Observationally calibrating snow-on-sea-ice model free parameters and estimating uncertainties using a Markov Chain Monte Carlo method
Bibliographic record
Abstract
Snow on Arctic sea ice plays many roles in Arctic climate feedbacks; in particular, through its impact on sea ice. Snow can have many, sometimes contrasting effects on sea ice thickness and extent. For example, during the ice growth season, snow can inhibit ice growth by insulating the ice from the cold atmosphere. Conversely, snow can allow sea ice to persist longer during the melt season, due to its high albedo. Furthermore, estimates of snow depth on Arctic sea ice are a key input for deriving sea ice thickness from satellite lidar altimetry measurements, such as those from ICESat-2. Due to the logistical challenges of making measurements in as remote a region as the Arctic, snow depth on Arctic sea ice is difficult to observationally quantify. To provide widespread estimates of the depth and density of snow on Arctic sea ice, models such as the NASA Eulerian Snow On Sea Ice Model (NESOSIM) can be used. The latest version of NESOSIM, version 1.1, is a 2-layer three-dimensional model with simple representations of snow accumulation, wind packing, loss due to blowing snow, and redistribution due to sea ice motion. Relative to version 1.0, among other changes, NESOSIM 1.1 features an extended model domain and reanalysis snowfall input from ERA5 scaled to observed snowfall derived from CloudSat satellite radar measurements. The free parameters in NESOSIM, which dictate the strength of the wind packing (densification) and blowing snow loss processes, cannot be directly constrained to observations. We present an indirect calibration of these free parameters, by calibrating NESOSIM output to observations from airborne snow depth observations from Operation IceBridge and in situ CRREL-Dartmouth snow buoy measurements, as well as historical Soviet drifting station density measurements, using a Metropolis Markov Chain Monte Carlo (MCMC) approach. This approach produces estimates of the free parameters and their uncertainty distributions, from which model snow depth and density uncertainties can be estimated. We find that introducing stricter observational constraints in the calibration produces narrower snow depth uncertainty distributions from NESOSIM. We then examine the impact of these uncertainties on sea ice thickness derived using NESOSIM output and freeboard measurements from ICESat-2.
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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 | 0.004 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".