Uneven impacts of climate change around the world and across the annual cycle of winegrapes
Bibliographic record
Abstract
Anthropogenic climate change has uneven impacts across the globe and throughout the year. Such unevenness poses a major challenge for human adaptation, especially for agricultural and other managed systems. Estimating effects beyond one region is challenging, however, because differences between regions make it difficult to know what seasonal periods of climate to compare. Both local climate and the planting decisions of a region affect the relevant periods for estimating changes in climate. Here, we use recent phenological models with a dataset of mean phenology for over 500 cultivars (varieties) to estimate climatic changes in growing regions across the globe for a major perennial crop that has been highly affected by climate change: winegrapes. We examine a suite of grower-relevant metrics, including temperatures during budburst, throughout the growing season and temperatures and precipitation surrounding harvest. We find that climate change has impacted all regions, especially for heat metrics across the full growing season (GDD, maximum temperature and days above 35°C). By far the largest shifts, however, are in European regions, where the number of hot days (>35°C) and maximum growing season temperatures are several standard deviations higher than before significant anthropogenic climate change. Including variety diversity in our estimates impacted only metrics at the start and end of the season, appearing most important for harvest-related climate metrics, and then only in ‘Old World’ regions, where most variety diversity is planted. Climate change impacts have thus been highly uneven across the world’s winegrowing regions and the impacts are variable across the growing season. Navigating how best to adapt the global winegrowing industry to climate change will require addressing these spatial and temporal complexities.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".