Descrizione del progetto
Modelli più accurati dello stoccaggio del carbonio negli oceani
La fotosintesi nell’oceano converte la CO2 in materia organica, di cui il 5-15 % scende nelle profondità oceaniche. La profondità raggiunta da questa materia organica è importante per controllare l’entità dello stoccaggio del carbonio negli oceani. La scarsa conoscenza della variabilità spazio-temporale dei processi che controllano l’attenuazione del flusso ostacola i tentativi volti a produrre mappe globali dell’attenuazione del flusso per comprendere meglio e prevedere lo stoccaggio del carbonio negli oceani. Il progetto ANTICS, finanziato dall’UE, affronterà questa lacuna di conoscenza utilizzando una sintesi innovativa di immaginografia in situ all’avanguardia, apprendimento automatico e nuove analisi dei dati per migliorare la comprensione dello stoccaggio del carbonio negli oceani. I risultati contribuiranno a convalidare e orientare la componente biogeochimica marina dei modelli del sistema Terra utilizzati per la previsione del ciclo del carbonio.
Obiettivo
Photosynthesis in the ocean converts approximately 100 Gt of carbon dioxide (CO2) into organic matter every year, of which 5-15% sinks to the deep ocean. The depth to which this organic matter sinks is important in controlling the magnitude of ocean carbon storage, as changes in this flux attenuation depth drive variations in atmospheric pCO2 of up to 200 ppm. Efforts to produce global maps of flux attenuation have yielded starkly contrasting global patterns, blocking our understanding of ocean carbon storage and our ability to predict it. The bottleneck is our ignorance of the spatiotemporal variability of the processes that control flux attenuation.
ANTICS will directly address this knowledge gap by using an innovative synthesis of cutting-edge in situ imaging, machine learning and novel data analyses to mechanistically understand ocean carbon storage. Use state-of-the-art imaging technologies, I will collect data on size, distribution and composition of organic matter particles and measure their sinking velocity in the upper 600 m across the Atlantic. I will design a neural network model that allows the conversion of in situ images into carbon fluxes, and develop analysis routines of particle size spectra that quantify the processes causing flux attenuation: remineralisation, physical aggregation/disaggregation, fragmentation/repackaging by zooplankton. By statistically linking these outputs to seasonality, depth, primary production and temperature, I will be able to determine which processes dominate under specific environmental conditions. This step change in our understanding will allow ANTICS to resolve flux attenuation spatially and temporally. I will use this pioneering knowledge to validate and inform the parametrization of the marine biogeochemical component of the UK’s earth system model used for carbon cycle forecasting in the next IPCC assessments.
Campo scientifico
- natural sciencescomputer and information sciencesdata science
- natural scienceschemical sciencesinorganic chemistryinorganic compounds
- natural sciencesearth and related environmental sciencesoceanographyocean chemistry
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Programma(i)
Argomento(i)
Meccanismo di finanziamento
ERC-STG - Starting GrantIstituzione ospitante
SO14 3ZH Southampton
Regno Unito