The Impact of Climate Change on the Annual Cycle of Freeze–Thaw Events in Eastern North America
Bibliographic record
Abstract
Abstract Freeze–thaw events are a common phenomenon occurring mostly during spring and fall in the midlatitudes. Accumulated over the years, these events inflict stress to infrastructures, and when occurring unexpectedly, they can be devastating for agriculture and other biological systems. In this study, the annual cycle of freeze–thaw days is analyzed over eastern North America in terms of its current climatology and projected changes using near-surface air temperature from a set of bias-adjusted phase 6 of the Coupled Model Intercomparison Project (CMIP6) global climate projections. For the shared socioeconomic pathway (SSP3-7.0) emissions scenario, the results show marked changes in the seasonality of freeze–thaw days toward 2070–2100, with southern Canada and northern United States showing an advance of the freeze–thaw season by about 2 weeks in the spring, and a delay of a similar duration in the fall. For the same regions, freeze–thaw days could become much more frequent during winter, and less frequent during fall and spring. As for their annual number, freeze–thaw days are projected to decrease significantly in the southern part of the United States due to the projected decrease in the number of frost days. For Canada and the northern part of the United States, the annual number of freeze–thaw days is projected to remain nearly constant due to a compensation between the projected increase in thaw days and the decrease in frost days. Overall, these changes in the climatological characteristics of freeze–thaw days are key aspects to consider in climate change adaptation strategies. Significance Statement Freeze–thaw events have negative impacts in several sectors, notably infrastructures such as buildings and roads, but they can also be critical for agriculture and other biological systems. In North America, there is a lack of information regarding these events, in terms of both their historical climatology and projected changes in the future. This research contributes to filling this gap by assessing the climatology of freeze–thaw days in eastern North America and providing average future projections for three greenhouse gas emissions scenarios using a high-resolution reanalysis and phase 6 of the Coupled Model Intercomparison Project (CMIP6) climate models.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".