Abstract
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.
Highlights
An indicator is a quantitative value, measured or calculated, that describes relevant aspects of the state of a defined system
To illustrate the construction of the matrix based on our example simulations, we show how the correlation between changes in global mean “surface air temperature” (A_sat) and “Northern Hemisphere sea ice area” (O_iceareaN) in the Representative Concentration Pathway (RCP) 8.5 emission scenario (Meinshausen et al, 2011) due to the parameter perturbations translates to the corresponding correlation matrix entry (Fig. 1)
To demonstrate the Systematic Correlation Matrix Evaluation (SCoMaE) method, we applied it to correlation matrices constructed with changes in Earth system variables of an intermediate-complexity Earth system model, with which we simulated three forcing scenarios
Summary
An indicator is a quantitative value, measured or calculated, that describes relevant aspects of the state of a defined system. To illustrate the SCoMaE method, we exemplarily select indicators to answer the following research question: “How are changes in the climate system influenced by the sensitivity of the marine and terrestrial biological system to temperature and CO2?” This example application enables us to (1) illustrate how a correlation matrix can be constructed given a specific research question, (2) identify a comprehensive indicator set, (3) show that an indicator set derived from a certain forcing scenario is not necessarily appropriate to assess a changed forcing scenario, (4) identify a common indicator set valid for multiple forcing scenarios, and (5) illustrate how the method could be used in an iterative process including expert judgment or previous knowledge of the given system These steps will serve as the guideline of this paper
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