Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1910.04470

Deep sea floor observations of typhoon driven enhanced ocean turbulence

Hans van Haren·Wu-Cheng Chi·Chu-Fang Yang·Yiing-Jang Yang·Sen Jan
View original source ↗Read source document ↗Export metadata ↓

Abstract

The impact of large atmospheric disturbances on deep benthic communities is not well known quantitatively. Observations are scarce but may reveal specific processes leading to turbulent disturbances. Here, we present high-resolution deep-ocean observations to study potential turbulent mixing by large atmospheric disturbances. We deployed an array of 100-Hz sampling-rate geophysical broadband Ocean Bottom Seismometers (OBSs) on the seafloor. Within the footprint of this array we also deployed an oceanographic 1-Hz sampling-rate vertical temperature sensor string covering the water phase between 7 and 207 m above the seafloor at about 3000 m depth off eastern Taiwan between June 2017 and April 2018. All instruments registered Category 4 cyclone Typhoon Talims passage northeast of the array one day ahead of the cyclones closest approach when the cyclones eye was more than 1,000 km away. For 10 days, a group of near-inertial motions appeared most clearly in temperature. The registration reflects the importance of barotropic response to cyclones and the propagation of inertio-gravity waves in weak density stratification. In addition to internal tides, these waves drove turbulent overturns larger than 200 m that were concurrently registered by OBSs. The turbulent signals were neither due to seismic activity nor to ocean-surface wave action. Cyclones can generate not only microseisms and earth hums, as well as turbulence in the water column, producing additional ground motions. Quantified turbulence processes may help constrain models on sediment resuspension and its effect on deep-sea benthic life.

Explore related subjects

physics.ao-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hans van Haren, Wu-Cheng Chi, Chu-Fang Yang, Yiing-Jang Yang, Sen Jan. 2020-04-03. Deep sea floor observations of typhoon driven enhanced ocean turbulence. https://arxiv.org/abs/1910.04470

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

KEEP EXPLORING

Related papers

arXiv2026

The Physical Limit of Neural Hypoxia Detection in the Black Sea from Satellite Observations

Coastal hypoxia (O_2 < 63 [mmol / m^3]) threatens ocean health worldwide. On continental shelves, summer stratification prevents bottom oxygen consumed by respiration from being renewed, making monitoring essential to protect vulnerable ecosystems and reduce biodiversity loss. Although satellite observations are increasingly available, their potential to infer subsurface oxygen remains largely unexplored. We frame this as a Bayesian inverse problem relating surface observations to the complete three-dimensional physical and biogeochemical states of the Black Sea. Here, we solve it using a deep generative neural network trained on numerical model outputs that provides a tractable and computationally efficient approximation of the true posterior distribution of sea states. We find that accurate state estimation is limited to the mixed layer, because its homogeneity makes surface conditions representative of subsurface states. During summer, we detect 38% of all hypoxic events shelf-wide with a precision of 47%. Improving the results will likely require longer assimilation windows or subsurface observations.

physics.ao-ph↗
arXiv2026

CNN-based forecasting of early winter NAO using sea surface temperature

The North Atlantic Oscillation (NAO) is the dominant mode of atmospheric variability over the North Atlantic sector, influencing temperature and precipitation across Europe. While the NAO's impact on North Atlantic sea surface temperatures (SSTs) is well understood, the NAO can also be driven by SST anomalies. However, this NAO response to SST anomalies is believed to be weak and nonlinear. Former studies highlight that during early winter (November-December), El Nino Southern Oscillation (ENSO) events modulate the NAO, with El Nino (La Nina) events being linked to positive (negative) NAO phases, and an opposite effect observed in late winter (January-February). Indian Ocean SSTs and the North Atlantic Horseshoe SST anomaly have also been suggested as contributors to early winter NAO variability. However, climate models often struggle to capture these SST-NAO teleconnections, particularly in early winter. To address this, a statistical framework based on convolutional neural networks (CNNs) is developed to predict the early winter NAO using observed SST fields one-, two-, and three-month before. A linear model serves as a benchmark, and both models are trained on ERA5 reanalysis data from 1940 to 2023. A sensitivity analysis is used to interpret the CNN's decision-making process, revealing that it focuses on regions such as the tropical Pacific and North Atlantic, confirming results from previous works. The CNN outperforms the linear model, highlighting the value of capturing nonlinear SST-NAO relationships. Prediction skill appears to be linked to ENSO, with strong ENSO events associated with greater skill in forecasting the NAO than neutral events. These findings underscore the potential of deep learning to build medium-range NAO prediction.

physics.ao-ph↗
arXiv2026

PepC-Global: A Basin-Tuned Probabilistic Tropical Cyclone Model with Enhanced Out-of-Sample Skill and Climate-Sensitive Over-Land Decay

We present PepC-Global, a global version of the Princeton environment-dependent probabilistic tropical cyclone (PepC) framework that uniquely implements basin-wise tuning within a single unified tropical cyclone climatology model. PepC-Global represents tropical cyclone climatology as a coupled stochastic process linking genesis, track, and intensity conditioned on large-scale environmental predictors. Each of the genesis, track, and intensity modules outperforms widely used linear models in out-of-sample tests. The intensity module also incorporates an environment-dependent over-land decay model, offering greater sensitivity to climate change signals than conventional fixed decay rate approaches. Systematic evaluation against observations demonstrates that PepC-Global closely reproduces genesis basin-wise frequency, seasonal cycles, interannual variability, and spatial distributions. The model also accurately captures basin-wise track patterns, along-coastline landfall frequency, and intensity statistics including lifetime maximum intensity and landfall intensity distributions. PepC-Global provides a versatile tool for probabilistic tropical cyclone hazard and risk assessment and a practical framework for investigating changes in tropical cyclone activity across future climate scenarios.

physics.ao-ph↗