Articles | Volume 8, issue 1
https://doi.org/10.5194/esd-8-211-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/esd-8-211-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
21 Mar 2017
Research article |
| 21 Mar 2017
On the meaning of independence in climate science
James D. Annan
CORRESPONDING AUTHOR
BlueSkiesResearch.org.uk, The Old Chapel, Albert Hill, Settle, BD24 9HE, UK
Julia C. Hargreaves
BlueSkiesResearch.org.uk, The Old Chapel, Albert Hill, Settle, BD24 9HE, UK
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- Accounting for skill in trend, variability, and autocorrelation facilitates better multi-model projections: Application to the AMOC and temperature time series R. Olson et al. 10.1371/journal.pone.0214535
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- Identifying climate models based on their daily output using machine learning L. Brunner & S. Sippel 10.1017/eds.2023.23
- Future Changes to El Niño–Southern Oscillation Temperature and Precipitation Teleconnections S. Perry et al. 10.1002/2017GL074509
- Ensemble optimisation, multiple constraints and overconfidence: a case study with future Australian precipitation change N. Herger et al. 10.1007/s00382-019-04690-8
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- Climate model pluralism beyond dynamical ensembles F. Mazzocchi & A. Pasini 10.1002/wcc.477
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- Accuracy in the prediction of disease epidemics when ensembling simple but highly correlated models D. Shah et al. 10.1371/journal.pcbi.1008831
- Quantifying uncertainty in European climate projections using combined performance-independence weighting L. Brunner et al. 10.1088/1748-9326/ab492f
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- Multi-model ensembles in climate science: Mathematical structures and expert judgements J. Jebeile & M. Crucifix 10.1016/j.shpsa.2020.03.001
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- Impacts of Climate Change and Variability on Precipitation and Maximum Flows in Devil’s Creek, Tacna, Peru E. Pino-Vargas et al. 10.3390/hydrology9010010
- ESD Reviews: Model dependence in multi-model climate ensembles: weighting, sub-selection and out-of-sample testing G. Abramowitz et al. 10.5194/esd-10-91-2019
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- Using multi‐model ensembles of CMIP5 global climate models to reproduce observed monthly rainfall and temperature with machine learning methods in Australia B. Wang et al. 10.1002/joc.5705
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- Similarities within a multi-model ensemble: functional data analysis framework E. Holtanová et al. 10.5194/gmd-12-735-2019
- Application-specific optimal model weighting of global climate models: A red tide example A. Elshall et al. 10.1016/j.cliser.2022.100334
- The Radiative Forcing of Aerosol–Cloud Interactions in Liquid Clouds: Wrestling and Embracing Uncertainty J. Mülmenstädt & G. Feingold 10.1007/s40641-018-0089-y
- An Assessment of Earth's Climate Sensitivity Using Multiple Lines of Evidence S. Sherwood et al. 10.1029/2019RG000678
- An investigation of weighting schemes suitable for incorporating large ensembles into multi-model ensembles A. Merrifield et al. 10.5194/esd-11-807-2020
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- Climate model Selection by Independence, Performance, and Spread (ClimSIPS v1.0.1) for regional applications A. Merrifield et al. 10.5194/gmd-16-4715-2023
- Interpreting the Probabilistic Language in IPCC Reports C. Dethier 10.3998/ergo.4637
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Discussed (final revised paper)
Latest update: 02 Nov 2024
Short summary
The concept of independence has been frequently raised in climate science, but has rarely been defined and discussed in a theoretically robust and quantifiable manner. Improved understanding of this topic is critical to better understanding of climate change. In this paper, we introduce a unifying approach based on the statistical definition of independence, and illustrate with simple examples how it can be applied to practical questions.
The concept of independence has been frequently raised in climate science, but has rarely been...
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