Storyline approach to extreme event characterization
- University of Reading, Meteorology, Berkshire, United Kingdom of Great Britain and Northern Ireland (theodore.shepherd@reading.ac.uk)
Extreme climate events are invariably highly nonlinear, complex events, resulting from the confluence of multiple causal factors, and often quite singular. In any complex system there is a tension between analysis methods that respect the singularity of the extreme events at the price of statistical repeatability, and those that emphasize statistical repeatability at the price of nonlinearity and complexity; this dichotomy is found across all areas of science. In the climate context, the ‘storyline’ approach has emerged in recent years as a way of following the first of these two pathways. I will discuss how the storyline approach can be cast within the mathematical framework of causal networks, which provides a way to bridge between the storyline and probabilistic approaches. This also provides a way to interpret data in an appropriately conditional manner, thereby aiding model-measurement comparison.
How to cite: Shepherd, T.: Storyline approach to extreme event characterization, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4896, https://doi.org/10.5194/egusphere-egu2020-4896, 2020
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Hi Ted! Very nice overview and very interesting results from Linda's new paper!
Interesting to see that the three ensemble members diverge most during the peak of the Russian heat wave. I assume that is due to variability of soil moisture? I would also be interested to know how the counterfactual world was set up?
Thank you! Kathrin
Thank you. The counter-factual was constructed by adjusting SSTs and GHGs in the usual way: namely we subtract an estimated anthropogenic warming signal from the observed SSTs, and we use pre-industrial levels of GHGs. We did not alter aerosols or land-use, so that's a limitation (though the effect of aerosols are included to some extent implicitly in the SSTs). As for the greater variance of the three ensemble members in the Russian heat wave, we can only speculate. Evidently the nudging in the free atmosphere seems to be constraining the surface temperature less strongly, so a stronger role of land-surface processes could be a potential reason (and we know they played a big role in this event). On the other hand the Russian domain is quite a bit smaller than the European domain so that might be relevant for the time series figure that is shown. It would be an interesting question to explore.
Thank you - really interesting presentation (btw I'm a great fan of your 2014 Nature geoscience paper). Would you therefore propose that the IPCC should include a counterfactual model ensemble in their CMIP experiment future scenarios, rather than an historical baseline?
That's an interesting suggestion. But I suppose that you could consider the different scenarios as counter-factuals; does anybody really believe that RCP2.6 can happen? In that respect I think there is a fundamental difference between the past and the future. For the past, the natural mindset is that of liability; namely, how much has climate change done so far? But for the future, it's about options, so we need to start from where we are now and I don't see any relevance in comparing future scenarios with pre-industrial; rather, it's about comparing different possible worlds. Since none of them will actually be realized, they are all counter-factuals.
On the one hand I completely agree that RCP2.6 is likely to be (is) a work of fiction, and of course none of the RCP scenarios are likely to represent the actual future outcome. On the other hand if I've understood correctly, there is an important conceptual difference between a counterfactual and an imaginary scenario. A future without adverse anthropogenic interference would make for quite a sobering baseline and a very compelling story. Perhaps that's the jolt we all need?