LCice 1.0 – a generalized Ice Sheet System Model coupler for LOVECLIM version 1.3: description, sensitivities, and validation with the Glacial Systems Model (GSM version D2017.aug17)
Bibliographic record
Abstract
Abstract. We have coupled an Earth system model of intermediate complexity (LOVECLIM) to the Glacial Systems Model (GSM) using the LCice 1.0 coupler. The coupling scheme is flexible enough to enable asynchronous coupling between any glacial cycle ice sheet model and (with some code work) any Earth system model of intermediate complexity (EMIC). This coupling includes a number of interactions between ice sheets and climate that are often neglected: dynamic meltwater runoff routing, novel downscaling for precipitation that corrects orographic forcing to the higher resolution ice sheet grid (“advective precipitation”), dynamic vertical temperature gradient, and ocean temperatures for sub-shelf melt. The sensitivity of the coupled model with respect to the selected parameterizations and coupling schemes is investigated. Each new coupling feature is shown to have a significant impact on ice sheet evolution. An ensemble of runs is used to explore the behaviour of the coupled model over a set of 2000 parameter vectors using present-day (PD) initial and boundary conditions. The ensemble of coupled model runs is compared against PD reanalysis data for atmosphere (2 m temperature, precipitation, jet stream, and Rossby number of jet), ocean (sea ice and Atlantic Meridional Overturning Circulation – AMOC), and Northern Hemisphere ice sheet thickness and extent. The parameter vectors are then narrowed by rejecting model runs (1700 CE to present) with regional land ice volume changes beyond an acceptance range. The selected subset forms the basis for ongoing work to explore the spatial–temporal phase space of the last two glacial cycles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".