Volume and Nutrient Transports Disturbed by the Typhoon Chebi (2013) in the Upwelling Zone East of Hainan Island, China

Using cruise observations before and after the typhoon Chebi in August 2013 and those without the typhoon in July 2012, this study investigates variations in current structure, nutrient distribution, and transports disturbed by a typhoon in a typical coastal upwelling zone east of Hainan Island in the northwestern South China Sea. The results show that along-shore northeastward flow dominates the coastal ocean with a volume transport of 0.64 × 106 m3/s in the case without the typhoon. The flow reversed southwestward, with its volume transport halved before the typhoon passage. After the typhoon passage, the flow returned back northeastward except the upper layer in waters deeper than 50 m and the total volume transport decreased to 0.10 × 106 m3/s. For the cross-shelf component, the flow kept shoreward, while transports crossing the 50 m isobath decreased from 0.25, 0.12 to 0.06 × 106 m3/s in the case without the typhoon as well as before and after typhoon passage, respectively. For the along-shore/cross-shelf nutrient transports, SiO32− has the largest value of 866.13/632.74 μmol/s per unit area, NO3− half of that, and PO43− and NO2− one order smaller in the offshore water without the typhoon. The values dramatically decreased to about one-third for SiO32−, NO3−, and PO43− after the typhoon, but changed little for NO2−. The disturbed wind field and associated Ekman flow and upwelling process may explain the variations in the current and nutrient transports after the typhoon.

Estimating Ocean Surface Currents from Satellite Observable Quantities with Machine Learning

Global surface currents are usually inferred from directly observed quantities like sea-surface height, wind stress by applying diagnostic balance relations (like geostrophy and Ekman flow), which provide a good approximation of the dynamics of slow, large-scale currents at large scales and low Rossby numbers.However, newer generation satellite altimeters (like the upcoming SWOT mission) will capture more of the high wavenumber variability associated with the unbalanced components, and applying these balances directly may lead to an incorrect un-physical estimate of the surface flow.In this study we explore Machine Learning (ML) algorithms as an alternate route to infer surface currents from satellite observable quantities.We train our ML models with SSH, SST and wind stress from available primitive equation ocean GCM simulation outputs as the inputs and make predictions of surface currents (u,v), which are then compared against the true GCM output. Using transformed 3 dimensional coordinates and a stencil of surrounding grid points as additional input features, the ML models are trained to effectively ``learn” spatial gradients.As a baseline example, we demonstrate that a linear regression model is ineffective at predicting velocities accurately beyond localized regions.In comparison, a relatively simple neural network (NN) can predict surface currents accurately over most of the global ocean, with lower mean squared errors than geostrophy+Ekman. The highest localized errors in the NN predictions are generally collocated with regions of higher Rossby numbers, thereby indicating that NNs can successfully learn the physics of geostrophic balance and Ekman flow.

Physics and Dynamics of the Oceans

This chapter focuses on the physics and dynamics of the ocean. It describes the variability of salinity and surface temperature, as well as the vertical temperature structure of the ocean, with the thermocline separating the variable top layer from the deeper ocean. It then describes the key forces in the ocean, as well as the geostrophic balance due to the Coriolis force and density differences. It derives the equations for the change of velocity with depth, the Ekman flow. Barotropic flow and baroclinic flow are elucidated and the general circulation of the ocean, with gyres and the effect of vorticity on their structure, is shown. The thermohaline circulation of the ocean with surface flow and returning deep ocean flows is described. Next, a simple model is used to show how salinity interacts with the thermohaline flow. Finally, as an example of ocean–land interaction, the El Niño phenomenon is described.

Generation of Subsurface Anticyclones at Arctic Surface Fronts due to a Surface Stress

Isolated anticyclones are frequently observed below the mixed layer in the Arctic Ocean. Some of these subsurface anticyclones are thought to originate at surface fronts. However, previous idealized simulations with no surface stress show that only cyclone–anticyclone dipoles can propagate away from baroclinically unstable surface fronts. Numerical simulations of fronts subject to a surface stress presented here show that a surface stress in the same direction as the geostrophic flow inhibits dipole propagation away from the front. On the other hand, a surface stress in the opposite direction to the geostrophic flow helps dipoles to propagate away from the front. Regardless of the surface stress at the point of dipole formation, these dipoles can be broken up on a time scale of days when a surface stress is applied in the right direction. The dipole breakup leads to the deeper anticyclonic component becoming an isolated subsurface eddy. The breakup of the dipole occurs because the cyclonic component of the dipole in the mixed layer is subject to an additional advection because of the Ekman flow. When the Ekman transport has a component oriented from the anticyclonic part of the dipole toward the cyclonic part then the cyclone is advected away from the anticyclone and the dipole is broken up. When the Ekman transport is in other directions relative to the dipole axis, it also leads to deviations in the trajectory of the dipole. A scaling is presented for the rate at which the surface cyclone is advected that holds across a range of mixed layer depths and surface stress magnitudes in these simulations. The results may be relevant to other regions of the ocean with similar near-surface stratification profiles.

Ekman Transport in Balanced Currents with Curvature

Ekman transport, the horizontal mass transport associated with a wind stress applied on the ocean surface, is modified by the vorticity of ocean currents, leading to what has been termed the nonlinear Ekman transport. This article extends earlier work on this topic by deriving solutions for the nonlinear Ekman transport valid in currents with curvature, such as a meandering jet or circular vortex, and for flows with the Rossby number approaching unity. Tilting of the horizontal vorticity of the Ekman flow by the balanced currents modifies the ocean response to surface forcing, such that, to leading order, winds parallel to the flow drive an Ekman transport that depends only on the shear vorticity component of the vertical relative vorticity, whereas across-flow winds drive transport dependent on the curvature vorticity. Curvature in the balanced flow field thus leads to an Ekman transport that differs from previous formulations derived under the assumption of straight flows. Notably, the theory also predicts a component of the transport aligned with the surface wind stress, contrary to classic Ekman theory. In the case of the circular vortex, the solutions given here can be used to calculate the vertical velocity to a higher order of accuracy than previous solutions, extending possible applications of the theory to strong balanced flows. The existence of oscillations, and the potential for resonance and instability, in the Ekman flow at a curved jet are also demonstrated.