The Nobel Prize in Physics 2021
nobelprize.org
nobelprize.org
The work of Parisi (and quite a few others) in the eighties (non-linear systems, chaos theory, attractors, universality of power laws etc) have given us a first glimpse of what lies beyond, but a true revolution is still in the future and will require some pretty mind-bending mathematical inventions.
Making serious progress is not just intellectually challenging, it is also of immense practical relevance for us understanding and moderating our impact on the biosphere. The Nobel committee, in their infinite wisdom, suggest as much.
[1] https://neurophys.biomedicale.parisdescartes.fr/wp-content/u... [2] https://ml-jku.github.io/hopfield-layers/
Imagine that you have a whole bunch generative models (its best if you imagine a fully connected Boltzmann machine in particular, whose states you can think of as a binary vector consisting only of zeros and ones) that have the same form but different random realizations of their parameters. This is a typical example of what a toy model of a so-called "spin glass" looks like in statistical physics (the spins are either up down down, usually represented as +1/-1). Each of these models, having been initialized randomly will have their up particular frequency of a particular location (also called site) of the boolean vector being either a one or a zero.
If the tendency of a site to be either or one or a zero was independent of every other site the analysis of such a model would be pretty straightforward: every model would just have a vector of N frequencies and we could compare how close the statistical behavior of each model was to the other by comparing how closely the N frequencies at each site matched one another. But in the general case there will be some interaction or correlation between sites in a given model. If the interaction strength is strong enough this can result in a model tending to generate groups of patterns in its sequence of zeros and ones that are close to one another. Furthermore if we compare the overlap of the apparent patterns between two such models, each with their own random parameters, we will find that some of them overlap more than others.
What we can then do is to ask the question of how much, on average do the patterns of these random models overlap with on another in the full set of all models. This leads us to the concept of an "overlap matrix". This matrix will have one set of values along the diagonal (corresponding to how much a models patterns tend to overlap with themselves) and off diagonal values capturing the overlap between. You can find through simulation or with some carefully constructed calculations that when the interaction strength between sites is small that the off diagonal elements don't tend to zero, but rather a single number different from the diagonal value. This is perhaps intuitive: these models were randomly initialized but they are going to overlap in their behavior in some places.
Where things get interesting though is when you increase the interaction strength you find that the overlap matrix starts to take on a block diagonal form, wherein clusters of models overlap with one another at a certain level and at a lower but constant level with out-of-cluster models. This is called one replica symmetry breaking (1RSB). These different clusters of models can be thought of as having learned different overall patterns with the similarity quantified by their overalp. If you keep increasing the interaction strength you will find that this happens again and again, with a k-fold replica symmetry braking (kRSB) with a sort of self similar block structure emerging in the overlap matrix (picture is worth a thousand words [1]).
Now the real wild part that Parisi figured out is what happens when you take this process to the regime of full replica symmetry breaking. You can't really do this with simulations and the calculations are very tricky (you have a bunch of terms either going to infinity or zero that need to balance out correctly) but Parisi ending up coming up with an expression for the distribution of overlaps for the infinitely sized matrix with full interaction strength in play. The expression is actually a partial differential equation that itself needs to be solved (I told you the calculations were tricky right), but amazingly, it seems to capture the behavior of these kinds of models correctly.
Whereas mathematicians have a pretty good idea of how to understand the 1RSB process rigorously, the Parisi full replica symmetry breaking scheme is very much not understood and remains of interest both to complex systems researches trying to understand their models and applied mathematicians (probability people in particular) trying to lay the foundations needed to explore the ideas being explored by theorists.
Hope that helps a bit!
[1] https://www.semanticscholar.org/paper/Spin-Glasses%2C-Boolea...
What the fuck?
Usually this phrase is used ironically, as in “they don’t know what the [expletive] they’re doing,” but the rest of your comment reads genuine. Because people seem split on this and because I don’t fully understand any of it, can you clarify one thing: are you throwin’ shade?
Granting Nobel prizes for science is not an exact science :-)
y' = x - y^2 (PANICC!!)
It is interesting to note his own view. On climate sensitivity calculation: "it is not advisable to take too seriously". On coupled model: "I am very proud of it".
> Wallace Broecker, who would later play a major role in climate change studies, recalled that it was the 1967 paper "that convinced me that this was a thing to worry about". Another scientist called it "arguably the greatest climate science paper of all time", for it "essentially settled the debate on whether carbon dioxide causes global warming". Experts in a 2015 poll agreed, naming it as the "most influential" of all climate change papers.
So Nobel committee was right to cite it. On the other hand, the author's "don't take it too seriously" comment should be taken seriously. 2 degrees value is spot on, but knowing what we know now, it couldn't be the right value for the right reason. The most important is that 1967 model had no ocean. (Manabe went on to make the first successful attack with ocean in 1969.) Another is lack of data. The following quote is pretty funny:
> Until satellite measurements became available later in the 1980s, most models used data from the 1950s that only gave averages by zones of latitude, and only for the Northern Hemisphere. Modelers mirrored the set to represent clouds in the Southern Hemisphere, with the seasons reversed -- although of course the distribution of land, sea, and ice is very different in the two halves of the planet.
I mean, mirroring? It is obviously wrong, but what can you do? Other than waiting for satellites and noting the result shouldn't be taken too seriously, that is. Now we have adequate data and model, it is proper to acknowledge great effort went into these pioneering works.
The last quote on amount of computation used (for 1969 paper). It was truly AlphaGo-scale computaiton of its time! It is interesting to note AlphaGo Zero trained for comparable time of 40 days.
> Their costly Univac 1108, a supercomputer by the standards of the time... Manabe and Bryan were confident enough of their model to undertake a heroic computer run, some 1100 hours long (more than 12 full days of computer time devoted to the atmosphere and 33 to the ocean).
1) Every person who has ever received a Nobel Prize in physics has richly deserved it;
2) Culture and politics influence the awarding of the Prize;
3) Several people who did not get the Prize would have if they had not been women.
(3) is a corollary to (2). One example is Jocelyn Bell Burnell, who discovered pulsars. The Nobel Prize for that was awarded to two men.
"First, demarcation disputes between supervisor and student are always difficult, probably impossible to resolve. Secondly, it is the supervisor who has the final responsibility for the success or failure of the project. We hear of cases where a supervisor blames his student for a failure, but we know that it is largely the fault of the supervisor. It seems only fair to me that he should benefit from the successes, too. Thirdly, I believe it would demean Nobel Prizes if they were awarded to research students, except in very exceptional cases, and I do not believe this is one of them. Finally, I am not myself upset about it – after all, I am in good company, am I not!"
I know of her statements about the incident. I think she’s being modest, and I disagree with her idea that awarding the Prize to someone who happened to be a student during the time of discovery would somehow demean the Prize. Her work was as crucial as theirs. It wasn’t just “legwork”, she discovered the regular signals and recognized them as something new and important.
Here's the criteria that Alfred Nobel set out in his will[0] for the literature prize (kinda badly translated from 19th century Swedish): "one part to whom in literature have produced the most excellent work in an ideal direction".
You have had 18 members of the Swedish Academy debating what the hell that means since forever.
[0] https://www.nobelprize.org/alfred-nobel/alfred-nobels-testam...
Let's take a look at the general concept though, from Manabe et al.'s 1975 paper (2):
> "The atmospheric part of the model incorporates the primitive equations of motion in a spherical coordinate system. The numerical problems associated with the treatment of mountains are minimized by using the “sigma” coordinate system in which pressure, normalized by surface pressure, is the vertical coordinate. For vertical finite differencing, nine levels are chosen so as to represent the planetary boundary layer and the stratosphere as well as the troposphere. For horizontal finite differencing, the regular latitude-longitude grid is used. To prevent linear computational instability in the time integration, Fourier filtering is applied in the longitudinal direction to all prognostic variables in higher latitudes such that the effective grid size of the model is approximately 500 km everywhere."
So, let's note that this general approach is applicable to planets like Mars and Venus as well as Earth. There is no ocean on those planets, however, but the atmospheric radiative-convection model approach is identical. Mars has something like 1% of Earth's surface pressure, Venus has 90X that pressure, but the same approach works. It's even applied to the gas giants. Note that 9 layers in the model is quite simple relative to modern models.
> "For the computation of radiative transfer, the distribution of water vapor, which is determined by the prognostic system of water vapor, is used. However, the distributions of carbon dioxide, ozone and cloudiness are prescribed as a function of latitude and height and assumed to be constant with time. The temperature of the ground surface is determined such that it satisfies the condition of heat balance."
Here's another key point - water vapor is modeled as a feedback, CO2 is modeled as a forcing. About 2/3 of radiative forcing in the atmosphere is due to water vapor, but that water vapor increases due to CO2 forcing (which has greatest effect higher in the atmosphere, closing windows that would allow IR to escape to space). This was verified by the Pinatubo explosion incidentally, in which predictions about water vapor feedback were highly accurate (3).
> "The prognostic system of water vapor includes the contribution of three-dimensional advection of water vapor and condensation in case of supersaturation. To simulate moist convection, a highly idealized procedure of moist convective adjustment is introduced. The prediction of soil moisture and snow depth is based upon the budget of water, snow and heat. Snow cover and sea ice are assumed to have much larger albedos than soil surface or open sea, and have a very significant effect upon the heat balance of the surface of the model."
So, that's the albedo effect, and as the poles melt albedo drops and you get more warming. You also get polewards heat transfer. Thus these scientists predicted warming at poles would be much faster than warming at equator, and that's been proven as well. Cloud feedbacks introduce a certain degree of variability, but definitely don't change the overall conclusions (see MIT's Richard Lindzen for that worn-out fossil-hyped argument if you like).
Now, I'll stop here but note that Manabe's other great contribution was linking the atmospheric model to the rather more difficult ocean circulation model. This allowed quantification of the lag effect, i.e. ocean warming absorbs a great % of the atmospheric forcing but warmer oceans warm the atmosphere and so on.
Incidentally, none of this would be at all controversial if human civilization had exhausted global fossil fuel reserves by 1980 and renewable adoption had been forced by necessity.
It's rather interesting though - science was once completely accepted by industry, but then scientific advances began undermining business profits - the discovery of industrial carcinogens, the discovery of fossil-fueled global warming, etc. really changed the dynamic and accurate science became as much of a threat to established interests as it was a boon.
[edit sources]: 1. https://www.climatefiles.com/exxonmobil/1978-exxon-memo-on-g...
2. https://journals.ametsoc.org/view/journals/phoc/5/1/1520-048...
3. https://www.science.org/doi/pdf/10.1126/science.296.5568.727
Irrespective of that this years NP in Physics is an achknowledgment of a "young" (in terms of NPs) but promising field.
For anyone interested, I found this podcast very informative:
https://www.preposterousuniverse.com/podcast/2020/08/17/110-...
I have a PhD in physics and have made sustainability my mission. I wish I didn't have to as fixing problems past generations stuck us with isn't my first passion, but I can't change the past.
As important as the science was to get us here, we have to move to the next stage, which is leadership. I don't mean just passing laws. Even prior to our twin problems of overconsumption and overpopulation, the damage we're suffering is the physical manifestation of our values, especially material growth, extraction, efficiency, externalizing costs, and comfort and convenience. Technology, innovation, laws, and markets augment those values. As long as we hold them as a culture and individuals, we will innovate technologies, laws, and markets that exacerbate the problem.
I will always support more research and value these scientists' work that enabled us to get past the science to restoring our values of stewardship: personal growth, enjoying what we have, humility to nature, resilience, responsibility for how our behavior affects others, meaning, purpose, and the satisfaction of a job well done. With those values, we will innovate solutions that increase Earth's ability to sustain life.
Again, as important as the science is, we must restore our personal and cultural values to solve the problems science revealed. That's leadership and teamwork. We can all act immediately. Since systemic change begins with personal transformation, the fastest, most effective way to change governments and corporations is to act here and now, learn from the experience, act more, and lead others to join.
Somehow, I don't think you'd have a PhD in physics had the industrial revolution never occurred.
To suggest that the prize is given simply in order to make a political statement seems like an insult to both the academy and the laureates. Besides, wouldn't it be just as bad to avoid awarding a prize that is politically sensitive, even though the actual science may well be prize worthy?
It's fine to not consider a laureate a worthy winner, for sure, but it's not like they gave the Nobel prize in physics to Greta Thunberg..
I'm no scientist by any stretch of the imagination, the last time when I read something related to higher physics was ~20 years ago, in college, but I have to ask how is that topic related to modern physics. You're correct, it sounds like like a worthy Nobel for an Earth Sciences Nobel, or a Meteorology one (to be more direct), if those two things existed (maybe they should), but, again, don't know how meteorology (what computer-based climate simulation basically is) is the same thing as higher physics.
The tag line sells it really poorly.
> In the 1960s, he led the development of physical models of the Earth’s climate and was the first person to explore the interaction between radiation balance and the vertical transport of air masses. His work laid the foundation for the development of current climate models.
As far as I understand (I am not a physicist), you're right: climate is not new, and climate models have been getting better for decades. And that's (in part) because of this guy's work, half a century ago. It sure pushed the boundaries of physics at the time, didn't it?
Obviously he discovered something a lot more noteworthy than that, but these one line summaries are doing a terrible job of hinting at what.
[1]: https://journals.ametsoc.org/view/journals/atsc/24/3/1520-04...
In this context, it's important to recall that the development of modern climate science is one of the most significant developments of 20th-century physics, and before this year, no Nobel Prize had been awarded for climate science. Similar prizes were awarded for the development of the integrated circuit and the light-emitting diode, and in these cases almost nobody finds it to be in the slightest untoward that the Prize is awarded in relation to the social impact of the work. (There are always a few crotchety purists, but more people pretend to be so when it suits them.)
So yes, awarding the Nobel for climate science is a political decision. Not awarding the Nobel for climate science would also be a political decision. In this context, it appears that the recipients are two of the most influential early theorists of climate science, who did their work decades ago.
The Nobel committee has no choice but to be politicized when science is politicized. They handled it admirably.
ps. Climate science is political only because those who don't believe in science politicize it.
Imagine if scientists just "believed" science and never tried to reproduce. Belief is an awfully terrible trait to have in science.
You're mixing up scientists-as-individuals with scientists-as-a-whole.
Scientists-as-a-whole should certainly reproduce results, both to check new claims, and to teach/learn/demonstrate old knowledge.
Scientists-as-individuals need belief, since there's no way to indivudally reproduce everything. For example, climate models rely on decades of measurements from Earth-observation satellites; if scientist shouldn't "believe", how would they go about reproducing those measurements for themselves?
Even if individual climate scientists began each of their projects by building and launching their own satellites to take decades of observations (which would lag behind existing data, in any case), how would they calibrate the instruments on those satellites (e.g. without "believing" in the zeroth law of thermodynamics)?
In principle, every individual could spend 10 years to learn the subject, then comb trough the evidence in one particular detail. That's not possible in reality.
Even climate scientists have to trust other climate scientists in details they are not experts in. Climate chemist has not checked 3D computational model and vice versa.
It is said that Thomas Young (1773 – 1829) was the last man who knew everything. He was a polymath who had studied most of human knowledge in detail.
If a subject is important to someone, they should try their best to understand the research and to more fully understand a claim, one would do well to research with a heavy dose of skepticism in everything, especially things that affirm their own bias.
If you think there is some ground truth in hard sciences, and science community provides best approximation of it, then "other side too" argument is not valid. Al Gore educated, only climate dentists politicized.
And this part is actual science: “for the discovery of the interplay of disorder and fluctuations in physical systems from atomic to planetary scales”
All this is saying is that the predictive models they devised were proven correct given climate behavior today, e.g rise of temperatures and a general uptrend in severe weather activity that followed.
before you ask me for sources, you haven't provided any, and you're quoting from a release with sources.
Less "politics" and more "hey his models were right."
For example, if you go back to the early 1950s this was not well known, and you had theories about how variations in the tides controlled climate and so on.
P.S. Exxon scientists knew in 1978 that the science was accurate, and yet their executives spent millions of dollars for decades lying about it - and are still at it.
Especially when world leaders ridicule the problem and dismantle efforts to fight it [1].
I personally believe that global warming and climate change are the most pressing issues facing humanity, but I oppose the politicization of prestigious award platforms. It's simultaneously obnoxious and unfair to the other candidates.
We see the same thing happen in racially-charged atmospheres for artistic awards.