Abstract
This paper designs a comprehensive approach based on the engineering machine/system concept, to
model, analyze, and assess the level of 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 (SFANOVA).
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 technology. 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.