Meteorological and cloud conditions during the Arctic Ocean 2018 expedition
Bibliographic record
Abstract
The Arctic Ocean 2018 (AO2018) took place in the central Arctic Ocean in August and September 2018 on the Swedish icebreaker Oden . An extensive suite of instrumentation provided detailed measurements of surface water chemistry and biology, sea ice and ocean physical and biogeochemical properties, surface exchange processes, aerosols, clouds, and the state of the atmosphere. The measurements provide important information on the coupling of the ocean and ice surface to the atmosphere and in particular to clouds. This paper provides (i) an overview of the synoptic-scale atmospheric conditions and their climatological anomaly to help interpret the process studies and put the detailed observations from AO2018 into a larger context, both spatially and temporally; (ii) a statistical analysis of the thermodynamic and near-surface meteorological conditions, boundary layer, cloud, and fog characteristics; and (iii) a comparison of the results to observations from earlier Arctic Ocean expeditions – in particular AOE1996 (Arctic Ocean Expedition 1996), SHEBA (Surface Heat Budget of the Arctic Ocean), AOE2001 (Arctic Ocean Experiment 2001), ASCOS (Arctic Summer Cloud Ocean Study), ACSE (Arctic Clouds in Summer Experiment), and AO2016 (Arctic Ocean 2016) – to provide an assessment of the representativeness of the measurements. The results show that near-surface conditions were broadly comparable to earlier experiments; however the thermodynamic vertical structure was quite different. An unusually high frequency of well-mixed boundary layers up to about 1 km depth occurred, and only a few cases of the “prototypical” Arctic summer single-layer stratocumulus deck were observed. Instead, an unexpectedly high amount of multiple cloud layers and mid-level clouds were present throughout the campaign. These differences from previous studies are related to the high frequency of cyclonic activity in the central Arctic in 2018.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".