Globally, the most intense uptake of anthropogenic carbon dioxide (CO) occurs in the Atlantic north of 50°N, and it has been predicted that atmospheric CO sequestration in the Arctic Ocean will increase as a result of ice-melt and increased primary production. However, little is known about the impact of pan-Arctic sea-ice decline on carbon export processes. We investigated the potential ballasting effect of sea-ice derived material on settling aggregates and carbon export in the Fram Strait by combining 13 years of vertical flux measurements with benthic eDNA analysis, laboratory experiments, and tracked sea-ice distributions. We show that melting sea-ice in the Fram Strait releases cryogenic gypsum and terrigenous material, which ballasts sinking organic aggregates. As a result, settling velocities of aggregates increased ≤10-fold, resulting in ≤30% higher carbon export in the vicinity of the melting ice-edge. Cryogenic gypsum is formed in first-year sea-ice, which is predicted to increase as the Arctic is warming. Simultaneously, less sea-ice forms over the Arctic shelves, which is where terrigenous material is incorporated into sea-ice. Supporting this, we found that terrigenous fluxes from melting sea-ice in the Fram Strait decreased by >80% during our time-series. Our study suggests that terrigenous flux will eventually cease when enhanced sea-ice melt disrupts trans-Arctic sea-ice transport and thus, limit terrigenous-ballasted carbon flux. However, the predicted increase in Arctic primary production and gypsum formation may enhance gypsum-ballasted carbon flux and compensate for lowered terrigenous fluxes. It is thus unclear if sea-ice loss will reduce carbon export in the Arctic Ocean.
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http://dx.doi.org/10.1093/pnasnexus/pgae081 | DOI Listing |
Environmental degradation due to the rapid increase in CO₂ emissions is a pressing global challenge, necessitating innovative solutions for accurate prediction and policy development. Machine learning (ML) techniques offer a robust approach to modeling complex relationships between various factors influencing emissions. Furthermore, ML models can learn and interpret the significance of each factor's contribution to the rise of CO.
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