Guest Post by Willis Eschenbach
Well, either it’s a genetic defect or I’m just a glutton for punishment, but I’m going to delve some more into the ice ages. This is a followup to my previous post, Into and Out Of The Icebox. Let me start by looking at the cycles in the insolation and the cycles in the geological temperature. I’ll use the same temperature proxy dataset used in the discussion by Science of Doom here and here, which is the Huybers ∂18O dataset . For the insolation, I’m using the same Berger dataset that I used in my last post. Figure 1 shows the cycles in the two datasets:
Figure 1. Periodogram of the Huybers temperature proxy dataset (blue) and the June insolation at 65°N from the Berger geological insolation dataset.
This graph demonstrates extremely clearly what is called the “100,000 year problem”. As you can see, the length of the ice ages has a very strong 100,000 year cycle, with a cycle amplitude greater than 40% of the swing of the data.
But in total contradiction to that, the June insolation at 65°N, which is the insolation that is supposed to cause the interruptions of the ice ages, has virtually no cycle strength in the 100,000 year (100 Kyr) range. The insolation has its greatest cycle strength between 19 and 24 Kya, and a smaller peak at 41 Kyr, but there is almost no power at all in the 100 Kya range.
It is worth noting that both the temperature and the insolation do show power in the ~ 23 Kyr and the ~ 41 Kyr range … but only the temperature has power in the 100 Kyr range.
Now, back in 2006 Gerald Roe wrote a paper called “In Defense of Milankovich”. In that paper, he said that the reason there was little relationship between the Northern Hemisphere insolation and the ice ages was that people were looking at the wrong thing. His point was that when the sun increases, the ice doesn’t immediately disappear. Instead, what changes is the rate of melting of the ice. This is also called the “first difference” of the ice volume. Roe used an earlier version of the same Huybers temperature proxy dataset I’m using to demonstrate his hypothesis, reasoning that the ice volume is a function of the global temperature.
So let’s start by looking at the effect of taking the first differences on the underlying cycles. Figure 2 is the same as Figure 1, except that I’m using first differences instead of using the raw Huybers temperature proxy data.
Figure 2. Periodogram of the first difference of the Huybers temperature proxy dataset (blue) and the June insolation at 65°N from the Berger geological insolation dataset.
Now, that is an interesting result. As you might imagine, it hasn’t introduced any new frequencies into the mix. However, it has greatly decreased the size of the 100 Kyr cycle, slightly increased the size of the 23 Kyr cycle, and slightly decreased the size of the 41 Kyr cycle.
And what would be the result of those changes? Well, the correlation will indeed be better, as Roe observed … but for the wrong reasons. The correlation will be greater because in the temperature data (blue) the ~ 20 Kyr cycle and 41 Kyr cycles are now about the same size as the 100 Kyr cycle. So those cycles will fit better … but we still have no explanation for the 100 Kyr cycle.
In any case, here’s the match between the June insolation at 65°N and the first difference of the temperature proxy:
Figure 3. A comparison of the June insolation at 65°N (red) and the first difference of the ∂18O temperature proxy. I am using the negative of the ∂18O data, so that increasing values show increasing temperatures.
Looks good, doesn’t it … but it’s not. Unfortunately, this is merely a wonderful example of the human propensity for seeing patterns. If you look at parts of this, it looks like a perfect match. The problem is, humans are shaped and bred by millions of years of evolution to find visual patterns … and as a result we find patterns even where no such patterns exist. The best example I can give you is that virtually every culture has found constellations in the stars. We identify Orion and Gemini and a host of others … and despite that, the stars contain no such patterns, just a random scatter.
And when we look closely at Figure 3, we can see that in many of the cases, the blue lines are in between the red lines … in all, they seem to be aligned at around 600 Kyr BP and also around the present, but badly out of alignment in between.
In order to keep ourselves from making such mistakes in pattern identification (among other reasons), we’ve invented an entire branch of mathematics called statistics. It allows us to do things like measure just how much of one variable is explained by another variable. The measure of this is called “R^2”. It varies from 0.0 (no relationship) to 1.0 (one variable totally explains the other).
And the R^2 value for the two variables above? How much of the first difference of the temperature variation is explained by the variation in northern insolation?
Well, the R^2 of the two is a mere 0.05 … that is to say, the June insolation at 65°N only explains about 5% of the variations in the first difference in temperature. Color me unimpressed.
Now, it’s possible that there is some lag in the data. To check that, we can run a “cross correlation”. This looks at the correlation, not just at the same time, but at a variety of time lags. Here is the cross correlation of the two variables:
Figure 4. Cross correlation of insolation and first difference of temperature. Positive lags show temperature changes lagging insolation changes. Blue lines show the level where the p-vaule is 0.05, which must be exceeded to achieve statistical significance.
So … there you have it. The relationship just barely achieves statistical significance. Is it true that looking at the first difference of the temperature improves the correlation? Yes, it is … but for the wrong reason. Taking the first difference of the temperature proxy reduces the amplitude of the 100 Kyr signal and increases the amplitude of the ~20 Kyr signal. Since the ~ 20 Kyr signal is the largest signal in the insolation, as a result the overall correlation increases … but this still doesn’t help us at all with the “100,000 year problem”. Not only that, but at the end of the day, the relationship is so weak as to scarcely achieve statistical significance.
Me, I’d say that Roe certainly didn’t solve the 100,000 year problem … although as always, YMMV …
Best wishes to everyone,
w.
My usual request—if you disagree with someone, please QUOTE THEIR EXACT WORDS THAT YOU DISAGREE WITH. This is the only way for everyone to be clear as to the exact ideas that you are objecting to.
Below is my latest I had sent something earlier but was not expressed the way I wanted it to be. This is.
Below are my thoughts about how the climatic system may work. It starts with interesting observations made by Don Easterbrook. I then reply and ask some intriguing questions at the end which I hope might generate some feedback responses. I then conclude with my own thoughts to the questions I pose.
1.From Don Easterbrook – Aside from the statistical analyses, there are very serious problems with the Milankovitch theory. For example, (1) as John Mercer pointed out decades ago, the synchronicity of glaciations in both hemispheres is ‘’a fly in the Malankovitch soup,’ (2) glaciations typically end very abruptly, not slowly, (3) the Dansgaard-Oeschger events are so abrupt that they could not possibility be caused by Milankovitch changes (this is why the YD is so significant), and (4) since the magnitude of the Younger Dryas changes were from full non-glacial to full glacial temperatures for 1000+ years and back to full non-glacial temperatures (20+ degrees in a century), it is clear that something other than Milankovitch cycles can cause full Pleistocene glaciations. Until we more clearly understand abrupt climate changes that are simultaneous in both hemispheres we will not understand the cause of glaciations and climate changes.
My reply :
I agree that the data does give rise to the questions Don Easterbrook, presents in the above. That data in turn leads me to believe along with the questions I pose at the end of this article, that a climatic variable force which changes often which is superimposed upon the climate trend has to be at play in the changing climatic scheme of things. The most likely candidate for that climatic variable force that comes to mind is solar variability and the primary and secondary effects associated with this solar variability which I feel are a significant player in glacial/inter-glacial cycles, counter climatic trends when taken into consideration with these factors which are , land/ocean arrangements , mean land elevation ,mean magnetic field strength of the earth(magnetic excursions), the mean state of the climate (average global temperature), the initial state of the earth’s climate(how close to interglacial-glacial threshold condition it is) the state of random terrestrial(violent volcanic eruption, or a random atmospheric circulation/oceanic pattern that feeds upon itself possibly) /extra terrestrial events (super-nova in vicinity of earth or a random impact) along with Milankovitch Cycles.
What I think happens is land /ocean arrangements, mean land elevation, mean magnetic field strength of the earth, the mean state of the climate, the initial state of the climate, and Milankovitch Cycles, keep the climate of the earth moving in a general trend toward either cooling or warming on a very loose cyclic or semi cyclic beat but get consistently interrupted by solar variability and the associated primary and secondary effects associated with this solar variability, and on occasion from random terrestrial/extra terrestrial events, which brings about at times counter trends in the climate of the earth within the overall trend. While at other times when the factors I have mentioned setting the gradual background for the climate trend for either cooling or warming, those being land/ocean arrangements, mean land elevation, mean state of the climate, initial state of the climate, Milankovitch Cycles , then drive the climate of the earth gradually into a cooler/warmer trend(unless interrupted by a random terrestrial or extra terrestrial event in which case it would drive the climate to a different state much more rapidly even if the climate initially was far from the glacial /inter-glacial threshold, or whatever general trend it may have been in ) UNTIL it is near that inter- glacial/glacial threshold or climate intersection at which time allows any solar variability and the associated secondary effects no matter how SLIGHT at that point to be enough to not only promote a counter trend to the climate, but cascade the climate into an abrupt climatic change. The back ground for the abrupt climatic change being in the making all along until the threshold glacial/inter-glacial intersection for the climate is reached ,which then gives rise to the abrupt climatic changes that occur and possibly feed upon themselves while the climate is around that glacial/inter-glacial threshold resulting in dramatic semi cyclic constant swings in the climate from glacial to inter-glacial while factors allow such an occurrence to take place.
The climatic back ground factors (those factors being previously mentioned) driving the climate gradually toward or away from the climate intersection or threshold of glacial versus interglacial, however when the climate is at the intersection the climate gets wild and abrupt, while once away from that intersection the climate is more stable. Although random terrestrial events and extra terrestrial events could be involved some times to account for some of the dramatic swings in the climatic history of the earth( perhaps to the tune of 10% ) at any time , while solar variability and the associated secondary effects are superimposed upon the otherwise gradual climatic trend, resulting in counter climatic trends, no matter where initial state of the climate is although the further from the glacial/inter-glacial threshold the climate is the less dramatic the overall climatic change should be, all other items being equal.
.The climate is chaotic, random, and non linear, but in addition it is never in the same mean state or initial state which gives rise to given forcing to the climatic system always resulting in a different climatic out-come although the semi cyclic nature of the climate can still be derived to a degree amongst all the noise and counter trends within the main trend.
QUESTIONS:
.Why is it when ever the climate changes the climate does not stray indefinitely from it’s mean in either a positive or negative direction? Why or rather what ALWAYS brings the climate back toward it’s mean value ? Why does the climate never go in the same direction once it heads in that direction?
Along those lines ,why is it that when the ice sheets expand the higher albedo /lower temperature more ice expansion positive feedback cycle does not keep going on once it is set into motion? What causes it not only to stop but reverse?
Vice Versa why is it when the Paleocene – Eocene Thermal Maximum ,once set into motion ,that being an increase in CO2/higher temperature positive feedback cycle did not feed upon itself? Again it did not only stop but reversed?
My conclusion is the climate system is always in a general gradual trend toward a warmer or cooler climate in a semi cyclic fashion which at times brings the climate system toward thresholds which make it subject to dramatic change with the slightest change of force superimposed upon the general trend and applied to it. While at other times the climate is subject to randomness being brought about from terrestrial /extra terrestrial events which can set up a rapid counter trend within the general slow moving climatic trend.
Despite this ,if enough time goes by (much time) the same factors that drive the climate toward a general gradual warming trend or cooling trend will prevail bringing the climate away from glacial/inter-glacial threshold conditions , it had once brought the climate toward , ending abrupt climatic change periods eventually or reversing over time dramatic climate changes from randomness. This however, subject to a rapid end maybe due to solar conditions superimposed and working in concert with the other factors driving the climate gradually toward a different climatic trend.
Just my usual carp about things this approach ignores.
1) Milankovich is about there being more days of summer and not just how intense the sun is on those days. You can’t just look at June and get anything that matches the actual Milankovich theory. (For example: North Pole is warmed more, and longer, when it points at the sun while furthest from the sun, since the orbital velocity is lower then; thus more days of summer and more melt, not less, despite lower insolation when further away by a small amount.)
2) Many (most?) planetary / lunar / solar cycles are not single mode. Sunspots tend to avoid the average, clustering on each side. Bond Events are an average of 1470 years, but have nodes each side (that seem to match to a 1500 year average lunar tidal cycle that is really bimodal at 1200 and 1800 years). Statistics that look for a match to ONE cycle frequency will fail on bimodal reality.
So, to me, it looks like you are inspecting non-Milancovitch theory (just more sun intensity) with unsuited tools (one fixed cycle) and then claiming Milancovitch must be wrong in consequence…
E.M.Smith February 2, 2015 at 10:32 am
In fact, these two cancel each other out. When the planet is closer to the sun it gets more sunlight over less time, and vice versa. Because of the inverse square nature of both light and gravity, the two effects end up with the same total amount of sun in both conditions. You can check this with real data using the CERES dataset. Currently the earth is closer to the sun in boreal winter, but the two hemispheres get the same amount of total insolation over the year.
In addition, according to your hypothesis we should be able to find a 100,000-year cycle if we looked at the entire half year of summer (AMJJAS) … but that shows no more of a 100 kyr cycle than does just June.
This is exactly why I use the periodogram. It reveals such bimodal cycles with great accuracy. However, it shows nothing of the sort regarding the insolation, unless you count the tiny (~1%) swings at ~ 100 kyrs shown in Figure 1 …
Sorry, simply not true. Whether we use just June or total solar summer input, and whether we look with tools such as the periodogram that would find more that “one fixed cycle”, there’s still nothing in the 100-kyr range to explain the cyclicity of the ice ages.
w.