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Record W3161004240 · doi:10.5194/egusphere-egu21-10357

Towards more physically constrained freshwater injection via eddy permitting simulations of the last glacial cycle

2021· article· en· W3161004240 on OpenAlexaffabout
Ryan Love, Heather Andres, Alan Condron, Xu Zhang, Gerrit Lohmann, Lev Tarasov

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsYounger DryasFreshwater ecosystemSurface runoffFreshwater fishGlacial periodGeologyClimate changeEnvironmental scienceOceanographyClimatologyGeomorphologyEcologyEcosystemFishery

Abstract

fetched live from OpenAlex

Freshwater, in the form of glacial runoff, is hypothesized to play a critical role in centennial to millennial scale climate variability, eg. the Younger Dryas and Dansgaard Oeschger events. Freshwater injection, or hosing, model experiments demonstrate that freshwater has the capability to generate abrupt climate transitions. However, in an attempt to mitigate the inability of most models to resolve the smaller-scale features relevant to freshwater transport (such as boundary currents and mesoscale eddies), these hosing experiments commonly apply the entirety of the freshwater directly to the regions of deepwater formation (DWF). Our results indicate that this can inflate the freshwater signal in those regions by as much as four times. We propose a novel method of freshwater injection for such low-resolution models that spatially distributes the freshwater in accord with the results of eddy-permitting modelling. Furthermore, this “freshwater fingerprint” method not only impacts the timing of simulated climate transitions but also can allow us to evaluate how much we are overestimating the effects of freshwater when injected directly into sites of DWF. The freshwater fingerprints we develop are based on a suite of freshwater injection experiments performed using an eddy permitting Younger Dryas configuration of the MITGCM. Freshwater injection locations include the Mackenzie River, Gulf of St. Lawrence, Gulf of Mexico and a location off the coast of Norway, with flux amounts bounded by glacial reconstructions. These simulations indicate that freshwater from the Mackenzie River and Fennoscandia have the largest impact on salinity in most of the conventional sites of DWF (GIN and Labrador Seas, and in these simulations, predominantly the northern North Atlantic due to extensive sea ice), while freshwater from the Gulf of St. Lawrence is effective at freshening only the northern North Atlantic. The Gulf of Mexico has little impact on any DWF region we consider, mostly because the lower but continual flux in our simulations does not allow freshwater to penetrate northward past the Gulf Stream. The dilution of the freshwater signal as it is transported from the site of injection to the DWF zones leads to a reduction in the effective freshwater forcing, making hosing directly over DWF zones even with realistic freshwater amounts unrealistic. Thus, we construct freshwater fingerprints from these simulations by extracting the freshwater anomaly spatial pattern averaged over the last 5 simulation years, vertically integrating the field and normalizing it. The freshwater fingerprint is then implemented in the COSMOS Earth Systems Model, which is run at resolutions typical for paleoclimate simulations (non-eddy permitting). Initial results show that freshwater from the Mackenzie River using our fingerprint method leads to a more gradual cooling than if the meltwater is released directly over the hosing region (50-70N). We conclude that hosing over DWF zones, even with realistic freshwater amounts, produces an unrealistically large climate response. Additional results for the remaining injection locations and with the fingerprint implemented in a simpler climate model will be presented.

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 score0.038
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.253
Teacher spread0.240 · 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
Published2021
Admission routes2
Has abstractyes

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