Climate models got their fundamentals right

15 04 2019

Everyone loves to hate climate models: climate deniers of course, but also the climate scientists who do not use these models — there are many, from observationalists to theoreticians. Even climate modelers love to put their models through harsh reality checks, and I confess to have indulged in that myself: about how models represent of sea-surface temperature variations in the tropical Pacific on various time scales (here, and here), or their ability to simulate the temperature evolution of the past millennium.   

There is nothing incongruous about that: climate models are the only physically-based tool to forecast the evolution of our planet over the next few centuries, our only crystal ball to peer into the future that awaits us if we do nothing — or too little — to curb greenhouse emissions. If we are going to trust them as our oracles, we had better make sure they get things right. That is especially true of climate variations prior to the instrumental record (around 1850), since they provide an independent test of the validity of the models’ physics and assumptions. 

In a new study in the Proceedings of the National Academy of Sciences, led by Feng Zhu, a third-year graduate student in my lab,  we evaluated how models fared at reproducing the spectrum of climate variability (particularly, its continuum)  over scales of 1 year to 20,000 years. To our surprise, we found that models of a range of complexity fared remarkably well for the global-mean temperature — much better than I had anticipated. This is tantalizing evidence that models contain the correct physics to represent this aspect of climate, with a big caveat: they can only do so if given the proper information (“initial and boundary conditions”). While the importance of boundary conditions has long been recognized, our study shakes up old paradigms in suggesting that the long memory of the ocean means that initial conditions are at least as important on these scales:  you can’t feed your model a wrong starting point and assume that fast dynamics make it forget all about it within a few years or decades. The inertia of the deep ocean means that you need to worry about this for hundreds — if not thousands — of years. This is mostly good news for our ability to predict the future (climate is predictable), but means that we need to do a lot more work to estimate the state of the deep ocean in a way that won’t screw up predictions. 

Why spectra?

Let us rewind a bit. The study originated 2 years ago in discussions with Nick McKay, Toby Ault, and Sylvia Dee, with the idea of doing a Model Intercomparison Project (a “MIP”, to be climate-hip) in the frequency domain. What is the frequency domain, you ask? You may think of it as a way to characterize the voices in the climate symphony. Your ear and your brain, in fact, do this all the time: you are accustomed to categorizing the sounds you hear in terms of pitch (“frequency”, in technical parlance) and have done it all your life without even thinking about it. You may even have interacted with a spectrum before:  if you have ever tweaked the equalizer on your stereo or iTunes, you understand that there is a different energy (loudness) associated with different frequencies (bass, medium, treble), and it sometimes need to be adjusted. The representation of this loudness as a function of frequency is a spectrum. Mathematically, it is a transformation of the waveform that measures how loud each frequency is, irrespective of when the sound happened (what scientists and engineers would call the phase). 


Fig 1: Time and Frequency domain. Credit:



If you’re a more visual type, “spectrum” may evoke a rainbow to you, wherein natural light is decomposed into its elemental colors (the wavelengths of light waves). Wavelengths and frequency are inversely related (by, it turns out, the speed of light). 


Fig 2: A prism decomposing “white” light into its constituent colors, aka wavelengths. Credit: DKfindout

So, whether you think of it in terms of sound, sight, or any other signal, a spectrum is a mathematical decomposition of the periodic components of a  signal, and says how intense each harmonic/color is.

Are you still with us? Great, let’s move on to the continuum. 

Why the continuum?

A spectrum is made of various features, which all have their uses. First, the peaks, which are associated with pure harmonics, or tones. The sounds we hear are made of pure voices, with a very identifiable pitch; likewise, the colors we see are a blend of elemental colors.  In the climate realm, these tones and colors  would correspond to very regular oscillations, like the annual cycle, or orbital cycles. All of these show up as sharp peaks in a spectrum. Then there are voices that span many harmonics, like El Niño, with its irregular, 2-7yr recurrence time, showing up as a broad-band peak in a spectrum. Finally, there is all the rest: what we call the background, or the continuum. You can think of the continuum as joining the peaks together, and in many cases it is at least as important as the peaks.

Why do we care about the continuum? Well, many processes are not periodic. To give but one example, turbulence theories make prediction about the energy spectrum of velocity fluctuations in a fluid, characterized by “scaling” or “power law”  behavior of the continuum.  There are no peaks there, but the slope of the spectrum (the “spectral exponent”) is all the rage: its value is indicative of the types of energy cascades that are at play. Therefore, these slopes are very informative of the underlying dynamics.   Hence the interest, developed by many previous studies, in whether models can reproduce the observed continuum of climate variability: if they can, it means they correctly capture energy transfers between scales, and that is what scientists call a “big fucking deal” (BFD). 

Our contribution


Fig 3: A spectral estimate of the global-average surface temperature variability using instrumental and paleoclimate datasets, as well as proxy-based reconstructions of surface temperature variability. See the paper for details.

One challenge in this is that we do not precisely know how climate varied in the past — we can only estimate it from the geologic record. Specifically, the data we rely on came from proxies, which are sparse, biased and noisy records of past climate variability. I was expecting to find all kinds of discrepancies between various proxies, and was surprised to find quite a bit of consistency in Fig 1. 

In fact, because we focused on global scales, the signal that emerges is quite clean: there are two distinct scaling regimes, with a kink around 1000 years. This result extends the pioneering findings of Huybers & Curry [2006], which were an inspiration for our study, as were follow-up by Laepple & Huybers [2013a,b, 2014].

It also leads to more questions:

– Why do the regimes have the slopes that they do?

– Why is the transition around 1,000 years?

– Can this be explained by a simple theory, based on fundamental principles?

This we do not know yet. What we did investigate was our motivating question: can climate models, from simple to complex, reproduce this two-regime affair? Why or why not?

What we found is that even simple models (“Earth System Models of Intermediate Complexity”) or a coarse-resolution Global Climate Model (GCM) like the one that generated TraCE-21k capture this feature surprisingly well (Fig 4).


Fig 4: The power spectral density (PSD) of transient model simulations. In the upper panel, β is estimated over 2-500 yr. The inset plot compares distributions of the scaling exponents (estimated over 2-500 yr) of GAST in PAGES2k-based LMR vs the PMIP3 simulations. The inset plot compares the β values of the model simulations (red, green, and blue dots) and that of the observations (gray dots). The gray curves are identical as those in Fig. 1. See Zhu et al [2019] for details

The TraCE-21k experiments were designed to simulate the deglaciation (exit from the last Ice Age, 21,000 years ago), and they know nothing about what conventional wisdom says are the main drivers of climate variations over the past few millennia: explosive volcanism, variations in the Sun’s brightness, land-use changes, or, more recently, human activities, particularly greenhouse gas emissions. And yet, over the past millennium, these simple models capture a slope of -1, comparable to what we get from state-of-the-art climate reconstructions. Coincidence?

More advanced models from the PMIP3 project also get this, but for totally different reasons. Most PMIP3 models don’t appear to do anything over the past millennium, unless whacked by violent eruptions or ferocious emissions of greenhouse gases (a.k.a. “civilization”). If you leave these emissions out, in fact, the models show too weak a variability, and too flat a spectral slope.  How could that be, given that they are much more sophisticated models  than the ones used to simulate the deglaciation?

Does the ocean still remember the last Ice Age?

The answer comes down to what information was given to these models: you can have the most perfect model, but it will fail every time if you give it incorrect information. In the PMIP experimental protocol, the past1000 simulations are given a starting point that is in equilibrium with the conditions of 850 AD. That makes sense if you’re trying to isolate the impact of climate forcings (volcanoes, the Sun, humans) and how they perturb this equilibrium. But that implicitly assumes that there is such a thing as equilibrium. Arguably, there never is: the Earth’s orbital configuration is always changing, and if the climate system is always trying to catch up with it. Only a system with goldfish memory of its forcing can be said to be in equilibrium at any given time. So how long is the climate system’s memory? 

What TraCE and the simple EMICs show is that you can explain the slope of the continuum over the past millennium by climate’s slow adjustment to orbital forcing, or abrupt changes that happened over 10,000 years ago. This means that climate’s memory is in fact quite long. Where does it reside?  Three main contenders:

  1. The deep ocean’s thermal and mechanical inertia
  2. The marine carbon cycle
  3. Continental Ice sheets

This is where we reach the limit of the experiments that were available to us: they did not have an interactive carbon cycle or ice sheets, so these effects had to be prescribed. However, even when all that is going on is the ocean and atmosphere adjusting to slowly-changing orbital conditions, we see a hint that the ocean carries that memory through the climate continuum, generating variability at timescales of decades to centuries, much shorter than the forcing itself. Though this current set of experiments cannot assess how that picture would change with interactive ice sheets and carbon cycle, it led to the following hypothesis (ECHOES): today’s climate variations at decadal to centennial scales are echoes of the last Ice Age; this variability arises because the climate system constantly seeks to adjust to ever-changing boundary conditions. 

If true, the ECHOES hypothesis would have a number of implications. The most obvious one is that global temperature is more predictable than has been supposed. The flipside is that this prediction requires giving models a rather accurate starting point. That is, model simulations starting in 1850 AD need to know about the state of the climate system at that time (particularly, the temperature distribution throughout the volume of the ocean), as this state encodes the accumulated memory of past forcings. Accurately estimating this state, even in the present day, is not trivial.

But first things first: is there any support for the ocean having such a long memory? Actually, there is: a recent paper by Gebbie & Huybers makes the point that today’s ocean is still adjusting to the Little Ice Age (around AD 1500-1800). Our results go a step further and suggest that the Little Ice Age, or the medieval warmth that preceded it, also lie on the continuum of climate variability. Though it is clear that they were influenced by short-term forcings, like the abrupt cooling due to large volcanic eruptions, part of these multi-century swings may simply be due to the ocean’s slow adjustment to orbital conditions. Fig 5 illustrates that idea. 


Fig 5: The impact of initial conditions on model simulations of the climate continuum. Models started with states in equilibrium with 850 AD show too flat a spectral slope unless whacked by the giant hammer of industrial greenhouse gas emissions. On the other hand, models initialized from a glacial state and adjusting to slowly-changing orbital conditions show a much steeper slope, comparable to reconstructions obtained from observations over the past 1000 years. This suggests that the continuum (some parts thereof, at least) is an echo of the deglaciation.


The ECHOES hypothesis is still only that: a hypothesis. It will take more work to test it in models and observations. But, if true, it would imply that even simple climate models correctly represent energy transfers between scales, i.e. that their physics are fundamentally sound. That’s a BFD if there ever was one in climate science. In the future, we would like to test this hypothesis with more complex models (with interactive carbon cycle and ice sheets), which will involve prodigious computational efforts.  If supported by observations, the ECHOES hypothesis will imply that to issue multi-century climate forecasts, one needs to initialize models from an estimate the ocean state prior to the instrumental era. This is a formidable challenge, but — trust us — we have ideas about that too. 


Friendly Advice to Graduate School Applicants

4 01 2018

Happy New Year!

In much of the Northern Hemisphere, this time of the year usually brings winter storms and graduate school applications.  I’ve gotten enough of those over the years that I thought I would compile a list of do’s and don’ts.  I am hoping it will help save your application from a chilly reception, and assist prospective students (PS’s) in their search for the most suitable prospective advisor (PA).

  • Know you audience: First, know that the people reading your application are all the professors from the department to which you are applying. If you are trying to reach a PA in particular, definitely get in touch with them prior to applying. This has 2 major benefits:
    1. you can save yourself some valuable time and money by figuring out if there is mutual interest in your applying at all.
    2. the PA will know who you are, and thus read your application with more interest, if there as been prior communication.
  • Who are you? The second thing to know is that your PA is quite busy, and has limited time to look you up. Thus, it is most helpful when you write to them if you can provide an up-to-date CV, or even better, have a webpage with your CV and examples of your work. Basically: it should not take them any time to figure out who you are, what your skills are, and why you’re applying.
  • Why are you applying? It is essential to show the relevance of your query. If you have a Bachelor’s degree in petroleum engineering, wanting to study climate is not the most obvious career switch. It is large enough an academic jump that you might want to justify it, so as to reassure your PA that you’re not just sending blanket applications to anyone in the geosciences without any notion of what they do, and how you could contribute to their field. In the aforementioned case, you might want to say “After 2 years learning about how to ruin our planet, I’ve decided to save it”, or something along those lines. The case is even more dramatic if you’re switching between the humanities and the sciences, say: the connection will be anything but obvious to anyone but you, so make sure you include an explanation for this (academically) sharp departure. On a more meta level, you should definitely ask yourself why you are applying for grad school. Is it only because you want to buy time before choosing a career? Are you trying to get your feet wet with a masters or  are you already committed to a PhD, and understand what that involves? (spoiler: several years of hard work, followed by more years of harder work). The PA has many roles, but  figuring out your life goals is not one of them.
  • Are you applying to a department or a lab? Some graduate programs are centrally run, admitting the best students they can, then having PA’s compete for the students. In other departments, like mine, you apply to work with one or more PAs, and need to open lines of communication with them, as they ultimately will be making the admission decision. In the rest of this post, I will be treating the second case: applying to a particular lab, not a department as a whole.
  • Show that you understand what your PA does.  Though contact with the PA is essential, my biggest turn-off is to receive an email from a PS asking me what research I do. The first quality of a researcher is doing research, so if you can’t even look up your PA on Google Scholar, you’re not fit for the job. Some PAs have lovely web pages that you can even read!   So do your research on the PA’s research and ask them direct questions about their work. You’ll find them much more willing to engage that way.
  • What is the funding situation? In many places, PhD fellowships are strongly tied to the PA’s research funding. It is a good idea to ask your PA about their current projects, and ask if they have any student funding on some of them. Even better: apply for externally funded PhD fellowships (e.g. from NSF) and come with funding in hand; you won’t find many labs to turn you down!
  • Can you write? Believe it or not, the single most important thing about being a scientist is whether you can communicate your science, and that usually involves writing in English. Therefore, anything that you have written in your own voice (preferably, but not necessarily, about the topic you wish to pursue in grad school) is a valuable datum for your PA. You need not have written a peer-reviewed paper before grad school (few people have!) but you surely have written something by that stage. If you have not (e.g., because you were educated in a language other than English), think of translating some of your academic writing into English to give PAs a chance to see how you think and write.
  • Programming: in my line of work (climate modeling and analysis), some programming knowledge is essential. That does not mean you need to be an expert programmer when you begin (I certainly wasn’t), but some classes help. What does not help are statements of the kind:

I have rich experience in  MATLAB programming and JAVA programming. Not to mention Microsoft Word, Microsoft Excel, Microsoft PowerPoint, and Photoshop software

MATLAB and JAVA are programming languages, but Microsoft Word? Excel?? Powerpoint??? Please! This sort of statement is akin to holding a giant placard over your head, screaming “I don’t have a clue about what programming is, but feel free to give me important tasks!”. It makes me want to “rm -rf”  your application altogether.

  • What kind of lab are you applying to? Is the PA running a boutique operation or a large enterprise? There are benefits to both. In a boutique shop you should get more 1-on-1 interactions, but there’s always a chance that funding might run out. Big labs are usually a sign of sustained funding (yay stability!) but often mean that it’s hard to get any face time with the PA, and you may wind up getting raised by their postdocs, more senior grad students, or – as happens frequently with famous PAs – being left to figure things out by yourself, “sink or swim” style. Figure out your learning style and whether your PA is a good fit for it.
  • Personal style: Ultimately, choosing a PA is not that different from choosing a life partner. It’s a decision that will affect you for the rest of your life, so think about it carefully. It’s not only about their prestige, or that of their institution. It’s also about whether you relate to them on a human level, because you’re about to spend a few years together. You might as well make those enjoyable.
  • Visit the department!  It’s not just about the PA, of course. How is the working environment? The fellow students? The campus? If you can, definitely visit the PA and spend as much time as you can with the students to figure out what life is like at their institutions, and in their research group. If you can track down some of the PA’s former students, they might have valuable insights too – and provide you with an idea of what your trajectory could look like.

Have I forgotten anything? Feel free to let me know below.



The Hockey Stick is Alive; long live the Hockey Stick

11 07 2017

Sometimes science progresses by overturning old dogmas, covering the minds behind luminaries in glory and celebrating individual genius. More often, however, it proceeds quietly, through the work of many scientists slowly accumulating evidence, much like sedimentary strata gradually deposited upon one another over the immensity of geologic time.

The recent PAGES 2k data compilation, which I had the privilege of helping carry across the finish line, falls squarely in the latter category.  To be sure, there is plenty of new science to be done with such an amazingly rich, well-curated, and consistently formatted dataset. Some of it is being done by the 2k collective, and published here. Some of it is carried out in my own research group. Most of it is yet to be imagined. But after slicing and dicing the dataset in quite a few ways, one thing is already quite clear: the Hockey Stick is alive and well.

For those who’ve been sleeping, the Hockey Stick is the famous graph, published in 1999 by Mann, Bradley and Hughes (MBH) and reproduced below. You only need a passing knowledge of climate science to know that it was pretty big news at the time, especially when, in 2001, it was featured in the summary for policy-makers of the Third Assessment Report of the IPCC. The graph was hailed as definitive proof of the human influence on climate by some, and disparaged as a “misguided and illegitimate investigation” by the ever-so-unbiased Joe Barton.


Because of its IPCC prominence, the graph, the science behind it, and especially its lead author (my colleague Michael E. Mann) became a lightning rod in the climate “debate” (I put the word in quotes to underline the fact that while a lot of the members of the public seem to be awfully divided about it, climate scientists aren’t, and very very few argue about the obvious reality that the Earth is warming, principally as a result of the human burning of fossil fuels).

Since 1998/1999, when the Hockey Stick work started to come out, the field has been very busy doing what science always does when a striking result first comes out: figuring out if is robust, and if so, how to explain it. Mike Mann did a lot of that work himself, but science always works best when a lot of independent eyes stare at the same question. Two things were at issue: the statistical methods, and the data, used by MBH.

Both matter immensely, and I’ve done my fair share of work on reconstruction methods (e.g. here and here). Yet many in the paleoclimate community felt that a lot more could be done to bolster the datasets that go into reconstructions of past temperature, regardless of the methods, and that they had a unique role to play in that. The PAGES 2k Network was thus initiated in 2006, with the goal of compiling and analyzing a global array of regional climate reconstructions for the last 2000 years. Their first major publication came in 2013, and was recounted here. Its most famous outcome was seen as a vindication of the Hockey Stick, to the point that the wikipedia page on the topic now shows the  original “Hockey Stick graph” from MBH99 together with the PAGES2k global “composite” (it cannot be called a reconstruction, because it was not formally calibrated to temperature).

Like many others, I had my issues with the methods, but that was not the point: compared to MBH and subsequent efforts, the main value of this data collection was that it was crowdsourced. Many experts covering all major land areas had collaborated to gather and certify a dataset of temperature-sensitive paleoclimate proxies.

The collective, however, felt that many things could be improved: the regional groups had worked under slightly different assumptions, and produced reconstructions that differed wildly in methodology and quality. The coverage over the ocean was practically non-existent, which is a bit of a problem given that they cover about two thirds of the planet (and growing, thanks to sea-level rise).  Finally, the data from the regional groups were archived in disparate formats, owing to the complete (and vexing) lack of data standard in the field.  So the group kept moving forward to address these problems.  Late in 2014 I got an email from Darrell Kaufman, who asked if I would help synthesizing the latest data collection to produce a successor to the 2013 PAGES 2k curve.  Not having any idea what I was getting myself into (or rather, what my co-blogger and dear friend Kevin Anchukaitis had gotten me into), I accepted with the foolish enthusiasm that only ignorance and youth can afford.

I knew it would work out in the end because the data wrangler in chief was Nick McKay, who can do no wrong. What I sorely under-estimated was how long it would take us to the point where we could publish such a dataset, and even more so, how long until a proper set of temperature reconstructions based on this dataset would be published. Much could be said about the cat-herding, the certifications, the revisions, the quality-control, and the references (oh, the references! Just close your eyes and imagine 317 “paper” reference and 477 data citations. Biblatex and the API saved out lives, but the process still sucked many months out of Nick’s, Darrel’s, and my time – a bit reminiscent of the machine in Princess Bride). Many thanks need to be given to many people, not least to then-graduate student Jianghao Wang, who developed an insane amount of code on short notice.

But that would take away from the main point: I promised you hockey sticks, and hockey sticks you shall get.  Back in 2007 Steve McIntyre (of ClimateAudit fame) told me “all this principal component business is misguided; the only way you really get the central limit theorem to work out in your favor is to average all the proxies together. This is variously known as “compositing” or “stacking”, but after raising a toddler I prefer the term “smushing” (those of you who have ever given a soft fruit to a toddler will know just what I mean).

Now, it may not be apparent, but I don’t think much of smushing.  On the other hand, the goal of this new PAGES 2k activity was to publish the dataset itself in a data journal, which entailed keeping all interpretations out of the “data descriptor” (the sexy name for such a paper). Many PAGES 2k collaborators wanted to publish proper reconstructions using this dataset, and this descriptor could not go into comparing the relative merits of several  statistical methods (a paper led by Raphi Neukom, which we hope to wrap up soon, is set to do just that). Yet a key value proposition of the data descriptor was to synthesize the 692 records we had from 648 locales, seeing if any large-scale signal emerged out of the inevitable noise of climate proxies. So I overcame my reticence and I went on a veritable smushing fest, on par with what a horde of preschoolers would do to a crate of over-ripe peaches.

The result? Peach purée. Hockey sticks! HOCKEY STICKS GALORE!!!


At first I did not think much of it. Sure, you could treat the data naïvely and get a hockey stick, and what did that prove? Nothing. So I started slicing and dicing the data set in all the ways I could think of.

By archive type? Mostly, that made other hockey sticks (except for marine sediments, which end too early, or have too much bioturbation, to show the blade). By start date? Hockey sticks.


Depending on how to screen the data? Nope, still a bunch of hockey sticks.


By latitude? Mostly, hockey sticks again, except over Antarctica (look forward to a article by the Ant2k regional group explaining why).



As a scientist, you have to go where the evidence takes you. You can only be smacked in the face by evidence so many times and not see some kind of pattern.  (you will never guess: a HOCKEY STICK!).

It’s been nearly 20 years since the landmark hockey stick study. Few things about it were perfect, and I’ve had more than a few friendly disagreements with Mike Mann about it and other research questions.  But what this latest PAGES 2k compilation shows, is that you get a hockey stick no matter what you do to the data.

The hockey stick is alive and well. There is now so much data supporting this observation that it will take nothing short of a revolution of how we understand all paleoclimate proxies to overturn this pattern. So let me make this prediction: the hockey stick is here to stay. In the coming years and decades, the scientific community will flesh out many more details about the climate of the past 2,000 years, the interactions between temperature and drought, their regional & local expressions, their physical causes, their impact on human civilizations, and many other fascinating research questions.  But one thing won’t change: the twentieth century will stick out like a sore thumb. The present rate of warming, and very likely the temperature levels, are exceptional in the past 2,000 years, perhaps even longer.

The hockey stick is alive; long live the hockey stick. Climate denialists will have to find another excuse behind which to hide.


PS: stay tuned for some exciting, smush-free results using this dataset and the framework of the Last Millennium Reanalysis.







Sweet nothings and fat cats

15 09 2016

Much has been written this week about the sugar industry’s dirty tactics to shift blame to fat in order to preserve (even expand) their business model and fatten their own bottom line. My news feed has been ablaze with expressions of surprise and anger at the sugar industry for deceiving the American public about what is now a public health crisis (obesity).

What’s surprising to me is that people are still getting surprised. Indeed, this is exactly the sort of thing many industries have done, and will keep doing until they get caught.

History shows that when you are in the business of selling an inherently harmful product, and under the inexorably expansionist logic of free-market capitalism, the profit motive will have you stop at nothing to keep selling more of it.  For a riveting (and appalling) account of this history, I highly recommend “Merchants of Doubt” by Oreskes & Conway (now a motion picture).


The basic tenet is simple: carefully manipulate public perception about your product to make it sound like we either don’t know that it’s dangerous, or that the harm is caused by something else.  Once the public is confused or thoroughly mislead, it’s impossible to get political consensus on any kind of regulation, and you can keep doing business as you see fit.

The tobacco industry is the most famous of such doubt-mongerers for having been caught in the act. But as “Merchants of Doubt” recounts, there are many other such examples (pesticides, mattress flame retardants, acid rain, the ozone hole, and most consequentially for our civilization, climate change). The  industries behind such campaigns knew full well that they were selling a harmful product, and they just kept doing it until they got caught.

Some of them are still at it, and will continue to do so until they are forced to throw the towel. For instance, the fossil fuel industry is still funding free-market think tanks to keep confusing the public about the causes and risk of climate change, and stave off meaningful regulation to prevent dangerous interference with the climate system.  The fracking lobby is still pretending it’s not polluting groundwater, emitting tons of natural gas at its wellheads, or inducing earthquakes in areas that previously experienced very few of them.

The sugar industry is merely the latest cat to have used the tobacco strategy to pay off unscrupulous scientists and shift blame to preserve its business model and get fatter.   Those of us still surprised by such examples of corporate malfeasance are encouraged to read/watch this admirable piece of work.

Global warming does not slow down

9 04 2013

Julien Emile-Geay

Thanks to the miracles of credit card reward programs, I have been gainfully receiving, for some time now, a weekly copy of the distinguished magazine The Economist. It is usually a fine read, especially since its editors distanced themselves from the laughable flat-Earthing of the Wall Street Journal a long time ago. It was therefore  a bit of a shock to read last week (on the cover, no less): “global warming slows down” (holding the quotation marks with gloves).  Had they been bought by Rupert Murdoch, or were they privy to new data, of which the  isolated climate scientist that I am had remained woefully ignorant? Well, neither, it seems.

Opening its pages with a mix of curiosity and skepticism (yes, we climate scientists are skeptics too – skepticism is not the privilege of deniers but the hallmark of healthy minds), I read the piece “Global Warming: apocalypse perhaps a little later”. It argued that since some recent estimates of climate sensitivity have been revised downward, the world might not scorch as early as we once thought. Still, they wisely argued that this was no excuse for inaction, and that the extra time should be used to devise plans to mitigate, and adapt to, man-made climate change.

Though I  agree with the consequent, I wholeheartedly disagree with the premise.

Digging a little deeper, it appears that they devoted a whole piece on climate sensitivity (“Climate Science: a sensitive matter“).  James Annan was right in pointing out its quality – it is a nuanced piece of science writing for a lay audience,  something all of my colleagues and I know the difficulty of achieving. It wasn’t the science journalist’s job to pick winners, but as a climate scientist I can tell you that not all the evidence presented therein carried equal weight. My issue has to do with the emphasis (here and elsewhere) that just because surface temperature  has been practically flat for the past decade (i.e.  the transient climate response (TCR) may not be as high as one would have concluded from the 1970-2000 warming), this means that equilibrium climate sensitivity (ECS) must also be lower.

Now, the “sensitive matter” piece does take care to distinguish the two concepts (transient vs equilibrium warming), but the message did not reach the editors, who conflate them with flying colors on the front page (“global warming slows down”). So while said editors can apparently hire good science writers, it would be even better if they  read their writings carefully.

My personal take is that TCR  is an ill-suited measure of climate response, because it only considers surface temperature. When energy is added to the climate system (e.g. by increasing the concentration of heat-trapping substances like carbon dioxide), it can go do any of 3 things:

  1. raise surface temperature
  2. help water change phase (from liquid to vapor, or from ice to liquid)
  3. raise temperature somewhere else (e.g. the deep ocean).

Tracking surface warming is certainly of paramount importance, but it’s clearly not the whole story. Focusing exclusively on it misses the other two outcomes. Do we have evidence that this might be a problem? Well, most glaciers in the world are losing mass, and a recent article by Balmaseda et al [2013], shows very clearly that the heat is reaching the deep ocean (see figure below). In fact, the rise in ocean heat content is more intense as depth than it is at the surface, for reasons that are not fully understood. To be fair, nothing involving the tri-dimensional ocean circulation is particularly straighforward when viewed through the lens of a few decades of observations, but the ocean heat content is quite a  robust variable to track, so even if the causes of this unequal warming are nebulous, the unequal warming isn’t.


The Balmaseda et al study concludes that, when considering the total rise in ocean heat content, global warming has not slowed down at all: it has in fact accelerated. I’m not quite sure I see it this way: warming is still decidedly happening, but it does look like this rate of increase has slowed down somewhat (as may be expected of internal climate variability). Therefore, to meet the Economist halfway, I am willing to embrace conservatism on this issue: the climate system is still warming, and let’s agree that the warming has neither slowed down nor accelerated.

In fairness, the Economist’s “sensitive matter” piece did quote the Balmaseda et al. study as part of its rather comprehensive review ; in my opinion, however, this is not just one data point on a complex issue (as their piece implies), but is a real game-changer. It confirms the findings of Meehl et al, [2011], who predicted exactly that: greenhouse energy surpluses need not materialize  instantly as surface warming ; in many instances, their model produced decades of relative “pause” in surface temperature amidst a century-long warming. It did so because the ocean circulation is quite variable, and sometimes kidnaps some of the heat to great depths, so it takes time before the whole of the climate system feels it. This is one reason for the essential distinction between ECS and TCR: the inherent variability of the climate system means that it may take a long time to reach equilibrium. That’s what’s really bothersome about ECS: it’s not observable on any time scale we care about, so it is of limited relevance in discussing reality (it is important to understanding climate models, however).  Perhaps TCR should be based on some measure of ocean heat content? This might already have been done, but I am not aware of it. Actually, sea level might be our best proxy for integrated ocean heat content plus melted ice, and non-surprisingly it is still going up.

So despite the jargon, the basic idea is quite simple: more CO2 means a warmer climate system as a whole, and sooner rather than later.  So it is becoming increasingly urgent to do something about it, as they point out.  Now, what would it take to convince the Wall Street Journal of that?

UPDATE (April 16, 3:30 PST): further accounts of The Economist’s unduly optimistic perspective are given here and here.  Another paper published this week in Nature Climate Change (nicely summarized here)  also emphasizes the important role of the ocean in mediating surface warming.

Tree rings and Drought Indices

21 11 2012

Kevin Anchukaitis

On November 14, Nature published a paper by  Justin Sheffield and colleagues with the title ‘Little change in global drought over the past 60 years’.  The paper describes the consequences of a bias in an index of drought, the Palmer Drought Severity Index (PDSI), which incorporates precipitation, temperature, and soil moisture storage into a single measure of drought severity.  The index is designed so as to indicate ‘normal’ (average) conditions at zero, with negative values indicating drought and positive values indicating pluvial (wetter) conditions.  Sheffield et al. note that the bias they identify arises from ‘a simplified model of potential evaporation [Thornthwaite model] that responds only to changes in temperature’. Indeed, this is something that scientists in the drought community have been aware of — For example, Aiguo Dai published a paper last year in the Journal of Geophysical Research in which he noted that

Another major complaint about the PDSI is that the [potential evaporation] calculated using the Thornthwaite equation (Thornthwaite,1948) in the original Palmer model could lead to errors in energy‐limited regions [Hobbins et al., 2008], as the Thornthwaite PE (PE_th) is based only on temperature, latitude, and month.

Similar to the Sheffield et al. paper, Dai looked at variants of the PDSI, including one calculated using the Penman-Monteith equation, which incorporates radiation, humidity, and wind speed as well as precipitation and temperature.  As their headline results, Sheffield et al. write that:

More realistic calculations, based on the underlying physical principles that take into account changes in available energy, humidity and wind speed, suggest that there has been little change in drought over the past 60 years.

While Dai came to a different conclusion:

The use of the Penman-Monteith PE and self-calibrating PDSI only slightly reduces the drying trend seen in the original PDSI.

This is one of those times to be wary of what Andrew Revkin has called ‘single study syndrome‘. As I am quoted saying in a ScienceNews article, I think the jury’s still out on the reason for the differences between the two groups and the implications.   Certainly, and as Sheffield et al. note, some of the difference is likely in part due to the treatment of uncertainties in the data for radiation, humidity, and wind speed that go into the Penman-Monteith equation.  Another solid resource on this issue is this Carbon Brief article by Freya Roberts.  In a larger sense, too, this is about what we actually mean by the term drought and how we chose to define and measure it.  John Fleck has this aspect extremely well-covered here, here, here, and here (also here, here, and here).

I wanted to address here, however, a question that arose in a Twitter string between journalists John Fleck and Keith Kloor, and climate scientists Jonathan Overpeck, Simon Donner, Ben Cook, Richard Betts, and myself — namely, since many tree-ring drought reconstructions are reconstructions of the PDSI, what does this mean for our understanding of past megadroughts?

Despite some of its weaknesses, the PDSI (even with the Thornthwaite model) still has some desirable characteristics.  First, it attempts to capture the influence of temperature on evapotranspiration, and so reflects more than just precipitation.  Second, since it describes dimensionless anomalies, it is theoretically comparable over large regions.  Third, since it simulates soil moisture storage, it can capture the importance of a previous season’s rain (or snow) on subsequence moisture availability. Finally, the Thornthwaite model can be calculated using precipitation and temperature, climate data that are more readily available back in time compared to the radiation, humidity, and wind speed data needed for  Penman-Monteith.  I should note that not all the assumptions implicit or explicit in the above hold true at all times or over all places.  Still, one is tempted to paraphrase Churchill on democracy.  Or was that Mark Twain?

So does the fact that many tree-ring reconstructions of drought — particularly the North American and Monsoon Asia Drought Atlases — use the Palmer Drought Severity Index as their ‘target’ (predictand) field mean that we have to re-evaluate our ideas about past megadroughts?  Not yet.  The first thing to keep in mind is that all the information we have about past drought comes from proxy measurements like tree-ring width (and lake sediments and speleothems and ice cores, to name a few) and not from observations of PDSI.  To put it another way, it is the sustained narrowness of growth rings in many trees over many decades and at many sites that leads us to infer something about the timing, extent, and relative magnitude of past droughts.  When we reconstruct the PDSI (or precipitation or temperature), we are in essence taking the part of the instrumental PDSI record that overlaps with the tree-ring record and using that period of overlap to calibrate and validate a statistical model that translates tree-ring width into an estimate of PDSI.  We are taking the tree-ring measurement data and putting them into units of climate.

Sheffield et al. talk briefly about the implications for paleoclimate reconstruction of drought and get this part right:

… the tree-ring data, which reflect real variations in climatic and non-climatic factors …

Or to put it another way, tree-ring width reflects climate, but PDSI might not accurately reflect drought severity because of the way temperature is used to calculate potential evapotranspiration.  As an example of this potential problem in the modern record, Sheffield et al. cite two papers they say show a diverging relationship (‘diverge from the instrumental-based PDSI_Th in recent decades’ between PDSI and ring width’) — unfortunately, this is where things start to go a bit wrong:

Fang, K. Y. et al. Drought variations in the eastern part of northwest China over the past two centuries: evidence from tree rings. Clim. Res. 38, 129–135 (2009).

deGrandpre, al. Seasonal shift in the climate responses of Pinus sibirica,Pinus sylvestris, and Larix sibirica trees from semi-arid, north-central Mongolia. Can. J. For. Res. 41, 1242–1255 (2011)

Both papers describe climate and tree growth relationships in semi-arid regions of China and Mongolia.  The second, by deGrandpre and coauthors (which, in the interest of disclosure, includes some of my own coauthors on related projects), doesn’t actually discuss any divergence between PDSI and tree-ring width.  The first, by Keyan Fang and coauthors, does show a period of separation between ring width and PDSI, but only between 1997 and 2003, the end of their record:

Fang et al. Figure 5

The authors of Fang et al. discuss this feature of their study, saying:

The abnormally dry conditions from 1997 to 2003 (Fig. 5) might have been caused by the significant drying trend and a poor PDSI model fit (Liang et al. 2007). That is, the current PDSI model for this region might have overestimated the effects of the warming trend on the local moisture conditions since 1997, resulting in abnormally dry PDSI values.

So, they are essentially (briefly) worrying about the same phenomenon that Sheffield et al. describe — that higher temperatures are biasing the calculation of the PDSI here.  I’m hesitant to read any more into a single study of a single species in a single location, but it is interesting to note that, if Fang et al. are correct and the lower PDSI values after 1997 are indeed a reflection of ‘overestimated the effects of the warming’  on the PDSI, their tree-rings didn’t ‘fall for it’ — they don’t follow PDSI into biased territory.  This should give us more confidence that the trees are reflecting moisture conditions, but it gives us less confidence in the PDSI.

Strangely, Sheffield and coauthors also briefly speculate about the Divergence Problem in formerly temperature-sensitive trees at some northern treeline sites.  Unfortunately, they get this part rather wrong — the mechanism they describe for the influence of temperature on tree-ring width isn’t correct and PDSI is completely unrelated to the divergence problem in these trees.

In case you’re still concerned about the existence of megadroughts identified in tree-ring chronologies (and other proxies) from places like the western United States, it is important to point out that a lot of the evidence for these events doesn’t even involve reconstructions of the PDSI.  For instance, Scott Stine’s important 1994 megadrought study is based on the dates of now-drowned Medieval trees in the Sierra Nevada of California (indicating sustained and dramatically lower lake levels at times prior to the 14th century). Henri Grissino-Mayer’s amazing 2000+ year reconstruction of drought from El Malpais in New Mexico is of water year precipitation, not PDSI.  And Dave Meko, Connie Woodhouse, and other have reconstructed streamflow, not PDSI, in the Colorado River basin.    Evidence for megadroughts comes from the proxies themselves, not the modern instrumental data like PDSI.

So why worry about the problems in the PDSI if you’re a paleoclimatologist like myself? It is possible that we’re on the cusp of this becoming a problem for the calibration of our statistical reconstruction models, although nothing like the ‘divergence’ seen in Fang et al. has thus far emerged (yet) at most tree-ring sites that I’m aware of.  Also, we’d like to be able to compare past droughts from the paleoclimate record with modern droughts from the instrumental data and future droughts from climate models.  But to do so, we need a metric that reliably will tell us the same thing across those epochs.  Evidence has been accumulating for years that PDSI presented a problem for this goal of integrating data and models and the past into understanding the future.  Our group has been looking into ways to deal with this for several years on issues including probabilistic drought forecasting and comparisons between models and paleoclimate data.  Hoerling and colleagues recently published a paper in the Journal of Climate looking at the PDSI and specifically predictions of imminent drying in the U.S. Great Plains.  They write  that:

PDSI is shown to be an excellent proxy indicator for Great Plains soil moisture in the 20th Century; however, its suitability breaks down in the 21st Century with the PDSI severely overstating surface water imbalances and implied agricultural stresses. Several lines of evidence and physical considerations indicate that simplifying assumptions regarding temperature effects on water balances especially concerning evapotranspiration in Palmer’s formulation compromise its suitability as drought indicator in a warming climate.

PDSI tracks soil moisture and precipitation well through the 20th century, but after that time becomes a biased indicator of drought conditions.

Hoerling et al. 2012 Figure 3

Richard Seager talked to John Fleck about this issue as well, pointing to this paper by Burke et al.

So what’s the solution for paleoclimatologists, if we’d still like to keep some of the attractive elements of the PDSI but enable better comparisons between past, present, and future droughts?  We might have to trade some of the desirable traits of the PDSI for potentially less-biased — but more uncertain or incomplete — drought metrics like modeled soil moisture. When comparing past climates and GCM simulations, we might utilize forward models to transform simulated climate into simulated tree-ring widths or other proxy measures, as opposed to the normal approach of transforming proxy data into climate variables.

In summary, the Sheffield et al. result doesn’t really cast any doubt on our knowledge of the existence, timing, during, and relative magnitude of past megadroughts.  What it does do — along with the other papers, particularly those by Hoerling and Dai, discussed above — is make us (dendroclimatologists) think about other drought metrics we might want to reconstruct to enable the most accurate comparisons across timescales and between paleoclimate data, instrumental observations, and climate model simulations.  Stay tuned.

UPDATE (via Skeptical Science): John Nielsen-Gammon, professor, atmospheric scientist, and Texas State Climatologist looks at the Sheffield paper and compares it with the earlier Dai article.  His summary:

So what’s the take-home message from all this?  It does indeed matter how you estimate evaporation, and better estimates do indeed correspond to smaller global drought trends over time, but those trends are probably not as small as Sheffield et al. calculates.  Drought is still getting worse on a globally-averaged basis.

New England Hurricanes, the forecast every time

12 11 2012

Kevin Anchukaitis

Let me start my first post here at Strange Weather by thanking Julien for the opportunity to join him here at his blog. I’ve been studiously preparing by listening to lots of Tom Waits albums, and although I hadn’t intended for my first post to be about hurricanes in the northeastern United States, some strange weather intervened.

I’m a recent arrival to the Massachusetts coast and now a scientist at the Woods Hole Oceanographic Institution, after spending the previous several years in New York City. While Superstorm Sandy, née Hurricane Sandy, was still several days from landfall in New Jersey, though, the region’s history of deadly hurricanes was already in the front of my mind. In August of 1991 Hurricane Bob scored a direct hit on Falmouth on Cape Cod in Massachusetts — my new home. At barbeques and gatherings this summer, my immediate neighbors were telling their stories of Bob’s “furious” landfall, the wind, the snapping trees, the storm surge, the flooding — and the long aftermath without power. So as Sandy lined up for her run at New England at the end of October, I admit I was somewhat nervously eyeing the tall but spindly locust trees near my house.

New England is not without a reason to keep one eye on the tropics in the late summer and autumn. Besides Bob, the New England Hurricane of 1938, the Great Atlantic Hurricane of 1944, Hurricane Carol in 1954, and Hurricane Donna in 1960. Wikipedia has a list of New England hurricanes. In 1821, a hurricane passed directly over New York City, resulting in 13 feet of storm surge and causing the East River to flow across lower Manhattan south of Canal Street. Yet another reason to be wary about hurricanes in New England lies in the mud and sand of the coastal marshes up and down the New England coast, several of which are disconcertingly within walking distance of my new home. These environments preserve a long record of storm activity in coastal New England going back hundreds or thousands of years.

Jeff Donnelly is a colleague of mine at Woods Hole Oceanographic Institution and one of the world’s experts in paleotempestology (Andrew Alden has a nice write-up on this science, here) — the study of past major storm activity from geological or biological evidence. Jeff uses sediments in coastal environments like marshes to identify and date past storms, but others have also used stable isotopes in tree rings, corals, and cave deposits, as well as historical records.

Marsh sediments from across New England tell a story of past hurricane strikes in the region, some clearly quite large. These environments record the passage of strong storms in the overwash deposits of sand that flood over their barrier with the sea during high waves and storm surges. Shore Drive in Falmouth is just such a barrier, and Sandy demonstrated quite clearly what an overwash deposit on its way to a backbarrier marsh looks like.

In a 2001 paper in the journal Geology, Jeff Donnelly and colleagues used multiple sediment cores extracted from a backbarrier march at Whale Beach, New Jersey, located between Ocean City and Sea Isle City, just to the south of Atlantic City, and close to where Hurricane Sandy made landfall, to reconstruct a history of beach overwash. They found deposits of sand associated with a 1962 nor’easter and another strong storm which they believed was the 1821 Hurricane. They dated a third deposit, thicker than the 1821 sand layer and probably related to an intense hurricane, to between 1278 and 1438 CE. In their article they note that the Whale Beach record suggests an annual landfall probability of 0.3%.

In a 2004 paper in Marine Geology, Donnelly and his team again looked at overwash deposits in New Jersey, this time from Brigantine, just to the north of Atlantic City. Here again they identified a layer of sand in the backbarrier marsh likely corresponding to the 1821 hurricane. They also dated large sand layers to the period between 550 – 1400 CE, which might correspond with the 13th or 14th century event identified at Whale Beach.

Donnelly et al. 2004, Marine Geology

Original caption from Donnelly et al. 2004, Figure 7: Cross-section of Transect 2 at Brigantine. ( p ) Location of radiocarbon-dated samples (see Table 1). Horizontal axis begins at the barrier/marsh boundary. The vertical datum is the elevation of the barrier/marsh interface (approximately mean highest high water).

Further to the east, in another 2001 paper Donnelly and his team used sediment cores from Succotash Marsh (near the fabulous Matunuck Oyster Bar near Point Judith in Rhode Island) to date hurricane strikes to known events in 1938 and 1954, as well as 1815 and the 1630s. Two other overwash deposits were dated to 1295-1407 and 1404-1446 CE. Donnelly and coauthors concluded that “at least seven hurricanes of intensity sufficient to produce storm surge capable of overtopping the barrier beach at Succotash Marsh have made landfall in southern New England in the past 700 yr”

One more, closer to home: In 2009, Anni Madsen and her coauthors (including Donnelly) published dates on hurricane deposits in Little Sippewissett Marsh in Falmouth, Cape Cod, Massachusetts, using optically stimulated luminescence (OSL) dating. This particular core shows a number of overwash deposits over the last 600 years, including probably Hurricane Bob and the 1976 Groundhog Day storm, but is also indicative of some of the difficulties and uncertainties in using backbarrier marshes to reconstruct hurricane strikes: Little Sippewissett Marsh doesn’t have sand layers that obviously date to recorded storms in 1938, 1944, 1954, 1815 and 1635, which include some of the largest to hit this region. Uncertainties arise from, amongst other things: a single core may not record all the storms at a site, storms themselves alter the height of the barrier and inlet channels, dating of events comes with analytical and depositional uncertainty, and in New England strong storms could be hurricanes or nor’easters.

Madsen et al. 2009, Geomorphology

Location of Little Sippewissett March, showing 19th and 20th century storm tracks across the region, from Madsen et al., A chronology of hurricane landfalls at Little Sippewissett Marsh, Massachusetts, USA, using optical dating, Geomorphology 109 (2009) 36–45, 2009

On Dot Earth, Andy Revkin has pointed toward his articles on Donnelly’s Caribbean research, as well as a 2002 paper by Anders Noren on millennium-scale storminess in the northeastern United States.

Bringing us back to Sandy, what does the history and geology of New England hurricanes tell us? There is evidence from all along the coast that powerful storms do occasionally make landfall in the region. The evidence from Whale Beach in New Jersey, near to where Sandy came ashore, records the very strong 1821 hurricane as well as another likely event in the 13th or 14th century. Other strong storms have hit the New England coast at other times in the past millennium. A 2002 article from the Woods Hole Oceanographic Institution quotes Donnelly:

“Most people have short memories,” says Donnelly. In fact, it is estimated that three-quarters of the population of the northeastern US has never experienced a hurricane. Donnelly’s research provides evidence to be heeded. “The geologic record shows that these great events do occur,” he says. “We need to make people aware that it can happen again. We’ve got to have better evacuation plans and we need to equip people to react to a big storm.”

I’m so far agnostic on the precise influence of human-caused climate changes on the track and characteristics of Sandy. The process of sorting out the influence of natural variability from the human-influence on this particular storm has just begun. As Justin Gillis notes in the New York Times Green Blog:

Some [climate scientists] are already offering preliminary speculations, true, but a detailed understanding of the anatomy and causes of the storm will take months, at least. In past major climate events, like the Russian heat wave and Pakistani floods of 2010, thorough analysis has taken years — and still failed to produce unanimity about the causes.

The influence of rising sea levels, particularly along the east coast of North America, no doubt has to be factored into understanding current and future storm surges. But what the geological and historical record indicate is that even in the absence of a human-influence on the strength, track, or magnitude of tropical storms, we would still need to be prepared for destructive coastal storms to strike areas of high population and considerable infrastructure. Paleoclimatology — in this case, paleotempestology — nearly always provides us with evidence of an even greater range and diversity of behavior of the climate system then we’ve witnessed over the relatively short period of instrumental observations, and gives an idea of some of the events — droughts, floods, and storms — that we need to keep in mind when figuring out how to build resilient communities.