Networks of tower-based CO2 mole fraction sensors have been deployed by various groups in and around cities across the world to quantify anthropogenic CO2 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 CO2 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. 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 CO2 fluxes showed that differences in phenology and drawdown intensities drove measured differences in enhancements. Forward modelled CO2 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 CO2 enhancements. The model-data total CO2 mismatch during the dormant season was low, − 0.1 ± 0.5 ppm. 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 CO2 enhancement could be misinterpreted as increased anthropogenic fluxes if the background ecosystem CO2 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 CO2 networks for cities with significant vegetation.

中文翻译：

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近场通量对季节性二氧化碳增强的影响：印第安纳波利斯通量实验（INFLUX）的结果

基于塔的二氧化碳摩尔分数传感器网络已被世界各地及其周围的城市中的各个小组部署，以量化大都市区的人为二氧化碳排放量。这些方法中的一个关键方面是将远距离源和汇（即背景）的大气特征与本地排放物和生物通量分开。作为印第安纳波利斯通量（INFLUX）项目的一部分，我们研究了与美国印第安纳州印第安纳波利斯的森林和农业背景塔相比，二氧化碳的增加随季节的变化，并将它们与模拟结果进行了比较。在INFLUX城市塔楼站点，每月生长期的日增重达到每月4.3-6.5 ppm，平均是休眠季节的2.6倍。增强效果因背景选择和一年中的不同时间而有显着差异，与农业背景塔相比，使用森林背景塔的六月提高了2.8 ppm，8月降低了1.8 ppm。基于土地覆盖和观测到的CO2通量的预测表明，物候和下降强度的差异驱使测得的增强效果差异。使用化石燃料和生物通量对CO2增强进行的正向建模表明，农业背景的生长期模型数据不匹配为1.1±1.7 ppm，森林背景的生长期模型数据不匹配为2.1±0.5 ppm，相当于模型CO2增强的25–29％。休眠季节期间，模型数据的总CO2不匹配率很低，为-0.1±0.5 ppm。由于背景塔的生长期生物通量很大，如果不考虑背景生态系统的CO2吸收，则必须将城市的增强与生物信号分离开来，并且将CO2增强的生长期增加误解为人为通量增加。增强的幅度和时间取决于土地覆盖类型和每个背景塔周围的净通量，因此，简单的箱形模型不适用于解释这些数据。使用高分辨率和详细的生物模型对研究区域中的生物通量的季节性和强度进行量化，对于解释具有大量植被的城市基于塔的城市CO2网络是必要的。如果不考虑背景生态系统的CO2下降，则CO2增强的生长季节的增加可能被误认为是人为通量的增加。增强的幅度和时间取决于每个背景塔周围的土地覆盖类型和净通量，因此，简单的箱形模型不适用于解释这些数据。使用高分辨率和详细的生物模型对研究区域中的生物通量的季节性和强度进行量化，对于解释具有大量植被的城市基于塔的城市CO2网络是必要的。如果不考虑背景生态系统的CO2下降，则CO2增强的生长季节的增加可能被误认为是人为通量的增加。增强的幅度和时间取决于土地覆盖类型和每个背景塔周围的净通量，因此，简单的箱形模型不适用于解释这些数据。使用高分辨率和详细的生物模型对研究区域中的生物通量的季节性和强度进行量化，对于解释具有大量植被的城市基于塔的城市CO2网络是必要的。