Pat 'n Chip play with climate models at WUWT

Updated - See below to find out just how wrong Pat'n'Chip are.


Paul C. “Chip” Knappenberger and Patrick J. Michaels are at it again at Anthony Watts' blog WUWT.  I don't think anyone else would have them apart from some right wing rags.

They are claiming that the models are all wrong.  They said they used the model runs as provided on the KNMI Climate Explorer website.  So I went to Climate Explorer and just downloaded the data for RCP8.5 and RCP2.6 and compared it to GISTemp.  This is the result:

Data Source: NASA and Climate Explorer

You'll notice there's little difference between RCP8.5 and RCP2.6.  They don't start to diverge for a few more years.

Bearing in mind there are no error bars shown and that the errors in the observations increase as you go back in time, what strikes me is how darn close the modeled surface temperature is to the observations.

So then I went back to WUWT to see why Pat'nChip say the models are "all wrong".  They wrote:

But very few  people know that the same situation has persisted for 25, going on 35 years, or that over the past 50-60 years (since the middle of the 20th century), the same models expected about 33 percent more warming to have taken place than was observed.

33% more warming since the 1950s? Now that's not what the data shows above.  The observations are fairly closely aligned to the model mean.

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Update

Here are two more charts.  I've started them at 1951 because Pat'nChip say that "over the past 50-60 years (since the middle of the 20th century)" the models expected 33% more warming.  That's balderdash as you'll see in these charts:

Data Source: NASA and Climate Explorer

Look at the above chart from 1951 to 2001.  The linear trend in the observations is almost indistinguishable from (and slightly steeper than) that of either of the two RCPs.  This completely contradicts the claim of Pat'n'Chip about the "situation has persisted" for last 50-60 years.

Next is 1951 to 2012.

Data Source: NASA and Climate Explorer

Over the period 1951 to the present, the trend for the models is slightly steeper than that of the observations. But certainly not by 33%! It's not even half that. The divergence is from around 2005 onwards.

End Update.

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So what did Pat 'n Chip do?  They haven't compared the models to the observations directly.  What they did was this:

We’ve calculated the trend in the global average surface temperature simulated to have occurred starting in every year since 1950 and ending in 2012 for every* run of every climate model used in the new IPCC report. In Figure 1, below, we compare the average (and spread) of these 106 model runs with the observed trend during each of the same periods.

They say they calculated the trend in °C/decade, rather than plotting the straight temperature.  I've played with the data a bit but I can't get anything like their chart shown below. (click to enlarge):

I'm guessing they've smoothed the derivatives for starters. However I don't understand how they got such different rates of warming between models and observations.  If you compare it to the result in my first chart, the models and observations correspond well, particularly in the period from around 1930 to around 2004-05.  (The observations show more variation because the model plot isn't a single run, so any variation is averaged out.)

Maybe someone can help me out here and tell me how Pat 'n Chip calculated the rate of warming, and why it appears to be different to what I, at least, would expect.

Anthony Watts makes Erroneous Assumptions

See update below.

Times are tough for deniers. There is not much denier material to work with these days.  Morano is recycling long debunked lies and Anthony is manufacturing new ones.  After so many years and so many fibs they no longer know what it's like to tell the truth.  Take this WUWT headline and opening lines for example:

A frank admission about the state of climate modeling by Dr. Gavin Schmidt

This is something I never expected to see in print. Climate modeler Dr. Gavin Schmidt of NASA GISS comments on the failure of models to match real world observations.

Mathematical Models in Social Sciences and Erroneous Assumptions

What was this 'frank admission' about climate modelling? None. There was no discussion of climate modelling, at least not till later - and Anthony missed that bit (see below).  It all started with a tweet by Nassim Taleb, who was tweeting about two of his online books about which he stated:

"Started a textbook-style document explaining ideas in technical style but linear form (updated progressively), mainly what people do not seem to get about The Black Swan and Antifragile. Letter in Nature explaining what Antifragile is NOT about and the central point that book reviewing fakes are missing"..  

This is the tweet from Nassim Taleb, author of The Black Swan and Fooled by Randomness, in which he promoted his on-line books. (click the date to go to the tweet):

The failure of mathematicall models in the social"sciences" shd be and can be debunked mathematically. Math = no BS. fooledbyrandomness.com/FatTails.html
— Nassim N. Taleb (@nntaleb) May 29, 2013

 Gavin Schmidt responded that

@nntaleb While errors in maths undoubtedly exist, the failure of models to match real world far more likely due to erroneous assumptions
— Gavin Schmidt (@ClimateOfGavin) June 1, 2013

Patience is not just a virtue...

If he hadn't jumped the gun, then Anthony could have chosen a tweet that was about climate models instead of one about mathematical models in the social sciences:

@ustafford @nntaleb We should always challenge the validity of assumptions in any model. Model credibility built on successful predictions
— Gavin Schmidt (@ClimateOfGavin) June 1, 2013

More on erroneous assumptions

I'm thinking that Anthony got it wrong because he started with an erroneous assumption.  Namely, that Gavin Schmidt never tweets about anything except climate science.

He also made another erroneous assumption (implicitly), that Dr Schmidt would "never" discuss the strengths and weaknesses of climate models.  Based on these observations, if I were to construct a model of Anthony Watts' behaviour, do you think the following assumptions would be valid or erroneous?

• Anthony Watts doesn't read RealClimate.org
• Anthony Watts does read RealClimate.org but doesn't understand it.
• The limit of Anthony Watts ability to comprehend is less than 140 characters at a time.
• Anthony Watts wouldn't know a climate model from a bar of soap.

Update

Anthony has changed his headline, removing the word "climate".  Which goes to prove what I wrote about his 'erroneous assumptions'.  The old post title is still in the URL:
http://wattsupwiththat.com/2013/06/01/a-frank-admission-about-the-state-of-climate-modeling-by-dr-gavin-schmidt/

Anthony's added bits and pieces at the bottom of his original article, trying to weasel his way out.  Instead of just admitting he 'jumped the gun', he rants on along the lines of "I don't want to pay tax therefore I don't 'believe in' climate science".

My comment: climate models have made huge advances.  They do have limitations.  For example, the arctic sea ice is melting faster than earlier models projected.  However, observed surface temperatures are within the range of the model projections.  Anthony Watts denies and is a disinformation propagandist because of his greed and ideology.  I base that assumption on the words that drip from his keyboard and his mouth - see 54 seconds in:

Interviewer: "What bothers you the most about the arguments that there is serious global warming?"

Watts: "They want to change policy, they want to apply taxes"!

The Mice Play - More Fake Forcings from 'Wondering' Willis on WUWT

I don't pretend to be an expert in mathematics, statistics or climate models but I'm going to make a short comment on 'Wondering' Willis Eschenbach's latest foray into fitting an equation to the outputs of climate models.

He's done this before on WUWT as he says, here and here.  What Willis does is fit a linear equation to climate model outputs.  Fortunately he doesn't go so far as make projections or predictions.

Willis' closing derogatory comments are nonsense. He writes:

Does this mean the models are useless? No. But it does indicate that they are pretty worthless for calculating the global average temperature. Since all the millions of calculations that they are doing are functionally equivalent to a simple lagged linear transformation of the inputs, it is very difficult to believe that they will ever show any skill in either hindcasting or forecasting the global climate.

Climate models might not be perfect but they are far from useless and are used for much more than surface temperature.  Indeed they used not just to make climate projections but increasingly to forecast weather on a seasonal basis. (More about POAMA here.)

Willis shows some skill with Microsoft Excel.  However he demonstrates a remarkable lack of understanding of climate forcings and climate models for someone who's been writing about them for so long.

To save you a Google search, let me point you to Tamino's explanation in case anyone is under the false impression that Willis' mathturbation™ is anything other than an exercise in curve-fitting after the event.  Here are some excerpts.  They are just as relevant in this case as they were to Willis' previous articles.

The first excerpt relates to Willis' adjustment for volcanic forcings:

Let me translate: the actual forcing didn’t fit his preconception, so he changed it to a fake forcing.

What he doesn’t do is make the connection: that the short-lived volcanic impulses have reduced impact, not because the GISS modelE treats them differently from all the others, but because they are short-lived and there’s more than one time scale for the model’s climate system response. There is for the real climate system, too — a potent argument for the fundamental soundness of the GISS modelE.

The second excerpt relates to curve fitting in general (as done by a recent visitor here - though Dan's equations were way more extravagant than Willis')

Bottom line: if you put in enough parameters, and fake the data because otherwise your model isn’t very good, you can get an excellent fit to the GISS modelE output. But it’s nothing but curve-fitting; the work of Willis Eschenbach and Paul_K is an outstanding example of mathturbation.

There’s no justification for them to fake the forcing, physical or mathematical. There’s no investigation of “effective forcing” to see how different forcings might actually have a different impact (in part because of feedbacks). That’s an effort which has been pioneered by James Hansen and colleagues. To contribute meaningfully, you’d have to do some actual science other than make an ad hoc change to the forcing data so you can impugn the results of somebody’s climate model.

For once Eric Worrall is spot on when he writes:

May 21, 2013 at 10:01 pm  HIlarious Willis...

Feel free to add your tuppenthworth

or maybe we should just ask Kenji :D

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Postscript: McIntyre's a dill, too

23 May 2013: In case anyone still harboured the false impression that The Auditor, Steven McIntyre knew what he was talking about when it comes to climate science, this comment from him should settle the matter.  Steve can't tell the difference between a curve-fitting exercise in Excel and a simple coupled climate model.  He is most impressed by Wondering Willis' fancy fudginations and has some wonderings of his own:

Steve McIntyre says:
May 22, 2013 at 9:55 am  Willis, nice spotting with the digitization and the fitting of the function. That there was a relatively simple relationship between model forcing and model global temperature is something that has been chatted about from time to time, but the fit here is really impressive. Wigley and Raper’s MAGICC program, used in past IPCC studies, also emulated key model outputs from forcings: I wonder if it does something similar.

According to this page, no.  MAGICC is a suite of models not a fudged curve fit, which is hardly a surprise. (But hey, who cares?  Now The Revered Auditor has elevated Wondering Willis' curve fitting fiasco to the level of 'real proper science' in the minds of the deluded Dismissives.  Doesn't matter that he's talking through his hat.)

MAGICC consists of a suite of coupled gas-cycle, climate and ice-melt models integrated into a single software package.

You can read more about MAGICC and its history here.

Bacci's "Delusional Dribble"

Talking "dribble" on climate models and tea leaves

This is (probably not) for people who listen to fake skeptics science mockers like bacci, who writes:

Source: HotCopper.com

Bacci starts off talking about modelling complex systems. He says the idea that 'we' can model the climate in 100 years is 'delusional'.  (I'd have to agree that any attempt by Bacci and mates to model complex systems would indicate delusion on their part, going by his posts.  Using his own imagery, bacci tends to dribble his drivel like a drip.)

He then shifts to weather forecasting, saying that in order to 'prove' a model of centennial trends in climate, one needs to model monthly weather.

Predicting monthly trends in weather

Actually, most people (Bacci excepted) don't need a model to broadly predict weather on the monthly scale.  Next month is the start of autumn down here and we know from experience that autumn brings milder temperatures (but it can still get a bit hot).  We can even predict with reasonable accuracy that in five months time (July) the average monthly temperature in southern Australia will be cooler than the average for this month (February) and there will likely be snow on the ranges, while in the northern hemisphere the ice in the Arctic will be melting.

Feel free to check back in July and tell me how wrong my prediction is!

One source for an indication of likely rainfall patterns in eastern and south-eastern Australia on a short term scale (weeks to months) is the Bureau of Meteorology's seasonal outlooks and also their ENSO wrap up.

Fake skeptic predictions

Fake skeptics have not done very well in their predictions. Some have even been so far off target with short term predictions that the 'delusional' descriptor may be appropriate.

John McLean's Delusional Drop

For example, bacci could have been talking about computer technician John McLean.  Back in March 2011, he 'predicted' that "2011 would be the coolest year since 1956, or even earlier".  He was forecasting a drop of 0.8 degrees Celsius in the average global surface temperature in a single year, from the record high of 2010. (The global average surface temperature has risen by about 0.8 degrees Celsius in the past century.  In 2010 it was 0.62 degrees above the twentieth century average.)

As it turned out, 2011 was the 11th warmest year on record and the warmest La Nina year on record.  So much for that fake skeptic's delusion.  2011 was 0.51 degrees Celsius above the twentieth century average, whereas the average temperature in 1956 was about 0.18 below the twentieth century average.  He was out by a whopping 0.69 degrees Celsius!

Click here to go to the NOAA source.

Other fake skeptics' tea leaves

Bacci says he might as well read tea leaves.  Maybe that's what fake skeptics do.  SkepticalScience.com has an animated gif comparing the predictions of 'skeptics' with IPCC temperature projections and actual observations.  Fake skeptics 'tea leaf' predictions don't stack up at all well, while the different years' IPCC projections have so far all been much closer to what was actually recorded.

The skepticalscience.com article goes into more detail and is worth a read.   It discusses some of the weaknesses of IPCC projections, such as the fact that sea levels may be rising faster and the fact that Arctic ice is definitely disappearing much faster than expected.

Realclimate.org does an annual comparison of models too, looking at global surface temperature, ocean heat content and summer Arctic sea ice cover as well as early projections from James Hansen.

To sum up, complex models based on physics and constructed by experts in climate science have been very good predictors of global trends and even of regional trends.  They are not perfect but as computing power increases along with knowledge of climate the models also improve.

Important factors that climate scientists have more difficulty in predicting in the medium to longer term are the amount of greenhouse gases and aerosols we choose to pour into the atmosphere.  (Also significant volcanic eruptions that might occur in the future.) That's why they use scenarios to model climate under different permutations of future pollution.

Isaac Held's blog is a really good place to peep under the hood of climate modelling.