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Earth System Dynamics An interactive open-access journal of the European Geosciences Union

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https://doi.org/10.5194/esd-2017-72
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
08 Aug 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Earth System Dynamics (ESD).
Systematic Correlation Matrix Evaluation (SCoMaE) – A bottom-up, natural science-based approach to identify Indicators
Nadine Mengis1,2, David P. Keller2, and Andreas Oschlies2,3 1Concordia University, Montreal, QC, Canada
2Helmholtz Center for Ocean Research Kiel (GEOMAR), Düsternbrooker Weg 20, 24105 Kiel, Germany
3Kiel University, D-24098 Kiel, Germany
Abstract. This study introduces the Systematic Correlation Matrix Evaluation (SCoMaE) method, a bottom-up approach which combines expert judgment and statistical information to systematically select transparent, non redundant indicators for a com- prehensive assessment of the state of the Earth system. The methods consists of three basic steps: 1) Calculation of a correlation matrix among variables relevant for a given research question, 2) Systematic evaluation of the matrix, to identify clusters of variables with similar behavior and respective mutually independent indicators, and 3) Interpretation of the identified clusters, enabling a learning effect from the selection of indicators. Optional further analysis steps include: 4) Testing the robustness of identified clusters with respect to changes in forcing or boundary conditions, 5) Enabling a comparative assessment of varying scenarios by constructing and evaluating a common correlation matrix, or 6) Inclusion of expert judgment such as to prescribe indicators, to allow for considerations other than statistical consistency. The exemplary application of the SCoMaE method to Earth system model output forced by different CO2 emission scenarios reveals the necessity of re-evaluating indicators identified in a historical scenario simulation for an accurate assessment of an intermediate-high, as well as a business-as-usual, climate change scenario simulation, which arises from changes in prevailing correlations in the Earth system under varying climate forcing. For a comparative assessment of the three climate change scenarios, we construct and evaluate a common correlation matrix, in which we identify robust correlations between variables across the three considered scenarios.

Citation: Mengis, N., Keller, D. P., and Oschlies, A.: Systematic Correlation Matrix Evaluation (SCoMaE) – A bottom-up, natural science-based approach to identify Indicators, Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2017-72, in review, 2017.
Nadine Mengis et al.
Nadine Mengis et al.
Nadine Mengis et al.

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Short summary
The Systematic Correlation Matrix Evaluation (SCoMaE) method applies statistical information to systematically select transparent, non-redundant 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 societal relevant assessment indicators.
The Systematic Correlation Matrix Evaluation (SCoMaE) method applies statistical information to...
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