Isn’t it time we gave a little recognition to our friend – Anthropogenic Global Warming

Raise the price of energy, we need to save the planet

…My hands are cold

…My fingers are cold

…I can’t see the drive

…I think my nose is actually froze

Turn down the heat, we must conserve

…My neck is cold

…My ears are cold

…My back is hurt

…My cheeks are froze

No incandescent bulbs, we must be efficient

…My eyes are cold

…My legs are cold

…My ears hurt

…My toes are probably solidly froze

We need emissions standards for small engines

…My car is cold

…Our school is closed

…My trees are ice and our yard is snow

…Our squirrels have likely froze

We must stop global warming before the planet burns

…Ice cold hair

…Ice cold nose

…Ice cold feet, especially toes

…Fortunately for me, my brain has still not froze.

——

Even at minus 3 Fahrenheit (-19C) I tell myself that we must thank fossil fuels for providing a fraction of the extra almost 1 degree of warmth we have luxuriated in this winter. Without it, we would have been a numbing minus 4 degrees F and that would have been a disaster!

Briffa 2013 Satellite Temperature Download

To look at the local temp data in comparison to the Briffa 2013 polar.mxd data, I downloaded both the RSS and UAH gridded lower troposphere satellite data.   Unlike ground temperature data, the lower troposphere data is a layer of air which extends miles above the surface of the earth (Blue curve in Figure 1).

Figure 1 – From World Climate Report

UAH and RSS regional trends are shown in Figures 2 and 3.

Figure 2

Figure 3

From past posts here, we have discussed that UAH estimates the polar regions whereas RSS leaves them out.   For fun, I plotted the difference of the two series globally and placed a green marker over the Yamal area where the data for Briffa 2013 was taken.

Figure 4

Data from that single gridcell for each series is sown in Figures 5 and 6.

Figure 5

Figure 6

The two series are mostly comprised of the same data.  UAH made the decision to change to station keeping satellites in recent years which eliminates some of the corrections necessary for satellites with orbits that decay over years.   Still the series are very highly correlated with an r^2 of 0.93.  Next,  I will get the ground data series for the region and then we can see how well things match up with the treerings.

I almost forgot:  R sourcecode is below and should be turnkey.

MXD Age Based Variance

I woke up the other day wondering if age of a tree would cause a different latewood density response to environmental factors.   I don’t remember reading anything about it so it seemed worthwhile to check.

I am working with 124 tree samples from the Polar.mxd file here.   This is the same data from my previous post.     I took each MXD curve which we showed previously has a long term age related signal and filtered that curve with a 51 year lowpass Gaussian filter.   The top of figure 1 shows the original curve (black) and the filtered curve (red).  Subtracting the red curve from the black curve gives the HF signal in the second pane of Figure 1 immediately below.  Basically a flat squiggly line.

I took all of the 124 trees and did the same thing.   Then I took the average for each year and calculated the confidence interval from the flattened high frequency curves.  The two sigma CI expands in a trumpet bell shape (dark gray) like the previous post so we know that differences in long term trend weren’t causing the whole shape.   The second pane below is the simple standard deviation (blue line).   Visually it has no appreciable trend over time meaning that as the trees age and gain mass, the response to 50 year and shorter signals isn’t a function of the age of the tree.     The trumpet bell shape of the dark gray region is therefore due to less samples in the older years and we don’t need to correct MXD variance for tree age.  It’s kind of a lame result, but at least we know the MXD answer.

Briffa MXD 2013 #1

I’ve spent a little time compiling MXD proxies from Briffa 2013. I don’t have a lot of results yet but I thought I would put them up. First, I created a simplified reconstruction by centering the proxies and then normalizing by standard deviation and averaging.  That is why Phi claimed to have done, and my data does seem to match his pretty well.   The graphs below have a 21 year Gaussian filter.

Phi stated in paraphrase that no age correction is needed for MXD data.   I took a look at that claim below.  By aligning the trees so that their first ring densities started in year 1 and averaging, I created the plot below.   The two sigma dark gray region assumes a normal distribution of data but you can see the red curve exceeds the no signal confidence interval expected if MXD didn’t demonstrate a signal by age.

Taking the examination a step further, I was concerned that younger trees or trees from different periods would show a different general trend with age.   Randomly, I split the trees into two groups, those whose birthday was before 1500AD and those after 1500AD.  Both sets of trees exhibited the same initial hump in the first 150 years of growth.  It is a bit concerning that they have such strong divergence in their older years.  If we normalized everything with a spline to figure 2, we might be able to create quite a blade on the end of our reconstruction.

From this, I think it is quite reasonable to make some kind of RCS based correction to the growth curves.  Ignoring that correction would lead to spurious trends as the data does have a strong tree age related signal.  Finding a reasonable sort of correction though is going to be a bit of work.

The source code is below, it is pretty messy so I will clean it up for future posts.

Flexible Thoughts

I’ve spent a little time over my years studying the math behind neural networks.   I find the self-organization of neural networks to be mathematically interesting in the same way that evolution is.  While we humans can’t claim to understand the whole of the math of even a single neuron, the basic concepts lead toward some interesting conclusions.   I think of a simplified neuron as a device with inputs and outputs that trigger in a binary fashion to analog style weighting of its own inputs.  The binary threshold, no matter what its nature, creates a mathematical stability when presented with a wide variety of simultaneous inputs.   That trigger threshold is known to self adjust as neurons learn and the combination of inputs from different stimuli on the same neuron means that a single neuron is capable of participation in unrelated decisions at different times.   The result is a certain flexibility of thought over time which is beneath the level of our own perception.

Neruons are known to continually change connections (mathematical weights) and have a random physical structure which by feedback grows in a fashion that is mathematically weighted toward non-random (learning).  This means that the comprehension, memory, and decision making environment in your mind is ever-changing.   Sure, some experiences will guide you, but how you perceive things will be guided by a different set of neural connections who’s weighting has taken over your current ones even if the results are the same.  In addition, the math behind the learning process is fully able to accept all kinds of interrelated information with the right kind of input and interpret even the most nonsensical information as truth.

It is particularly important to understand that emotion plays a role in learning as emotions can direct the physical learning process to absorb or reject information.  If  a mind wants to believe something is true, it will find extraordinary ways to rationalize how that information comports with reality and be satisfied for the effort. Perhaps emotion encourages or discourages connections of reason or maybe it just participates in the assembling of incomplete information, the actual function isn’t as critical for this post as knowing of its existence.  Even more than emotion, learning is especially influenced by being exposed to certain facts in repetition.   It isn’t an unreasonable statement that after years of dedication to the study of a particular phenomenon, confirmation bias is one of the greatest hurdles that a scientist faces.

For these reasons, I believe that the human mind has more flexibility of perception than people often realize.  Rather than being more complex and massive, I believe our personalities are actually smaller and more fluid than is often believed and that highly malleable personalities actually drive us.  You literally won’t be the same person in 10 years that you are today.  I don’t have any physiological evidence to support my statement, so perhaps it is what I want to believe, but it does seem to support my general observations.

So it is a common contention that we humans are all susceptible to this kind of bias, I have a few examples outlandish conclusions people have reached based in my understanding of these principles.

– The oft-stated belief by certain climate scientists that skeptics are oil funded.  I like this one because it is relatively benign, but seems to ignore the possibility that oil companies don’t mind the supply restrictions being caused in the energy industry by production reducing regulations.   I’ve often wondered why people like Michael Mann and Stephan Lewandowsky believe that oil companies don’t want supply limitations which cause people to pay more for oil? Paying more per production gallon means more profit.  No, I’m not saying that some money from oil companies isn’t spent in all directions, but it doesn’t take a big leap of understanding to know why the oil companies fund mainstream climate science and promote expensive “green” energy on their signs.

– Belief that industry or capitalism are making people poor.   This is a very common conclusion today and it is no wonder considering the vast repetition of this concept being fed to society, but people are wealthier in capitalist pro-industry society than at any time in our history.  The bifurcation of logic is extraordinary, yet this message has come to be believed by more people than not.   The popular thought today is that it is the fault of business that people are poor, not the fault of the poor individuals who on immediate inspection are not producing anything of value.

– Lewandowsky and Mann “According to the World Health Organization, climate change is already claiming more than 150,000 lives annually (Patz, Campbell-Lendrum, Holloway, & Foley, 2005), and estimates of future migrations triggered by unmitigated global warming run as high as 187 million refugees “   Despite there being literally zero evidence supporting this statement, this is something these authors have come to fully believe.

– Belief that global warming will not only destroy the world but will destroy it soon.  This belief is common among those who are immersed in the field of climate science.  For example, it isn’t hard to visualize James Hanson literally screaming wildly at his keyboard for this old quote from an article which literally compared coal to the “death trains” of humanities darkest history:

“The climate is nearing tipping points. Changes are beginning to appear and there is a potential for explosive changes, effects that would be irreversible, if we do not rapidly slow fossil-fuel emissions over the next few decades. As Arctic sea ice melts, the darker ocean absorbs more sunlight and speeds melting. As the tundra melts, methane, a strong greenhouse gas, is released, causing more warming. As species are exterminated by shifting climate zones, ecosystems can collapse, destroying more species.

The public, buffeted by weather fluctuations and economic turmoil, has little time to analyze decadal changes. How can people be expected to evaluate and filter out advice emanating from those pushing special interests? How can people distinguish between top-notch science and pseudo-science?

Those who lead us have no excuse – they are elected to guide, to protect the public and its best interests. They have at their disposal the best scientific organisations in the world, such as the Royal Society and the US National Academy of Sciences. Only in the past few years did the science crystallize, revealing the urgency. Our planet is in peril. If we do not change course, we’ll hand our children a situation that is out of their control. One ecological collapse will lead to another, in amplifying feedbacks.”

Clearly, while there are climate models predicting warming, there is no evidence of the kind of destruction tormenting Dr. Hanson’s mind.  He clearly believes in what he is saying, yet for those who haven’t been regularly cuddled by “pro-green” message, we cannot see any of these possibilities as rational.  It is my impression that nothing frustrates Dr. Hansen more than humanities refusal to jump off the cliff with him.

Rationalization:

Now clearly, all of the above anti-reason comments can be rationalized by an individual.  I can say that easily because so many people find the above concepts certain and true despite what appear to be such nonsensical roots.  You don’t even have to agree that ALL of the statements are nonsensical, just pick one of them that is nonsense and we can agree that rational irrationality is part of our human nature.  The human ability to rationalize anything is so powerful that entire governments and societies are based on the unprovable certainty of superiority of their deity over another.  Still, the truly brutal impact of those governments on their populations does disturbingly little to perturb their popular beliefs.

We can conclude from this, that it is an unfortunate fact that it isn’t the quality of the information which determines the number or percentage of humans who subscribe to a belief, nor even the difficulty of rationalization, but rather the emotional investment and degree of repetition which makes the final determination of what you believe.  Today, many IPCC climate scientists fret over the fact that so many of us cannot see their version of reality, while simultaneously recommending extremely dangerous economic measures like massive curtailing of meat and energy production, or the use of food for production of transportation fuel.  These extreme anti-industrial measures clearly contribute to impoverishment and starvation of the poorest societies of the world the scientists profess to protect.  Some find the solution to be to give these poor money, which obviously just enslaves them to the payments. All of this rationalization to address a planetary destruction which at this point isn’t even remotely in evidence.  They are completely immersed in the field, with their like-minded peers reinforcing their political and scientific certainty.  For some, it seems that every piece of their self image has become tied up in this certainty of knowledge of the future climate as well as their authoritarian style global government solutions.

The whole situation is crazy at this point but the global political news machine is still repeating the global doom message at top volume, reinforcing all necessary aspects of global climate doom and promoting the wrong-think it supports.   So far there have been a lot of people who have declared the end of the world, and so far they are batting a perfect zero.

So, since climate models are running hotter than measurements by 2X or more, and since not a single destructive change definitively related to climate change has been recorded in the entirety of human history, how about we just settle down for say 20 or 30 years and see where climate is then?    Doesn’t that seem a tweak more reasonable than making regulations which preemptively shut down energy production or drive food and fuel prices through the roof?