The multi-scale carbon-carbon dioxide (C-CO₂) dynamics of subtropical urban forests and other green and grey infrastructure types were explored in an urbanized campus near Shanghai, China. We integrated eddy covariance (EC) C-CO₂ flux measurements and the Agroscope Reckenholz-Tänikon footprint tool to analyze C-CO₂ dynamics at the landscape-scale as well as in local-scale urban forest patches during one year. The approach measured the C-CO₂ flux from different contributing areas depending on wind directions and atmospheric stability. Although the study landscape was a net carbon source (2.98 Mg C ha⁻¹ yr⁻¹), we found the mean CO₂ flux in urban forest patches was −1.32 μmol m⁻²s⁻¹, indicating that these patches function as a carbon sink with an annual carbon balance of −5.00 Mg C ha⁻¹. These results indicate that urban forest patches and vegetation (i.e., green infrastructure) composition can be designed to maximize the sequestration of CO₂. This novel integrated modeling approach can be used to facilitate the study of the multi-scale effects of urban forests and green infrastructure on CO₂ and to establish low-carbon emitting planning and planting designs in the subtropics.

Shanghai, China, is a city that is relatively representative of various cities in China due to its geographical location and socio-economic dynamics. The role of urban vegetation in the carbon cycle of urban developments in these types of cities is now being studied. We focus on identifying which urban plant community types have a greater influence on CO₂ flux in cities, thus providing a scientific basis for low-carbon urban greening. Based on the eddy covariance (EC) observation system, ART Footprint Tool, plant inventory, and ecological community classification, we show that the CO₂ flux characteristics of different plant communities vary temporally. The carbon sink duration during summer was the longest (up to 10 h) and the carbon sink duration was the shortest during winter (7.5 h). In addition, we discovered that the CO₂ flux contribution rates of different plant community types are distinct. The annual average CO₂ contribution rates of the Cinnamomum camphora-Trachycarpus fortunei community and the Metasequoia glyptostroboides+Sabina chinensis community are 11.88% and 0.93%, respectively. The CO₂ flux contribution rate of the same plant community differs according to season. The CO₂ contribution rate of the Cinnamomum camphora-Trachycarpus fortunei community exhibits local maxima during winter and summer, with a maximum difference of 11.16%. In contrast, the Metasequoia glyptostroboides+Sabina chinensis community has a CO₂ contribution rate of 0.35% during the same period. In general, summer is the season with the lowest CO₂ flux contribution rate of plant communities, and winter is the season with the highest CO₂ flux contribution rate. However, the Cinnamomum camphora+Salix babylonica community and the Cinnamomum camphora+Sabina chinensis community present the opposite pattern. Finally, the diurnal variation characteristics of CO₂ flux in different communities have the same trend, but the peak values differ significantly. Overall, daily CO₂ flux peak value of the Metasequoia glyptostroboides community and the Cinnamomum camphora-Trachycarpus fortunei community indicate that these two plant communities exhibit a strong capacity for CO₂ absorption in the study area. According to these research results, urban greening efforts in subtropical climates can increase the green space covered by the Cinnamomum camphora-Trachycarpus fortunei and the Metasequoia glyptostroboides community types when urban greening, so as to appropriately reduce the CO₂ emitted into the atmosphere.

Understanding plant phenological change is of great concern in the context of global climate change. Phenological models can aid in understanding and predicting growing season changes and can be parameterized with gross primary production (GPP) estimated using the eddy covariance (EC) technique. This study used nine years of EC-derived GPP data from three mature subtropical longleaf pine forests in the southeastern United States with differing soil water holding capacity in combination with site-specific micrometeorological data to parameterize a photosynthesis-based phenological model. We evaluated how weather conditions and prescribed fire led to variation in the ecosystem phenological processes. The results suggest that soil water availability had an effect on phenology, and greater soil water availability was associated with a longer growing season (LOS). We also observed that prescribed fire, a common forest management activity in the region, had a limited impact on phenological processes. Dormant season fire had no significant effect on phenological processes by site, but we observed differences in the start of the growing season (SOS) between fire and non-fire years. Fire delayed SOS by 10 d ± 5 d (SE), and this effect was greater with higher soil water availability, extending SOS by 18 d on average. Fire was also associated with increased sensitivity of spring phenology to radiation and air temperature. We found that interannual climate change and periodic weather anomalies (flood, short-term drought, and long-term drought), controlled annual ecosystem phenological processes more than prescribed fire. When water availability increased following short-term summer drought, the growing season was extended. With future climate change, subtropical areas of the Southeastern US are expected to experience more frequent short-term droughts, which could shorten the region’s growing season and lead to a reduction in the longleaf pine ecosystem’s carbon sequestration capacity.

The seasonal dynamics of plant communities are important indicators for assessment of long‐term vegetation patterns and provide valuable information to predict ecosystem responses to climate change. However, increased frequency of extreme weather events can force ecosystems into unstable states, which leads to greater uncertainty in determining phenological metrics (e.g., growing season length). To better understand these uncertainties, we utilized 9 years of eddy covariance and remote sensing data to parameterize models of seasonal ecosystem respiration (Re) for two subtropical longleaf pine forests (mesic and xeric), with similar vegetation but different water holding capacity. We compared two commonly used algorithms to extract phenology metrics, the growth rate (GR) and third derivative (TD) methods, which are usually used without justification. We determined the impact of algorithm selection on estimating key biological dates related to plant community carbon dynamics (e.g., start, end, and length of physiologically active season, specifically Re), characterized the model's response to extreme weather events, and compared estimates to those derived via remotely sensed greenness from the enhanced vegetation index (EVI). We observed that periods of winter warming increased duration of physiological activity in terms of Re, and summer water limitation caused multi‐peaked, asymmetric behavior, creating significant uncertainties. We found that choice of phenology metric extraction algorithm significantly impacted biological event dates; the GR method estimated longer phenophases than the TD in both sites, as well as earlier starting and later ending dates for phenophases. Because the TD method was unable to give estimates during the buffer period of phenophase transition under certain weather conditions, the GR method may be more suitable for studies in subtropical forests. Dates derived from EVI greenness rarely matched those of plant community seasonal dynamics models, especially in spring and summer. The estimated length of Re from the model was significantly longer than that derived from EVI, indicating that the use of EVI could result in shorter growing season estimates and greater uncertainty. Our results provide direction for optimization of future approaches to extract phenological metrics and better scientific understanding of forest land surface phenology, as weather anomalies become more common with climate change.