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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Main Author: Clements, Daniel
Other Authors: Bianchi, Daniele
Format: Other/Unknown Material
Language:English
Published: eScholarship, University of California 2023
Subjects:
Online Access:https://escholarship.org/uc/item/21c6j0sm
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spelling 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
institution 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
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