Iceland's Great Frost Winter of 1917/1918 and its representation in reanalyses of the twentieth century
Bibliographic record
Abstract
Climate variability during the twentieth century is a subject of considerable interest as it represents a way to place the changes that we are currently experiencing into a long‐term context. The characterization of climatic extremes and their representation in models is of particular importance as these events have a significant impact on communities and ecosystems. Much of the focus on these extremes has been on events that occurred after the establishment of the upper‐air observing network in the 1950s. The recent availability of reanalyses that extend throughout the twentieth century now allow extreme events that occurred before this time to be more fully investigated. The winter of 1917/1918 is referred to as the Great Frost Winter in Iceland. It was the coldest winter in the region during the twentieth century. It was remarkable for the presence of sea ice in Reykjavik Harbour as well as for the unusually large number of polar bear sightings in northern Iceland. Here we use observations as well as two reanalyses that span the twentieth century to document this event. We show that throughout much of the region, January 1918 was the coldest winter month on record. The North Atlantic Oscillation (NAO) attained one of its most negative values during January 1918 and the westward shift in its northern centre of action allowed cold Arctic and Greenlandic air to penetrate south towards Iceland. We also show that the two reanalyses diverged in their ability to represent the temperature anomalies during the event. Differences in the sea‐surface temperature (SST) and sea ice concentration fields used to force the underlying models contributed to this divergence. These results stress the important role that boundary conditions play in determining the fidelity with which reanalyses can represent extreme climate events in data‐sparse regions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".