Ed Gerber

Ed Gerber

Professor at the Courant Institute

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!

Numerical impacts on tracer transport: A proposed intercomparison test of Atmospheric General Circulation Models has been substantially restructured to more clearly present and motivate our experiments and results. It also includes more details on the inner workings of the dynamical cores, and how they relate to comprehensive AGCMs. An anonymous reviewer provided the most careful, detailed review that I have received in my career; he/she really went the extra 1.61 km to make our paper stronger.

Downward migration of the zonal-mean circulation in the tropical atmosphere now includes more detailed analysis of the dynamics driving these intriguing circulation anomalies in the tropical circulation. We also delve into the tricky subject of what it means to be “annular” in some new supplemental figures. We unfortunately had to jettison some interesting experiments with a dry dynamical core to fit the new analysis, but I do feel these would be better presented in a longer, follow up manuscript.

Great work Aman and Kevin!

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Stressed out jet streams!

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Xingjian (Ken) Yan, a precocious undergraduate (now bound for a PhD at MIT) working with Lei Wang and I just submitted a paper exploring the utility of the traffic jam theory of blocking onset for perdiction to Geophysical Research Letters. Ken defined and explored “flux exceedance events”, meteorological situations where the jet stream gets overloaded with storm activity. Nakamura and Huang suggested that this overloaded jet situation creates a pile up storm activity – a traffic jam – leading to blocking events. Ken found that the climatological structure of exceedance events is remarkably similar to that of blocks, but that they appear to be distinct phenomenon: an overloaded jet stream is unfortunately not a reliable harbinger of an atmospheric block.

Protection without poison

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Aaron Match developed a new theory for why the ozone layer is up in the stratosphere, reaching a maximum 26 km above the surface in paper submitted to the journal of Atmospheric Chemistry and Physics. In 1880, Walter Hartley deduced that ozone must be absorbing UV-B and UV-C radiation from the sun, but since it isn’t present at the surface (except in polluted air), this ozone must be somewhere “up there”. The stratosphere wasn’t discovered for a couple more decades, but it turns out that ozone that shields us from this harmful UV radiation is largely between 16 and 40 km above us. It’s a good thing its there: ozone is toxic. If it were uniformly distributed through the atmosphere, the concentration at the surface would be 8 times the EPA safety limit. Why is ozone safely up in the stratosphere, where it protects us without poisoning us? Advanced chemistry climate models can accurately predict the distribution of ozone, but in turns out our text book understanding of the ozone layer were incomplete and gave the wrong explanation for why it reaches a maximum in the stratosphere at 26 km.

Using Explainable AI and Transfer Learning to understand and predict the maintenance of Atlantic blocking with limited observational data

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Huan Zhang submitted our paper on how to use machine learning to predict the persistence of blocking events to the new Journal of Geophysical Research Machine Learning and Computation, published by the AGU. Blocking events are persistent high pressure systems that “block” the flow of the jet stream. They are associated with extreme weather, as they shift the direction of storms, and, in summer, create heat domes that drive heat waves. A key element of a block is its persistence. Huan developed a convolutional neural network to predict whether an nascent blocking anomaly would persist, or fade away, and then interrogated the network to understand why it worked, and how it could be trained with the short observational record.

So, you have an imperfect data-driven parameterization for climate modeling. How can you make it better?

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Minah Yang submitted a paper on how to combat data imbalance in regression problems to JAMES. The goal is to improve the performance of data-driven paramterizations, particularly for profiles that are rare, but important. This is a data imbalance problem: we need ensure that the parameterization works well on input-output pairs that are seldom seen in training. Minah proposes a technique based on histogram equalization, visualized below with help from Cece, Minah’s faithful companion! The idea is to oversample or reweight these rare cases during training, to ensure the method learns from them.