Defining Southern Ocean fronts using unsupervised classification

Paper: https://doi.org/10.5194/os-17-1545-2021

Preprint: https://doi.org/10.5194/os-2021-40

Presentation at AGU2021: https://doi.org/10.1002/essoar.10507114.1

Short description

In the Southern Ocean, fronts delineate water masses, which correspond to upwelling and downwelling branches of the overturning circulation. Classically, oceanographers define Southern Ocean fronts as a small number of continuous linear features that encircle Antarctica. However, modern observational and theoretical developments are challenging this traditional framework to accommodate more localized views of fronts [Chapman et al. 2020].

Here we present code for implementing two related methods for calculating fronts from oceanographic data. The first method uses unsupervised classification (specifically, Gaussian Mixture Modeling or GMM) and a novel interclass metric to define fronts. This approach produces a discontinuous, probabilistic view of front location, emphasising the fact that the boundaries between water masses are not uniformly sharp across the entire Southern Ocean.

The second method uses Sobel edge detection to highlight rapid changes [Hjelmervik & Hjelmervik, 2019]. This approach produces a more local view of fronts, with the advantage that it can highlight the movement of individual eddy-like features (such as the Agulhas rings).

1. Chapman, C. C., Lea, M.-A., Meyer, A., Sallee, J.-B. & Hindell, M. Defining Southern Ocean fronts and their influence on biological and physical processes in a changing climate. Nature Climate Change (2020). https://doi.org/10.1038/s41558-020-0705-4

2. Maze, G. et al. Coherent heat patterns revealed by unsupervised classification of Argo temperature profiles in the North Atlantic Ocean. Progress in Oceanography (2017). https://doi.org/10.1016/j.pocean.2016.12.008, https://doi.org/10.5281/zenodo.3906236

3. Hjelmervik, K. B. & Hjelmervik, K. T. Detection of oceanographic fronts on variable water depths using empirical orthogonal functions. IEEE Journal of Oceanic Engineering (2019). https://doi.org/10.1109/JOE.2019.2917456

I metric for K=5

Getting started

• Make the environment:

  bash make env

• Activate the environment in conda:

  bash conda activate ./env

• Change the settings in src.constants to set download location etc.

• Download data (get_zip: 1694.64639 s):

bash python3 src/data_loading/bsose_download.py

• Make I-metric:

bash python3 src/models/batch_i_metric.py

• Make figures:

bash python3 main.py

Project Organization

``txt LICENSE Makefile <- Makefile with commands likemake envormake ` README.md <- The top-level README for developers using this project. main.py <- The main python script to run. | figures <- .png images with non-enumerated names.

requirements <- Directory containing the requirement files.

setup.py <- makes project pip installable (pip install -e .) so src can be imported from jupyter notebooks etc. | src <- Source code for use in this project. | | init.py <- Makes src a Python module | | data <- KO fronts to plot, other data.

data_loading     <- Scripts to download and name data.

models           <- Make I-metric and Sobel edge detection directory.

plot             <- plotting functions directory

| | plotutils <- plotting utilities directory | | preprocessing <- preprocessing scripts (to transform to density etc.). | | | tests <- Scripts for unit tests of your functions | | | animate.py <- animate i-metric. | constants.py <- contains majority of run parameters that can be changed. | makefigures.py <- make all figures in one long script. | movefigures.py <- Move figures script (now unnecessary). | | Changes figure names to Figure-X.png etc. | timewrapper.py <- time wrapper to time parts of the program.

setup.cfg <- setup configuration file for linting rules ```

Requirements

• Anaconda, with conda working in shell.
• make in shell.
• Python 3.6+ (final run for paper used python==3.8.8)

Project template created by the Cambridge AI4ER Cookiecutter.