Ed Gerber

Ed Gerber

Professor at the Courant Institute

The building blocks of Northern Hemisphere stationary waves

1 minute read

Published:

Please see our new paper exploring stationary waves in the Northern Hemisphere, just accepted in the Journal of Climate. What are stationary waves, you ask? In laymen’s terms, they are variations in climate with longitude, for example, the reason why the weather in Madrid is quite different from that here in New York, even though we’re both situated at nearly the exact same latitude.

This study, lead by Chaim Garfinkel, investigates the processes that create these variation in climate, focussing on the atmospheric circulation. We can quantify stationary waves by looking at variations in geopotential height, which is related to the lows and highs on a weather map. First, we constructed a simple model (the blue curve in the top panel above) that does just as good a job at creating variations in the real circulation (green curve) as state-of-the-art climate models (all the thin lines). This wasn’t a trivial result: a handful of us are competing with world class modeling centers! (Of course their models are better for climate prediction; our model is designed more for understanding the climate.)

We then broke down the processes that create these variations, including topography, land sea constrast, and variations in sea surface temperatures. The response of the atmosphere to these “forcings” is nonlinear, that is, more than just the sum of the invidual parts, as shown in the panel of the figure above.

This research was based on an idealized atmospheric model, MiMA developed by my former postdoc, Martin Jucker. I’ve been fortunate to continue collaborating with Martin, who’s a coauthor on this study.

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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!