I have recently started a new position as a Lecturer at the University of Auckland where I will continue researching regional climate change and extreme event predictability with a touch of machine learning. Anyone interested in working on these problems Down Under-er please reach out!
This research was part of my postdoctoral fellowship with Stanford Data Science and I am eternally grateful for the funding and freedom to dedicate to this project.
Our new paper shows how recent prescribed (Rx) burns in the western US impacted later wildfires. We find that Rx fires reduced wildfire severity + net smoke emissions, even when factoring in smoke from Rx fires. But, we find that these Rx fires were less effective in the wildland-urban interface.
implying that ocean variability provides additional predictability of regional warming.
This paper demonstrates that multi-year extreme event prediction can be tackled through targeted methodologies that identify extreme-event covariates that are more predictable than the extremes themselves
Future TC risk is uncertain — but here’s the twist: what drives that uncertainty changes with your risk model setup.
We unpacked that. Full paper here: www.science.org/doi/10.1126/...
@adamsobel.bsky.social @scamargo.bsky.social @nblmndl.bsky.social and other wonderful co-authors 🙏!
We first show that abrupt jumps in regional average summertime temperatures correspond to a significantly heightened likelihood of experiencing a three-day heat event over the same period.
Our new preprint proposes a framework for predicting summertime temperature jumps on 1-5 year timescales.
eartharxiv.org/repository/v...
Then, we train simple machine learning models to predict the onset of these summertime warming jumps in climate models, and verify on observations. We show skill in predicting warming jumps, independent of the warming signal...