An enhanced engineering perspective of global climate systems and statistical formulation of terrestrial CO₂ exchanges

This paper designs a comprehensive approach based on the engineering machine/system concept, to model, analyze, and assess the level of carbon dioxide (CO₂) exchange between the atmosphere and terrestrial ecosystems, which is an important factor in understanding changes in global climate. The focus of this article is on spatial patterns and on the correlation between levels of CO₂ fluxes and a variety of influencing factors in eco-environments. The engineering/machine concept used is a system protocol that includes the sequential activities of design, test, observe, and model. This concept is applied to explicitly include various influencing factors and interactions associated with CO₂ fluxes. To formulate effective models of a large and complex climate system, this article introduces a modeling technique that will be referred to as stochastic filtering analysis of variance (SF-ANOVA). The CO₂ flux data observed from some sites of AmeriFlux are used to illustrate and validate the analysis, prediction, and globalization capabilities of the proposed engineering approach and the SF-ANOVA technique. The SF-ANOVA modeling approach was compared to stepwise regression, ridge regression, and neural networks. The comparison indicated that the proposed approach is a valid and effective tool with similar accuracy and less complexity than the other procedures.