Standard

Harvard

APA

Vancouver

Author

BibTeX

@article{769b1d9214d749b98fb1b47e3c62cbf3,
title = "pCO2 Algorithms for the Baltic Sea Based on Ship, Modelled, and Satellite Data",
abstract = "The concentration of carbon dioxide (CO2) in seawater is an important parameter of the global carbon cycle in the ocean. Marginal seas, like the Baltic Sea, are an understudied component of this cycle. The variability of CO2 in seawater depends on physical, chemical and biological processes, which are assessed in this study using remotely sensed and modelled data–satellite data (1) euphotic layer depth (Zeu), (2) photosynthetically active radiation (PAR), (3) concentration of chlorophyll-a (Chl-a), (4) particulate organic and inorganic carbon (POC, PIC); modelled data (5) sea surface temperature (SST), (6) mixed layer depth (MLD) and (7) salinity. This paper covers the spatial, multi-year and seasonal variability of the partial pressure of carbon dioxide (pCO2) in the Baltic Sea based on ship data from the Surface Ocean CO2 Atlas (SOCAT v.2023).We propose new models for the assessment of pCO2 using the multilayer perceptron machine learning algorithm. As a result, maps of monthly average values of pCO2 for the twelve months of 2022 were constructedbased on the optimal pCO2 models.",
keywords = "Baltic Sea, carbon dioxide partial pressure, machine learning, marginal seas, remote sensing",
author = "Кузьмина, {Софья Константиновна} and Лобанова, {Полина Вячеславовна}",
note = "Kuzmina S.K., Lobanova P.V. pCO2 Algorithms for the Baltic Sea Based on Ship, Modelled, and Satellite Data // Oceanology, 2024, Vol. 64, Suppl. 1, pp. S97–S106",
year = "2025",
month = feb,
doi = "10.1134/S0001437024700917",
language = "English",
volume = "64",
pages = "S97--S106",
journal = "Oceanology",
issn = "0001-4370",
publisher = "МАИК {"}Наука/Интерпериодика{"}",
number = "S1",

}

RIS

TY - JOUR

T1 - pCO2 Algorithms for the Baltic Sea Based on Ship, Modelled, and Satellite Data

AU - Кузьмина, Софья Константиновна

AU - Лобанова, Полина Вячеславовна

N1 - Kuzmina S.K., Lobanova P.V. pCO2 Algorithms for the Baltic Sea Based on Ship, Modelled, and Satellite Data // Oceanology, 2024, Vol. 64, Suppl. 1, pp. S97–S106

PY - 2025/2

Y1 - 2025/2

N2 - The concentration of carbon dioxide (CO2) in seawater is an important parameter of the global carbon cycle in the ocean. Marginal seas, like the Baltic Sea, are an understudied component of this cycle. The variability of CO2 in seawater depends on physical, chemical and biological processes, which are assessed in this study using remotely sensed and modelled data–satellite data (1) euphotic layer depth (Zeu), (2) photosynthetically active radiation (PAR), (3) concentration of chlorophyll-a (Chl-a), (4) particulate organic and inorganic carbon (POC, PIC); modelled data (5) sea surface temperature (SST), (6) mixed layer depth (MLD) and (7) salinity. This paper covers the spatial, multi-year and seasonal variability of the partial pressure of carbon dioxide (pCO2) in the Baltic Sea based on ship data from the Surface Ocean CO2 Atlas (SOCAT v.2023).We propose new models for the assessment of pCO2 using the multilayer perceptron machine learning algorithm. As a result, maps of monthly average values of pCO2 for the twelve months of 2022 were constructedbased on the optimal pCO2 models.

AB - The concentration of carbon dioxide (CO2) in seawater is an important parameter of the global carbon cycle in the ocean. Marginal seas, like the Baltic Sea, are an understudied component of this cycle. The variability of CO2 in seawater depends on physical, chemical and biological processes, which are assessed in this study using remotely sensed and modelled data–satellite data (1) euphotic layer depth (Zeu), (2) photosynthetically active radiation (PAR), (3) concentration of chlorophyll-a (Chl-a), (4) particulate organic and inorganic carbon (POC, PIC); modelled data (5) sea surface temperature (SST), (6) mixed layer depth (MLD) and (7) salinity. This paper covers the spatial, multi-year and seasonal variability of the partial pressure of carbon dioxide (pCO2) in the Baltic Sea based on ship data from the Surface Ocean CO2 Atlas (SOCAT v.2023).We propose new models for the assessment of pCO2 using the multilayer perceptron machine learning algorithm. As a result, maps of monthly average values of pCO2 for the twelve months of 2022 were constructedbased on the optimal pCO2 models.

KW - Baltic Sea

KW - carbon dioxide partial pressure

KW - machine learning

KW - marginal seas

KW - remote sensing

UR - https://www.mendeley.com/catalogue/0ed58af3-41b3-3c0a-94cc-4d09f40df65d/

U2 - 10.1134/S0001437024700917

DO - 10.1134/S0001437024700917

M3 - Article

VL - 64

SP - S97-S106

JO - Oceanology

JF - Oceanology

SN - 0001-4370

IS - S1

ER -

ID: 132687151