We use process-based modeling techniques to characterize the temporal features of natural biologically controlled surface CO(2) fluxes and the relationships between the assimilation and respiration fluxes. Based on these analyses, we develop a signal-enhancing technique that combines a novel time-window splitting scheme, a simple median filtering, and an appropriate scaling method to detect potential signals of leakage of CO(2) from geologic carbon sequestration sites from within datasets of net near-surface CO(2) flux measurements. The technique can be directly applied to measured data and does not require subjective gap filling or data-smoothing preprocessing. Preliminary application of the new method to flux measurements from a CO(2) shallow-release experiment appears promising for detecting a leakage signal relative to background variability. The leakage index of +/- 2 was found to span the range of biological variability for various ecosystems as determined by observing CO(2) flux data at various control sites for a number of years.