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Record W4220917787 · doi:10.5194/egusphere-egu22-8288

Ocean sequestration of carbon dioxide and heat under global warming in a climate model with an eddy-rich ocean

2022· preprint· en· W4220917787 on OpenAlexaff
Ivy Frenger, Carolina O. Dufour, Julia Getzlaff, Stephen M. Griffies, Wolfgang Koeve, Jorge L. Sarmiento

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsMesoscale meteorologyEddyAtmospheric sciencesCarbon dioxideClimatologyEnvironmental scienceChemistryMeteorologyTurbulencePhysicsGeology

Abstract

fetched live from OpenAlex

To robustly estimate how much carbon dioxide (CO2) we may still emit while staying below a certain level of global warming, we need to know uncertainty in oceanic sequestration of CO2 and heat. We here address uncertainty in oceanic CO2 and heat sequestration that arises due to the representation of ocean mesoscale features. Such features are fundamental components of ocean circulation and mixing, though with spatial scales smaller than 100 km they are typically not resolved by climate models. We compare three configurations of the GFDL climate model that differ in the spatial resolution of the ocean, namely "eddy-rich" (0.10o resolution) that simulates a rich field of mesoscale features such as mesoscale eddies and fronts, "eddy-present" (0.25o) that simulates mesoscale features to a lesser extent, and "eddy-param" (1o grid spacing) that does not resolve mesoscale features but represents effects of mesoscale eddies with parameterizations. The three models are run under preindustrial conditions and then exposed to an idealized increase of atmospheric CO2 levels of one percent per year, until CO2 doubling is reached. We find that ocean mesoscale processes act to enhance the oceanic uptake of heat under global warming, while they act to reduce the uptake of CO2 (eddy-rich relative to eddy-present). The greater heat sequestration is due to a greater reduction of the Atlantic Meridional Overturning Circulation, which redistributes heat from the Pacific to the Atlantic oceans, but also leads to an enhanced, albeit small, net global heat gain of several percent. Potential causes for the reduced sequestration of CO2 (eddy-rich takes up 7% less relative to eddy-present) include reduced surface solubility of CO2 due to the larger heating, or a different preindustrial state, e.g., of the buffer capacity. Eddy-param appears to not capture this effect; in contrast, it sequesters 13% more CO2 than eddy-rich. While eddy-param largely captures the redistribution of heat between the Pacific and Atlantic oceans, it does not capture the enhanced net global heat gain. Despite the lower oceanic heat sequestration, eddy-param features a lower global atmospheric near surface warming of 0.4oC at CO2 doubling compared to eddy-rich and eddy-present, because heat is sequestered deeper in the ocean. Our results suggest that opting either for resolving ocean mesoscale processes in climate models or parameterizing their effects will affect the proportion of ocean heat versus carbon sequestration, with potential implications for the relationship of cumulative CO2 emissions and global warming.

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)0.002
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 score0.115
Threshold uncertainty score0.229

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.010
GPT teacher head0.228
Teacher spread0.218 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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