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Record W2755015987 · doi:10.5194/bg-2017-328

The devil's in the disequilibrium: sensitivity of ocean carbon storage to climate state and iron fertilization in a general circulation model

2017· article· en· W2755015987 on OpenAlexafffund
Sarah Eggleston, Eric D. Galbraith

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsMcGill University
FundersMinisterio de Economía y CompetitividadCompute CanadaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsIron fertilizationBiogeochemical cycleAntarctic Bottom WaterRadiative forcingBiological pumpEnvironmental scienceClimatologyAtmospheric sciencesOceanographyCarbon cycleThermohaline circulationClimate changeGeologyChemistryEcosystemPhytoplanktonEcologyBiology

Abstract

fetched live from OpenAlex

Abstract. Ocean dissolved inorganic carbon (DIC) storage can be conceptualized as the sum of four components: saturation (DICsat), disequilibrium (DICdis), carbonate (DICcarb) and soft tissue (DICsoft). Among these, DICdis and DICsoft have the potential for large changes that are relatively difficult to predict. Here we explore changes in DICsoft and DICdis in a large suite of simulations with a complex coupled climate-biogeochemical model, driven by changes in orbital forcing, ice sheets and the radiative effect of CO2. Both DICdis and DICsoft vary over a range of 40 μmol kg−1 in response to the climate forcing, equivalent to changes in atmospheric CO2 on the order of 50 ppm for each. We find that, despite the broad range of climate states represented, changes in global DICsoft can be well-approximated by the product of deep ocean ideal age and the global export production flux, while global DICdis is dominantly controlled by the fraction of the ocean filled by Antarctic Bottom Water (AABW). Because the AABW fraction and ideal age are inversely correlated between the simulations, DICdis and DICsoft are also inversely correlated. This inverse correlation could be decoupled if changes in deep ocean mixing were to alter ideal age independently of AABW fraction, or if independent ecosystem changes were to alter export and remineralization, thereby modifying DICsoft. As an example of the latter, iron fertilization causes DICsoft to increase, and causes DICdis to also increase by a similar or greater amount, to a degree that depends on climate state. We propose a simple framework to consider the global contribution of DICsoft + DICdis to ocean carbon storage as a function of the surface preformed nitrate and DICdis of dense water formation regions, the global volume fractions ventilated by these regions, and the global nitrate inventory. More extensive sea ice increases DICdis, and when sea ice becomes very extensive it also causes significant O2 disequilibrium, which may have contributed to reconstructions of low O2 in the Southern Ocean during the glacial. Global DICdis reaches a minimum near modern CO2 because the AABW fraction reaches a minimum, which may have contributed to preventing further CO2 rise during interglacial periods.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.219
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2017
Admission routes2
Has abstractyes

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