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 European Centre for Medium-range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), the Japanese Meteorological Agency’s Japanese 55-year Reanalysis (JRA-55), and NASA’s Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), 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. 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 the 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. The models perform comparably when evaluated against IceBird airborne snow depth observations and in situ depth and density observations from the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC). Trends are found to be region-dependent, and central Arctic Ocean snow depth trends are 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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".