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Record W4410382113 · doi:10.3389/fclim.2025.1554685

Climate science for 2050

2025· article· en· W4410382113 on OpenAlexaff
Guy Brasseur, Detlef Stammer, Pierre Friedlingstein, Gabriele C. Hegerl, Tiffany A. Shaw, Kevin E. Trenberth, Jadwiga H. Richter, Carolina Vera, André Berger, Helen Cleugh, Steve Easterbrook, Paul Edwards, Daniela Jacob, Michael Mann, Valérie Masson‐Delmotte, Gavin A. Schmidt, Mary Scholes, Thomas F. Stocker, Martin Visbeck, Guoxiong Wu

Bibliographic record

VenueFrontiers in Climate · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of TorontoMinistry of the Environment, Conservation and Parks
FundersNational Oceanic and Atmospheric AdministrationNational Center for Atmospheric ResearchNational Science Foundation
KeywordsClimate scienceClimate changeEnvironmental scienceBiologyEcology

Abstract

fetched live from OpenAlex

Knowledge of the functioning of the climate system, including the physical, dynamical and biogeochemical feedback processes expected to occur in response to anthropogenic climate forcing, has increased substantially over recent decades. Today, climate science is at a crossroads, with new and urgent demands arising from the needs of society to deal with future climate change, and the need for the climate science community to refine its strategic goals to meet these demands rapidly. All possible—but currently unknown—worlds in 2050, with a larger global population, unprecedented climate conditions with higher temperatures, more frequent extreme weather events, sea level rise, disrupted ecosystems, changes in habitability and increased climate-induced displacement and migration, and the emergence of new geopolitical tensions, will require limiting society’s vulnerability both through mitigation measures to minimize further warming and through the implementation of innovative adaptation initiatives. The development of a skillful climate information system, based on the most advanced Earth system science, will be required to inform decision-makers and the public around the world about the local and remote impacts of climate change, and guide them in optimizing their adaptation and mitigation agendas. This information will also help manage renewable resources in a warmer world and strengthen resilience to the expected interconnected impacts of climate change. In this paper, we summarize the major advances needed to understand the multiscale dynamics of the Earth system. We highlight the need to develop an integrated information system accessible to decision-makers and citizens in all parts of the world, and present some of the key scientific questions that need to be addressed to inform decisions on mitigation and adaptation. Finally, we speculate about the values and ethics of climate science and the nature of climate research in a world that will be increasingly affected by global warming in a geopolitical context very different from that of recent decades.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.137
Threshold uncertainty score0.458

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0050.005
Open science0.0010.004
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.1370.120

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.

Opus teacher head0.012
GPT teacher head0.276
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2025
Admission routes1
Has abstractyes

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