For over 20 years, atmospheric measurements of CO2 dry air mole fractions have been used to derive estimates of CO2 surface fluxes. Historically, only a few research laboratories made these measurements. Today, many laboratories are making CO2 observations using a variety of analysis techniques and, in some instances, using different calibration scales. As a result, the risk of biases in individual CO2 mole fraction records, or even in complete monitoring networks, has increased over the last decades. Ongoing experiments comparing independent, well-calibrated measurements of atmospheric CO2 show that biases can and do exist between measurement records. Biases in measurements create artificial spatial and temporal CO2 gradients, which are then interpreted by an inversion system, leading to erroneous flux estimates. Here we evaluate the impact of a constant bias introduced into the National Oceanic and Atmospheric Administration (NOAA) quasi-continuous measurement record at the Park Falls, Wisconsin (LEF), tall tower site on CarbonTracker flux estimates. We derive a linear relationship between the magnitude of the introduced bias at LEF and the CarbonTracker surface flux responses. Temperate North American net flux estimates are most sensitive to a bias at LEF in our CarbonTracker inversion, and its linear response rate is 68 Tg C yr−1 (∼10% of the estimated North American annual terrestrial uptake) for every 1 ppm of bias in the LEF record. This sensitivity increases when (1) measurement biases approached assumed model errors and (2) fewer other measurement records are available to anchor the flux estimates despite the presence of bias in one record. Flux estimate errors are also calculated beyond North America. For example, biospheric uptake in Europe and boreal Eurasia combined increases by 25 Tg C yr−1 per ppm CO2 to partially compensate for changes in the North American flux totals. These results illustrate the importance of well-calibrated, high-precision CO2 dry air mole fraction measurements, as well as the value of an effective strategy for detecting bias in measurements. This study stresses the need for a monitoring network with the necessary density to anchor regional, continental, and hemispheric fluxes more tightly and to lessen the impact of potentially undetected biases in observational networks operated by different national and international research programs.