ReviewScience advances2025
Confronting Earth System Model trends with observations.
Review in Science advances, 2025. 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.
- Space-time signatures of surface warming accelerations since 1970.Nature communications · 2026Article
- Bridging the weather and climate divide with artificial intelligence.Nature communications · 2026Review
- Observed fingerprint of global warming exposes model biases in regional climate attribution.Science advances · 2026Article
- Tropical origins of the recent trends in Northern Hemisphere wintertime jet streams.Nature communications · 2026Article
- Tropical impacts of the Southern Ocean underestimated by mean-state biases.Science advances · 2026Article
- Towards disentangling human-induced drivers of precipitation trends from naturally occurring ones.Nature · 2026Article
- Article
- Machine learning helps to strongly reduce future warming uncertainty.Nature communications · 2026Article
- Unlocking the benefits of transparent and reusable science for climate risk management.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A strengthening yet less extreme Atlantic-European jet during winter.Nature geoscience · 2026Article
- Contextualizing contemporary seasonality variations in East Asian tropical cyclone landfalls with a multi-century historical baseline.NPJ climate and atmospheric science · 2026Article
- CMIP6 models cannot capture long-term forced changes in the tropical Pacific sea surface temperature gradient.Nature communications · 2025Article
- Projection of ENSO using observation-informed deep learning.Nature communications · 2025Article
- Robust impact of tropical Pacific SST trends on global and regional circulation in boreal winter.NPJ climate and atmospheric science · 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
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Anthropogenically forced climate change signals are emerging from the noise of internal variability in observations, and the impacts on society are growing. For decades, Climate or Earth System Models have been predicting how these climate change signals will unfold. While challenges remain, given the growing forced trends and the lengthening observational record, the climate science community is now in a position to confront the signals, as represented by historical trends, in models with observations. This review covers the state of the science on the ability of models to represent historical trends in the climate system. It also outlines robust procedures that should be used when comparing modeled and observed trends and how to move beyond quantification into understanding. Finally, this review discusses cutting-edge methods for identifying sources of discrepancies and the importance of future confrontations.
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.