Climate science for 2050
Bibliographic record
Abstract
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".