Ed Gerber

Ed Gerber

Professor at the Courant Institute

The life and times of a trace gas in the stratosphere

1 minute read

Published:

Please see our paper exploring the role of “isentropic mixing” on the transport of trace gases through the stratosphere, just submitted to the Journal of Geophysical Research: Atmospheres.

This study, led by Marianna Linz explores the processes that transport ozone, water vapor, and other important trace gases through the stratosphere. Using an idealized tracer, the “age-of-air”, we develop tools to diagnose the relative roles of the mean overturning circulation vs. the large scale mixing of air by breaking eddies in transporting trace gases. The age-of-air measures the time air has spent in the stratosphere, and as discussed in the paper, can be estimated from observations of nitrous oxide and carbon dioxide. The study contrasts integrations with a very simple GCM with simulations of a top shelf climate model, the Whole Atmosphere Community Climate Model, or WACCM, to improve our fundemental understanding of stratospheric transport and provide quantitative estimates of transport in our atmosphere. Co-authors include Alan Plumb and my former student, Aman Gupta.

Key Points

  • A new metric for adiabatic mixing in the stratosphere is developed from the age of air tracer
  • We examine hemispheric asymmetry and discuss the slow time evolution of the flow
  • We calculate this metric to perform a preliminary comparison between models and satellite data

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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!