Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection
Bibliographic record
Abstract
Abstract. Reliable basin-scale estimates of sea ice thickness are urgently needed to improve our understanding of recent changes and future projections of polar climate. Data collected by NASA’s ICESat-2 mission have provided new, high-resolution, estimates of sea ice freeboard across both hemispheres since data collection started in October 2018. These data have been used in recent work to produce estimates of winter Arctic sea ice thickness using snow loading estimates from the NASA Eulerian Snow On Sea Ice Model (NESOSIM). Here we provide an impact assessment of upgrades to both the ICESat-2 freeboard data (ATL10) and NESOSIM snow loading on estimates of winter Arctic sea ice thickness. Misclassified leads were removed from the freeboard algorithm in the third release (rel003) of ICESat-2 freeboard data, which increased freeboards in January and April 2019, and increased the fraction of low freeboards in November 2018, compared to rel002. These changes improved comparisons of sea ice thickness (lower mean biases and standard deviations, higher correlations) with monthly gridded thickness estimates produced from ESA’s CryoSat-2 (using the same input snow and ice density assumptions). Later releases (rel004 and rel005) of ICESat-2 ATL10 freeboards result in less significant changes in the freeboard distributions and thus thickness. The latest version of NESOSIM (version 1.1), forced by CloudSat-scaled ERA5 snowfall, has been re-calibrated using snow depth estimates obtained by NASA’s Operation IceBridge airborne mission. The upgrade from NESOSIM v1.0 to v1.1 results in only small changes in snow depth which have a less significant impact on thickness compared to the rel002 to rel003 freeboard changes. Finally, we present our updated monthly gridded winter Arctic sea ice thickness dataset and highlight key changes over the past three winter seasons of data collection (November 2018–April 2021). Strong differences in total winter Arctic thickness across the three winters are observed, linked to clear differences in the multiyear ice thickness at the start of each winter. Interannual changes in snow depth provide significant impacts on our thickness results on regional and seasonal scales. Our analysis of recent winter Arctic sea ice thickness variability is provided online in a novel Jupyter Book format to increase transparency and user engagement with our derived gridded monthly thickness dataset.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".