Research output: Contribution to journal › Article › peer-review
pCO2 Algorithms for the Baltic Sea Based on Ship, Modelled, and Satellite Data. / Кузьмина, Софья Константиновна; Лобанова, Полина Вячеславовна.
In: Oceanology, Vol. 64, No. S1, 02.2025, p. S97-S106.Research output: Contribution to journal › Article › peer-review
}
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