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| SASIP Newsletter June 2024 |
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![](https://mcusercontent.com/87628ce091a6cd0b5a69e6bfa/images/6570bcfc-d7a4-23ba-5d21-dc4d36a6f1bd.png) |
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Energetics and Transfer of Submesoscale Brine Driven Eddies at a Sea Ice Edge |
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This new article led by Anna Lo Piccolo has been published in the Journal of Physical Oceanography.
Submesoscale eddies grow from baroclinic instabilities along a sea-ice edge under freezing conditions, via an inverse energy cascade. They cause a faster overturning of the front and a lateral expansion of the frontal region, which presents some common behavior under different atmospheric temperatures. The continuously-forced submesoscale-eddies of the polar regions are fundamentally different from the initial-condition frontal-adjustment problem valid for mid-latitude oceans. |
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![](https://mcusercontent.com/87628ce091a6cd0b5a69e6bfa/images/c022df27-b7a2-7007-d188-905267383041.jpeg) |
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Tailoring data assimilation to discontinuous Galerkin models |
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Models using a discontinuous Galerkin (DG) solver, like neXtSIM_DG, partially resolve the model solution within each grid cell as a combination of polynomials. In this work published in the Quarterly Journal of the Royal Meteorological Society, Ivo Pasmans, Yumeng Chen et al. looked at the possibilities of exploiting this structure to improve the combination of model output with observations in a process called data assimilation. In particular, they show that the DG structure allows to effectively more than one observation per grid cell and to construct a scale-dependent localisation scheme to remove sampling error at little additional computational cost. |
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![](https://mcusercontent.com/87628ce091a6cd0b5a69e6bfa/images/f001ee59-0f38-aa3f-a5d6-c1fb779c996e.jpeg) |
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Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology |
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In this article published in The Cryosphere, Yumeng Chen et al. show that multivariate data assimilation can provide improved state estimation for the all model state variables in an idealised setup using dynamics-only Maxwell-elasto-brittle sea ice model. Using the same model, they also show that data assimilation can improve the parameter estimation with current observation network. Further investigation will be needed with more realistic setup and models. |
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Towards a GPU-Parallelization of the neXtSIM-DG Dynamical Core |
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In recent years, a number of frameworks have become available that promise to simplify general purpose GPU programming. In this preprint, Robert Jendersie and colleagues compare multiple such frameworks, including CUDA, SYCL, Kokkos and PyTorch, for the parallelization of \nextsim, a finite-element based dynamical core for sea ice. They evaluate the different approaches according to their usability and performance. |
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This work was also presented as part of the SASIP June webinar. Watch the recording available below! |
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![](https://mcusercontent.com/87628ce091a6cd0b5a69e6bfa/video_thumbnails_new/1cff167f3e5ca317678041b8127edd67.png) |
Investigating Ocean Circulation Dynamics Through Data Assimilation: A Mathematical Study Using the Stommel Box Model with Rapid Oscillatory Forcings |
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The Stommel box model is the simplest model able to describe the Atlantic Meriodional Overtuning Circulation (AMOC). In this new preprint co-authorshipped by Ivo Pasmans, students from Miami University, Ohio and the University of Minnesota test whether data assimilation can be used to estimate its model parameters from MetOffice EN4 observations and estimate the probability of an AMOC reversal under different levels polar warming and Greenland ice sheet melting. |
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🧑🏫 Ivo Pasmans will present his work on applying the ensemble Kalman filter in the latent space of a variational autoencoder to address non-Gaussian errors in sea ice models in a poster, presented at the University of Reading, UK, on 13th June, and a presentation in Malaga, Spain, on 3rd July. |
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🧑💻SASIP June Webinar will be held by Tim Spain from NERSC on June 19, 3pm CEST! The nextSIM-DG sea ice model: rewriting, refactoring and reusing The nextSIM-DG sea ice model is at the core of the modelling in SASIP. It is derived from the existing Lagrangian nextSIM model (Rampal et al., 2016) and has been designed to be more flexible for both end users and coupling to other models. This webinar will cover what was necessary to go from the previous to the future version of model. |
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👩 Welcome to Laetitia Drumare who joined WP2 as a Ph.D. candidate. She will be working on modelling sea ice with a novel continuum approach of the phase field type which she will use to investigate the mechanical transition between a healthy, dense sea ice pack and a fragmented sea ice cover. |
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