Ed Gerber

Ed Gerber

Professor at the Courant Institute

Using machine learning to improve climate prediction!

less than 1 minute read

Published:

Zac Espinosa’s study to replace a physics-based gravity wave parameterization with a neural network based emulator was just accepted in Geophysical Research Letters!

Atmospheric gravity waves (GWs) or “buoyancy waves” are generated by perturbations in a stably-stratified environment. They mediate momentum transport between the lower and middle atmospheres and play a leading-order role in driving middle atmospheric circulation. Due to computational constraints, global climate models (GCMs) “parameterize” or estimate the effect of GWs on the large-scale flow. Current climate predictions are sensitive to uncertainties in these representations. Here, we examine whether machine learning, given limited data, can be used for gravity wave parameterization (GWP) in climate prediction. This approach represents an appealing technique to build data-driven GWPs that can reduce existing uncertainties by incorporating observations.

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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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Published:

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!