The spatio-temporal structure of the Lateglacial to early Holocene transition reconstructed from the pollen record of Lake Suigetsu and its precise correlation with other key global archives: Implications for palaeoclimatology and archaeology
Bibliographic record
Abstract
Leads, lags, or synchronies in climatic events among different regions are key to understanding mechanisms of climate change, as they provide insights into the causal linkages among components of the climate system. The well-studied transition from the Lateglacial to early Holocene ( ca . 16–10 ka) contains several abrupt climatic shifts, making this period ideal for assessing the spatio-temporal structure of climate change. However, comparisons of timings of past climatic events among regions often remain hypothetical because site-specific age scales are not necessarily synchronised to each other. Here we present new pollen data ( n = 510) and mean annual temperature reconstruction from the annually laminated sediments of Lake Suigetsu, Japan. Suigetsu's 14 C dataset is an integral component of the IntCal20 radiocarbon calibration model, in which the absolute age scale is established to the highest standard. Its exceptionally high-precision chronology, along with recent advances in cosmogenic isotope studies of ice cores, enables temporally coherent comparisons among Suigetsu, Greenland, and other key proxy records across regions. We show that the onsets of the Lateglacial cold reversal (equivalent to GS-1/Younger Dryas) and the Holocene were synchronous between East Asia and the North Atlantic, whereas the Lateglacial interstadial (equivalent to GI-1/Bølling-Allerød) started ca . two centuries earlier in East Asia than in the North Atlantic. Bimodal migration (or ‘jump’) of the westerly jet between north and south of the Tibetan plateau and Himalayas may have operated as a threshold system responsible for the abruptness of the change in East and South (and possibly also West) Asia. That threshold in Asia and another major threshold in the North Atlantic, associated with switching on/off of the Atlantic meridional overturning circulation (AMOC), were crossed at different times, producing a multi-centennial asynchrony of abrupt changes, as well as a disparity of climatic modes among regions during the transitional phases. Such disparity may have disturbed zonal circulation and generated unstable climate during transitions. The intervening periods with stable climate, on the other hand, coincided with the beginnings of sedentary life and agriculture, implying that these new lifestyles and technologies were not rational unless climate was stable and thus, to a certain extent, predictable.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".