Meeting Documents

Characterising Submesoscale Statistics Globally in a High-Resolution Model using Pangeo Tools

Abstract

Characterising submesoscale dynamics globally requires extremely high resolution model runs. One example of a submesoscale-permitting global simulation is the MITgcm LLC4320 run [1], but the sheer scale of the output data (5 PetaBytes in total) has so far presented a serious challenge for analysis.

Separately, Balwada et al. [2] showed that analysing flowfield variables via their Joint Probability Distribution Functions (JPDFs) can provide a useful reduced representation of regional submesoscale dynamics.

We extend this JPDF technique to analyse the entire global ocean surface, at the 1/48th degree and hourly resolution used in the LLC4320 run. We argue that then clustering the resultant regional JPDFs by similarity partitions the ocean according to the local dynamical turbulence regime, a new conceptual framework for thinking about submesoscale turbulence globally.

Performing this analysis required computational tools which can operate on large-scale datasets, and we also describe some of our work on the Pangeo toolstack [3] that made this analysis possible.

[1] Rocha, C. B., Gille, S. T., Chereskin, T. K., & Menemenlis, D. (2016). Seasonality of submesoscale dynamics in the Kuroshio Extension. Geophysical Research Letters, 43(21), 11-304.

[2] Balwada, Dhruv, et al. "Vertical fluxes conditioned on vorticity and strain reveal submesoscale ventilation." Journal of Physical Oceanography 51.9 (2021): 2883-2901.

[3] Petabyte-scale ocean data analytics on staggered grids via the grid ufunc protocol in xGCM, Thomas Nicholas et al., SciPy Conference 2022 doi.org/10.25080/majora-212e5952-042