Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2304.13225

Intrinsically episodic Antarctic shelf intrusions of circumpolar deep water via canyons

Ellie Q. Y. Ong·Edward Doddridge·Navid C. Constantinou·Andrew McC. Hogg·Matthew H. England
View original source ↗Read source document ↗Export metadata ↓

Abstract

The structure of the Antarctic Slope Current at the continental shelf is crucial in governing the poleward transport of warm water. Canyons on the continental slope may provide a pathway for warm water to cross the slope current and intrude onto the continental shelf underneath ice shelves, which can increase rates of ice shelf melting, leading to reduced buttressing of ice shelves, accelerating glacial flow and hence increased sea level rise. Observations and modelling studies of the Antarctic Slope Current and cross-shelf warm water intrusions are limited, particularly in the East Antarctica region. To explore this topic, an idealised configuration of the Antarctic Slope Current is developed, using an eddy-resolving isopycnal model that emulates the dynamics and topography of the East Antarctic sector. Warm water intrusions via canyons are found to occur in discrete episodes of large onshore flow induced by eddies, even in the absence of any temporal variability in external forcings, demonstrating the intrinsic nature of these intrusions to the slope current system. Canyon width is found to play a key role in modulating cross-shelf exchanges; warm water transport through narrower canyons is more irregular than transport through wider canyons. The intrinsically episodic cross-shelf transport is found to be driven by feedbacks between wind energy input and eddy generation in the Antarctic Slope Current. Improved understanding of the intrinsic variability of warm water intrusions can help guide future observational and modelling studies in the analysis of eddy impacts on Antarctic shelf circulation.

Explore related subjects

physics.ao-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ellie Q. Y. Ong, Edward Doddridge, Navid C. Constantinou, Andrew McC. Hogg, Matthew H. England. 2024-03-07. Intrinsically episodic Antarctic shelf intrusions of circumpolar deep water via canyons. https://doi.org/10.1175/jpo-d-23-0067.1

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

KEEP EXPLORING

Related papers

arXiv2026

GPROF-IR: An Improved Single-Channel Infrared Precipitation Retrieval for Merged Satellite Precipitation Products

The Integrated Multi-Satellite Retrievals for the Global Precipitation Measurement (IMERG) product combines precipitation estimates from passive microwave (PMW) and infrared (IR) sensors to provide quasi-global precipitation estimates at half-hourly resolution. Because IR observations are primarily sensitive to cloud-top properties, however, IR-based precipitation estimates are generally less accurate than PMW estimates and can introduce artifacts into the merged precipitation fields. We introduce GPROF-IR, a neural-network-based precipitation retrieval designed to provide precipitation estimates from single-channel geostationary IR observations for the upcoming IMERG V08. GPROF-IR uses a convolutional neural network to exploit spatiotemporal information from sequences of half-hourly IR observations. We validate GPROF-IR against independent surface-based precipitation measurements over land and ocean. GPROF-IR substantially improves upon existing single-channel IR retrievals, including the IR retrieval used in IMERG V07 and more recent neural-network-based approaches. During overpasses of flagship PMW sensors, GPROF-IR is more accurate than the corresponding IMERG PMW estimates over CONUS and Austria, but not Korea. Over CONUS, GPROF-IR outperforms the merged IMERG V07 product for hourly and daily precipitation accumulations. Against shipborne disdrometer observations, GPROF-IR is more accurate than IMERG V07 in the tropics but slightly less accurate in the extratropics. Since geostationary IR observations are continuously available across much of the globe since 1983, GPROF-IR provides a promising basis for improving merged precipitation products and developing long-term, temporally consistent precipitation records.

physics.ao-ph↗
arXiv2026

FAST-ML: A Hybrid Physics-Machine Learning Framework for Tropical Cyclone Intensity Forecasting

Rapid intensification (RI) remains one of the most consequential and difficult aspects of tropical cyclone (TC) forecasting. Although full-physics numerical weather prediction models can represent the processes governing RI, resolving storm-environment interactions remains computationally expensive, while purely data-driven approaches often lack physical interpretability. We present FAST-ML, a hybrid framework that bridges data-driven efficiency with physical constraints. A physically informed dual-stream neural parameterization ingests 3D ERA5 fields to diagnose ventilation controls---environmental wind shear and mid-level entropy deficit. By optimizing these parameters end-to-end through a differentiable FAST intensity model, this architecture establishes a robust new paradigm for observation-driven parameter optimization, ensuring storm evolution remains strictly governed by thermodynamic principles. By better capturing the storm's continuous intensity evolution, FAST-ML improves upon its physical baseline, reducing ensemble CRPS across forecast lead times, with a reduction of approximately 31% at 60 h and nearly halving the RI false alarm ratio without sacrificing detection skill. In a 100-member ensemble configuration, FAST-ML produces intensity forecasts comparable to FNV3 for selected storms under the evaluated input configurations. Furthermore, zero-shot tests on selected Eastern Pacific storms provide encouraging evidence of cross-basin transferability. FAST-ML provides a modular intensity forecasting framework that can be coupled with externally supplied storm tracks and environmental fields. It demonstrates that observation-driven parameter learning within physically constrained dynamics simultaneously enhances accuracy, interpretability, and computational efficiency.

physics.ao-ph↗
arXiv2026

West-WRF AI 2-km: High-Resolution Prediction of Integrated Vapor Transport and Precipitation

We introduce a stretched-grid artificial intelligence (AI) weather forecasting model with 2-km resolution over the western United States and part of the Northeast Pacific and approximately 31-km resolution elsewhere globally. Forecasting over the western U.S. is challenging because complex topography and atmospheric rivers (ARs) strongly influence orographic precipitation. West-WRF AI 2-km builds on a global model pretrained with a 40-year European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) dataset and is fine-tuned with the Center for Western Weather and Water Extremes (CW3E) 2-km regional reanalysis to produce autoregressive 6-hourly forecasts of precipitation and integrated vapor transport (IVT). Forecasts are evaluated over winters 2020-2023 using gridded precipitation observations, rain gauges, and AR Reconnaissance dropsondes and are benchmarked against coarser-resolution AI forecasts and regional and global numerical weather prediction (NWP) systems. West-WRF AI 2-km reproduces observed precipitation-intensity distributions, retains fine-scale spectral variability, and produces sharper narrow coastal precipitation bands and localized, terrain-sensitive extremes. Its broader-scale performance remains comparable to coarser-resolution configurations while preserving large-scale skill despite higher resolution. Dropsonde verification shows lower errors and improved categorical skill at the most extreme IVT threshold. Overall, West-WRF AI 2-km provides its greatest value for localized precipitation extremes and intense AR-related moisture transport.

physics.ao-ph↗