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Ocean Alkalinity: Reducing Local CO₂ Emissions

A new study shows that increasing marine alkalinity could theoretically absorb several gigatonnes of CO₂ each year. The research details the actual capacities, AI-based methodology, and practical implications for local communities considering this form of geoengineering.

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Rédaction Weather IA

mercredi 7 octobre 2026 à 19:138 min
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Ocean Alkalinity: Reducing Local CO₂ Emissions

Recent research suggests that increasing seawater alkalinity by tens of millimoles per liter could theoretically enable the ocean to absorb several gigatonnes of CO₂ annually. This prospect has sparked debate around geoengineering as a potential 'miracle solution' for offsetting industrial emissions. However, scientists emphasize that implementing this at a local scale carries major uncertainties, both chemically and ecologically. The article published in Nature Climate Change explores the limits of this mechanism using high-resolution simulations and satellite observations. At its core is a series of predictive models combining neural networks and atmospheric data to evaluate potential sequestration gains and associated risks.

The study's findings: Absorption capacities and limits

The authors indicate that if alkalinity were increased by 30 mmol L-1 in coastal zones, the ocean could theoretically remove several gigatonnes of CO₂ annually, a figure that appears impressive on a global scale. However, this potential is strongly dependent on location: cold waters rich in nutrients absorb more than already carbon-saturated tropical regions. Moreover, increasing alkalinity promotes calcium carbonate precipitation, which can alter seafloor chemistry and impact calcifying organisms.

When these figures are scaled down to the level of a local community, gains are notably more modest. Pilot experiments described in the study, conducted in protected bays, showed measurable reduction in atmospheric CO₂, but far from the global scale gigatonnes. Prediction uncertainty remains high, particularly because local biogeochemical processes are poorly characterized and current models do not always capture small-scale ecological feedbacks.

How researchers quantified the effect: Methodology and models

To assess alkalinity's potential, the team integrated satellite data from Copernicus, temperature and salinity fields provided by ECMWF, and in-situ pH measurements. These datasets fed an AI-trained neural network capable of predicting calcium carbonate dissolution and CO₂ absorption based on added alkalinity. The predictive model was coupled with systems like GraphCast and Pangu-Weather to incorporate atmospheric fluxes and high-resolution weather variations.

The validation process relied on comparing simulations with observations of CO₂ concentration in the upper ocean layers using Sentinel-6 mission sensors. Researchers quantified prediction uncertainty by...

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