Journal metrics

Journal metrics

  • IF value: 3.769 IF 3.769
  • IF 5-year value: 4.522 IF 5-year 4.522
  • CiteScore value: 4.14 CiteScore 4.14
  • SNIP value: 1.170 SNIP 1.170
  • SJR value: 2.253 SJR 2.253
  • IPP value: 3.86 IPP 3.86
  • h5-index value: 26 h5-index 26
  • Scimago H index value: 22 Scimago H index 22
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
Systematic Correlation Matrix Evaluation (SCoMaE) – a bottom–up, science-led approach to identifying indicators
Nadine Mengis1,a, David P. Keller2, and Andreas Oschlies2,3 1Geography, Planning, and Environment, Concordia University, Montréal, QC, Canada
2GEOMAR Helmholtz Centre for Ocean Research Kiel, Department of Marine Biogeochemistry, 24105 Kiel, Germany
3Kiel University, 24098 Kiel, Germany
aformerly at: GEOMAR Helmholtz Centre for Ocean Research Kiel, Department of Marine Biogeochemistry, 24105 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, nonredundant indicators for a comprehensive assessment of the state of the Earth system. The methods consists of two basic steps: (1) the calculation of a correlation matrix among variables relevant for a given research question and (2) the systematic evaluation of the matrix, to identify clusters of variables with similar behavior and respective mutually independent indicators. Optional further analysis steps include (3) the interpretation of the identified clusters, enabling a learning effect from the selection of indicators, (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, and (6) the inclusion of expert judgment, for example, to prescribe indicators, to allow for considerations other than statistical consistency. The example application of the SCoMaE method to Earth system model output forced by different CO2 emission scenarios reveals the necessity of reevaluating 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. This necessity 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, science-led approach to identifying indicators, Earth Syst. Dynam., 9, 15-31, https://doi.org/10.5194/esd-9-15-2018, 2018.
Publications Copernicus
Download
Short summary
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...
Share