Indicators of Global Climate Change 2022
Bibliographic record
Abstract
This dataset contains standardised data for figures and results used in the Indicators of Global Climate Change paper: Forster, P. M., Smith, C. J., Walsh, T., Lamb, W. F., Lamboll, R., Hauser, M., Ribes, A., Rosen, D., Gillett, N., Palmer, M. D., Rogelj, J., von Schuckmann, K., Seneviratne, S. I., Trewin, B., Zhang, X., Allen, M., Andrew, R., Birt, A., Borger, A., Boyer, T., Broersma, J. A., Cheng, L., Dentener, F., Friedlingstein, P., Gutiérrez, J. M., Gütschow, J., Hall, B., Ishii, M., Jenkins, S., Lan, X., Lee, J.-Y., Morice, C., Kadow, C., Kennedy, J., Killick, R., Minx, J. C., Naik, V., Peters, G. P., Pirani, A., Pongratz, J., Schleussner, C.-F., Szopa, S., Thorne, P., Rohde, R., Rojas Corradi, M., Schumacher, D., Vose, R., Zickfeld, K., Masson-Delmotte, V., and Zhai, P.: Indicators of Global Climate Change 2022: annual update of large-scale indicators of the state of the climate system and human influence, Earth Syst. Sci. Data, 15, 2295–2327, https://doi.org/10.5194/essd-15-2295-2023, 2023 The below table details the author(s) of each dataset contained within the repository, and their homepages. Dataset Author(s) Original code repository Attribution of historical warming 1850-2022 Tristram Walsh, Aurélien Ribes, Nathan Gillett, Chris Smith https://github.com/ClimateIndicator/anthropogenic-warming-assessment https://github.com/ESMValGroup/ESMValTool/tree/forster23 Earth's energy imbalance 1971-2022 Matthew Palmer, Karina von Schuckmann https://github.com/ClimateIndicator/ocean-heat-content Effective radiative forcing 1750-2022 Chris Smith, Piers Forster https://github.com/ClimateIndicator/forcing-timeseries Global mean surface temperature anomalies 1850-2022 Blair Trewin https://github.com/ClimateIndicator/GMST Global temperature extreme anomalies 1950-2022 Mathias Hauser, Dominik Schumacher, Sonia Seneviratne https://github.com/ClimateIndicator/cip_extremes Greenhouse gas concentrations 1750-2022 Chris Smith https://github.com/ClimateIndicator/forcing-timeseries Greenhouse gas emissions 1750-2022 William Lamb https://github.com/ClimateIndicator/GHG-Emissions-Assessment Remaining carbon budgets in 0.1°C increments Robin Lamboll https://github.com/Rlamboll/CarbonBudget Each data file has associated metadata in YML format with details on the contact author and original repository of the source code (note no code is retained on this data repository). The metadata files include additional information about each dataset, including short descriptions, file sizes and MD5 hashes. .md and YML format files can be opened by any text editor (Notepad etc.). This release contains the indicators of global climate change updated to the end of 2022. Datasets included are: Attribution of historical warming 1850-2022 Earth's energy imbalance 1971-2022 Effective radiative forcing 1750-2022 Global mean surface temperature anomalies 1850-2022 Global temperature extreme anomalies 1950-2022 Greenhouse gas concentrations 1750-2022 Greenhouse gas emissions 1750-2022 Remaining carbon budgets in 0.1°C increments v2023.06.02: Update to attributed warming summary table following update to Gillett et al. in v2023.05.25 Simplified metadata YML files for each data file v2023.05.25: Update of Gillett et al. attributed warming to include percentiles and fix bug v2023.05.24: Scope of F-gas emissions changed to original AR6 categories (i.e. excluding Montreal gas emissions). Land use emissions reduced to remove double-counting of emissions reported in both PRIMAP-hist and GFED databases. Greenhouse gas emissions are reported both on native emissions units and CO2e for CH4 and N2O, and all source datasets provided. Metadata updated to include additional code repository source for historical climate change attribution. v2023.05.01: Original 2022 Climate Indicators data
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.063 |
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 source (direct Gemma or distilled Codex), 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".