Abstract
This paper explores the application of emerging machine learning methods from image super resolution (SR) to the task of statistical downscaling. We specifically focus on convolutional neural network–based generative adversarial networks (GANs). Our GANs are conditioned on low-resolution (LR) inputs to generate high-resolution (HR) surface winds emulating Weather Research and Forecasting (WRF) Model simulations over North America. Unlike traditional SR models, where LR inputs are idealized coarsened versions of the HR images, WRF emulation involves using nonidealized LR and HR pairs, resulting in shared-scale mismatches due to internal variability. Our study builds upon current SR-based statistical downscaling by experimenting with a novel frequency-separation (FS) approach from the computer vision field. To assess the skill of SR models, we carefully select evaluation metrics and focus on performance measures based on spatial power spectra. Our analyses reveal how GAN configurations influence spatial structures in the generated fields, particularly biases in spatial variability spectra. Using power spectra to evaluate the FS experiments reveals that successful applications of FS in computer vision do not translate to climate fields. However, the FS experiments demonstrate the sensitivity of power spectra to a commonly used GAN-based SR objective function, which helps interpret and understand its role in determining spatial structures. This result motivates the development of a novel partial frequency-separation scheme as a promising configuration option. We also quantify the influence on GAN performance of nonidealized LR fields resulting from internal variability. Furthermore, we conduct a spectrum-based feature-importance experiment, allowing us to explore the dependence of the spatial structure of generated fields on different physically relevant LR covariates.
Significance Statement
We use artificial intelligence algorithms to mimic wind patterns from high-resolution climate models, offering a faster alternative to running these models directly. Unlike many similar approaches, we use datasets that acknowledge the essentially stochastic nature of the downscaling problem. Drawing inspiration from computer vision studies, we design several experiments to explore how different configurations impact our results. We find evaluation methods based on spatial frequencies in the climate fields to be quite effective at understanding how algorithms behave. Our results provide valuable insights into and interpretations of the methods for future research in this field.
Abstract
This paper explores the application of emerging machine learning methods from image super resolution (SR) to the task of statistical downscaling. We specifically focus on convolutional neural network–based generative adversarial networks (GANs). Our GANs are conditioned on low-resolution (LR) inputs to generate high-resolution (HR) surface winds emulating Weather Research and Forecasting (WRF) Model simulations over North America. Unlike traditional SR models, where LR inputs are idealized coarsened versions of the HR images, WRF emulation involves using nonidealized LR and HR pairs, resulting in shared-scale mismatches due to internal variability. Our study builds upon current SR-based statistical downscaling by experimenting with a novel frequency-separation (FS) approach from the computer vision field. To assess the skill of SR models, we carefully select evaluation metrics and focus on performance measures based on spatial power spectra. Our analyses reveal how GAN configurations influence spatial structures in the generated fields, particularly biases in spatial variability spectra. Using power spectra to evaluate the FS experiments reveals that successful applications of FS in computer vision do not translate to climate fields. However, the FS experiments demonstrate the sensitivity of power spectra to a commonly used GAN-based SR objective function, which helps interpret and understand its role in determining spatial structures. This result motivates the development of a novel partial frequency-separation scheme as a promising configuration option. We also quantify the influence on GAN performance of nonidealized LR fields resulting from internal variability. Furthermore, we conduct a spectrum-based feature-importance experiment, allowing us to explore the dependence of the spatial structure of generated fields on different physically relevant LR covariates.
Significance Statement
We use artificial intelligence algorithms to mimic wind patterns from high-resolution climate models, offering a faster alternative to running these models directly. Unlike many similar approaches, we use datasets that acknowledge the essentially stochastic nature of the downscaling problem. Drawing inspiration from computer vision studies, we design several experiments to explore how different configurations impact our results. We find evaluation methods based on spatial frequencies in the climate fields to be quite effective at understanding how algorithms behave. Our results provide valuable insights into and interpretations of the methods for future research in this field.
Abstract
This study describes an automated analysis of real-time tropical cyclone (TC) aircraft reconnaissance observations to estimate TC surface winds. The wind analysis uses an iterative, objective, data-weighted analysis approach with different smoothing constraints in the radial and azimuthal directions. Smoothing constraints penalize the data misfit when the solutions deviate from smoothed analyses and extend the aircraft information into areas not directly observed. The analysis composites observations following storm motion taken within five hours prior and three hours after analysis time and makes use of prescribed methods to move observations to a Common Flight Level (CFL; 700-hPa) for analysis and reduce reconnaissance observations to the surface. Comparing analyses to several observed and simulated wind fields shows that analyses fit the observations while extending observational information to poorly observed regions. However, resulting analyses tend toward greater symmetry as observational coverage decreases, and show sensitivity to the first guess information in unobserved radii. Analyses produce reasonable and useful estimates of operationally important characteristics of the wind field. But, due to the radial and azimuthal smoothing and the under-sampling of typical aircraft reconnaissance flights, wind maxima are underestimated, and the radii of maximum wind are slightly overestimated. Varying observational coverage using model-based synthetic aircraft observations, these analyses improve as observational coverage increases, and for a typical observational pattern (two transects through the storm) the root-mean-square error deviation is < 10 kt (< 5 m s−1).
Abstract
This study describes an automated analysis of real-time tropical cyclone (TC) aircraft reconnaissance observations to estimate TC surface winds. The wind analysis uses an iterative, objective, data-weighted analysis approach with different smoothing constraints in the radial and azimuthal directions. Smoothing constraints penalize the data misfit when the solutions deviate from smoothed analyses and extend the aircraft information into areas not directly observed. The analysis composites observations following storm motion taken within five hours prior and three hours after analysis time and makes use of prescribed methods to move observations to a Common Flight Level (CFL; 700-hPa) for analysis and reduce reconnaissance observations to the surface. Comparing analyses to several observed and simulated wind fields shows that analyses fit the observations while extending observational information to poorly observed regions. However, resulting analyses tend toward greater symmetry as observational coverage decreases, and show sensitivity to the first guess information in unobserved radii. Analyses produce reasonable and useful estimates of operationally important characteristics of the wind field. But, due to the radial and azimuthal smoothing and the under-sampling of typical aircraft reconnaissance flights, wind maxima are underestimated, and the radii of maximum wind are slightly overestimated. Varying observational coverage using model-based synthetic aircraft observations, these analyses improve as observational coverage increases, and for a typical observational pattern (two transects through the storm) the root-mean-square error deviation is < 10 kt (< 5 m s−1).
Abstract
Mesoscale eddy buoyancy fluxes across continental slopes profoundly modulate the boundary current dynamics and shelf–ocean exchanges but have yet to be appropriately parameterized via the Gent–McWilliams (GM) scheme in predictive ocean models. In this work, we test the prognostic performance of multiple GM variants in noneddying simulations of upwelling slope fronts that are commonly found along the subtropical continental margins. The tested GM variants range from a set of constant eddy buoyancy diffusivities to recently developed energetically constrained, bathymetry-aware diffusivities, whose implementation is augmented by an artificial neural network (ANN) serving to predict the mesoscale eddy energy based on the topographic and mean flow quantities online. In addition, an ANN is employed to parameterize the cross-slope eddy momentum flux (EMF) that maintains a barotropic flow field analogous to that in an eddy-resolving model. Our tests reveal that noneddying simulations employing the bathymetry-aware forms of the Rhines scale–based scheme and GEOMETRIC scheme can most accurately reproduce the heat contents and along-slope baroclinic transports as those in the eddy-resolving simulations. Further analyses reveal certain degrees of physical consistency in the ANN-inferred eddy energy, which tends to grow (decay) as isopycnal slopes are steepened (flattened), and in the parameterized EMF, which exhibits the correct strength of shaping the flow baroclinicity if a bathymetry-aware GM variant is jointly used. These findings provide a recipe of GM variants for use in noneddying simulations with continental slopes and highlight the potential of machine learning techniques to augment physics-based mesoscale eddy parameterization schemes.
Significance Statement
This study evaluates the predictive skill of parameterization schemes of water mass transports induced by ocean mesoscale eddies across continental slopes. Correctly parameterizing these transports in noneddying ocean models (e.g., ocean climate models) is crucial for predicting the ocean circulation and shelf–ocean exchanges. This work highlights the importance of bathymetric effects on eddy transports, as parameterization schemes that account for the influence of a sloping seafloor outperform those developed specifically for a flat-bottomed ocean. This work also highlights the efficacy of machine learning techniques to augment physics-based mesoscale eddy parameterization schemes, for instance, by estimating the mesoscale eddy energy online to realize energy-dependent parameterization schemes in noneddying simulations.
Abstract
Mesoscale eddy buoyancy fluxes across continental slopes profoundly modulate the boundary current dynamics and shelf–ocean exchanges but have yet to be appropriately parameterized via the Gent–McWilliams (GM) scheme in predictive ocean models. In this work, we test the prognostic performance of multiple GM variants in noneddying simulations of upwelling slope fronts that are commonly found along the subtropical continental margins. The tested GM variants range from a set of constant eddy buoyancy diffusivities to recently developed energetically constrained, bathymetry-aware diffusivities, whose implementation is augmented by an artificial neural network (ANN) serving to predict the mesoscale eddy energy based on the topographic and mean flow quantities online. In addition, an ANN is employed to parameterize the cross-slope eddy momentum flux (EMF) that maintains a barotropic flow field analogous to that in an eddy-resolving model. Our tests reveal that noneddying simulations employing the bathymetry-aware forms of the Rhines scale–based scheme and GEOMETRIC scheme can most accurately reproduce the heat contents and along-slope baroclinic transports as those in the eddy-resolving simulations. Further analyses reveal certain degrees of physical consistency in the ANN-inferred eddy energy, which tends to grow (decay) as isopycnal slopes are steepened (flattened), and in the parameterized EMF, which exhibits the correct strength of shaping the flow baroclinicity if a bathymetry-aware GM variant is jointly used. These findings provide a recipe of GM variants for use in noneddying simulations with continental slopes and highlight the potential of machine learning techniques to augment physics-based mesoscale eddy parameterization schemes.
Significance Statement
This study evaluates the predictive skill of parameterization schemes of water mass transports induced by ocean mesoscale eddies across continental slopes. Correctly parameterizing these transports in noneddying ocean models (e.g., ocean climate models) is crucial for predicting the ocean circulation and shelf–ocean exchanges. This work highlights the importance of bathymetric effects on eddy transports, as parameterization schemes that account for the influence of a sloping seafloor outperform those developed specifically for a flat-bottomed ocean. This work also highlights the efficacy of machine learning techniques to augment physics-based mesoscale eddy parameterization schemes, for instance, by estimating the mesoscale eddy energy online to realize energy-dependent parameterization schemes in noneddying simulations.
Abstract
As a follow-on to a previous study that examined the tilt and precession evolution of tropical cyclones (TCs) in a critical shear regime, this study examines the processes leading to the subsequent divergent evolutions in tilt and intensity. The control experiment fails to resume its precession and reintensify, while the perturbed experiments with enhanced upper-level inner-core vorticity resume the precession after a precession hiatus period. In the control experiment, a mesoscale negative absolute vorticity region forms at the upper levels due to tilting in strong downtilt convection. This upper-level, negative-vorticity region is inertially unstable, causing the inward acceleration of upper-level radial inflow. This upper-level inflow subsequently becomes negatively buoyant due to diabatic cooling and descends, bringing midlevel, low equivalent potential temperature (θE ) air into the inner-core TC boundary layer, significantly disrupting the low-level TC circulation. Consequently, the disrupted TC vortex in the control is unable to recover. The upper-level negative vorticity region is absent in the perturbed experiments due to weaker downtilt convection, preventing the emergence of the disruptive inner-core downdraft. The weaker downtilt convection is caused by several factors. First, a stronger circulation aloft advects hydrometeors farther downwind, resulting in greater separation of the cooling-driven downdraft from the convective updraft region, and thus weaker dynamically forced lifting at low levels. Second, the mean θE of the low-level air feeding downtilt convection is smaller. Third, there is stronger and deeper adiabatic descent uptilt, causing more low-θE air diluting the downtilt updraft region. These results show how the full vortex structure is important to diverging TC evolutions in moderately sheared environments.
Abstract
As a follow-on to a previous study that examined the tilt and precession evolution of tropical cyclones (TCs) in a critical shear regime, this study examines the processes leading to the subsequent divergent evolutions in tilt and intensity. The control experiment fails to resume its precession and reintensify, while the perturbed experiments with enhanced upper-level inner-core vorticity resume the precession after a precession hiatus period. In the control experiment, a mesoscale negative absolute vorticity region forms at the upper levels due to tilting in strong downtilt convection. This upper-level, negative-vorticity region is inertially unstable, causing the inward acceleration of upper-level radial inflow. This upper-level inflow subsequently becomes negatively buoyant due to diabatic cooling and descends, bringing midlevel, low equivalent potential temperature (θE ) air into the inner-core TC boundary layer, significantly disrupting the low-level TC circulation. Consequently, the disrupted TC vortex in the control is unable to recover. The upper-level negative vorticity region is absent in the perturbed experiments due to weaker downtilt convection, preventing the emergence of the disruptive inner-core downdraft. The weaker downtilt convection is caused by several factors. First, a stronger circulation aloft advects hydrometeors farther downwind, resulting in greater separation of the cooling-driven downdraft from the convective updraft region, and thus weaker dynamically forced lifting at low levels. Second, the mean θE of the low-level air feeding downtilt convection is smaller. Third, there is stronger and deeper adiabatic descent uptilt, causing more low-θE air diluting the downtilt updraft region. These results show how the full vortex structure is important to diverging TC evolutions in moderately sheared environments.
Abstract
While mesoscale eddy-induced temperature and salinity (T and S) variations at depth levels were widely reported, those on isopycnal surfaces have been largely unexplored so far. This study investigates temperature and salinity anomalies (T′ and S′; dubbed “spiciness anomalies”) on isopycnal surfaces induced by mesoscale eddies in the Kuroshio Extension (KET) region, with a focus on the North Pacific Intermediate Water (NPIW) layer of 26.3–26.7σθ . Cyclonic eddies (CEs) and anticyclonic eddies (AEs) tend to cluster on the northern and southern flanks of the KET jet, respectively. These eddies are characterized by a large radius (CEs: 61.94 km; AEs: 68.05 km), limited zonal movement, and a tendency of meridional movement (CEs: 0.35 cm s−1 southward; AEs: 0.66 cm s−1 northward). The average eddy-induced T′ and S′ are −0.25°C (0.06°C) and −0.05 psu (0.01 psu) for CEs (AEs) in the 26.3–26.7σθ layer. We propose two mechanisms for the generation of subsurface spiciness anomalies, respectively, for moving eddies that travel over long distances with trapped waters and quasi-stationary meander eddies that are generated by the meanders of the KET front. The T′ and S′ induced by moving eddies cumulatively drive cross-front water exchanges. Meander eddies shift the position of the front and induce redistribution of properties. However, these anomalies do not contribute to heat and salt exchanges between water masses. This work provides a useful benchmark for model simulations of mesoscale isopycnal variability in subsurface waters.
Abstract
While mesoscale eddy-induced temperature and salinity (T and S) variations at depth levels were widely reported, those on isopycnal surfaces have been largely unexplored so far. This study investigates temperature and salinity anomalies (T′ and S′; dubbed “spiciness anomalies”) on isopycnal surfaces induced by mesoscale eddies in the Kuroshio Extension (KET) region, with a focus on the North Pacific Intermediate Water (NPIW) layer of 26.3–26.7σθ . Cyclonic eddies (CEs) and anticyclonic eddies (AEs) tend to cluster on the northern and southern flanks of the KET jet, respectively. These eddies are characterized by a large radius (CEs: 61.94 km; AEs: 68.05 km), limited zonal movement, and a tendency of meridional movement (CEs: 0.35 cm s−1 southward; AEs: 0.66 cm s−1 northward). The average eddy-induced T′ and S′ are −0.25°C (0.06°C) and −0.05 psu (0.01 psu) for CEs (AEs) in the 26.3–26.7σθ layer. We propose two mechanisms for the generation of subsurface spiciness anomalies, respectively, for moving eddies that travel over long distances with trapped waters and quasi-stationary meander eddies that are generated by the meanders of the KET front. The T′ and S′ induced by moving eddies cumulatively drive cross-front water exchanges. Meander eddies shift the position of the front and induce redistribution of properties. However, these anomalies do not contribute to heat and salt exchanges between water masses. This work provides a useful benchmark for model simulations of mesoscale isopycnal variability in subsurface waters.
Estimating Profiles of Dissipation Rate in the Upper Ocean Using Acoustic Doppler Measurements Made from Surface-Following PlatformsAbstract
High-resolution profiles of vertical velocity obtained from two different surface-following autonomous platforms, Surface Wave Instrument Floats with Tracking (SWIFTs) and a Liquid Robotics SV3 Wave Glider, are used to compute dissipation rate profiles ϵ(z) between 0.5 and 5 m depth via the structure function method. The main contribution of this work is to update previous SWIFT methods to account for bias due to surface gravity waves, which are ubiquitous in the near-surface region. We present a technique where the data are prefiltered by removing profiles of wave orbital velocities obtained via empirical orthogonal function (EOF) analysis of the data prior to computing the structure function. Our analysis builds on previous work to remove wave bias in which analytic modifications are made to the structure function model. However, we find the analytic approach less able to resolve the strong vertical gradients in ϵ(z) near the surface. The strength of the EOF filtering technique is that it does not require any assumptions about the structure of nonturbulent shear, and does not add any additional degrees of freedom in the least squares fit to the model of the structure function. In comparison to the analytic method, ϵ(z) estimates obtained via empirical filtering have substantially reduced noise and a clearer dependence on near-surface wind speed.
Abstract
High-resolution profiles of vertical velocity obtained from two different surface-following autonomous platforms, Surface Wave Instrument Floats with Tracking (SWIFTs) and a Liquid Robotics SV3 Wave Glider, are used to compute dissipation rate profiles ϵ(z) between 0.5 and 5 m depth via the structure function method. The main contribution of this work is to update previous SWIFT methods to account for bias due to surface gravity waves, which are ubiquitous in the near-surface region. We present a technique where the data are prefiltered by removing profiles of wave orbital velocities obtained via empirical orthogonal function (EOF) analysis of the data prior to computing the structure function. Our analysis builds on previous work to remove wave bias in which analytic modifications are made to the structure function model. However, we find the analytic approach less able to resolve the strong vertical gradients in ϵ(z) near the surface. The strength of the EOF filtering technique is that it does not require any assumptions about the structure of nonturbulent shear, and does not add any additional degrees of freedom in the least squares fit to the model of the structure function. In comparison to the analytic method, ϵ(z) estimates obtained via empirical filtering have substantially reduced noise and a clearer dependence on near-surface wind speed.
Abstract
Modification of grasslands into irrigated and non-irrigated agriculture in the Great Plains results in significant impacts on weather and climate. However, there has been lack of observational data-based studies solely focused on impacts of irrigation on the PBL and convective conditions. The Great Plains Irrigation Experiment (GRAINEX) during the 2018 growing season collected data over irrigated and non-irrigated land uses over Nebraska to understand these impacts. Specifically, the objective was to determine whether the impacts of irrigation are sustained throughout the growing season.
The data analyzed include latent and sensible heat flux, air temperature, dew point temperature, equivalent temperature (moist enthalpy), PBL height, lifting condensation level (LCL), level of free convection (LFC), and PBL mixing ratio. Results show increased partitioning of energy into latent heat compared to sensible heat over irrigated areas while average maximum air was decreased and dewpoint temperature was increased from the early to peak growing season. Radiosonde data suggest reduced planetary boundary layer (PBL) heights at all launch sites from the early to peak growing season. However, reduction of PBL height was much greater over irrigated areas compared to non-irrigated croplands. Compared to the early growing period, LCL and LFC heights were also lower during the peak growing period over irrigated areas. Results note, for the first time, that the impacts of irrigation on PBL evolution and convective environment can be sustained throughout the growing season and regardless of background atmospheric conditions. These are important findings and applicable to other irrigated areas in the world.
Abstract
Modification of grasslands into irrigated and non-irrigated agriculture in the Great Plains results in significant impacts on weather and climate. However, there has been lack of observational data-based studies solely focused on impacts of irrigation on the PBL and convective conditions. The Great Plains Irrigation Experiment (GRAINEX) during the 2018 growing season collected data over irrigated and non-irrigated land uses over Nebraska to understand these impacts. Specifically, the objective was to determine whether the impacts of irrigation are sustained throughout the growing season.
The data analyzed include latent and sensible heat flux, air temperature, dew point temperature, equivalent temperature (moist enthalpy), PBL height, lifting condensation level (LCL), level of free convection (LFC), and PBL mixing ratio. Results show increased partitioning of energy into latent heat compared to sensible heat over irrigated areas while average maximum air was decreased and dewpoint temperature was increased from the early to peak growing season. Radiosonde data suggest reduced planetary boundary layer (PBL) heights at all launch sites from the early to peak growing season. However, reduction of PBL height was much greater over irrigated areas compared to non-irrigated croplands. Compared to the early growing period, LCL and LFC heights were also lower during the peak growing period over irrigated areas. Results note, for the first time, that the impacts of irrigation on PBL evolution and convective environment can be sustained throughout the growing season and regardless of background atmospheric conditions. These are important findings and applicable to other irrigated areas in the world.
Abstract
Small-scale processes at the southwestern boundary of the Ulleung Basin (UB) in the Japan/East Sea (JES) were examined using combined ship-based and moored observations along with model output. Model results show baroclinic semidiurnal tides are generated at the shelf break and corresponding slope connecting the Korea/Tsushima Strait with the UB and propagate into the UB with large barotropic-to-baroclinic energy conversion over the slope. Observations show high-frequency internal wave packets and indicate strong velocity shear and energetic turbulence associated with baroclinic tides in the stratified bottom layer. Solitary-like waves with frequencies from 0.2N to 0.5N (buoyancy frequency N) were found at the edge of the shelf break with supercritical flow. For subcritical flow, a hydraulic jump formed over the shelf break with weakly dispersive internal lee waves with frequencies varying from 0.5N to N. These high-frequency lee waves were trapped in the stratified bottom layer, with wave stress similar to the turbulent stress near the bottom. The power loss due to turbulent bottom drag can be an important factor for energy loss associated with the hydraulic jump. Turbulent kinetic energy dissipation rates of ∼10−4 W kg−1 were found. Large downward heat and salt fluxes below the high-salinity core mix warm/salty Tsushima Current Water with cold/low-salinity JES Intermediate Water. Mixing over the shelf break could be very important to the JES circulation since the calculated diapycnal upwelling (1–6 m day−1) at the shelf break and slope is substantially greater than the basin-averaged estimate from chemical tracers and modeling studies.
Significant Statement
The Japan/East Sea (JES) is a marginal sea, enclosed by Japan, Korea, and Russia. This study describes mixing processes over the shelf break connecting the northern Korea/Tsushima Strait (KTS) with the southern Ulleung Basin (UB), where the warm, high-salinity Kuroshio water carried by the Tsushima Current interacts with southward-flowing subsurface water masses in the JES. Our analysis suggests that the shelf break and slope between the KTS and the UB are vital areas for water-mass exchange in the southern JES. The enhanced mixing at the shelf break may impact water masses and circulation over the entire JES.
Abstract
Small-scale processes at the southwestern boundary of the Ulleung Basin (UB) in the Japan/East Sea (JES) were examined using combined ship-based and moored observations along with model output. Model results show baroclinic semidiurnal tides are generated at the shelf break and corresponding slope connecting the Korea/Tsushima Strait with the UB and propagate into the UB with large barotropic-to-baroclinic energy conversion over the slope. Observations show high-frequency internal wave packets and indicate strong velocity shear and energetic turbulence associated with baroclinic tides in the stratified bottom layer. Solitary-like waves with frequencies from 0.2N to 0.5N (buoyancy frequency N) were found at the edge of the shelf break with supercritical flow. For subcritical flow, a hydraulic jump formed over the shelf break with weakly dispersive internal lee waves with frequencies varying from 0.5N to N. These high-frequency lee waves were trapped in the stratified bottom layer, with wave stress similar to the turbulent stress near the bottom. The power loss due to turbulent bottom drag can be an important factor for energy loss associated with the hydraulic jump. Turbulent kinetic energy dissipation rates of ∼10−4 W kg−1 were found. Large downward heat and salt fluxes below the high-salinity core mix warm/salty Tsushima Current Water with cold/low-salinity JES Intermediate Water. Mixing over the shelf break could be very important to the JES circulation since the calculated diapycnal upwelling (1–6 m day−1) at the shelf break and slope is substantially greater than the basin-averaged estimate from chemical tracers and modeling studies.
Significant Statement
The Japan/East Sea (JES) is a marginal sea, enclosed by Japan, Korea, and Russia. This study describes mixing processes over the shelf break connecting the northern Korea/Tsushima Strait (KTS) with the southern Ulleung Basin (UB), where the warm, high-salinity Kuroshio water carried by the Tsushima Current interacts with southward-flowing subsurface water masses in the JES. Our analysis suggests that the shelf break and slope between the KTS and the UB are vital areas for water-mass exchange in the southern JES. The enhanced mixing at the shelf break may impact water masses and circulation over the entire JES.
Abstract
Energetic internal tides (ITs) are generated from the Luzon Strait (LS) and propagate westward into the South China Sea (SCS). Owing to the lack of large-scale synchronous measurements, the propagation features and seasonal variations of diurnal ITs remain unclear. From 2018 to 2019, mode-1 diurnal ITs west of the LS were continuously observed using a large-scale moored array of 27 pressure-recording inverted echo sounders (PIESs) and a thermistor chain. Measurements confirmed that diurnal ITs radiate from the LS with a north–south asymmetrical pattern, with the most energetic channel located in the middle and south of the LS. The total energy radiated into the SCS across 120°E is 2.67 GW for the K1 ITs and 1.54 GW for the O1 ITs, approximately 2 times larger than those inferred from satellite observations. K1 dominates among the diurnal ITs, with its maximum isopycnal displacement (amplitude) and energy input to the SCS being the strongest in summer (i.e., 16.3 m and 2.81 GW, respectively). The propagation speed of K1 is higher in summer and autumn along the main channel (i.e., 4.33and 4.36 m s−1, respectively). Seasonal stratification and circulation play important roles in the seasonal variation of amplitude and propagation speed of the K1 ITs. The seasonal variability of diurnal-band ITs, which includes all diurnal constituents, is location-dependent and primarily results from the superposition of the K1 and P1 ITs. In particular, vertical displacement is strong in summer and winter along the main channel of the K1 and P1 ITs. The seasonal amplitude of K1 can modulate this seasonal feature.
Significance Statement
Internal tides (ITs) are internal waves at tidal frequencies. The Luzon Strait (LS) is one of the most energetic sites to generate large-amplitude ITs. The ITs propagate into the South China Sea (SCS), interact with mesoscale eddies, large-scale circulations, etc., and influence local hydrodynamics as well as ecosystem and sediment transport. This motivated an observation plan to investigate the ITs at the western entrance of the LS. From June 2018 to August 2019, an array of 28 PIESs was deployed in the northeastern SCS, almost covering the western entrance of the LS, to investigate the propagation properties of ITs including their amplitude, phase speed, wavelength, propagation direction, and energy fluxes and their annual and seasonal variations. Here, we primarily focus on the mode-1 diurnal ITs. The new insights enrich our understanding of IT dynamics and seasonal variations and support further improvements in numerical simulations.
Abstract
Energetic internal tides (ITs) are generated from the Luzon Strait (LS) and propagate westward into the South China Sea (SCS). Owing to the lack of large-scale synchronous measurements, the propagation features and seasonal variations of diurnal ITs remain unclear. From 2018 to 2019, mode-1 diurnal ITs west of the LS were continuously observed using a large-scale moored array of 27 pressure-recording inverted echo sounders (PIESs) and a thermistor chain. Measurements confirmed that diurnal ITs radiate from the LS with a north–south asymmetrical pattern, with the most energetic channel located in the middle and south of the LS. The total energy radiated into the SCS across 120°E is 2.67 GW for the K1 ITs and 1.54 GW for the O1 ITs, approximately 2 times larger than those inferred from satellite observations. K1 dominates among the diurnal ITs, with its maximum isopycnal displacement (amplitude) and energy input to the SCS being the strongest in summer (i.e., 16.3 m and 2.81 GW, respectively). The propagation speed of K1 is higher in summer and autumn along the main channel (i.e., 4.33and 4.36 m s−1, respectively). Seasonal stratification and circulation play important roles in the seasonal variation of amplitude and propagation speed of the K1 ITs. The seasonal variability of diurnal-band ITs, which includes all diurnal constituents, is location-dependent and primarily results from the superposition of the K1 and P1 ITs. In particular, vertical displacement is strong in summer and winter along the main channel of the K1 and P1 ITs. The seasonal amplitude of K1 can modulate this seasonal feature.
Significance Statement
Internal tides (ITs) are internal waves at tidal frequencies. The Luzon Strait (LS) is one of the most energetic sites to generate large-amplitude ITs. The ITs propagate into the South China Sea (SCS), interact with mesoscale eddies, large-scale circulations, etc., and influence local hydrodynamics as well as ecosystem and sediment transport. This motivated an observation plan to investigate the ITs at the western entrance of the LS. From June 2018 to August 2019, an array of 28 PIESs was deployed in the northeastern SCS, almost covering the western entrance of the LS, to investigate the propagation properties of ITs including their amplitude, phase speed, wavelength, propagation direction, and energy fluxes and their annual and seasonal variations. Here, we primarily focus on the mode-1 diurnal ITs. The new insights enrich our understanding of IT dynamics and seasonal variations and support further improvements in numerical simulations.
Abstract
The spatiotemporal variability of oceanic fronts in the Indonesian seas was investigated using high-resolution satellite observations. The study aimed to understand the underlying mechanism driving these fronts and their impact on chlorophyll-a variability. A high value of frontal probability was found near the coasts of major islands, exhibiting a distinct seasonal cycle with peaks occurrences during austral winter. The distribution variability of chlorophyll-a was generally consistent with the presence of active frontal zones, although a significantly positive relationship between fronts and chlorophyll-a was limited to only some specific areas, e.g., south Java Island and the Celebes Sea. Wind-driven upwelling played a major role in front generation in the Java upwelling region and enhanced frontal activity can promote the growth of phytoplankton, leading to higher chlorophyll-a. Furthermore, the study demonstrated that wind patterns preceded variations in front probability and chlorophyll-a by approximately two months. This lag suggests that the spatiotemporal variability of fronts and chlorophyll-a in this region is primarily influenced by the monsoon system. In addition, the sea surface temperature (SST) simultaneously modulated the chlorophyll-a variability. Negative SST anomalies were typically associated with positive anomalies in front probability the chlorophyll-a in most areas. Notably, the interannual variability of fronts and chlorophyll-a are prominent in the Java upwelling region. During El Niño years, this region experienced an enhanced monsoon, resulting in a negative SST anomaly alongside positive anomalies in front probability and chlorophyll-a. A comprehensive description and underlying dynamics of frontal activity in the Indonesian seas are provided by this study. The findings are helpful to delineate the variability in chlorophyll-a, thereby facilitating the future understanding of local primary production and the carbon cycle.
Significance Statement
As typical mesoscale processes, oceanic fronts have significant impacts on biological processes and fisheries in marginal seas. The complex spatiotemporal variability of fronts and their effects on biological processes in the Indonesian seas remain poorly understood. This study aimed to address this knowledge gap by investigating the seasonal and interannual variability of fronts and their influence on chlorophyll-a, a key indicator of phytoplankton biomass and primary productivity. The study identified a high frontal probability in south Java Island during austral winter and El Niño years. Wind-driven upwelling was found to be a major factor in front generation and promoting phytoplankton growth. The findings of this study will improve the theoretical knowledge of regional dynamics, local primary production, and the carbon cycle in the Indonesian seas, benefiting fisheries management and ecosystem conservation efforts.
Abstract
The spatiotemporal variability of oceanic fronts in the Indonesian seas was investigated using high-resolution satellite observations. The study aimed to understand the underlying mechanism driving these fronts and their impact on chlorophyll-a variability. A high value of frontal probability was found near the coasts of major islands, exhibiting a distinct seasonal cycle with peaks occurrences during austral winter. The distribution variability of chlorophyll-a was generally consistent with the presence of active frontal zones, although a significantly positive relationship between fronts and chlorophyll-a was limited to only some specific areas, e.g., south Java Island and the Celebes Sea. Wind-driven upwelling played a major role in front generation in the Java upwelling region and enhanced frontal activity can promote the growth of phytoplankton, leading to higher chlorophyll-a. Furthermore, the study demonstrated that wind patterns preceded variations in front probability and chlorophyll-a by approximately two months. This lag suggests that the spatiotemporal variability of fronts and chlorophyll-a in this region is primarily influenced by the monsoon system. In addition, the sea surface temperature (SST) simultaneously modulated the chlorophyll-a variability. Negative SST anomalies were typically associated with positive anomalies in front probability the chlorophyll-a in most areas. Notably, the interannual variability of fronts and chlorophyll-a are prominent in the Java upwelling region. During El Niño years, this region experienced an enhanced monsoon, resulting in a negative SST anomaly alongside positive anomalies in front probability and chlorophyll-a. A comprehensive description and underlying dynamics of frontal activity in the Indonesian seas are provided by this study. The findings are helpful to delineate the variability in chlorophyll-a, thereby facilitating the future understanding of local primary production and the carbon cycle.
Significance Statement
As typical mesoscale processes, oceanic fronts have significant impacts on biological processes and fisheries in marginal seas. The complex spatiotemporal variability of fronts and their effects on biological processes in the Indonesian seas remain poorly understood. This study aimed to address this knowledge gap by investigating the seasonal and interannual variability of fronts and their influence on chlorophyll-a, a key indicator of phytoplankton biomass and primary productivity. The study identified a high frontal probability in south Java Island during austral winter and El Niño years. Wind-driven upwelling was found to be a major factor in front generation and promoting phytoplankton growth. The findings of this study will improve the theoretical knowledge of regional dynamics, local primary production, and the carbon cycle in the Indonesian seas, benefiting fisheries management and ecosystem conservation efforts.
