ArticleNature communications2023
Advancing research on compound weather and climate events via large ensemble model simulations.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Rethinking future flood hazard: Hourly data challenge daily flood projections in Alpine catchments.Science advances · 2026Article
- The variability fingerprint of a warming world informing climate risk and solutions.Scientific reports · 2026Article
- Article
- Global record-shattering breadbasket droughts emerge from moderately extreme regional events.Nature communications · 2026Article
- Article
- Climate research in the Global South.Nature communications · 2025Article
- Robust assessment of climatic risks to crop production.Nature food · 2025Article
- Changes in event soil moisture-temperature coupling can intensify very extreme heat beyond expectations.Nature communications · 2025Article
- Future increase in compound soil drought-heat extremes exacerbated by vegetation greening.Nature communications · 2024Article
- Review
- Significant challenges to the sustainability of the California coast considering climate change.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Generative emulation of weather forecast ensembles with diffusion models.Science advances · 2024Article
- Storyline attribution of human influence on a record-breaking spatially compounding flood-heat event.Science advances · 2023Article
- Addressing path dependencies in decision-making processes for operationalizing compound climate-risk management.iScience · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Societally relevant weather impacts typically result from compound events, which are rare combinations of weather and climate drivers. Focussing on four event types arising from different combinations of climate variables across space and time, here we illustrate that robust analyses of compound events - such as frequency and uncertainty analysis under present-day and future conditions, event attribution to climate change, and exploration of low-probability-high-impact events - require data with very large sample size. In particular, the required sample is much larger than that needed for analyses of univariate extremes. We demonstrate that Single Model Initial-condition Large Ensemble (SMILE) simulations from multiple climate models, which provide hundreds to thousands of years of weather conditions, are crucial for advancing our assessments of compound events and constructing robust model projections. Combining SMILEs with an improved physical understanding of compound events will ultimately provide practitioners and stakeholders with the best available information on climate risks.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.