How do mesoscale #eddies shape the Southern Ocean #CarbonBudget? 🌊🌀
The Southern Ocean takes up about a quarter of anthropogenic CO₂. A 27-yr eddy resolving simulation conducted by Salinas-Matus et al. shows anticyclonic eddies to be most efficient at absorbing atmospheric CO₂, while cyclones & peripheries are weaker. These eddies cause daily & seasonal variability and act as overall carbon sinks, contributing ~10 % of total uptake & ~1 % of anomalous uptake.🔍
Chlorophyll Eddies
Instruments aboard NASA’s PACE mission are able to distinguish far more about phytoplankton blooms than previous satellites. This image shows chlorophyll concentrations in the Norwegian Sea in July 2025. Chlorophyll acts as a proxy for phytoplankton, which produce the chemical as they process sunlight into food and oxygen.
Despite their microscopic size, phytoplankton have enormous collective effects. Scientists estimate that phytoplankton produce as much as half of the Earth’s oxygen in addition to helping transport carbon dioxide from the atmosphere into the deep ocean. They are also the foundation of the marine food web, feeding nearly all life in the ocean. (Image credit: W. Liang; via NASA Earth Observatory)
#eddies #flowVisualization #fluidDynamics #physics #phytoplankton #satelliteImage #science
Baltic Bloom
June and July brings blooming phytoplankton to the Baltic Sea, seen here in late July 2025. On-the-water measurements show that much of this bloom was cyanobacteria, an ancient type of organism among the first to process carbon dioxide into oxygen. These organisms thrive in nutrient- and nitrogen-rich waters. Here, they mark out the tides and currents that mix the Baltic. Zoom in on the full image, and you’ll see dark, nearly-straight lines across the swirls; these are the wakes of boats. (Image credit: M. Garrison; via NASA Earth Observatory)
#eddies #flowVisualization #fluidDynamics #mixing #ocean #physics #phytoplankton #satelliteImage #science
This was a pretty decent portion of #fishnchips offered by #Eddies in #Tammisaari / #Ekenäs.
As you can see, all the proper condiments!
30-May-2025
Small #currents, big impact: #Satellite breakthrough reveals hidden #ocean forces
Texas A&M's Jinbo Wang and an international team use SWOT satellite data to uncover powerful submesoscale #eddies, reshaping our understanding of #OceanClimateDynamics.
https://www.eurekalert.org/news-releases/1085746 #science #OceanCurrents
While scientists have long studied currents of large eddies, the smaller ones — called submesoscale eddies — are notoriously difficult to detect. These currents, which range from several kilometers to 100 kilometers wide, have been the “missing pieces” of the ocean’s puzzle — until now. Using data from the new Surface Water and Ocean Topography (SWOT) satellite, a Texas A&M researcher and his collaborators at JPL, CNES and Caltech finally got a clear view of these hard-to-see currents, and they are a lot stronger than anyone thought.
Corduroy Trouser Rampage – Ep. 51
In which we skip diagonally along on a corduroy trouser rampage, tread lightly lest we tread on your flags, and consider the character of Eddy’s compatriot. PD === Don’t just say to yourself “this one’s for the birds”, click on the player. You know you ought to…. […]
https://offgrid.tlmb.net/2025/04/27/corduroy-trouser-rampage-ep-51/
💡 New Preprint!
What are the benefits of an online versus offline learning approach? Explore our application of online training approach to learning subgrid parameterisations of baroclinic ocean #eddies.
👉 Find out more: https://arxiv.org/abs/2411.14106
For reasons of computational constraint, most global ocean circulation models used for Earth System Modeling still rely on parameterizations of sub-grid processes, and limitations in these parameterizations affect the modeled ocean circulation and impact on predictive skill. An increasingly popular approach is to leverage machine learning approaches for parameterizations, regressing for a map between the resolved state and missing feedbacks in a fluid system as a supervised learning task. However, the learning is often performed in an `offline' fashion, without involving the underlying fluid dynamical model during the training stage. Here, we explore the `online' approach that involves the fluid dynamical model during the training stage for the learning of baroclinic turbulence and its parameterization, with reference to ocean eddy parameterization. Two online approaches are considered: a full adjoint-based online approach, related to traditional adjoint optimization approaches that require a `differentiable' dynamical model, and an approximately online approach that approximates the adjoint calculation and does not require a differentiable dynamical model. The online approaches are found to be generally more skillful and numerically stable than offline approaches. Others details relating to online training, such as window size, machine learning model set up and designs of the loss functions are detailed to aid in further explorations of the online training methodology for Earth System Modeling.
MPI für Meteorologie
Nicole Sharp
roweb
Kristoffer Lawson
michael
Off-Grid
Benjamin Carr, Ph.D. 👨🏻💻🧬
AI4PEX