The basic idea is fine: make initialized predictions of Florence with observed background state, and then with a background state from which you subtract the estimated anthropogenic warming/moistening. Then see what's different.
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Some people have asked about this attempt to do real time attribution of the impacts of #florence to anthropogenic climate change.https://twitter.com/EricHolthaus/status/1040007537234530304 …
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Each experiment is an ensemble - in this case using perturbed physics - and the estimated attribution (in rainfall amounts/intensity etc.) are estimated using the differences in the ensemble means.
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While the overall direction of the results are unexpected (higher rainfall, longer time as an intense storm), the specific numbers are not going to be as robust. A few questions to ask...
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What is the spread amongst the ensemble members? How well is the ensemble mean characterized? How robust would this be with a different model? (hard) Does it matter how good the prediction is? (ie does the attribution depend very sensitively on the exact path or intensification?)
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But more importantly, what are we gaining from doing this in real time vs. doing it more comprehensively later on? One correspondent suggests that it's simply to demonstrate that there is a climate change signal in what is occurring while people are paying attention.
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There's something to be said for that. But it's also clear this is a 'first draft'. Post-storm analyses will be better & more robust. Will they change the main thrust of these results? Unlikely - similar thermodynamic effects can be seen in many previous studies (Harvey, Katrina)
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thanks for this thread, Gavin.
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Without climate change slowing the jet streams and making them wavier, the storm wouldn’t have taken the path that it did, or move slowly over land like Harvey. Not sure if this study is useful at all?
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Thoughts
@JosephMajkut?
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Don't instigate.
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