Ben Matthews

I’m not convinced this plot bears much relation to the Hansen et al paper. The plot combines SSPs - socioeconomic scenarios, RCPs - levels of ‘radiative forcing’ (all gases together - the numbers 1.9 to 8.5 are RF in W/m2) and temperature.
Hansen et al (OP) are suggesting that the climate sensitivity may be higher than the IPCC range, which would imply a higher temperature for any given RCP level. This suggestion is derived from the recent history of aerosols and temperature, as I discuss in another comment (above/below?).
However this doesn’t change which curve we are on (in reality none of them), as the big difference between curves is from projections of future emissions depending on technology, population, policies etc. Also the near-term projections have little relation to the long term, due to inertia but also the range of different models and assumptions used to make them (some years ago). So you can’t look at very recent data to derive which curve we are on.

I was (years ago) part of the process that designed the IPCC scenario structure of splitting the SSPs from RCPs, to allow the two modelling groups to work in parallel rather than one waiting for the other, so they could get on with connecting feedbacks (not sure this happened…).
But IPCC ends up re-blending too many varying factors in one plot, because they have page limits, which can be somewhat confusing. Work of 10,000 scientists over six years condensed into one summary report… - is that the most effective method of communication ? That’s why I prefer to make an interactive model.

Fundamentally, the useful question is not ‘where are we going’ but ‘where do we want to go’ - taking into account all the feedbacks and inertia in the systems, but making choices about our common future. To get to the lowest (1.9) curve we’d need a global green revolution tomorrow, but it’s not physically impossible. As for the 8.5 curve it’s very unlikely it’s part of the set as similar to earlier high-end scenarios run by IPCC since 1990s, which might have happened if we’d stayed in the coal age (still useful for comparing the high end of the physical models). The others are still in play. Extrapolating current trends and policies (especially tech and popn in China) I’d say the most likely is somewhere between 2.6 and 4.5 curves, but we could do better. While if you are building sea-walls you should anticipate worse. But don’t be fatalistic, we still have many options.

[ Side remark in response to comment - as for Jan 2025, it’s really not ‘climate’ to conclude anything from one month. Even the El-Niño cycle depends on heat transfer between surface and deep ocean, which is a slow process, you need to integrate over much longer time. There may be a warm anomaly recently in tropical oceans, and also the high-arctic, but it’s actually quite cold where most northern people live (due to Rossby waves whose amplitude is increasing, as we expected in an hand-waving kind of way). ]

My model, no not yet clathrates or specific permafrost feedback, although I’m concerned about these and would like to put such feedbacks in (with wide adjustable parameter ranges to reflect high uncertainty). My model is interactive, you can play with it in a browser, so it’s hardly typical.
However, in relation to the OP and the Hansen paper, it’s important to understand that the usual definition of climate sensitivity does not include such “slow” feedbacks - it’s assumed to assume fast atmospheric feedbacks e.g. physics of clouds, but not slower biogeochemistry. CS has been used for decades for comparing models, so it’s useful that the definition remains the same, simply the equilibrium (multi-century) response to CO2 doubling, it’s not any kind of prediction. That’s why it’s surprising that he would draw strong conclusions from a number (4.5) that’s well beyond the normal range.
Complex 3d physics models derive CS, while integrated assessment models use CS as a calibration parameter for one component of a complex system, including socioeconomic drivers, emissions policies, land use change, etc. Most models (including mine) do have some climate - biogeochemistry feedbacks (for example, there is faster soil respiration at higher temperature), which are included in such ‘real’ scenario projections, but wouldn’t change the CS.

I think the Guardian article may be somewhat exaggerating what the Hansen et al paper says. I’ve been studying this kind of problem for 30 years. It’s indeed true, that there are many ways to explain the historical temperature rise, by adjusting the balance of positive (mainly greenhouse gases) and negative (sulphate and white-carbon aerosols, volcanos etc.) forcings. So if you think the aerosol effect was greater, you also have to assume the greenhouse warming was greater to balance - hence deriving a higher climate sensitivity (CS). In this case, they are arguing that we underestimated the (former) cooling effect of shipping sulphate - I’d agree this is not a trivial factor (and similarly for the warming effect of aviation induced cirrus, which we could also change quickly with global transport policy). However, I really doubt this change is sufficient to justify such a big shift from the long-developed consensus range of CS.
In general, the recent historical data series has never been a sufficient constraint on CS (I know from having tried a similar approach for probabilistic analysis with earlier versions of my own model). So we have to use other methods too, and the IPCC consensus for the likely range of 2.5-4ºC for CS is derived from a wide range of methods and sources, particularly but not only big physics-based models (GCMs). I’d be very cautious about overturning this based on any one study, despite my respect for Hansen and colleagues. Of course, this contributes an interesting new view on this important topic, but it does not justify the headline of the article.

Fine map, good to see the old names. But some of these routes are pretty impassable even today - for example I doubt the Wakhan corridor was ever a major route, even the bottom of that narrow valley rises above 4000m. And note Torugart pass (been there…) is north of Kashgar on the way to Issyk Kul (missing lake), not on the way to Osh. So, considering the mountains, I guess a larger fraction than indicated crossed the steppe further north - horses wouldn’t need roads or cities, but it’s easier.

Can use Scala to gradually transition away from java - convert code module by module, interop just works, until eventually no java left, can then compile instead as js, native or even wasm (i recently tried this for my climate-system model which evolved from old java). Also, btw, made in europe, not big-tech, and scala3 looks more like python.

Strange that Genoa controlled off bits of coast (yellow) all the way to Crimea, Azov sea (is Tana now Azov?) , and Circassia, how did they manage all that? And who’s in that purple-coloured “Theodoro”?

Also remember Bush did the same for Kyoto protocol. Loop repeats again.

I’ve wondered through favelas in Rio. Nice people, tasty snacks, fine views (they are on hillsides), some great music. But that was over a decade ago, I heard situation got worse again during Temer/Bolso time, hope it’s improving now.

This is hardly surprise news, it’s just what the demographics projected. And if trends continue, China’s population will halve by 2100. The big one to watch now is India, and then central Africa, those are harder to project.
You can experiment with my model - click the ‘model pop’ plot on the left side.

Indeed trade links relevant, so navigable rivers played a big role - before railways, our main transport was either boats or horses (or camels). Horses needed a lot of grass, which thrives in drier mid-continental climates where trees don’t survive wildfires. For example the Mongol empire was good at trade and connecting cultures, covered a huge area, but not (for long) near coasts, and still demanded intense tribal loyalty (elements of such culture was absorbed by the next empire which gradually pushed it back…).

Maybe add some major tributaries - eg the Inn, Sava, Tisza … ? Sometimes, depending rain on the mountains, these have greater water flow.

I think if you plotted political inclination as a function of distance from coast, globally, you might find quite a good correlation. Various plausible explanations. For example, in tougher mid-continental climates you need to store and protect stuff to survive the winter / dry-season, so people there evolved (including self-filtering migration) to have more tribal loyalty.

I was concerned about the gap between climate science and policy. So, having learned to program as a kid, I made an interactive model to help bridge that gap, to let people experiment. This evolved over 25 years - recently moved to scala.js, still developing, not for money but because we have to keep trying to solve complex problems.

In high latitude winters, snow provides light and contrast - without it the world is just shades of black brown grey. Some people there may think a little extra warmth could be welcome, but -2ºC with snow is much better than +2ºC with cold grey drizzle.

As a kid, I learned to write i = i +1, before school maths taught me it can’t. The point is, computers do iteration well, especially to model dynamics of real non-linear systems, while classical maths is good at finding algebraic solutions to equilibria - typically more theoretical than real. Calculus is great for understanding repeatable dynamics - such as waves in physics, also integrating over some distributions. But even without knowing that well you could still approximate stuff numerically with simple loops, test it, and if an inner-loop turns out to be time-critical or accuracy-critical (most are not), ask a mathematical colleague to rethink it - believe in iteration rather than perfect solutions.

I’m aware of the racism issue, I even observed it myself in China, even many years ago.
But i’ve also seen similar problems in other corners of the world. Such cultural concepts can change slowly, as they did over here.
Anyway, I doubt this would dissuade people trying to connect what will become the world’s main supply of surplus young labour, with the world’s greatest demand for care-services, combined with spare apartments, money, and a milder climate. Not saying it’s good or bad, just trying to anticipate future changes.

I agree. For global discussions, are many Indians going to learn Chinese, Swahili, Hausa, Arabic, and vice-versa ? Meanwhile international-english is the new latin… Even within India, the south insists to keep english as an official language, to avoid being dominated by more populous hindi-speaking north.
Alternatively LLM-translation may facilitate multi-lingual discussion, but in this case the language of software development may still be influential during such transition.
By the way - this is an important topic for future of lemmy, which should expand more towards the south - where’s a good place to develop it (beyond such set of replies)?

That seems like a big change in one year. It may to some extent reflect delay, as on average chinese used to pair-up at a younger age than typical in europe, also maybe some feel old traditions aren’t necessary to keep a stable family with children. But the article says, the core factors are economic. Even so, as they have built so many surplus apartments, the [real] prices must drop, I wonder how many years before they are trying to sell the chinese dream to migrants from Africa or elsewhere.

Hmm. I’m still using a 2014 iMac, as its 27" 5k screen still very good for coding (with added memory). Sometimes develops a bunch of thin vertical lines, which come and go maybe dependent on temperature, but hasn’t changed for for ten years and i can live with those. Just wish they’d continue providing security updates for it.

Odd that Estacio de França is now the terminus only of lines from the opposite direction - but it makes sense to run all across the centre.