Mesoscale Wind and Temperature Changes over Peatlands of the Hudson Bay Lowlands: impacts on the Surface Energy Balance and Net CO2 Exchange
Bibliographic record
Abstract
Although northern peatlands in general are currently a net carbon sink, there is still considerable uncertainty in the long-term combined response of plant productivity and ecosystem respiration to global warming and moisture changes. The Hudson Bay Lowlands (HBL) region of Canada is the second largest peat-accumulating complex in the world that is strongly influenced by the cold air masses originating off the Hudson Bay. Recent warming has caused observed changes in the sea ice dynamics and energy budget of the Hudson Bay, yet it is presently unknown how these climatic changes in the Bay will impact the surface energy and carbon balance of the adjacent HBL. As a globally significant peatland carbon pool, the HBL will play an important role in future climate warming and permafrost carbon feedback. The primary aim of this study is to improve our understanding of the warming-induced changes in the advective influence of the Hudson Bay and its linkage to the changes in the surface energy and carbon balance over peatlands of the HBL. I use a combined model and assimilated climate dataset to investigate the mesoscale wind and temperature changes in the HBL and their impacts on the surface energy balance (1979–2018). Furthermore, I employ a satellite data-driven light-use efficiency model, calibrated and validated with eddy covariance tower measurements at a fen and bog to examine the response of net ecosystem CO2 exchange to climatic changes (2000–2019). The results reveal that differential rates of warming between offshore and onshore winds have produced significant changes in the advective role of the Hudson Bay as evident in the increased frequency and strength of onshore winds. Also, the results show contrasting net CO2 exchange between the fen and bog sites. The anomalies in gross primary production and ecosystem respiration were associated with strong trends in temperature and moisture, and the Hudson Bay had a more pronounced advective influence on peatland respiration than photosynthesis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".