I developed the model as part of my PhD at @climateanalytics.org together with @carlschleussner.bsky.social @marleneclimate.bsky.social and Tobias Geiger in 2020.
Since then, I'm doing the forecasts every year. Now from the climate causality and attribution group (lim-climate-causality.github.io)

Here is the model the description of the model: wcd.copernicus.org/articles/1/3...

The slightly above average ACE matches very well with seasonal forecasts 🥳

seasonalhurricanepredictions.bsc.es/forecast/sea...

For years climate change research has shown that seasons would struggle with stability issues, perhaps even with less storms, but when a storm breaks through, it is much more likely to rapidly intensify to a top tier hurricane due to heated water. 2/

The analysis is based on CESM2 simulations: large ensemble and single forcing large ensembles

How the estimates of extreme event attribution are affected by aerosols depends strongly on the region and on the timing of the studied event. But in most regions, it's worth considering the aerosols in the analysis.

The same applies for other regions and we show that considering aerosol optical depth in the probabilistic extreme event attribution approach considerably improves the estimates.

Over Germany, heatwaves were dampened during a period of high aerosol emissions between 1950-2000. In a standard extreme weather attribution analysis that only considers global mean temperature, this effect of the aerosols is neglected -> with implications for the estimates