Reconstructing warm-season temperatures using brGDGTs and assessing biases in Holocene temperature records in northern Fennoscandia
Bibliographic record
Abstract
Understanding Holocene climate variability is crucial for predicting future climate change, which will disproportionally affect high-latitude regions. Summer temperature (Tsummer) reconstructions in regions such as northern Finland are mainly derived from microfossil data. We reconstructed Tsummer spanning the interval 10-1 cal ka BP using branched glycerol dialkyl glycerol tetraethers (brGDGTs) from lake-sediment record from Lake Annan Juomusjärvi (AJU) in northern Finland. The reconstruction shows cool early Holocene conditions, ∼2 °C below the long-term mean (defined by the last 8.5 kyr), followed by persistent warming to a thermal maximum around ∼7.0 cal ka BP, a relatively stable climate (∼0.5 °C above the long-term mean) from 7.0 to 3.5 cal ka BP, and then a long-term cooling trend (−0.1 °C·kyr−1) since 3.5 cal ka BP. This temperature history is remarkably well replicated by the nearby pollen-TJuly reconstruction from Lake Loitsana. However, Lake Loitsana chironomid and macrofossil data argue for a much earlier thermal maximum at ∼10 cal ka BP. Comparison of chironomid versus pollen records from across northern Fennoscandia confirms this inter-proxy discrepancy on the timing of Holocene peak warmth is a regional-scale phenomenon. Previous studies had raised the possibility that non-climatic noise in certain pollen records, due to local-scale overrepresentation of certain pollen types in the early and mid Holocene, may be contributing to an artificial lag in the thermal maximum. However, brGDGTs are unaffected by terrestrial flora and corroborate a mid-Holocene thermal maximum, which challenges the notion that pollen records are generally prone to misrepresenting the early to mid-Holocene climate history. Alternatively, proxy-specific environmental or mean seasonality biases may explain inter-proxy discrepancies in the timing of peak warmth. Continued diversification of the proxy network will help to better understand inter-proxy differences and refine knowledge of Holocene climate in northern Fennoscandia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".