Estimating carbon pools in the European Shelf sea environment: replacing reanalysis by model-informed machine learning?
arXiv:2508.10178v3 Announce Type: replace-cross Abstract: Shelf seas are important for the economy and the carbon cycle, but shelf sea observations for carbon pools are often sparse, or highly uncertain. An alternative can be provided by carbon reanalyses (whether assimilating proxy variables, such as chlorophyll-$a$, or directly carbon), but these are often expensive to run. We propose to use a computationally cheap ensemble of neural networks (i.e. deep ensemble) to learn the relationship between the directly observable (atmospheric, riverine and ocean) variables and marine carbon pools from
The increasing availability of AI/ML techniques and the growing urgency to accurately monitor environmental carbon dynamics are converging to produce more efficient estimation methods.
This development allows for more accurate and cost-effective monitoring of critical carbon sinks, enhancing our understanding of climate change drivers and the effectiveness of mitigation strategies.
The reliance on expensive, compute-intensive carbon reanalysis models can be reduced or supplemented by more agile and computationally cheaper AI-driven approaches, making carbon accounting more accessible.
- · Climate scientists
- · Environmental monitoring agencies
- · Azure, AWS, and GCP machine learning services
- · European Shelf sea economy
- · Traditional oceanographic modeling centers (if they don't adapt)
- · High-cost, high-compute reanalysis services
Improved resolution and frequency of carbon pool estimates in critical marine environments.
More reliable data for policy-making concerning carbon emissions and climate change mitigation.
Potential for integration of AI/ML carbon estimation into real-time global environmental observatories, influencing carbon credit markets and international climate agreements.
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Read at arXiv cs.LG