Large scale extreme events in surface temperature during 1950--2003: an observational and modeling study
Bibliographic record
Abstract
The present study describes the climatology of events of surface temperature. Using both data analysis and model simulations, we investigated the frequency, duration and of events. After developing objective methods to identify extreme events, we consistently applied them to all data used in this study, focusing mainly on two particular regions: Europe and North America. We utilized 54 years (1950--2003) of Reanalysis data and we compared and contrasted the space-time of events during 1950--1976 and 1977--2003. We found that there has been an increase in the warm extremes in Europe and continental Canada and a general decrease of cold spells. Analyzing the spatial-temporal scale of their structure we found that an event, either warm or cold, that affects a larger area is expected to last longer, and longer and extended warm events are also more intense. The techniques and methods used for analysis of Reanalysis data were also applied to model simulations with increasing concentration of greenhouse gases to evaluate how the occurrence of warm and cold events might change in the future. Even though the models agree in projecting a significant increase of warm events and decrease of cold events in the 21st century, they give different estimates of events on regional scales. We identified the Mediterranean SSTA as a common feature for major warm events in the central-western European region. The European heat wave of summer 2003 was then taken as a case study. After a detailed diagnosis of the event, AGCM simulations were performed to investigate the role of ocean and land boundary conditions. In particular we tested the influence of the global SST and local Mediterranean SST in creating and maintaining the heat wave. We successfully simulated the summer 2003 heat wave by using global observed SST. We also found that the local Mediterranean SSTA played an important role in the evolution of the heat wave, explaining more than half of the global SST effect.
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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 | 0.001 | 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 | 0.000 |
| 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".