For some years now, I’ve been beaking off (and, from time to time, writing) about the unreliability of methodologies designed to enable analysis of “alternative futures” as a means of informing the policy and force development and planning processes. I don’t object to the whole “speculative fiction” part of the enterprise; as an avid sci-fi fan who traces his fascination with “what-if” literature all the way back to Danny Dunn and the Automatic House, I love a good yarn, and the more convincingly-told, the better. Heck, Asimov’s Foundation series is predicated on the idea that rigorous, methodologically valid science (“psychohistory”) will someday be able to predict the human future; and some of Robert Heinlein’s most famous narratives derive from his “future history” series of novels.
My problem with the present mania for FSA
derives from a methodological concern that can be summed up in
the question, “how do we get there from here?” The answer is simple:
you can either use some form of projection of existing trends, or you can
guess. I won’t bother addressing the latter, as there’s no methodology to
critique. In trend assessment, however, the methodologies boil down to
two: projection of historical trends; or modelling, with varying degrees of
rigour. Both methodologies are crippled by the same flaw: the fact that
non-linear trends are virtually impossible to project or model, because they
tend to be interdependent (often with trends about which we may know little or
nothing), and because they may be exquisitely sensitive to minute changes in
initial conditions. Edward Lorenz sketched out the first inklings of what
we refer to today as “chaos theory” when he attempted to model weather
patterns for purposes of prediction in the 1960s. He noticed that
infinitesimal changes in starting conditions for an experiment can quickly lead
to vast divergences between sequential experimental runs.
Here’s an example. One of the simplest
equations that demonstrates instability in response to a slight change in
initial conditions is the “logistic equation”, which in a mapping function can
be expressed as:
Xn+1 = RXn(1-Xn) [Note
A]
Setting the value of R to 4 (any higher integer
causes the equation to quickly produce near-infinite results), let’s look at
two curves, both of which follow this equation through 100 iterations for a
starting value of 0.300000:
And what happens if we increase the starting
value by another three ten-thousandths of a percent? Check it out:
This is a demonstration of the sensitivity to
initial conditions displayed by nonlinear phenomena.[Note B] This was Lorenz’s
key insight: that because weather systems are chaotic and nonlinear, long-term
behaviour is exquisitely dependent upon minute variations in starting
conditions, and thus attempts to predict the behaviour of weather systems will
always be enormously less reliable as time goes on. The nonlinearity of
the system means that variations in starting conditions that are so small as to
be undetectable can very quickly result in massive differences in
outcome. This is why weather forecasts are more or less reliable a day or
two into the future, pretty spotty about a week out, and utterly worthless
beyond two weeks. No matter how good your understanding of the equations,
or how minutely you are able to measure initial conditions, predictive accuracy
rapidly goes out the window because the system is nonlinear.
To further illustrate the problem,
imagine we were able to achieve a one hundred-fold increase in our ability
to accurately measure initial conditions; i.e., the difference between the
starting values for two curves is 0.0000001, or in the case of the next
chart, three millionths of one percent.
The bottom line is that anyone who claims
predictive accuracy in long-term modelling of the behaviour of nonlinear
systems isn’t doing science; they’re doing something else.
Why does this matter? Because it
demonstrates a crippling weakness inherent in attempts to model the
future behaviour of nonlinear systems, and it highlights the far greater
predictive validity of methodologies that employ historical trend projection
based on observed data. I mention this because yesterday, the American
Astronomical Society (AAS) dropped a fairly significant bombshell when it
stated that all major empirical measures of what’s going on with the Sun
suggest that we are in for a major and prolonged decline in solar activity
leading to cooling likely to last for decades, with consequent impacts on Earth’s
environment.[Note C]
The Sun exhibits a number of important
cycles; the one most folks are familiar with is the 11-year sunspot
cycle. We’re currently in the midst of Cycle 24 (they’re numbered from
when we began assigning them numbers back in the 17th Century), and the Sun has
been working up towards the peak of that cycle, exhibiting (for example)
increasing if somewhat erratic sunspot activity.
However, this is only one of the Sun’s major
cycles; the overall trend in sunspot activity and irradiance is beginning to
decline. First, the average magnetic field strength of sunspots is
declining, and when it drops below 1500 Gauss, sunspots will no longer be
visible on the Sun’s surface. Based on observed data, that’s likely to
happen in the next decade or so.
The absence of polar jetstreams points to a very weak, possibly missing, Cycle
25. The combination of all three phenomena - missing jetstreams,
declining magnetic field strength in sunspots, and lower activity near the
solar poles suggests that even as the Sun is becoming more active as a
consequence of peaking in Cycle 24, it is almost certainly heading for
a very weak Cycle 25, and very likely for a long-term period of
dormancy. Richard Altrock, the manager of the US Air Force’s Coronal
Research Programme, put it this way:
“Cycle 24 started out late and slow and
may not be strong enough to create a rush to the poles, indicating we’ll see a
very weak solar maximum in 2013, if at all. If the rush to the poles fails to
complete, this creates a tremendous dilemma for the theorists, as it would mean
that Cycle 23’s magnetic field will not completely disappear from the polar
regions (the rush to the poles accomplishes this feat). No one knows what the
Sun will do in that case.”
All three of these lines of research to
point to the familiar sunspot cycle shutting down for a while.
Dr. Frank Hill of the US National Solar
Observatory had this to say about the observed results and analysis:
“This is highly unusual and unexpected,”
Dr. Frank Hill, associate director of the NSO’s Solar Synoptic Network, said of
the results. “But the fact that three completely different views of the Sun
point in the same direction is a powerful indicator that the sunspot cycle may
be going into hibernation.”
[...]
“If we are right,” Hill concluded, “this
could be the last solar maximum we’ll see for a few decades. That would affect
everything from space exploration to Earth’s climate.”
Of course, this is only a problem if you happen
to believe that “solar forcings” spawned by that big ball of
fusion in the sky have anything to do with Earth’s climate. The IPCC
has dismissed solar forcing effects as a potential driver, calling the impact
of the Sun on Earth’s climate “very small compared to the differences in
radiative forcing estimated to have resulted from human activities.”[Note D]
If the IPCC says the Sun doesn’t matter, then
should we really be concerned? Well, the AAS results aren’t the only bit
of research to come out recently. Harking back to my first point - that
it’s really hard to figure out where you’re going if you have no idea where you’ve
been - I was fascinated by a new paper out of Brown University last week,
which, using ice core data, attributes the extinction of the medieval Viking settlements
in Greenland to “rapid climate change” - i.e., cooling. It’s not the
first time that this has been posited, but it IS the first time that somebody
has supported the argument with local empirical data.
According to this paper [Note E], beginning
around 1100 AD, temperatures in the lakes around the Viking settlements dropped
by 4 degrees centigrade over a period of about 80 years (observe that Al Gore
and the IPCC are sounding the tocsin because the average global temperature has
risen about 0.6 degrees centigrade over the past 100 years):
“You have an interval when the summers
are long and balmy and you build up the size of your farm, and then suddenly
year after year, you go into this cooling trend, and the summers are getting
shorter and colder and you can’t make as much hay. You can imagine how that
particular lifestyle may not be able to make it,” D’Andrea said.
Archaeological and written records show
the Western Settlement persisted until sometime around the mid-1300s. The
Eastern Settlement is believed to have vanished in the first two decades of the
1400s.
What was going on? Well, the
Medieval Warm Period - which enabled the Viking settlements to be
established in the first place in the 10th Century, and which the more ardent
proponents of the anthropogenic global warming thesis have tried so valiantly
to make disappear (see the controversy over Michael Mann’s infamous “Hockey
Stick” graph, which purported to show that global temperatures were unchanging
for the past 1000 years until humans started burning lots of oil in the
1900s) - was followed by the Little Ice Age. While all civilizations
suffer from cooling, it’s important to note that the ones that went
extinct were not the civilizations with high technology or excess capacity
or forgiving climates, but rather those that existed on
the outskirts of more clement regions, surviving on the narrowest margins
of crop and livestock viability. A 10% reduction in the length of a
growing season might not be important in a tropical country, but as we’ve seen
in that past few weeks, with millions of acres in the US Midwest going
unplanted due to unseasonal cold and rains, it can make the difference between
subsistence and catastrophe. Look to see food prices skyrocketing this
fall, by the way - and this time, it won’t just be because the US government
has mandated ethanol inclusions in motor gasoline. It’ll be because the
cold, wet spring prevented grain from being planted in the optimal growing
season in the countries that traditionally produce a surplus of grains.
When you live where Canadians and Americans live, or where the Greenland
Vikings lived, “global warming” is not the problem, especially when it isn’t
happening. Global cooling is.
And what causes cooling? Well, climate,
as noted above, is a complex interdependent system, so it’s probably the
result of a lot of interrelated things. But the only phenomenon
that the long-term cooling trends have been demonstrated to correlate
with is lowered solar activity (and, at much longer time scales, the Earth’s
axial tilt, and the passage of the Solar System through the galactic spiral
arms - but that’s a topic for another day). The Little Ice Age correlated
with a stark decline in solar activity that culminated with the Maunder
Minimum, when the Sun’s visible activity all but ceased for more than half a
century. During the Medieval Warm Period, when the Vikings colonized
Greenland, grapes grew in England; during the Maunder Minimum, when
the Greenland settlements died out, Britons regularly walked across
the frozen Thames. History is a great teacher, especially when it
correlates with what we know about the Sun’s behaviour over the past four
centuries.
That’s NASA data, by
the way - and the reason it doesn’t go beyond 2007 is because sunspot
numbers show another massive plunge that’s something of an “inconvenient
truth” for those insistent upon fingering human-produced carbon
dioxide as the principal culprit in climate change. You see, carbon
dioxide concentrations have been climbing spectacularly, but both temperatures
and solar activity have been declining. Correlation may not be
causation, but it certainly suggests where you ought to be looking for
causal relationships.
If the sunspot cycle,
as historical trends and observed data suggest, is indeed going “into
hibernation” for a prolonged period, then the implications for mankind are
significant. If you want to see “negative impacts of climate change”, you
won’t have to wait for the postulated four-degree-per-century warming that the
IPCC says will happen, and isn’t; you simply have to wait for the cooling
that measured solar data suggest is on the way, and that temperature
measurements tell us has been going on for more than a decade:
Or to put it a
different way, as
Steve Goddard likes to:
This shows James Hansen's temperature predictions based on CO2 concentration in the atmosphere. The solid black line shows "business as usual" CO2 emissions (actually they've been higher than that); while the Scenario C line basically shows what would happen if human beings had disappeared from the planet in the year 2000. The red and blue lines track measured temperatures. In other words, temperatures are lower than if humans had drastically curtailed CO2 emissions 12 years ago, which we know did not happen.
Empirical data demonstrate that there is no correlation, and therefore no possibility of a causal linkage, between human-produced CO2 and global temperatures. There is simply no data to support the principle contention of the anthropogenic global warming theorists.
It comes down to a choice between basing your analysis either on something concrete, like measured data, or on something artificial, like model outputs. In my opinion, our responsibility as strategic analysts is to ground our guesses about the future in empirical data and the parsimonious, conservative projection of demonstrable historical trends, always acknowledging that nonlinear systems - whether the logistic equation, climate, or human society - are exquisitely sensitive to minor variations in initial conditions, and that predictive confidence therefore drops off rapidly the further out we look. Sure, using actual data and acknowledging the constraints of this kind of methodology is a lot more challenging than just citing the IPCC, the Stern Report, or second- and third-hand analytical derivations penned by organizations of dubious scientific credentials and objectivity (it’s also harder than simply making stuff up, which is both easy AND fun); but working from hard data has the virtue of being methodologically rigorous, and the added bonus that you might possibly be proven right by design rather than simply by luck.
Because that last chart shows what happens when people rely on models of nonlinear systems to try and predict the future. It doesn't work.
Which sort of brings
us to the last principle of trend analysis: when the data don’t confirm
our predictions, which do we change? The data? Or the way we make
predictions? How you answer that question determines
whether you’re doing science...or something else.
Cheers,
B) There’s an excellent discussion, using some of the same examples, of how
chaos theory invalidates the AGW hypothesis, here [http://wattsupwiththat.com/2011/06/13/the-chaos-theoretic-argument-that-undermines-climate-change-modelling/#more-41556].
C)
[http://wattsupwiththat.com/2011/06/14/all-three-of-these-lines-of-research-to-point-to-the-familiar-sunspot-cycle-shutting-down-for-a-while/]
D) IPCC 4th Assessment Report, Report of Working Group
1, Chapter 2, p. 137.
E)
[http://news.brown.edu/pressreleases/2011/05/vikings]
