Climate Bathtub Chartjunk

I just ran across Twist and Shout: Images and Graphs in Skeptical Climate Media, a compendium of cherry picking and other chartjunk abuses.

I think it misses a large class of (often willful) errors: ignoring the climate bathtub. Such charts typically plot CO2 emissions or concentration against temperature, with the implication that any lack of correlation indicates a problem with the science. But this engages in a combination of a pattern matching fallacy and fallacy of the single cause. Sometimes these things make it into the literature, but most live on swampy skeptic sites.

An example, reportedly from John Christy, who should know better:

Notice how we’re supposed to make a visual correlation between emissions and temperature (even though two integrations separate them, and multiple forcings and noise influence temperature). Also notice how the nonzero minimum axis crossing for CO2 exaggerates the effect. That’s in addition to the usual tricks of inserting an artificial trend break at the 1998 El Nino and truncating the rest of history.

Silver Lining to the White House Climate Panel?

The White House is reportedly convening a panel to reexamine the scientific consensus on climate. How does that work, exactly? Are they going to publish thousands of new papers to shift the apparent balance of opinion in the scientific literature? And hasn’t analysis of consensus already been done to death, with a null result for the skeptics?

The problem is that there isn’t much for skeptics to work with. There aren’t any models that make useful predictions with very low climate sensitivity. In fact, skeptical predictions haven’t really panned out at all. Lindzen’s Adaptive Iris is still alive – sort of – but doesn’t result in a strong negative feedback. The BEST reanalysis didn’t refute previous temperature data. The surfacestations.org effort used crowdsourcing to reveal some serious weather station siting problems, which ultimately amounted to nothing.

And those are really the skeptics’ Greatest Hits. After that, it’s a rapid fall from errors to nuts. No, satellites temperatures don’t show a negative trend. Yes, Fourier and wavelet analyses are typically silly, but fortunately tend to refute themselves quickly. This list could grow long quickly, though skeptics are usually pretty reluctant to make testable models or predictions. That’s why even prominent outlets for climate skepticism have to resort to simple obfuscation.

So, if there’s a silver lining to the proposed panel, it’s that they’d have to put the alleged skeptics’ best foot forward, by collecting and identifying the best models, data and predictions. Then it would be readily apparent what a puny body of evidence that yielded.

 

Fancy Stats and Silly Climate Contests

Climate skeptics seem to have a thing for contests and bets. For example, there’s Armstrong’s proposed bet, baiting Al Gore. Amusingly (for data nerds anyway), the bet, which pitted a null forecast against the taker’s chosen climate model, could have been beaten easily by either a low-order climate model or a less-naive null forecast. And, of course, it completely fails to understand that climate science is not about fitting a curve to the global temperature record.

Another instance of such foolishness recently came to my attention. It doesn’t have a name that I know of, but here’s the basic idea:

  • The author generates 1000 time series:

Each series has length 135: the same length as that of the most commonly studied series of global temperatures (which span 1880–2014). The 1000 series were generated as follows. First, 1000 random series were obtained (for more details, see below). Then, some of those series were randomly selected and had a trend added to them. Each added trend was either 1°C/century or −1°C/century. For comparison, a trend of 1°C/century is greater than the trend that is claimed for global temperatures.

  • The challenger pays $10 for the privilege of attempting to detect which of the 1000 series are perturbed by a trend, winning $100,000 for correctly identifying 90% or more.

The best challenger managed to identify 860 series, so the prize went unclaimed. But only two challenges are described, so I have to wonder how many serious attempts were made. Had I known about the contest in advance, I would not have tried it. I know plenty about fitting dynamic models to data, though abstract statistical methods aren’t really my thing. But I still have to ask myself some questions:

  • Is there really money to be made, or will the author simply abscond to the pub with my $10? For the sake of argument, let’s assume that the author really has $100k at stake.
  • Is it even possible to win? The author did not reveal the process used to generate the series in advance. That alone makes this potentially a sucker bet. If you’re in control of the noise and structure of the process, it’s easy to generate series that are impossible to reliably disentangle. (Tellingly, the author later revealed the code to generate the series, but it appears there’s no code to successfully identify 90%!)

For me, the statistical properties of the contest make it an obvious non-starter. But does it have any redeeming social value? For example, is it an interesting puzzle that has something to do with actual science? Sadly, no.

The hidden assumption of the contest is that climate science is about estimating the trend of the global temperature time series. Yes, people do that. But it’s a tiny fraction of climate science, and it’s a diagnostic of models and data, not a real model in itself. Science in general is not about such things. It’s about getting a good model, not a good fit. In some places the author talks about real physics, but ultimately seems clueless about this – he’s content with unphysical models:

Moreover, the Contest model was never asserted to be realistic.

Are ARIMA models truly appropriate for climatic time series? I do not have an opinion. There seem to be no persuasive arguments for or against using ARIMA models. Rather, studying such models for climatic series seems to be a worthy area of research.

Liljegren’s argument against ARIMA is that ARIMA models have a certain property that the climate system does not have. Specifically, for ARIMA time series, the variance becomes arbitrarily large, over long enough time, whereas for the climate system, the variance does not become arbitrarily large. It is easy to understand why Liljegren’s argument fails.

It is a common aphorism in statistics that “all models are wrong”. In other words, when we consider any statistical model, we will find something wrong with the model. Thus, when considering a model, the question is not whether the model is wrong—because the model is certain to be wrong. Rather, the question is whether the model is useful, for a particular application. This is a fundamental issue that is commonly taught to undergraduates in statistics. Yet Liljegren ignores it.

As an illustration, consider a straight line (with noise) as a model of global temperatures. Such a line will become arbitrarily high, over long enough time: e.g. higher than the temperature at the center of the sun. Global temperatures, however, will not become arbitrarily high. Hence, the model is wrong. And so—by an argument essentially the same as Liljegren’s—we should not use a straight line as a model of temperatures.

In fact, a straight line is commonly used for temperatures, because everyone understands that it is to be used only over a finite time (e.g. a few centuries). Over a finite time, the line cannot become arbitrarily high; so, the argument against using a straight line fails. Similarly, over a finite time, the variance of an ARIMA time series cannot become arbitrarily large; so, Liljegren’s argument fails.

Actually, no one in climate science uses straight lines to predict future temperatures, because forcing is rising, and therefore warming will accelerate. But that’s a minor quibble, compared to the real problem here. If your model is:

global temperature = f( time )

you’ve just thrown away 99.999% of the information available for studying the climate. (Ironically, the author’s entire point is that annual global temperatures don’t contain a lot of information.)

No matter how fancy your ARIMA model is, it knows nothing about conservation laws, robustness in extreme conditions, dimensional consistency, or real physical processes like heat transfer. In other words, it fails every reality check a dynamic modeler would normally apply, except the weakest – fit to data. Even its fit to data is near-meaningless, because it ignores all other series (forcings, ocean heat, precipitation, etc.) and has nothing to say about replication of spatial and seasonal patterns. That’s why this contest has almost nothing to do with actual climate science.

This is also why data-driven machine learning approaches have a long way to go before they can handle general problems. It’s comparatively easy to learn to recognize the cats in a database of photos, because the data spans everything there is to know about the problem. That’s not true for systemic problems, where you need a web of data and structural information at multiple scales in order to understand the situation.

Climate and Competitiveness

Trump gets well-deserved criticism for denying having claimed that the Chinese invented climate change to make  US manufacturing non-competitive.

climatechinesehoax

The idea is absurd on its face. Climate change was proposed long before (or long after) China figured on the global economic landscape. There was only one lead author from China out of the 34 in the first IPCC Scientific Assessment. The entire climate literature is heavily dominated by the US and Europe.

But another big reason to doubt its veracity is that climate policy, like emissions pricing, would make Chinese manufacturing less competitive. In fact, at the time of the first assessment, China was the most carbon-intensive economy in the world, according to the World Bank:

chinaintensity

Today, China’s carbon intensity remains more than twice that of the US. That makes a carbon tax with a border adjustment an attractive policy for US competitiveness. What conspiracy theory makes it rational for China to promote that?

How many things can you get wrong on one chart?

Let’s count:

  1. stupidGraphTruncate records that start ca. 1850 at an arbitrary starting point.
  2. Calculate trends around a breakpoint cherry-picked to most favor your argument.
  3. Abuse polynomial fits generally. (See this series.)
  4. Report misleading linear trends by simply dropping the quadratic term.
  5. Fail to notice the obvious: that temperature in the second period is, on average, higher than in the first.
  6. Choose a loaded color scheme that emphasizes #5.
  7. Fail to understand that temperature integrates CO2.
  8. Fallacy of the single cause (only CO2 affects temperature – in good company with Burt Rutan).

Summary for Suckers

The NIPCC critique is, ironically, a compelling argument in favor of the IPCC assessment. Why? Well, science is about evaluation of competing hypotheses. The NIPCC report collects a bunch of alternatives to mainstream climate science in one place, where it’s easy to see how pathetic they are. If this is the best climate skeptics can muster, their science must be exceedingly weak.

The NIPCC (Nongovernmental International Panel on Climate Change, a.k.a. Not IPCC) is the Heartland Institute’s rebuttal of the IPCC assessments. Apparently the latest NIPCC report has been mailed to zillions of teachers. As a homeschooling dad, I’m disappointed that I didn’t get mine. Well, not really.

It would probably take more pages to debunk the NIPCC report than it occupies, but others are chipping away at it. Some aspects, like temperature cherry-picking, are like shooting fish in a barrel.

The SPM, and presumably the entire report that it summarizes, seems to labor under the misapprehension that the IPCC is itself a body that conducts science. In fact, the IPCC assessments are basically a giant literature review. So, when the Heartland panel writes,

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

we must remember that “the IPCC” is shorthand for a vast conspiracy of scientists, coordinated by an invisible hand.

The report organizes the IPPC argument into 3 categories: “Global Climate Model (GCM) projections,” “postulates,” and “circumstantial evidence.” This is a fairly ridiculous caricature of the actual body of work. Most of what is dismissed as postulates could better be described as, “things we’re too lazy to explore properly,” for example. But my eye strays straight to the report’s misconceptions about modeling.

First, the NIPCC seems to have missed the fact that GCMs are not the only models in use. There are EMICS (models of intermediate complexity) and low-order energy balance models as well.

The NIPCC has taken George Box’s “all models are wrong, some are useful” and run with it:

… Global climate models produce meaningful results only if we assume we already know perfectly how the global climate works, and most climate scientists say we do not (Bray and von Storch, 2010).

How are we to read this … all models are useless, unless they’re perfect? Of course, no models are perfect, therefore all models are useless. Now that’s science!

NIPCC trots out a von Neumann quote that’s almost as tired as Box:

with four parameters I can fit an elephant, and with five I can make him wiggle his trunk

In models with lots of reality checks available (i.e. laws of physics), it just isn’t that easy. And the earth is a very big elephant, which means that there’s a rather vast array of data to be fit.

The NIPCC seems to be aware of only a few temperature series, but the AR5 report devotes 200 pages (Chapter 9) to model evaluation, with results against a wide variety of spatial and temporal distributions of physical quantities. Models are obviously far from perfect, but a lot of the results look good, in ways that exceed the wildest dreams of social system modelers.

NIPCC doesn’t seem to understand how this whole “fit” thing works.

Model calibration is faulty as it assumes all temperature rise since the start of the industrial revolution has resulted from human CO2 emissions.

This is blatantly false, not only because it contradicts the actual practice of attribution, but because there is no such parameter as “fraction of temp rise due to anthro CO2.” One can’t assume the answer to the attribution question without passing through a lot of intermediate checks, like conforming to physics and data other than global temperature. In complex models, where the contribution of any individual parameter to the outcome is likely to be unknown to the modeler, and the model is too big to calibrate by brute force, the vast majority of parameters must be established bottom up, from physics or submodels, which makes it extremely difficult for the modeler to impose preconceptions on the complete model.

Similarly,

IPCC models stress the importance of positive feedback from increasing water vapor and thereby project warming of ~3-6°C, whereas empirical data indicate an order of magnitude less warming of ~0.3-1.0°C.

Data by itself doesn’t “indicate” anything. Data only speaks insofar as it refutes (or fails to refute) a model. So where is the NIPCC model that fits available data and yields very low climate sensitivity?

The bottom line is that, if it were really true that models have little predictive power and admit many alternative calibrations (a la the elephant), it should be easy for skeptics to show model runs that fit the data as well as mainstream results, with assumptions that are consistent with low climate sensitivity. They wouldn’t necessarily need a GCM and a supercomputer; modest EBMs or EMICs should suffice. This they have utterly failed to demonstrate.

 

Equation Soup

Most climate skepticism I encounter these days has transparently crappy technical content, if it has any at all. It’s become boring to read.

But every once in a while a paper comes along that is sufficiently complex and free of immediately obvious errors that it becomes difficult to evaluate. One recent example that came across my desk is,

Polynomial cointegration tests of anthropogenic impact on global warming Continue reading “Equation Soup”

Climate incentives

Richard Lindzen and many others have long maintained that climate science promotes alarm in order to secure funding. For example:

Regarding Professor Nordhaus’s fifth point that there is no evidence that money is at issue, we simply note that funding for climate science has expanded by a factor of 15 since the early 1990s, and that most of this funding would disappear with the absence of alarm. Climate alarmism has expanded into a hundred-billion-dollar industry far broader than just research. Economists are usually sensitive to the incentive structure, so it is curious that the overwhelming incentives to promote climate alarm are not a consideration to Professor Nordhaus. There are no remotely comparable incentives to the contrary position provided by the industries that he claims would be harmed by the policies he advocates.

I’ve always found this idea completely absurd, but to prep for an upcoming talk I decided to collect some rough numbers. A picture says it all:

Data

Notice that it’s completely impractical to make the scale large enough to see any detail in climate science funding or NGOs. I didn’t even bother to include the climate-specific NGOs, like 350.org and USCAN, because they are too tiny to show up (under $10m/yr). Yet, if anything, my tally of the climate-related activity is inflated. For example, a big slice of US Global Change Research is remote sensing (56% of the budget is NASA), which is not strictly climate-related. The cleantech sector is highly fragmented and diverse, and driven by many incentives other than climate. Over 2/3 of the NGO revenue stream consists of Ducks Unlimited and the Nature Conservancy, which are not primarily climate advocates.

Nordhaus, hardly a tree hugger himself, sensibly responds,

As a fifth point, they defend their argument that standard climate science is corrupted by the need to exaggerate warming to obtain research funds. They elaborate this argument by stating, “There are no remotely comparable incentives to the contrary position provided by the industries that he claims would be harmed by the policies he advocates.”

This is a ludicrous comparison. To get some facts on the ground, I will compare two specific cases: that of my university and that of Dr. Cohen’s former employer, ExxonMobil. Federal climate-related research grants to Yale University, for which I work, averaged $1.4 million per year over the last decade. This represents 0.5 percent of last year’s total revenues.

By contrast, the sales of ExxonMobil, for which Dr. Cohen worked as manager of strategic planning and programs, were $467 billion last year. ExxonMobil produces and sells primarily fossil fuels, which lead to large quantities of CO2 emissions. A substantial charge for emitting CO2 would raise the prices and reduce the sales of its oil, gas, and coal products. ExxonMobil has, according to several reports, pursued its economic self-interest by working to undermine mainstream climate science. A report of the Union of Concerned Scientists stated that ExxonMobil “has funneled about $16 million between 1998 and 2005 to a network of ideological and advocacy organizations that manufacture uncertainty” on global warming. So ExxonMobil has spent more covertly undermining climate-change science than all of Yale University’s federal climate-related grants in this area.

Money isn’t the whole story. Science is self-correcting, at least if you believe in empiricism and some kind of shared underlying physical reality. If funding pressures could somehow overcome the gigantic asymmetry of resources to favor alarmism, the opportunity for a researcher to have a Galileo moment would grow as the mainstream accumulated unsolved puzzles. Sooner or later, better theories would become irresistible. But that has not been the history of climate science; alternative hypotheses have been more risible than irresistible.

Given the scale of the numbers, each of the big 3 oil companies could run a climate science program as big as the US government’s, for 1% of revenues. Surely the NPV of their potential costs, if faced with a real climate policy, would justify that. But they don’t. Why? Perhaps they know that they wouldn’t get a different answer, or that it’s far cheaper to hire shills to make stuff up than to do real science?

Minds are like parachutes, or are they dumpsters?

Open Minds has yet another post in a long series demolishing bizarre views of climate skeptics, particularly those from WattsUpWithThat. Several of the targets are nice violations of conservation laws and bathtub dynamics. For example, how can you believe that the ocean is the source of rising atmospheric CO2, when atmospheric CO2 increases by less than human emissions and ocean CO2 is also rising?

The alarming thing about this is that, if I squint and forget that I know anything about dynamics, some of the rubbish sounds like science. For example,

The prevailing paradigm simply does not make sense from a stochastic systems point of view – it is essentially self-refuting. A very low bandwidth system, such as it demands, would not be able to have maintained CO2 levels in a tight band during the pre-industrial era and then suddenly started accumulating our inputs. It would have been driven by random events into a random walk with dispersion increasing as the square root of time. I have been aware of this disconnect for some time. When I found the glaringly evident temperature to CO2 derivative relationship, I knew I had found proof. It just does not make any sense otherwise. Temperature drives atmospheric CO2, and human inputs are negligible. Case closed.

I suspect that a lot of people would have trouble distinguishing this foolishness from sense. In fact, it’s tough to precisely articulate what’s wrong with this statement, because it falls so far short of a runnable model specification. I also suspect that I would have trouble distinguishing similar foolishness from sense in some other field, say biochemistry, if I were unfamiliar with the content and jargon.

This reinforces my conviction that words are inadequate for discussing complex, quantitative problems. Verbal descriptions of dynamic mental models hide all kinds of inconsistencies and are generally impossible to reliably test and refute. If you don’t have a formal model, you’ve brought a knife, or maybe a banana, to a gunfight.

There are two remedies for this. We need more formal mathematical model literacy, and more humility about mental models and verbal arguments.

A natural driver of increasing CO2 concentration?

You wouldn’t normally look at a sink with the tap running and conclude that the water level must be rising because the drain is backing up. Nevertheless, a physically similar idea has been popular in climate skeptic circles lately.

You actually don’t need much more than a mass balance to conclude that anthropogenic emissions are the cause of rising atmospheric CO2, but with a model and some data you can really pound a lot of nails into the coffin of the idea that temperature is somehow responsible.

This notion has been adequately debunked already, but here goes:

This is another experimental video. As before, there’s a lot of fine detail, so you may want to head over to Vimeo to view in full screen HD. I find it somewhat astonishing that it takes 45 minutes to explore a first-order model.

Here’s the model: co2corr2.vpm (runs in Vensim PLE; requires DSS or Pro for calibration optimization)

Update: a new copy, replacing a GET DATA FIRST TIME call to permit running with simpler versions of Vensim. co2corr3.vpm