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September 10, 2025Earth System DynamicsOpen Access

A causality-based method for multi-model comparison: application to relationships between atmospheric, oceanic and marine biogeochemical variables

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Authors

GBGermain BénardCentre National de la Recherche Scientifique MGMarion GehlenCentre National de la Recherche Scientifique MVMathieu VracInstitut Supérieur d'Électronique de Paris

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Implication

This approach examines causal relationships among atmospheric, oceanic, and biogeochemical variables, highlighting their impact on nutrient dynamics and production.

Key Points

The causality-based method reveals how vertical mixing contributes to nutrient concentration in the North Atlantic.
Analysis indicates that winter vertical mixing has a stronger influence on nutrient levels than horizontal transport.
Stratification affects spring bloom net primary production differently across various Earth system models.
The PCMCI+ algorithm effectively identifies causal relationships among atmospheric and biogeochemical variables.

Cite This Study

Bénard et al. (2025) studied this question.

synapsesocial.com/papers/68c1c22554b1d3bfb60ef33f https://doi.org/10.5194/esd-16-1085-2025

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Implication

This approach examines causal relationships among atmospheric, oceanic, and biogeochemical variables, highlighting their impact on nutrient dynamics and production.

A causality-based method for multi-model comparison: application to relationships between atmospheric, oceanic and marine biogeochemical variables

Discussion

Member takes

Implication

Key Points

Cite This Study

A causality-based method for multi-model comparison: application to relationships between atmospheric, oceanic and marine biogeochemical variables

Discussion

Member takes

Implication

Key Points

Cite This Study

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