The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Toward Seamless Prediction: Calibration of Climate Change Projections Using Seasonal Forecasts
Open accessAbstract
Trustworthy probabilistic projections of regional climate are essential for society to plan for future climate change, and yet, by the nonlinear nature of climate, finite computational models of climate are inherently deficient in their ability to simulate regional climatic variability with complete accuracy. How can we determine whether specific regional climate projections may be untrustworthy in the light of such generic deficiencies? A calibration method is proposed whose basis lies in the emerging notion of seamless prediction. Specifically, calibrations of ensemblebased climate change probabilities are derived from analyses of the statistical reliability of ensemblebased forecast probabilities on seasonal time scales. The method is demonstrated by calibrating probabilistic projections from the multimodel ensembles used in the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC), based on reliability analyses from the seasonal forecast Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) dataset. The focus in this paper is on climate change projections of regional precipitation, though the method is more general.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Malaria early warnings based on seasonal climate forecasts from multi-model ensembles
Thomson, M. C., Doblas-Reyes, F. J., Mason, S. J., Hagedorn, R., Connor, S. J., Phindela, T., Morse, A. P., & Palmer, T. N. (2006) · Nature · doi:10.1038/nature04503 · Cited by 470
Self-citation
Cited by
Seasonal climate predictability and forecasting: status and prospects
Doblas‐Reyes, F. J., García‐Serrano, J., Lienert, F., Biescas, A. P., & Rodrigues, L. R. L. (2013) · WIREs Climate Change · doi:10.1002/wcc.217 · Cited by 478
Self-citationMurphy, J. M., Booth, B. B. B., Collins, M., Harris, G. R., Sexton, D. M. H., & Webb, M. J. (2007) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2007.2077 · Cited by 316
Should we believe model predictions of future climate change?
Knutti, R. (2008) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2008.0169 · Cited by 279
Initialized Earth System prediction from subseasonal to decadal timescales
Meehl, G. A., Richter, J. H., Teng, H., Capotondi, A., Cobb, K., Doblas-Reyes, F., Donat, M. G., England, M. H., Fyfe, J. C., Han, W., et al. (2021) · Nature Reviews Earth & Environment · doi:10.1038/s43017-021-00155-x · Cited by 231
Self-citationCalibration of Pre-trained Transformers
Desai, S., & Durrett, G. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.21 · Cited by 209
Metadata
- Authors
- Palmer, T. N., Doblas-Reyes, F. J., Weisheimer, A., & Rodwell, M. J.
- Venue
- Bulletin of the American Meteorological Society
- Publisher
- American Meteorological Society
- Published
- 2008-04
- Volume
- 89
- Issue
- 4
- Pages
- 459-470
- ISSN
- 0003-0007 (ISSN-L)
- 1520-0477
- OpenAlex
- W2047635155
- Cited by
- 294
- References
- 1
- FWCI
- 23.50
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Toward Seamless Prediction: Calibration of Climate Change Projections Using Seasonal Forecasts. https://app.cochat.ai/papers/10.1175/bams-89-4-459
- The paper
- Palmer, T. N., Doblas-Reyes, F. J., Weisheimer, A., & Rodwell, M. J. (2008). Toward Seamless Prediction: Calibration of Climate Change Projections Using Seasonal Forecasts. Bulletin of the American Meteorological Society. https://doi.org/10.1175/bams-89-4-459
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.