Trend Analysis of Surface Shortwave (Solar) Radiation in Continental Northwest Territories, Canada (1980–2023)
Bibliographic record
Abstract
Abstract Identifying historical trends in surface shortwave (solar) radiation (SSR) is essential for understanding the energy balance in northern high latitudes and its effects on lake ecosystems undergoing climate-induced changes, such as those in the Northwest Territories (NWT), Canada. Using Daily Surface Meteorology and Hydrology Data (Daymet) SSR—validated against CERES measurements with an R2 of 0.95—this study quantified SSR trends from 1980 to 2023, investigated the relationship between total cloud cover (TCC) and SSR, and assessed the sensitivity of lake surface water temperature (LSWT) to SSR changes across continental NWT. The Mann–Kendall trend analysis showed contrasting SSR trends before and after 2000 across NWT ecozones. Over the 44-yr period, western zones such as the Taiga Cordillera and Taiga Plain exhibited negative SSR trends of approximately −1.4 and −0.7 W m−2 decade−1, respectively, whereas the eastern Taiga Shield showed an increase of about +0.6 W m−2 decade−1 (p ≤ 0.05 for most areas). Seasonally, summer SSR trends were mainly negative in western regions and positive in eastern regions, underscoring complex SSR dynamics in NWT. Regarding the influence of TCC on SSR, Pearson’s correlation analyses using both modeled and in situ TCC observations revealed a moderate-to-high negative correlation, particularly during spring, mainly in central and western NWT, excluding areas near the high-elevation mountainous boundaries of the Taiga Cordillera and Taiga Plains ecozones. The relationship between LSWT and SSR was varied; small lakes showed a positive correlation, while medium and large lakes located in the north exhibited negative correlations. Our findings suggest that lake thermal responses to SSR could be modulated by lake size and location, offering novel insights into NWT lake energy dynamics essential for NWT communities dependent on lakes and their ecosystems.
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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 | 0.001 | 0.004 |
| 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".