by you of a perfect picture so long as the signal strength is adequate.

The information that comes to you via your TV set is borne by a message consisting of a stream of 0s and 1s. In telecommunications a message comes to us from the past. In control systems engineering the message must come to us from the future.

In telecommunications engineering the message can be carried by a signal. In control systems engineering it cannot be carried by a signal for this “signal” would have to travel at a speed that exceeding the speed of light and this would be impossible under Einsteinian relativity. Rather than being carried by a signal the information is carried (if it is carried at all) by a scientific theory.

That a theory is “scientific” implies that it is predictive and carries information about the outcomes of events of the future. There is no error correcting code and thus the possibility of correcting errors does not exist. One of the consequences is for mathematical statistics to be probabilistic.

One of the many mistakes that have been made by climatologists in conducting their study of global warming it to speak airily of an “anthropogenic signal.” As the goal of this study is control of the climate, global warming climatology is properly an exercise in control systems engineering and thus the signal power is nil. Thus far, global warming climatologists have spent the money that has been entrusted to them in attempting to tune into a signal that does not exist while ignoring the possibility of tuning into a message that does exist. This message is a time sequence of outcomes of climatological events not yet identified. An available technology makes it conceivable for us to tune into this message but this technology not used by climatologists.

DAV,

The textbook principle has CO2 as a cause of warming which means CO2 rise in concentration must lead the rise in temperature always.

“See also my forthcoming paper: ‘Chickens do not lay eggs, because they have been observed to hatch from them’.”

Shamelessly pilfered from: http://scienceblogs.com/deltoid/2008/03/22/remember-eg-becks-dodgy/comment-page-2/#comment-38385

It is an error to assume effects observed in simple systems can be carried directly over to a complex system. You continuously make this error.

How does CO2 know when it is in the lab vs. when it is in is in the atmosphere?

DAV:

To conflate predictions with projections is to facilitate applications of the equivocation fallacy that are having perfectly ghastly consequences for mankind. One of my articles on this topic is at https://www.wmbriggs.com/blog/?p=7923 . Questions and comments on the article are always welcome.

Bob Kurland wrote:
““Does that number come from God, or did you make it up?”
“The 50% comes from the Principle of Indifference or as it is otherwise known, the Principle of Insufficient Reason…Google that if you’re ignorant of this foundational notion in probability.”

Not applicable, since we do have reasons to expect the climate to warm — GHG emissions.

Brandon: CO2 does not “know” if it’s a lab or not. However, an astute scientist would know that controls conditions in a lab where most variables are known is very different from an open system, like atmosphere, where many variables are unknown. The CO2 may react identically, but the over outcome can be miles apart. The same mistake has been made in climatology testing ocean reduction in alkalinity on corals and other such creatures. Their reaction to the changes in pH in the lab was quite different from in the field research.

Brandon Gates wrote:
“How does CO2 know when it is in the lab vs. when it is in is in the atmosphere?”

CO2 doesn’t know anything, but we know the extra CO2 in the atmosphere is coming from fossil carbon and not natural carbon by looking at its the radioactive carbon isotopes. Fossi carbon is ancient, and has little radioactivity left. And the radioactive carbon in the atmosphere has been decreasing.

There are other reasons; see section 3 here: “How do we know the CO2 is ours?”
http://climatechangenationalforum.org/teaching-climate-change-through-six-questions/

David Appell:
“Not applicable, since we do have reasons to expect the climate to warm — GHG emissions.”
Unless you’re using the royal or editorial we, that pronoun “we” in your statement is not appropriate. YOU may have reasons, but I and many others do not.

Sheri wrote:
“Brandon: CO2 does not “know” if it’s a lab or not. However, an astute scientist would know that controls conditions in a lab where most variables are known is very different from an open system, like atmosphere, where many variables are unknown. ”

The molecular absorption spectrum of atmospheric CO2 can be changed by the atmophere through (1) the Doppler shift (2) atmospheric pressure, (3) scattering, (4) collision-induced absorption. Scientists take all of them into account, such as:

“The Effect of Pressure Broadening of Spectral Lines on Atmospheric Temperature,” Strong and Plass, The Astrophysical Journal, Nov 1950
http://adsabs.harvard.edu/full/1950ApJ…112..365S

Pressure broadening is especially important on Venus.

Mr. Appell: Yes, I know that. That was my point. Perhaps you should direct your comment to Brandon, who seemed to believe there was no difference. He can then clarify if that is indeed what he meant.

Terry: I’m working through your comment. The TV signal example helped. It may take me some time to wrap my head around this, and I do appreciate your continued patience.

DAV wrote:
“The textbook principle has CO2 as a cause of warming which means CO2 rise in concentration must lead the rise in temperature always. The observed relationship is CO2 concentration lagging temperature rise more often than not. If anything, the causal chain must lead from temperature to CO2 concentration and not vice versa unless you are proposing effect leading cause. ”

Often repeated, and flat out wrong.

CO2 and temperature are in a mutual feedback loop. An increase in one causes an increase in the other. The temperature change between recent glacial and interglacial periods, ~8-10 C, would only be about half as much if CO2 didn’t cause a positive feedback.

CO2-created warming happens when independent agents (humans) are busy transferring ancient carbon (fossil fuels) into the atmosphere by burning it. They are doing this regardless of the Earth’s temperature. Thus, in this situation, CO2 leads temperature.

stefanthedenier wrote:
“…many different factors influence the prosperity of a tree.”

Gee, then I wonder why so many scientists have spent so many decades working on dendrochronology, and I wonder why you would think you know something all of them don’t and have asked a question that just — duh — never occurred to them at all.

Fundamentals of Tree Ring Research Paperback – February 1, 2012
by James H. Speer
http://www.amazon.com/Fundamentals-Tree-Research-James-Speer/dp/0816526850

Ima Debatin wrote:
“Re the Pages2k model mentioned earlier. The model has been revised, and the MWP is back, at least mostly. http://www.nature.com/articles/sdata201426.”

How does this paper change the PAGES 2k result (which already does show Arctic warming circa 1000 AD), “There were no globally synchronous multi-decadal warm or cold intervals that define a worldwide Medieval Warm Period…”

viz. that the MWP wasn’t a global phenonemon?

Terry Oldberg wrote:
“You claim the projections show emitting carbon on the current scale will cause significant warming yet projections are subject neither to falsification nor to validation. Isn’t that so?”

No. While it’s true no given projection will exactly follow any given scenario, the models that project can be used to hindcast, using the known numbers for, say, the 20th century; see IPCC 5AR WG1 Ch11 Box 11.1 Fig 1, which is reproduced here (the second graph on the page):
http://www.skepticalscience.com/Climate-Models-Show-Remarkable-Agreement-with-Recent-Surface-Warming.html

Climate models also can be used to study historical examples of climate change, such as the Pliocene or Eemian or the PETM. They’re big picture only, but then, that’s the worry now too, and not what the exact surface temperature will be 23.792 years from now.

And scientists compare what models projected 20 years ago to what actually happened, even though the models of 20 years ago were less complex than today’s. In fact, the entire “hiatus” hubbub is about scientists studying what happened over the last 20 years and trying to understand what they missed, so they can make their models even better. (That includes both the climate models AND the data models.) But that’s exactly what happens in ANY field of science.

Climate models are a limited tool. The best they can give for the long-term future (century scale) is a probability distribution for climate parameters. They cannot and will not tell us the exact surface temperature in 2050 or 2102 or 2224. We will have to make decisions about what to do (or not do) about manmade climate change in the face of considerable uncertanties. But humans and societies do that all the time.

Models are only useful if they can forecast. Hindcasting just means you can fit the data. Yes, models are “trained” on past data . But if I have a model that shows a 95-97% probability of a 3 degree temperature rise in the next ten years, and instead, no increase occurs, all the hindcasting in the world does not matter. The model was WRONG. Now, if you want to take apart the pieces, see what went wrong, and make a newer, improved version that only predicts a .5 degree rise in ten years, you then have to wait ten years and see if the model was better. No amount of hindcasting proves a model. Only if the future and model match, does it matter.

As for “exact temperatures”, that’s interesting. We should not expect extreme accuracy in a field that graph in .1 of a degree? We were right about those graphs being overly confident? Seems so.

Terry Oldberg,

Sorry but projections are predictions if only of the “if this goes on” variety. Mincing words is a tiresome game.

Gates,

How does CO2 know when it is in the lab vs. when it is in is in the atmosphere?

It doesn’t and wouldn’t mean anything even if it could. What a silly thing to ask.

David Appell,

CO2 and temperature are in a mutual feedback loop. An increase in one causes an increase in the other. The temperature change between recent glacial and interglacial periods, ~8-10 C, would only be about half as much if CO2 didn’t cause a positive feedback.

Until they somehow stop feeding each other? What is your evidence for this supposition of mutual causality?

They are doing this [releasing CO2] regardless of the Earth’s temperature. Thus, in this situation, CO2 leads temperature.

Yet while CO2 rises temperatures have stabilized. What is your evidence that CO2 concentration has any real affect on atmospheric temperatures? Is supposition your only tool?

I wonder why so many scientists have spent so many decades working on dendrochronology

You aren’t the only one wondering.

David Appell:

Thanks for taking the time to reply and for sharing your views.

No hindcast is congruent with a global temperature time series. Thus, the conclusion can be drawn that all of the various models are falsified by the evidence. However, IPCC climatologists do not draw this conclusion. They avoid it by the implicit argument that one or more projections are “about right.” “About” is a polysemic word which in this case supports an application of the equivocation fallacy; under this application the projections are “about right.” This conclusion, however, violates the logical prohibition on drawing a conclusion from an equivocation. As few members of the general public are aware of the equivocation fallacy this tactic scores many victories for equivocating climatologists.

DAV:

You err in equating “disambiguation” to “mincing words.” To “mince words” is to “soften the effect of one’s words” [ http://idioms.thefreedictionary.com/mince+words ]. To “disambiguate” is “to establish a single semantic or grammatical interpretation for” [ http://www.merriam-webster.com/dictionary/disambiguate ]. To oppose mincing words is to to favor frankness in communication. To oppose disambiguation is to favor reaching conclusions from logically faulty arguments that appear to be sound.

They drew something like confidence intervals on the model outputs. They put them there for a reason. Now these things aren’t really true confidence intervals but more like pseudo containers on the the extent of different model runs, and the basis for these has always been dubious mathematically.

One assumes those who put these there used expert judgment and the intent was that temperatures were expected to stay within this cone of outputs. There are different ones for different emission profiles, but they all look the same for the first few decades regardless of emission profile.

Of course those who drew these cannot account for major events such as large volcanic eruptions and so forth post model run. We are quite close to falling out of this cone of expectations in less than 2 decades without any easily explainable major climate events. This looks bad. The real question is why they drew this “cone of expectations” so narrow in the first place. I have never understood this. Were they really that confident? I find that hard to believe, but maybe they actually were. Perhaps it suited their political (errrr…science) objectives at the time. Anyway overconfidence in results is the quickest path to credibility loss, and they seemed to be actively courting this outcome with the narrow distribution of expected outcomes. We shall see if the models get better over the next 10 years.

Terry,

There is indeed that idiom but “mince” also means to finely chop. This is what you are doing with words in your “disambiguation”.

mince (mns) v. minced, minc·ing, minc·es
v.tr.
1.
a. To cut or chop into very small pieces.
b. To subdivide (land, for example) into minute parts.
2. To pronounce in an affected way, as with studied elegance and refinement.
3. To moderate or restrain (words) for the sake of politeness and decorum; euphemize

But, since we are using Free Dictionary, note that one of the synonyms of “projection” is “prediction”. There is no point in a “projection” if it isn’t for the purpose of “prediction”. Regardless of your fine distinctions, a “projection” is still a “prediction” in intent if not in actuality.

pro·jec·tion
prəˈjekSH(ə)n/
noun
plural noun: projections

1. an estimate or forecast of a future situation or trend based on a study of present ones.
“plans based on projections of slow but positive growth”
synonyms: forecast, prediction, prognosis, outlook, expectation, estimate

There is a second meaning as used in “movie projection” which doesn’t apply here.

DAV:

There is no logical downside to preserving a semantic distinction between a “prediction” and a “projection.” There is a logical downside to failing to preserve this distinction. The downside is for people to draw logically illicit conclusions from arguments about global warming. Why, then, to you fight tooth and nail for the members of your audience to fail to make this distinction? It is hard to avoid the conclusion that you wish for them to draw logically illicit conclusions from global warming arguments.

Terry,

Because it is central to the “do GCMs” work discussion. The modelers and the IPCC both want to say CO2 rising will (vs. might or could lead to temperature rise while simultaneously claiming they can’t be faulted if it doesn’t happen despite their bracketing their “projection” with so-called confidence intervals (see Tom’s post). This is a cop out and you seem determined to not call them on it.

If the situation were different and temperatures continued to rise they would now be crowing about how right they were. They want it both ways. In my view they predicted a temperature rise with rising CO2 concentration and, when it didn’t occur, their “projection” née “prediction” has been falsified.

Yes, “née” meaning “name at birth” is appropriate here. They made a prediction and now want to disown it.

DAV wrote:
“The modelers and the IPCC both want to say CO2 rising will (vs. might or could lead to temperature rise…”

It’s will. There isn’t an iota of doubt that CO2 isn’t a heat-trapping gas. I don’t know any scientist doubts that, though some kook probably does somewhere.

“… while simultaneously claiming they can’t be faulted if it doesn’t happen despite their bracketing their “projection” with so-called confidence intervals….”

The misunderstand is yours. CO2 is a greenhouse gas, but natural variability still exists, and it is going to influence short-term climate too. Only until you account for natural variability can you begin to look seriously at CO2’s impact.

“If the situation were different and temperatures continued to rise they would now be crowing about how right they were.”

Actually 2014 is going to be a record-breaking year for the surface temperature record. The previous record holder was 2010, before that it was 2005, earlier it was 1998… do you see the pattern?

It’s been even more of a record-breaking year for sea-surface temperatures — the five warmest months ever recorded are the last five months.

And the best measure of global warming by far, the ocean — because it is such a huge heat reservoir; about 93% of the extra trapped heat goes into the ocean — and its heat content has been steadily increasing for decades:
http://www.nodc.noaa.gov/OC5/3M_HEAT_CONTENT/

DAV:

Thank you for explaining your motivation.

If the language of the IPCC arguments is disambiguated in the manner that I develop in the cited article then the models of AR4 made projections rather than predictions. There was something seriously wrong with these models. What was wrong, though, was not that they made faulty predictions. As Trenberth stated unequivocally in his post to Nature they did not make predictions at all.

To point out that the models did not make predictions does not let the IPCC off the hook for its duplicity as a model that makes no predictions is: a) non-falsifiable b) unscientific c) uninformative and d) unsuitable for controlling the climate yet in AR4 promoted control of the climate through the use of climate models and claimed the basis for this position to be scientific. Legitimate scientists can beat the IPCC into submission but not by joining them in their tawdry game.

P Molitor wrote:
“When speaking of anomalies and baseline, shouldn’t one add 290 or 300 to make the scale an absolute temperature scale? Real scientists know that Kelvin rules.”

No. We’re interested in temperature changes, not the absolute temperature.

If you want to add 288 K to each anomaly, go right ahead. All the conclusions about warming/cooling will stay the same.

Terry Oldberg: Instead of continually dissing climate models, why don’t you specify for everyone all the information necessary for them to make a prediction — or explain how to make a prediction without this knowledge?

I’ll list (most of) them again:

1) what is the rate of all GHG emissions from going forward? You can supply each year’s annual total, if that’s easier. Be sure to include these values for each of the 18 GHGs on this list:
http://en.wikipedia.org/wiki/IPCC_list_of_greenhouse_gases
2) what will the Sun’s average radiance be for each year going forward?
3) What aerosols will be emitted by humans from now until forever? Annual data is OK, but you also have to specify their locations, because that matters.

And if you want a short-range prediction (<~30 yrs):
4) what ENSOs will occur between now and then, and what will their time series of Nino 3.4 values be for each?
5) What volcanoes will erupt between now and then? Please also specify the amount of aerosols each will emit.
6) When will the PDO flip phase? The AMO?
7) What will happen with the ozone hole between now and then?

David Appell,

It’s will. There isn’t an iota of doubt that CO2 isn’t a heat-trapping gas.

So far the ONLY evidence of it affecting temperature is within simple systems. It is erroneous to assume simple systems can be transferred directly into a complex systems. Where is the evidence that it has any noticeable effect in Earth’s atmospheric temperatures?

The misunderstand is yours. CO2 is a greenhouse gas, but natural variability still exists

You don’t say? Maybe an example of one of the difficulties in transferring simple system results to something as complex as the atmosphere? Yet you continue to want to do so. I asked for your evidence of:

1) That CO2 concentration has a noticeable effect on the atmosphere
2) The mutual causal relationship between temperature and CO2 concentration.

You don’t have any?

Only until you account for natural variability can you begin to look seriously at CO2’s impact.

So you are supposing why the GCMs are failing? And what exactly makes up “natural variability”? Is CO2 iself and the human population excluded from it?
See: https://www.wmbriggs.com/blog/?s=natural+variability

Actually 2014 is going to be a record-breaking year for the surface temperature record.

Whatever “record breaking” means. So far it doesn’t seem to mean “warmer than usual” in 2014. The pattern seems to be El-Nino and that hasn’t happened yet. Maybe you meant 2015?

David Appel

FYIL
By eyeball estimate and extrapolation from past entries in the UAH global temperature time series the 2014 average is going to be about 0.2 C below the peak. Since about 2001 the change in the yearly average has fluctuated about 0 while the CO2 concentration and projected temperature have moved inexorably upward.

DAV wrote:
“So far the ONLY evidence of it affecting temperature is within simple systems.”

Not true at all. You can’t explain modern warming with CO2. You can’t explain much of anything in planetary science without including the greenhouse effect. You can’t explain the interglacial-glacial gap without CO2. You can’t explain past eposides of warming without CO2 (such as the PETM). You can’t explain Venus’s climate without CO2. Or the climate of Mars. You can’t explain why the Earth’s surface isn’t at the temperature the Sun would provide, 255 K (-19 C).

If CO2 absorbs infrared radiation in the laboratory (discovered: 1859), why wouldn’t it absorb infrared radiation in the atmosphere? It has the same molecular properties throughout the Universe. It does, and there’s plenty of evidence for that:

http://www.giss.nasa.gov/research/briefs/schmidt_05/curve_s.gif

as well as

“Increases in greenhouse forcing inferred from the outgoing longwave radiation spectra of the Earth in 1970 and 1997,” J.E. Harries et al, Nature 410, 355-357 (15 March 2001).
http://www.nature.com/nature/journal/v410/n6826/abs/410355a0.html

“Comparison of spectrally resolved outgoing longwave data between 1970 and present,” J.A. Griggs et al, Proc SPIE 164, 5543 (2004). http://spiedigitallibrary.org/proceedings/resource/2/psisdg/5543/1/164_1

“Spectral signatures of climate change in the Earth’s infrared spectrum between 1970 and 2006,” Chen et al, (2007) http://www.eumetsat.int/Home/Main/Publications/Conference_and_Workshop_Proceedings/groups/cps/documents/document/pdf_conf_p50_s9_01_harries_v.pdf

“Radiative forcing – measured at Earth’s surface – corroborate the increasing greenhouse effect,” R. Phillipona et al, Geo Res Letters, v31 L03202 (2004)
http://onlinelibrary.wiley.com/doi/10.1029/2003GL018765/abstract

“Measurements of the Radiative Surface Forcing of Climate,” W.F.J. Evans, Jan 2006
https://ams.confex.com/ams/Annual2006/techprogram/paper_100737.htm

“Satellite-Based Reconstruction of the Tropical Oceanic Clear-Sky Outgoing Longwave Radiation and Comparison with Climate Models,” Gastineau et al, J Climate, vol 27, 941–957 (2014).
http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-13-00047.1

“Evaluations of atmospheric downward longwave radiation from 44 coupled general circulation models of CMIP5,” Qian Ma et al, JGR Atmospheres, Volume 119, Issue 8, pages 4486–4497, April 27, 2014.
http://onlinelibrary.wiley.com/doi/10.1002/2013JD021427/abstract

More papers along these lines are listed here:
http://agwobserver.wordpress.com/2009/08/02/papers-on-changes-in-olr-due-to-ghgs/

Terry Oldberg wrote:
“By eyeball estimate and extrapolation from past entries in the UAH global temperature time series the 2014 average is going to be about 0.2 C below the peak. Since about 2001 the change in the yearly average has fluctuated about 0 while the CO2 concentration and projected temperature have moved inexorably upward.”

Yes, about 0.2 C from the monster El Nino year 1998. So what? Every year isn’t going to break a record in an AGW-world. UAH’s trend is still upward, showing 0.5 C of warming since it began in 1979. UAH just had the warmest October in their dataset, which will likely continue since the global SST is so warm.

Then there’s a question if UAH’s temperature numbers are right. (This is a question with ALL data, and they all come from models.) It takes a complicated algorithm to convert microwave readings to LT temperatures, especially accounting for satellite drift, instrumentation heating, and more. For many years scientists kept pointing out that UAH was running too cool, and the UAH team fought vigorously against such claims, but they lost and made several adjustments to their data:
http://en.wikipedia.org/wiki/UAH_satellite_temperature_dataset#Corrections_made

Right now UAH and RSS, which both try to measure LT temperatures via satellite data, disagree more than ever. Until that gets sorted out, it’s not clear either’s data can be trusted.

DAV wrote:
“That CO2 concentration has a noticeable effect on the atmosphere.”

I just gave it to you:
https://www.wmbriggs.com/blog/?p=14718#comment-133265

In particular, measuring the Earth’s outgoing radiation spectrum via satellite shows it very clearly:
http://www.giss.nasa.gov/research/briefs/schmidt_05/curve_s.gif

DAV wrote:
“So far it doesn’t seem to mean “warmer than usual” in 2014.”

What do you mean by “seem?”

Here is the data for the globe from NASA:
http://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt

Graphs on this page, but don’t include 2014 yet:
http://data.giss.nasa.gov/gistemp/graphs_v3/

@Briggs, related to your comment here:

You did reject partial use of that smoother, but not the whole use.

Oh but I did.

waarom deze keuze van data, immers de maandwaarden bevatten meer informatie, waarom deze keuze van smoother, en tenslotte, deze parameter.

roughly translates to.

Why this choice of data (he switched to yearly means)
Why did you choose a smoother
Why did you choose this parameter

Second, if ones says in Dutch that a trend shows that there is no warming, the classical H0 should be that warming >= 0, so you could reject it. Failure to reject Ho: trend = 0 is not equivalent to “the trend shows that there is no warming”. At least not in Dutch and also, I thought, not in English. The trend shows nothing. Not rejecting a hypothesis is not equivalent to accepting the alternative.

Seeing that the rest of the “audience” here is more interested in their own discussions than in the original subject if this post, I’ll leave it here. But I have to add mr. Briggs, you made a basic error (too). A statistician (and I should say every consultant) should start with a careful analysis of the data before drawing any conclusions. In this case you did not do that, you could not read the Dutch, you interpreted the document out of context (it was not meant as an analysis, but only showed the calculations that supported the analysis) and you never read the analysis at all. It’s on Marcel’s site, not in my pdf.

DAV wrote:
“So you are supposing why the GCMs are failing? And what exactly makes up “natural variability”? Is CO2 iself and the human population excluded from it?”

Climate models aren’t “failing,” because they DON’T PREDICT SHORT-TERM TEMPERATURE TRENDS.

How many times must this be repeated?

“And what exactly makes up “natural variability””

ENSOs. Volcanoes. Solar irradiance. Ocean cycles like the PDO, AMO, MJO, IOD, NAO, AO, etc. The ozone hole. Changes in winds. Changes in cloud cover. Etc.

Yes, natural variability doesn’t include humans. We’re a perturbation on the natural system. But natural processes still exist, and they still help shape climate and climate change.

But you do need sociological data about what humans are emitting to make an accurate prediction. Nor one has yet figured out how to obtain such data for the future.

“NOAA: First 10 Months of 2014 Were Hottest Recorded,”
http://robertscribbler.wordpress.com/2014/11/20/noaa-first-10-months-of-2014-were-hottest-recorded/

http://www.ncdc.noaa.gov/sotc/global/2014/10

Mr. Appell: I am curious how hundreds of new lows can be set and Buffalo buried under 6 to 8 feet of snow, yet it’s always getting hotter. By my calculations, it would have to 150F somewhere to average out. None of this makes a bit of sense when you look at the so-called continued heating. Plus, statistically, 300 plus months of temperature increases is not likely. One could achieve this by “adjusting” the past or by using .001 increase and declaring victory. Either one seems very dishonest. Yes, I am familiar with the graphs and the “adjustments” and “homogenization” done to data. (Homogenization is great for milk. Not so much for data. Though I suspect that is the purpose of the technique.)

You are saying, if I read your comment correctly, that we cannot know what will happen with CO2 except that it adds heat. How much, when, where–and nature can overcome all of this for unknown periods. When this started, it was explicitly stated that CO2 was THE driver and nature could not overcome. And yet, here we are…….

You can’t explain it without CO2 so you assume you’re correct? I recall that diabetes was obviously caused by eating too much sugar. Consuming sugar killed, sugar was in the urine, so the cause was the sugar, or so it seemed. It wasn’t but a straight-line cause and effect could easily have been drawn. Had everyone decided the cause was what was obvious, our health care would be much cheaper now and our population lower. While it is “obvious” to global warming advocates that CO2 is an evil villian that must be reigned in, let’s hope there are still those who don’t flatly accept the role and keep researching, just in case……

Plugging in CO2 into models designed to show that CO2 is necessary is a problem. Sometimes they run the models with each component removed, but not often, which results in circular reasoning.

If CO2 absorbs infrared radiation in the laboratory (discovered: 1859), why wouldn’t it absorb infrared radiation in the atmosphere?

Who said it didn’t? A match will noticeably warm a small test tube but have next to no effect on a large pot of water. I asked what effect it actually has on atmospheric temperatures and what evidence there is for it.

You can’t explain much of anything in planetary science without including the greenhouse effect. You can’t explain the interglacial-glacial gap without CO2.

Nonsense. Those are circular argument as are all of the CO2 forcing calculations. In fact, if CO2 can explain these then you are claiming to know the size of the effect of CO2 concentration and specifically you know exactly what dT=F(CO2 conc.) is so 1) how was it verified? All of this was discussed here some time ago and I don’t feel like looking for it. Feel free to do so yourself. Natural variation (whatever that is) couldn’t possibly be the answer of course. You do realize you are making an argument from ignorance when you say can’t explain things any other way, don’t you?

Right now UAH and RSS, which both try to measure LT temperatures via satellite data, disagree more than ever. Until that gets sorted out, it’s not clear either’s data can be trusted.

As if the land surface thermometer record with its strange distribution on the surface — not to mention it’s only 25% of the surface — is any better. At least the satellite data are more inclusive.

Mr. Appell: Excuse me–humans are not part of the system??? What are we? Aliens. We most certainly are part of the system and were since we first appeared on earth.
I am curious how hundreds of new lows can be set and Buffalo buried under 6 to 8 feet of snow, yet it’s always getting hotter. By my calculations, it would have to 150F somewhere to average out. None of this makes a bit of sense when you look at the so-called continued heating. Plus, statistically, 300 plus months of temperature increases is not likely. One could achieve this by “adjusting” the past or by using .001 increase and declaring victory. Either one seems very dishonest. Yes, I am familiar with the graphs and the “adjustments” and “homogenization” done to data. (Homogenization is great for milk. Not so much for data. Though I suspect that is the purpose of the technique.)
You are saying, if I read your comment correctly, that we cannot know what will happen with CO2 except that it adds heat. How much, when, where–and nature can overcome all of this for unknown periods. When this started, it was explicitly stated that CO2 was THE driver and nature could not overcome. And yet, here we are…….
You can’t explain it without CO2? I recall that diabetes was obviously caused by eating too much sugar. Sugar killed, so the cause was the sugar, or so it seemed. It wasn’t but a straight-line cause and effect could easily have been drawn. Had everyone decided the cause was what was obvious, our health care would be much cheaper now. Plugging in CO2 into models designed to show that CO2 is necessary is a problem. Sometimes they run the models with each component removed, but not often, which results in circular reasoning. (As DAV commented while I was typing.)

Jan van Rongen: I have often wondered about how accurately things get translated. Thank you for clarifying. And, yes, things generally get off track on posts about global warming. People are very passionate about the idea and often trade barbs and links. It does detract from the original posting. It would be great if we could actually discuss the statistics, but it never seems to happen.

“You can’t explain much of anything in planetary science without including the greenhouse effect. You can’t explain the interglacial-glacial gap without CO2.”
“Nonsense.”

It’s not nonsense. Go read any textbook on planetary climate. A good deal of it will be discussing the effects of GHGs, since they are the 2nd largest influence on a planet’s temperature. I recommend “Principles of Planetary Climate” by Raymond Pierrehumbert:
http://cips.berkeley.edu/events/rocky-planets-class09/ClimateVol1.pdf

Sheri wrote:
“Mr. Appell: Excuse me–humans are not part of the system???”

Learn to read — I said we weren’t part of the natural system.

DAV wrote:
“n fact, if CO2 can explain these then you are claiming to know the size of the effect of CO2 concentration and specifically you know exactly what dT=F(CO2 conc.)”

You keep asking questions that are covered in any undergraduate course on climate? Have you taken one? Because all of them are fair questions, and all of them were asked and answered long ago.

You strike me as like many others: You don’t know the first thing about climate science, yet you’re sure it’s all wrong. How does that happen?

BTW, the solution to T=F(CO2) is exactly what a climate model does — it numerically solves the partial differential equations that describe the processes that determine climate. Although F is a function of many more things than CO2 — the next of which is soot (a warming factor) and air pollution (a cooling factor). Right now traditional air pollution — aerosols — are counteracting about 1/2 of CO2’s warming factor. But there isn’t the data to calculate that that with much certainty.

So if we are not part of the “natural” system, what are we? Aliens? How can something born on earth, evolved on earth and part of the systems on earth be alien to the planet? Even Darwin didn’t call us aliens and yet you do. (PS—I can read. It was just such an utterly stupid comment it threw me off for a moment.)

It’s not nonsense.

If it’s not nonsense then someone with a PhD could surely give an elevator speech on how it is not. You certainly don’t seem to be able to. Why is it that GCM pushers can’t ever seem to summarize their positions? Please expand on how CO2 can be the only explanation of anything in planetary science and the interglacial-glacial gap.

You said earlier: Only until you account for natural variability can you begin to look seriously at CO2’s impact. Please expand on how “natural variability” (especially the “etc.” part you gave above) was accounted for to which made CO2 the ONLY explanation of the interglacial-glacial gap.

solution to T=F(CO2) is exactly what a climate model does

Perhaps, but the result is not verifiable so what’s the point?

David Appell
20 NOVEMBER 2014 AT 5:11 PM

DAV wrote:
“So far it doesn’t seem to mean “warmer than usual” in 2014.”

What do you mean by “seem?”

Here is the data for the globe from NASA:
http://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt

Of all of the datasets, GISS is the outlier on the warm side. When people start citing GISS, I just shake my head.

w.

David Appell:

One can make a prediction without knowing all of the details through the use of abstract states in building the model. An “abstract state” is a description of a system that is abstracted “removed” from selected details as, for example, the abstract state “male OR female” is removed from the detail of gender differences (logical OR implied).

Oh, yeah! I forgot:
RE: dT=F(CO2 conc.)
You keep asking questions that are covered in any undergraduate course on climate?

Yet you avoid answering them. Why? Have you taken these courses yourself? If so, have you forgotten them or maybe barely passed them? Are they really too complex to post here?

If they cover these then they can easily explain how to determine dT=F(CO2 conc.) which apparently needs a GCM to get the actual value or so you’ve implied.

Over at Judy Curry’s site there is a page discussing CO2 forcing in which roughly dT(CO2)=K*RF(CO2). Let’s stick with this, the simplest of all, for now.

The problem, as I see it, is to determine K but all that’s known is CO2 concentration and maybe RF(CO2). Not enough knowns to solve for the unknowns. It is impossible to measure dT(CO2). At Judy’s, it was arrived at with GCM runs using only CO2 changes. However, since it’s impossible to measure dT(CO2) and maybe even RF(CO2), the results are impossible to verify. It’s far worse if the actual equation is more complex than a simple linear relationship. The net result is a circular argument.

Willis,
Amen.

Willis E wrote:
“Of all of the datasets, GISS is the outlier on the warm side. When people start citing GISS, I just shake my head.”

Of course you do. Anything that conflicts with your construction of an alternate reality must necessarily be discarded.

BTW, UAH LT and C&W have a higher trend than GISS over the last magical 18 years.

Terry Oldberg wrote:
“One can make a prediction without knowing all of the details through the use of abstract states in building the model.”

Which is EXACTLY what climate modelers have been doing for years, using their many different economic “scenarios,” now called RCPs (Representative Concentration Pathways).

DId you really not know this??

BTW, these are still just scenarios. None will turn out to be what actually happens.

MattS,

You want proof of this, take the temperature anomaly series (global or any single station), add the baseline value used for calculating the anomalies back in and plot the data with a vertical scale with a 25.0 or 30.0 degree Celsius range and see if you can see any trend to the data.

That’s a data presentation critique. I specifically asked you about calculation. But ok, I’ll roll with it. What if the temperature data are in Fahrenheit? Do you set the bottom of the vertical scale to 32 instead of 0? What are the min/max values on the y-axis of your graph if the stations you’re most interested in are located in Antarctica?

As you yourself pointed out, the one true zero is on the Kelvin scale. Not only would a plot of surface temperature from 0-350 K not show any visible trend, you would strain your eyeballs to get any other meaningful information out of the graph. And why pick 350 K as the upper limit? How about a nice round number like 1,000? That would obfuscate things quite nicely indeed.

The other method is not unspecified, Briggs has already specified it, you look at the real data with no anomalies on a meaningful vertical scale that represents the full natural seasonal variability of the data.

That works just fine if all you’re looking at are data from one station, or stations from several locations whose absolute temperatures are roughly similar. But if you’re comparing equatorial trends to polar trends, your eyeballs might have a better time of it if those curves are lying right on top of each other instead of separated by 40 °C on the plot.

But if you’re comparing equatorial trends to polar trends, your eyeballs might have a better time of it if those curves are lying right on top of each other instead of separated by 40 °C on the plot.

Translating a plot on the Y-axis is not the same thing as plotting deltas from a baseline average.

David Appell
20 NOVEMBER 2014 AT 6:49 PM

Willis E wrote:

“Of all of the datasets, GISS is the outlier on the warm side. When people start citing GISS, I just shake my head.”

Of course you do. Anything that conflicts with your construction of an alternate reality must necessarily be discarded.

“Discarded”? I said nothing about discarding. I just said I shake my head.

BTW, UAH LT and C&W have a higher trend than GISS over the last magical 18 years.

Not sure what you’re smoking, but GISS LOTI since 1998 has a higher trend than both UAH and RSS. Can’t say about “C&W”, since I haven’t a clue what that might stand for. Crick and Watson? Country and Western?

w.

David Appell:

I’m not sure that you know what I mean by an “abstract state.” An example of one can be formed by taking all of the values of the global temperature lying in the interval between 14 and 15 Celsius and placing them in an exclusive disjunction. This produces the abstract state T1 OR T2 OR… where T1, T2… represent the values in this interval. States of this type are suitable replacements for temperature values as the outcomes of global warming events. Use of them would avoid the uncomfortable choice between equivocation by the climatologist and falsification of his model.

DAV wrote:
> You keep asking questions that are covered in any undergraduate course on climate? <
"Yet you avoid answering them. Why?"

Because I'm not here to be your tutor. Go learn something before you wish to participate in a debate, not after.

"Have you taken these courses yourself?"

No; on climate I'm self-taught. But it's just applied physics.

"If they cover these then they can easily explain how to determine dT=F(CO2 conc.) which apparently needs a GCM to get the actual value or so you’ve implied."

Writing down the function F(CO2, aerosols, +everythign else) is enormously complicated. But it's the first step it, and the models the IPCC uses are getting more complicated all the time:

See 2nd figure here:
https://www.niwa.co.nz/our-science/climate/information-and-resources/clivar/models
and
http://davidappell.blogspot.com/2011/04/progress-in-climate-models.html

"Over at Judy Curry’s site there is a page discussing CO2 forcing in which roughly dT(CO2)=K*RF(CO2). Let’s stick with this, the simplest of all, for now."

That's (a) well-known, and (b) quite a simplication from a real solution.

Terry Oldberg wrote:
“I’m not sure that you know what I mean by an “abstract state.” An example of one can be formed by taking all of the values of the global temperature lying in the interval between 14 and 15 Celsius and placing them in an exclusive disjunction. This produces the abstract state T1 OR T2 OR… where T1, T2… represent the values in this interval. States of this type are suitable replacements for temperature values as the outcomes of global warming events. ”

That sounds completely useless if you’re trying to calculate future climates.

Sheri,

However, an astute scientist would know that controls conditions in a lab where most variables are known is very different from an open system, like atmosphere, where many variables are unknown.

I’m aware that some pretty astute scientists perform the lab tests characterizing CO2’s spectral properties. So astute that they do, in fact, take things like temperature and pressure, even isotopic composition(!), into account. REALLY astute scientists would check atmospheric radiative models against observation directly, and … well gosh, they have: http://ramanathan.ucsd.edu/files/pr129.pdf

DAV,

What a silly thing to ask.

It’s difficult to take you seriously when you ask such questions as: “What is your evidence for this supposition of mutual causality?”

Brandon: No reason why you can’t put more than one set of data on a graph. You just can’t homogenize or otherwise mix data. Two lines are fine.

Mr. Appell–And there it is. The third thing clueless climate people do “Go learn it yourself”. Save time and just say you are clueless and be done with it. You’ve just outed yourself big time. (Or are you and Brandon trading emails and he’s coaching you?)
Really, self taught is a-okay for your majesty but the rest of us not? Could you be any more narcissistic?

David Appell:

In building a model one has to choose between abstract states and equivocation in making arguments. If equivocation is the choice then conclusions may not be logically drawn from these arguments. Thus far builders of climatological models have opted for equivocation and conclusions drawn illogically from arguments. This practice leaves us with a logically unsound basis for making policy.

Willis wrote:
“Not sure what you’re smoking, but GISS LOTI since 1998 has a higher trend than both UAH and RSS. ”

Here’s what I find for the linear trends for each:

GISS: +0.10 C/decade
UAH LT: +0.14 C/decade
Cowtan & Way: +0.13 C/decade

Terry Olderg wrote:
“In building a model one has to choose between abstract states and equivocation in making arguments. If equivocation is the choice then conclusions may not be logically drawn from these arguments. Thus far builders of climatological models have opted for equivocation and conclusions drawn illogically from arguments. This practice leaves us with a logically unsound basis for making policy.”

Yada yada. You must be a philospher or theoretical mathematician who’s never had to actually calculate anything, yet somehow manages to think all those big complicated words let you determine CO2’s climate sensitivity. Humorous though.

Models get their hands in the actual muck, about 30,000 ft (maybe light-years) below whatever realm you inhabit.

David Appell said: “NOAA: First 10 Months of 2014 Were Hottest Recorded, by Robert Scribbler,”

David, you are recommending a post from Robert Scribbler – the biggest conman of them all in the Warmist Organised Crime (WOC)
People, if you want to know about prof Robert Scribbler; I reviewed his post, see whom and what David is promoting, you to David, face the real truth: http://globalwarmingdenier.wordpress.com/2014/11/13/fusion-for-electricity-or-only-for-rip-off/

Sheri wrote:
“Mr. Appell–And there it is. The third thing clueless climate people do “Go learn it yourself”. Save time and just say you are clueless and be done with it.”

I don’t think people like DAV are clueless — I think they don’t know much about climate science, yet somehow manage to think they’re completely right and everyone else is completely wrong, and that somehow in the vast scientific enterprise no one ever thought about these questions until the moment they clicked “Post Comment.”

Frankly, I’d rather try to understand THAT trait, which is legion, than go over the same things time and again.

That’s (a) well-known, and (b) quite a simplication from a real solution.

Yes, it is a simplification but still illustrates the impossibility of verification. I notice you avoided addressing that.

Because I’m not here to be your tutor.

Of course not but your avoidance of stating what YOU think you understand allows you to continue to imply that you do understand without exposing your own ignorance. A happy coincidence.

You have contradicted yourself as I pointed out here so I’m thinking you are attempting damage control by employing the lame “it’s out there somewhere”. How impressive.

PS: I also think that if you’re going to engage in a debate about something technical, you have an obligation to know something about it first.

Yeah that IS such a nasty corner your stuck in. You have only yourself to blame though.

Sheri,

No reason why you can’t put more than one set of data on a graph.

If all you care about is trend, there’s no reason not to adjust the y values so that those two curves lie on top of one another instead of being separated by 40 units. Doing so does not change the slope (shape, whatever) of either curve.

You just can’t homogenize or otherwise mix data. Two lines are fine.

There are temperature observations from tens of thousands of sources. That’s a lot of squiggles to pack into one graph.

David Appell
20 NOVEMBER 2014 AT 7:48 PM

Willis wrote:

“Not sure what you’re smoking, but GISS LOTI since 1998 has a higher trend than both UAH and RSS. ”

Here’s what I find for the linear trends for each:

GISS: +0.10 C/decade
UAH LT: +0.14 C/decade
Cowtan & Way: +0.13 C/decade

Thanks, David, but without a specification of start and end dates, and the particular GISS dataset used, and a link to the Cowtan and Way dataset, I fear it’s less than convincing. You may be right … but that doesn’t show it.

Regards,

w.

Sheri,

Or are you and Brandon trading emails and he’s coaching you?

What, you and DAV haven’t been trading emails all this time? Get with the program already!

Dr. Appell (and you can address me as Dr. Kurland), you have taken on a very nasty tone–is it the case that you can’t convince by logic and factual evidence, so vituperation is in order?
With respect to the NOAA claim that 2014 is the hottest year ever, as Sheri has pointed out, why hasn’t this been reflected in observed temperatures here? Are temperature sensors in parking lots and near air conditioners still being used, as in previous years?
(See: 2013-2014 Northeast Winter Stats at http://www.weatherworksinc.com/winter-statistics-2013-2014
for more accurate, if mundane data that shows cooling, not warming.)

Let me put in a personal note. In the late 80’s and 90’s I too was a warmist and advocated all necessary measures to reduce CO2 production. I then read articles by Richard Lindzen and Fred Singer, and the work of the Canadian statisticians that demolished Mann’s hockey stick. Then came Climategate–the emails that showed the lies, the unscientific attacks on “deniers”, the falsification and omission of data–that convinced me that AGW was, as then (and currently) practiced, a pseudo-science, a useful vehicle for politicians and scientists on the make.
So your discussion in this post is as fruitless in convincing those who do not believe there is scientific merit to AGW as presently set forth, as our factual evidence and logic is fruitless in convincing you, the true believer. The discussion is remarkable in resembling that between religious faithful and evangelical atheists. Rational arguments are not effective.
By the way, please in future comments, don’t try to impress the scientific hoi-polloi by comments such as “these are solutions of partial differential equations.” Solving a partial differential equation that does not reflect reality is an empty exercise, and some of us who have tossed around boundary-value problems and integral equations don’t think it adds to your argument.

David Appell:

Your speculations about my CV bear little relationship to the reality. I’m a retired engineer with a background in the design and management of scientific studies. Among the tasks that I have completed successfully were to head all theoretical aspects and some observational aspects of a $100 million study of the reliability of materials in the cores of light water reactors. This work resulted in the existence of a computer-resident system for control of success or failure for the zirconium alloy sheaths that surrounded the uranium oxide pellets of a fuel rod. In the 1970s colleagues and I pioneered the use of information theory in bringing complex systems under control. Follow on work by one of these colleagues resulted in the existence of the first statistically validated long range weather forecasting models.

David, using the UAH LT dataset and starting in January 1998 for the global data, I get a trend of 0.07°C/decade …

w.

Brandon: Maybe if you elaborated more than “does CO2 know it’s in the lab”, it would help. I’ll check your link. It looks interesting.
Agreed, you can adjust the Y values so the two curves are not on top of each other. Not a problem.
Why must we compare all the sources at once? Global warming is not equal globally. Why can’t we look at various areas and compare? Or run a program to compare the differences. I’m sure a super-computer and programmer could do that. Without mushing data all into one compact number. Don’t have time right now to give examples, but maybe later on I’ll put a blog entry.

Dang! You’re on to me! (concerning emails—however, I can neither confirm nor deny with whom the emails are being exchanged)

DAV,

Translating a plot on the Y-axis is not the same thing as plotting deltas from a baseline average.

I’ll grant that the latter is a bit more specific about the calculation used, yet I’ll be darned if I understand why both of them are not translating plots along the Y-axis. Please explain.

Terry Oldberg:

I’ve seen you on the Internet before, trying to bamboozle your way with logic, as on Judith Curry’s blog. It’s just intellectual masturbation, with little connection to real science or how real scientists think. If you want to show off your understanding of logic, fine, but don’t confuse it with an ability to address scientific questions or use the answers to help make policy.

both of them are not translating plots along the Y-axis. Please explain.

It’s not particularly important but subtracting the same constant (such as a “global average”) from both plots does not cause the plots to overlay each other. To do that one of the plots should not be altered. If you are interested in comparing the plots it may be better to subtract one from the other. It’s the same operation used in CPUs to compare one number with another. If they have the same rates of change the result would be a constant horizontal line though not necessarily zero.

I’m not so sure overlaying them would tell you much anyway. The tropics are nearly constant the year round while those further north and south will have larger seasonal variation. You could use yearly averages but that is starting down the road of replacing data with non-data.

Bob Kurland wrote:
“With respect to the NOAA claim that 2014 is the hottest year ever, as Sheri has pointed out, why hasn’t this been reflected in observed temperatures here?”

Here being the US? The US is 1.9% of the globe, that’s why.

Here are the global temperature averages for October; notice all the red:
https://twitter.com/afreedma/status/535628264006766592/photo/1

Here’s the reanalysis for today (11/20):
http://cci-reanalyzer.org/DailySummary/index_ds.php

It’s quite cold in the US. It’s quite warm in Alaska, Greenland, northern Europe, Africa, Asia, and Australia.

David Appell:

I’m into logical discourse. You’re not. So long.

Bob Kurland wrote:
“Are temperature sensors in parking lots and near air conditioners still being used, as in previous years?”

I don’t know — I’m sure you can find that information for yourself.

“(See: 2013-2014 Northeast Winter Stats at http://www.weatherworksinc.com/winter-statistics-2013-2014
for more accurate, if mundane data that shows cooling, not warming.)”

The Northeast is, what, 10% of the US by area? So it’s ~0.2% of the globe. No such particular area has much weight in the global average.

David Appell commented : ” Gee, then I wonder why so many scientists have spent so many decades working on dendrochronology, and I wonder why you would think you know something all of them don’t”

David, David; ”Warmist Scientist” only ”pretend” that they are ”researching” because before even they start, they know which lie they need to discover; their work is a ”sandpit job” David, you are one of the ”Warmist White Collar Criminals” (WWCC) you know yours and their honesty. Lying is bread and butter for the ”Warmist Organized Crime” (WOC) – they should be all in the same jail cell as Bernard Madoff, only longer jail therm; because they have squandered more billions$$$, for the PHONY global warming, than Bernard – here is the best proof : http://globalwarmingdenier.wordpress.com/2014/07/12/cooling-earth/

Bob Kurland,

If your standards are Fred Singer, no wonder you believe as you do. Singer is IMO one of the most corrupt scientists of all time, selling his opinion for whoever pays him the most. It happened first with tobacco, and now it’s happening with climate. change. He made a pretty penny to write the NIPCC report.

I heard Singer talk at Portland State University in 2011. He science was, I’m sorry to say, pathetic, unconvincing, and also sad:

http://davidappell.blogspot.com/2011/05/video-of-fred-singers-talk-at-portland.html
http://davidappell.blogspot.com/2011/05/fred-singers-lecture-at-portland-state.html
http://davidappell.blogspot.com/2012/02/more-whoppers-from-fred-singer.html

Singer may have once done good science. But his place in history will forever be that of a hired shill.

stefanthedenier wrote:
“David, you are one of the ”Warmist White Collar Criminals” (WWCC) you know yours and their honesty.”

Insult me all you want. All it shows is that you don’t have a better scientific argument, or any scientific arguments at all.

David Appell commented: ”In particular, measuring the Earth’s outgoing radiation spectrum via satellite shows it very clearly”

David, that’s outdated pagan belief, what you are peddling!

The new and correct science is: ”the cold vacuum” that the planet is traveling trough – is penetrating into the earth’s atmosphere and neutralizing the heat. NO any heat ”radiates” from the atmosphere to out of space – so: THE SATELLITE CANNOT ”DETECT” any heat ”radiating out”
By 25km altitude – the temp is minus -90C (-139F) because no heat radiates out! Longwave radiation from kinetic heat is only few microns; read my post and learn for your own benefit; the ”real proofs” always win on the end:: http://globalwarmingdenier.wordpress.com/2014/07/12/cooling-earth/

Terry Oldberg wrote:
“I’m into logical discourse. You’re not.”

Good for you, if you like that. But it has very little to do with how science gets made.

Here’s something you will likely abhore. Since about 1950 quantum field theorists have been doing calculations whose results are often infinity. Yet they’ve developed techniques to subtract one infinity from another and get a finite answer — some such predictions are correct to 10 significant figures:
http://en.wikipedia.org/wiki/Anomalous_magnetic_dipole_moment#Electron

If logic prevailed, they’d still be back in 1950, doing no calculations and not having discovered a thing about the real world.

DAV wrote:
“You could use yearly averages but that is starting down the road of replacing data with non-data.”

That’s purely ridiculous. Removing a seasonal signal is a straightforward mathematical operation that is completely equivalent to the first description of the data. You can transfer between them any time you want without losing any information at all. It’s constantly done in climate science, and it’s done in economics, astronomy, and probably more sciences if I thought about it.

Insult me all you want. All it shows is that you don’t have a better scientific argument, or any scientific arguments at all.

Yeah, your science position should include scientific terms like “intellectual masturbation”, “paid shill” and more. Avoid insulting with presentation of facts or responding honestly to questions and criticisms. Whenever possible, imply the other person is an ignoramus for even thinking of speaking to a PhD holder as if they are somehow peers. No respect for authority! I mean, really! Stick to the low road. The high road is for fools and idiots. By all means, downplay logic and promote mindless calculation.

stefanthedenier wrote:
“The new and correct science is: ”the cold vacuum” that the planet is traveling trough – is penetrating into the earth’s atmosphere and neutralizing the heat. NO any heat ”radiates” from the atmosphere to out of space.”

This is gobbleygook. And pseudoscience — and pretty lame pseudoscience at that.

Keep ranting, though, if it makes you feel better.

Dr. Appell, you denigrate Fred Singer’s work because he’s a hired tool of whomever–well, what about all the pseudoscientists making big money out of the government–they’re not fudging and cherrypicking data for fun, but for grant money. Fred Singer has better credentials than I in this subject, and from what I judge of your arguments, better than yours. And, in any case,financial support is irrelevant to the logic of argumentation and the truth–factual truth–of what arguments predict. By the way, you still have not addressed the issue of AGW failure to predict (A theory or model which gives predictions for which only 50% occur , is not a reliable theory.) And what about the statistical demolition of the Penn State person’s hockey stick representations (I don’t want to name him for fear of being sued). and what about the lies, cherry-picking and unscientific behavior shown in the climategate emails?
My thesis director E.B. Wilson, Jr., in his book ” An Introduction to Scientific Research”, said that the two most important qualities a scientist should have are the ability to criticize his own work and scientific integrity. As the climategate emails have shown, these qualities are sadly lacking in most prominent proponents of AGW.
And with these remarks I’m out of here. Your comments are not interesting and do not lead to more stimulating intellectual excursions. Time can and should be spent more productively.

Sheri,

Maybe if you elaborated more than “does CO2 know it’s in the lab”, it would help.

It was in direct response to DAV’s statement: “It is an error to assume effects observed in simple systems can be carried directly over to a complex system.” I am jerking his chain because it is a gross error to assume that working climatologists have don’t understand the complexities of the situation better than both of us.

Global warming is not equal globally. Why can’t we look at various areas and compare?

http://climexp.knmi.nl/selectfield_obs2.cgi?id=someone@somewhere

Have fun.

Without mushing data all into one compact number.

I really don’t understand the opposition to using a global index as a yardstick measuring a global phenomenon.

Dang! You’re on to me! (concerning emails—however, I can neither confirm nor deny with whom the emails are being exchanged)

A gentleman never asks …

I knew it was time to disengage with Dr. Appell at the point at which he had lost in our debate on all of the logical issues. He responded to his plight by fabricating a picture of me. He proceeded to attack the character of the person he had fabricated as though not knowing that the character of one’s opponent is irrelevant to a scientific issue.

DAV,

It’s not particularly important but subtracting the same constant (such as a “global average”) from both plots does not cause the plots to overlay each other.

Calculating anomalies is done at the station level, usually by month. Computing global/hemispheric/regional averages comes after the anomaly process, not before.

If you are interested in comparing the plots it may be better to subtract one from the other.

Well sure, but that’s a different operation from what I was discussing with MattS. If I want to know the temperature differential at a point in time between Dallas and Vancouver, using anomaly data would not be appropriate. If I’m after slope of a regression line, it matters not one bit whether I calculate it based on anomalies or raw. His main complaint has nothing to do with that basic algebraic fact, but rather how scaling the y-axis affects eyeball analysis of trends.

I’m not so sure overlaying them would tell you much anyway. The tropics are nearly constant the year round while those further north and south will have larger seasonal variation.

Unless the trend period is quite short, I don’t see that seasonal variations will have much impact on trend until reaching into high polar latitudes.

You could use yearly averages but that is starting down the road of replacing data with non-data.

Well I’m not big on eyeballing charts for analytical purposes, so if a monthly (or daily) plot is too messy to look at I calculate the trend against the high resolution data and then do the pretty to look at chart based on summary data.

Terry Oldberg,

Your character? When did I do that? I said your ideas weren’t at all how science gets done. You choose to take that as a statement about your character, not me.

Bob Kurland wrote:
“what about all the pseudoscientists making big money out of the government–they’re not fudging and cherrypicking data for fun, but for grant money.”

1) Which “pseudoscientists.”
2) what data has been “fudged,” and how?
3) What data has been “cherrypicked,” and how?

“And, in any case,financial support is irrelevant to the logic of argumentation and the truth–factual truth–of what arguments predict. ”

Ha ha. I guess all that oil and tobacco money just goes down Singer’s drain, right?

I wonder how many smokers were killed by Singer’s “science.” I wonder how many future people will be killed by his “climate science.”

“By the way, you still have not addressed the issue of AGW failure to predict (A theory or model which gives predictions for which only 50% occur , is not a reliable theory.”

Climate models don’t predict.
And what is the basis for your 50% number?

“And what about the statistical demolition of the Penn State person’s hockey stick representations”

Are you referring to the one paper that never went anywhere and was shown to be flawed, or the dozen papers that have confirmed and replicated the Mann et al hockey stick, some using independent mathematical techniques?

“,,,the lies, cherry-picking and unscientific behavior shown in the climategate emails?”

Which ones, exactly?

(I don’t want to name him for fear of being sued). and what about the lies, cherry-picking and unscientific behavior shown in the climategate emails?

DAV, This is an online debating forum. If you don’t like being corrected, maybe it’s not for you. If you don’t like being criticized for not knowing much climate science yet being sure it’s all wrong, you probably belong elsewhere.

And you still haven’t explained how you dismiss the science without understanding it.

Stefandenier wrote:
“THE SATELLITE CANNOT ”DETECT” any heat ”radiating out”:

Here is one such measurement:
http://www.giss.nasa.gov/research/briefs/schmidt_05/curve_s.gif

There are many others — look up the work of John Harries in the UK.

“NO any heat ”radiates” from the atmosphere to out of space”

If no energy escapes the top of Earth’s atmosphere, how did the Apollo astronauts take all those pictures of Earth?

David Appell Nov. 20 at 7:73 PM:

Yada yada. You must be a philospher or theoretical mathematician who’s never had to actually calculate anything, yet somehow manages to think all those big complicated words let you determine CO2’s climate sensitivity. Humorous though.

David Appell Nov. 20 at 10:01 PM:

I’ve seen you on the Internet before, trying to bamboozle your way with logic, as on Judith Curry’s blog. It’s just intellectual masturbation, with little connection to real science or how real scientists think. If you want to show off your understanding of logic, fine, but don’t confuse it with an ability to address scientific questions or use the answers to help make policy.

David Appell Nov. 20 at 11:42 PM:

Your character? When did I do that? I said your ideas weren’t at all how science gets done. You choose to take that as a statement about your character, not me.

OK, I was wrong, apparently you have calculated a few things in your live — if logically I can accept your commentary discourse on this subject.

However, I do think the tripe you write here is intellectual masterbation — and so, logically, I do therefore think you’re an intellectual masterbator. I also think you try to bambozzle people with your logical discourse, and I think such logical discourse has nothing at all to do with how science gets done. I don’t see any value at all in this kind of writing:

http://judithcurry.com/2011/02/15/the-principles-of-reasoning-part-iii-logic-and-climatology/

None. You criticize everything about climate science, while contributing absolutely nothing to it. But maybe I’m wrong. Why haven’t you applied all your immense logical theory to the problem of climate change and given us all the answer? To 10 significant figures, please.

If you don’t like being corrected,

Whatever that means.

Maybe this is it?
And you still haven’t explained how you dismiss the science without understanding it.

Actually, I’m more into dismissing you. You are the one who backed into the corner and put on the pointy hat with contradicting statements. After I called you out on it, you responded by implying it somehow meant I lacked knowledge in the subject at hand and started using the “go look it up” smoke screen.

Which contradictory statements?

David Appeal ask: ”If no energy escapes the top of Earth’s atmosphere, how did the Apollo astronauts take all those pictures of Earth?”

David, I asked you twice, to read that post – obviously you are scared from ”real proofs that can be proved now” no need to wait 100y. The answers are there!!!

The old /popular beliefs are: heat radiating out, albedo crap. NOP! you can take a photo of a deep freezer inside, don’t confuse with Apollo. b]You give lots of ”links” to other people to go for brainwashing on Warmist dysentery; but when you are given a link, to learn the truth -> you run for cover. In other words: you know that you are wrong!! Your brains-trust Robert Scribbler after getting on my blog and read only one post -> he disappeared from his own blog for two weeks! Ask him how painful the real proofs are for the ”Warmist Organized Crime” (WOC) Why don’t you try?
(he should stand by, for much more real proofs, instead of running under his rock)

Stefan: Read what post? (I don’t keep up with all the comments.)

I “know that I’m wrong.” What does that even mean?? What I know is what I’ve written, and what I’ve written is what I know. Are you reverting to some logical discourse here?

Which contradictory statements?

Oy veh! What a putz!

I called you out on it here and reminded you of it but since then I’ve pretty much lost interest in anything you have to say not that you would have any sensible response anyway. But please! Do enjoy the Streisand Effect.

Bye. Bye.

Your link
https://www.wmbriggs.com/blog/?p=14718#comment-133281

goes to a comment of mine, not a comment by you.

There are too many comments to keep up with, so if you want this addressed you’ll need to repeat it or give a better link.

When I click on it, it goes here.
20 November 2014 at 5:44 pm

You have already responded with idiotic evasion. I doubt you would do better now.

So long.

Do you mean this:

“Please expand on how CO2 can be the only explanation of anything in planetary science and the interglacial-glacial gap.”

That’s easy — I never wrote such a thing.

Or if you mean this:

“Please expand on how “natural variability” (especially the “etc.” part you gave above) was accounted for to which made CO2 the ONLY explanation of the interglacial-glacial gap.”

I never said that, either.

Terry Oldberg: By the way, your equation (1) on this page is wrong:
http://judithcurry.com/2011/02/15/the-principles-of-reasoning-part-iii-logic-and-climatology/

The climate sensitivity to a forcing, call it S, is defined by

S = dT/dF

where F is the radiative forcing. For CO2, near present values

dF(C) = alpha*ln(C/Co)

where alpha = 5.35 W/m2. (This equation has additional terms when CO2 gets above 700 ppm or so.)

Then, dT= S*alpha*ln(C/Co)

so if C doubles, the temperature T2 is

T2 = S*alpha*ln(2)

S = T2/alpha*ln(2)

Climate sensitivity has units of Kelvin per W/m2, though sometimes people just call T2 the climate sensitivity, but it’s only true for CO2 values near where we’re at now. Above about 700-800 ppm, or at a different starting temperature (like in the past or in the future), or at lower values of CO2, T2 will not be so simply related to S.

No, S isn’t an observable, but it can be calculated, and it’s ridiculous to write:

“Thus, the proposition that “ΔT lies in the range of 2oC to 4.5oC” is not an observable. However, an outcome of an event is an observable. Thus, the proposition that “ΔT lies in the range of 2oC to 4.5oC” is not an outcome. WG1’s claim that it is “likely” that, for a doubling of C, ΔT lies in the range of 2oC to 4.5oC references no outcome. As the claim references no outcome, it conveys no information about an outcome from a policy decision to a policy maker.”

or that you need 3900 years of surface temperature data. Your logical error: calculating climate is not the same as calculating weather — they are two fundamentally different problems: the first a boundary value problem, and the second an initial value problem

So estimates of S and the observed dT’s are, of course highly relevant to policy makers.

If you’re going to get all logical on people, you need to get the equations right, and you must understand what climate is and what it is not.

A brief recap:

20 November 2014 at 4:10 pm

Only until you account for natural variability can you begin to look seriously at CO2’s impact.

20 November 2014 at 5:19 pm
“And what exactly makes up “natural variability”

ENSOs. Volcanoes. Solar irradiance. Ocean cycles like the PDO, AMO, MJO, IOD, NAO, AO, etc. The ozone hole. Changes in winds. Changes in cloud cover. Etc.

20 November 2014 at 5:19 pm

You can’t explain much of anything in planetary science without including the greenhouse effect. You can’t explain the interglacial-glacial gap without CO2.

—
“…which made CO2 the ONLY explanation of the interglacial-glacial gap.”
I never said that, either.

Well, yes you did. See the link to 20 November 2014 at 5:19 pm. Perhaps the “ONLY” was a bit of an exaggeration but If “natural variation” was accounted for, which you claim is necessary to look at the impact of CO2, then pretty much the only thing left is CO2, yes?

Oh my, the second reference to 20 November 2014 at 5:19 pm should have been 20 November 2014 at 4:48 pm. Ah well.

DAV,

Perhaps the “ONLY” was a bit of an exaggeration …

A tad, yes.

Ditto, Brandon.

Actually Matt we can and do check our model of temperature against old data.

Before I get to that I want to thank you for extolling the virtues of not splicing data. When a station changes it’s a new station.

On old data. We build a field. That field is a prediction of would have been measured in the locations where we
Have no data. This field is built from the data we do
Have. As fate would have it we now have new old data.
That is, station data recently recovered. Old records never used before. Stations not used to build the field. New old data.

Basically the global average is not an average. It’s a prediction of what would have been measured had we had an instrument at a given time and place in the past.
Sometimes we end up finding new old data. This allows us to test our prediction of the past.

Steven Mosher
22 NOVEMBER 2014 AT 1:52 PM

Actually Matt we can and do check our model of temperature against old data.

Before I get to that I want to thank you for extolling the virtues of not splicing data. When a station changes it’s a new station.

Mosh, good to hear from you as always. I have pointed out directly to you a huge problem with this method, which you call the “scalpel” method, both here in this thread and elsewhere. The problem is that if you have a trendless “sawtooth” shaped record, as is quite common in climate data due to things like routine maintenance and station moves (see my examples above), the scalpel method gives you a very erroneous result with a trend.

I have asked both you and Zeke Hausfather about this problem several times, and you both have been quite assiduous about not answering. And even our estimable host, who recommended the method, appears to be averse to answering my question.

So … here’s your chance to finally settle the question. Since we know that your scalpel method can easily convert a trendless sawtooth shaped record to a record containing a statistically significant trend … how do you guard against this or remove this effect from your results?

It’s a big issue because in my experience, the teeth of the sawtooth wave in climate data tend to point up, which would result in a falsely indicated larger warming than actually exists in the data.

w.

David Appell, the importance of the problem is another arbitrary assumption.

aaron wrote:
“David Appell, the importance of the problem is another arbitrary assumption.”

So is the problem of Ebola.

willis

“Here’s the problem. In between paintings, the temperature gradually rises, due to increased absorption as the paint gets old, darkens, and gets dirty. So let’s assume that there is absolutely no temperature change over the period. We end up with a sawtoothed wave, where the measured temperature gradually rises for ten years, and then drops back down when the Screen is painted.”

actually anthonys field test of this showed NOTHING OF THE KIND

“Mosh, good to hear from you as always. I have pointed out directly to you a huge problem with this method, which you call the “scalpel” method, both here in this thread and elsewhere. The problem is that if you have a trendless “sawtooth” shaped record, as is quite common in climate data due to things like routine maintenance and station moves (see my examples above), the scalpel method gives you a very erroneous result with a trend.

I have asked both you and Zeke Hausfather about this problem several times, and you both have been quite assiduous about not answering. And even our estimable host, who recommended the method, appears to be averse to answering my question.”

1. the cause you imagine has been tested. there was no effect.
2. The situation you define as common is in fact not common at all.
3. the scalpeling method.
a) we split stations where the meta data suggests that it be split. As briggs suggests.
b) we also test for breaks. we scalpel where the break test suggests a discontinuity.
1. hiding the meta data we actually tested the ability of the breakpoint
code to identify the hidden metadata changes. For example, we dont
slice a series that shows and instrument change. run the code and it
identifies a break in the series that is co incident with the instrument change
2. We ran extensive sensitivity test on the additional slicing. That is only slice where metadata says to ( 0 additional slicing) to slicing with very lax
requirements for slicing. additional slicing ( your supposed saw tooth problem ) does very little to the global series. It only improves the local
scale.

In short the problem you suppose is not a problem. You, RomanM, Briggs, all agree that if a station changes location or instrument that its a new series.
We do that. We also tested using statistics ( break point) to indentify undocumented changes. Keeping a series intact that should be sliced is
basically trusting the metadata– being over certain that the metadata record captures all changes. We also tested from 0 additional slicing to very agressive slicing. The global answer does not change. What changes is the smoothness of the local field.

Steven,

I caught this over at Venema’s place a few days ago: http://variable-variability.blogspot.ch/2014/11/participate-in-best-validation-study.html

Participate in the best validation study for daily homogenization algorithms
Rachel Warren is working on the validation of homogenization methods that remove non-climatic changes from the distribution of daily temperature data. Such methods are used to make trend estimates for changes in weather extremes and weather variability more accurate.

To study this, she has just released a numerical validation dataset. Everyone is invited to apply their homogenization method to this dataset. It looks to be the most realistic validation dataset produced up to now. Thus it promises to become an important paper for the homogenization community.

Can you comment whether the BEST team will be participating?

Climate Corruption — Blame Gauss

In the article “Netherlands Temperature Controversy: Or, Yet Again, How Not To Do Time Series,” (https://www.wmbriggs.com/blog/?p=14718%C2%A0 ) William Briggs makes a strong case against data processing statistical techniques used in climate analysis. He shows how data can be “tortured” and regression applied to conclude different results from the same data.

But he doesn’t discuss the history of regression involving it’s inventor, Carl Friedrich Gauss, who used the method to discover the orbit of the planetoid Ceres. As discussed in “Gauss and Ceres,” Leorah Weiss (http://www.math.rutgers.edu/~cherlin/History/Papers1999/weiss.html ), Gauss’ succeeded where others didn’t by using a good physical model of the motion (from Kepler) in his fitting process. this model came from physics and was external to the regression technique Gauss had developed.

It is precisely this connection between physics and math (regression) that is missing in climate temperature analysis. Gauss gave us the idea of regression, but did not warn us that people might misuse it to “justify” models with no underpinnings in physics.

So, I blame Gauss for the current climate corruption, well, not really.

Steven Mosher
22 NOVEMBER 2014 AT 4:05 PM

“Mosh, good to hear from you as always. I have pointed out directly to you a huge problem with this method, which you call the “scalpel” method, both here in this thread and elsewhere. The problem is that if you have a trendless “sawtooth” shaped record, as is quite common in climate data due to things like routine maintenance and station moves (see my examples above), the scalpel method gives you a very erroneous result with a trend.

I have asked both you and Zeke Hausfather about this problem several times, and you both have been quite assiduous about not answering. And even our estimable host, who recommended the method, appears to be averse to answering my question.”

1. the cause you imagine has been tested. there was no effect.
2. The situation you define as common is in fact not common at all.
3. the scalpeling method.
a) we split stations where the meta data suggests that it be split. As briggs suggests.
b) we also test for breaks. we scalpel where the break test suggests a discontinuity.
1. hiding the meta data we actually tested the ability of the breakpoint
code to identify the hidden metadata changes. For example, we dont
slice a series that shows and instrument change. run the code and it
identifies a break in the series that is co incident with the instrument change
2. We ran extensive sensitivity test on the additional slicing. That is only slice where metadata says to ( 0 additional slicing) to slicing with very lax
requirements for slicing. additional slicing ( your supposed saw tooth problem ) does very little to the global series. It only improves the local
scale.

And you truly expect me to just take your word on this, Mosh? Aren’t you the “free the data, free the code” man? Where is the data and the code to back up your claims?

For example you say:

1. hiding the meta data we actually tested the ability of the breakpoint code to identify the hidden metadata changes. For example, we dont slice a series that shows and instrument change. run the code and it identifies a break in the series that is co incident with the instrument change

While that gives me a warm fuzzy feeling, I fear that without numbers it is meaningless. How many tests did you run? How many were successful? How many failed? What was the ratio of false positives to false negatives?

Like I said, without data and code your claims are meaningless.

As to whether the situation is common, here is the common situation I described above:

This is a common situation in climate science due to the urban heat island. We have a thermometer near town. Let’s assume the temperature is steady for a long time. The town gets built up over the years, and the measured temperature rises due to UHI. Finally someone says hey, the temperature is reading way high, there’s pavement all around, let’s move it to the airport. Then, of course, over the years the area surrounding the airport is built up and once again the temperature gradually rises … so they move the thermometer out of the airport and to some more rural area.

Now, if you consider the town record and the airport record as separate records, all you are doing is baking in a non-existent trend. Remember that the temperature is stable … but the two individual records both show trends. So what you’ve done is removed the important information, the part where the temperature drops precipitously back down to or near the real value.

Are you seriously claiming that this situation, where a station moves out of town to the airport, is uncommon? Or are you claiming that there is no UHI in the town, and that after that, there is no UHI around the airport? It’s unclear what your claim is, since you didn’t respond to this example.

Finally, you say:

Steven Mosher
22 NOVEMBER 2014 AT 3:52 PM

willis

“Here’s the problem. In between paintings, the temperature gradually rises, due to increased absorption as the paint gets old, darkens, and gets dirty. So let’s assume that there is absolutely no temperature change over the period. We end up with a sawtoothed wave, where the measured temperature gradually rises for ten years, and then drops back down when the Screen is painted.”

actually anthonys field test of this showed NOTHING OF THE KIND

Since Anthony’s test didn’t last for ten years, and it definitely showed a difference in temperature between whitewash, latex, and bare wood … I fear your shouting is not very convincing.

Anthony’s tests, for example, showed that on a typical day bare wood runs about 2°C warmer than whitewash, and about degree warmer than latex. So if the paint is flaking off (as is quite common, particularly in the third world), Anthony’s tests showed that as I said, repainting would lead to a cooling of the record.

In any case …

Cite? Where did Anthony’s field test show anything about repainting after ten years?

w.

Global warming, when purported science becomes religion.

Tim wrote:
“Global warming, when purported science becomes religion.”

So do you think CO2 doesn’t absorb infrared radiation?
Or instead, do you think the Earth doesn’t emit it?

Dave Appell,

Science is based on theories and hypotheses. Models are approximations of them. And climate models are seriously missing lots of important items – either because climatalogists don’t understand, the equations are too difficult to compute accurately, or because they’re thinking ‘linearly’ and say that the change over time is too little to have an effect (and need to read up on non-linear mathematics).

anng wrote:
“And climate models are seriously missing lots of important items – either because climatalogists don’t understand, the equations are too difficult to compute accurately, or because they’re thinking ‘linearly’ and say that the change over time is too little to have an effect (and need to read up on non-linear mathematics).”

Which important physical processes are missing from climate models?

Well, this is a lively thread, but it is apparently not going to result in an outcome that all contributors will accept. That’s what the scientific method is about – hypotheses and the testing thereof.

I have not yet read all the contributions, since there are far too many to absorb, and they have become a bit repetitive. I just want to say that this thread began with a discussion of the Netherlands temperature record, specifically the De Bilt record. This prompted me to consult my “archives” to see what a relatively early version of the GHCN record has to say, and how my analyses of it went. It turned out to be very interesting and a worthwhile exercise, which I would recommend to any serious contributor. My findings of around ten years ago turned out to be exactly repeatable, and I was encouraged by that to collect the most recent data set which starts at 1901 and finished at October 2014. Analyses of this data confirmed what I’d done earlier, but of course brought things up to date.

However, it is not the arrival of new data that I’d like to write briefly about, but mainly the period 1970 to 1990, which holds at least one surprise for those who have not examined it closely. Clearly changes have occurred – there’s been a rise in temperature, evidenced by fitting a regression to generate an estimate of “trend”, and having established this some might compute the residuals or attempt some inferential statistics, which is not such a straightfoward task. And then if one is interested in monthly means, as I am, there is the seasonal effect to be dealt with. And what about “anomalies” with respect to some arbitrary time interval. For me, the sensible period over which to calculate “anomalies” the the entire span of the data that one is examining. Why choose any other?

I recommend a very simple approach to the seasonal problem. Using the complete time period in which one is interested, just subtract the overall mean for each month from the individual data values, creating what I call a “monthly difference” rather than an “anomaly”, which I feel implies some kind of prejudicial feel to the calculated quantity. Now comes the interesting bit. Form a derived time series by computing the cumulative sum of the monthly differences. Note that this derived series contains all the data from the original one except the base value used for the cusum calculation. There is NO smoothing, and the original data can be recovered from its cusum by simple subtractions. If you used the “monthly differences” as your series the base value is of course zero. If you choose instead to use the data without allowing for seasonal effects the normal base value would be the overall mean of the time series. In both these cases the cusum will end with a value zero, which both simplifies further examination and ensures that plots of the cusum will be readily plottable. Cusums of a parameter that is “stable” plot approximately as a straight line (with excursions that represent noise or a change in the observed data that persists for several observations. If the “stable” value happens to be above the base value the cusum will show a positive slope, whose value is the difference of the observations from the base value. Curving cusums show that the original data were changing. It follows that a sharp and enduring change in the appearance of the cusum indicates an “event”, which may well be a step change in the data.

Cusums were developed, in the 1950s I believe, to help statistical quality control practitioners to detect at the earliest possible moment any change in production line quality. With historical data their use is to attempt to identify major climate regimes and any changes in the regimes.

Cusums are readily calculated in spreadsheets, though I use a stats package.

Returning to the complete data for De Bilt, the cusum is very striking. From 1901 to 1987 the general form of the plot is a gentle positive curve which immediately indicates to someone familiar with interpreting cusums that a fairly constant increase in values – with of course fairly brief excursions – held sway. Then a striking break in the smooth progression of the cusum occurs, with no prior warning. After then discontinuity there is little sign of a curve in the cusum. Recalculating the cusum but for the period 1970 to 1990, using the monthly averages for that period in computing the monthly differences, gives a close up of the behaviour of the original series. It indicates that at September 1987 a drastic and enduring change in monthly differences occurred. Further detailed work indicates that this was a temperature change of about 0.6 C, and it became evident from one month to the next.

How can this be? I have no idea, but I am convinced that it is real. The detailed meta data from De Bilt makes no mention of 1987, though several notes cover changes in equipment or siting, though none of these coincides with anything noticeable in the cusum.

Clearly this needed some further work, and so I checked the Groningen data, and find exactly the same step at the same time. Not an instrument problem then. What about other sites. Well, repeat the exercise with various data from MeteoSuisse and you will find exactly the same thing there. Try the CET data, or the Armagh series, and there is the 1987 discontinuity. The same sort of discontinuity can be seen in data from Moscow. It can hardly be a delusion or an accident I feel.

Dividing the De Bilt data into two segments at Sept 1987, and fitting trends (regressions) to these as well as the full data set is very instructive. The slight linear upward trend prior to Sep 1987 surmised from the cusum plot is confirmed, but is not significant at the usual probability level; the post Sep 1987 to the present segment has a totally non-significant positive slope. The full series regression has of course a very highly significant positive slope (we all knew that in advance of course), but the two segment fitted lines are separated at Sep 1987 by 0.6 deg C. This is my estimate of the size of the step change.

The highly significant slope of the full data set is clearly accounted for in virtually its entirety by the Sept 1987 step change.

Any suggestions as to the origin of this striking feature will be welcomed. I have none to offer.

Robin Edwards wrote:
“And what about “anomalies” with respect to some arbitrary time interval. For me, the sensible period over which to calculate “anomalies” the the entire span of the data that one is examining. Why choose any other?”

Because changing the baseline has no consequences for the science. Data with one baseline can easily be converted into data with another baseline.

David Appell
24 November 2014 at 2:35 pm
“Which important physical processes are missing from climate models?”

Climate models don’t do simulation of physical processes. They use statistical descriptions, parameterized to optimize the hindcasting. THe parameters are guesses.

You can do that, but then your model is a guess – you must now test whether it has predictive skill. We are in this test. The last 18 years show that the guesses for the parameters were wrong.

Climate science can now make a new guess, and we will wait again.

DirkH wrote:
“Climate models don’t do simulation of physical processes. They use statistical descriptions, parameterized to optimize the hindcasting. THe parameters are guesses.”

That’s completelly. Climate models are numerical solutions to the underlying partial differential equations that describe the physics.

Here’s the description of the science in one global climate model. You should look at it:
http://www.cesm.ucar.edu/models/atm-cam/docs/description/description.pdf

“The last 18 years show that the guesses for the parameters were wrong.

Climate science can now make a new guess, and we will wait again.”

Also wrong. Climate models don’t predict short-term changes in climate variables — they only do so over the long-term (~> 3+ decades). To do short-term forecasts they’d need to know what ENSOs will happen in the next couple of decades, what volcanoes will erupt, what changes there will be in the sun’s radiance, and more.

Weather models solve an initial value problem. Climate models do not — their major contraint is conservation of energy.

First sentence in previous reply should have been “That’s completely wrong.”

PS: Yes, there are parametricized pieces in a climate model. That’s either to (1) summarize physics that is too complex to describe and calculate, or (2) have the model compute results in a reasonable amount of time. (And they can already run for ~2 months.) That they hindcast the 20th century shows the models have some value.

What would your model do differently, to avoid parametrizations?