[This post was quite long, so I excerpted it; if you want all the details, please go to the link at the bottom of the post.]
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 40 or even 80 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: (see video)
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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 M. G. Wyatt and J. A. 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
Mann, Steinman and Miller, in their 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.
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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.
Mann, Michael, Byron Steinmann, and Sonya Miller. 2014. On Forced Temperature Changes, Internal Variability and the AMO. Geophysical Research Letters. DOI: 10.1002/2014GL05923
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