Guest Post by Werner Brozek, Professor Robert Brown from Duke University and Just The Facts
Image Credit: Josh
In order for climate science to be settled, there are many requirements. I will list four for now, although I am sure you can think of many more. Then I will expand on those.
1. We must know all variables that can affect climate.
2. We must know how all variables are changing over time.
3. We must know how each changing variable affects climate.
4. We must know about all non-linear changes that take place as a result of changes to variables.
As for the variables affecting climate, Just The Facts has done a superb job compiling many of them on WUWT’s Potential Climatic Variables Reference Page.
If you have an hour, there is lots of good reading here. For now, I will just give the main topics, but note that all main topics have an array of sub topics.
1. Earth’s Rotational Energy
2. Orbital Energy, Orbital Period, Orbital Spiral, Elliptical Orbits (Eccentricity), Tilt (Obliquity), Wobble (Axial precession) and Polar Motion
4. Solar Energy
5. Geothermal Energy
6. Outer Space/Cosmic/Galactic Effects
7. Earth’s Magnetic Field
8. Atmospheric Composition
13. Known Unknowns
14. Unknown Unknowns
If you know some more that should be added, please let us know.
The above covers my point 1 above. As for points 2 and 3, for all of the items listed above, we need to know if the changes, if any, are linear, exponential, logarithmic, sinusoidal, random or some other pattern. For example, depending on who you talk to and the interval you are considering, our emissions of carbon dioxide could be exponential, but the increase in the atmosphere could be linear, but the effect could be logarithmic. Then there are asteroids which could be totally random. As for point 4 above, the easiest example would be to consider a ball with air at 30 C and a relative humidity of 90%. When this is cooled, the gas molecules do not simply slow down indefinitely. At a certain point, the water molecules move so slowly that the hydrogen bonds cause molecules to stick together after collisions to cause liquid water or ice to form. Further cooling causes the various gases to condense to their liquid states and then to freeze to their solid state.
October 2, 2015 at 10:36 am
t’s not a law of nature, but outside of Le Chatelier’s principle, a more modern version (in case anyone is still reading this thread) is Prigogene’s Self-Organization of dissipative systems.
Self-organization as a concept preceded Prigogene, but he quantified it and moved it from the realm of philosophy and psychology and cybernetics to the realm of physics and the behavior of nonlinear non-equilibrium systems.
To put it into a contextual nutshell, an open, non-equilibrium system (such as a gas being heated on one side and cooled on the other) will tend to self-organize into structures that increase the dissipation of the system, that is, facilitate energy transport through the system. The classic contextual example of this is the advent of convective rolls in a fluid in a symmetry breaking gravitational field. Convection moves heat from the hot side to the cold side much, much faster than conduction or radiation does, but initially the gas has no motion but microscopic motions of the molecules and (if we presume symmetry and smoothness in the heated surface and boundaries) experiences only balanced, if unstable, forces. However, those microscopic motions contain small volumes that are not symmetric, that move up or down. These small fluctuations nucleate convection, at first irregular and disorganized, that then “discovers” the favored modes of dissipation, adjacent counter rotating turbulent rolls that have a size characteristic of the geometry of the volume and the thermal imbalance.
The point is that open fluid dynamical differentially heated and cooled systems spontaneously develop these sorts of structures, and they have some degree of stability or at least persistence in time. They can persist a long time — see e.g. the great red spot on Jupiter. The reason that this is essentially a physical, or better yet a mathematical, principle is evident from the wikipedia page above — Prigogene won the Nobel Prize because he showed that this sort of behavior has a universal character and will arise in many, if not most open systems of sufficient complexity. There is a deep connection between this theory and chaos — essentially that an open chaotic system with “noise” is constantly being bounced around in its phase space, so that it wanders around through the broad stretches of uninteresting critical points until it enters the basin of attraction of an interesting one, a strange attractor. At that point the same noise drives it diffusively into a constantly shifting ensemble of comparatively tightly bound orbits. At that point the system is “stable” in that it has temporally persistent behavior with gross physical structures with their own “pseudoparticle” physics and sometimes even thermodynamics. This is one of the things I studied pretty extensively back when I did work in open quantum optical systems.
There is absolutely no question that our climate is precisely a self-organized system of this sort. We have long since named the observed, temporally persistent self-organized structures — ENSO, the Monsoon, the NAO, the PDO. We can also observe more transient structures that appear or disappear such as the “polar vortex” or “The Blob” (warm patch in the ocean off of the Pacific Northwest) or a “blocking high”. Lately, we had “Hurricane Joaquin”. Anybody can play — at this point you can visit various websites and watch a tiny patch of clouds organize into a thunderstorm, then a numbered “disturbance with the potential for tropical development”, then a tropical depression, and finally into a named storm with considerable if highly variable and transient structure.
All of these structures tend to dissipate a huge amount of energy that would otherwise have to escape to space much more slowly. They are born out of energy in flow, and “evolve” so that the ones that move energy most efficiently survive and grow.
Once again, one has to bemoan the lack of serious math that has been done on the climate. This in some sense is understandable, as the math is insanely difficult even when it is limited to toy systems — simple iterated maps, simple ODE or PDE systems with simple boundary conditions. However, there are some principles to guide us. One is that in the case of self-organization in chaotic systems, the dynamical map itself has a structure of critical points and attractors. Once the system “discovers” a favorable attractor and diffuses into an orbit, it actually becomes rather immune to simple changes in the driving. Once a set of turbulent rolls is established, as it were, there is a barrier to be overcome before one can make the number of rolls change or fundamentally change their character — moderate changes in the thermal gradient just make the existing rolls roll faster or slower to maintain heat transport. However, in a sufficiently complex system there are usually neighboring attractors with some sort of barrier in between them, but this barrier is there only in an average sense. In many, many cases, the orbits of the system in phase space have a fractal, folded character where orbits from neighboring attractors can interpenetrate and overlap. If there is noise, there is a probability of switching attractors when one nears a non-equilibrium critical regime, so that the system can suddenly and dramatically change its character. Next, the attractors themselves are not really fixed. As one alters (parametrically for example) the forcing of the system or the boundary conditions or the degree of noise or… one expects the critical points and attractors themselves to move, to appear and disappear, to get pushed together or moved apart, to have the barriers between them rise or fall. Finally (as if this isn’t enough) the climate is not in any usual sense an iterated map. It is usually treated as one from the point of view of solving PDEs (which is usually done via an iterated map where the output of one time step is the input into the next with a fixed dynamics). This makes the solution a Markov Process — one that “forgets” its past history and evolves locally in time and space as an iterated map (usually with a transition “rule” with some randomness in it).
But the climate is almost certainly not Markovian, certainly not in practical terms. What it does today depends on the state today, to be sure, but because there are vast reservoirs where past dynamical evolution is “hidden” in precisely Prigogene’s self-organized structures, structures whose temporal coherence and behavior can only be meaningfully understood on the basis of their own physical description and not microscopically, it is completely, utterly senseless to try to advance a Markovian solution and expect it to actually work!
Two examples, and then I must clean my house and do other work. One is clearly the named structures themselves in the climate. The multidecadal oscillations have spatiotemporal persistence and organization with major spectral components out as far as sixty or seventy years (and may well have longer periods still to be discovered — we have crappy data and not much of it that extends into the increasingly distant past). Current models treat things like ENSO and the PDO and so on more like noise, and we see people constantly “removing the influence of ENSO” from a temperature record to try to reductively discern some underlying ENSO-less trend. But they aren’t noise. They are major features of the dynamics! They move huge amounts of energy around, and are key components of the efficiency of the open system as it transports incident solar energy to infinity, keeping a reservoir of it trapped within along the way. It is practically speaking impossible to integrate the PDEs of the climate models and reproduce any of the multidecadal behavior. Even if multidecadal structures emerge, they have the wrong shape and the wrong spectrum because the chaotic models have a completely different critical structure and attractors as they are iterated maps at the wrong resolution and with parameters that almost certainly move them into completely distinct operational regimes and quite different quasiparticle structures. This is instantly evident if one looks at the actual dynamical futures produced by the climate models. They have the wrong spectrum on pretty much all scales, fluctuating far more wildly than the actual climate does, with the wrong short time autocorrelation and spectral behavior (let alone the longer multidecadal behavior that we observe).
The second is me. I’m precisely a self-organized chaotic system. Here’s a metaphor. Climate models are performing the moral equivalent of trying to predict my behavior by simulating the flow of neural activity in my brain on a coarse-grained basis that chops my cortex up into (say) centimeter square chunks one layer thick and coming up with some sort of crude Markovian model. Since the modelers have no idea what I’m actually thinking, and cannot possibly actually measure the state of my brain outside of some even more crudely averaged surface electrical activity, they just roll dice to generate an initial state “like” what they think my initial state might be, and then trust their dynamics to eventually “forget” that initial state and move the model brain into what they imagine is an “ensemble” of my possible brain states so that after a few years, my behavior will no longer depend on the ignored details (you know, things like memories of my childhood or what I’ve learned in school). They run their model forward twenty years and announce to the world that unless I undergo electroshock therapy right now their models prove that I’m almost certainly destined to become an axe murderer or exhibit some other “extreme” behavior. Only if I am kept in a dark room, not overstimulated, and am fed regular doses of drugs that essentially destroy the resolution of my real brain until it approximates that of their model can they be certain that I won’t either bring about World Peace in one extreme or cause a Nuclear War in the other.
The problem is that this whole idea is just silly! Human behavior cannot be predicted by a microscopic physical model of the neurons at the quantum chemistry level! Humans are open non-Markovian information systems. We are strongly regulated by our past experience, our memory, as well as our instantaneous input, all folded through a noisy, defect-ridden, and unbelievably complex multilayer neural network that is chemically modulated by a few dozen things (hormones, bioavailable energy, diurnal phase, temperature, circulatory state, oxygenation…)
As a good friend of mine who was a World’s Greatest Expert (literally!) on complex systems used to say: “More is different”. Emergent self-organized behavior results in a cascade of structures. Microscopic physics starts with quarks and leptons and interaction particles/rules. The quarks organize into nucleons. The nucleons organize into nuclei. The electrons bond to the nuclei to form atoms. The physics and behavior of the nuclei are not easily understood in terms of bare quark dynamics! The physics and behavior of the atoms are not easily understood in terms of the bare quark plus lepton dynamics! The atoms interact and form molecules, more molecules, increasingly complex molecules. The molecules have behavior that is not easily understood in terms of the “bare” behavior of the isolated atoms that make them up. Some classes of molecular chemistry produce liquids, solids, gases, plasmas. Again, the behavior of these things is increasingly disconnected from the behavior of the specific molecules that make them up — new classes of universal behavior emerge at all steps, so that all fluids are alike in certain ways independent of the particular molecules that make them up, even as they inherent certain parametric behavior from the base molecules. Some molecules in some fluids become organic biomolecules, and there is suddenly a huge disconnect both from simple chemistry and from the several layers of underlying physics.
If more is different, how much is enough? There is a whole lot of more in the coupled Earth-Ocean-Atmosphere-Solar system. There is a whole lot less, heavily oversimplified and with the deliberate omission of the ill-understood quasiparticle structures that we can see dominating the weather and the climate, in climate models.
Could they work? Sure. But one really shouldn’t expect them to work, one
should expect them to work no better than a simulated neural network “works” to simulate actual intelligence, which is to say, it can sometimes produce understandable behaviors “like” intelligence without ever properly resembling the intelligence of any intelligent thing and without the slightest ability to predict the behavior of an intelligent thing. The onus of proof is very much on the modelers that wish to assert that their models are useful for predicting long term climate, but this is a burden that so far they refuse to acknowledge, let alone accept! If they did, large numbers of climate models would have to be rejected because they do not work in the specific sense that they do not come particularly close to predicting the behavior of the actual climate from the instant they entered the regime where they were supposed to be predictive, instead of parametrically tuned and locked to match up well with a reference interval that just happened to be the one single stretch of 15-25 years where strong warming occurred in the last 85 years. There are so very, very many problems with this — training any model on a non-representative segment of the available data is obviously likely to lead to a poor model — but suffice it to say that so far, they aren’t working and nobody should be surprised.
In the sections below, as in previous posts, we will present you with the latest facts. The information will be presented in three sections and an appendix. The first section will show for how long there has been no warming on some data sets. At the moment, only the satellite data have flat periods of longer than a year. The second section will show for how long there has been no statistically significant warming on several data sets. The third section will show how 2015 so far compares with 2014 and the warmest years and months on record so far. For three of the data sets, 2014 also happens to be the warmest year. The appendix will illustrate sections 1 and 2 in a different way. Graphs and a table will be used to illustrate the data.
This analysis uses the latest month for which data is available on WoodForTrees.com (WFT). All of the data on WFT is also available at the specific sources as outlined below. We start with the present date and go to the furthest month in the past where the slope is a least slightly negative on at least one calculation. So if the slope from September is 4 x 10^-4 but it is – 4 x 10^-4 from October, we give the time from October so no one can accuse us of being less than honest if we say the slope is flat from a certain month.
1. For GISS, the slope is not flat for any period that is worth mentioning.
2. For Hadcrut4, the slope is not flat for any period that is worth mentioning.
3. For Hadsst3, the slope is not flat for any period that is worth mentioning.
4. For UAH, the slope is flat since May 1997 or 18 years and 5 months. (goes to September using version 6.0)
5. For RSS, the slope is flat since February 1997 or 18 years and 8 months. (goes to September)
The next graph shows just the lines to illustrate the above. Think of it as a sideways bar graph where the lengths of the lines indicate the relative times where the slope is 0. In addition, the upward sloping blue line at the top indicates that CO2 has steadily increased over this period.
Note that the UAH5.6 from WFT needed a detrend to show the slope is zero for UAH6.0.
When two things are plotted as I have done, the left only shows a temperature anomaly.
The actual numbers are meaningless since the two slopes are essentially zero. No numbers are given for CO2. Some have asked that the log of the concentration of CO2 be plotted. However WFT does not give this option. The upward sloping CO2 line only shows that while CO2 has been going up over the last 18 years, the temperatures have been flat for varying periods on the two sets.
For this analysis, data was retrieved from Nick Stokes’ Trendviewer available on his website. This analysis indicates for how long there has not been statistically significant warming according to Nick’s criteria. Data go to their latest update for each set. In every case, note that the lower error bar is negative so a slope of 0 cannot be ruled out from the month indicated.
On several different data sets, there has been no statistically significant warming for between 11 and 22 years according to Nick’s criteria. Cl stands for the confidence limits at the 95% level.
The details for several sets are below.
For UAH6.0: Since December 1992: Cl from -0.009 to 1.688
This is 22 years and 10 months.
For RSS: Since March 1993: Cl from -0.014 to 1.597
This is 22 years and 7 months.
For Hadcrut4.4: Since January 2001: Cl from -0.048 to 1.334
This is 14 years and 9 months.
For Hadsst3: Since July 1995: Cl from -0.002 to 1.949
This is 20 years and 3 months.
For GISS: Since September 2004: Cl from -0.033 to 2.020
This is 11 years and 1 month.
This section shows data about 2015 and other information in the form of a table. The table shows the five data sources along the top and other places so they should be visible at all times. The sources are UAH, RSS, Hadcrut4, Hadsst3, and GISS.
Down the column, are the following:
1. 14ra: This is the final ranking for 2014 on each data set.
2. 14a: Here I give the average anomaly for 2014.
3. year: This indicates the warmest year on record so far for that particular data set. Note that the satellite data sets have 1998 as the warmest year and the others have 2014 as the warmest year.
4. ano: This is the average of the monthly anomalies of the warmest year just above.
5. mon: This is the month where that particular data set showed the highest anomaly. The months are identified by the first three letters of the month and the last two numbers of the year.
6. ano: This is the anomaly of the month just above.
7. y/m: This is the longest period of time where the slope is not positive given in years/months. So 16/2 means that for 16 years and 2 months the slope is essentially 0. Periods of under a year are not counted and are shown as “0”.
8. sig: This the first month for which warming is not statistically significant according to Nick’s criteria. The first three letters of the month are followed by the last two numbers of the year.
9. sy/m: This is the years and months for row 8. Depending on when the update was last done, the months may be off by one month.
10. Jan: This is the January 2015 anomaly for that particular data set.
11. Feb: This is the February 2015 anomaly for that particular data set, etc.
19. ave: This is the average anomaly of all months to date taken by adding all numbers and dividing by the number of months.
20. rnk: This is the rank that each particular data set would have for 2015 without regards to error bars and assuming no changes. Think of it as an update 45 minutes into a game.
If you wish to verify all of the latest anomalies, go to the following:
For UAH, version 6.0beta3 was used. Note that WFT uses version 5.6. So to verify the length of the pause on version 6.0, you need to use Nick’s program.
For Hadsst3, see: http://www.cru.uea.ac.uk/cru/data/temperature/HadSST3-gl.dat
For GISS, see:
To see all points since January 2015 in the form of a graph, see the WFT graph below. Note that UAH version 5.6 is shown. WFT does not show version 6.0 yet. Also note that Hadcrut4.3 is shown and not Hadcrut4.4, which is why the last few months are missing for Hadcrut.
As you can see, all lines have been offset so they all start at the same place in January 2015. This makes it easy to compare January 2015 with the latest anomaly.
In this part, we are summarizing data for each set separately.
The slope is flat since February 1997 or 18 years, 8 months. (goes to September)
For RSS: There is no statistically significant warming since March 1993: Cl from -0.014 to 1.597.
The RSS average anomaly so far for 2015 is 0.320. This ties it as 4th place. 1998 was the warmest at 0.55. The highest ever monthly anomaly was in April of 1998 when it reached 0.857. The anomaly in 2014 was 0.255 and it was ranked 6th.
The slope is flat since May 1997 or 18 years and 5 months. (goes to September using version 6.0beta3)
For UAH: There is no statistically significant warming since December 1992: Cl from -0.009 to 1.688. (This is using version 6.0 according to Nick’s program.)
The UAH average anomaly so far for 2015 is 0.225. This would rank it as 3rd place. 1998 was the warmest at 0.483. The highest ever monthly anomaly was in April of 1998 when it reached 0.742. The anomaly in 2014 was 0.188 and it was ranked 5th.
The slope is not flat for any period that is worth mentioning.
For Hadcrut4: There is no statistically significant warming since January 2001: Cl from -0.048 to 1.334.
The Hadcrut4 average anomaly so far for 2015 is 0.702. This would set a new record if it stayed this way. The highest ever monthly anomaly was in January of 2007 when it reached 0.832. The anomaly in 2014 was 0.564 and this set a new record.
For Hadsst3, the slope is not flat for any period that is worth mentioning. For Hadsst3: There is no statistically significant warming since July 1995: Cl from -0.002 to 1.949.
The Hadsst3 average anomaly so far for 2015 is 0.558. This would set a new record if it stayed this way. The highest ever monthly anomaly was in August of 2014 when it reached 0.644. This is prior to 2015. The anomaly in 2014 was 0.479 and this set a new record. The September 2015 anomaly of 0.729 also sets a new record.
The slope is not flat for any period that is worth mentioning.
For GISS: There is no statistically significant warming since September 2004: Cl from -0.033 to 2.020.
The GISS average anomaly so far for 2015 is 0.81. This would set a new record if it stayed this way. The highest ever monthly anomaly was in January of 2007 when it reached 0.97. The anomaly in 2014 was 0.75 and it set a new record.
After reading this article, do you think climate science is settled? If not, do you think it will be settled in your lifetime?