Investigating the impact of reanalysis snow input on an observationally calibrated snow-on-sea-ice reconstruction
Bibliographic record
Abstract
Abstract. A key uncertainty in reanalysis-based snow-on-sea-ice reconstructions is the choice of reanalysis product used for snowfall input. Although reanalysis products have many similarities in their precipitation output over the Arctic Ocean, they nevertheless have relative biases that impact derived snow-on-sea-ice estimates. In this study, snowfall from the ERA5, JRA-55 and MERRA-2 reanalysis products is used as input to the NASA Eulerian Snow On Sea Ice Model (NESOSIM). A Markov chain Monte Carlo (MCMC) approach is used to calibrate the wind packing and blowing snow parameters in NESOSIM run with these different snowfall inputs. A multi-input-averaged snow-on-sea-ice product is then constructed from NESOSIM run with the three reanalysis products. JRA-55 shows the largest departure from the previously-used values (Bayesian priors) when the MCMC calibration is run, and also has the largest posterior uncertainty due to parameter uncertainties. The MCMC calibration reconciles snow depths between NESOSIM run with different reanalysis snowfall inputs, but produces larger discrepancies in snow densities, due to the sensitivity of snow density in NESOSIM to parameter values and weak observational constraints on density. Regional climatologies and trends in the calibrated products are examined and compared to another reanalysis-based snow-on-sea-ice reconstruction, SnowModel-LG. NESOSIM and SnowModel-LG show close agreement in snow depth climatologies in the Central Arctic Ocean region, but differ more in peripheral seas. Trends are found to be region-dependent, and the magnitude of Central Arctic Ocean snow depth trends is more sensitive to the choice of reanalysis input than to the choice of model.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".