Three-Dimensional Hydrostatic Curved Channel Flow Simulations Using Non-Staggered Triangular Grids

Non-staggered triangular grids have many advantages in performing river or ocean modeling with the finite-volume method. However, horizontal divergence errors may occur, especially in large-scale hydrostatic calculations with centrifugal acceleration. This paper proposes an unstructured finite-volume method with a filtered scheme to mitigate the divergence noise and avoid further influencing the velocities and water elevation. In hydrostatic pressure calculations, we apply the proposed method to three-dimensional curved channel flows. Approximations reduce the numerical errors after filtering the horizontal divergence operator, and the approximation is second-order accurate. Numerical results for the channel flow accurately calculate the velocity profile and surface elevation at different Froude numbers. Moreover, secondary flow features such as the vortex pattern and its movement along the channel sections are also well captured.

Probabilistic Sea Level Rise Flood Projections Using a Localized Ocean Reference Surface

Projecting sea level rise (SLR) impacts requires defining ocean surface variability as a source of uncertainty. We analyze data from a Regional Ocean Modeling System (ROMS) reanalysis for the region surrounding the main Hawaiian Islands to incorporate the ocean surface uncertainty in mapping SLR flood probabilities. By analyzing the ocean surface height component of the ROMS reanalysis, we create an ocean surface reference (ORS) as a proxy for MHHW. We model the NOAA Intermediate, Intermediate-high and High regional SLR scenarios for the years 2050 and 2100 at three field sites around Oʻahu; Waikīkī, Hauʻula, Haleʻiwa. We calculate a probability density function (PDF) by convolving the PDF of water level derived from the ROMS reanalysis data with the PDF of error associated with a digital elevation model of the study sites. The resulting joint-PDF of flood depth allows us to create two types of probability-based flood projections: (1) Maps illustrating varying flood depths for a given probability threshold and, (2) maps illustrating varying probability for a specific flood depth. We compare 80% probability flood projections using our ORS approach to projections using the TCARI grid, the standard NOAA method. We highlight the importance of uncertainty and user-defined probability in identifying pixels that function as tipping points distinguishing flooding styles.

Two Different Descriptions of a Thermocline in the Persian Gulf

The Persian Gulf (PG) is a shallow sea connected to the rest of the world by the Strait of Hormuz. Temperature changes in the water column, which indicate the thermocline, are typically explained by the depth of the mixed layer and the thermocline. The thermocline is caused by a sudden decrease in temperature in the water column's subsurface layer, resulting in stratification in the PG from winter to summer. The parameters are approximated numerically through the Princeton Ocean Modeling (POM) method and compared to those determined by some CTD profiles collected in the PG. The most obvious method for approximating thermocline depth is to find the maximum negative slope \(\frac{\partial T}{\partial z}\) in a temperature profile. The method produces applied results with sufficient depth resolution and smooth temperature changes with depth. This method is a component of the Princeton Ocean Modeling (POM) framework for numerically modeling temperature variation in the water basins used in this study. The depth of the mixed layer is approximated by the surface equality temperature (Sea Surface Temperature), regardless of the thermocline approximation. The variable isotherm behavior accurately approximates the thermocline depth. Thermocline formation occurs in the PG during the summer, and this article will conclude using two methods, observational and numerical modeling.

ROMSPath v1.0: Offline Particle Tracking for the Regional Ocean Modeling System (ROMS)

Offline particle tracking (OPT) is a widely used tool for the analysis of data in oceanographic research. Given the output of a hydrodynamic model, OPT can provide answers to a wide variety of research questions involving fluid kinematics, zooplankton transport, the dispersion of pollutants, and the fate of chemical tracers, among others. In this paper, we introduce ROMSPath, an OPT model designed to complement the Regional Ocean Modelling System (ROMS). Based on the Lagrangian TRANSport (LTRANS) model (North et al., 2008), ROMSPath is written in Fortran 90 and provides advancements in functionality and efficiency compared to LTRANS. First, ROMSPath now calculates particle trajectories using the ROMS native grid, which provides advantages in interpolation, masking, and boundary interaction, while improving accuracy. Second, ROMSPath enables simulated particles to pass between nested ROMS grids, which are an increasingly popular tool to simulate the ocean over multiple scales. Third, the ROMSPath vertical turbulence module enables the turbulent (diffusion) time step and advection time step to be specified separately, adding flexibility and improving computational efficiency. Lastly, ROMSPath includes new infrastructure enabling input of auxiliary parameters for added functionality. In particular, Stokes drift can be input and added to particle advection. Here we describe the details of these updates and improvements.

A Deep Learning Model for Forecasting Velocity Structures of the Loop Current System in the Gulf of Mexico

Despite the large efforts made by the ocean modeling community, such as the GODAE (Global Ocean Data Assimilation Experiment), which started in 1997 and was renamed as OceanPredict in 2019, the prediction of ocean currents has remained a challenge until the present day—particularly in ocean regions that are characterized by rapid changes in their circulation due to changes in atmospheric forcing or due to the release of available potential energy through the development of instabilities. Ocean numerical models’ useful forecast window is no longer than two days over a given area with the best initialization possible. Predictions quickly diverge from the observational field throughout the water and become unreliable, despite the fact that they can simulate the observed dynamics through other variables such as temperature, salinity and sea surface height. Numerical methods such as harmonic analysis are used to predict both short- and long-term tidal currents with significant accuracy. However, they are limited to the areas where the tide was measured. In this study, a new approach to ocean current prediction based on deep learning is proposed. This method is evaluated on the measured energetic currents of the Gulf of Mexico circulation dominated by the Loop Current (LC) at multiple spatial and temporal scales. The approach taken herein consists of dividing the velocity tensor into planes perpendicular to each of the three Cartesian coordinate system directions. A Long Short-Term Memory Recurrent Neural Network, which is best suited to handling long-term dependencies in the data, was thus used to predict the evolution of the velocity field in each plane, along each of the three directions. The predicted tensors, made of the planes perpendicular to each Cartesian direction, revealed that the model’s prediction skills were best for the flow field in the planes perpendicular to the direction of prediction. Furthermore, the fusion of all three predicted tensors significantly increased the overall skills of the flow prediction over the individual model’s predictions. The useful forecast period of this new model was greater than 4 days with a root mean square error less than 0.05 cm·s−1 and a correlation coefficient of 0.6.

Unprecedented acceleration of winter discharge of Upper Yenisei River inferred from tree rings

The Yenisei River is the largest contributor of freshwater and energy fluxes among all rivers draining to the Arctic Ocean. Modeling long-term variability of Eurasian runoff to the Arctic Ocean is complicated by the considerable variability of river discharge in time and space, and the monitoring constraints imposed by a sparse gauged-flow network and paucity of satellite data. We quantify tree growth response to river discharge at the upper reaches of the Yenisei River in Tuva, South Siberia. Two regression models built from eight tree-ring width chronologies of Larix sibirica are applied to reconstruct winter (Nov–Apr) discharge for the period 1784-1997 (214 years), and annual (Oct–Sept) discharge for the period 1701–2000 (300 years). The Nov–Apr model explains 52% of the discharge variance whereas Oct–Sept explains 26% for the calibration intervals 1927–1997 and 1927-2000, respectively. This new hydrological archive doubles the length of the instrumental discharge record at the Kyzyl gauge and resets the temporal background of discharge variability back to 1784. The reconstruction finds a remarkable 80% upsurge in winter flow over the last 25 years, which is unprecedented in the last 214 years. In contrast, annual discharge fluctuated normally for this system, with only a 7% increase over the last 25 years. Water balance modeling with CRU data manifests a significant discrepancy between decadal variability of the gauged flow and climate data after 1960. We discuss the impact on the baseflow rate change of both the accelerating permafrost warming in the discontinuous zone of South Siberia and widespread forest fires. The winter discharge accounts for only one-third of the annual flow, yet the persistent 25-year upsurge is alarming. This trend is likely caused by Arctic Amplification, which can be further magnified by increased winter flow delivering significantly more freshwater to the Kara Sea during the cold season.

Plume spreading test case for coastal ocean models

We present a test case of river plume spreading to evaluate numerical methods used in coastal ocean modeling. It includes an estuary–shelf system whose dynamics combine nonlinear flow regimes with sharp frontal boundaries and linear regimes with cross-shore geostrophic balance. This system is highly sensitive to physical or numerical dissipation and mixing. The main characteristics of the plume dynamics are predicted analytically but are difficult to reproduce numerically because of numerical mixing present in the models. Our test case reveals the level of numerical mixing as well as the ability of models to reproduce nonlinear processes and frontal zone dynamics. We document numerical solutions for the Thetis and FESOM-C models on an unstructured triangular mesh, as well as ones for the GETM and FESOM-C models on a quadrilateral mesh. We propose an analysis of simulated plume spreading which may be useful in more general studies of plume dynamics. The major result of our comparative study is that accuracy in reproducing the analytical solution depends less on the type of model discretization or computational grid than it does on the type of advection scheme.

Seasonal-To-Interannual Variability of Sea-Surface Temperatures in The Inter-Americas Seas: Pattern-Dependent Biases in The Regional Ocean Modeling System

The Inter-Americas Seas (IAS), involving the Gulf of Mexico, the Caribbean and a section of the eastern tropical Pacific Ocean bordering Central America, Colombia and Ecuador, exhibits very active ocean-land-atmosphere interactions that impact socio-economic activities within and beyond the region, and that are still not well understood or represented in state-of-the-art models. On seasonal-to-interannual timescales, the main source of variability of this geographical area is related to interactions between the Pacific and the Atlantic oceans, involving to anomalous sea-surface temperature (SST) patterns like El Niño-Southern Oscillation (ENSO), and regional features in the Caribbean linked to the bi-modal seasonality of the Caribbean Low-Level Jet. This study investigates seasonal-to-interannual IAS surface-temperature anomalies in observations, and their representation in am eddy-permitting, 1/9o-resolution simulation using the Regional Ocean Modeling System (ROMS), interannually-forced by the Climate Forecast System Reanalysis. Here, rather than analyzing model biases locally (i.e., gridbox-by-gridbox), a non-local SST pattern-based diagnostic was conducted via a principal component analysis. The approach allowed to identify magnitude, variance and spatial systematic errors in SST patterns related to the Western Hemisphere Warm Pool, ENSO, the Inter-American Seas Dipole, and several other variability modes. These biases are mainly related to errors in surface heat fluxes, misrepresentation of air-sea interactions impacting surface latent cooling in the Caribbean, and too strong sub-surface thermal stratification, mostly off the coast of Ecuador and northern Peru.