Volcanism & climate, part 1

Volcanoes are among the most iconic of Earth’s structures, active portals conveying the Hadean depths to the heavens, and all the ecosystems in between.  When the topic of volcanoes comes up, most people think of Kilauea’s lavas flows, Eyjafjallajökull’s ash cloud grounding air traffic, or lovers caught in a last embrace at Pompeii.  Volcanoes turn out to also be an important forcing of Earth’s climate, and not just because they spew greenhouse gases (at this point, they release carbon dioxide 100 times slower  that our civilization). The most recent, climatically significant eruption was that of Mount Pinatubo in 1991, immortalized in this picture. 

The driver of a pick-up truck desperately tries to overrun a cloud of ash spewing from the volcanic eruption of Mt. Pinatubo in 1991. Credit: ALBERTO GARCIA

Read more: https://globalnation.inquirer.net/100047/filipinos-pinatubo-photo-named-among-greatest-of-all-time#ixzz6GzFFGzsK 

Terrifying though that moving mountain of ash and burning gas may be, it does not, in fact, have much effect on climate: that ash is short-lived and shallow,  easily scavenged out of the lower atmosphere by rain. What does matter for climate are the sulfur emissions (specifically, SO2), which coagulate into fine droplets that are rather effective at reflecting sunlight.  Explosive eruptions produce buoyant plumes that can catapult such reflectors into the stratosphere (the region of the atmosphere just over the cruising altitude of commercial jetliners), where they are isolated from the stormy turmoil of the troposphere (the lower layer that we breathe).  In the stable heights of the stratosphere, volcanic aerosols persist for several years, during which they have ample time to circumnavigate a good chunk of the globe, thanks to the stratosphere’s vigorous circulation.  The typical climate impacts begin shortly after an eruption, usually peaking within a year, with a gradual return to “normal” (if there is such a thing for a chaotic system like Earth’s climate) within 5 years. 

At least, that’s the picture painted from the painfully short instrumental record, which has seen only a handful of climatically-significant eruptions — Pinatubo and El Chichon (1982) being the only ones that were recorded by satellites.  For this reason, the way stratospheric aerosol forcing is implemented in climate models mimics what happened during Pinatubo, because that’s the largest one we have observed with any global coverage.

However, we also have ample geologic and documentary evidence for much larger eruptions prior to that: for instance, the “year without a summer” due to the Tambora eruption of 1816, or the Samalas eruption of 1257, the largest of the Common Era, and probably the third largest of the last 10,000 years, according to ice cores. Yes, what falls out of the stratosphere eventually falls to ice caps, where they can recorded within the annual layers of fast-accumulating ice deposits.  (for more on how we reconstruct volcanic forcing from ice cores, see this article). So paleoclimatologists, volcanologists, archeologists, geographers, historians and climate modelers have long been interested in how volcanoes influence climate and society.

Another reason study the climate response to volcanic aerosols: they serve as our best analog for a form of geoengineering called “solar radiation management”, which is — to the immense dismay of reasonable people — now considered as a viable policy proposal to mitigate the worst impacts of climate change (while creating a host of gruesome side effects that such proposals tend to ignore). 

All of this to say that volcanoes offer incredible natural experiments with which to probe Earth’s climate system, and how it is represented by climate models (the computer codes used to project climate’s evolution over the next century, for instance). The last assessment report of the Intergovernmental Panel on Climate Change (IPCC), in particular, summarized the peer-reviewed literature as of 2013, and highlighted the conundrum illustrated here: 

Fig 1: Superposed epoch analysis (SEA) on simulated and reconstructed temperature response to the 12 strongest volcanic eruptions since 1400 AD, reproduced from IPCC AR5 (Masson- Delmotte et al., 2013) Fig. 5.8b

The figure shows that, on average, climate model simulations of northern hemisphere temperature tended to produce a response to volcanic cooling that was stronger, peaked earlier, and recovered more rapidly, than temperature reconstructed from paleoclimate indicators (tree-rings, corals, ice cores, sediment cores, etc). On the face of it, such a discrepancy could call into question the fidelity of climate simulations, the accuracy of paleoclimate reconstructions, the quality of the forcing, and perhaps all three. Like many IPCC figures, it has stimulated many of us to think deeply about what is going on.

In 2017, my group was lucky enough to receive funding from the National Oceanographic and Atmospheric Administration to study this and other climate impacts of volcanic eruptions through the new prism of paleoclimate data assimilation, particularly the Last Millennium Reanalysis. One of the goals of the project is to understand (and hopefully correct) the discrepancy shown above. After some very careful work (and a lot of Python code), USC graduate student Feng Zhu reports in a recent paper (soon to be published in Geophysical Research Letters) that the discrepancies can indeed be reconciled, with a few asterisks.

At the risk of spoiling the suspense, here’s the same picture, after accounting for known effects due to (1) the location of paleoclimate records (mostly, trees); (2) the seasonality of these proxies (extratropical trees only grow during the summer), and (3) focusing on the observations types that are least affected by a process called biological memory (maximum latewood density, or MXD).

Fig 2: (a) Same as Fig 1, but applying the LMR after resolving differences in the model and proxy domains associated with seasonality, spatial distribution, and biological memory. (b) Same as (a) but using the NTREND network of MXD records (Wilson et al, 2016; Anchukaitis et al, 2017)

This figure shows that, for well-characterized eruptions and a careful, like-to-like comparison of climate models and MXD records, the discrepancy is resolved to a shocking extent. Remarkably, this holds true for two very different sets of paleoclimate observations, a handpicked selection like NTREND, or a more permissive selection like PAGES 2k. For details, the reader is invited to read the paper (in press at Geophysical Research Letters).

Now, before you take this to the bank, there are a few asterisks. The first thing to mention is that we are not the first to point out that these effects matter; indeed our study was motivated by decades of research by many careful scientists. Standing on the shoulders of giants, as always. Our main contribution is to weave all these factors together to enable a systematic comparison of models and reconstructions over multiple eruptions. Yet a big caveat is that the comparison could only be done on 6 eruptions, highlighted in this figure:

Fig 3: Comparison between the volcanic forcing of Gao et al., 2008 (used in many PMIP3 models , as well as the iCESM simulation of Stevenson et al., 2019; Brady et al., 2019) and the eVolv2k version 3 Volcanic Stratospheric Sulfur Injection (VSSI) compilation (Toohey & Sigl, 2017). The triangles denote the selected 6 large events between 1400 and 1850 CE. 

While the promise of paleoclimatology is to sample many more and larger events than captured over the instrumental record, we found our sample still limited, for three reasons:

1. Forcing uncertainties: many of the model simulations used the Gao et al 2008 forcing, which has now been considerably refined by the incredibly careful work of Michael Sigl and co-authors. As a result, many of the eruptions were just not comparable, or we had to manually adjust the timing of eruptions to really compare like to like. The comparison should be much richer when PMIP4 simulations, which use the evolv2k dataset, come online.
2. Data coverage: the “pull of the recent” is as real in paleoclimatology as it is in paleobiology. What this means is that the most recent eruptions are the one that are best captured by our proxy network; the old ones, not so much. This remains a problem for the 1257 (Samalas) and 1450’s (Kuwae + others?) eruptions, as we discuss at length in the paper. We therefore had to focus on the last 4 centuries — not the full Common Era as we were hoping for.
3. Cluster effects: some eruptions tend to happen in clusters, making it hard to separate their effects. To keep the comparison clean, Feng chose to focus on isolated eruptions, which further reduces the number of eruptions.

So while our careful analysis of the data enables a spectacular resolution of discrepancies between models and reconstructions, that is only the case for this small set of 6 eruptions. Therefore, our conclusion (that the physics of the climate models is not the main culprit here ; only the way the comparison was previously done) presently applies only to these 6 eruptions. At least, for now: there is obviously a large potential for expanding the set of eruptions for this comparison in PMIP4, and as more MXD chronologies are incorporated in data syntheses like PAGES 2k or N-TREND.

One of the more interesting questions is whether our conclusion would hold true for much larger eruption like Samalas, mentioned earlier. As pointed out before, models and reconstructions disagree fiercely on that one. In 2015, Markus Stofffel and his group did some exciting work showing that you could reduce much of the difference by looking at carefully screened long MXD chronologies, considering seasonality (as we did) and improving how stratospheric sulfur aerosols are represented in models. These “self-limiting” feedbacks have long been known to become important for very high sulfate loading, yet a lot of climate models did not incorporate them. Using the IPSL model with a particular formulation of this feedback, Stoffel et al concluded that the improved stratospheric physics was key to bringing models and reconstructions in line. Does this contradict our result?

We don’t know yet. It remains to be seen how their comparison would work for a broader set of models and observations. The good news is that Feng made all his open-source (Python) code freely available to reproduce and extend the results of the paper. I, for one, would love to have more simulations to play with, incorporating such improved physics and seeing whether the reconstructions can help discriminate between competing parameterizations. If you have ideas for how you’d like to extend this study, please reach out! This type of work lends itself well to long quarantines.

Reflections on a pandemic, part 2

a radial survey of topics on my mind in the midst of the COVID-19 pandemic, continued from Part 1.

Risk Perceptions

I’ve come to appreciate that most of our fights are fundamentally about risk perceptions, even when they are couched in different words: are we better off with strong social safety net or a strong military? Can we balance environmental health and economic growth? (that’s a false choice, but one that is still repeatedly offered.) Can society allow gay couples to marry without threatening the institution of marriage? (again a false choice, but a common argument). Where you land on these questions depends on how you weight the risk of one action or inaction vs the other.

COVID-19 has been an interesting mirror to calibrate how (ir)rational we are in our perceptions of risk. Certainly the death toll in places like Italy (nearly 10%, according to these data) is extremely worrisome. Yet, it needs to be put in the context of other morbidity factors (see table below for USC data): it’s not like heart disease went on confinement during the COVID-19 pandemic. In fact, given that it’s now much more difficult to exercise and that (in my observation) junk food was the first to be snatched from supermarket shelves, I would imagine that deaths from heart disease will only get amplified by COVID-19, though we may not see that right away.

Leading cause of death in the United States in 2017. Source: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_06-508.pdf

Missing from a lot of these tables is also the breakdown by age or the existence of pre-existing health conditions. So how is one to make a rational assessment about the right level of concern? Amidst all these extremes, I was glad to hear the level-headed appraisal of Amesh Adalja, which I heartily recommend.

This made me think more broadly about why COVID-19 is eliciting a panic response for much of the world, whereas climate change (arguably, an even more formidable threat) has failed to do so. Much has been written on this, including this excellent piece co-written by my colleague Eric Galbraith.

The timescales are critical here: our ability to perceive risk is the result of our evolution, which has rewarded the ability to avoid or thwart immediate, tangible threats like this coronavirus, but has yet to select us on whether we correctly assess long-term threats like climate change. To paraphrase the terminology of Daniel Kahneman , our instinctive “System 1” is peculiarly bad at assessing abstract, long-term threat, which it discounts entirely in favor of more immediate concerns (however mundane). System 2 involves careful thinking, and our society has evolved that in the form of scientific institutions and (in some countries, anyway) governments that actually listen to those scientists. What should be clear from the work of many cognitive psychologists, beginning with Kahneman and Tversky, is that we are not nearly as smart as we think we are, and that we should place our trust in the hands of experts. Experts are fallible too, but, as Naomi Oreskes reminds us, the thought collective they form, and its process of critical interrogation, guarantees that their message gets ever closer to the truth.

That is why I listen to epidemiologists when it comes to COVID-19, and that is why everyone should listen to the quasi-absolute consensus on man-made climate change.

The lesson so far is that we seem to be quite bad at learning our lesson: only when smacked in the face with consequences do most of us finally overcome our reluctance to enacting the recommendations of experts. Inaction on COVID-19 should not be surprising: even when our own health is clearly at stake, many of us procrastinate about the advice given by our primary care physician until a strong warning sign comes, usually in the form of illness. It would be wise to view COVID-19 as a glaring illustration of the current inability of our society to take prompt and necessary action on crises even when we have seen them coming for a while. As should be clear, we can’t afford to procrastinate on major crises any longer, so perhaps we should take that subtle cue to re-assess our societal priorities, and start by engineering economic system that serve the planet and its people, not the other way around.

COVID and Climate

COVID-19 is forcing some societal changes that many people once argued were impossible: “the American way of life is not up for negotiation. Period”, G.H.W. Bush once said. One submicron-size viral pandemic later, that way of life got upended pretty quickly, without the need for any negotiation.

To me this is a vibrant demonstration of how fundamentally changeable our society is. For decades now, climate scientists like myself have rung the alarm bells about the devastating costs of unmitigated carbon levels in the atmosphere, requiring nothing short of a war-time mobilization effort to rewire our economies in carbon-neutral ways. For the most part, the body politic chose to do nothing, for many reasons. One argument you often hear, is that it would be too difficult to re-organize our society to meet that challenge. Clearly it is not. In fact, as Western societies rethink how to generate income from the comfort of their own home, we are seeing the potential for what a radically more local existence could mean.

On one level, COVID-19 is forcing us to enact many of the same changes that a low-carbon life would require. But the parallel goes deeper: as this brilliant visualization makes clear, there are two major reasons COVID-19 is so disruptive to our way of life and our economy:

1. We’re late to the fight. In an exponential game, the early bird catches the worm. If drastic international travel restrictions had been enacted as soon as the virus was detected in, well, 2019, much of the world wouldn’t be in confinement right now, and this economic crisis could have been minimized. We are now witnessing the true cost of procrastination.
2. We’re not working together: there could have been a whole lot more international cooperation to stem this crisis. Despite calls by the WHO, there was not. Be that as it may, there are a lot of things local governance can do here, at the level of countries, states, and cities. But within these entities, we all need to play along. Social responsibility is following the advice of public health experts and officials; supporting each other through these tough times; at the very least, not falling into a narrative of scarcity that leads some to stockpile toilet or other necessities that leaves others in the cold. We’re all in this together, and if too many people buck the trend, there might as well be no measures in place. Unfortunately, the Faux News demographic is not helping here.

In policy parlance, this is called a “collective action problem“, of which climate change is the poster child: there is a cost to action, which no one wants to incur unless everybody does. Even though there is a much, MUCH higher cost to inaction, it is well-known in game theory that this asymmetry can result in paralysis. COVID-19 and climate change both show that unless all of us are aware of the risk and committed to addressing it, the needle won’t move, or move too little too late. COVID-19 will help us see this one way or the other: it has already shown the willingness of some societies to drastically reshape themselves to protect their most vulnerable members. In ours, it will either show collaboration to be a success, or it will show the lack of collaboration to be a terrible failure. Either way, we should heed the lesson.

That is, if we see things for what they are. In the Age of Misinformation, that is far from a guarantee. Here again, COVID-1, climate change, and other societal risks share a common enemy: science denialism.

Denialism

David Michaels makes the apt point that the US dilly-dallying on this crisis shares many of the epistemic roots that have plagued the accurate appraisal of climate risk. Namely, that when science (any science) is seen as threatening to an economic system, no amount of corporate malfeasance will be spared to undermine that science. This was abundantly shown in Merchants of Doubt. Because science denialism has been perfected by many industries (starting with the astonishing PR innovations of the tobacco industry) it is a well-oiled machine that extends its tendrils to the heart of right-wing media conglomerates, from Fox News to talk radio, who benefit from and amplify its message. And because their audiences have now been primed for decades to deny or minimize the warmings of experts, even educated Republicans in demographics at high risk of severe harm by COVID-19 continue to put their heads in the sand about the issue.

This is no accident. This is the result of a decades-long war on science that has pitted every scientific issues that conflicts with the bottom-line of entrenched industries against the momentous disinformation apparatus of modern right-wing media, from Fox News to Breitbart. If I were a social Darwinist, I’d say that we should let those who refuse to hear the evidence die of the consequences of this refusal. Unfortunately, we’re all crewmates on Spaceship Earth: my actions affect them, and their actions (or lack thereof) affect all of us. So there is no choice but for everyone to get on board and accept the evidence, preferably before it is too late.

Beyond COVID-19, the concerning pandemic here is the denial of reality, exemplified by Trump’s wishful thinking about the issue. My only hope is that this crisis underlies the true costs of denying reality, and rehabilitates critical thinking at all levels of our society. For that to happen, the tech giants that profit from the spread of misinformation need to change, and it seems to be leaving aside its supposed “neutrality” in the public interest. Great! Can we do the same for climate change, racial equality, vaccines, gun control, and other major societal issues, please?

Viral Conspiracies

For a brief time early in this millennium, “going viral” acquired a sheen synonymous with celebrity, fame and fortune (as well as, occasionally, disrepute, opprobrium, and ridicule). The SARS-CoV-2 virus abruptly reminded us of the its biological (and sometimes sinister) of viral.

The underlying mathematics are the same, however, and they may in large part be understood through graph theory. I first became acquainted with it through Duncan Watts’ “Six Degrees“, which lifted the curtain on the astonishing variety of natural and social phenomena that can be understood through graph theory (including paleoclimate reconstructions, if you can believe it).

I empathize with the many people throughout the world who feel abandoned by such institutions as crises multiply around them. While it is not the only problem, I do think that scientists being too distant from the public they serve is one reason why we see such conspiracy theories multiply (Russian trolls being another, non-negligible one). This blog is one attempt at communicating science a bit more directly than through the stilted prism of peer-reviewed technical journals.

There is a perilous parallel in how similarly infections and misinformation spread, and the small world property has a lot to do with that. Of particular interest to me today are conspiracy theories, like “climate scientists are only in it for the money” — no doubt referring to those fat suitcases full of unmarked $100 bills that the money-hungry solar industry regularly hands me, which is why I am currently sitting in a fortress in the middle of the ocean from which I pull strings with a cat on my lap. There are many other examples of such conspiratorial ideation, from the myth that the 1969 moon landing was a hoax, to anti-vaccine paranoia, to the idea that a cure for cancer is being withheld by vested interests, to Flat Earthers contending that NASA has been brainwashing us that the Earth is round when in fact it is flat. Common to many (all?) of these conspiracy theories is a strong disillusion in institutions, not only government but also scientific bodies.

In a refreshingly clear and concise article, David Robert Grimes proposed a simple mathematical model to quantify the limited viability of conspiratorial beliefs; it’s basically viral contagion in reverse. Imagine that you are part of a conspiracy trying to rule the world, and (naturally) you don’t want people to find out. Despite your best efforts to keep it a tight-lipped secret, information always gets out, sooner or later: you might be talking in your sleep, blurting out secrets to your spouse without even knowing; or there might be a whistleblower in your organization, worried about your plot to take over the world for solar panels. Either way, your network of conspirators is leaky, and sooner or later someone is going to call The Guardian with some damning revelations.

I’m going to skip over the math (looks like Grimes had gone a little fast himself, and that some aspects needed a correction), but one way to look at this is to ask: even in the best of circumstances, for how long can a global conspiracy endure before someone spills the beans, voluntarily or not? The answer depends obviously on how many people are “in on it”, how carefully they communicate, etc. To give just one of the example (one that hits close to home), here is the probability of spilling the beans on that climate change conspiracy as a function of time:

Failure curves for the climate change “hoax”: The blue solid line depicts failure probability with time if all scientific bodies endorsing the scientific consensus are involved, the red-dotted line presents the curve if solely active climate researchers were involved

Assuming a fixed probability of an intrinsic leak (i.e. how likely is each conspirator to spill the beans?), the main variable is the number of conspirators. With just those evil, blood-thirsty climate scientists doing the conspiring (about 30,000 of us, by Grimes’ estimate), it would take about 27 years for the climate “hoax” to have a 95% probability of being exposed (red dashed curve). Add to that the entirety of NASA, the American Geophysical Union, and other gatherings of shape-shifting vampires (about 400,000 total), and you get the blue curve, reaching the same 95% probability in just 4 years. In other words, if man-made global warming truly were a hoax, the beans would very likely have been spilled many times over.

You can argue about the exact numbers picked by Grimes for the probability of an intrinsic leak, but there is no going around the fact that keeping secrets between large numbers of people is tough business. That’s why Dr Evil only works with a select cadre of co-conspirators, not 400,000 of them. Also, he kills a sizable fraction of them when Mr Bigglesworth gets upset, which keeps the numbers low. Grimes’ article is considerably more interesting, though it involves fewer murderous cats. It is well worth a read.

May it become one more argument against the ludicrous theories peddled by science denialists of all stripes.

Virus as Medicine

While SARS-CoV-2 was not my idea of a good time, it has changed our life practically overnight, and is forcing a fundamental rethinking of pretty much every aspect of modern existence, from travel, work and schooling to artistic and political expression. Politico ran a nice piece over the week-end, with over 30 thinkers sharing their view of how COVID-19 was reshaping our world, in many cases, I’d argue, for the better. But no one said it quite as beautifully as Lynn Ungar:

Pandemic by Lynn Ungar

What if you thought of it
as the Jews consider the Sabbath—
the most sacred of times?
Cease from travel.
Cease from buying and selling.
Give up, just for now,
on trying to make the world
different than it is.
Sing. Pray. Touch only those
to whom you commit your life.
Center down.

And when your body has become still,
reach out with your heart.
Know that we are connected
in ways that are terrifying and beautiful.
(You could hardly deny it now.)
Know that our lives
are in one another’s hands.
(Surely, that has come clear.)
Do not reach out your hands.
Reach out your heart.
Reach out your words.
Reach out all the tendrils
of compassion that move, invisibly,
where we cannot touch.

Promise this world your love—
for better or for worse,
in sickness and in health,
so long as we all shall live.

Advice to Climate Scientists on how to Avoid being Swift-boated and how to become Public Intellectuals

My friend Kevin pointed me to this great post by Juan Cole, which is well worth it’s own weight in wisdom. The recent vilification of climate scientists in the public sphere underscores the need for my profession to urgently become media-savvy. Though it seems that experience will be the best teacher, i found the following advice very useful. It is always nice to hear you are not alone in a dark room. Happy reading. El Niño.

Climate Scientists continue to see persuasive evidence of global warming and climate change when they speak at academic conferences, even though, as Andrew Sullivan rightly put it, the science is being ‘swift-boated before our eyes.’ (See also Bill McKibben at Tomdispatch.com on Climate Change’s OJ Simpson moment).

This article at mongabay.com includes some hand-wringing from scientists who say that they should have responded to the attacks earlier and more forcefully in public last fall, or who worry that scientists are not charismatic t.v. personalities who can be persuasive on that medium.

Let me just give my scientific colleagues some advice, since as a Middle East expert I’ve seen all sorts of falsehoods about the region successfully purveyed by the US mass media and print press, in such a way as to shape public opinion and to affect policy-making in Washington:

1. Every single serious climate scientist should be running a blog. There is enormous thirst among the public for this information, and publishing only in technical refereed journals is guaranteed to quarantine the information away from the general public. A blog allows scientists to summarize new findings in clear language for a wide audience. It makes the scientist and the scientific research ‘legible’ to the wider society. Educated lay persons will run with interesting new findings and cause them to go viral. You will also find that you give courage to other colleagues who are specialists to speak out in public. You cannot depend on journalists to do this work. You have to do it yourselves.

2. It is not your fault. The falsehoods in the media are not there because you haven’t spoken out forcefully or are not good on t.v. They are there for the following reasons:

a. Very, very wealthy and powerful interests are lobbying the big media companies behind the scenes to push climate change skepticism, or in some cases (as with Rupert Murdoch’s Newscorp/ Fox Cable News) the powerful and wealthy interests actually own the media.

b. Powerful politicians linked to those wealthy interests are shilling for them, and elected politicians clearly backed by economic elites are given respect in the US corporate media. Big Oil executives e.g. have an excellent rollodex for CEOs, producers, the bookers for the talk shows, etc. in the corporate media. They also behind the scenes fund “think tanks” such as the American Enterprise Institute to produce phony science. Since the AEI generates talking points that aim at helping Republicans get elected and pass right wing legislation, it is paid attention to by the corporate media.

c. Media thrives on controversy, which produces ratings and advertising revenue. As a result, it is structured into an ‘on the one hand, on the other hand’ binary argument. Any broadcast that pits a climate change skeptic against a serious climate scientist is automatically a win for the skeptic, since a false position is being given equal time and legitimacy. It was the same in the old days when the cigarette manufacturers would pay a ‘scientist’ to go deny that smoking causes lung cancer. And of course we saw all the instant Middle East experts who knew no Arabic and had never lived in the Arab world or sometimes even been there who were paraded as knowledgeable sources of what would happen if the United States invaded Iraq and occupied it.

d. Journalists for the most part have to do as they are told. Their editors and the owners of the corporate media decide which stories get air time and how they are pitched. Most journalists privately admit that they hate their often venal and ignorant bosses. But what alternative do most of them have?

e. Journalists for the most part do not know how to find academic experts. An enterprising one might call a university and be directed to a particular faculty member, which is way too random a way to proceed. If I were looking for an academic expert, I’d check a citation index of refereed articles, but most people don’t even know how to find the relevant database. Moreover, it is not all the journalists’ fault. journalism works on short deadlines and academics are often teaching or in committee and away from email. Many academics refuse (shame on them) to make time for media interviews.

f. Many journalists are generalists and do not themselves have the specialized training or background for deciding what the truth is in technical controversies. Some of them are therefore fairly easily fooled on issues that require technical or specialist knowledge. Even a veteran journalist like Judy Miller fell for an allegation that Iraq’s importation of thin aluminum tubes in 2002 was for nuclear enrichment centrifuges, even though the tubes were not substantial enough for that purpose. Many journalists (and even Colin Powell) reported with a straight face the Neocon lie that Iraq had ‘mobile biological weapons labs,’ as though they were something you could put in a winnebago and bounce around on Iraq’s pitted roads. No biological weapons lab could possibly be set up without a clean room, which can hardly be mobile. Back in the Iran-Iraq War, I can remember an American wire service story that took seriously Iraq’s claim that large numbers of Iranian troops were killed trying to cross a large body of water by fallen electrical wires; that could happen in a puddle but not in a river. They were killed by Iraqi poison gas, of course.

The good journalists are aware of their limitations and develop proxies for figuring out who is credible. But the social climbers and time servers are happy just to host a shouting match that maybe produces ‘compelling’ television, which is how they get ahead in life.

3. If you just keep plugging away at it, with blogging and print, radio and television interviews, you can have an impact on public discourse over time. I could not quantify it, but I am sure that I have. It is a lifetime commitment and a lot of work and it interferes with academic life to some extent. Going public also makes it likely that you will be personally smeared and horrible lies purveyed about you in public (they don’t play fair– they make up quotes and falsely attribute them to you; it isn’t a debate, it is a hatchet job). I certainly have been calumniated, e.g. by poweful voices such as John Fund at the Wall Street Journal or Michael Rubin at the American Enterprise Institute. But if an issue is important to you and the fate of your children and grandchildren, surely having an impact is well worth any price you pay.

Juan Cole, Feb 28 2010 blogpost

The Heisenberg Principle of Climatology

OK, I have to admit this is a pretty eccentric idea . It occurred to me around New Year’s time. Please read this as entertainment, not science – or at best, entertaining science. I assume the esteemed reader to be familiar with Heisenberg’s uncertainty principle .

These days, even small-time lawyers are using the concept – as we were reminded by the Coen brothers in the magnificently quirky “The Man who wasn’t there” :

Forgetting the Coen twist, the uncertainty principle states that there is a fundamental limit to the accuracy with which you can jointly determine the position () and the momentum () of a particle in quantum world. Which one can write :

wherein is the root mean square operator and is the familiar Planck’s constant.

This has pretty deep philosophical implications because it means that we must abandon utopias of ever knowing those quantities simultaneously with arbitrary accuracy. Most sobering is that it is a direct consequence of the fundamental principles of quantum mechanics.

It occurred to me the other day while having a shower (which is a well-known fountain of ideas), that one could formalize a similar principle in climatology. Indeed, the basic curse of paleoclimatology is that the farther back in time you try to estimate temperatures, the more uncertain they become. We think we know last year’s globally average temperature pretty well (say within 0.05 C ). Last decade might have similar or even lower uncertainties because of the central limit theorem knocking down some measurement errors for you… but try going back 100 years and the measurement error and sampling bias become so large you’ll be lucky to have an accuracy of 0.5 C. And that is during a period broadly known in the field as “instrumental”, that is to say the one over which we have a reasonable number of physical measurements of temperature. As you can see on the following graph from Brohan et al, 2006, this uncertainty grows back in time rather quickly already.

Before about 1850 A.D., we no longer have enough direct measurements, so we have to rely instead on proxy indicators. All of them (corals, tree rings, ice cores, sediments, documentary evidence) have their pros and cons, but even if they can give a surprisingly coherent view of past climates, they are necessarily approximate. Hence, go back 1000 years and arguably, we don’t know this within 0.7 C, perhaps even 1 C (this is a very controversial number and I’d be surprised if no one picks up on it… With wacky ideas come rather loose numbers that one should not take too literally). Go back 10,000 years and a degree C or two might be all that you could hope for. And so on and so forth : the more time elapses and eons pass by, the more water flows under bridges, the more overprinted, worn and tired is the geologic record – and so grows the uncertainty in the estimated temperature.

Say you are trying to estimate said temperature, , over some period (or equivalently, the frequency ). The longer the period (i.e., the smaller the frequency), the more uncertain the estimate, so one could write something of the form :

.

Which is pretty naive and assumes an inverse relationship between the two variables, and is by no means a “universal” constant. More generally one could write :

where the subscript denotes a reference period (say, the last decade), and and are a positive constants, whose precise values are as yet undetermined. So one could play games with that and try to estimate them from a linear fit in log-log space… I’m not sure they would mean much, but who knows ? Perhaps one day we’ll have enough reliable data to be able to characterize this and it won’t seem so quirky. In any case, it’s now pretty far from Heisenberg, who must be shifting in his grave and the mere idea that I am using is august name for such silliness. Nevertheless, it is an uncertainty principle of sorts. And for no more fundamental reason that the degradation of geological records over time, which I guess one could view as a broad consequence of disequilibrium thermodynamics. But only loosely so, because bioturbation holds a large part of the blame, and darned if we have a consistent theory of living organisms that’s grounded in statistical mechanics. But I digresss.

In my defense, I would like to declare that this nonsense was scribbled on a piece of paper while leaning on a garbage can outside the Metropolitan Avenue subway station in Williamsburg, Brooklyn. Which we all know to be home to some pretty crazy stuff.

Now here’s the truly insane part of the story. The next day was my adoptive bigger sister’s birthday. ( If you happen to live far from home, I highly recommend you adopt, or get adopted by, a bigger sister. It’s loads of fun, especially when they have the same linguistic schizophrenia as yours). I was working from the Columbia University library that day, and decided to drop by to bring her a gift (she lives around the corner). I had no sooner entered her building and stepped into the elevator that a white-haired gentleman nimbly entering the lobby asked me to hold the doors. I happily obliged, and he promptly jumped into the cage a few seconds afterwards, with a mischievous smile on his face.

– “Piso cuatro, por favor “, he said with a distinguished Spanglish accent.

– “Si señor”, I replied, and pushed button 4.

He looked at me from top to bottom and asked in a spotless New England English (I must have looked really freaky) :

– “Are you a physicist ?”

– “Almost”, I replied, “I’m a geophysicist”.

– “Ah well, I am a physicist”, he went on. “And I recently had a very interesting epiphany about Heisenberg’s uncertainty principle.”

 

Before I could pick up my jaw from the floor, Mr Heisenberg Jr had disappeared into the depths of the 4th floor, only perceptible through the rustling of his raincoat as his walked down the corridor. And so it was decided that even though my Heisenberg idea might not pass into posterity past breakfast, it should at least be worthy of a little post.