Study of the Planetary Boundary Layer using GEM model simulations in support of wind power projections for the Canadian Arctic
Bibliographic record
Abstract
Temperature inversions are a common feature of the Arctic climate, affecting the surface energy budget and planetary boundary layer transports. The temperature inversion, in conjunction with the wind, is used to define the transition between two stable planetary boundary layer regimes, the weakly stable boundary layer, and the very stable boundary layer. This regime shift is sharply defined in terms of wind speed and temperature inversions, so accurate modeling of those two characteristics is crucial to a good representation of the Arctic climate. This study investigates the evolution of large-scale temperature inversions in the context of a changing climate, in support of wind power projections for the Canadian Arctic. To this end, two five-member Regional Climate Model (RCM) ensembles, driven by the Canadian Earth System Model, spanning the 1950–2099 period, corresponding to two greenhouse gas emission scenarios (RCP 4.5 and 8.5), are considered. An ERA-Interim driven simulation for the 1979–2005 period is also considered to assess model performance. A comparison of observed atmospheric soundings with the boundary layer variations in the reanalysis-driven simulation indicates that the model captures the temperature inversion characteristics reasonably well, with some positive biases in the temperature inversion strength and frequency. The transient regional climate change simulations suggest substantial decreases in both temperature inversion strength and frequency in winter in future climate for both emission scenarios. These changes are consistent with the reduced sea ice cover and the associated increase in cloud cover that reduce the surface radiative cooling necessary for the formation of strong temperature inversions. Some increases in the frequency and strength of temperature inversions are projected for summer over the Arctic Ocean, possibly linked with increased poleward moisture transport. With the changes in the Arctic atmosphere seen in the future climate, the current predominant clear cold atmospheric state becomes less frequent. Cloudy and warm conditions that inhibit the formation of temperature inversions become more common. This scenario will increase the frequency of the atmospheric regime where temperature inversions are weaker and the wind speed is higher, leading to increased wind power potential to the Arctic region and positive prospects to a transition to a clean energy regimen
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".