Ed Gerber

Ed Gerber

Professor at the Courant Institute

News

2021

Learning forecasts of rare stratospheric transitions from short simulations

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Published:

Please see our new paper adopting a novel prediction framework from computational chemistry to forecast extreme meteorological events, just submitted to Monthly Weather Review. The paper, led by Justin Finkel, presents a proof of concept study using a stochastically forced version of the classic Holton and Mass (1976) model of Sudden Stratospheric Warming events. We establish the “committor”, which provides the ideal combination of variables for predicting SSWs (where an SSW is a transition between the two fixed points in the Holton-Mass model). We also establish a method to compute it from relatively short integrations, i.e., integrations that are short relative to the time scale of the event, and much shorter than the return time scale of events.

Virtual Presentations at EGU this April

less than 1 minute read

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As my family and I cannibalize each other on our solitary descent into the abyss that is remote elementary education, intrepid collaborators will be hitting the virtual road to present at the vEGU this April! Check out these presentations:

Modeling our atmosphere with help from the machines

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Despite tremendous advances in our understanding of the atmosphere and our capability to simulate it with numerical models on the fastest computers in the world, their remain processes that we can not accurately represent from basic physical principles. In some cases, it is an issue of computational power: we cannot resolve all relevant scales for climate prediction, from planetary scale weather systems (10^6=1,000,000’s of meters) to cloud and aerosol particles on the microscale (10^-6=0.000001 m). In other cases, we do not yet know all the relevant physics! We still need to do our best to represent these processes based on what we can simulate. Traditionally this has been done with physically motivated schemes, but there’s growing in interest in using machine learning to help. Here we take the first steps of using an artificial neural network to help parameterize atmospheric gravity waves.

2020

Papers in print and press

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Our review article on Sudden Stratospheric Warmings, led by Mark Baldwin and Blanca Ayarzuguena, was just accepted for publication in Reviews in Geophysics. We’ve learned a great deal about “explosionartigen Stratosphärenerwärmungen” since they were first discovered by Prof. Dr. Scherhag almost 70 years ago!

Postdoctoral Positions in Machine Learning and Atmospheric Science

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A number of postdoctoral positions are available through a project funded by NSF’s Cyberinfrastructure for Sustained Scientific Inquiry (CSSI) program. This highly collaborative project between four institutions will develop data-driven parameterizations of atmospheric gravity waves and explore their impact on climate variability and change. The project will involve novel balloon-based observations, high-resolution atmospheric model simulations, machine learning, and atmospheric modeling.

Two good papers just got better

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I am aware that things have been rather quiet on my blog in the last months. In addition to my new found profession as an elementary school teacher (alas, not a very good one, but our efforts to get the kids transferred to another class were fruitless), we’ve been hard at work on revisions. Some very detailed and careful reviews allowed us to make two good papers even better!

2019

Extratropical Stratosphere-Troposphere Coupling

3 minute read

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Please see our chapter on extratropical stratosphere-troposphere coupling, just submitted as Chapter 6 of the SPARC Reanalysis Intercomparison Project, S-RIP. I’m quite pleased by the final (or at least, submitted) product! Kudos to Patrick Martineau, my co-lead on this 17 author effort which started over 6 years ago. Six years ago, a far away time when I only had one kid. To say that I’m very relieved to have this submitted is an understatement!

Speakers at Atmospheric Circulation in a Changing Climate

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An updated on 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.

Check out our line up of invited speakers!

Imagining Simpler Worlds to Understand the Complexity of Our Own

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Just submitted to JAMES: our commentary on a nice paper by Zhihong Tan, Orli Lachmy, and Tiffany Shaw that recently appeared in the same journal. We make the case that models of simpler atmospheres – which are distinct from simple models of our atmosphere – can help us understand the circulation response of our atmosphere to global warming, and enable us to build better climate prediction models!

Now in press: model hierarchies for understanding atmospheric circulation

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Our manuscript for Reviews of Geophysics, Model hierarchies for understanding atmospheric circulation, was just accepted! Way to go Penny! In particular, I like our new figure illustrating the web of models around state-of-the-art Atmospheric General Circulation Models (AGCMs). These hierarchies of simpler models enables us to understand and improve our weather and climate prediction systems.

Why is Rome so much warmer than New York?

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While Rome and New York receive the same amount of energy from the sun (being situated at the same latitude), the former experiences a much warmer climate, particularly in the winter months. This is due to large variations in the atmospheric flow with longitude, known as “stationary waves”. It has long been known that these variations are generated by differences between land and sea, topography, and variations in sea surface temperatures. But just how do these different components add up to produce our climate?

Coast to coast speaking tours!

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The whole group is hitting the road. Check out talks in Boulder, Palo Alto, and uptown, to be helf over the next couple weeks!

2018

What limits our ability to characterize the variability of the large scale circulation of the extratropics?

less than 1 minute read

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Please see our paper Quantifying the variability of the annular modes: Reanalysis uncertainty vs. sampling uncertainty, just accepted in Atmospheric Chemistry and Physics. Patrick Martineau and I show that reanalyses have gotten quite good, and we are chiefly limited by the finite length of the observational records. In this sense, we are starved for data, not model physics!

Can we make useful forecasts beyond a couple weeks?

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Deterministic weather forecast are only possible for one to two weeks. (Or in other words, we just can’t predict whether it will be sunny or rainy 14 days from now.) But can we say something about the weather over the next few weeks, for example, will it be warmer and drier than average, even if we can’t say exactly which days will be sunny?

Why do only some Sudden Stratospheric Warmings bring stormy weather?

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Why do some Sudden Stratospheric Warmings appear to influence the troposphere, shifting the jet stream equatorward over the next 2-3 months, while others don’t? Much of the issue is tropospheric variability, which can overwhelm the influence of the stratosphere. However, our recent study, The Downward Influence of Sudden Stratospheric Warmings: Association with Tropospheric Precursors shows that there are regional patterns that can help us predict whether a Sudden Warming is more likely to have an influence on the troposphere!