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Record W4392602378 · doi:10.5194/egusphere-egu24-12592

Sea ice and climate impacts from Antarctic ice-mass loss in the SOFIA multi-model ensemble

2024· preprint· en· W4392602378 on OpenAlexaff
Andrew G. Pauling, Neil C. Swart, Torge Martin, Rebecca L. Beadling, Jiajia Chen, Matthew H. England, Riccardo Farneti, Stephen M. Griffies, Tore Hattermann, F. Alexander Haumann, Qian Li, John Marshall, Morven Muilwijk, Ariaan Purich, Jeff Ridley, Inga J. Smith, Max Thomas

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsSea iceClimatologyEnvironmental scienceClimate modelOceanographyClimate changeGeology

Abstract

fetched live from OpenAlex

We assess the response of Antarctic sea ice, the Southern Ocean, and global climate to mass loss from the Antarctic continent in a new multi-model ensemble. Antarctic ice-mass loss from ice sheets and ice shelves is increasing and is projected to increase further as the climate warms. The fresh water entering the Southern Ocean due to this ice-mass loss has been proposed as a mechanism responsible for the lack of decline in Antarctic sea ice area between 1979 and 2015, in contrast to the sea-ice loss seen in the Arctic. The fresh water impacts sea ice by increasing the density gradient between the near-surface waters and deeper waters around the Antarctic continent, which inhibits vertical transport of warmer, deeper water to the surface. This results in surface cooling and increased sea ice growth, as has been shown in previous studies. Though this increased Antarctic ice-mass loss is expected to impact climate it is absent from almost all models in the current Coupled Model Intercomparison Project (CMIP6), which typically enforce that the continent remain in perpetual mass balance, with no gain or loss of mass over time. Further, previous non-CMIP6 model experiments that include changing Antarctic ice-mass loss suggest that the climate response depends on the model used, and that the reasons for this model dependence are not clear.We present results from the Southern Ocean Freshwater Input from Antarctica (SOFIA) Initiative, an international model intercomparison, in which freshwater is added to the ocean surrounding Antarctica to simulate the otherwise missing ice-sheet mass loss. This unique suite of models allows us compare the response to Antarctic mass loss across climate models, identify reasons for model discrepancies, and quantify the potential impact of the absence of increasing Antarctic ice-mass loss on Antarctic sea ice and climate. We will give an overview of the SOFIA initiative including the experiment design and participating models. We will present results from the “antwater” experiment outlined in the SOFIA protocol in which a constant freshwater input of 0.1 Sv is distributed evenly around the Antarctic continent at the ocean surface in an experiment with pre-industrial control forcing. We show that there is a spread of up to a factor of 3 across models in the Antarctic sea ice area response to identical freshwater forcing. There are also substantial differences in the spatial pattern of the sea ice response depending on the model used. We explore the dependence of the response on the mean state of Antarctic sea ice and the Southern Ocean in the pre-industrial control runs, as well as the response of the ocean stratification and oceanic deep convection in the models. We also explore the seasonality of the sea ice and oceanic response.

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 score0.077
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.021
GPT teacher head0.251
Teacher spread0.230 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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