Ed Gerber

Ed Gerber

Professor at the Courant Institute

How certain are we about the impact of stratospheric ozone loss on the atmospheric circulation?

1 minute read

Published:

Please see our on the impact of stratospheric ozone loss on the atmosphere across the latest reanalysis products, just accepted in Atmospheric Chemistry and Physics.

This study, led by Andrew Orr at the British Antarctic Survey explores trends associated with the ozone hole in all of the modern “reanalysis products”. Reanalyses are our best guess estimate of the atmospheric state as a function of time. We must always be careful about trends in a reanalysis, as changes in the observing network can cause spurious signals. When new satellites or observational networks turn on or off, we suddenly have more (or less) information about our atmosphere. The atmosphere can appear to change, but it’s only because we’re now in a better position to see it (or we just lost our glasses, in the case of losing an observing system).

In this paper we show that the response of the atmosphere to ozone loss is remarkably consistent across several reanalyses, including dynamical (eddy) trends that are less directly observed. The internal consistency of the trends in the zonal mean circulation and the eddies, and the consistency of these trends between different reanalyses, gives us more confidence that we’re seeing the real impact of stratospheric ozone loss. Shaving cream, refridgerators, and other CFC sources really moved the jet stream in the Southern Hemisphere. And as a corollary the Montreal Protocol – an international agreement to phase out CFCs – prevented a great deal of further damage. We can protect our planet if we find both the way and the will.

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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!