Proper Cherry Picking

Recognised talbes of tidal periods usually run to 18.6 years I think. The direct input of tidal energy dispersed through frictional losses is small as you correctly point out. However, if there is inter-annual to decadal scale bulk displacement of water this could cause more energy to be captured from Mr Sun:
Sure, it could. Although I’ve heard more plausible arguments suggesting that atmospheric tides can modulate the tropospheric height, hence the DALR, hence the GHE. Either way, MORE plausible is still far from convincing. It’s just like sunspots/solar activity. There are tantalizing hints of correlation, if you pick the interval to look at and ignore the places where a mental linear model doesn’t work. OTOH, if you look at all of the data and try to establish a simple linear one-parameter model effect it is far from compelling.
None of which rules out nonlinear multivariate or differential effects, but looking for that degree of complexity is difficult without some specific model to motivate it and nobody seems to discuss them much in climate science outside of throwing a boatload of more or less linear stuff into a GCM and letting it stir the pot. Maybe if one puts solar activity on one axis, the ENSO index on another axis, your tidal activity voodoo on yet another axis, and then plots the time derivative of GASTA on still another axis, it loops around in a beautiful coherent Poincare cycle trajectory that slowly varies in a predictable way with CO_2 concentration. Unless and until somebody goes positively crazy with cone-head quantities of data, some truly excellent analytic tools, and nothing but time on their hands, we may never know. Especially when “coherence” might emerge from phenomena that in projection look like ill-correlated noise only when one gets up to ten dimensions. Or twenty. We just don’t know.
IMO a lot of the climate data probably could be understood in terms of local attractors and/or Hurst-Kolmogorov statistics — locally stable climate (that is, macroscopic and decadal in scope) global states where the climate whirls around in some sort of demented set of epicycles — diurnal epicycles, annual epicycles, solar cycle epicycles, hell, why not, tidal epicycles, multidecadal oscillation epicycles that are at once a semistable response to all of this cyclic time evolution with strong feedbacks and themselves epicyclic factors in the dynamics. Because the climate oscillates (not coherently, but as in is bound to around some sort of comparatively stationary set point) we know there are strong negative feedbacks that push it back towards the set point — if it gets “too warm” for the set point it cools, if it gets “too cool” for the set point it warms. The climate certainly is not an undirected random walk or we would cook or freeze in short order.
But the climate also clearly has a kind of “inertia” — once too much heating establishes a cooling mode, the cooling mode persists long enough to overshoot the set point, once it gets too cool and establishes a heating mode it lasts long enough to overshoot the other way. Sometimes — because these modes are themselves multivariate and subject to the whirling epicyclic evolution of their many components. Instead of getting a clean oscillation with a particularly clear fourier signal (outside of the obvious diurnal and seasonal signals for the purely periodic drivers) one gets chaos.
Underneath all of this, the locally stable climate states — the climate “attractors” as it were — are not really stable at all. They are only stable on a scale of decades to centuries, and may well be jinking around on even shorter time scales. If I understand the idea behind CAGW, it is that the steady addition of CO_2 is supposed to systematically tilt the “forces” that act on these attractors to make the set points move ever further in the warmer direction, and that as it does this tilting water vapor will chime in to amplify the tilt instead of doing the exact opposite — push it back — which is what one would expect purely on the basis of the overall semi-stability of the space of attractor set points visible in the climate record at various scales.
But sadly, we do not know anything at all about the factors and epicycles that determine the dynamics of the attractors themselves — we just pretend that we do when we build a simple linear model without even understanding the nature of the underlying macroscopic dynamics.
I’ll end my diatribe du jour with an analogy. My mentor in the general field of complex systems, Dr. Richard Palmer (who was once on the shortest list for the directorship of the Santa Fe institute before a series of strokes tragically affected his career) had an adage which was very useful to understand the motivation for the study of complex systems: More is Different.
This is clearly visible when one attempts to move from the microscale to the macroscale in physics. Microscopically everything is linear, reversible, time symmetric, deterministic (the latter in quantum mechanics only if one considers complete/closed systems, and irrelevant in chaotic classical mechanics where one cannot sufficiently precisely specify an initial state and hence true but irrelevant in both). Consideration of the fundamental interactions leads one to an understanding of how e.g. quarks combine to form nucleons, how nucleons combine to form more or less stable nuclei, how electrons bind to nuclei to form atoms, how atoms — now a set with its own rules for stability and dynamics that are in no way obviously linked to the “bare” laws of the symmetry broken field — combine to form molecules, how molecules — now with their own laws that make up an entire standalone science, “chemistry” — develop enough complexity that chemistry itself fractures into subsets each with its own UNIQUE set of rules plus rules for more general interactions, organic chemistry for molecules with carbon, the chemistry of ceramics, metals, semiconductors, acids, bases, how chemistry (apparently) self-organizes organic chemistry into organic BIO-chemistry where proteins and amino acids transform into self-replicating factories, are shaped by evolution (a whole new selection paradigm that is in no way relatable to e.g. electrodynamic theory) into life. Life has its own rules, and those rules look nothing at all like the rules of physics, chemistry, organic chemistry. In other directions, gravity assembles atoms and molecules into larger bodies, heating them as they fall together, until some of the largest bodies “ignite” with the strong nuclear force of fusion and become enormously complex objects forging the heavier atoms (the picture above was logical, not temporally ordered) and scattering them in supernovae to be re-forged into smaller bodies that follow complex orbits around the larger bodies and develop surface interfaces with sufficient chemistry to permit the growth of complexity with its own rules leading to life. Stars have numerous rules of their own, rules that can in some sense be related to understood physics and chemistry but nevertheless rules for macroscopic structures that are “discrete” — not really thought of in terms of the individual rules and motions of their enormous numbers of constituent parts.
In physics, one generally knows better than to try to understand Shakespeare by considering the unified field applied to the 10 to the thirty-something elementary particles (at least) that make up the brain, not including the massless particles of the field itself that have no meaningful count. Or one should. The physics of the dozens of layers of meta-structure between the unified field simply cannot be traced quantitatively through the layers except by developing rules for the many intermediate layers by a mix of observation and induction. That is, we do well to understand the physics of each transition from one layer to the next, and perhaps to be able to do at least some computations in both worlds to give ourselves comfort that our hierarchical decomposition is sound. Hence it is lovely to be able to use quantum chemistry to solve for the chemical laws that govern at least simple molecules and to help us understand particular features of the internal structure and dynamics of more complex molecules even though we know that it would be silly to try to solve a Schrodinger equation for the dynamics of a neuron, or for hemoglobin in an electrolyte water-based fluid. It’s nice to be able to derive the ideal gas law and the VanderWaals gas law from comparatively simple principles even though real gases often do not fit either one perfectly, especially near phase transitions or when lots of chemical stuff is going on. We end up with a decent bridge of verified consistency from the microscale where things are simple and comparatively easy to understand and compute (well, ok, maybe not THAT easy) to the semantic content of the Gettysburg Address, with only a few “gaps” that are difficult to bride or at best still rather speculative.
Consider, then, what we are trying to do in climate science. Consider, in fact, GCMs — an effort to compute what the climate will do based on a meso-scale microdynamic model. By that I mean that we take the planet and chop its surface and atmosphere and some depth of the ocean into chunks, establish what we hope are accurate physics-based rules for the time evolution of each chunk based on its own state, its interaction with the surrounding chunks, and with various external forces (or “forcings” in context). The rules of this sort of game are actually known to us from experience in other contexts.
We do not really know the dynamics of the quasiparticles of the climate system. By this I mean that the climate system and its drivers have numerous named structures, both specific large scale structures and generic small scale structures, that have sufficient spatiotemporal coherence that we can observe them and give them names: Thunderstorms, tornadoes, hurricanes, droughts, high pressure centers, low pressure centers, waves, ridges, Hadley cells, the jet stream, troposphere, stratosphere, the Gulf stream, the global thermohaline circulation, ENSO, monsoon, cumulus clouds, Santa Ana winds, the Sun, solar cycles, elliptical orbits. All of these objects mutually conspire in one fundamental process — the transport of nuclear energy released in the heart of the sun 100,000 years ago (or so) to deep space through the tiny solid angle subtended by the planet Earth. That’s it. Energy from the sun arrives on Earth, hangs out for a while, and then continues on its merry way to infinity and beyond, to entropic oblivion. Our entire climate and life itself is nothing but self-organized structure involved in dissipating energy in an open system.
I’m a quasiparticle in the climate system. So are you. The entire climate debate is about quasiparticles in the climate system inventing new high level rules for quasiparticle activity that might or might not impact the transfer efficiency of the entire system (or consideration of the possibility that quasiparticle dynamics to date has already affected it and will continue to affect it).
In simulations of complex systems, if the resolution of the simulation is not sufficient to represent the important quasiparticles of the system, the simulations often fail, even simulations of far simpler systems than the climate. We can easily understand why, of course. You cannot describe a laser in terms of blackbody radiation — the latter is all completely understandable and physically correct in context, but it makes certain assumptions in its averaging that are, in fact, not well represented by all kinds of optical phenomena, especially things like lasers that are monochromatic, coherent, and not even vaguely “thermal”.
Understanding the evolution of a fluid system in closed container with convection from conduction with at most one convective roll, through a state with numerous stable convective rolls (determined non-uniquely by boundary conditions, the degree of forcing, and non-Markovian time evolution from various microscopic nucleations), to a fully turbulent state by solving microscopic Navier-Stokes equations will not work if one restricts the granularity of the spatial decomposition to be commensurate with the sizes of the intermediate convective rolls. It will not deterministically tell you what a real system will do even if it is remarkably fine grained — the specific patterns of convective rolls that emerge as being stable depend in some detail on how the system is nucleated, how the structures emerge from the initial INTERNAL motions of the system, and in numerous critical regions the apparent “stability” of the quasiparticle structure is, well, not. Stable. Right up to where turbulence and true chaos emerge, where the quasiparticles of one domain break down and are replaced by turbulent rolls, by eddies on all length scales jostling and bouncing off of one another, forming and dissipating as they help carry energy from one place to another in the service of Entropy.
GCMs use cells that are not uniform in area — because the writers of the GCMs are (I have to suppose) too lazy to implement a rescalable unbiased e.g. icosahedral tiling of the sphere, they use the “easy” latitude/longitude decomposition with cells determined as integer numbers of degrees in a coordinate system with a horrendous polar-singular Jacobean. This undersamples the equator and oversamples the high latitudes (and sometimes they heuristically correct for this, a bit). It means that cells are nearly square near the equator and wedge shaped near the poles. IIRC, there are few or no GCMs with resolution less than 1 degree, so equatorial cells are order of 70 miles to the side or larger. The motivation for using a lat/long grid is that rectilinear cells are easy to loop over and write nearest neighbor dynamics for. This presumes that one can correct for the cell distortions due to the Jacobean in a PDE solution based on a rectangular grid more easily than one can develop rescalable icosahedral routines for solving partial differential equations on a non-rectangular grid. GCMs by their nature cannot, therefore, resolve quasiparticle structures less than several grid cells in size — to do a centered circulation for example would require at least 9 cells in a 3×3 grid — a central “defect” cell and eight bordering cells such that the integral of the “wind velocity” assigned to the cell around the loop of cells is nonzero. That is a unit order of 200 miles square in the tropics and 200 miles not so square elsewhere.
200 miles, of course, is the order of the size of large scale weather quasiparticles — hurricanes, for example. It is completely blind to most mundane thunderstorms, all tornadoes, and even simple local things like a daily/cyclic updraft over a farmer’s plowed field and an accompanying downdraft over a nearby cold lake, or the effect of hills and valleys. The coastal microclimates of e.g. California are lost to it — smeared out so that land that is basically hot desert may be considered to be nicely damp and temperature in temperature because the west slope of a mountain range through a cell is waterlogged and cool where the east slope is hot and dry. North Carolina has four or five distinct named microclimates (all basically climate “quasiparticles) between the mountains and the coast. The coast itself is obviously a coastal microclimate, but in NC the coast is penetrated by enormous sounds and broad rivers so that a truly complex modulation of temperatures and weather by water occurs in almost a fractal way as one moves inland. Then there are the sandhills — so named because the soil is very sandy with different water-retention and evaporation properties — which have a unique pattern of thunderstorms and quite distinct weather from the piedmont, where I live (we still sometimes get weather with sandhills-like patterns even though our soil is basically hardpack clay under a thin layer of loam) up to the actual rocky mountains to the north and west, with a gradual ascension in height, reduction of surface water flow, and with more and more rock replacing the clay. Greensboro has a noticeably different climate than Durham, Asheville a very different climate than Durham. All of this complexity has to be reduced to around five cells across, around two or three cells deep, and the models AFAIK do not contain parameters to correct for things like surface area occupied by lakes and waterways, water retention of the soil, rock versus clay versus sand. They at best have some sort of mean height and local albedo
Is it any surprise, really, that Pielke Sr. reports that GCMs do terribly at representing rainfall patterns? Those things depend on the ignored features of the terrain at a spatial resolution well beneath what the models can handle, whether or not the cells themselves are distorting outcomes along with their shape. And then there is the vertical decomposition of the atmosphere above each cell, and the vertical decomposition (if any) of the ocean below all of the cells that cover some 70% of the Earth. Do these cells matter? ENSO says that they do, they matter very much, they matter so much that models that cannot replicate ENSO haven’t got a prayer of tracking the actual climate and this is just one complex transport quasiparticle/structure in the system where heat flows in one place, is stored at depth in the ocean, is laterally transported as much as thousands of miles, and then re-emerges as hot surface water, is transported rapidly up to the stratosphere, and then spreads out globally to substantially alter the entire quasiparticle pattern of heating and cooling and rainfall and drought everywhere for an immediate effect lasting years. A sufficiently strong ENSO can clearly shift the attractors of the climate system, effectively reset the quasi-stable set point of the system, and influence the climate on a multidecadal time scale even across multiple El Nino and La Nina events.
Until we understand the scale and importance of the many, many quasiparticles in the self-organized system — quasiparticles that are driven by dissipation, that generally grow and become more efficient when fed energy — we are going to have a hard time even knowing how to build a functional GCM. It might involve (for example) using a variable resolution icosahedral grid where grid cells are decorated with another five or six or ten parameters that have (in aggregate, over time) non-negligible impacts on the time evolution of both weather and climate. It might require the insertion of quasiparticle time evolution rules to replace or correct supposedly “physics based” cell microdynamics — we have a hard time predicting the time evolution of hurricanes, for example, for exactly this sort of reason — tiny fluctuations (noise at the grid level) make microscopic/ensemble methods “blur” past a certain point until their predictions are no longer useful, where semi-heuristic rules involving macro-scale structures like ridges and high and low pressure centers and atmospheric shear might do better, and where most hurricane predictions rely on a mix of both with a touch of human judgement based on experience as to what sorts of errors both methods are prone to.
We can do that with hurricanes, but how can we do that with climate? Hurricanes spin a week, two weeks, and then are gone. The effect a hurricane has on the climate cannot be limited. The GCMs themselves produce a staggering range of possible output climates including some that are as cool or cooler than what we are experiencing from even smaller perturbations of initial conditions. It’s that damn butterfly in Brazil again, beating its wings and changing everything by far more than any simple heuristic rule can manage.
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