The newly minted Institute for Mathematical and Statistical Innovation has organized a virtual conference on climate change from 1-5 March, 2021. Speakers including experts in applied mathematics and climate research – and both, including my colleague Laure Zanna! It’s my understanding that the meeting is free to atttend, but that you must register in advance.
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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.
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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:
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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.
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Suppose you could build your own planet: create continents, lift mountains, carve out the bathymetry of the ocean to help direct its currents! What would you need to do to create the monsoonal circulation on Earth, the sharp seasonal transitions in rainfall that play such a huge role in the climate of South and East Asia?