Ed Gerber

Ed Gerber

Professor at the Courant Institute

A QBO Cookbook

1 minute read

Published:

Please see our new paper providing a recipe for generating and optimizing a Quasi-Biennial Oscillation in your very own GCM, just submitted to JAMES.

The most prominent mode of variability in the tropical stratosphere is the quasi-biennial oscillation (QBO), however only relatively recently have comprehensive models begun to simulate a QBO spontaneously, and even in these models the representation of the QBO typically suffers from biases. Here we elucidate the sensitivities of the QBO to a wide range of model parameters, and explore how these parameters affect the QBO behavior. We expect that these results will be helpful for tuning of more comprehensive models.

The paper, led by Chaim Garfinkel, explores the QBO in MiMA, an idealized atmospheric model developed in my group a few years back. Martin Jucker’s postdoctoral work keeps paying dividends!

The Key Points:

  • The sensitivity of the QBO to resolution, dissipation, wave forcing, and parameterized gravity waves is explored in a single framework.
  • The QBO period can be tuned independently of its amplitude, but the vertical structure (particularly at lower levels) is harder to capture.
  • The influence of factors on the QBO can be related to their impact on wave-induced momentum fluxes in the deep tropics.

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See the forest and the trees!

1 minute read

Published:

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

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?

less than 1 minute read

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!