Ed Gerber

Ed Gerber

Professor at the Courant Institute

Transition Path Theory: A framework to explore rare events in the atmospheric circulation!

1 minute read

Published:

To conserve the number of papers in my queue, Justin Finkel submitted a new manuscript just as our MWR paper was accepted. Exploring stratospheric rare events with transition path theory and short simulations, submitted to the Journal of the Atmospheric Sciences, takes a deeper dive into the idealized Holton and Mass (1976) model of Sudden Stratospheric Warming events using Transition Path Theory, TPT for short.

The paper builds on first paper, exploring more TPT metrics to characterize rare transitions from the strong and weak vortex states of the Holton and Mass model. The committor, forward and backward lead times, and reactive densities and currents provide a comprehensive description of the transitions betwen these two attracting states in phase space. The Dynamic Galerkin Approximation (DGA) method allows us to construct this picture based on a larger number of short simulations, simulations that are orders of magnitude shorter than the return time between transitions, and even short relative to the time scale of transitions themselves. TPT has been used more extensively in chemistry, but a growing number of papers are showing that it can help us understand the Earth system as well.

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See the forest and the trees!

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Congratulations to Dave Connelly, who just submitted his first paper! It is about the use of regression forest to represent atmospheric gravity wave momentum transport to JAMES! The manuscript makes two important steps forward. First, it shows that a “boosted forest” approach, where you train each subsequent decision tree on the residual (as sketched below), can out perform a “random forest” where you combine a number of decision trees, averaging the result. This was well known in the ML community, but less so in the climate sciences. Second, Dave found that techniques from interpretable AI could be used to improve the training of a data driven parameterization. Using feature importance metrics, he found that his origional boosted forest wasn’t using enough information about latitude. By forcing the method to predict the latitude as well, he could build trees that incorporate this information more effectively!

The graft-versus-host problem…

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Ofer Shamir just submitted a paper on the use of machine learning to represent atmospheric gravity wave momentum transport to QJRMS. He developed an idealized, one-dimensional model of the Quasi-Biennial Oscillation, where we could systematically compare different data-driven methods. In particular, how can one calibrate a data-driven scheme to work in a biased model (the graft-host problem), and how well can schemes generalize to new conditions, as in a climate change scenario?

Um, what happened to me?

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Published:

I flatter myself to think you may have stopped by my webpage over the last year and half and wondered, what happened to Ed? No new papers? No research? Did he drop off the face of the Earth? Sort of. I was on sabbatical at Free University Berlin and Ludwig Maximillian University, Munich for an academic year!