Arctic sea ice anomalies during the MOSAiC winter 2019/20
Bibliographic record
Abstract
Abstract. As the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) project went into effect during the winter of 2019/2020, the Arctic Oscillation (AO) has experienced some of the largest shifts from a highly negative index in November 2019 to an extremely positive index during January-February-March (JFM) 2020. Here we analyse the sea ice thickness (SIT) distribution based on CryoSat-2/SMOS satellite data augmented with results from the hindcast simulation by the fully coupled Regional Arctic System Model (RASM) for the time period from November 2019 through March 2020. A notable result of the positive AO phase during JFM 2020 were large SIT anomalies, up to 1.3 m, which emerged in the Barents-Sea (BS), along the northeastern Canadian coast and in parts of the central Arctic Ocean. These anomalies appear to be driven by nonlinear interactions between thermodynamic and dynamic processes. In particular, in the Barents- and Kara Seas (BKS) they are a result of an enhanced ice growth connected with the colder temperature anomalies and the consequence of intensified atmospheric-driven sea ice transport and deformations (i.e. divergence and shear) in this area. Low-pressure anomalies, which developed over the Eastern Arctic during JFM 2020, increased northerly winds from the cold Arctic Ocean to the BS and accelerated the southward drift of the MOSAiC ice floe. The satellite-derived and model-simulated sea ice velocity anomalies, which compared well during JFM 2020, indicate a strong acceleration of the Transpolar Drift relative to the mean for the past decade, with intensified speeds up to 6 km/day. As a consequence, sea ice transport and deformations driven by atmospheric wind forcing accounted for bulk of SIT anomalies, especially in January and February 2020. The unusual AO shift and the related sea ice anomalies during the MOSAiC winter 2019/20 are within the range of simulated states in the forecast ensemble. RASM intra-annual ensemble forecast simulations, forced with different atmospheric boundary conditions from November 1, 2019 through April 30, 2020, show a pronounced internally generated variability in the sea ice volume. A comparison of the respective SIT distribution and turbulent heat fluxes during the positive AO phase in JFM 2020 and the negative AO phase in JFM 2010 further corroborates the conclusion, that winter sea ice conditions of the Arctic Ocean can be significantly altered by AO variability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".