Ed Gerber

Ed Gerber

Professor at the Courant Institute

What drives the impact of global warming on stratospheric ozone in the tropics?

1 minute read

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Aaron Match just submitted a very elegant paper on the impact of global warming on ozone in the tropical stratosphere to GRL! Combining the classic leaky pipe model of the stratosphere with Chapman photochemistry and a simple representation of tropospheric ozone destruction, we show that the apparant upward shift of ozone in response to global warming is actually due to fortituous overlap of several different processes.

The ozone layer absorbs ultraviolet light otherwise harmful to life. Due to the Montreal Protocol, the ozone layer is generally recovering from depletion. In response to global warming, ozone is predicted to increase in the upper stratosphere but decrease in the tropical lower stratosphere. These decreases have previously been attributed to strengthening of stratospheric upwelling or to the deepening of the troposphere under global warming. We draw on elements of these prevailing explanations to quantify a new mechanism, which explains roughly half of the decrease: deepening of the troposphere converts stratospheric air into tropospheric air, the low ozone anomalies from which are then transported upwards into the tropical lower stratosphere by the background upwelling.

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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…

less than 1 minute read

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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!