the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Cloud-based framework for inter-comparing submesoscale-permitting realistic ocean models
Takaya Uchida
Julien Le Sommer
Charles Stern
Ryan P. Abernathey
Chris Holdgraf
Aurélie Albert
Laurent Brodeau
Eric P. Chassignet
Xiaobiao Xu
Jonathan Gula
Guillaume Roullet
Nikolay Koldunov
Sergey Danilov
Qiang Wang
Dimitris Menemenlis
Clément Bricaud
Brian K. Arbic
Jay F. Shriver
Fangli Qiao
Bin Xiao
Arne Biastoch
René Schubert
Baylor Fox-Kemper
William K. Dewar
Alan Wallcraft
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ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
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Inaccuracies in air–sea heat fluxes severely degrade the accuracy of ocean numerical simulations. Here, we use artificial neural networks to correct air–sea heat fluxes as a function of oceanic and atmospheric state predictors. The correction successfully improves surface and subsurface ocean temperatures beyond the training period and in prediction experiments.
FINAM is not a model), a new coupling framework written in Python to dynamically connect independently developed models. Python, as the ultimate glue language, enables the use of codes from nearly any programming language like Fortran, C++, Rust, and others. FINAM is designed to simplify the integration of various models with minimal effort, as demonstrated through various examples ranging from simple to complex systems.
This study introduces a new 3D lake–ice–atmosphere coupled model that significantly improves winter climate simulations for the Great Lakes compared to traditional 1D lake model coupling. The key contribution is the identification of critical hydrodynamic processes – ice transport, heat advection, and shear-driven turbulence production – that influence lake thermal structure and ice cover and explain the superior performance of 3D lake models to their 1D counterparts.
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fastjmd95
: Numba implementation of Jackett &
McDougall (1995) ocean equation of state, Zenodo [code], https://doi.org/10.5281/zenodo.4498376,
2020. a
xgcm
: General Circulation Model Postprocessing with
xarray, Zenodo [code], https://doi.org/10.5281/zenodo.3634752, 2021b. a, b
xhistogram
: Fast, flexible,
label-aware histograms for numpy and xarray, Zenodo [code], https://doi.org/10.5281/zenodo.5757149,
2021c. a
xmitgcm
: Read
MITgcm mds binary files into xarray, Zenodo [code], https://doi.org/10.5281/zenodo.596253,
2021d. a
python-seawater
: Python re-write of the CSIRO seawater
toolbox SEAWATER-3.3 for calculating the properties of sea water, Zenodo [code],
https://doi.org/10.5281/zenodo.11395, 2014. a
GSW-python
:
Python implementation of the Thermodynamic Equation of Seawater 2010
(TEOS-10), Zenodo [code], https://doi.org/10.5281/zenodo.5214122, 2021. a
zarr
: A format for the
storage of chunked, compressed, N-dimensional arrays, Zenodo [code],
https://doi.org/10.5281/zenodo.3773450, 2020. a
xrft
: Fourier
transforms for xarray data, Zenodo [code], https://doi.org/10.5281/zenodo.1402635, 2021. a