Mechanismen der Meereisvariabilität aus Beobachtungen
Bibliographic record
Abstract
The cryosphere is coupled closely to the ocean as well as to the atmosphere and therefore reacts very sensitive on fluctuations in the climate system. This leads to a strong retreat in sea ice due to the ongoing global warming, as well as a high amount of internal variability, which can in turn influence the climate system through several feedback-processes. According to this the dominant pattern in the variability of winterly sea-ice, in more detail in ice concentration as well as extension, in Arctic and Antarctic have been investigated. For this purpose empirical orthogonal functions were calculated from the fields of sea ice concentration and the timeseries of the sea ice area was investigated via singular spectrum analysis (SSA). Results were connected to the atmosphere via PEARSON correlation-analysis. Different sets of satellite-data for sea ice reaching from 1982 to 2014 were used, which are offered by NSIDC, Hadley Centre and NOAA. Data for sea level pressure, two metre airtemperature as well as sea surface temperature were gained from the ERA-Interim dataset offered by the EZMWF, additionally several climate-indices from the NOAA were used. The NAO has been found as the most dominant factor influencing the ice concentration in the northern hemisphere, a weaker influence of the AO could be found additionally. The former is driving out-of-phase fluctuations between the Labrador-Sea and easterly North Atlantic, which displays the leading mode of winterly sea ice variability. SSA-Analysis has shown a significant influence of the AO on the sea ice extension in the Arctic. For Antarctic, a known pattern of anomalies of alterning sign in temperature, pressure and sea ice has been found, which are related to the so called Antarctic Circumpolar Wave and travel around the Antactic in a timeperiod of eight to ten years.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.774 | 0.030 |
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; both teacher heads agree on what is shown here.
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".