Don't just do something, stand there! (Sometimes good policy in complex systems is counterintuitive)
Fast forward to 4:40 to hear the former Vice Chairman of GM wondering how CO2 can go up while temperature goes down.
Setting aside the fact that, smoothed over a reasonable period to suppress natural variability, temperatures haven’t gone down, Bob clearly needs to brush up on his bathtub dynamics.
Atmospheric CO2 doesn’t drive temperature, and temperature doesn’t drive CO2. They drive each other, in a feedback loop. Each relationship involves integration – CO2 accumulates temperature changes through mechanisms like forest growth and ocean uptake, and temperature is the accumulation of heat flux controlled by the radiative effects of CO2.
This has been obvious for at least decades, yet it still eludes many. A favorite counter-argument for an influence of CO2 on temperature has long been the observation that temperature appears to lead CO2 at turning points in the ice core record. Naively, this violates the requirement for establishing causality, that cause must precede effect. But climate is not a simple system with binary states and events, discrete time and single causes. In a feedback system, the fact that X lags Y by some discernible amount doesn’t rule out an influence of Y on X; in fact such bidirectional causality is essential for simple oscillators.
A newish paper by Shakun et al. sheds some light on the issue of ice age turning points. It turns out that much of the issue is a matter of data – that ice core records are not representative of global temperatures. But it still appears that CO2 is not the triggering mechanism for deglaciation. The authors speculate that the trigger is northern hemisphere temperatures, presumably driven by orbital insolation changes, followed by changes in ocean circulation. Then CO2 kicks in as amplifier. Simulation backs this up, though it appears to me from figure 3 that models capture the qualitative dynamics, but underpredict the total variance in temperature over the period. To me, this is an interesting step toward a more complete understanding of ice age terminations, but I’ll wait for a few more papers before accepting declarations of victory on the topic.
Predictably, climate skeptics hate this paper. For example, consider Master Tricksed Us! at WattsUpWithThat. Commenters positively drool over the implication that Shakun et al. “hid the incline” by declining to show the last 6000 years for proxy temperature/CO2 relationship.
I leave the readers to consider the fact that for most of the Holocene, eight
millennia or so, half a dozen different ice core records say that CO2 levels were rising pretty fast by geological standards … and despite that, the temperatures have been dropping over the last eight millennia …
But not so fast. First, there’s no skepticism about the data. Perhaps Shakun et al. omitted the last 6k years for a good reason, like homogeneity. A spot check indicates that there might be issues – series MD95-2037 ends in the year 6838 BP, for example. So, perhaps the WUWT graph merely shows spatial selection bias in the dataset. Second, the implication that rising CO2 and falling temperatures somehow disproves a CO2->temperature link is yet another failure to appreciate bathtub dynamics and multivariate causality.
This credulous fawning over the slightest hint of a crack in mainstream theory strikes me as the opposite of skepticism. The essence of a skeptical attitude, I think, is to avoid early lock-in to any one pet theory or data stream. Winning theories emerge from testing lots of theories against lots of constraints. That requires continual questioning of models and data, but also questioning of the questions. Objections that violate physics like accumulation, or heaps of mutually exclusive objections, have to be discarded like any other failed theory. The process should involve more than fools asking more questions than a wise man can answer. At the end of the day, “no theory is possible” is itself a theory that implies null predictions that can be falsified like any other, if it’s been stated explicitly enough.
I’ve always thought Burt Rutan was pretty cool, so I was rather disappointed when he signed on to a shady climate op-ed in the WSJ (along with Scott Armstrong). I was curious what Rutan’s mental model was, so I googled and found his arguments summarized in an extensive slide deck, available here.
It would probably take me 98 posts to detail the problems with these 98 slides, so I’ll just summarize a few that are particularly noteworthy from the perspective of learning about complex systems.
Data Quality
Rutan claims to be motivated by data fraud,
In my background of 46 years in aerospace flight testing and design I have seen many examples of data presentation fraud. That is what prompted my interest in seeing how the scientists have processed the climate data, presented it and promoted their theories to policy makers and the media. (here)
This is ironic, because he credulously relies on much fraudulent data. For example, slide 18 attempts to show that CO2 concentrations were actually much higher in the 19th century. But that’s bogus, because many of those measurements were from urban areas or otherwise subject to large measurement errors and bias. You can reject many of the data points on first principles, because they imply physically impossible carbon fluxes (500 billion tons in one year).
Slides 32-34 also present some rather grossly distorted comparisons of data and projections, complete with attributions of temperature cycles that appear to bear no relationship to the data (Slide 33, right figure, red line).
Slides 50+ discuss the urban heat island effect and surfacestations.org effort. Somehow they neglect to mention that the outcome of all of that was a cool bias in the data, not a warm bias.
Bathtub Dynamics
Slides 27 and 28 seek a correlation between the CO2 and temperature time series. Failure is considered evidence that temperature is not significantly influenced by CO2. But this is a basic failure to appreciate bathtub dynamics. Temperature is an indicator of the accumulation of heat. Heat integrates radiative flux, which depends on GHG concentrations. So, even in a perfect system where CO2 is the only causal influence on temperature, we would not expect to see matching temporal trends in emissions, concentrations, and temperatures. How do you escape engineering school and design airplanes without knowing about integration?
Correlation and causation
Slide 28 also engages in the fallacy of the single cause and denying the antecedent. It proposes that, because warming rates were roughly the same from 1915-1945 and 1970-2000, while CO2 concentrations varied, CO2 cannot be the cause of the observations. This of course presumes (falsely) that CO2 is the only influence on temperatures, neglecting volcanoes, endogenous natural variability, etc., not to mention blatantly cherry-picking arbitrary intervals.
Slide 14 shows another misattribution of single cause, comparing CO2 and temperature over 600 million years, ignoring little things like changes in the configuration of the continents and output of the sun over that long period.
In spite of the fact that Rutan generally argues against correlation as evidence for causation, Slide 46 presents correlations between orbital factors and sunspots (the latter smoothed in some arbitrary way) as evidence that these factors do drive temperature.
Feedback
Slide 29 shows temperature leading CO2 in ice core records, concluding that temperature must drive CO2, and not the reverse. In reality, temperature and CO2 drive one another in a feedback loop. That turning points in temperature sometimes lead turning points in CO2 does not preclude CO2 from acting as an amplifier of temperature changes. (Recently there has been a little progress on this point.)
Too small to matter
Slide 12 indicates that CO2 concentrations are too small to make a difference, which has no physical basis, other than the general misconception that small numbers don’t matter.
Computer models are not evidence
So Rutan claims on slide 47. Of course this is true in a trivial sense, because one can always build arbitrary models that bear no relationship to anything.
But why single out computer models? Mental models and pencil-and-paper calculations are not uniquely privileged. They are just as likely to fail to conform to data, laws of physics, and rules of logic as a computer model. In fact, because they’re not stated formally, testable automatically, or easily shared and critiqued, they’re more likely to contain some flaws, particularly mathematical ones. The more complex a problem becomes, the more the balance tips in favor of formal (computer) models, particularly in non-experimental sciences where trial-and-error is not practical.
There’s also no such thing as model-free inference. Rutan presents many of his charts as if data speaks for itself. In fact, no measurements can be taken without a model of the underlying process to be measured (in a thermometer, the thermal expansion of a fluid). More importantly, event the simplest trend calculation or comparison of time series implies a model. Leaving that model unstated just makes it easier to engage in bathtub fallacies and other errors in reasoning.
The bottom line
The problem here is that Rutan has no computer model. So, he feels free to assemble a dog’s breakfast of data, sourced from illustrious scientific institutions like the Heritage Foundation (slide 12), and call it evidence. Because he skips the exercise of trying to put everything into a rigorous formal feedback model, he’s received no warning signs that he has strayed far from reality.
I find this all rather disheartening. Clearly it is easy for a smart, technical person to be wildly incompetent outside his original field of expertise. But worse, it’s even easy for them to assemble a portfolio of pseudoscience that looks like evidence, capitalizing on past achievements to sucker a loyal following.
The whole global climate negotiation process is a bit of a sideshow, in that negotiators don’t have the freedom to actually agree to anything meaningful. When they head to Poznan, or Copenhagen, or Durban, they get their briefings from finance and economic ministries, not environment ministries. The mandates are evidently that there’s no way most countries will agree to anything like the significant emissions cuts needed to achieve stabilization.
That’s particularly clear at the moment, with Europe imposing a carbon fee on flights using their airspace, and facing broad opposition. And what opponent makes the biggest headlines? India’s environment minister – possibly the person on the planet who should be happiest to see any kind of meaningful emissions policy anywhere.
Clearly, climate is not driving the bus.
NPR has a nice piece on the US natural gas boom.
Henry Jacoby, an economist at the Center for Energy and Environmental Policy Research at MIT, says cheap energy will help pump up the economy.
“Overall, this is a great boon to the United States,” he says. “It’s not a bad thing to have this new and available domestic resource.” He says cheap energy can boost the economy, and he notes that natural gas is half as polluting as coal when it’s burned for electricity.
“But we have to keep our eye on the ball long-term,” Jacoby says. He’s concerned about how cheap gas will affect much cleaner sources of energy. Wind and solar power are more expensive than natural gas, and though those prices have been coming down, they’re chasing a moving target that has fallen fast: natural gas.
“It makes the prospects for large-scale expansion of those technologies more chancy,” Jacoby says.
From an environmental perspective, natural gas could help transition our economy from fossil fuels to clean energy. It’s often portrayed as a bridge fuel to help us through the transition, because it’s so much cleaner than coal and it’s abundant. But Jacoby says that bridge could be in trouble if cheap gas kills the incentive to develop renewable industry.
“You’d better be thinking about a landing of the bridge at the other end. If there’s no landing at the other end, it’s just a bridge to nowhere,” he says.
(For those who don’t know, Jake Jacoby is not a warm-fuzzy greenie; he’s a hard line economist who leads a big general equilibrium modeling project, but also takes climate science seriously).
For me, the key takeaways are:
The last item is a problem. In the US, the price of greenhouse emissions from gas (or anything else) is approximately zero. The effective prices of other environmental consequences – air quality, pollution from fracking, etc. – are also low. Depletion rents for gas are probably also too low, because the abundance of gas is overhyped, and public resources were suboptimally over-allocated decades ago. Low depletion rents encourage a painful boom/bust of gas supply.
Not only physical assets are mispriced. Another part of the story is learning-by-doing, deliberate R&D, and economies of scale – positive feedbacks that grow the market for low-emissions technologies. Firms producing new tech like PV or wind turbines are only able to appropriate part of the profits of their innovations. The rest spills over to benefit society more generally. Too-cheap gas undercuts these reinforcing mechanisms, so gas substitutes aren’t available when scarcity inevitably returns, hence the “bridge to nowhere” dynamic.
Long-term renewable deployment in the U.S. is going to depend primarily on policy. Is there enough concern about environmental consequences to put in place incentives for renewable energy?
- Trevor Houser, energy analyst, Rhodium Group
They key is, what kind of policy? Currently, we rely primarily on performance standards and subsidies. These aren’t getting the job done, for structural reasons. For example, subsidies are self-extinguishing, because they get too expensive to sustain when the target gets too big (think solar feed-in-tariffs in Europe). They’re also politically vulnerable to apparently-cheap alternatives:
“If those prices hang around for another three or four years, then I think you’ll definitely see reduced political will for renewable energy deployment, ” Houser says
The basic problem is that the mindset of subsidizing or requiring “good” technologies makes them feel like luxuries for rich altruists, even though the apparently-cheap alternatives may be merely penny-wise and pound-foolish. The essential alternative is to price the bads, with the logic that people who want to use technologies that harm others ought to at least pay for the privilege. If we can’t manage to do that, I don’t think there’s much hope of getting gas or climate policy right.
I seldom run across an example of so many things that can go wrong with linear regression in one place, but one just crossed my reader.
A new paper examines the relationship between CO2 concentration and flooding in the US, and finds no significant impact:
Has the magnitude of floods across the USA changed with global CO2 levels?
R. M. Hirsch & K. R. Ryberg
Abstract
Statistical relationships between annual floods at 200 long-term (85–127 years of record) streamgauges in the coterminous United States and the global mean carbon dioxide concentration (GMCO2) record are explored. The streamgauge locations are limited to those with little or no regulation or urban development. The coterminous US is divided into four large regions and stationary bootstrapping is used to evaluate if the patterns of these statistical associations are significantly different from what would be expected under the null hypothesis that flood magnitudes are independent of GMCO2. In none of the four regions defined in this study is there strong statistical evidence for flood magnitudes increasing with increasing GMCO2. One region, the southwest, showed a statistically significant negative relationship between GMCO2 and flood magnitudes. The statistical methods applied compensate both for the inter-site correlation of flood magnitudes and the shorter-term (up to a few decades) serial correlation of floods.
There are several serious problems here.
First, it ignores bathtub dynamics. The authors describe causality from CO2 -> energy balance -> temperature & precipitation -> flooding. But they regress:
ln(peak streamflow) = beta0 + beta1 × global mean CO2 + error
That alone is a fatal gaffe, because temperature and precipitation depend on the integration of the global energy balance. Integration renders simple pattern matching of cause and effect invalid. For example, if A influences B, with B as the integral of A, and A grows linearly with time, B will grow quadratically with time. The situation is actually worse than that for climate, because the system is not first order; you need at least a second-order model to do a decent job of approximating the global dynamics, and much higher order models to even think about simulating regional effects. At the very least, the authors might have explored the usual approach of taking first differences to undo the integration, though it seems likely that the data are too noisy for this to reveal much.
Second, it ignores a lot of other influences. The global energy balance, temperature and precipitation are influenced by a lot of natural and anthropogenic forcings in addition to CO2. Aerosols are particularly problematic since they offset the warming effect of CO2 and influence cloud formation directly. Since data for total GHG loads (CO2eq), total forcing and temperature, which are more proximate in the causal chain to precipitation, are readily available, using CO2 alone seems like willful ignorance. The authors also discuss issues “downstream” in the causal chain, with difficult-to-assess changes due to human disturbance of watersheds; while these seem plausible (not my area), they are not a good argument for the use of CO2. The authors also test other factors by including oscillatory climate indices, the AMO, PDO and ENSO, but these don’t address the problem either.
Third, the hypothesis that streamflow depends on global mean CO2 is a strawman. Climate models don’t predict that the hydrologic cycle will accelerate uniformly everywhere. Rising global mean temperature and precipitation are merely aggregate indicators of a more complex regional fingerprint. If one wants to evaluate the hypothesis that CO2 affects streamflow, one ought to compare observed streamflow trends with something like the model-predicted spatial pattern of precipitation anomalies. Here’s North America in AR4 WG1 Fig. 11.12, with late-21st-century precipitation anomalies, for example:
The pattern looks suspiciously like the paper’s spatial distribution of regression coefficients:
The eyeball correlation in itself doesn’t prove anything, but it’s suggestive that something has been missed.
Fourth, the treatment of nonlinearity and distributions is a bit fishy. The relationship between CO2 and forcing is logarithmic, which is captured in the regression equation, but I’m surprised that there aren’t other important nonlinearities or nonnormalities. Isn’t flooding heavy-tailed, for example? I’d like to see just a bit more physics in the model to handle such issues.
Fifth, I question the approach of estimating each watershed individually, then examining the distribution of results. The signal to noise ratio on any individual watershed is probably pretty horrible, so one ought to be able to do a lot better with some spatial pooling of the betas (which would also help with issue three above).
I think that it’s actually interesting to hold your nose and use linear regression as a simple screening tool, in spite of violated assumptions. If a relationship is strong, you may still find it. If you don’t find it, that may not tell you much, other than that you need better methods. The authors seem to hold to this philosophy in the conclusion, though it doesn’t come across that way in the abstract. Not everyone is as careful though; Roger Pielke Jr. picked up this paper and read it as,
Are US Floods Increasing? The Answer is Still No.
A new paper out today in the Hydrological Sciences Journal shows that flooding has not increased in the United States over records of 85 to 127 years. This adds to a pile of research that shows similar results around the world. This result is of course consistent with our work that shows that increasing damage related to weather extremes can be entirely explained by societal changes, such as more property in harm’s way. In fact, in the US flood damage has decreased dramatically as a fraction of GDP, which is exactly whet you get if GDP goes up and flooding does not.
Actually, the paper doesn’t even address whether floods are increasing or decreasing. It evaluates CO2 correlations, not temporal trends. To the extent that CO2 has increased monotonically, the regression will capture some trend in the betas on CO2, but it’s not the same thing.
Funny stuff from the Daily Show. It would be funnier if it weren’t a bit too real.
Climate skeptics’ opinions about global temperatures and the BEST project are a moving target:
August 27, 2010 (D’Aleo & Watts), there is no warming:
SUMMARY FOR POLICY MAKERS
1. Instrumental temperature data for the pre-satellite era (1850-1980) have been so widely, systematically, and uni-directionally tampered with that it cannot be credibly asserted there has been any significant “global warming” in the 20th century.
February 11, 2011 (Watts), an initial lovefest with the Berkeley Earth Surface Temperature (BEST) project:
Good news travels fast. I’m a bit surprised to see this get some early coverage, as the project isn’t ready yet. However since it has been announced by press, I can tell you that this project is partly a reaction and result of what we’ve learned in the surfacesations project, but mostly, this project is a reaction to many of the things we have been saying time and again, only to have NOAA and NASA ignore our concerns, or create responses designed to protect their ideas, rather than consider if their ideas were valid in the first place. …Note: since there’s been some concern in comments, I’m adding this: Here’s the thing, the final output isn’t known yet. There’s been no “peeking” at the answer, mainly due to a desire not to let preliminary results bias the method. It may very well turn out to agree with the NOAA surface temperature record, or it may diverge positive or negative. We just don’t know yet.
February 19, 2011 (Fred Singer @ wattsupwiththat):
The Berkeley Earth Surface Temperature (BEST) Project aims to do what needs to be done: That is, to develop an independent analysis of the data from land stations, which would include many more stations than had been considered by the Global Historic Climatology Network. The Project is in the hands of a group of recognized scientists, who are not at all “climate skeptics” — which should enhance their credibility….
I applaud and support what is being done by the Project — a very difficult but important undertaking. I personally have little faith in the quality of the surface data, having been exposed to the revealing work by Anthony Watts and others. However, I have an open mind on the issue and look forward to seeing the results of the Project in their forthcoming publications.
… The approaches that I’ve seen during my visit give me far more confidence than the “homogenization solves all” claims from NOAA and NASA GISS, and that the BEST result will be closer to the ground truth that anything we’ve seen.
… I think, based on what I’ve seen, that BEST has a superior method. Of course that is just my opinion, with all of it’s baggage; it remains to be seen how the rest of the scientific community will react when they publish.
In the meantime, never mind the yipping from climate chihuahuas like Joe Romm over at Climate Progress who are trying to destroy the credibility of the project before it even produces a result (hmmm, where have we seen that before?) , it is simply the modus operandi of the fearful, who don’t want anything to compete with the “certainty” of climate change they have been pushing courtesy NOAA and GISS results.
…
But here’s the thing: I have no certainty nor expectations in the results. Like them, I have no idea whether it will show more warming, about the same, no change, or cooling in the land surface temperature record they are analyzing. Neither do they, as they have not run the full data set, only small test runs on certain areas to evaluate the code. However, I can say that having examined the method, on the surface it seems to be a novel approach that handles many of the issues that have been raised.
As a reflection of my increased confidence, I have provided them with my surfacestations.org dataset to allow them to use it to run a comparisons against their data. The only caveat being that they won’t release my data publicly until our upcoming paper and the supplemental info (SI) has been published. Unlike NCDC and Menne et al, they respect my right to first publication of my own data and have agreed.
And, I’m prepared to accept whatever result they produce, even if it proves my premise wrong. I’m taking this bold step because the method has promise. So let’s not pay attention to the little yippers who want to tear it down before they even see the results. I haven’t seen the global result, nobody has, not even the home team, but the method isn’t the madness that we’ve seen from NOAA, NCDC, GISS, and CRU, and, there aren’t any monetary strings attached to the result that I can tell. If the project was terminated tomorrow, nobody loses jobs, no large government programs get shut down, and no dependent programs crash either. That lack of strings attached to funding, plus the broad mix of people involved especially those who have previous experience in handling large data sets gives me greater confidence in the result being closer to a bona fide ground truth than anything we’ve seen yet. Dr. Fred Singer also gives a tentative endorsement of the methods.
My gut feeling? The possibility that we may get the elusive “grand unified temperature” for the planet is higher than ever before. Let’s give it a chance.
I still believe that BEST represents a very good effort, and that all parties on both sides of the debate should look at it carefully when it is finally released, and avail themselves to the data and code that is promised to allow for replication.
March 31, 2011 (Watts), beginning to grumble when the results don’t look favorable to the no-warming point of view:
There seems a bit of a rush here, as BEST hasn’t completed all of their promised data techniques that would be able to remove the different kinds of data biases we’ve noted. That was the promise, that is why I signed on (to share my data and collaborate with them). Yet somehow, much of that has been thrown out the window, and they are presenting some results today without the full set of techniques applied. Based on my current understanding, they don’t even have some of them fully working and debugged yet. Knowing that, today’s hearing presenting preliminary results seems rather topsy turvy. But, post normal science political theater is like that.
… I’ll point out that on the front page of the BEST project, they tout openness and replicability, but none of that is available in this instance, even to Dr. Pielke and I. They’ve had a couple of weeks with the surfacestations data, and now without fully completing the main theme of data cleaning, are releasing early conclusions based on that data, without providing the ability to replicate. I’ve seen some graphical output, but that’s it. What I really want to see is a paper and methods. Our upcoming paper was shared with BEST in confidence.
The Berkeley Earth Surface Temperature project puts PR before peer review
… [Lots of ranting, primarily about the use of a 60 year interval] …
So now (pending peer-review and publication) we have the interesting situation of a Koch institution, a left-wing boogy-man, funding an unbiased study that confirms the previous temperature estimates, “consistent with global land-surface warming results previously reported, but with reduced uncertainty.”
Oct. 21, 2011 (Keenan @ wattsupwiththat), an extended discussion of smoothing, AR(1) noise and other statistical issues, much of which appears to be founded on misconceptions*:
This problem seems to invalidate much of the statistical analysis in your paper.
Oct. 22, 2011 (Eschenbach @ wattsupwiththat), preceded by a lot of nonsense based on the fact that he’s too lazy to run BEST’s Matlab code:
PS—The world is warming. It has been for centuries.
* Update: or maybe not. Still, the paper has nothing to do with the validity of the BEST version of the observational record.
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
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