A gridded data set to study historical climate impacts in Switzerland since 1763
Bibliographic record
Abstract
For Switzerland, high-resolution gridded data of daily mean temperature and daily precipitation sums have recently been developed based on a large amount of early instrumental data for a period from 1763 to 1960. These temperature and precipitation fields were reconstructed with the analogue resampling method and subsequently improved using data assimilation for the temperature fields and bias correction for the precipitation fields. This new data set together with present-day meteorological fields since 1961 allows us to study a wide range of historical extreme weather events in Switzerland and their impacts on past societies. However, to study the impact of historical weather events in more detail, other variables are often needed, such as sunshine duration, wind speed, and humidity, but also minimum and maximum temperature.Here, we present the daily gridded Swiss reconstruction of daily temperature and precipitation, as well as the extension of the Swiss reconstruction to more variables, focusing mainly on sunshine duration, relative humidity, and wind speed. These additional reconstructions are based as well on the analogue resampling method, however, with a partly different reference period for the analogue pool compared to the temperature and precipitation reconstructions, and with a different data source for the resampled field. The extended Swiss gridded reconstructions make it possible to perform impact studies of historical weather and climate events, for example through agricultural modelling and calculating impact-based indices.Furthermore, we explore the potential of the extended reconstructions by evaluating historical and contemporary wildfire events in Switzerland. Wildfires are analysed using commonly used fire weather indices, such as the Canadian Fire Weather Index. We compare these indices with historical documents reporting on the fire events in Switzerland and, where possible, with the synoptic conditions over Switzerland and Europe leading up to the event.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.006 | 0.002 |
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".