Ed Gerber

Ed Gerber

Professor at the Courant Institute

Three papers in press!

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While I was returning from summer travels, the journals went into overdrive and three papers were accepted and/or came out in press!

A second paper from Aman Gupta’s thesis work was accepted in the Journal of the Atmospheric Sciences: Numerical impacts on tracer transport: Diagnosing the influence of dynamical core formulation and resolution on stratospheric transport. We take a deep dive into the difference in trace gas transport between state-of-the-art atmospheric models, demonstrating that the numerical formulation of a model has a significant impact on it’s representation of transport.

The same day, Justin Finkel’s paper applying novel forecast metrics to a conceptual model of Sudden Stratospheric Warming events was accepted in Monthly Weather Review: Learning forecasts of rare stratospheric transitions from short simulations. Here we show how the “committor” and related lead time metrics provide a powerful description of rare events.

Finally, a week later Xichen Li’s review of the impact of the tropical oceans on Antarctica came out in Nature Reviews Earth & Environment:Tropical teleconnection impacts on Antarctic climate changes. This tour-de-force review explains how natural variability and trends in the tropical oceans have had profound impacts on Antarctica and the Southern Ocean.

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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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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!