Ed Gerber

Ed Gerber

Professor at the Courant Institute

Atmospheric Circulation in a Changing Climate

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Please come to our workshop on the Atmospheric Circulation in a Changing Climate.

22-25 October 2019, Madrid

A joint DynVarMIP, SPARC DynVar, and SNAP meeting, hosted by Universidad Complutense Madrid, Instituto de Geociencias. A substantial amount of travel support is available for Early Career Scientists and participants from underrepresented nations!

Scientific Program

  • A mechanistic understanding of atmospheric circulation trends
  • The role of atmospheric dynamics in climate model biases
  • Atmospheric heat and momentum budgets in a changing climate
  • Stratosphere-troposphere coupling and tropospheric predictability

Abstract + Travel Support Deadline: ​30 June 2019

For more details, please see our first announcement and the conference website.

Support for the meeting has been provided by the Universidad Complutense Madrid, Instituto de Geociencias, NSF, WMO, WCRP, SPARC, and IUGG.

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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!