A new paper (commentary) on the so-called “pause in global warming” puts it all together.
First let’s establish this as a starting point. When climate science contrarians refer to a “pause” or “hiatus” in global warming, they usually mean that the process of warming of the Earth’s surface caused by the human release of greenhouse gas is not a thing. They are usually implying, or overtly claiming, that the link between CO2 and other greenhouse gas pollutants and surface warming was never there to begin with, and previous warming, warming before “the pause,” was natural variation. Many even go so far as to claim that the Earth’s surface temperature will go down to levels seen decades ago.
“The Pause” is not, in their minds, a slowdown in the rate of warming. It is a disconnect, either there all along or produced somehow recently, between the physics of the greenhouse effect and reality.
Also, the evidence adduced for this “pause” is often bogus. Sometimes badly calibrated satellite data is used to show a flat Earth surface. Er, flat Earth surface temperature. Sometimes a line is drawn from an unusually warm, even under conditions of global warming, El Niño year, to later years, lately excluding record breaking recent warm years, in order to make the line flatter. So, that part of the denier pause is a different kind of lie. See this post and this post for recent research on this issue.
Having said all that, there have been frequent slowdowns and speed-ups in the rate of the planet’s surface warming throughout the entire instrumental record. (Even though the instrumental record begins in 1850 or 1880, depending on which data set you use, greenhouse gas pollution started before that, so some greenhouse warming has been happening all along).
Prior some date, like 1970 perhaps, the up and down variations in surface temperature has been a combination of natural variation and human caused variation, with both being strong factors. The human caused variation includes particulate pollution (from burning coal, mainly) which pushes the temperature down, and greenhouse gas release and its associated effects, which push the temperature up.
For the last third or so of the 20th century, through the present, while both natural and human-caused effects matter, the role of human effects has increased to be the dominant force in the overall trend. The natural variations continue to contribute to the shape of the curve, but this contribution is attenuated by the increased abundance of human generated greenhouse gas.
For the last few years, we have seen several research projects that look at the “pause.” Many of these projects helped to explain the slowdown by showing that it wasn’t really as much of a slowdown as previously thought. For example, some research showed that the surface warming in recent decades has been under-measured because the Arctic (and probably the interior of Africa) were getting warmer faster, compared to other regions, and those areas were under-sampled by the usual data sets. Also, heat has been moving in and out of the ocean all along, and that has had an effect on the surface temperaturews.
But even after accounting for all of these effects, there is still a slowdown.
John C. Fyfe, Gerald A. Meehl, Matthew H. England, Michael E. Mann, Benjamin D. Santer, Gregory M. Flato, Ed Hawkins, Nathan P. Gillett, Shang-Ping Xie, Yu Kosaka and Neil C. Swart have just published a commentary in Nature Climate Change called “Making sense of the early-2000s warming slowdown” that looks at what caused this partial flattening out of the upward trend in global surface temperatures.
Part of this investigation compares the earlier part of the 20th century, when there was a much more significant slowdown in warming, with the more recent slowdown. Fyfe et al note that there are two major contributors to variation in surface temperature aside from greenhouse gases. One is the abundance of aerosols, such as industrial pollution (more of a factor during the earlier hiatus) and the output of volcanoes (such as the 1991 eruption of Mount Pinatubo). The other is multi-decade scale variation in the interaction between the oceans and the atmosphere. The following figure compares the two periods of reduced rate of warming.
As noted in the caption, the two periods are representations of how far off from expected (based on simple greenhouse warming) each period is. It happens that these two periods of slowdown in rate of warming are associated with the negative phase of a major ocean-atmosphere interaction, during which the ocean was eating up some of the extra heat, removing it from the atmosphere. The intervening period of increased rate of warming (from the mid 1970s to about 2000) is associated with a period when this system was in positive phase, putting heat out into the atmosphere. As I’ve noted before, the ocean, which takes up most of the global warming caused heat, is the dog, and the atmosphere is the tail. This is a graph of a dog wagging its tail.
It is not clear when the multi-decade scale ocean-atmosphere interactions will shift to a positive phase. If you look at just the raw numbers, it seems like this may have started a few years ago (around late 2013) but the index for this phenomenon varies enough (goes positive and negative and back over shorter time periods, briefly) that this is not certain. More recently, we have an El Nino causing the belching of heat form the ocean to the air, heating up the surface. This may or may not be related to the multi-decade pattern. Having said all that, we may be concerned that over the next ten years or so, starting about now, we will be in a positive phase during which the rate of warming will be accelerated. This may not be the case. Or it might be the case. No one is actually betting on this yet.
You’ve heard much about the so-called “pause” or “hiatus” in global warming.
One of the implications of a multi-year “pause” in global warming is that the science of global warming must be somehow wrong, because with CO2 rising in atmosphere, due to human activity, how can the surface not warm? However, surface temperatures have been rising, but at a somewhat slower rate than at some other times.
The truth is that there is a lot of variation in that upward trending surface temperature value, measured as an anomaly above expected temperatures. Sometimes the variation pushes the rate of warming up, sometimes it pushes the rate of warming down. This has always happened, and will always happen.
So there was something of a lowering of rate of surface warming, but at the same time, no such reduction in rate of ocean warming. Most of the heat from global warming is added to the ocean, not the surface. So, the reality is, global warming has been continuing apace.
One of the factors involved in a slowdown is probably the fact that the Pacific Ocean has been absorbing more heat, for a longer period, relatively uninterrupted by large El Ninos (which reverse that trend), for longer than usual. This year’s El Nino is returning some of that heat to the atmosphere. But even before El Nino kicked in, we were having month after month of record breaking heat (with the very rare month not being a record breaker) for a long time.
Anyway, a couple of papers have recently been published that look once more at the “pause” and I wanted to point them out. The best way to get at these papers is to read the guest commentary by tephan Lewandowsky, James Risbey, and Naomi Oreskes on RealClimate.org: Hiatus or Bye-atus?
The idea that global warming has “stopped” has long been a contrarian talking point. This framing has found entry into the scientific literature and there are now numerous articles that address a presumed recent “pause” or “hiatus” in global warming. Moreover, the “hiatus” also featured as an accepted fact in the latest IPCC report (AR5). Notwithstanding its widespread use in public and apparent acceptance in the scientific community, there are reasons to be skeptical of the existence of a “hiatus” or “pause” in global warming …. We have examined this issue in a series of three recent papers, which have converged on the conclusion that there is not now, and there never has been, a hiatus or pause in global warming.
We drove north for two days, to arrive at a place that existed almost entirely for one reason: To facilitate the capture and, often, consumption of wild fish. The folks who run the facility make a living providing shelter, food, boats, fishing tackle, easy access to a fishing license, and they can be hired as guides. The whole point is to locate, capture, butcher, cook, and eat the fish. The fish themselves have little say in the matter.
And while talking to the people there we got a lot of advice as to how to find and capture the fish, and offers were made to assist with the butchering and culinary preparation of the piscine prey. After a bit of final preparation and a few final words of advice, we were ready to go fish hinting.
“Except for those, fish,” the woman we were talking to said, pointing towards a particularly long dock extending into the vast lake, one of the largest lakes in North America. “Don’t catch those fish.”
“Why?” I asked perplexed.
“We named them,” she said. “You can go down and look at them, the fish that hang out at the end of that dock. A couple of Northern Pike. Don’t catch those fish.”
“OK,” I said. And off we went in the other direction to catch some different fish. I figured there were about 200 million fish of a pound or more in size in this particular lake. We could skip the ones with names.
For many decades, probably for over a century, there has been an observable, measurable, increase in global surface temperature caused by human greenhouse gas pollution. For the first several decades, this increase is a clear trend, but a mild one, and there is a lot of up and down fluctuation, with periods of several years of decrease as well as increase. Then the upward trend becomes stronger, and some time around 1970 it becomes virtually relentless, going up a good amount every decade. But still, there are fluctuations in the curve.
What causes these fluctuations? Several things. The total amount of CO2, the main greenhouse gas causing this heating, has been going up during this period without stopping. Because CO2 added to the atmosphere stays there for a long time, so even if the amount released into the air by burning fossil fuels varies, there is always an upward trend. This causes the general increase, and it is why the increase in the last 50 years or so has been stronger; more CO2 has been released each year more recently.
There are large scale interactions between the ocean, which is also heating up, and the atmosphere and sea surface, the latter being what is measured in graphs of “surface temperature.” These fluctuations are decades long, and influence the degree to which the surface is warm vs very warm. There are shorter term ocean-air interactions such as La Nina (periods when the ocean is taking in more heat) and El Nino (periods when the ocean is pumping out more heat).
As the Arctic has warmed, it has been less icy, and other parts of the Northern Hemisphere have been less snowy, so there has been less sunlight reflected away, another source of fluctuation. Also, since we are talking about the Arctic, there are fewer measurements there so the traditional curves showing global warming have not included increased rates of warming there to the degree they should. Some of the fluctuations in the surface temperature curve are caused by this kind of bias, a shifting bias (because of relatively more warming in under-sampled areas) in the data set.
Humans and volcanoes make dust. Humans used to make a lot more dust before environmental regulation required that factories and power plants clean up their act. There are varying amounts of widespread low level volcanic activity and the occasional enormous eruption. This dust affects the surface temperature curve, and the dust varies quite a bit over time.
If the earth was simpler … a rocky surface, no ocean, no volcanoes, no vegetation (and thus no wildfires as well), but a similar atmosphere, changes in the amount of CO2 or other greenhouse gasses in the atmosphere would be reflected in changes in surface temperature much more smoothly. If that was the case, the amount of variation in energy supplied by the sun would probably be visible in the curve (that factor is so small compared to the other factors that it is very hard to see in the actual data). The curve on the simple earth would probably jiggle up and down a bit but there would be relatively smooth.
If you look at the temperature curve, you can see periods of greater or lesser upward change in temperature. You can even name them. I decided to do this. I chose common baby names, half male and half female, giving male names to the periods with slower increase, female names to the periods with faster increase. It looks like this:
In recent years, in what is at the root a corporate funded, and rather nefarious effort to delay addressing the most important existential issue of our time, climate change caused by human greenhouse gas pollution, science deniers have come up with their own name for one of the fluctuations in the ever increasing upward march of global surface temperatures. They call it “hiatus” (aka “pause”). The purpose of naming this part of the curve is to pretend that global warming is not real. It looks like this:
I am not impressed. And neither should you be. This is like those fish at the end of the dock. Except for the fish it is an affectation of a few people having fun, whereas with the science deniers it is a bought and paid for attempt to cause another hiatus, a hiatus in taking action to save our future.
There is a new study, the Nth in a spate of studies looking at the “Hiatus,” that asks experts on trends (economists, mainly) to look at the surface temperature trend as though it was something other than surface temperatures (they were told it was global agricultural production), to see if they identify the hiatus.
The study is by Lewandowsky, Risbey, and Oreskes, and is “The “Pause” in Global Warming: Turning a Routine Fluctuation Into A Problem For Science. It is here.
There has been much recent published research about a putative “pause” or “hiatus” in global warming. We show that there are frequent fluctuations in the rate of warming around a longer-term warming trend, and that there is no evidence that identifies the recent period as unique or particularly unusual. In confirmation, we show that the notion of a “pause” in warming is considered to be misleading in a blind expert test. Nonetheless, the most recent fluctuation about the longer-term trend has been regarded by many as an explanatory challenge that climate science must resolve. This departs from long-standing practice, insofar as scientists have long recognized that the climate fluctuates, that linear increases in CO2 do not produce linear trends in global warming, and that 15-year (or shorter) periods are not diagnostic of long-term trends. We suggest that the repetition of the “warming has paused” message by contrarians was adopted by the scientific community in its problem-solving and answer-seeking role and has led to undue focus on, and mislabeling of, a recent fluctuation. We present an alternative framing that could have avoided inadvertently reinforcing a misleading claim.
The authors show that there is no unique pause in the data. They also discuss biases in the measurements themselves which suggested a slowing in warming that actually did not occur once the data were de-biased. Finally, they reported on recent work that displayed a common error when people compare climate models to measurements (climate models report surface air temperatures while observations use a mixture of air and sea surface temperatures). With this as a backdrop, the authors take a step back and ask some seemingly basic questions.
Speaking of John Abraham, he just sent me this new graphic based on the latest surface temperature measurements. This is a good moment to have a look at it:
There are two new scientific research papers looking at variation over the last century or so in global warming. One paper looks at the march of annual estimates of global surface temperature (air over the land plus sea surface, not ocean), and applies a well established statistical technique to ask the question: Was there a pause in global warming some time over the last couple of decades, as claimed by some?
The other paper looks at the so called global warming “pause” and interrogates the available data to see if the pause is supported. It concludes that it isn’t. The paper is written up in a blog post by one of the authors, here.
I’ll give you an overview of the findings below, but first, a word from the world of How Science Works.
It’s the variation, stupid
No, I’m not calling you stupid. Probably. I’m just paraphrasing Bill Clinton to underscore the importance of variation in science. The new paper examines variation in the global surface temperature record, so this is an opportunity to make this point.
Much of the time, science is about measuring, understanding, explaining, and predicting variation. This is a point non-scientists would do well to grasp. One of the reasons non-scientists, especially those engaged in policy making (from voter to member of Congress to regulatory agent to talking head) need to understand this better is because variation is one of the most useful tools in the denier tool kit. If your objective is to deny or obfuscate science, variation is there to help you.
Global warming, the increase in the Earth’s surface and ocean temperatures caused by the human caused increase in greenhouse gas, is a system with plenty of variation. The sources of variation are myriad, and the result is that the measurement of air temperature, sea surface temperature, and deeper ocean temperature appears as a set of squiggly lines.
In many systems, variation exists at more than one scale.
So, at the centennial scale, we see global surface temperatures not varying much century by century for a thousand years, then the 20th century average is higher than the previous centuries, and the 21st century average, estimated by 15% of the years of a century, is higher still. That is the effect of industrialization, where we shift from using energy from human and animal work, together with a bit of wind and water power, to using energy stored in carbon bonds in fossil fuels. This combined with population increase and increasing demands to support a consumer-driven comfort-based lifestyle have caused us to release fossil carbon into the atmosphere at an alarming rate.
At the decadal scale, we see a few recent decades that stick up above the others, and a few that are lower than others or at least don’t go up as much as others. Over the last 100 years, the decadal average temperatures have gone up on average, but with variation. The primary explanation for this variation is two fold. First, there is an increase in the absolute amount of greenhouse gas, and the rate at which we are adding greenhouse gasses to the atmosphere, so over time, greenhouse gasses have become the main determinant of temperature change (causing an increase). Earlier on, when greenhouse gas concentration was lower, other factors had a bigger impact. The second (and related) explanation is variation in aerosols, aka dust, in the atmosphere from various industrial processes, volcanoes, and such. Decadal or multidecadal variation over the last century has been mainly caused by aerosols, but with this effect diminishing in its importance as it gives way to the increasingly important role of greenhouse gas.
At a finer scale, of a year or a few years, we see variation caused mainly by the interaction of the surface (the air and the sea surface) and the upper ocean (this is sometimes examined for the top, say, 700 meters, other times, for the top 2000 meters, etc.) When we look at just ocean temperatures or just surface temperatures, we see a lot of squiggling up and down on an ever increasing upward trend. When we look at both together, we note that the squiggles cancel out to some extent. The ocean warmed considerably during recent years when the surface warmed more slowly. This is because heat is being exchanged back and forth between the surface and the deeper sea in a away that itself varies.
That is the simple version. In reality things are more complex. Even though ocean and surface temperatures vary from year to year, with the major variations caused by El Nino and La Nina events in the ENSO cycle, there are longer term variations in how this exchange of heat trends. This time scale is in the order of several decades going in one direction, several decades going in the other direction. (see this post) Then, this sort of variation may have much larger scales of change, at century or even millennial time scales, as ocean currents that facilitate this exchange, undergo major changes, which in turn alters the interaction of the surface and the sea. And, of course, both sea and ocean temperature can affect the major ocean currents, so there is a complex causal interaction going in both directions between those two sources of variation.
This is not a digression but it is annoying
Have you ever been annoyed by someone who makes a claim about the health benefits, or negative effects, of some kind of food or other ingestible substance? You know, one of those totally non-scientific “findings” from the usual internet sources. Here is a little trick you can do if you want to challenge such a claim.
In order to truly evaluate a health related claim, and have that evaluation be credible, you have to be able to do one of the following things, depending on the claim. Being able to do this is not enough to validate your expertise, but it is a starting point. It is a gate-keeper thought experiment. If you can’t do this, then you can’t really make the claim you are making with any credibility.
Name all the parts of a cell and what they do (for many health claims, especially those that have to do with diet, energy, metabolism, etc.)
Name all the different components of the immune system and explain how they work in detail (for many disease or illness related claims).
Describe, in detail, the digestive process, i.e., the process of food sitting on a plate being ingested and eventually being used by a human body, at the molecular level (for many claims about the beneficial or negative effects of certain foods, or the benefits of various dietary supplements).
You might be a climate scientist if …
All that stuff I said above about variation is the very simple version of what happens in the climate with respect to global surface temperature imbalance and global warming. If you read what I wrote and the whole time were thinking things like “yeah, but, he’s totally glossing this” or “no, it isn’t that simple, what really happens is…” then you might be a climate scientist.
If, on the other hand, this extensive tl;dr yammering on variation seemed senseless or a waste of time, or you didn’t find it interesting or don’t get the point, the you may not be prepared to evaluate claims like the one about the so-called “pause” or “hiatus” in global warming. More importantly, there is a good chance that a person making the claim that there has been such a pause is unprepared to do so, just as the person claiming that wearing a $50 fragment of a discarded circuit board around their neck will protect them from EMF can not really make that claim because they are a total dumb-ass when it comes to energy fields and cell biology.
Or, the person making the claim (most common in the area of global warming) is just trying to fool somebody. They are like the person who sells the fragment of the discarded circuit board.
A long series of data may demonstrate the outcome of a set of variables where all the variables act in similar ways over time, and any trend plus or minus variation will be clear. But if the variables change in their level of effect over time, it may be that parts of the long term data series need to be treated separately. This requirement has led to the development of a number of statistical techniques to break a series of data up into different segments, with each segment having a different statistical model applied to it.
The statistical approaches to this problem initially arose in an effort to better understand variation in the process of making key electronic components in the telecommunications industry. An early method was the “Control Chart” developed by Walter A. Shewhart at Bell Labs. The method allowed engineers to isolate moments in time when a source of variation contributing to mechanical failure changed, perhaps because some new factor came into play.
More recently, the statistical method of “Change Point Analysis” was developed to provide a better statistical framework for identifying and assessing the statistical significance of changes in sources of variation. The process involves determining whether or not a change in the sources of variation has occurred, and also, estimating if multiple change points have occurred. The process is pretty complicated, numerically, but is automated by a number of available statistical tools.
The new paper attempts to assess the reality of a “pause” or “hiatus” in global surface temperature increase using change point analysis. The change point analysis used four of the major commonly used data sets reflecting surface temperature changes. In each case, they found three change points to be sufficient to explain the medium to long term variation in the data. Most importantly, the most recent detectable change point was in the 1970s, after which there is no detectable change in the trend of increasing global temperature.
The results of the analysis are summarized in this graphic:
Figure 1. Overlaid on the raw data are the mean curves predicted by the three CP model. The grey time intervals display the total range of the 95% confidence limits for each CP. The average rates of rise per decade for the three latter periods are 0.13 ± 0.04 °C, ?0.03 ± 0.04 °C and 0.17 ± 0.03 °C for HadCRUT, 0.14 ± 0.03 °C, ?0.01 ± 0.04 °C and 0.15 ± 0.02 °C for NOAA, 0.15 ± 0.05 °C, ?0.03 ± 0.04 °C and 0.18 ± 0.03 °C for Cowtan and Way and 0.14 ± 0.04 °C, ?0.01 ± 0.04 °C and 0.16 ± 0.02 °C for GISTEMP.
Those who claim that there was a pause in global warming point to certain dates as the origin of that pause. The authors tested that idea by forcing the change point analysis to assume that this was correct. The alleged starting points for a global warming hiatus failed the statistical test. They are not real. The authors determined that the change point analysis “…provides strong evidence that there has been no detectable trend change in any of the global temperature records either in 1998 or 2001, or indeed any time since 1980. Note that performing the CP analysis on the global temperature records excluding the 2013 and 2014 observations does not alter this conclusion.”
In addition, even though the alleged starting points for a global warming hiatus were found to be bogus, they were found to be more bogus in one of the four data sets, that developed by Cowtan and Way, which in turn is generally thought to be the data set that eliminates most of the biases and other problems found in this sort of information. In other words, using the best representation available of global surface temperature increase, the so called hiatus is not only statistically insignificant, it is even less significant!
But that wasn’t enough. The authors took it even a step further.
Finally to conclusively answer the question of whether there has been a ‘pause’ or ‘hiatus’ we need to ask: If there really was zero-trend since 1998, would the short length of the series since this time be sufficient to detect a CP? To answer this, we took the GISTEMP global record and assumed a hypothetical climate in which temperatures have zero trend since 1998. The estimated trend line value for 1998 is 0.43 °C (obtained by running the CP analysis on the original data up to and including 1998). Using this, we simulated 100 de-trended realizations for the period 1998–2014 that were centered around 0.43 °C. We augmented the GISTEMP data with each hypothetical climate realization and ran the four CP model on the augmented data sets. This allowed us to observe how often a fourth CP could be detected if the underlying trend for this period was in fact zero. Results showed that 92% of the time the four CP model converged to indicate CPs in approximately 1912, 1940, 1970 and a fourth CP after 1998. Thus, we can be confident that if a significant ‘pause’ or ‘hiatus’ in global temperature did exist, our models would have picked up the trend change with a high probability of 0.92.
One is forced, sadly, to think about what deniers might say about any new climate change study. In this case, I think I know what they might say. Look again at the graph shown above. We see two periods when temperatures seem to be going down, and two periods when temperatures seem to be going up. So, half the time, they are going down and half the time they are going up, right? So, what happens if, as suggested by some climate deniers, we are due for a downward trend? Maybe there will be enough multi-decadal downward trends over the next century or so to significantly attenuate the overall trend. Hey, we might even see cooling. Right?
Well, no. For one thing, as mentioned above, the overall pattern has been an increase in the importance of greenhouse gasses as the variable controlling surface temperatures. Whatever factors caused the flattish or downward trends many decades ago may still be in place but are relatively less important from now on, even if we quickly curtail CO2 output. Also, one of those factors, aerosols, is reduced permanently (we hope). Industrial pollution, in the past, caused a lot of aerosols to be released into the atmosphere. This has been reduced by changing how we burn things, so that source attenuation of surface temperatures is reduced. Also, as noted above, there are multi-decadal changes in the relationship between the surface (air and sea surface) and the ocean, and at least one major study suggests that over coming decades this will shift into a new phase with more surface heating.
I asked author Stefan Rahmstorf to comment on the possibility of a future “hiatus.” He told me that one is possible, but “I don’t expect that a grand solar minimum alone could do it (see Feulner and Rahmstorf ERL 2010). Maybe an exceptionally large volcanic eruption could do it but it would have to be far bigger than Pinatubo, which did not cause one.” He also notes that some IPCC climate models have suggested a future slowdown, and the possibility of cooling in not non-zero. The key point, he notes, is “it just has not happened thus far, as the data analysis shows.”
Author Andrew Parnell noted, “I think anybody who claims that these current data demonstrate a hiatus is mistaken.”
Think there is a global warming hiatus? Slow down a second…
The second paper is “Lack of evidence for a slowdown in global temperature” by Grant Foster and John Abraham. Foster and Abraham start out by noting that there is a widely held belief, even among the climate science community, “…that the warming rate of global surface temperature has
exhibited a slowdown over the last decade to decade and a half.” They examine this idea “…and find no evidence to support claims of a slowdown in the trend.”
The authors note that most of the discussion of global warming involves reference to “ the relatively small thermal reservoir of the lower atmosphere” (what I refer to above as the “surface”), but since this is only a small part of the planets heat storage, this can be misleading. When the ocean is taken into account, we see no slowdown in warming. The paper by Foster and Abraham refers to the above discussed paper on change point analysis, so I’ll skip that part. The remaining thrust of the paper is to apply some basic statistical tests to the temperature curves to see if there is a statistically valid slowdown.
They derived residuals, using the GISS data set, for the last several decades, indidating the divergence of each year from an expected value given an upward trend. This looks like this:
They then took sets of adjoining residuals, and tested the hypothesis “This set of numbers is different from the other numbers.” If there was a statistically significant decrease, or increase, in temperature change for several years it would show up in this analysis. The statistical test of this hypothesis failed. As beautiful as a pause in global warming may seem, the idea has been killed by the ugly fact of ever increasing temperatures. To coin a phrase.
As a last attempt to find evidence of a trend in the residuals, we allowed for models in which not only the slope (the warming rate) changes, but the actual value itself. These are discontinuous trends, which really do not make sense physically … but because our goal is to investigate as many possible changes as is practical, we applied these models too. This is yet another version of change-point analysis, in which we test all practical values of the time at which the slope and value of the time series change. Hence it too must be adjusted for multiple trials.
Again, no statistical significance. If you look at the global temperature curve, and see a pause, what you are really seeing is noise.
Foster and Abraham conclude:
Our results show that the widespread acceptance of the idea of a recent slowdown in the increase of global average surface temperature is not supported by analytical evidence. We suggest two possible contributors to this. First, the natural curiosity of honest scientists strongly motivates them to investigate issues which appear to be meaningful even before such evidence arrives (which is a very good thing). Second, those who deny that manmade global warming is a danger have actively engaged in a public campaign to proclaim not just a slowdown in surface temperature increase, but a complete halt to global warming. Their efforts have been pervasive, so that in spite of lack of evidence to back up such claims, they have effectively sown the seeds of doubt in the public, the community of journalists, and even elected officials.
The Earth’s climate is warming. The upper oceans are warming, the sea surface temperatures are elevated, the air in the lower Troposphere, where we live, is warming. This warming is caused almost entirely by the increase in human generated greenhouse gasses and the positive (not positive in a good way) feedbacks caused by that. The effects that increase the global heat imbalance and the effects that decrease it (such as greenhouse gas increase and aerosols — dust — from volcanoes, respectively) vary over time in their effect, which causes some variation in the upward march of global surface and ocean temperatures. Meanwhile heat is going back and forth between the surface (atmosphere and sea surface) and the deeper (but still upper) ocean, which causes either of these parts of the Earth system to wiggle up and down in temperature. There are other effects causing other wiggles.
Every now and then the wiggling results in a seemingly rapid increase in temperature. Every now and then the wiggling results in a seeming slowdown in increase in temperature. When the latter happen, those who wish you to believe that climate change is not real point, jumping up and down, giddy, and say, “See, there is a pause in global warming, therefore global warming is not happening!” They are wrong for the reasons just stated.
Dana Nuccitelli (author of this book), over at the Guardian, has written a blog post that nicely summarizes current scientific thinking on the so-called pause (or so-called hiatus) which has been named by witty climate scientists as the #FauxPause. Because it is faux.
My post on the paper describes the basic findings, but at the time I wrote that, I had a number of questions for the study authors. I sent the questions off noting that there was not a big hurry to get back to me, since the climate wasn’t going anywhere any time soon. Lead Author, Patrick Brown, in the mean time, underwent something of a trial by fire when the denialosphere went nuts in the effort to misinterpret the study’s results. I guess I can’t blame them. There is no actual science to grab on to in the effort to deny the reality or importance of anthropogenic climate change, so why not just make stuff up?
Anyway, Patrick Brown addressed all the questions I sent him, and I thought the best way to present this information is as a straight forward interview. As follows.
Amidst the reactions I’ve seen on social media, blogs, etc. to your paper, I see the idea that your study suggests a downward shift in the (severity of, literally, GMT) resulting from greenhouse gas pollution than what was previously thought. However, I don’t think your paper actually says that. Can you comment?
Reply: You are correct, our paper does not say that. How much warming you get for a given change in greenhouse gasses is termed ‘climate sensitivity’ and our study does not address climate sensitivity at all. In fact, the words ‘climate sensitivity’ do not even appear in the study so we are a little frustrated with this interpretation.
It seems to me that between RCP 4.5, 6 and 8.5, you are suggesting that they differ in their ability to predict, with 6 being the best, 4.5 not as good (but well within the range) and 8.5 as being least good, possible but depending on conditions maybe rejectable.
Reply: Yes and this is just over the recent couple decades, our study does not address how likely these scenarios will be by next year or 2050 or 2100.
At this point I think the following characterizes your work; 1) Taking a somewhat novel look at models and data, what we were thinking before seems by and large confirmed by your work; The central trend of warming with increased greenhouse gas is confirmed in that models and data are by and large aligned in both central tendency (the trend line) and variation. Is this correct?
Reply: We found that models largely get the ‘big picture’ correct when it comes to how large the natural chaotic variability is. We already knew the multi-model mean was not getting the trend correct over the past decade-or-so but we knew that this could have been due to random natural variation. Our study just quantified how large the underlying global warming progression could be given that we saw little warming over the recent past.
Your paper seems to confirm that the more likely scenarios are more likely and the less likely scenarios are as previously thought, possible but less likely. More extreme scenarios have not been taken off the table, though there may be refinement in how we view them. Is that a fair characterization?
The amount of noise in the climate system (EUN) is sufficiently high that much of the observed squiggling around a central trend line is accounted for by that noise and does not require questioning the models (that is my rewrite of "We find that the empirical EUN is wide enough so that the interdecadal variability in the rate of global warming over the 20th century does not necessarily require corresponding variability in the rate-of-increase of the forced signal” in your paper) Is it correct to say that unforced squiggling/EUN/noise would naturally go away with longer sampling intervals (going from years to decades, for example) but these results suggest that even interdecadal variability is likely a result of noise, not forcing.
Reply: Yes, we do not rule out that forcing may be responsible but we are saying that this inderdecadal variability doesn’t necessarily require forcing.
Would it be accurate to say that your paper speaks mainly to the nature of variation observed temperature over time, the squiggling of the signal up and down along a trend line, in relation to variation that is seen in models?
Reply: Just to clarify, in the paper we refer to the component of GMT change that is due to external radiative forcings (e.g., greenhouse gasses) as the ‘signal’ and the component due to chaotic unforced variability as ‘noise’. We don’t necessarily expect either of these to be linear or to follow a trend line. We estimated how large the noise was and used this estimate to see what we might be able to infer regarding the underlying signal, given recent observations.
Noise in this signal is presumably dampened by averaging out the numbers over time (widening the sampling interval, if you will) so as we go from years to decades we get a straighter line that should be more in accord with the correct model. Your paper seems to be suggesting that natural/internal variation (EUN, noise) often operates at a scale larger than we would dampen by looking at the data at the decade-long scale. Is that correct? If so, is it the case that an excursion (such as the so called pause/hiatus) that is 10–20 years long does not fall out of the range of expectations (of noise effects) according to your work?
Reply: Yes. No recent trend was completely outside of the range of possibility – even for RCP8.5. However, it’s naturally the case that a steeper signal (like RCP8.5) is less likely than a slower progressing signal (like RCP6.0) over a time period of no warming.
There seems to be some confusion about your conclusions regarding RCP8.5. Does this paper suggest that RCP8.5 should be rejected? Or does it suggest that it is less likely than previous work suggests?
Reply: First it must be said that we were not looking at how likely RCP8.5 is in the long run. We are simply asking the question “if it hasn’t warmed in 11 years (2001–2013) how likely is it that we have been on RCP 8.5 during that time? We find that it is not very likely but still possible.
Asking this a slightly different way (to address the confusion that is out there) does your paper confirm that 8.5 is less likely than RCP 6.0 as previously thought? If RCP 8.5 is less likely than previously thought does this mean that the entire probability distribution estimate for climate sensitivity needs to be shifted downward, or, alternatively, does it only mean that the upper tail is less fat than previously thought, and if so, how much less fat?
Reply: We may have seen less warming than RCP8.5 because the forcings have been overestimated in RCP8.5 relative to reality over the past decade. If forcings have been overestimed than we expect less warming, even with high climate sensitivity. Because of this possibility, our study cannot make conclusions about the climate sensitivity distribution.
Schurer et al did something similar to what you’ve done here a couple of years ago. Comparing their work and yours the question arises, can you get adequate constraint on the forced and internal variability separately from the paleodata and paleo-forced simulations? Or is there too much noise in the two systems that differencing between two noisy data sets is affected by too much noise amplification? In other words, you have partitioned the problem into model outputs vs. empirical, while Shurer separate between forced and internal. Does your (relatively orthogonal) take an additional risk?
Reply: It is certainly a challenge to know how much can be inferred from the paleo record. Our goal, however, was simply to use the paleo-record in a sensible way to estimate the magnitude of unforced variability. We feel that we adequately account for uncertainty in this estimation as we came up with over 15,000 different estimates which sampled uncertainty in different parameters.
Finally, your study goes up to 2013. The year 2013 (or thereabouts) may be considered as part of a sequence of years with little increase in surface temperature. However, starting in March 2014 we have seen only very warm months (starting earlier than that, but excluding February). Predictions on the table suggest 2015 will be warm, and actually, 2016 as well. If it turns out that 2014, 2015, and 2016 are each warmer than the previous year, and your entire study was redone to go to the end of 2016, would your results change? If so, how? (I?m thinking not because the time scale of your work is so large, but I need to ask!)
Reply: The study was submitted before the 2014 datpoint was added to the record which is why it stops there. If by 2016, we are back in the middle of the distribution for RCP8.5 then it would imply that we might be back on the RCP8.5 scenario. This wouldn’t actually change the results of the study since the study was only concerned with what had already occurred. New data will not change that it did not warm from 2002–2013 so our probability calculations of how likely it was that we are on RCP8.5 over those 11 years would not change.
I keep hearing about this 17 year long pause in global warming. So I went and looked. I did a regression analysis of the last 17 full years of surface temperatures from the GISS database. There is an upward trend in warming during this period and it is statistically significant.
Then I calculated a “running slope” over 17 year long periods from the beginning of the record (plus 8 years) to the end of the record (minus 8 years). For each slope I tested to see if the slope was less than +0.1 (the average slope across the record is 0.75). If a year centered on any 17 year period had a low or negative slope as defined, I counted it as a year in a Hiatus. I then made a chart showing when these hiatuses happened. They used to be more common, but it has been quite a while since the last one:
Since 2014 is not over yet, I did not include it. But, the last “year” (12 month interval) was the warmest 12 month interval for the entire record. 2014 is likely to be in the top two or three warmest years globally on record, quite possibly the warmest. That is not going to help the now discredited hiatus theory very much.
Just a quick item on the pause in global warming that is said to have happened over the last X number of years. I took NOAA’s instrumental record since the late 19th century and calculated the average deviation for “surface” temperatures from a baseline for the entire period. Surface temperatures refer to the lower part of the atmosphere and sea surfaces. When you look at a graph of “global warming” expressed in temperatures, this is almost always what is meant (this leaves out a lot of things, including the poles, much of Africa, and deeper ocean waters). But it is a standard and a fairly useful one.
If there was a significant pause in the overall upswing of temperatures for any period of time, I reasoned, it would show up as a cluster of negative years … years where the temperature for that year is lower than the previous years. More to the point, “hiatuses” (and I actually don’t like that word because it is being used correctly …. “pause” is a better word) if they happen on a regular basis should show up as a cluster, not necessarily continuous, of negative years.
Look at the graph above. This is simply a graph that shows a point for each year that is cooler than the previous year. There are tests that one could do on this data. For example, a sign test or a run test would tell if there was any clustering of negatives. But I’m not going to bother with this at this point.
It seems to me that negative years are fairly uniformly distributed at the large scale and seem random. There may in fact be some real clusters in here, but if they are, they are not recent.
The weatherologists have more or less stopped saying there might be an El Niño this year. Now they are saying there will be an El Niño, and they are starting to consider how strong it will be. Well, actually, they’ve stopped doing that too and are now talking about whether it will be a mondo-El Niño or a mondo-mondo-El Niño.
Here is a newly released video by Peter Sinclair and the Yale Climate Forum about the coming El Niño:
I have a prediction to make. First a bit of background.
You know about the so-called “hiatus” in global warming, because every Tea Partier with a mouth is yammering about it. (See this for a nice response to Sean Hannity’s most recent yammering.) There is a slowing down of the increase of what we call “surface temperatures” over the last several years. However, there are at least two major categories of cause to consider when thinking about this slowdown. First, “surface temperatures” do not reflect the totality of global warming. Surface temperature is a summary of several important parts of the planet, mainly the lower part of the atmosphere and the very top layer of the ocean. The vast majority of the heat added to the system by increasing CO2 in the atmosphere actually goes somewhere else; it goes into the oceans. I’ve talked about this problem of measurement here and here. So, we expect that actual “global warming” (the total warming of the Earth because of added CO2) to be one thing, and “surface temperatures” to reflect but not perfectly track that. (Having said that the term “Global Warming” usually refers just to surface temperatures, a terminological glitch, in my opinion, that arises from the historic uses of the terms among scientists.)
The second feature of the so called hiatus, aka #FauxPause, is that even within that part of the planetary system that is measured and summarized as “surface temperature” there are biases. Much of interior Africa and huge regions of the poles are not as accurately measured, or are simply not included in the squiggle that we use to represent global warming. Recent analysis, however, has estimated the degree and direction of that bias, and it turns out that the bias is towards the negative — we have been missing heat. When you put that heat back into the squiggle, the so-called hiatus gets less hiatusy.
What does all this have to do with El Niño? And my prediction? Here’s the thing. El Niño is part of a larger ongoing continuous climatological phenomenon referred to as ENSO. This is a cyclic (but not periodic) phenomenon having to do with surface heat being plowed into the deeper ocean for a period of time then coming out later. When the heat comes out, the “surface temperatures” go up. If we have the mondo-, or especially the mondo-mondo- El Niño people are expecting, a whopping pile of heat that has been hiding in the Pacific Ocean is going to spring from the sea and heat up the air. Expect heat.
In Anthropology we have a concept called the “Nature-Nurture Dichotomy.” You know what this is because you have made references to it frequently in day to day life. Nature is learned, enculturated, received behavior or personality or whatever. If you water your plants more or less they grow more or less. If we raise our children to be more or less violent, we may get a society that is more or less violent. That sort of thing. Nature is the built in part. A gene causes men to be more violent than women, or women to be more nurturing than men. The “men are from Mars” and “women are from Venus” thing is a statement — unsupported by science and way oversimplified to the extent of being stupid and useless, I quickly add — about nature. According to the Nature-Nurture dichotomy model, we, our plants, other living things, can be described as the outcome of innate (genetic) causes and external, learned or environmental causes. Indeed, according to this framework, we can characterize a feature of a person or a plant or whatever as X percent nature and Y percent nurture, adding up to 100%.
The nature-nurture dichotomy is a falsehood. It is wrong. It is incorrect. It is not supported by the data. This is not to say that there are not “built in” features of living systems and “environmental” features of living systems. But, the stark distinction between the two, the lack of consideration of interaction between the two, and the simplified partitioning of causality to add up to 100% between the two are all demonstrably wrong. Not only are those things wrong, but Nature-Nurture dichotomy thinking is misleading.
What does this have to do with El Niño and global warming and stuff?
If global warming was not happening, the surface temperature measurements would look like a flat squiggle over time, going up and down but averaging out over just a few years or a decade or two. There are many “natural” features of the climate system that act like this, such as the strength of the sun or the effects on surface temperatures of aerosols (i.e. volcanic dust). ENSO is such a thing, a natural squiggling of effects on surface temperatures.
Human release of Carbon Dioxide into the atmosphere is also a squiggle, potentially varying from year to year, but the effects of CO2 release have been to increase the baseline temperature over time.
The following graph is a made up version of this. The squiggles in the lower part of the graph represent a set of natural factors that influence the final outcome; they vary with their own up and down pattern. Just above that is a single upward tending variable. The line in the topmost part of the graph is the sum of all of these.
When temperatures started to flatten out over the last few years (they did not decrease, and they kept rising, but at a slower rate) climate change denialists made the claim that this was because anthropogenic global warming (represented by that middle, upward trending line in the made up graph) wasn’t real. Climate scientists argued that there were two or three reasons the graph flattened out a bit. One of those reasons is the simple, overarching claim that natural variation (like ENSO) makes the graph squiggle up and down independently of anthropogenic global warming. The climate scientists were correct, the science denialists were wrong, of course.
Now, with ENSO about to produce an El Niño, we are probably going to see the squiggle that is the sum of all squiggles squiggle upward, perhaps rather dramatically. The heat that has been hiding in the ocean will return to the “surface” (lower atmosphere and top layer of the oceans).
I predict that denialists will claim that this is not global warming, but rather, natural variation, so therefore global warming is not real.
The first part of that is sort of true, but if so, it was ALSO TRUE that the flattening out of the overall surface temperature curve, and much or all of the squiggle in that line over many decades, is explained by natural variation.
In other words, denialists will have ended up saying: “The pause is true because AGW is not true. The upward swing in 2014/5 is not real because is is natural.”
Climate scientists will say “The pause was an artifact of the natural process of heat plowing into the ocean and the resurgence of surface temperatures in 2014/5 is due to the natural process of that heat coming back to the surface, all of this playing out on a generally upward trending surface temperature graph.”
See the difference?
I’d like to add, taking the aforementioned anthropological perspective, that the nature-nurture (natural/anthropogenic) dichotomy is actually present in this argument and mucking it up a bit. The resurgence in temperature increase we are likely to see over the next year or so is not a “natural” occurrence in that the actual heat is “natural.” Some of that heat, we humans made. So you can’t really call it natural variation. This is more of a linguistic point than anything else, because the natural variations of which we speak are “variations” more than they are “natural” or “not natural.” And the science of climate is, like science in general, all about variation. In the end, the trend of temperature change over the last half of the 20th century up to the present and for decades to come is an increase (a kind of variation) pretty much all caused by human release of CO2 and related effects. In the end, the squiggly nature of the line that represents that trend will end up being a combination of natural features and changes in the human effect. This has been true for decades, it is true right now, and it will continue to be true for a long time.
It is said that global warming has taken a break over the last decade or so. This is not true. Surface temperatures (air, sea surface, and ice) have increased over this period of time, though less so than previous years. Also, there are various indicators that the coming year or so may be extra warm, depending on what happens in the Pacific Ocean. Perhaps more importantly, deep sea temperatures seem to have gone up, and since most of the effects of anthropogenic global warming are seen in the ocean (over 90% of the extra heat goes there), changes in the rate of global warming at the surface can easily be the result of short term changes in exactly where the heat goes. (I discuss this in detail here: The Ocean is the Dog. Atmospheric Temperature is the Tail and About That Global Warming Hiatus… #Fauxpause.)
Recent research has suggested that part of the recent slow down in global surface warming, and other fluctuations, have resulted from the fact that the Earth’s surface is not as evenly sampled as one would like, and certain areas that have heated up quite a bit lately such as the Arctic and interior Africa are underrepresented in the data.
Some of the variation in surface warming has been attributed by some researchers to a phenomenon known as the Atlantic Multidecadal Oscillation (AMO). “Oscillations” are a common phenomenon in climatology. Generally speaking, this is where a major variable (temperature or air pressure) in a given area or between two areas shifts back and forth around a mean. The AMO in particular has been a bit difficult to figure out, or for that matter, to prove that it really even exists. Part of the problem is that a single oscillation, which involves seas surface temperatures over the Atlantic Ocean, may have a period of forty or even eighty years. For this reason, the high quality record of surface temperature change allows us to only see a couple of full oscillations, and this makes it hard to characterize and even harder to explain causally.
According to Michael Mann, lead author of a paper just out addressing the pause and its relationship to the AMO, “Some researchers have in the past attributed a portion of Northern Hemispheric warming to a warm phase of the AMO. The true AMO signal, instead, appears likely to have been in a cooling phase in recent decades, offsetting some of the anthropogenic warming temporarily.”
One application to understanding recent changes in the rate of warming in the context of the AMO is the so-called “Stadium Wave.” This is an actual Stadium Wave, a phenomenon seen at sporting events:
The climate Stadium Wave idea as proposed by Judith Curry suggests that certain changes in surface conditions related to the AMO result in swings in surface temperature that actually explain the long term “global warming curve” enough to discount or reduce the presumed effects of global warming. Curry’s Stadium Wave is a kind of emergent property of climate, where this and that thing happens and results in a large effect because of compounding variables.
It’s complicated. Here is an abstract from a paper by MG Wyatt and JA Curry explaining it:
A hypothesized low-frequency climate signal propagating across the Northern Hemisphere through a network of synchronized climate indices was identified in previous analyses of instrumental and proxy data. The tempo of signal propagation is rationalized in terms of the … Atlantic Multidecadal Oscillation. Through multivariate statistical analysis of an expanded database, we further investigate this hypothesized signal to elucidate propagation dynamics. The Eurasian Arctic Shelf-Sea Region, where sea ice is uniquely exposed to open ocean in the Northern Hemisphere, emerges as a strong contender for generating and sustaining propagation of the hemispheric signal. Ocean-ice-atmosphere coupling spawns a sequence of positive and negative feedbacks that convey persistence and quasi-oscillatory features to the signal. Further stabilizing the system are anomalies of co-varying Pacific-centered atmospheric circulations. Indirectly related to dynamics in the Eurasian Arctic, these anomalies appear to negatively feed back onto the Atlantic‘s freshwater balance. Earth’s rotational rate and other proxies encode traces of this signal as it makes its way across the Northern Hemisphere.
This led to a number of statements and predictions by Curry, which have been parsed out here.
For the past 15+ years, there has been no increase in global average surface temperature…
The stadium wave hypothesis provides a plausible explanation for the hiatus in warming and helps explain why climate models did not predict this hiatus. Further, the new hypothesis suggests how long the hiatus might last.
The ‘hiatus’ will continue at least another decade
Climate models are too sensitive to external forcing
Hiatus persistence beyond 20 years would support a firm declaration of problems with the climate models
Incorrect accounting for natural internal variability implies: Biased attribution of 20th century warming [and] Climate models are not useful on decadal time scales
So, the Stadium Wave model goes a long way to explain recent surface temperature trends, and seriously calls into question the viability of climate models that show a strong human influence on global warming and that predict future catastrophic warming. For this reason, the Stadium Wave hypothesis brings up key questions, and if there is evidence either supporting it or falsifying it, that would be of utmost importance.
The paper under consideration here, “On Forced Temperature Changes, Internal Variability and the AMO” by Michael Mann, Byron Steinman, and Sonya Miller, addresses the Stadium Wave issue (and other matters). This is a very complicated study and if you really want to understand it I recommend getting at least a Masters Degree in Atmospheric Science then sitting down with it for a long time. The way I got through the paper was asking the lead author a bunch of questions. Here, I mainly want to address the Stadium Wave issue. The short version of the story is this: Curry’s Stadium Wave is an artifact of her methods. A second and probably more important finding is that the AMO, previously thought to have contributed to warming surface temperatures over the last ten years, is now thought, based on this new analysis, to have contributed to a relative flattening out of the warming, and thus may account for the so-called “hiatus” in part.
Previous work, including that done by Curry but also others, treated the AMO as a long term change in sea surface temperature that could be identified by removing other signals using some standard statistical techniques, most notably “detrending.” Detrending is where you have a known (or presumed) signal that imposes a certain pull on the system over time. This is then numerically removed from the signal as a linear adjustment. For example, if I want to know the average heart beat rate of a set of people, I could just hook them up to a monitor and collect data and get an average. But say I don’t want my signal to be messed up by certain factors, such as caffeine intake, aerobic exercise, or watching episodes of exciting TV shows. So, I estimate the effects of these other activities on heart rate using some independent information and come up with a linear fudge factor. Then, I record when my subjects are drinking their Latte, engaged in their Cardio-Kick class, or watching The Walking Dead. For those periods of time I adjust the heart rate data based my numerical model of those effects, and the result is the detrended heart rate.
A more straight forward use is found in climate studies. We know that there is long term global warming caused by the release of fossil Carbon (mainly as Carbon Dioxide) into the atmosphere. So if we want to observe something like the AMO all by itself, we take the long term temperature record of sea surface in the Atlantic, subtract a numerical value representing anthropogenic global warming over time, and what is left should be the AMO.
But there is a problem with that technique.
The relationship between different variables in a complicated system has to be known or assumed to do this kind of adjustment. For example, let’s say that drinking a latte before Cardio-Kick makes the effects of Cardio-Kick five times more intense on the heart rate. If you didn’t know that, than your detrending of heart rate would get messed up. If you knew about this non-linear relationship, you could adjust for it, but if you don’t know about it, or assume it to be not significant and thus ignore it, than your results will be wrong.
Here’s another analogy that may help. Let’s say you know how to drive a car. That includes how to steer the car through a turn. This involves turning the wheel in a certain direction a certain amount as the car goes through the curve, then straightening out the wheel to go straight after the curve. Now, lets say you get a job flying a high performance fighter jet. But, you slept through flight school. Now, you are flying the jet and you want to make a small turn, so you turn the “wheel” of the plane a bit, then straighten it out to continue in a straight line after the turn.
If you did that, you would actually tilt the plane with your first turn of the wheel, and it would stay tilted indefinitely thereafter, continuing with the turn. To properly turn the jet you have to tilt it, let it start flying in the new direction, then untilt it. In other words, if you fly a jet fighter like you drive a car, you will fly it wrong because you made incorrect assumptions about the relationships between the key variables leading to the final outcome (the direction you are going in). I recommend that you don’t do that with fighter planes or climate data.
Mann, Steinman and Miller, in this new paper, tried something interesting. They recreated a set of scenarios in which they could observe the AMO and other climate variables over time, but rather than having the AMO be a variable subject to emergence after other factors are accounted for, they introduced a known AMO. This way they could see the exact effects of the AMO on surface temperatures and other variables and explore the relationship between the variables. They call this the “differenced-AMO approach.” Knowing the true AMO signal they were able to produce a correct climate signal, and when the AMO signal was detrended in this scenario, the final result failed to match known internal variability. In other words, using the previously applied techniques, such as used by Curry, the modeling did not work. More importantly, the detrended AMO signal had an artificially increased amplitude, with lower lows and higher highs, and these peaks occurred at the wrong times.
Go back to the fly vs. drive analogy. Imagine you are now driving something … a car or a plane … with a blindfold. Your job is to drive or fly around for a while then later show your path on a map. You know how to drive a car. You drive around a bit at a regular speed, make four left turns, and when you are done you may be able to draw your path on a map with reasonable accuracy because you have an accurate expectation of what happens when you turn the wheel of a car. Now, do it with the high performance jet fighter but using your car-driving expectations. You think that first turn to the left made your path turn 90 degees to the left but it really sent you into an unending circle. Now you make two more left turns and you think you’d be back to the starting point like you would be in a car, but what you’ve really done is to send the jet into a tighter and tighter turn and while you think you flew in a big square, your actual path is more like something a kid might draw with a Sprograph(TM). That appears to be what Judith Curry did.
The Stadium Wave is alleged to happen when the AMO and other related climate factors peak and wane in sync, but this new paper shows that this is a statistical artifact. According to Mann, “Past studies arguing for a large AMO temperature signal with a substantial contribution to recent warming have assumed that the forced component of climate change (human factors such as greenhouse gases and sulphate aerosols, as well as natural factors such as volcanoes and solar output changes) is a simple straight line, a linear trend. That is the null hypothesis they assume. They subtract off that linear trend and interpret what is left over as an “oscillation”. But the significance of that oscillation rests upon the validity of the null hypothesis of a simple linear forced signal. That null hypothesis is just wrong.”
Driving a jet plane like a car.
“We estimate the forced signal (which includes a cooling component from 1950s–1970s due to human-generated sulphate aerosols) using a variety of climate model results, and show that the residual “internal variability” that results when you subtract off a more valid estimate of the forced climate trend is very different. The AMO signal turns out to be much smaller (and the estimated amplitude is consistent with findings from coupled model simulations that exhibit an AMO oscillation).”
So, the Stadium Wave hypothesis now looks more like this:
As I mention above, another important finding of this work is that the AMO probably accounts for part of the recent decade’s warming being less than previous years. According to Mann, “Rather than contributing to recent warming, the correctly-estimated AMO signal appears to have contributed cooling over the past decade, i.e. it offset some greenhouse warming.”
The previously used detrending also missed the contribution of other factors that probably make the AMO look like something it isn’t. There have been a number of other effects on surface temperatures that are left behind after anthropogenic warming is detrended out of the data, especially the effects of sulfate aerosols, which come from power plants and such. “These aerosols have cooled substantial regions of the Northern Hemisphere continents in recent decades, thus masking some of the warming we otherwise would have seen,” Mann told me. “But aerosols have tailed off in recent decades thanks to the Clean Air Acts, etc. That has allowed the hidden warming to emerge in recent decades. If you subtract off a straight line from the temperature trend, you will appear to have an “oscillation”, but that oscillation is just mostly due to the non-linear nature of the long-term forcing, with a substantial positive forcing (warming through 1950s, then slight warming or even cooling from the 1950s–1970s due to a large sulphate aerosol cooling contribution), followed by the accelerated warming in recent decades as aerosols have tailed off. We show in the paper that subtracting off a simple linear trend when you have this more complicated time history of human forcing of climate, gives rise to a spurious apparent “oscillation”.”
Go back, if you dare, to the abstract from Curry’s paper. Back when I used to teach multi-variate statistics for grad students (co-taught with a brilliant statistician, I quickly add) this is the kind of abstract we would look for to use in class. It demonstrates an all too common error, or at least potentially demonstrates it well enough to examine as an exemplar of what not to do. Climate systems are complex. There are a lot of known variables and accessible data sets, but those variables and data sets have often hidden relationships, or important factors are unknown, either entire variables or relationships between variables. If you take a set of possible causal variables and one or two ideal outcome variables, it is possible to mix and match among the candidate causal variables until you get a model that matches the outcome. Perhaps, in doing so, you’ve figured something out. Or, perhaps you just made up some stuff. One way to know if you’ve really explained a phenomenon is to have a sensible, even expected, physical process that links things together. In other words, you have a logical cause as well as a statistical link. The latter without the former is potentially wrong. A second way to evaluate your finding is to seek internal statistical or numerical relationships that result in apparent meaning but that are actually artifacts of your methods. In this case, Mann et al have done this; as demonstrated in this new paper, Curry’s stadium wave is one possible, but meaningless, outcome from the process of making statistical stone soup. Such is the way many theories of everything, large or small, seem to go.
Mann also told me that some of the other large scale oscillations that make up part of the standard descriptions of Earth climate systems could be subject to similar artifactual effects. It will be interesting to see if further work allows further refinement of our understanding of these systems over coming months or years. The models climate scientists use are pretty good, but this would make them more useful and accurate.