Analysis of Particulate Carbon Export in the Global Ocean using in situ Observations and Machine Learning
The abundance and size distribution of marine organic particles are two major factors controlling biological carbon sequestration in the ocean. These quantities are the result of complex physical-biological interactions that are difficult to observe, and their spatial and temporal patterns remain un...
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2023
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ftcdlib:oai:escholarship.org:ark:/13030/qt21c6j0sm 2023-05-15T18:25:51+02:00 Analysis of Particulate Carbon Export in the Global Ocean using in situ Observations and Machine Learning Clements, Daniel Bianchi, Daniele 2023-01-01 application/pdf https://escholarship.org/uc/item/21c6j0sm en eng eScholarship, University of California qt21c6j0sm https://escholarship.org/uc/item/21c6j0sm public Chemical oceanography Biological oceanography Biological Pump Climate Change Machine Learning Particulate Carbon etd 2023 ftcdlib 2023-02-13T18:45:00Z The abundance and size distribution of marine organic particles are two major factors controlling biological carbon sequestration in the ocean. These quantities are the result of complex physical-biological interactions that are difficult to observe, and their spatial and temporal patterns remain uncertain. This dissertation describes our analysis of particle size distributions (PSD) and the resulting export, from a global compilation of \textit{in situ} Underwater Vision Profiler 5 (UVP5) optical measurements. In Chapter 2, we demostrate the ability to extrapolate sparse UVP5 observations to the global ocean from well-sampled oceanographic variables, using a machine learning algorithm. We reconstruct global maps of the biogenic PSD parameters (biovolume and slope) for particles at the base of the euphotic zone. These reconstructions reveal consistent global patterns, with high chlorophyll regions generally characterized by high particle biovolume and flatter PSD slope, i.e., a high relative abundance of large vs. small particles. The resulting negative correlations between particle biovolume and slope further suggest amplified effects on sinking particle fluxes. Our approach and estimates provide a baseline for understanding the export of organic matter from the surface ocean. Chapter 3 describes how applying a simple empirical relationship to our reconstructions of the PSD, we can calculate the total export. In this Chapter, we explore the seasonal and spatial patterns of carbon export. Taking advantage of the high vertical resolution of the UVP5, we quantify the export from the surface using two previously established depth horizons. We identify a larger export from the Southern Ocean than most other models of export. Similarly, we find the lower part of the euphotic zone to be dominated by heterotrophy, rather than autotrophy. Being able to reconstruct the PSD and particle flux at multiple depths allows for further exploration of the full 3-dimensional particle field. Chapter 4 describes a full 3-D model, ... Other/Unknown Material Southern Ocean University of California: eScholarship Southern Ocean |
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Open Polar |
collection |
University of California: eScholarship |
op_collection_id |
ftcdlib |
language |
English |
topic |
Chemical oceanography Biological oceanography Biological Pump Climate Change Machine Learning Particulate Carbon |
spellingShingle |
Chemical oceanography Biological oceanography Biological Pump Climate Change Machine Learning Particulate Carbon Clements, Daniel Analysis of Particulate Carbon Export in the Global Ocean using in situ Observations and Machine Learning |
topic_facet |
Chemical oceanography Biological oceanography Biological Pump Climate Change Machine Learning Particulate Carbon |
description |
The abundance and size distribution of marine organic particles are two major factors controlling biological carbon sequestration in the ocean. These quantities are the result of complex physical-biological interactions that are difficult to observe, and their spatial and temporal patterns remain uncertain. This dissertation describes our analysis of particle size distributions (PSD) and the resulting export, from a global compilation of \textit{in situ} Underwater Vision Profiler 5 (UVP5) optical measurements. In Chapter 2, we demostrate the ability to extrapolate sparse UVP5 observations to the global ocean from well-sampled oceanographic variables, using a machine learning algorithm. We reconstruct global maps of the biogenic PSD parameters (biovolume and slope) for particles at the base of the euphotic zone. These reconstructions reveal consistent global patterns, with high chlorophyll regions generally characterized by high particle biovolume and flatter PSD slope, i.e., a high relative abundance of large vs. small particles. The resulting negative correlations between particle biovolume and slope further suggest amplified effects on sinking particle fluxes. Our approach and estimates provide a baseline for understanding the export of organic matter from the surface ocean. Chapter 3 describes how applying a simple empirical relationship to our reconstructions of the PSD, we can calculate the total export. In this Chapter, we explore the seasonal and spatial patterns of carbon export. Taking advantage of the high vertical resolution of the UVP5, we quantify the export from the surface using two previously established depth horizons. We identify a larger export from the Southern Ocean than most other models of export. Similarly, we find the lower part of the euphotic zone to be dominated by heterotrophy, rather than autotrophy. Being able to reconstruct the PSD and particle flux at multiple depths allows for further exploration of the full 3-dimensional particle field. Chapter 4 describes a full 3-D model, ... |
author2 |
Bianchi, Daniele |
format |
Other/Unknown Material |
author |
Clements, Daniel |
author_facet |
Clements, Daniel |
author_sort |
Clements, Daniel |
title |
Analysis of Particulate Carbon Export in the Global Ocean using in situ Observations and Machine Learning |
title_short |
Analysis of Particulate Carbon Export in the Global Ocean using in situ Observations and Machine Learning |
title_full |
Analysis of Particulate Carbon Export in the Global Ocean using in situ Observations and Machine Learning |
title_fullStr |
Analysis of Particulate Carbon Export in the Global Ocean using in situ Observations and Machine Learning |
title_full_unstemmed |
Analysis of Particulate Carbon Export in the Global Ocean using in situ Observations and Machine Learning |
title_sort |
analysis of particulate carbon export in the global ocean using in situ observations and machine learning |
publisher |
eScholarship, University of California |
publishDate |
2023 |
url |
https://escholarship.org/uc/item/21c6j0sm |
geographic |
Southern Ocean |
geographic_facet |
Southern Ocean |
genre |
Southern Ocean |
genre_facet |
Southern Ocean |
op_relation |
qt21c6j0sm https://escholarship.org/uc/item/21c6j0sm |
op_rights |
public |
_version_ |
1766207536882515968 |