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ESD | Articles | Volume 9, issue 1
Earth Syst. Dynam., 9, 15-31, 2018
https://doi.org/10.5194/esd-9-15-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Earth Syst. Dynam., 9, 15-31, 2018
https://doi.org/10.5194/esd-9-15-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 15 Jan 2018

Research article | 15 Jan 2018

Systematic Correlation Matrix Evaluation (SCoMaE) – a bottom–up, science-led approach to identifying indicators

Nadine Mengis et al.
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Cited articles  
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Bitz, C. M., Holland, M. M., Weaver, A. J., and Eby, M.: Simulating the ice-thickness distribution in a coupled climate model, J. Geophys. Res., 106, 2441–2463, https://doi.org/10.1029/1999JC000113, 2001. a
Böhringer, C. and Jochem, P. E. P.: Measuring the immeasurable – A survey of sustainability indices, Ecol. Econ., 63, 1–8, 2007. a
Callendar, G. S.: The artificial production of carbon dioxide and its influence on temperature, Q. J. Roy. Meteorol. Soc., 64, 223–240, 1938. a
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The Systematic Correlation Matrix Evaluation (SCoMaE) method applies statistical information to systematically select, transparent, nonredundant indicators for a comprehensive assessment of the Earth system state. We show that due to changing climate forcing, such as anthropogenic climate change, the ad hoc assessment indicators might need to be reevaluated. Within an iterative process, this method would allow us to select scientifically consistent and societally relevant assessment indicators.
The Systematic Correlation Matrix Evaluation (SCoMaE) method applies statistical information to...
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