Jamie Hilditch
Postdoctoral Scholar, Earth System Science
All Publications
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Symmetric instability drives exchange between surface and bottom waters in a coastal front.
Science advances
2026; 12 (19): eaeb9841
Abstract
The coastal ocean in the northern Gulf of Mexico is highly productive and socioeconomically important. Here, stratification can inhibit vertical exchange, with consequences for ecosystem health and hypoxia. Previous work emphasized the role of wind-driven turbulent mixing as a mechanism to overcome the stratification barrier. We identify symmetric instability (SI) as an additional and more energy-efficient pathway linking surface and bottom waters. In high-resolution observations, diagonal bands of overturning motions, a telltale sign of SI, connect the sea surface to the bottom and produce intrusions of temperature and oxygen anomalies. Overturning persists for 2 days after instability-favorable wind ceases. During this time, vertical advective fluxes exceed turbulent fluxes by an order of magnitude, ventilating low-oxygen bottom waters and transporting surface heat downward. Our results show that SI facilitates vertical exchange that can outlive direct wind forcing, highlighting an instability-driven mechanism that may be important in coastal oceans more generally.
View details for DOI 10.1126/sciadv.aeb9841
View details for PubMedID 42102197
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Trapping of near-inertial waves and critical layer formation in baroclinic currents
JOURNAL OF FLUID MECHANICS
2026; 1031
View details for DOI 10.1017/jfm.2026.11336
View details for Web of Science ID 001720204400001
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Observations of Subduction, Downward Heat Flux and Dense Filament Collapse in the Northern Gulf of Mexico
JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS
2025; 130 (11)
View details for DOI 10.1029/2025JC022570
View details for Web of Science ID 001609635700001
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Refraction of near-inertial waves by submesoscale vorticity filaments
JOURNAL OF FLUID MECHANICS
2025; 1020
View details for DOI 10.1017/jfm.2025.10637
View details for Web of Science ID 001582887800001
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Expected correlation in time-series analysis.
Physical review. E
2025; 111 (2-1): 024121
Abstract
Time-series analysis often involves the characterization of order or predictability, qualities that are related to internal structure and autocorrelation. Investigating a recently proposed algorithm for solving a density prediction task, we demonstrate that if the same system can be viewed on multiple time scales, there is an inevitable degree of expected order and predictability that increases as the system size grows. In particular, we introduce bounds on the expected second-order structure function and autocorrelation function of a time series where multiple observation scales are available, and conclude with a lower bound on the expected correlation time. Such a lower bound shows that there is an inevitable degree of correlation induced when time-series data is aggregated, quantifying a previously overlooked source of bias towards high correlations.
View details for DOI 10.1103/PhysRevE.111.024121
View details for PubMedID 40103100
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Blocked Drainpipes and Smoking Chimneys: Discovery of New Near-Inertial Wave Phenomena in Anticyclones
Oceanography
2024
View details for DOI 10.5670/oceanog.2024.304
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Parametric subharmonic instability of inertial shear at ocean fronts
JOURNAL OF FLUID MECHANICS
2023; 966
View details for DOI 10.1017/jfm.2023.452
View details for Web of Science ID 001028820600001
https://orcid.org/0000-0002-7164-9953