Unsupervised clustering identifies thermohaline staircases in the Canada Basin of the Arctic Ocean
Thermohaline staircases are a widespread stratification feature that impact the vertical transport of heat and nutrients and are consistently observed throughout the Canada Basin of the Arctic Ocean. Observations of staircases from the same time period and geographic region form clusters in temperat...
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crescholarship:10.31223/x5v67w 2024-05-12T07:59:40+00:00 Unsupervised clustering identifies thermohaline staircases in the Canada Basin of the Arctic Ocean Schee, Mikhail Rosenblum, Erica Lilly, Jonathan Grisouard, Nicolas 2024 http://dx.doi.org/10.31223/x5v67w unknown California Digital Library (CDL) posted-content 2024 crescholarship https://doi.org/10.31223/x5v67w 2024-04-18T08:47:18Z Thermohaline staircases are a widespread stratification feature that impact the vertical transport of heat and nutrients and are consistently observed throughout the Canada Basin of the Arctic Ocean. Observations of staircases from the same time period and geographic region form clusters in temperature-salinity ($T$--$S$) space. Here, for the first time, we use an automated clustering algorithm called the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), to detect and connect individual well-mixed staircase layers across profiles from Ice-Tethered Profilers (ITPs). Our application only requires an estimate of the typical layer thickness and expected salinity range of staircases. We compare this method to two previous studies that used different approaches to detect layers, and reproduce several results including the mean lateral density ratio $R_L$ and that the difference in salinity between neighboring layers is a magnitude larger than the salinity variance within a layer. We find that we can accurately and automatically track individual layers in coherent staircases across time and space between different profiles. In evaluating the algorithm's performance, we find evidence of different physical features, namely splitting or merging layers and remnant intrusions. Further, we find a dependence of $R_L$ on pressure, whereas previous studies have reported constant $R_L$. Our results demonstrate that clustering algorithms are an effective and parsimonious method of identifying staircases in ocean profile data. Other/Unknown Material Arctic Arctic Ocean canada basin eScholarship Repository (University of California) Arctic Arctic Ocean Canada |
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Thermohaline staircases are a widespread stratification feature that impact the vertical transport of heat and nutrients and are consistently observed throughout the Canada Basin of the Arctic Ocean. Observations of staircases from the same time period and geographic region form clusters in temperature-salinity ($T$--$S$) space. Here, for the first time, we use an automated clustering algorithm called the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), to detect and connect individual well-mixed staircase layers across profiles from Ice-Tethered Profilers (ITPs). Our application only requires an estimate of the typical layer thickness and expected salinity range of staircases. We compare this method to two previous studies that used different approaches to detect layers, and reproduce several results including the mean lateral density ratio $R_L$ and that the difference in salinity between neighboring layers is a magnitude larger than the salinity variance within a layer. We find that we can accurately and automatically track individual layers in coherent staircases across time and space between different profiles. In evaluating the algorithm's performance, we find evidence of different physical features, namely splitting or merging layers and remnant intrusions. Further, we find a dependence of $R_L$ on pressure, whereas previous studies have reported constant $R_L$. Our results demonstrate that clustering algorithms are an effective and parsimonious method of identifying staircases in ocean profile data. |
format |
Other/Unknown Material |
author |
Schee, Mikhail Rosenblum, Erica Lilly, Jonathan Grisouard, Nicolas |
spellingShingle |
Schee, Mikhail Rosenblum, Erica Lilly, Jonathan Grisouard, Nicolas Unsupervised clustering identifies thermohaline staircases in the Canada Basin of the Arctic Ocean |
author_facet |
Schee, Mikhail Rosenblum, Erica Lilly, Jonathan Grisouard, Nicolas |
author_sort |
Schee, Mikhail |
title |
Unsupervised clustering identifies thermohaline staircases in the Canada Basin of the Arctic Ocean |
title_short |
Unsupervised clustering identifies thermohaline staircases in the Canada Basin of the Arctic Ocean |
title_full |
Unsupervised clustering identifies thermohaline staircases in the Canada Basin of the Arctic Ocean |
title_fullStr |
Unsupervised clustering identifies thermohaline staircases in the Canada Basin of the Arctic Ocean |
title_full_unstemmed |
Unsupervised clustering identifies thermohaline staircases in the Canada Basin of the Arctic Ocean |
title_sort |
unsupervised clustering identifies thermohaline staircases in the canada basin of the arctic ocean |
publisher |
California Digital Library (CDL) |
publishDate |
2024 |
url |
http://dx.doi.org/10.31223/x5v67w |
geographic |
Arctic Arctic Ocean Canada |
geographic_facet |
Arctic Arctic Ocean Canada |
genre |
Arctic Arctic Ocean canada basin |
genre_facet |
Arctic Arctic Ocean canada basin |
op_doi |
https://doi.org/10.31223/x5v67w |
_version_ |
1798841252246978560 |