A new analysis by Hansen and colleagues concludes that both the impact of recent cuts in sun-blocking shipping pollution, which has raised temperatures, and the sensitivity of the climate to increasing fossil fuels emissions are greater than thought.
The group’s results are at the high end of estimates from mainstream climate science but cannot be ruled out, independent experts said. If correct, they mean even worse extreme weather will come sooner and there is a greater risk of passing global tipping points,
Evwrtyhings on course for the collpaee if civilisation under the weight of human stupidity :)
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.
Honest questions:
Does your model take into account that the ‘methane clathrate gun’ seems to have been fired?
https://www.spacedaily.com/m/reports/Study_links_climate_change_to_explosive_methane_release_in_Siberian_permafrost_999.html
That more and more of the arctic is rapidly converting from a carbon sink into a carbon source?
https://news.mongabay.com/2024/05/polar-warning-warming-temperatures-mean-more-than-melted-ice/
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.
it’d be interesting to see (without knowing whether this is actually already the case with most of the models scientists use) a model that looks at the predictions we’ve made, discoveries that have led to changes, and factored in the likely changes to come in the future… it seems our refinement of these models has often been “oops we didn’t think about this source” and it happens pretty regularly