Precipitation and Temperature Trends and Cycles Derived from Historical 1890–2019 Weather Data for the City of Ottawa, Ontario, Canada
Bibliographic record
Abstract
Patterns in historical climate data were analyzed for Ottawa, Ontario, Canada, for the interval 1890–2019. Variables analyzed included records of annual, seasonal, and extreme temperature and precipitation, diurnal temperature range, and various environmental responses. Using LOWESS regressions, it was found that annual and seasonal temperatures in Ottawa have generally increased through this interval, precipitation has shifted to a less snowy, rainier regime, and diurnal temperature variation has decreased. Furthermore, the annual growing season has lengthened by 23 days to ~163 days, and the annual number of frost-free days increased by 13 days to ~215 days. Despite these substantial climatic shifts, some variables (e.g., extreme weather events per year) have remained largely stable through the interval. Time-series analyses (including multitaper spectral analysis and continuous and cross wavelet transforms) have revealed the presence of several strong cyclical patterns in the instrumental record attributable to known natural climate phenomena. The strongest such influence on Ottawa’s climate has been the 11-year solar cycle, while the influence of the El Niño-Southern Oscillation, Arctic Oscillation, North Atlantic Oscillation, and Quasi-Biennial Oscillation were also observed and linked with the trends in annual, seasonal, and extreme weather. The results of this study, particularly the observed linkages between temperature and precipitation variables and cyclic climate drivers, will be of considerable use to policymakers for the planning, development, and maintenance of city infrastructure as Ottawa continues to rapidly grow under a warmer, wetter climate regime.
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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 | 0.002 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".