The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
A climate model projection weighting scheme accounting for performance and interdependence
Open accessAbstract
Uncertainties of climate projections are routinely assessed by considering simulations from different models. Observations are used to evaluate models, yet there is a debate about whether and how to explicitly weight model projections by agreement with observations. Here we present a straightforward weighting scheme that accounts both for the large differences in model performance and for model interdependencies, and we test reliability in a perfect model setup. We provide weighted multimodel projections of Arctic sea ice and temperature as a case study to demonstrate that, for some questions at least, it is meaningless to treat all models equally. The constrained ensemble shows reduced spread and a more rapid sea ice decline than the unweighted ensemble. We argue that the growing number of models with different characteristics and considerable interdependence finally justifies abandoning strict model democracy, and we provide guidance on when and how this can be achieved robustly.
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
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016) · Geoscientific Model Development · doi:10.5194/gmd-9-1937-2016 · Cited by 11,981
Self-citationThe use of the multi-model ensemble in probabilistic climate projections
Tebaldi, C., & Knutti, R. (2007) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2007.2076 · Cited by 1,862
Self-citationRobustness and uncertainties in the new CMIP5 climate model projections
Knutti, R., & Sedláček, J. (2013) · Nature Climate Change · doi:10.1038/nclimate1716 · Cited by 1,569
Self-citationClimate model genealogy: Generation CMIP5 and how we got there
Knutti, R., Masson, D., & Gettelman, A. (2013) · Geophysical Research Letters · doi:10.1002/grl.50256 · Cited by 918
Self-citationRisks of Model Weighting in Multimodel Climate Projections
Weigel, A. P., Knutti, R., Liniger, M. A., & Appenzeller, C. (2010) · Journal of Climate · doi:10.1175/2010jcli3594.1 · Cited by 386
Self-citationShould 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
Self-citationStatistical significance of climate sensitivity predictors obtained by data mining
Caldwell, P. M., Bretherton, C. S., Zelinka, M. D., Klein, S. A., Santer, B. D., & Sanderson, B. M. (2014) · Geophysical Research Letters · doi:10.1002/2014gl059205 · Cited by 153
Self-citation
Cited by
High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections
Beck, H. E., McVicar, T. R., Vergopolan, N., Berg, A., Lutsko, N. J., Dufour, A., Zeng, Z., Jiang, X., van Dijk, A. I. J. M., & Miralles, D. G. (2023) · Scientific Data · doi:10.1038/s41597-023-02549-6 · Cited by 917
Taking climate model evaluation to the next level
Eyring, V., Cox, P. M., Flato, G. M., Gleckler, P. J., Abramowitz, G., Caldwell, P., Collins, W. D., Gier, B. K., Hall, A. D., Hoffman, F. M., et al. (2019) · Nature Climate Change · doi:10.1038/s41558-018-0355-y · Cited by 748
Self-citationUncertainty in ensembles of global biodiversity scenarios
Thuiller, W., Guéguen, M., Renaud, J., Karger, D. N., & Zimmermann, N. E. (2019) · Nature Communications · doi:10.1038/s41467-019-09519-w · Cited by 513
(2023) · Climate Change 2021 – The Physical Science Basis · doi:10.1017/9781009157896.003 · Cited by 298
Emergent constraints on equilibrium climate sensitivity in CMIP5: do they hold for CMIP6?
Schlund, M., Lauer, A., Gentine, P., Sherwood, S. C., & Eyring, V. (2020) · Earth System Dynamics · doi:10.5194/esd-11-1233-2020 · Cited by 178
Self-citationWeakening of springtime Arctic ozone depletion with climate change
Friedel, M., Chiodo, G., Sukhodolov, T., Keeble, J., Peter, T., Seeber, S., Stenke, A., Akiyoshi, H., Rozanov, E., Plummer, D., et al. (2023) · Atmospheric Chemistry and Physics · doi:10.5194/acp-23-10235-2023 · Cited by 18
Metadata
- Authors
- Knutti, R., Sedláček, J., Sanderson, B. M., Lorenz, R., Fischer, E. M., & Eyring, V.
- Venue
- Geophysical Research Letters
- Publisher
- American Geophysical Union (AGU)
- Published
- 2017-02-18
- Volume
- 44
- Issue
- 4
- Pages
- 1909-1918
- ISSN
- 0094-8276 (ISSN-L)
- 1944-8007
- OpenAlex
- W2586615157
- Cited by
- 491
- References
- 7
- FWCI
- 22.60
- 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
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.
