Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2002.07537

Slow persistent mixing in the abyss

Hans van Haren
View original source ↗Read source document ↗Export metadata ↓

Abstract

Knowledge about deep-ocean turbulent mixing and flow circulation above abyssal hilly plains is important to quantify processes for the modelling of resuspension and dispersal of sediments in areas where turbulence sources are expected to be relatively weak. Turbulence may disperse sediments from artificial deep-sea mining activities over large distances. To quantify turbulent mixing above the deep-ocean floor around 4000 m depth, high-resolution moored temperature sensor observations have been obtained from the near-equatorial southeast Pacific (7{\deg}S, 88{\deg}W). Models demonstrate low activity of equatorial flow dynamics, internal tides and surface near-inertial motions in the area. The present observations demonstrate a Conservative Temperature difference of about 0.012{\deg}C between 7 and 406 meter above the bottom (hereafter, mab, for short), which is a quarter of the adiabatic lapse rate. The very weakly stratified waters with buoyancy periods between about six hours and one day allow for slowly varying mixing. The calculated turbulence dissipation rate values are half to one order of magnitude larger than those from open-ocean turbulent exchange well away from bottom topography and surface boundaries. In the deep, turbulent overturns extend up to 100 m tall, in the ocean-interior, and also reach the lowest sensor. The overturns are governed by internal-wave-shear and -convection. The turbulence inertial subrange is observed to extend into the internal wave frequency band. The associated mixing is not related to bottom friction processes but to internal wave breaking and near-inertial shear. The mixing facilitates long (hours to day) and high (exceeding 100 mab) dispersal of suspended sediments.

Explore related subjects

physics.ao-ph

Keep this discovery

BibTeXRIS

Hans van Haren. 2020-02-18. Slow persistent mixing in the abyss. https://doi.org/10.1007/s10236-019-01335-6

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Windowed Envelope Statistics for Time-Domain Significant Wave Height Estimation From HF Radar

Significant wave height (SWH) retrieval from high-frequency (HF) radar typically relies on a weak second-order Doppler continuum that is sensitive to noise, interference, and spectral leakage. This letter presents a Windowed Envelope Statistics Estimator (WESE) that operates directly on beam-formed time-domain voltages. A second-order term obtained from a Neumann expansion of the rough-surface field equation motivates quadratic compensation of localized radar features. WESE extracts the mean, standard deviation, or variance from overlapping windows of the in-phase, quadrature, or envelope-magnitude sequence, followed by quadratic compensation, rank ordering, least-squares regression, and causal smoothing. Evaluation used 335 synchronized hourly observations from a 13.385 MHz, 12-element WERA system at Argentia, Newfoundland and Labrador. The optimal configuration used quadrature variance, a 16-sample window, 896 retained chronological samples, and 30-h smoothing, achieving an RMSE of 0.152 m and a Pearson correlation of 0.978. This represents RMSE reductions of 32.1% and 18.7% relative to previously reported linear and second-order compensated ordered-statistics models, respectively. The results demonstrate robust time-domain SWH estimation without explicit Doppler-spectrum construction.

physics.ao-ph↗
arXiv2026

KiloDA: Reconstructing kilometer-scale near-surface wind states from sparse station observations

Accurate kilometer-scale near-surface winds are important for understanding atmospheric processes over complex terrain, yet remain difficult to reconstruct from sparse and unevenly distributed observations. Here we introduce KiloDA, a diffusion framework for hourly kilometer-scale wind reconstruction from surface stations. KiloDA learns the statistical distribution and spatial structure of wind fields from historical 3-km Weather Research and Forecasting (WRF) model forecasts. At each reconstruction time, no contemporaneous WRF field is used. Instead, station observations provide the only constraints on the current atmospheric state and guide posterior sampling from the learned prior. In idealized WRF experiments, KiloDA recovers localized wind structures when only 0.24% of grid cells are observed and shows an overall advantage over conventional interpolation across terrain conditions and wind speed regimes. This capability largely transfers to real observations. In a fully withheld region, KiloDA reduces the median wind speed root mean square error (RMSE) by 19% relative to ERA5 reanalysis, using only observations outside the region, with the largest improvements over high-elevation and high-relief terrain. A random station holdout further confirms that this advantage extends across different complex-terrain locations and holdout configurations. These results show that historical model archives can provide useful structural knowledge for reconstructing kilometer-scale wind fields from sparse observations without requiring an accurate model estimate of the current atmospheric state.

physics.ao-ph↗