Automated Calibration of a Snow‐On‐Sea‐Ice Model
Bibliographic record
Abstract
Abstract Snow on Arctic sea ice has many, contrasting effects on ice thickness and extent. Furthermore, estimates of snow depth on Arctic sea ice are a key input for ice thickness estimates from satellite altimeters such as ICESat‐2. Models such as the NASA Eulerian Snow on Sea Ice Model (NESOSIM) have been recently utilized by the sea ice community to provide time‐varying basin‐wide estimates of snow depth and density on Arctic sea ice. NESOSIM is a two‐snow‐layer model with simple representations of snow accumulation, wind packing, loss due to blowing snow, and redistribution due to sea ice motion. Two free parameters in NESOSIM, which dictate the bulk effect of wind packing (densification) and blowing snow processes, lack direct observational constraints. We present an indirect calibration of these parameters using a Markov Chain Monte Carlo (MCMC) approach. NESOSIM output is calibrated to observations of snow depth from Operation IceBridge and CRREL‐Dartmouth buoys, and density from historical drifting stations. OIB measurements alone are found to more strictly constrain the blowing snow parameter, and including additional observations yields more physically reasonable density estimates. The MCMC‐calibrated model output is further used to estimate sea ice thickness and uncertainty from model parameter uncertainty using ICESat‐2 freeboard measurements. Despite visible differences in density, the change in ice thickness is minimal. We also find that the model is relatively insensitive to parameter variations, and hence, the snow model uncertainty contribution to ice thickness is small compared to the systematic uncertainty from snow in the current ICESat‐2 thickness product.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".