Search NASA⌕ Search

DOE OSTI · 3024420

Water Mass Transformation Budgets in Finite‐Volume Generalized Vertical Coordinate Ocean Models

Abstract

Water Mass Transformation (WMT) theory provides conceptual tools that in principle enable innovative analyses of numerical ocean models; in practice, however, these methods can be challenging to implement and interpret, and therefore remain under-utilized. Our aim is to demonstrate the feasibility of diagnosing all terms in the water mass budget and to exemplify their usefulness for scientific inquiry and model development by quantitatively relating water mass changes, overturning circulations, boundary fluxes, and interior mixing. We begin with a pedagogical derivation of key results of classical WMT theory. We then describe best practices for diagnosing each of the water mass budget terms from the output of Finite-Volume Generalized Vertical Coordinate (FV-GVC) ocean models, including the identification of a non-negligible remainder term as the spurious numerical mixing due to advection scheme discretization errors. We illustrate key aspects of the methodology through the analysis of a polygonal region of the Greater Baltic Sea in a regional demonstration simulation using the Modular Ocean Model v6 (MOM6). We verify the convergence of our WMT diagnostics by brute-force, comparing time-averaged (“offline”) diagnostics on various vertical grids to timestep-averaged (“online”) diagnostics on the native model grid. Finally, we briefly describe a stack of xarray-enabled Python packages for evaluating WMT budgets in FV-GVC models (culminating in the new xwmb package), which is intended to be model-agnostic and available for community use and development.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Drake, Henri F. [Univ. of California, Irvine, CA (United States)] (ORCID:0000000301350814), Bailey, Shanice [Columbia Univ., New York, NY (United States)] (ORCID:0000000281769465), Dussin, Raphael [University Corporation for Atmospheric Research, Boulder, CO (United States); National Oceanic and Atmospheric Administration (NOAA), Princeton, NJ (United States). Geophysical Fluid Dynamics Laboratory], Griffies, Stephen M. [National Oceanic and Atmospheric Administration (NOAA), Princeton, NJ (United States). Geophysical Fluid Dynamics Laboratory] (ORCID:000000023711236X), Krasting, John [NOAA Geophysical Fluid Dynamics Laboratory Princeton NJ USA; Princeton Univ., NJ (United States)] (ORCID:0000000246509844), MacGilchrist, Graeme [Univ. of St. Andrews, Scotland (United Kingdom)] (ORCID:0000000314098937), Stanley, Geoffrey [Canadian Centre for Climate Modelling and Analysis Environment and Climate Change, Victoria (Canada)] (ORCID:0000000345368602), Tesdal, Jan‐Erik [Princeton Univ., NJ (United States)] (ORCID:0000000217020097), Zika, Jan D. [Univ. of New South Wales, Sydney, NSW (Australia)] (ORCID:0000000334623559). 2025-03-08. Water Mass Transformation Budgets in Finite‐Volume Generalized Vertical Coordinate Ocean Models. https://doi.org/10.1029/2024ms004383

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Water4Energy Step-1 Band-M Ready-to-Train Samples for TVA Weeks-to-Years Prediction, Version 0

AI-ready Band-M (monthly) labelled training pack for the Water4Energy Genesis Task-1 project on weeks-to-years prediction of Tennessee Valley temperature and precipitation. The deposit includes leakage-aware issue-time samples (samples_M_v0.nc; N=486), train-only scalers, issue-time split table, supporting monthly panels, and Python generation scripts to recreate the pack from the companion Tier-1 raw observation collection (https://doi.org/10.13139/ORNLNCCS/3398576). Each sample pairs a 12-month lookback of teleconnection indices and SST box anomalies with TVA-mean ERA5 anomaly targets (t2m, tp, msl) at leads 1–3 months.

54 ENVIRONMENTAL SCIENCES↗

Multi-Angle Snowflake Camera, particle analysis

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES↗

Multi-Angle Snowflake Camera, time bins

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES↗