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Glen Peters @Peters_Glen
, 8 tweets, 5 min read Read on Twitter
Does model bias effect outcomes of the #IPCC 1.5°C emission scenarios #SR15?

The scenario database is over represented by some model frameworks. Taking statistics will over represent some models (eg, REMIND, AIM), discount others (eg @IEA), & not all models included!

The bias varies by scenario category, here for 1.5°C scenarios with "no or low overshoot" as highlighted in #SR15. Should REMIND & POLES get more weight then GCAM or MERGE?
I often take medians of scenarios to simplify communication, trying to reveal the "key characteristics" of 1.5°C (trying to find robust messages). The scenario community does not like this approach
I would counter that the problem exists whenever you take statistics of a biased sample, as the #IPCC #SR15 routinely does. There is no point taking moral ground here. The statistical sample is biased & incomplete. Fullstop!
One approach is to select individual scenarios (figure shows 4 #SR15 illustrative scenarios), which is fine in principle, but suffers the same problem. This helps shows how scenarios vary, but the choice is inherently value based (not statistical).
An alternative is to plot all scenarios & label them, something I have done with #IPCC #AR5 scenarios (as have others). This makes it quite clear that models can give very different outcomes... Figure shows CCS in <2°C scenarios.…
More robust, perhaps, is that model groups flag one scenario as their base case to be used in model ensembles (select one 1.5°C scenario from each model framework). This was done in some figures in #AR5. Other scenarios can be used for sensitivity analyses. 7/
Important to not that emission scenarios cannot be treated statistically as climate scenarios. Many of the same problems exist, but it is much harder to get a representative statistical sample of the scenario space (easier to sample climate space for a given scenario). 8/8
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