Research
My research lies at the intersection of physical oceanography and computational science. Current and past research projects include:
- Ocean mesoscale eddies
- Machine learning for ocean modeling
- Uncertainty quantification & observing system design
- Oceanic teleconnections in the North Atlantic
- Development of open source software tools
Ocean Mesoscale Eddies
Ocean mesoscale eddies are energetic motions that have horizontal scales of tens to hundreds of kilometers. Despite their relatively small scale, these eddies play an important role in transporting momentum, heat, salt, carbon, and nutrients throughout the world’s oceans. My research focuses on the energy cycle of the ocean mesoscale eddy field – its generation, its interaction with the large-scale flow, and its dissipation. To diagnose the energy cycle, I am using high-resolution idealized models. With a better understanding of the ocean eddy energy cycle, we can improve the representation of mesoscale processes in global ocean models.
Related publications: Loose et al., JPO (2022); Loose et al., JAMES (2023); Marques, Loose et al., GMD (2022).
Machine Learning for Ocean Modeling
Mesoscale eddies can persist in the ocean for months to years and thus eddy fluxes may carry a memory of past ocean states. Even so, eddy parameterizations are typically local in time. In this work, I am using machine learning techniques together with physical arguments to introduce memory into ocean eddy parameterizations, with the goal to make ocean models more accurate.
Uncertainty Quantification & Observing System Design
Ocean observing systems are expensive to build and maintain, and therefore have to be designed carefully. I am interested in questions such as:
- What dynamical information is contained in already existing observation networks?
- What is the optimal instrument configuration, which is both cost-efficient and capable to monitor key processes and ocean variability?
To tackle these questions, I perform quantitative observing system design, through a combination of Bayesian inverse methods and uncertainty quantification in a data assimilation framework. By means of these computational tools, quantitative observing system design suggests an optimal observing strategy and supports effective instrument placements in the future.
Related publications: Loose et al., J. Geophys. Res (2020); Loose and Heimbach, JAMES (2021); Fujii et al., Front. Mar. Sci. (2019).
Oceanic Teleconnections in the North Atlantic
The seas around Greenland, Iceland, and Norway transport heat from the North Atlantic toward the Arctic. I am using adjoint-derived sensitivities in the ECCO state estimate to identify drivers and locations that affect ocean heat transport in this region. Due to oceanic teleconnections, high-latitude heat transport is sensitive to local and remote(!) wind forcing.
Related publications: StoryMap; Loose et al., J. Geophys. Res (2020); Loose, PhD Dissertation, 2019.
Development of Open Source Software Tools
I am engaged with developing open-source software tools to enable our Earth Science community to perform data analysis in an efficient and reproducible way. Recently, my focus has been on the python package GCM-Filters.
GCM-Filters is a python package that allows scientists to perform spatial filtering analysis in an easy, flexible, efficient, and reproducible way. GCM-Filters is designed to work with gridded data that is produced by General Circulation Models (GCMs) of ocean, weather, and climate. Users can employ GCM-Filters on either CPUs or GPUs, with NumPy or CuPy input data. Moreover, GCM-Filters leverages Dask and Xarray to support filtering of larger-than-memory datasets and computational flexibility. Community contributions are welcome!
Check out this presentation on GCM-Filters!
Related publications: Loose et al., JOSS (2022); Grooms, Loose et al., JAMES (2021); Yu et al..