Not that Certain

A new paper (A probabilistic analysis of human influence on recent record global mean temperature changes–Kokic, Crimp, Howden) states a 99.999% certainty humans are causing the warming on the planet, IF the model contains all factors with significant (ie measurable and large enough to affect the outcome) influence on climate.

The model only has four factors: CO2 (GHG as measured by Kyoto Protocol), ENSO, TSI, and volcanoes. It’s highly unlikely that there are not more factors–for example oceans storing heat, albedo of arctic and antarctic ice, back radiation, convection currents, etc, just to name a few I have read about on various sites. If any of these have a large effect, the model does not match reality and any outcome or prediction may be useful by chance but most probably useless other than to grab headlines.

Also, if the measurements of any of the factors is not accurate, the conclusion is void. That does not mean the conclusion is not true–it means the models and statistics used to create the model and certainty are invalid. In other words, the model is back to an unproven hypothesis. It is possible for an incomplete model might be useful in some ways, but the 99.999% certainty is most certainly exaggerated and should be scrapped. A four factor model of climate that shows this kind of “certainty” is very unlikely to be accurate or even useful.

The modeler’s use a bootstrap calculation, something that seems to be used more and more in the studies I have been reading. In theory, the bootstrap yields multiple data sets to increase the likelihood that the model cover all data. (Correct me if my explanation of this is poorly stated. I am sometimes not very good at explaining statistics so it makes sense to readers.) They ran the bootstrap 100,000 times both leaving in and leaving out GHG. From this, they reached the incredible (or perhaps not-so-credible) 99.999% number.

There is no information on whether or not the model was run eliminating other factors one at a time in the same fashion as GHG. This is vital to gauge whether something else may have just as strong an effect.

The model B also indicated only an approximate 25% of 304 months of continuous record breaking temperatures, but that was one of the original questions in the model–how likely are 304 months of record breaking temperatures without human influence? That would seem to indicate the model missed the mark. Model E also showed only about a 53% chance of this temperature streak happening. Why can’t the model reproduce the 304 months of record setting temperatures? With 99.999 % certainty, one would expect nothing less.

An interesting thing that did show up in the study was the prediction of periods of flat or colling temperatures and the number of periods of cooling was closer to observed in the runs with GHG left in than those without. The number was still not matching actual recoded data but was closer with GHG.

What does this tell us about humans, GHG and certainty? IF the models are sufficiently accurate, there could be a strong case for humans causing warming. However, the small number of variable in the model call into question whether all significant factors have been included. Without a 99.999% certainty that these are the only factors needed the conclusion is not valid. If any measurements of input variables are even slightly off, the conclusion does not hold.

All in all, the study, while it addressed some interesting points fell far short of being definitive proof of humans causing climate change. The certainty is far over stated when one compares reality to the model and its conclusions.

And the hypocrisy continues

Interesting email today from one of the “advocates” of climate change.  Said advocate noted he does not read this blog because he can’t take the face-painting and head-banging (I think he has me confused with some other site, but who knows?).  Now, if I recall correctly (and I do because I have all kinds of backup material), one of the objections to skeptics is that they don’t read the actual science pages, only their own pages, to avoid learning anything new.  WOW, now I get confirmation that advocates do EXACTLY THE SAME THING.  Isn’t that most interesting?  Apparently advocates are afraid of the truth, right?  I mean, that’s their interpretation of people who avoid opposing views, so it’s not like I made it up.  It’s their standard.  Okay, it’s been known for years and there’s really no way to fix it, but I do feel obligated to point out that climate science really isn’t about the science and only about agreement whenever evidence drops in.

As an aside, there are some awesome mammatus clouds this morning, but it’s too dark yet to get picture.


Another thing I should address is people who disagree with me telling me to take a class in science presuming that if I had taken science I would immediately recognize how indisputable their position is.  I will list the classes I have taken and if you have one that isn’t on the list, I will consider it.  For those of you who change the subject or tell someone else to take a science or physics class, I can’t reach someone who ignores his/her own requirements.

High school—chemistry, physics, geometry, trigonometry, algebra

College—general chemistry, analytical chemistry, organic chemistry, instrumental chemistry, physical chemistry, statistics for non-science majors, statistics for science majors, calculus  (several biology classes also)

Online—science based course on climate change from MIT

As for those saying to “read a research paper”, been there, done that too.  I am always open for suggestions on what papers I could read.


Scientific Badger

Scientific Badger

Statistical significance and climate change

I am linking to a blog post on statistical significance that may help explain why I am such a skeptic on the whole “human caused warming” claim:

Here’s an excerpt:

“Which is to say that according to my real, genuine, mathematically legitimate, scientifically fabricated scientific statistical scientific model (calculated on a computer), I was able to produce statistical significance and reject the “null” hypothesis of no cooling. Therefore there has been cooling. And since cooling is the opposite of warming, there is no more global warming. Quod ipso facto. Or something.

I was led to this result because many (many) readers alerted me to a fellow named Lord Donoughue, who asked Parliament a question which produced the answer that “the temperature rise since about 1880 is statistically significant.” Is this right?

Not according to my model. So who’s model, the Met Office’s or mine, is right?

Well, that’s the beauty of statistics. Neither model has to be right; plus, anybody can create their own.”

Read on:

The post explains very clearly, at least to me, why statistical significance may not be significant at all.  Another important excerpt:

“His model, which is frankly absurd, is to say the change in global temperatures is a straight linear combination of the change in “anthropogenic contributions” to temperature plus the change in “natural variability” of temperature plus the change in “measurement error” of temperature. (Hilariously, he claims measurement error is of the order +/- 0.03 degrees Celsius; yes, three-hundredths of a degree: I despair, I despair.)”  (Bold is mine)

Proxies cannot possibly yield the accuracy required for the claims made by climate science concerning warming.  They simply lack the degree of accuracy needed.  We really can’t even measure the accuracy except in very modern times.  Perhaps if we gave the proxy to 25 unrelated scientists and had them all give their interpretation of the proxy, it would become apparent that this is not an accurate measurement.  If you lack accurate measurements, then your conclusion cannot be said to be accurate based on those measurements.  There are too many assumptions and too much use of “estimated” and proxy data to give any kind of accurate result, except by random chance.  We simply do not have the data for these types of claims.

Scientific badger

Scientific badger

Why consensus research is flawed

This site has a very detailed write-up on Cook and further back, some on Lewandowsky.  He’s quite thorough in his explanation about why the research is bad.

(Note:  Yes, I know he says he “believes” there is a consensus, and he believes that some of AGW is valid.   It is not necessary to agree with every argument a person puts forward.  His treatment of the psychology aspect of the consensus is very good and worth reading.)

It’s not warming, it’s dying

A new campaign from the denizens of death group, brought to you in an effort to scare you into carbon taxes and one world energy policies.

Interesting campaign. First, if it’s not warming, what’s it dying from? Second, after billion of years of evolution, ice ages, hot houses and climate flip-flops, we are to believe that this time it’s for good–all over, dead as a doornail, kaput? Wow, humans accomplished what asteroid strikes, frozen oceans and five mass extinctions could not. We’re here and witnessing the death of the earth. Oh, wait….you mean destroyed it? We’re more powerful than asteroids? Amazing when you think about t.

One would expect another response could be–great, party, have a good time while it lasts. Crank up the A/C, buy a Hummer and go out in style. You know, like the denizens of death heroes Al Gore and Bill McKibben. You don’t see these folks cutting back, do you?

The button used in the campaign is itself about as clever as….well, I can’t really come up with anything that simplistic and inane. A button that goes from black to green on a gradient? Really? Had they gone with Hansen’s boiling oceans, now that would have been graphically terrifying. Maybe a 3D spherical look would have helped. How much mining and manufacturing went into producing these things? Just pushing us that much closer to “its death”.

With advocates like this…..

(Question-off topic and I don’t know the answer: If what humans do to the landscape causes global warming, and we do know weather patterns are affected by landscapes, why didn’t the dinosaurs really mess up the climate with the huge amounts of vegetation they consumed?)

Open thread—post away

I am moving over a series of questions asked on another blog. My statements are in italics, the questions are in regular type.

1. The latest IPCC report seems to have removed a large portion of quantitative predictions and just goes with “may increase”, “may cause bad things”, etc.

“Seems”? How about some specific examples? What basis for comparison was used to arrive at this determination? Word counts? What?

2. That’s not really a scientific statement. ( The latest IPCC report seems to have removed a large portion of quantitative predictions and just goes with “may increase”, “may cause bad things”, etc.)

Pick a paper from some field other than climatology which you trust. Count how many times it uses qualifiers such as “may”, “might”, “could” etc. Keep in mind that a good scientific study is honest about uncertainty.

3. Virtually all the predictions from the models were barely within the error margins or outside the margins.
Which predictions, when? Over what period of time? Are we talking the last 20 years here or something else? Which models? Were some closer than others? Why were some more off than others?

4. More extreme weather?
When were those predictions made? What were the specific predictions made? What period of time did they cover (ie, how far in the future did the predictions cover from the time they were made)?

5. Arctic ice melting–yes, but faster than the models predicted, so another fail.

And that falsifies CO2 warming how exactly? Think about what you just wrote — a predicted effect of warming happened faster than predicted, and that falsifies the theory that CO2 causes warming?

6. Some thought Arctic and Antarctic ice melts would mirror each other, but no, that didn’t happen.
Specifics, Sheri. Who said this? When did they say it? Are you talking Antarctic sea ice or land ice? (Two very different animals). Same question as previous: how does a failed prediction of a secondary effect of warming falisify the underlying theory behind the warming itself?

7. Lack of snow–some places, some not.
Which places? How far off were the predictions? When were the predictions made? Snowmelt is an effect of warming: how does a failed prediction of an effect of warming falsify the underlying theory behind the warming itself?

8. Species extinctions–nope.
Who made the predictions? When did they make them? Over what period of time were the predictions supposed to take place?


My responses (note that this is a holiday weekend and I will be away from my computer much of the time.  Answers when I have time.  Other readers feel free to jump in with answers.  Makes it more fun!)

Number 2:  Clarification.  Yes, papers do use the terms “may”, “might”, etc.  This is indicative that what is being studied is still not fully understood or researched.  It is not known with near certainty.   If the point of the comment was much of science is uncertain, yes, it certainly is.  Which is why we should not be making policies and pretending like we do have certainty.  Without actual numbers and data, there’s no objective measures of “may” “might”.  For something to be scientifically likely, one needs to know what the probability, using an actual number, is and how that probability was arrived at.  As I am  prone to repeat, “There may be unicorns”, but that’s not a scientific statement.  There are unicorns is scientific and only true if the scientist can produce a unicorn.  There is a 10% chance there are unicorns would require the data and method used to arrive at the 10% to be examined before one can accept the statement as accurate.  I can say “The earth may be going into an ice age” and that statement is true.  Let’s say we have calculated to probability thereof accurately and it’s 2%.  The statement “The earth may be going……” is still very true.  It’s just not very probable based on current data.  Science needs to be more quantitative about its “mays”.  After all, science is about rules, quantification, verification.   Philosophy is about “may”.  (I do not have the time right now to search for the requested papers—may be able to do so later.)  

Number 5:  Yes, the arctic melting faster than predicted does call into question the theory.  If I have a theory that it will rain tomorrow and then it rains today and not tomorrow, my theory was wrong.  It did rain, but not when I predicted it would.  If I predict there will be a first snow later than the average date of September 15th (made up date, by the way) and it snows September 12th, my theory was wrong.   Theories being true require that the predictions are accurate in both quantity and timing.  If the Arctic ice is melting faster, then either there is another factor at play or the calculations of warming are inaccurate.  I put this example in frequently because people grab onto things that show warming, but completely ignore the time predictions.  If the time predictions are wrong, then theory may be wrong and it certainly is of very little value since its predictions are not accurate over time.

I will return later with more on the questions.  Part of the problem with climate science is the lack of time periods over which things will take place.  It will get warmer is not really a useful statement without some idea of how hot and when it will happen.  It’s just a prediction of a vague outcome.  Unfortunately we see it all the time in climate science.  Examples coming soon.


Check out Climate4kids.  There are new posts about climate change written at children’s level.  Share it with kids you know.

Scientific Badger

Scientific Badger

Time flies

It looks like I’ve been missing in action on this blog for quite sometime.  It’s summer and between mowing, gardening and spending time at the cabin, I can’t seem to keep up with the computer work.  I am posting a letter to the editor I recently sent to my newpaper in response to another letter that was printed.  Hope to get back to posting more soon!

In response to Matt Armstrong’s letter:

Consensus is formed on the basis of many things–money, politics, power. It may start out based on facts, but can digress at any point if a new factor outweighs the others. Climate science seems to have digressed.

Scientists since the mid-19th century have been examining CO2 and its effects on temperature. That just means it’s not a new idea, not a correct one. Newton studied alchemy.

Even the IPCC admits there’s a pause or slowing in temperature. Berkeley Earth put out a paper on it. One can call it a slowing down or a pause. However, models did not predict any such slowing in any of the projections. Models all showed a consistent rise.

The use of the term “denialist” is indicative of a person with a very, very weak case. People who know their ideas are valid present their case and let the data and methodology speak for itself.

In spite of claims to the contrary, models lack sufficient resolution to incorporate clouds, a very important greenhouse factor. So “parameterization” is done. You don’t know the value, so you just use a number to represent this. Values varying from model to model. Also, natural phenomena like El Nino and La Nina cannot be included in the models. The models the IPCC used and most climate modelers used showed far more warming than actually occurred, meaning the models have serious flaws. The latest IPCC document, in the science section, admitted that models cannot forecast with any degree of certainty: “- Projections of future climate change are not like weather forecasts. It is not possible to make deterministic, definitive predictions of how climate will evolve over the next century and beyond as it is with short- term weather forecasts.”