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- Author or Editor: Sarah T. Gille x
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Abstract
Four years of ocean vector wind data are used to evaluate statistics of wind stress over the ocean. Raw swath wind stresses derived from the Quick Scatterometer (QuikSCAT) are compared with five different global gridded wind products, including products based on scatterometer observations, meteorological analysis winds from the European Centre for Medium-Range Weather Forecasts, and reanalysis winds from the National Centers for Environmental Prediction. Buoy winds from a limited number of sites in the Pacific Ocean are also considered.
Probability density functions (PDFs) computed for latitudinal bands show that mean wind stresses for the six global products are largely in agreement, while variances differ substantially, by a factor of 2 or more, with swath wind stresses indicating highest variances for meridional winds and for zonal winds outside the Tropics. Higher moments of the PDFs also differ. Kurtoses are large for all wind products, implying that PDFs are not Gaussian. None of the available gridded products fully captures the range of extreme wind events seen in the raw swath data.
Frequency spectra for the five gridded products agree with frequency spectra from swath data at low frequencies, but spectral slopes differ at higher frequencies, particularly for frequencies greater than 100 cycles per year (cpy), which are poorly resolved by a single scatterometer. In the frequency range between 10 and 90 cpy that is resolved by the scatterometer, spectra derived from swath data are flatter than spectra from gridded products and are judged to be flatter than ω −2/3 at all latitudes.
Abstract
Four years of ocean vector wind data are used to evaluate statistics of wind stress over the ocean. Raw swath wind stresses derived from the Quick Scatterometer (QuikSCAT) are compared with five different global gridded wind products, including products based on scatterometer observations, meteorological analysis winds from the European Centre for Medium-Range Weather Forecasts, and reanalysis winds from the National Centers for Environmental Prediction. Buoy winds from a limited number of sites in the Pacific Ocean are also considered.
Probability density functions (PDFs) computed for latitudinal bands show that mean wind stresses for the six global products are largely in agreement, while variances differ substantially, by a factor of 2 or more, with swath wind stresses indicating highest variances for meridional winds and for zonal winds outside the Tropics. Higher moments of the PDFs also differ. Kurtoses are large for all wind products, implying that PDFs are not Gaussian. None of the available gridded products fully captures the range of extreme wind events seen in the raw swath data.
Frequency spectra for the five gridded products agree with frequency spectra from swath data at low frequencies, but spectral slopes differ at higher frequencies, particularly for frequencies greater than 100 cycles per year (cpy), which are poorly resolved by a single scatterometer. In the frequency range between 10 and 90 cpy that is resolved by the scatterometer, spectra derived from swath data are flatter than spectra from gridded products and are judged to be flatter than ω −2/3 at all latitudes.
Abstract
Acoustically tracked float data from 16 experiments carried out in the North Atlantic are used to evaluate the feasibility of estimating eddy heat fluxes from floats. Daily float observations were bin averaged in 2° by 2° by 200-db-deep geographic bins, and eddy heat fluxes were estimated for each bin. Results suggest that eddy heat fluxes can be highly variable, with substantial outliers that mean that fluxes do not converge quickly. If 100 statistically independent observations are available in each bin (corresponding to 500–1000 float days of data), then results predict that 80% of bins will have eddy heat fluxes that are statistically different from zero. Pop-up floats, such as Autonomous Lagrangian Circulation Explorer (ALACE) and Argo floats, do not provide daily sampling and therefore underestimate eddy heat flux. The fraction of eddy heat flux resolved using pop-up float sampling patterns decreases linearly with increasing intervals between float mapping and can be modeled analytically. This implies that flux estimates from pop-up floats may be correctable to represent true eddy heat flux.
Abstract
Acoustically tracked float data from 16 experiments carried out in the North Atlantic are used to evaluate the feasibility of estimating eddy heat fluxes from floats. Daily float observations were bin averaged in 2° by 2° by 200-db-deep geographic bins, and eddy heat fluxes were estimated for each bin. Results suggest that eddy heat fluxes can be highly variable, with substantial outliers that mean that fluxes do not converge quickly. If 100 statistically independent observations are available in each bin (corresponding to 500–1000 float days of data), then results predict that 80% of bins will have eddy heat fluxes that are statistically different from zero. Pop-up floats, such as Autonomous Lagrangian Circulation Explorer (ALACE) and Argo floats, do not provide daily sampling and therefore underestimate eddy heat flux. The fraction of eddy heat flux resolved using pop-up float sampling patterns decreases linearly with increasing intervals between float mapping and can be modeled analytically. This implies that flux estimates from pop-up floats may be correctable to represent true eddy heat flux.
Abstract
Autonomous Lagrangian Circulation Explorer (ALACE) floats were designed to measure subsurface velocities throughout the global ocean. In order to transmit their data to satellite, they spend 24 h at the ocean surface during each 10–25-day cycle. During this time the floats behave as undrogued drifters. In the Southern Ocean, floats tend to advect downwind and, in accordance with Ekman theory, slightly to the left of the wind during their time at the surface. Mean displacements are likely to carry floats northward and, correspondingly, with each cycle, the Southern Ocean floats will move into warmer water with higher dynamic height. Because of large variability, the northward trend may not be discernible for any single float: in 2 years' worth of 10-day cycles, a typical float will be displaced 100 ± 270 km northward relative to a float that never surfaces. Float surface velocities and wind speed are statistically correlated at the 95% confidence level. Compared with drogued drifters, floats tend to move more rapidly, are advected more strongly downwind, and are more sensitive to changes in wind speed. Regression coefficients estimated from the differences between float and drogued drifter velocities suggest that floats may be used to estimate the mean upper ocean currents in regions where drogued drifter data are not available.
Abstract
Autonomous Lagrangian Circulation Explorer (ALACE) floats were designed to measure subsurface velocities throughout the global ocean. In order to transmit their data to satellite, they spend 24 h at the ocean surface during each 10–25-day cycle. During this time the floats behave as undrogued drifters. In the Southern Ocean, floats tend to advect downwind and, in accordance with Ekman theory, slightly to the left of the wind during their time at the surface. Mean displacements are likely to carry floats northward and, correspondingly, with each cycle, the Southern Ocean floats will move into warmer water with higher dynamic height. Because of large variability, the northward trend may not be discernible for any single float: in 2 years' worth of 10-day cycles, a typical float will be displaced 100 ± 270 km northward relative to a float that never surfaces. Float surface velocities and wind speed are statistically correlated at the 95% confidence level. Compared with drogued drifters, floats tend to move more rapidly, are advected more strongly downwind, and are more sensitive to changes in wind speed. Regression coefficients estimated from the differences between float and drogued drifter velocities suggest that floats may be used to estimate the mean upper ocean currents in regions where drogued drifter data are not available.
Abstract
The high vertical resolution of temperature and salinity measurements from expendable conductivity–temperature–depth (XCTD) instruments can be useful for inferring small-scale mixing rates in the ocean. However, XCTD temperature profiles show distinct spectral spikes at frequencies of 5 and 10 Hz, corresponding to 1 and 2 cycles per five measurement points. Peaks at these same frequencies are often present in the conductivity spectra as well. The spectral spikes occur in XCTD profiles from both the subtropical and subpolar regions. They appear to originate as digital electronic noise within the probes. A finite impulse response filter design procedure was used to develop filters that could remove the spectral spikes while retaining as much high vertical resolution as possible. For most purposes, the application of an 11-point, least squares, low-pass filter proves sufficient for removing the spectral energy at 5 and 10 Hz, and results in an effective minimum vertical resolution of about 0.7 m.
Abstract
The high vertical resolution of temperature and salinity measurements from expendable conductivity–temperature–depth (XCTD) instruments can be useful for inferring small-scale mixing rates in the ocean. However, XCTD temperature profiles show distinct spectral spikes at frequencies of 5 and 10 Hz, corresponding to 1 and 2 cycles per five measurement points. Peaks at these same frequencies are often present in the conductivity spectra as well. The spectral spikes occur in XCTD profiles from both the subtropical and subpolar regions. They appear to originate as digital electronic noise within the probes. A finite impulse response filter design procedure was used to develop filters that could remove the spectral spikes while retaining as much high vertical resolution as possible. For most purposes, the application of an 11-point, least squares, low-pass filter proves sufficient for removing the spectral energy at 5 and 10 Hz, and results in an effective minimum vertical resolution of about 0.7 m.
Identifying Ocean Swell Generation Events from Ross Ice Shelf Seismic DataAbstract
Strong surface winds under extratropical cyclones exert intense surface stresses on the ocean that lead to upper-ocean mixing, intensified heat fluxes, and the generation of waves, that, over time, lead to swell waves (longer than 10-s period) that travel long distances. Because low-frequency swell propagates faster than high-frequency swell, the frequency dependence of swell arrival times at a measurement site can be used to infer the distance and time that the wave has traveled from its generation site. This study presents a methodology that employs spectrograms of ocean swell from point observations on the Ross Ice Shelf (RIS) to verify the position of high wind speed areas over the Southern Ocean, and therefore of extratropical cyclones. The focus here is on the implementation and robustness of the methodology in order to lay the groundwork for future broad application to verify Southern Ocean storm positions from atmospheric reanalysis data. The method developed here combines linear swell dispersion with a parametric wave model to construct a time- and frequency-dependent model of the dispersed swell arrivals in spectrograms of seismic observations on the RIS. A two-step optimization procedure (deep learning) of gradient descent and Monte Carlo sampling allows detailed estimates of the parameter distributions, with robust estimates of swell origins. Median uncertainties of swell source locations are 110 km in radial distance and 2 h in time. The uncertainties are derived from RIS observations and the model, rather than an assumed distribution. This method is an example of supervised machine learning informed by physical first principles in order to facilitate parameter interpretation in the physical domain.
Abstract
Strong surface winds under extratropical cyclones exert intense surface stresses on the ocean that lead to upper-ocean mixing, intensified heat fluxes, and the generation of waves, that, over time, lead to swell waves (longer than 10-s period) that travel long distances. Because low-frequency swell propagates faster than high-frequency swell, the frequency dependence of swell arrival times at a measurement site can be used to infer the distance and time that the wave has traveled from its generation site. This study presents a methodology that employs spectrograms of ocean swell from point observations on the Ross Ice Shelf (RIS) to verify the position of high wind speed areas over the Southern Ocean, and therefore of extratropical cyclones. The focus here is on the implementation and robustness of the methodology in order to lay the groundwork for future broad application to verify Southern Ocean storm positions from atmospheric reanalysis data. The method developed here combines linear swell dispersion with a parametric wave model to construct a time- and frequency-dependent model of the dispersed swell arrivals in spectrograms of seismic observations on the RIS. A two-step optimization procedure (deep learning) of gradient descent and Monte Carlo sampling allows detailed estimates of the parameter distributions, with robust estimates of swell origins. Median uncertainties of swell source locations are 110 km in radial distance and 2 h in time. The uncertainties are derived from RIS observations and the model, rather than an assumed distribution. This method is an example of supervised machine learning informed by physical first principles in order to facilitate parameter interpretation in the physical domain.
Numerical Simulations to Project Argo Float Positions in the Middepth and Deep Southwest PacificAbstract
Argo float trajectories are simulated in the southwest Pacific basin (25°–45°S, 170°E–165°W) using velocity fields from a 1/12° Southern Ocean model and a Lagrangian particle tracking model programmed to represent the vertical motions of profiling Argo floats. The system is applied to simulate both core Argo floats (typically parked at 1000-m depth and profiling to 2000-m depth) and Deep Argo floats (parked 500 m above the seafloor). The goal is to estimate probability density functions (PDFs) predicting future float positions. Differences are expected in the trajectory statistics, largely because of limitations in the temporal and spatial resolution of the model fields and uncertainties associated with a random walk component included in the particle advection scheme to represent this unresolved variability. Nonetheless, the core Argo float displacements over ~100-day time intervals are mostly consistent with the derived PDFs, particularly in regions with stable midlayer flows. For the Deep Argo floats, which are released into the open ocean and parked near the bottom, the simulations predict an average total displacement of less than 50 km within 100 days, in good agreement with the Deep Argo floats deployed as part of a pilot study. The study explores both the representativeness and the predictability of float displacements, with an aim to contribute to planning for the float observing system.
Abstract
Argo float trajectories are simulated in the southwest Pacific basin (25°–45°S, 170°E–165°W) using velocity fields from a 1/12° Southern Ocean model and a Lagrangian particle tracking model programmed to represent the vertical motions of profiling Argo floats. The system is applied to simulate both core Argo floats (typically parked at 1000-m depth and profiling to 2000-m depth) and Deep Argo floats (parked 500 m above the seafloor). The goal is to estimate probability density functions (PDFs) predicting future float positions. Differences are expected in the trajectory statistics, largely because of limitations in the temporal and spatial resolution of the model fields and uncertainties associated with a random walk component included in the particle advection scheme to represent this unresolved variability. Nonetheless, the core Argo float displacements over ~100-day time intervals are mostly consistent with the derived PDFs, particularly in regions with stable midlayer flows. For the Deep Argo floats, which are released into the open ocean and parked near the bottom, the simulations predict an average total displacement of less than 50 km within 100 days, in good agreement with the Deep Argo floats deployed as part of a pilot study. The study explores both the representativeness and the predictability of float displacements, with an aim to contribute to planning for the float observing system.
Harmonic Analysis of Non-Phase-Locked Tides with Red Noise Using the red_tide PackageAbstract
A novel tidal analysis package (red_tide) has been developed to characterize low-amplitude non-phase-locked tidal energy and dominant tidal peaks in noisy, irregularly sampled, or gap-prone time series. We recover tidal information by expanding conventional harmonic analysis to include prior information and assumptions about the statistics of a process, such as the assumption of a spectrally colored background, treated as nontidal noise. This is implemented using Bayesian maximum posterior estimation and assuming Gaussian prior distributions. We utilize a hierarchy of test cases, including synthetic data and observations, to evaluate this method and its relevance to analysis of data with a tidal component and an energetic nontidal background. Analysis of synthetic test cases shows that the methodology provides robust tidal estimates. When the background energy spectrum is nearly spectrally white, red_tide results replicate results from ordinary least squares (OLS) commonly used in other tidal packages. When background spectra are red (a spectral slope of −2 or steeper), red_tide’s estimates represent a measurable improvement over OLS. The approach highlights the presence of tidal variability and low-amplitude constituents in observations by allowing arbitrarily configurable fitted frequencies and prior statistics that constrain solutions. These techniques have been implemented in MATLAB in order to analyze tidal data with non-phase-locked components and an energetic background that pose challenges to the commonly used OLS approach.
Abstract
A novel tidal analysis package (red_tide) has been developed to characterize low-amplitude non-phase-locked tidal energy and dominant tidal peaks in noisy, irregularly sampled, or gap-prone time series. We recover tidal information by expanding conventional harmonic analysis to include prior information and assumptions about the statistics of a process, such as the assumption of a spectrally colored background, treated as nontidal noise. This is implemented using Bayesian maximum posterior estimation and assuming Gaussian prior distributions. We utilize a hierarchy of test cases, including synthetic data and observations, to evaluate this method and its relevance to analysis of data with a tidal component and an energetic nontidal background. Analysis of synthetic test cases shows that the methodology provides robust tidal estimates. When the background energy spectrum is nearly spectrally white, red_tide results replicate results from ordinary least squares (OLS) commonly used in other tidal packages. When background spectra are red (a spectral slope of −2 or steeper), red_tide’s estimates represent a measurable improvement over OLS. The approach highlights the presence of tidal variability and low-amplitude constituents in observations by allowing arbitrarily configurable fitted frequencies and prior statistics that constrain solutions. These techniques have been implemented in MATLAB in order to analyze tidal data with non-phase-locked components and an energetic background that pose challenges to the commonly used OLS approach.
Optimizing the Biogeochemical Argo Float DistributionAbstract
The core Argo array has operated with the design goal of uniform spatial distribution of 3° in latitude and longitude. Recent studies have acknowledged that spatial and temporal scales of variability in some parts of the ocean are not resolved by 3° sampling and have recommended increased core Argo density in the equatorial region, boundary currents, and marginal seas with an integrated vision of other Argo variants. Biogeochemical (BGC) Argo floats currently observe the ocean from a collection of pilot arrays, but recently funded proposals will transition these pilot arrays to a global array. The current BGC Argo implementation plan recommends uniform spatial distribution of BGC Argo floats. For the first time, we estimate the effectiveness of the existing BGC Argo array to resolve the anomaly from the mean using a subset of modeled, full-depth BGC fields. We also study the effectiveness of uniformly distributed BGC Argo arrays with varying float densities at observing the ocean. Then, using previous Argo trajectories, we estimate the Argo array’s future distribution and quantify how well it observes the ocean. Finally, using a novel technique for sequentially identifying the best deployment locations, we suggest the optimal array distribution for BGC Argo floats to minimize objective mapping uncertainty in a subset of BGC fields and to best constrain BGC temporal variability.
Abstract
The core Argo array has operated with the design goal of uniform spatial distribution of 3° in latitude and longitude. Recent studies have acknowledged that spatial and temporal scales of variability in some parts of the ocean are not resolved by 3° sampling and have recommended increased core Argo density in the equatorial region, boundary currents, and marginal seas with an integrated vision of other Argo variants. Biogeochemical (BGC) Argo floats currently observe the ocean from a collection of pilot arrays, but recently funded proposals will transition these pilot arrays to a global array. The current BGC Argo implementation plan recommends uniform spatial distribution of BGC Argo floats. For the first time, we estimate the effectiveness of the existing BGC Argo array to resolve the anomaly from the mean using a subset of modeled, full-depth BGC fields. We also study the effectiveness of uniformly distributed BGC Argo arrays with varying float densities at observing the ocean. Then, using previous Argo trajectories, we estimate the Argo array’s future distribution and quantify how well it observes the ocean. Finally, using a novel technique for sequentially identifying the best deployment locations, we suggest the optimal array distribution for BGC Argo floats to minimize objective mapping uncertainty in a subset of BGC fields and to best constrain BGC temporal variability.
Abstract
Finescale estimates of diapycnal diffusivity κ are computed from CTD and expendable CTD (XCTD) data sampled in Drake Passage and in the eastern Pacific sector of the Southern Ocean and are compared against microstructure measurements from the same times and locations. The microstructure data show vertical diffusivities that are one-third to one-fifth as large over the smooth abyssal plain in the southeastern Pacific as they are in Drake Passage, where diffusivities are thought to be enhanced by the flow of the Antarctic Circumpolar Current over rough topography. Finescale methods based on vertical strain estimates are successful at capturing the spatial variability between the low-mixing regime in the southeastern Pacific and the high-mixing regime of Drake Passage. Thorpe-scale estimates for the same dataset fail to capture the differences between Drake Passage and eastern Pacific estimates. XCTD profiles have lower vertical resolution and higher noise levels after filtering than CTD profiles, resulting in XCTD κ estimates that are, on average, an order of magnitude higher than CTD estimates. Overall, microstructure diffusivity estimates are better matched by strain-based estimates than by estimates based on Thorpe scales, and CTD data appear to perform better than XCTD data. However, even the CTD-based strain diffusivity estimates can differ from microstructure diffusivities by nearly an order of magnitude, suggesting that density-based fine-structure methods of estimating mixing from CTD or XCTD data have real limitations in low-stratification regimes such as the Southern Ocean.
Abstract
Finescale estimates of diapycnal diffusivity κ are computed from CTD and expendable CTD (XCTD) data sampled in Drake Passage and in the eastern Pacific sector of the Southern Ocean and are compared against microstructure measurements from the same times and locations. The microstructure data show vertical diffusivities that are one-third to one-fifth as large over the smooth abyssal plain in the southeastern Pacific as they are in Drake Passage, where diffusivities are thought to be enhanced by the flow of the Antarctic Circumpolar Current over rough topography. Finescale methods based on vertical strain estimates are successful at capturing the spatial variability between the low-mixing regime in the southeastern Pacific and the high-mixing regime of Drake Passage. Thorpe-scale estimates for the same dataset fail to capture the differences between Drake Passage and eastern Pacific estimates. XCTD profiles have lower vertical resolution and higher noise levels after filtering than CTD profiles, resulting in XCTD κ estimates that are, on average, an order of magnitude higher than CTD estimates. Overall, microstructure diffusivity estimates are better matched by strain-based estimates than by estimates based on Thorpe scales, and CTD data appear to perform better than XCTD data. However, even the CTD-based strain diffusivity estimates can differ from microstructure diffusivities by nearly an order of magnitude, suggesting that density-based fine-structure methods of estimating mixing from CTD or XCTD data have real limitations in low-stratification regimes such as the Southern Ocean.
Using Existing Argo Trajectories to Statistically Predict Future Float Positions with a Transition MatrixAbstract
The Argo array provides nearly 4000 temperature and salinity profiles of the top 2000 m of the ocean every 10 days. Still, Argo floats will never be able to measure the ocean at all times, everywhere. Optimized Argo float distributions should match the spatial and temporal variability of the many societally important ocean features that they observe. Determining these distributions is challenging because float advection is difficult to predict. Using no external models, transition matrices based on existing Argo trajectories provide statistical inferences about Argo float motion. We use the 24 years of Argo locations to construct an optimal transition matrix that minimizes estimation bias and uncertainty. The optimal array is determined to have a 2° × 2° spatial resolution with a 90-day time step. We then use the transition matrix to predict the probability of future float locations of the core Argo array, the Global Biogeochemical Array, and the Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) array. A comparison of transition matrices derived from floats using Argos system and Iridium communication methods shows the impact of surface displacements, which is most apparent near the equator. Additionally, we demonstrate the utility of transition matrices for validating models by comparing the matrix derived from Argo floats with that derived from a particle release experiment in the Southern Ocean State Estimate (SOSE).
Abstract
The Argo array provides nearly 4000 temperature and salinity profiles of the top 2000 m of the ocean every 10 days. Still, Argo floats will never be able to measure the ocean at all times, everywhere. Optimized Argo float distributions should match the spatial and temporal variability of the many societally important ocean features that they observe. Determining these distributions is challenging because float advection is difficult to predict. Using no external models, transition matrices based on existing Argo trajectories provide statistical inferences about Argo float motion. We use the 24 years of Argo locations to construct an optimal transition matrix that minimizes estimation bias and uncertainty. The optimal array is determined to have a 2° × 2° spatial resolution with a 90-day time step. We then use the transition matrix to predict the probability of future float locations of the core Argo array, the Global Biogeochemical Array, and the Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) array. A comparison of transition matrices derived from floats using Argos system and Iridium communication methods shows the impact of surface displacements, which is most apparent near the equator. Additionally, we demonstrate the utility of transition matrices for validating models by comparing the matrix derived from Argo floats with that derived from a particle release experiment in the Southern Ocean State Estimate (SOSE).
