Background Networks of tower-based CO 2 mole fraction sensors have been deployed in and around cities across the world to quantify anthropogenic CO 2 emissions from metropolitan areas. A critical aspect in these approaches is the separation of atmospheric signatures from distant sources and sinks (i.e., the background) from local emissions and biogenic fluxes. We examined CO 2 enhancements compared to forested and agricultural background towers in Indianapolis, Indiana, USA, as a function of season and compared them to modeled results, as a part of the Indianapolis Flux (INFLUX) project. Results At the INFLUX urban tower sites, daytime growing season enhancement on a monthly timescale was up to 4.3 – 6.5 ppm, 2.6 times as large as those in the dormant season, on average. The enhancement differed significantly depending on choice of background and time of year, being 2.8 ppm higher in June and 1.8 ppm lower in August using a forested background tower compared to an agricultural background tower. A prediction based on land cover and observed CO 2 fluxes showed that differences in phenology and drawdown intensities drove measured differences in enhancements. Forward modelled CO 2 enhancements using fossil fuel and biogenic fluxes indicated growing season model-data mismatch of 1.1 ± 1.7 ppm for the agricultural background and 2.1 ± 0.5 ppm for the forested background, corresponding to 25 – 29 % of the modelled CO 2 enhancements. The model-data total CO 2 mismatch during the dormant season was low, – 0.1 ± 0.5 ppm. Conclusions Because growing season biogenic fluxes at the background towers are large, the urban enhancements must be disentangled from the biogenic signal, and growing season increases in CO 2 enhancement could be misinterpreted as increased anthropogenic fluxes if the background ecosystem CO 2 drawdown is not considered. The magnitude and timing of enhancements depend on the land cover type and net fluxes surrounding each background tower, so a simple box model is not appropriate for interpretation of these data. Quantification of the seasonality and magnitude of the biological fluxes in the study region using high-resolution and detailed biogenic models is necessary for the interpretation of tower-based urban CO 2 networks for cities with significant vegetation.