Offline advection schemes allow for low-computational-cost simulations using existing model output. This study presents the approach and assessment for passive offline tracer advection within the Regional Ocean Modeling System (ROMS). An advantage of running the code within ROMS itself is consistency in the numerics on- and offline. We find that the offline tracer model is robust: after about 14 d of simulation (almost 60 units of time normalized by the advection timescale), the skill score comparing offline output to the online simulation using the
The ability to integrate Eulerian tracer fields offline or separate from the online original full simulation is attractive because of the improved computational efficiency. Once an online simulation has been run, any number of offline simulations can be run, forced by the stored online model output, using a larger time step, and only needing to integrate the transport field itself. This allows for many simulations when, in contrast, fewer would have been possible with the online simulation. This study presents the development and assessment of an offline passive tracer advection model that is part of the Regional Ocean Modeling System (ROMS), version 904, in the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) modeling system
Previous work has been done in this area with other models. An offline tracer model for the Massachusetts Institute of Technology (MIT) general circulation model (MITgcm) was developed and showed good accuracy
The offline tracer model described in this paper is integrated into and derived from the ROMS model: preprocessor flag choices allow access to the offline capability. While not being derived from a specific ocean model allows for wider potential use, as in some of the previously described models, there may be an advantage in using the offline model that is a derivative of the offline model to ensure consistency, using the exact same numerics and setup. The expected user for this software is someone who uses ROMS for their ocean modeling needs and wants to have the ability to run more tracer simulations, decoupled from their more expensive online simulations. Another type of user may simply have some ROMS output available, and this code will allow them to leverage it beyond its originally intended use.
The experimental setup is described in Sect.
The online model is set up for the northern Gulf of Mexico (25.6–30.6
Model domain and initial blob of passive dye at the surface for the first set of numerical experiments.
A series of online and offline simulations were run to evaluate the comparative performance of offline tracer advection. The first set of numerical experiments presented in this paper were initialized with a discrete Gaussian blob of dye southwest of the Mississippi River delta in a regional model of the northern Gulf of Mexico (see Fig.
These offline simulations were run using one of the two tracer advection schemes, with either
The relevant controlling timescale for this simulation is the advection timescale. Results from online simulations of the dye advection show a representative length scale of about
Another set of simulations were run to apply the lessons learned in the first set to a more realistic test case (Fig.
Second experiment: discrete Gaussian blob of dye at 800 m depth. Subplots show the domain and bathymetry
The main metric used to evaluate the performance of this model is a skill score, SS
Often skill scores are calculated with respect to a reference. For example, for numerical model performance, the difference between model and data in the numerator may be compared with the difference between climatology and data in the denominator in order to assess how much better the model is performing than simple climatology
Percent error is used to demonstrate the accuracy of the second set of simulations in space, because it is not averaged over spatial dimensions like the skill score. The percent error at time
Simulations were performed on a Linux cluster with 84 processors for online simulations and 28 processors for offline simulations. The number used was not optimized. Analysis was performed in a Jupyter notebook
The accuracy of selected offline simulations is presented here. As there were over 300 offline simulations, only selected results are shown to best illustrate specific points and show the overall performance of the model under a range of parameter choices. Offline simulations are forced by snapshots of online output (
Instantaneous difference in dye concentration (online minus offline simulation) after about 13.2 d. Alternating rows show the results from the two tracer advection schemes tested, and the columns show the different experiments. All pairs of experiments except D forced the vertical salinity diffusion coefficient
Instantaneous differences in dye concentration demonstrate the spatial structure of the offline simulation errors (Fig.
Skill scores for several subsets of offline simulations.
Skill scores (Eq.
The importance of
Several issues are demonstrated in Fig.
Several other issues were investigated but not plotted (they can be seen in the paper GitHub repository). Passive tracers are conserved in online ROMS simulations
An overview of results is shown in Fig.
Summary of skill score results. Shown are the offline computational time per simulation day (
Snapshots of the dye at 13.75 d for the online
Vertical cross section comparisons of the online and offline simulations; the cross section location is indicated in Fig.
The biggest difference in the second set of simulations (initialization shown in Fig.
Spatial differences in the accuracy of the experiments are shown for depth slices (Fig.
Results are similar for the vertical cross section (Fig.
The context of the performance difference found as a function of
Power spectral density for speed at a single location near the center of the dye patch. Overlaid (gray dashed) are lines marking frequencies at which online model output was saved for forcing offline simulations; these are marked with their corresponding
Comparing a relevant dynamical timescale to
We should expect that the offline time step is controlled by the horizontal Courant number and that our results destabilize as the number increases toward 1. An estimate of the horizontal Courant number, with the largest horizontal velocity of 1 and the smallest horizontal cell width of about 3800 m, for the offline time steps gives a range from 0.005 for the offline time step matching the online time step up to about 0.1 and 0.25 for offline time step
This paper presents a description and evaluation of an offline tracer advection model developed within ROMS. The advantage of this is the ease and consistency with which ROMS users can employ existing model output to force offline tracer simulations at low computational cost. The main approach of the offline model is to force variables
We tested two tracer advection schemes,
A second set of simulations were run to demonstrate performance in a more realistic, application-driven experiment – in this case, with a discrete blob of dye at depth. The good-resolution case with online forcing at a frequency of
Final skill score (percent) of offline simulations after 14 d, sorted by
While preprocessor flags for offline simulations already existed in the ROMS and COAWST code base, we found that the offline simulations did not work as desired. In this section, we describe changes made to the code base so that offline passive tracer advection works properly by receiving the necessary forced variables. Generally, the offline code works by forcing previously simulated online model output that is input as climatological forcing. Typically, climatology would be used in a ROMS simulation to nudge boundary conditions toward mean values, but in this case all grid cells are fully forced.
Code changes were made to avoid repeating processes offline that were already included online. Initialization is now minimal for offline simulations (
The offline simulation is missing much of the complex time stepping in an online ROMS simulation due to the missing numerics, leading to necessary code adjustments (
The
To fix a problem with reading in the climatology at the correct time step, a condition was added (
Requirements and considerations for setting up online and offline simulations in ROMS or COAWST with the offline passive tracer advection code are provided below.
In the project input file (the Choose whether to save output as snapshots at a single time or averages across time intervals (ROMS Output necessary variables for forcing the offline simulation. Variables Choose output frequency (parameter For this online simulation, point to file
In the project header file (the Choose a tracer advection scheme. We tested two schemes and found both accurately reproduced the online results offline, though Use
In the project input file (the The output frequency ( A reasonable choice for the offline simulation time step All physics should be off in the offline case, except for anything directly impacting the offline tracer field ( boundaries should all be closed except for offline tracer fields, e.g., parameter river forcing and other sources or sinks that were forced in the online simulation should be turned off; winds, bulk fluxes, etc, should not be forced from the online simulation; the model should not be nudged to climatology, even if used in the online simulation (climatology, the output from the online simulation, will be entirely enforced); flags for climatology forcing for sea surface height ( Only the sea surface height (zeta) and the offline dye(s) ( Input as the climatology forcing ( For this offline simulation, point to file
In the project header file (the Use the For the best results, use the same tracer advection scheme as the online run. The schemes do not have to match, but the skill score between the simulations will diminish substantially (Fig. Forcing the vertical salinity diffusivity
The current versions of the related code and data are available online, all under the MIT license. The offline tracer model is available from
KMT edited the code, performed the final simulations and analysis, and wrote the text. DK, VRX, and LQ edited the ROMS code and ran simulations. XC created the regional model setup in ROMS. RDH participated in discussions and provided ideas.
The authors declare that they have no conflict of interest.
This research was made possible by a grant from the Gulf of Mexico Research Initiative. In addition to the locations noted in the “Code and data availability” section, data are publicly available through the Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC) at
The authors are grateful to the Texas A&M High Performance Research Computing center for hosting simulations.
This research has been supported by the Gulf of Mexico Research Initiative (grant no. SA 18-10).
This paper was edited by Qiang Wang and reviewed by two anonymous referees.