Tag Archives: Climate Models

Update on climate models and heat waves

Climate Models Accurately Predict Warming

Climate models employ piles of data and sophisticated computational techniques to predict what will happen in the future. Sometimes they predict what happened in the past as well. That is important to test the models (because we might know what happened in the past), or to fill in the blanks (we don’t always know exactly what happened in the past) or to understand complex climate systems better.

If you glance at the science denier rhetoric (mainly on blogs, you won’t find much in the peer reviewed literature because it isn’t good science) you’ll see repeated claims that climate models that try to predict global warming don’t match the actual observations of global warming. Most of the time, this claim is simply wrong. Perhaps an improper measurement of warming (like temperatures up higher in the atmosphere where we actually expect cooling rather than warming) is being used, which constitutes a lie. In other cases observed warming is within the model projections, but tracks off to one side (usually the low side) of the average expectation, but remains within the margin of error. This is either a misunderstanding of how the science works, or a willful misrepresentation. (Again, a lie.) But there are actually two legitimate areas where certain climate models seem to overstate observed warming. A recent post by John Abraham at the Guardian explores these areas.

First there is the question of where the warming is observed. We measure warming in several parts of the Earth’s surface. (See “What does “Global Warming” Mean?) One is surface temperatures at about head height, over land, via a gazillion weather stations many of which have been in operation since the 19th century. The other is at the surface of the sea, using a combination of older measurements taken from ships and more recent satellite observations. In addition, we have measurements of the deeper ocean itself, usually averaged out over some depth such as the top 700 meters, or the top 2,000 meters. This combines older and new ship our buoy measurements but tend to not go back in far in time as the land and sea surface measurements.

John Abraham has spent a lot of effort looking at ocean temperatures at depth. He and I recently published this item, and he’s done a lot of additional work on it. The total amount of heat added to the Earth’s surface from anthropogenic warming (caused mainly by greenhouse gasses such as carbon dioxide) is divided between the ocean and the surface, with the ocean taking up much of that heat. I liken the system to a dog with a wagging tail. The ocean is the dog, the surface temperatures, making up a small part of the overall system and being much more variable, is the tail.

In “The oceans are warming faster than climate models predicted,” Abraham notes:

We separated the world’s oceans into the Atlantic, Pacific, and Indian. All three of these oceans are warming with the Atlantic warming the most. We also calculated the ocean heating by using 40 state-of-the-art climate models. Over the period from 1970, the climate models have under-predicted the warming by 15%.

And here you can see a number of climate models superimposed over the observed heating in the top 700 meters of the ocean (the red line):


In a more recent post, Abraham asks the question, “ how have the models done at predicting the changes in air temperatures?”

As noted, global surface temperature is estimated in part from a bunch of thermometers around the globe, but these thermometers were not placed there for this purpose. They are weather stations set up to help track the weather, not to address questions of climate change. They are not evenly distributed, and there are huge gaps in the surface coverage, most notably in the Arctic and interior Africa, both regions where recent warming has probably been greater than elsewhere. In order for these temperature data to be used, they have to be carefully employed in a computational system that helps fill in the gaps. There are other complexities beyond our scope here. Suffice it to say that when a bunch of different groups of scientists (i.e., the British meteorology office, NASA, NOAA, the Japan Meteorological agency, and various university based groups) take on the task of estimating surface temperatures, they all do it a bit differently and thus turn up with slightly different curves.

This is true as well with sea surface temperatures. There is more than one way to measure sea surface temperature, or more exactly, to take existing data and turn it into a useful estimate of sea surface temperatures.

In addition, the data are cleaned up over time as mistakes are found, the basic computational approach used may be updated to make it more precise, and the overall approach to computation may be changed to make it more accurate.

Over time, two clear patterns have emerged. First, if you take all of the different measurements of surface temperature over time spanning from the 19th century to the present, lay them all out in front of you and step back about two meters and squint slightly, you can’t see the difference among them. They all look the same, they all tell the same story. They all have a handful of notable ups and downs along the generally upward march of surface temperatures with industrial pollution. You have to look at the graphs all on the same axes, together, to see the differences, and the differences are minor. This tells us that all the different approaches to processing a largely overlapping set of data end up with the same basic result. So many smart minds working with the best available science all produce the same result. How boring. But also, how reassuring that the science is being done right.

The second pattern emerges when we look at these graphs as they are produced over time. Various groups have said, “hey wait a minute, we’re missing this” or “we’re missing that” factor. Urban heat island effects may change the data! What about the Arctic! Interior Africa! Our satellites were recalibrated what does that do? Etc.

Over time, and honest, well informed, diligent effort by many groups to improve the measurement of the Earth’s surface temperature has resulted in a series of slightly different graphs, and in each and every case of which I’m aware, the resulting, more recent and better done graphs show more warming, and various periods of relative flatness have become steeper (going upwards).

So, what John Abraham has done is to take some of the more recently processed, better quality data and compared it to the usual models to see how well the models have done. They did well.

He based his discussion on a comparison of the most recent climate model simulations with actual global surface temperature measurements as numerically summarized by NASA’s Gavin Schmidt, shown here:


John has superimposed 2015 so far (the star).

This shows the most current computer model results and five current temperature data sets. The dark line is the average of the models, and the various colored lines are the temperature measurements.

The dashed line is slightly above the colored markers in recent years, but the results are quite close. Furthermore, this year’s temperature to date is running hotter than 2014. To date, 2015 is almost exactly at the predicted mean value from the models. Importantly, the measured temperatures are well within the spread of the model projections.

Too Hot

This is the year of the heatwave. We’ve had heat waves off and on for the last few years, but it seems that there are more now than ever before. While some have tried to argue that global warming can’t really cause warming (sometimes expressed as heat waves), it does.

Climate Scientist Stefan Rahmstorf has a blog post, in German, about a current heat wave in Europe. He notes (rough translation):

Europe is currently experiencing the second major heat wave this summer. On 5 July, according to the German Weather Service , a never before measured in Germany temperature reached 40.3 ° C in Kitzingen. But a month later, on August 7, this century record has been revised…One might speculate that a single heat wave could be simply due to chance. If you look at the temperature data, however, in their entirety it is immediately clear that extreme heat has become more common over several decades. (Apologies for errors in translation.)

And he shows this graph:


Percentage of global land area where the temperatures over a month were two or three standard deviations above the average from 1950 to 1980. Two standard deviations (orange) could be described as “very hot”, three standard deviations (dark red) as “extremely hot”. Source: Coumou and Robinson 2013

Meanwhile, in Egypt, there is another heat wave. According to ABC News,

Egypt’s state news agency says 21 more people have died due to a scorching heat wave, raising this week’s death toll to more than 60.

The official MENA news agency said Wednesday that the latest deaths are from the previous day, mostly elderly people. It says 581 people are in hospital for heat exhaustion.

The Mideast has been hit by a heat wave since late July. Egyptian summers are usually hot, but temperatures this week soared to 46 degrees Celsius (114 degrees Fahrenheit) in the south.

At least 40 people had died on Sunday and Monday, including detainees and patients in a psychiatric hospital, according to officials. It wasn’t immediately clear whether Tuesday’s death toll includes a German national living in the southern city of Luxor who died from heatstroke.

Additional items of interest:

It is not the sun: Corrected sunspot history suggests climate change not due to natural solar trends

It is not just the editorial page: Study Finds WSJ’s Reporting On Climate Change Also Skewed

It is not a hiatus or pause: The alleged hiatus in global warming didn’t happen, new research shows

It is not just the heat, it is also sea level rise: Catastrophic Sea Level Rise: More and sooner

We are expecting a major Carbon Dioxide sink to eventually stop grabbing CO2 and, possibly, to start releasing it: Global Warming: Earth, Wind, Fire, and Ice

Bad Climate Science Debunked

Recently, a paper published in a Chinese journal of science by Monckton, Soon and Legates attracted a small amount of attention by claiming that climate science models “run hot” and therefore overrepresent the level of global warming caused by human greenhouse gas pollution. The way they approached the problem of climate change was odd. The Earth’s climate system is incredibly complex, and climate models used by mainstream climate scientists address this complexity and therefore are also complex. Monckton et al chose to address this complexity by developing a model they characterize as “irreducibly simple.” I’m not sure if their model is really irreducibly simple, but I am pretty sure that a highly complex dynamic system is not well characterized by a model so simple that the model’s creators can’t think of a way to remove any further complexity.

The same journal, Science Bulletin, has now published a paper, “Misdiagnosis of Earth climate sensitivity based on energy balance model results,” by Richardson, Hausfather, Nuccitelli, Rice, and Abraham that evaluates the Monckton et al paper and demonstrates why it is wrong.

From the abstract of the new paper:

Monckton et al. … use a simple energy balance model to estimate climate response. They select parameters for this model based on semantic arguments, leading to different results from those obtained in physics-based studies. [They] did not validate their model against observations, but instead created synthetic test data based on subjective assumptions. We show that [they] systematically underestimate warming … [They] conclude that climate has a near instantaneous response to forcing, implying no net energy imbalance for the Earth. This contributes to their low estimates of future warming and is falsified by Argo float measurements that show continued ocean heating and therefore a sustained energy imbalance. [Their] estimates of climate response and future global warming are not consistent with 29 measurements and so cannot be considered credible.

The Monckton model does not match observed temperatures, and consistently underestimates them. We don’t expect a model to perfectly match measurements, but when a model is wrong so much of the time in the same direction, the model is demonstrably biased and needs to be either tossed or adjusted. However, you can’t adjust an “irreducibly simple” model, by definition. Therefore the Monckton model is useless. And, as pointed out by Richardson et al, the basic values used in the model were badly selected.

Figure 2 from Richardson et al demonstrate the problem with bias. The pink band in the upper figure and the red/pink line in the lower figure show the Monckton model tracking across time from 1850, compared to several sets of actual observations. The irreducibly simple model may not be irreducibly wrong, but it is irreducibly useless.


Monckton et al rely on the assumption that the Earth’s surface temperature varies by only 1% around a long term (810,000 year) average. This “thermostasis”, they argue, means that there are no positive feedbacks that move the Earth’s temperature higher. This ignores the fact that for that entire record one of the main determinants of global surface temperature, greenhouse gases, has also not varied from a fairly narrow range. But now human greenhouse gas pollution has pushed greenhouse gas concentrations well outside that long term range, and heating has resulted.

The Monckton model is contradicted by observation of global ocean heat content. However, recent Argo measurements of ocean heat content indicate significant warming over the last decade.

During recent years, the rate at which global mean surface temperatures have gone up has been somewhat reduced, and various factors have been suggested as explanations. Of these explanations, Monckton et al. assume only one of these to be true, specifically, that the climate models used by all the other climate scientists are wrong. They ignore other very likely factors. Monckton et al state that models used by the IPCC “run hot” without any reference to the fairly well developed literature that examines differences between observed temperatures and model ranges. They also misinterpreted IPCC estimates of various important feedbacks to the climate system.

I asked paper author John Abraham if it is ever the case that a simpler model would work better when addressing a complex system. “While simple models can give useful information, they must be executed correctly,” he told me. “The model of Monckton and his colleagues is fatally flawed in that it assumes the Earth responds instantly to changes in heat. We know this isn’t true. The Earth has what’s called thermal inertia. Just like it takes a while for a pot of water to boil, or a Thanksgiving turkey to heat up, the Earth takes a while to absorb heat. If you ignore that, you will be way off in your results.”

I also contacted author Dana Nuccitelli, who recently published the book “Climatology versus Pseudoscience,” to ask him to place the Monckton et al. study in the broader context of climate science contrarianism. He told me, “In my book, I show that mainstream climate models have been very accurate at projecting changes in global surface temperatures. Monckton et al. created a problem to solve by misrepresenting those model projections and hence inaccurately claiming that they “run hot.” The entire premise of their paper is based on an inaccuracy, and it just goes downhill from there.”

This is not Nuccitelli’s first rodeo when it comes to the Monckton camp. “It’s perhaps worth noting that these same four authors (Legates, Soon, Briggs, and Monckton) wrote another error-riddled paper two years ago, purporting to critique the paper my colleagues and I published in 2013, finding a 97% consensus on human-caused global warming in the peer-reviewed climate science literature,” he told me. “The journal quickly published a response from Daniel Bedford and John Cook, detailing the many errors those four authors had made. There seems to be a pattern in which Monckton, Soon, Legates, and Briggs somehow manage to publish error-riddled papers in peer-reviewed journals, and scientists are forced to spend their time correcting those errors.”

Monckton et al cherry-picked the available literature, thus ignoring a plethora of standing arguments and analysis that would have contradicted their study. They get the paleoclimate data wrong, ignore over 90% of the climate system (the ocean), selected inappropriate parameters, and seem unaware of prior work comparing models and data. Monckton et al also fail to provide a useful alternative valid model.

Monckton et al failed in their attempt to demonstrate that IPCC estimates of climate sensitivity run hot. Their alternative model does not perform well, and is strongly biased in one direction. They estimate future warming based on “assumptions developed using a logically flawed justification narrative rather than physical analysis,” according to Richardson et al. “The key conclusions are directly contradicted by observations and 450 cannot be considered credible.”

Also of interest

Aside from the obvious and significant problems with the Monckton et al paper, it is also worth noting that the authors of that work are well known as “climate science deniers” or “contrarians.” You can find out more about Soon here. Monckton has a long history of attacking mainstream climate science as well as the scientists themselves. To be fair, it is also worth noting that two of the new paper’s authors have been engaged in this discussion as well. John Abraham has been eDebating Monckton for some time. (See also this conversation with me, John Abraham, and Kevin Zelnio.)

Author Dana Nuccitelli is the author of this recent book on climate science deniers and models.

An Improved Classification And Explanation For El Nino (New Research)

A new study seems to provide a better way to categorize El Nino climate events, and offers an explanation for how different kinds of El Nino events emerge.

El Nino is part of a large scale, very important climate phenomenon in the Pacific Ocean, generally referred to as the El Nino Southern Oscillation (ENSO). Over time (years) wind and water currents move heat into the upper levels of the Equatorial Pacific (La Nina). Then, over time (months) the heat comes back out – that is an El Nino. The effects can be dramatic. During El Nino years, trade winds and monsoons may behave differently than normal. How much precipitation falls and where it falls can change over large regions. Deserts become lakes, good croplands are drought stricken, sea levels change across large portions of the coast.

It is interesting to contemplate the following thought experiment (sorry, a bit of a digression). Imagine if all of the conditions associated with El Nino happened all the time, and had been happening for centuries. An El Nino that is always there is not really an El Nino. It is normal. Those parts that are dry would be dry; Plants and animals, including people, would be dry adapted there in physiology, ecology, and behavior. Same for wet places. It wouldn’t be a desert covered with a lake, it would just be a lake. It wouldn’t be a drought, but just a desert. Etc. The point of this is to underscore the real meaning of El Nino: change. It isn’t so much what it does, but rather, that this climate event’s effects are sudden, dramatic, and occasional.

El Nino has been off and on in the news over the last year or so because it looked like there was going to be a really big one in 2014, but it never materialized. (Even without an El Nino, which warms the surface of the Earth, 2014 was still a record breaking warm year.) Now, El Nino is in the news again because finally we kinda sorta are having one, and a future (this year and next) super double El Nino is being predicted.

Why is El Nino prediction so difficult, and why, when an El Nino happens, it may be very different from some other El Nino that happened before in its overall intensity and in the details of what it causes to happen elsewhere in the world?

You can hear them screaming. The climatologists. “Why? Why? WHY?!?!?” Because this is a really a big thing and it would be really nice to be better at predicting it.

A new study has taken an important step in understanding, and ultimately, predicting El Nino. “Strong influence of westerly wind bursts on El Nino diversity” by Chen et al, published in Nature Geoscience, makes two related points. First, the authors presupposed the existence of three kinds of El Ninos. It has long been thought that El Ninos can be classified into different categories, but the number and nature of those categories varies across groups of researchers. I asked the author if they tried using other a priori numbers for the El Nino categories. “Yes, we did try using other cluster numbers,” Dr. Chen told me. “If it’s set to 2, we would have the extreme El Nino and a broad cluster that include both the canonical and the warm-pool El Nino. If it’s set to 4, we would still see the 3 types we identified but with a 4th type that’s not well separated from the canonical El Nino. In any case we had only one type of La Nina. These discussions will be included in a long paper to be submitted to Journal of Climate.”

Chen et al used a method of modeling El Nino that is different than what is usually done and with this method successfully classified all of the El Nino events over a 50 year time period into these three categories. Second, Chen et al show that the main variable that determines what kind of El Nino happens is the intensity and location of westerly wind bursts (WWBs). I also asked if other variables used in their model (discussed below) were changed to see what would happen. Dr. Chen noted, “we did play with different model settings and parameters, and the outcome turned out to be fairly robust. We are very confident with our results.”

First a brief note on the method. The usual way of managing the complex phenomenon of El Nino … of measuring stuff and stuffing the measurements into a mathematical model … is called empirical orthogonal function (EOF) analysis. This involves measuring key variables across a grid covering the Pacific Equatorial region. Then you take the measurements and simplify how they are organized and turn a multidimensional time-space problem in to a one with fewer dimensions. There are different ways to do this but they all fall into the widely used methodology that included principle component analysis and other things you may be familiar with. You take something really complicated and derive simplified (somewhat) data that is more usable for characterizing a phenomenon or predicting the phenomenon’s behavior while at the same time not throwing away too much of the meaningful variation in the system. This method, however, if fairly linear and deterministic. A bunch of variables are thought to cause outcome A (which has variants), and this bunch of variables are combined so you have only X and Y causing A.

Chen et al applied a different (but well established) technique that presumes less about the linear nature of the model’s components and allows for complex interrelationships that may vary across conditions to remain. It is called fuzzy clustering method. In this method, the data are allowed to decide on their own (more or less) how they should be organized, and (this is the fuzzy part) individual bits of data are actually allowed to occupy more than one cluster. For many systems, the two methods would result in similar outcomes, but when a system is less linear the second method may be more realistic.

When this method is used, the role of WWBs turns out to be very important. This is not entirely new because we already knew that westerly winds across the Equatorial Pacific were important in ENSO cycles. The ENSO cycle involves, to simplify a bit, heat at the surface of the Pacific moving westward and then into the deep (but not to deep) ocean where it builds up. This process is maintained by currents and winds moving from east to west. It is a little like the air near your ceiling growing ever hotter if you burn wood in your stove; In that case the property of warm air rising causes the upper few feet of your living room to get much hotter than the floor. The Western Pacific gets hotter over time because winds and currents push the heat there.

This build up in heat (and other factors) eventually cause a change in the movement of heat and we see warm water moving east, surfacing, and transferring heat energy into the air. That is an El Nino.

From the abstract of the paper:

We propose a unified perspective on El Nin?o diversity as well as its causes, and support our view with a fuzzy clustering analysis and model experiments. Specifically, the interannual variability of sea surface temperatures in the tropical Pacific Ocean can generally be classified into three warm patterns and one cold pattern, which together constitute a canonical cycle of El Nin?o/ La Nin?a and its different flavours. Although the genesis of the canonical cycle can be readily explained by classic theories, we suggest that the asymmetry, irregularity and extremes of El Nin?o result from westerly wind bursts, a type of state-dependent atmospheric perturbation in the equatorial Pacific. Westerly wind bursts strongly affect El Nin?o but not La Nin?a because of their unidirectional nature. We conclude that properly accounting for the interplay between the canonical cycle and westerly wind bursts may improve El Nin?o prediction.

The authors demonstrate that accounting for WWBs does a better job of retroactively predicting the different kinds of El Nino events that have happened over the last fifty years. They conclude that El Nino may result from a combination of the built in see-saw effect of build up of ocean heat in the west and the reversal of movement of warm water on one hand and WWB perturbations, with the pattern of westerly winds affected by the oscillation itself. (I am over simplifying ENSO here, see below for resources on how it works.) Whether or not an El Nino happens is predicted by the classic oscillation model, but which kind of El Nino results is better predicted by the WWBs. From the study:

Such a scenario is appealing because it reconciles hotly debated issues related to the classification and genesis of various El Nin?o events, by killing three birds — diversity, asymmetry and extremes — with one stone. But one must not dwell on the simplicity of the picture painted here. Our intention is to emphasize the strong influence of WWBs on El Nin?o diversity, but not to downplay other processes that may play significant roles in El Nin?o dynamics and thus contribute to the complexity of its diversity.

The research reported here does not address, but may relate to, a set of questions that have been on my mind as I’ve watched El Nino and the discussion surrounding it develop over the last year or so. Is El Nino (or ENSO, more broadly) changing because of climate change? Since El Nino was already hard to predict, we can chalk up this last round of lousy predictions as El Nino being El Nino. But we might also ask the question, is it possible that as more surface and upper ocean heat enters the system, are there changes? Chen et all actually do note that “… real-time El Nin?o forecasting remains an elusive and formidable goal. This is probably because predictability estimates were mainly based on models dominated by a single mode of El Nin?o variability or on hindcast skills of relatively large El Nin?o events, whereas in reality El Nin?o has a variety of flavours, especially in the past decade” (emphasis added). So, these folks, referring to other research, note that El Nino has changed. Is this random variation with no important linear time dimension, or is it a “new normal” for the already normal-defying El Nino, or, perhaps, is it the first part of a period during which ENSO changes dramatically to a climate controlling phenomenon that acts differently in important ways?

Michael Tobis, an expert on atmospheric and ocean systems, suggested to me that “…the real action in climate change is where the warm water goes, not what the wind does. The wind will respond, and may reinforce or mitigate what the ocean does, true. But as the ocean water mass gets further from its recent near-equilibrium, eventually wind stress coupling becomes a smaller deal and the water will go where it will go.” Tobis also notes, and Chen et al acknowledge this may be important, that “the most salient feature in the oceans right now is the large and persistent warm blob in the eastern North Pacific.” This implies (i.e., causes me to speculate or, really, guess) that a warming ocean may shift the balance of what is important in driving, or resulting from, ENSO dynamics. That does not detract from Chen et al’s apparent ability to both classify and explain the differences between El Nino events with WWBs being the key factor.

I asked Dr. Chen to go out on a limb a bit to discuss what the future may hold as climate changes.

Question: With global warming, ocean heat (both at some depth and SST) has increased. Since heat in the ocean is a key variable in ENSO cycles, is it possible that El Nino dynamics would change in some important way, for example, of the three flavors of El Nino, the relative likelihood of which flavor manifesting changing? Is there evidence that such a change may have already occurred, thus the dismal level of predicability of 2014/5? Or, would you expect such a change in the future? My gut feeling is that El Nino dynamics is a barely stable metastable system that is in sufficiently weak equilibrium that it could change to a different equilibrium if important inputs are changed a lot.

Answer: I think your gut feeling is right, in the sense that El Nino is changing under global warming. The questions are how and why. Observations over the last 15 years seem to indicate that the system is now dominated by the warm-pool El Nino, while some people use IPCC model projections to argue that in the future the extreme El Nino will become more frequent. These are still open questions.

Second version of the same question: I’ve heard El Nino/ENSO described as a quasi equilibrium. The essential feature of this system is shifting back and forth between recharge and discharge of ocean heat. Is a different system imaginable where this is not a cyclic system, but rather, a steady state system (such as we see with the Atlantic Conveyor or other climate systems) with heat going in (somewhere) and coming out (somewhere else) more or less steadily? Since we are entering global temperature levels not seen in a long time (and thus only represented in ancient paleo records of lower quality) it seems like it can’t be ruled out (other than it being a rather extreme idea)

Answer: In the recent history and perhaps also during many periods in the past, ENSO did behave like a self-sustaining oscillation. However, it is quite possible that the system might enter a steady state — a permenant El NIno or La Nina state — when external forcing changes.

Question: Figure 4 (see top of post) seems to show something rather astonishing (aside from that figure’s use to demonstrate WWB and WWV association with different kinds of El Nino): WWB in 2014 was very high and uniquely so. Why? Other than the apparent fact that this WWB was not followed by a strong El Nino (a key point of your paper) is there anything else interesting about this?

It all depends on the interplay between the WWB and the basic cycle (measured by WWV). Only when the former occurs at the right phase of the latter a large El Nino will take place. It is true that WWBs were very strong in 2014, but only in the early year, not over the entire spring season. Further experiments will be needed to clarify whether or not the relationship between WWB and WWV will change under global warming.

It will certainly be interesting to see, over the next 24 months or so, if we end up having a strong El Nino, a double El Nino, or if we have lapsed into an extended period of what some are calling El Annoyingo.

For more information about El Nino:

Is a Powerful El Niño Brewing in the Pacific Ocean?

Fishing in pink waters: How scientists unraveled the El Niño mystery

El Niño/Southern Oscillation (ENSO) Technical Discussion

El Niño: How it works, how we observe it

Check out: The First Earth Day, an epoch journey into politics, explosions, folk music, and old boats floating on stinking rivers.

Volcanoes, Tree Rings, and Climate Models: This is how science works.

Mark Your Cosmic Calendar: 774/775

One wonders if anyone felt it. Did Charlemagne feel it as he led his forces across Pagan Saxon Westphalia, knocking down Irminsuls and making everyone pretend to be Christian or else? Did the people of Bagdad, just becoming the world’s largest city, notice anything aside from their own metro-bigness? Did the Abbasid Caliph Muhammad ibn Mansur al-Mahdi have the impression something cosmic was going on that year, other than his own ascendancy to power? Or was it mainly some of the Nitrogen molecules in the upper atmosphere that were changed, not forever but for an average of 5,730 years, by the event?

The bent tree like object is said by some to be the, or a, Irminsul, the "pagan" sacred object, destroyed by Charlemagne much as one might destroy a hypothesis, either with, or about, trees.
The bent tree like object is said by some to be the, or a, Irminsul, the “pagan” sacred object, destroyed by Charlemagne much as one might destroy a hypothesis, either with, or about, trees.
A long time ago, probably in our galaxy but kind of far away, a cosmic event happened that caused the Earth to be bathed in Gamma rays in AD 774 or 775. No one seems to have noticed. There is a mention, in 774, of an apparition in the sky that could be related, but talk of apparitions in the sky were more common back then, before they had certified astronomers to check things out. There is chemical and physical evidence, though, of the Gamma ray burst. The best evidence is the large scale conversion of stable Nitrogen isotopes into unstable Carbon–14 isotopes in the upper atmosphere. As you know, radioactive (meaning, unstable) Carbon–14 is created continuously but at a somewhat variable rate in the upper atmosphere. Some of that Carbon is incorporated, along with regular stable Carbon, into living tissues. After the living tissue is created and further biological activity that might retrofit some of the Carbon atoms ends (i.e., the thing dies) the ratio of radioactive Carbon to stable Carbon slowly changes as the radioactive Carbon changes back into Nitrogen. By measuring the ratio now, we can estimate how many years ago, plus or minus, the originally living thing lived and died.

But it does vary. Solar activity, nuclear testing, other things, can change the amount of Carbon–14 that gets produced. And, a cosmic event that happened in 774/775 caused the production of enough Carbon–14 to throw off the chronology by hundreds of years. This is seen in the close examination of Carbon in the tissue of trees placed in a tree ring chronology. For example:

Screen Shot 2014-07-29 at 2.00.05 PM

Original Caption: High-resolution radiocarbon ages, superimposed on annually resolved radiocarbon measurements from Japan and Europe (grey lines and crosses) as well as the IntCal calibration curve based on decadal samples (blue shading), re-sampled at 5 year intervals (light blue crosses). Radiocarbon ages (that is, using 14C, 13C and 12C isotopes) were determined at ETHZ with the MICADAS system.

See the inverted spike there? That is, apparently, gamma rays messing up the Radiocarbon chronology. Hold that thought.

Climate Change Is Hard

When volcanoes erupt, they typically spew crap into the air. Some of this material stays in the atmosphere for a while (called aerosol, but not your underarm deodorant exactly) which will in turn reflect sunlight back out into space prematurely. This causes cooling. It is essential to know how much cooling of the atmosphere happens from aerosols because this is a potentially important factor in global warming. The effect of aerosols caused by volcanoes or industrial activity is an important term in the big giant equation that puts all the different factors together to produce global warming (or cooling). It is important that climate models be able to accurately and realistically incorporate the effects of aerosols. If the science isn’t right on aerosols, climate models may not run true when aerosols are included.

Caldera of Mount Tambora.  When Tambora erupted in 1815 we experienced a year without a summer (1816). Tambora was small compared to many earlier volcanoes which may have produced a few summer-less years in a row.
Caldera of Mount Tambora. When Tambora erupted in 1815 we experienced a year without a summer (1816). Tambora was small compared to many earlier volcanoes which may have produced a few summer-less years in a row.
And indeed there is an apparent problem. When climate models are run and include aerosols, and the results are compared with real life data where we have good proxyindicators of past climate, the model predictions and the real life measurements don’t line up when aerosols are involved at any significant level. A big volcano goes off, but the proxy record consisting mainly of things like tree rings doesn’t show the level of cooling models predict. This has titillated denialists, as you might imagine, because it shows how the science has it all wrong and the only way to truly understand the climate change is to spend hours in the basement with your spreadsheet and a good internet connection, like Galileo would have done.

In fact, this was an interesting problem that needed to be addressed. The modeling methods had to be wrong, or the paleodata had to be wrong, or something had to be wrong.

In 2012 Michael Mann, Jose Fuentes and Scott Rutherford published a paper in Nature Geoscience proposing a hypothesis to explain this discrepancy. The problem was that when a known volcano went off, the tree ring record in particular tended to show only an anemic result. Volcanoes that were thought to totally mess up the weather seemed to have little effect on trees. This even applied to volcanoes which were very directly observed in recent times, when we know there was an effect because people were putting on sweaters and measuring things with actual thermometers.

Mann et al proposed that rather than having little effect on tree growth, the volcanoes had a huge effect on tree growth. What was being seen by the Dendrochronologists (tree daters, like tree huggers but more serious) as a normal, average growth ring at the time of a volcanic eruption was actually the ring for the next year in line; they were missing, understandably, one or more growth rings. The volcano goes off, the trees don’t grow at all. (The masquerading ring would typically be the year before the missing ring since dendrochronology is done backwards, since we know what year it is now.)

You don’t have to imagine a year in which no tree grows ever anywhere to accept this idea. The trees being used as temperature proxies are more the sensitive type. They respond to temperature changes by growing more or less (warmer vs. cooler). Trees that don’t do this are not chosen for study. This has to do with the species and the setting the tree grows in, combining to make temperature the key limiting factor most years, so that growth ring width reflects temperature more than any other factor. So yeah, when it gets very cool because of a big-ass volcanic eruption, one of those “year with out a summer” deals, the very sensitive trees respond by not growing at all that year. They may have a growth period of a few weeks, but trees don’t simply lay down wood every day they are biologically acvite. They usually start with leaves, then many move on to reproduction, and once they have finished reproducing, have a cigarette, wash up, whatever, they may lay down wood or roots. (Different species have different patterns). So a very short growing season can mean no ring at all. If a really bad nuclear-winter-esque volcano happens this may go on for a few years. This leads to the growth ring corresponding to the year of the volcano simply not being noticed by the dendrochronologists, with a different year standing in. Over time the record can be thrown off by several years, if there are a few volcanoes and one or more of them affects growth for more than one year.

So two things happen. Years with a very strong cold signal are lost entirely, and the record is quasi-randomly offset by a few years in some but not all tree records (because some will be thrown off while others are not) so the collective record gets out of alignment. A strong uptick in the signal (the zero growth year) does not contribute to the paleoclimate squiggle of temperature at all, and the other possibly contributing years (after the worst is over) are moved around in relation to each other and average in with less cold years. It’s a mess.

Consider the following made up numbers representing temperature over time. The top table is the hypothetical raw data of tree ring growth in relation to temperature across a very strong cold anomaly as might be caused by a massive volcanic eruption. Depending on the tree, there is one or more years of zero growth. The lower table is the same set of numbers but with the earlier years (top) shifted down to cover the zeros, because that is what would happen if a dendrochronologist was looking at the rings from more recent (bottom) to oldest; there would just be this void and it would be filled with the next data in line.

Screen Shot 2014-07-30 at 7.20.34 PM

Here are the same data graphed showing a clear anomaly in the top chart, but the very clear anomaly utterly disappears because of missing rings and shifting sequences in the lower chart. This is an existential problem for ancient climate events. I squiggle therefore I am.

Screen Shot 2014-07-30 at 7.16.41 PM

Mann Et Al proposed adjustments to the record of proxyindicators of temperature that accounted for missing tree rings at the time of major volcanic events. They made a good case, but it was a bit complicated and relied on some fairly complicated modeling.

Since the publication of Mann et al there has been quite a bit of back and forth between the climate modelers and the dendrochronologists. I’ve assembled a list of publications and blog posts below. I’ll only very briefly summarize here.

The dendrochronologists had a bit of an academic fit over the idea that they had missed rings. Understandably so. As an archaeologist, I’m partly trained in dendrochronology. There was actually a time when I considered making it my specialty, so I had read all the literature on the topic. I can tell you that missing rings was a serious concern, and taken seriously, and seriously addressed. Seriously, there’s no way modern dendrochronologists would totally miss an entire year’s growth rings. They had ways of dealing with missing rings.

The thing is, it is actually possible to miss rings. Here’s why. The assumption in Dendrochronology is that rings can be missed (or for that matter, added) for reasons that allow for correction by cross dating growth ring sequences with other trees or even other samples in a single tree. A particular part of a tree can be missing a ring while another is not (especially vertically; the lower part of a tree grows last in many species), or some trees in an area may be missing a ring, but others have that growth ring. This assumption is probably almost always valid; missing rings can ben adjusted for by cross checking across samples. But, if all of the trees of a given species and sampling area have one or more missing rings because of a major volcanic event, that won’t work. But this is not something Dendrochronologists are used to.

2 + 2 = 774/775

Eventually Mann and his Colleagues put two and two together and realize that the Dendrochronologists had a way to test the hypothesis that would not rely on fancy dancy climate modeling techniques, and that would potentially allow a better calibration of the tree growth ring record for certain time periods. It was that Gamma ray burst.

That moment in time is a clear marker. Any system involving Carbon–14 spanning this time interval should show the spike. Well, what about tree ring records that span both a major volcano and the 774/775 event? If Mann et all are right, an uncorrected tree ring record would show a lack of correspondence of any spike at 774/775. But, if missing rings are assumed for sensitive tree records at the time of the volcano, and the tree ring sequence for those trees shifted, perhaps the records will line up. That would be a test of the hypothesis.

And this is the gist of a letter to Nature from Scott Rutherford and Michael Mann. Very simply put, Mann and his colleagues took this graph, from an earlier paper:
Screen Shot 2014-07-30 at 8.11.52 PM
And changed it to this graphic which shows mainly (see caption) the tree ring sequences that span both the 1258 volcanic eruption, which was a big one, and the 774/775 event.
Screen Shot 2014-07-30 at 8.11.35 PM
This is a gauntlet, being respectfully thrown down. Mann et al erected a hypothesis, that missing tree rings are virtually universal in large parts of the dendrochronological sample for some events, were not accounted for in the tree ring chronology, and have thus messed up the tree rings as a proxyindicator for temperature. Various attempts to knock it down have not worked out. Now, Mann has himself provided an excellent way to assail his own idea. It is now up to the tree ring experts to try to knock this hypothesis down. I suspect Charlemagne might have had an easier time knocking down the Irminsul.

I asked Michael Mann how he felt about this latest development in the ongoing saga of the missing (probably) growth rings. He said, “I’m very pleased that we’ve reached some level of reconciliation with our dendroclimatology colleagues: there’s an objective test that is available to determine if there are indeed missing rings in some of the regional chronologies as we have speculated to be the case. I look forward to seeing the results of those tests. We proposed a hypothesis, other scientists were skeptical of the hypothesis, and now there is a way forward for testing the hypothesis. In the end, a fair amount of good science will have been done, and we will have learned something. This is the way science is supposed to work.”

This is going to make a great Master’s thesis for someone.

As promised, a list of writings on this topic, organized by date:

2012 Mann, M.E., Fuentes, J.D., Rutherford, S., Underestimation of volcanic cooling in tree-ring- based reconstructions of hemispheric temperatures, Nature Geoscience, doi:10.1038/NGEO1394, 2012. Press release here.

2012 Mann Et Al. Global Temperatures, Volcanic Eruptions, and Trees that Didn’t Bark. Real Climate.

2012 (November) Kevin J. Anchukaitis, Petra Breitenmoser, Keith R. Briffa, Agata Buchwal, Ulf Büntgen, Edward R. Cook, Rosanne D. D’Arrigo, Jan Esper, Michael N. Evans, David Frank, Håkan Grudd, Björn E. Gunnarson, Malcolm K. Hughes, Alexander V. Kirdyanov, Christian Körner, Paul J. Krusic, Brian Luckman, Thomas M. Melvin, Matthew W. Salzer, Alexander V. Shashkin, Claudia Timmreck, Eugene A. Vaganov & Rob J. S. Wilson. Tree rings and volcanic cooling. Nature Geoscience 5, 836–837 (2012) doi:10.1038/ngeo1645

2012 (November) Mann, Fuentes and Rutherford Reply to ‘Tree rings and volcanic cooling’. Nature Geoscience. 5, 837–838 (2012) doi:10.1038/ngeo1646

2012 Gavin at RealClimate Responses to volcanoes in tree rings and models

2012 Esper et al. Testing the hypothesis of post-volcanic missing rings in temperature sensitive dendrochronological data Dendrochronologia. Volume 31, Issue 3, 2013, Pages 216–222

2012 Esper et al. European summer temperature response to annually dated volcanic eruptions over the past nine centuries. Bulletin of Volcanology. June 2013, 75:736

2013 George et al. The rarity of absent growth rings in Northern Hemisphere forests outside the American Southwest. Geophysical Research Letters. 40(14) 3727-3731.

2013 D’Arrigo et al. Volcanic cooling signal in tree ring temperature records for the past millennium Journal of Geophysical Research: Atmospheres. Volume 118, Issue 16, pages 9000–9010, 27 August 2013

2014 Jull et al. Excursions in the 14C record at A.D. 774–775 in tree rings from Russia and America. Geophysical Research Letters. Volume 41, Issue 8, pages 3004–3010, 28 April 2014

2013 Mann, Michael, Scott Rutherford, Andrew Schurer, Simon Tett, Jose Fuentes. Discrepancies between the modeled and proxy-reconstructed response to volcanic forcing over the past millennium: Implications and possible mechanisms. J. of Geophysical Research: Atmospheres, Vol 118, 7617–7627.

2014 Büntgen. Et Al. Extraterrestrial confirmation of tree-ring dating. Nature Climate Change 4: 404-405.

2014 [Rutherford, Scott and Michael Mann. Missing tree rings and the AD 774–775 radiocarbon event](http://www.nature.com/nclimate/journal/v4/n8/full/nclimate2315.html?WT.ec_id=NCLIMATE–201408]. Nature Climate Change. Vol 4, August 2014.