Thermal boundary conditions on western Greenland: Observational constraints and impacts on the modeled thermomechanical state
Bibliographic record
Abstract
Abstract The surface and basal boundary conditions exert an important control on the thermodynamic state of the Greenland Ice Sheet, but their representation in numerical ice sheet models is poorly constrained due to the lack of observations. Here we investigate a land‐terminating sector of western Greenland and (1) quantify differences between new observations and commonly used boundary condition data sets and (2) demonstrate the impact of improved boundary conditions on simulated thermodynamics in a higher‐order numerical flow model. We constrain near‐surface temperature with measurements from two 20 m boreholes in the ablation zone and 10 m firn temperature from the percolation zone. We constrain basal heat flux using in situ measurement in a deep bedrock hole at the study area margin and other existing assessments. To assess boundary condition influences on simulated thermal‐mechanical processes, we compare model output to multiple full‐thickness temperature profiles collected in the ablation zone. Our observation‐constrained basal heat flux is 30 mW m −2 less than commonly used representations. In contrast, measured near‐surface temperatures are warmer than common surface temperature data sets by up to 15°C. Application of lower basal heat flux increases a model cold bias compared to the measured temperature profiles and causes frozen basal conditions across the ablation zone. Temperate basal conditions are reestablished by our warmer surface boundary. Warmer surface ice and firn can introduce several times more energy to the modeled ice mass than what is lost at the bed from reduced basal heat flux, indicating that the thermomechanical state of the ice sheet is highly sensitive to near‐surface effects.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".