Ed Gerber

Ed Gerber

Professor at the Courant Institute

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conference

in press

Two papers in press!

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The kitchen has been busy: two papers are now in press. First, Marianna Linz’s paper on mixing in the stratosphere was just accepted by the Journal of Geophysical Research: Atmospheres. We show how the vertical gradients in age allow us to quantify the exchange of air between the tropics and extratropics. Increased mixing leads to better baking, right? In this case, it’s very important for transporting ozone and water vapor through the stratosphere, two trace gases that impact us on the surface, protecting us from UV radiation and keeping us a bit warmer, respectively.

Three papers in press!

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While I was returning from summer travels, the journals went into overdrive and three papers were accepted and/or came out in press!

The SPARC Reanalysis Intercomparison Project (S-RIP) has been released!

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The SPARC Reanalysis Intercomparison Project report has been published in an early online release! This has been nearly a decade in the making – I had only one child when I first got involved – but the long effort has really paid off. I am of course partial to Chapter 6, Extratropical Stratosphere-troposphere Coupling, but the entire report is full of information about the reanalyses and the stratosphere.

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!

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.

in review

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!

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?

meeting announcement

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!

meeting announcment

news

now in press

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

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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!

positions available

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.

presentations

revised

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!

submitted

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.

On the stability of the stratospheric ozone layer

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Aaron Match submitted a paper on how photochemistry can compensate or amplify perturbations to the ozone layer to journal of Atmospheric Chemistry and Physics, published by the EGU. It has been observed that photochemistry can partially compensate for ozone loss due to CFCs and other ozone depleting substances. The process is known as self healing: decreases in tropical ozone aloft are associated with a counterintuitive increase at lower levels. It is not enough to fully compensate for the loss, but mitigates it. More recently, greenhouse gases have begun to cool the stratosphere, leading to increasing ozone aloft, which is partially compensated for by ozone reduction below (referred to as “reverse self healing”, which is admittedly a rather contorted phrase!). These responses are attributed to the fact that ozone loss increases the penetration of high energy UV radiation, which leads to more ozone production below (and skin cancer for us on the surface), vice versa for ozone increase due to cooling. Aaron asked whether this compensating response is generic, and found it is not! In upper stratosphere, photochemistry can amplify a perturbation, such that ozone loss would lead to more loss. But fortunate for us on the surface, compensation in the lower stratosphere is actually much more significant than appreciated before!

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?

What drives the impact of global warming on stratospheric ozone in the tropics?

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Aaron Match just submitted a very elegant paper on the impact of global warming on ozone in the tropical stratosphere to GRL! Combining the classic leaky pipe model of the stratosphere with Chapman photochemistry and a simple representation of tropospheric ozone destruction, we show that the apparant upward shift of ozone in response to global warming is actually due to fortituous overlap of several different processes.

Transition Path Theory: A framework to explore rare events in the atmospheric circulation!

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To conserve the number of papers in my queue, Justin Finkel submitted a new manuscript just as our MWR paper was accepted. Exploring stratospheric rare events with transition path theory and short simulations, submitted to the Journal of the Atmospheric Sciences, takes a deeper dive into the idealized Holton and Mass (1976) model of Sudden Stratospheric Warming events using Transition Path Theory, TPT for short.

Learning forecasts of rare stratospheric transitions from short simulations

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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.

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.

Extratropical Stratosphere-Troposphere Coupling

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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!

talks and seminars

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!

thesis defense

upcoming talks

Virtual Presentations at EGU this April

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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:

update

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!