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AI-PhytoClim

Financiado

Elucidating the function-specific response of marine phytoplankton to climate ch...

Elucidating the function-specific response of marine phytoplankton to climate change in the Southwestern Atlantic Ocean and its influence on regional CO2 fluxes. An AI-based approach Phytoplankton is the base of the trophic marine ecosystem and a main regulator of global CO2 fluxes. Climate-related shifts in the dominant phytoplankton functional types (PFTs), their blooming periods, and overall productivity, a... ver más

31/08/2027

CSIC

181K€

Presupuesto del proyecto: 181K€

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AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGA... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 2-3 | 552M€

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-05-13

HORIZON-MSCA-2023-PF-01-01: MSCA Postdoctoral Fellowships 2023 ExpectedOutcome:Project results are expected to contribute to the following outcomes:

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1 Participantes

CSIC

181.15K€ | Lider

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Duración del proyecto: 39 meses Fecha Inicio: 2024-05-13
Fecha Fin: 2027-08-31

Líder del proyecto

AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGA... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 2-3 | 552M€

Presupuesto del proyecto 181K€

Descripción del proyecto Phytoplankton is the base of the trophic marine ecosystem and a main regulator of global CO2 fluxes. Climate-related shifts in the dominant phytoplankton functional types (PFTs), their blooming periods, and overall productivity, are expected to have major implications on oceans’ carbon cycle. The Southern Atlantic Ocean (SAO), renowned as one of the world's most biologically productive regions and the largest carbon sink, is already experiencing profound environmental changes due to climate change. Using AI-techniques, AI-PhytoClim seeks to analyze how climate-driven changes in physical forcings influence the functioning and structure of phytoplankton, and the implications these changes have in surface ocean partial pressure (pCO2sw) and CO2 fluxes. This study is founded on the analysis of a comprehensive dataset, encompassing 26 years of satellite data (ocean color), numerical modeling results, and in situ data. The first goal is to establish quantitative relationships between climate-driven physical forcings in the SAO and phytoplankton responses at the PFT level, employing neural network pattern recognition and classification algorithms (2S-SOM, AI). The second goal aims to assess the impact of the climate variability/change on the PFTs and the influence on regional CO2 fluxes. The responses each PFT to climate variability and interconnections between each PFTs variability and the pCO2sw and CO2 fluxes will be explored through time-series analysis (CENSUS X-13) and statistical approaches (e.g. cross-wavelets, correlations). Knowing the coupled physical-PFTs relationships over the last 26 years and using the forecasted environmental changes during the next decades (CIMP6 model), I will assess how PFTs are likely to evolve in the future ocean and the resulting implications for regional CO2 fluxes. The methodologies developed in AI-PhytoClim will provide important new insights on the ocean-climate nexus and on its influence on global climate.

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