ArticleScience advances2025
Should we think of observationally constrained multidecade climate projections as predictions?
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Machine learning helps to strongly reduce future warming uncertainty.Nature communications · 2026Article
- Projection of ENSO using observation-informed deep learning.Nature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Empirical evidence indicates that the range of model-projected future warming can be successfully narrowed by conditioning the projected warming on past observed warming. We demonstrate that warming projections conditioned on the entire instrumental annual surface temperature record are of sufficiently high quality and should be considered as long-term predictions rather than merely as projections. We support this view by considering the skill of predicted 20- and 50-year lead temperature changes under the Shared Economic Pathway (SSP)1-2.6 and SSP5-8.5 emission scenarios in climates of different sensitivities. Using climate model simulations, we show that adjusting raw multimodel projections of future warming with the Kriging for Climate Change (KCC) method eliminates most biases and reduces the uncertainty of warming projections irrespective of the sensitivity of the climate being considered. Simpler methods, or using only the more recent part of the temperature record, provide less effective constraints. The high-skill future warming predictions obtained via KCC have a serious place in informing global climate policies.
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.