Examining the Characteristics and Evolution of Wintertime Temperature Whiplash Events in the U.S. Southern Plains
Bibliographic record
Abstract
Abstract Rapid extreme temperature swings, termed “temperature whiplashes,” can lead to significant socioeconomic impacts. Few studies have considered temperature whiplashes over continental or global domains, but regional characteristics of temperature whiplash events and the potential for long-range predictions of such events remain to be studied. This study focuses on defining and characterizing temperature whiplash events in the U.S. southern plains during the winter. Two types of whiplashes are defined: 1) hot to cold and 2) cold to hot. Using the fifth generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis from 1950 to 2023 during December–February, temperature whiplash events are defined using a temperature swing index (TSI), a measure of day-to-day changes in temperature, area-averaged across the southern plains. Days where the TSI exceeds the 90th percentile are labeled “high swing days” (HSDs). Temperature whiplash events occur when a HSD coincides with a highly negative (hot to cold) or positive (cold to high) shift in temperatures. Lagged geopotential height composites of hot-to-cold whiplashes illustrate an amplifying Rossby wave train and atmospheric blocking pattern in the North Pacific in the midtroposphere, coinciding with an increasingly distorted and “stretched” stratospheric polar vortex at 50 hPa. Coinciding with a statistically significant upward flux over eastern Siberia/western Alaska and downward flux over Canada, our results suggest possible stratospheric wave reflection prior to the HSD. These characteristics present particular forecasts of opportunity that may improve predictions of these extreme temperature events on a subseasonal to seasonal scale. Significance Statement Rapid changes in temperature within a few days, which we term “temperature whiplash events,” can have significant impacts on agriculture, transportation, and daily life. Although some past studies have looked at these temperature whiplash events on very large scales, regional events and their potential long-range prediction have not been quantified. This study focuses on temperature whiplash events in the southern plains of the United States, an area known for highly variable weather. Analyses of the atmospheric conditions reveal that stronger-than-normal high pressure builds in the North Pacific in the days leading up to the hot-to-cold temperature whiplash event, which helps to funnel colder air down into the southern plains days later. There are also precursor signals in the stratosphere, where the polar vortex shows signs of becoming more elliptical than circular in shape, a known precursor for cold-air surges into central North America. Together, these signals may be useful to improve our forecast skill of these very impactful extreme temperature events.
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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 | 0.001 |
| 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".