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Climate change will bring about changes in meteorological and ecological factors that are currently used in global-scale models to calculate biogenic emissions. By comparing long-term datasets of biogenic compounds to modeled emissions, this work seeks to improve understanding of these models and their driving factors. We compare speciated biogenic volatile organic compound (BVOC) measurements at the Virginia Forest Research Laboratory located in Fluvanna County, VA, USA, for the year 2020 with emissions estimated by the Model of Emissions of Gases and Aerosols from Nature version 3.2 (MEGANv3.2). The emissions were subjected to oxidation in a 0-D box model (F0AM v4.3) to generate time series of modeled concentrations. We find that default light-dependent fractions (LDFs) in the emissions model do not accurately represent observed temporal variability in regional observations. Some monoterpenes with a default light dependence are better represented using light-independent emissions throughout the year (LDFα-pinene=0, as opposed to 0.6), while others are best represented using a seasonally or temporally dependent light dependence. For example, limonene has the highest correlation between modeled and measured concentrations using an LDF =0 for January through April and roughly 0.74–0.97 in the summer months, in contrast to the default value of 0.4. The monoterpenes β-thujene, sabinene, and γ-terpinene similarly have an LDF that varies throughout the year, with light-dependent behavior in summer, while camphene and α-fenchene follow light-independent behavior throughout the year. Simulations of most compounds are consistently underpredicted in the winter months compared to observed concentrations. In contrast, day-to-day variability in the concentrations during summer months are relatively well captured using the coupled emissions–chemistry model constrained by regional concentrations of NOX and O3. 

Solar-induced chlorophyll fluorescence (SIF) is widely accepted as a proxy for gross primary productivity (GPP). Among the various SIF measurements, tower-based SIF measurements allow for continuous monitoring of SIF variation at a canopy scale with high temporal resolution, making it suitable for monitoring highly variable plant physiological responses to environmental changes. However, because of the strong and close relationship between SIF and absorbed photosynthetically active radiation (aPAR), it may be difficult to detect the influence of environmental drivers other than light conditions. Among the drivers, atmospheric dryness (vapor pressure deficit, VPD) is projected to increase as drought becomes more frequent and severe in the future, negatively impacting plants. In this study, we evaluated the tower-based high-frequency SIF measurement as a tool for detecting plant response to highly variable VPD. The study was performed in a mixed temperate forest in Virginia, USA, where a 40-m-tall flux tower has been measuring gas and energy exchanges and ancillary environmental drivers, and the Fluospec 2 system has been measuring SIF. We show that a proper definition of light availability to vegetation can reproduce SIF response to changing VPD that is comparable to GPP response as estimated from eddy covariance measurement: GPP decreased with rising VPD regardless of how aPAR was defined, whereas SIF decreased only when aPAR was defined as the PAR absorbed by chlorophyll (aPARchl) or simulated by a model (Soil Canopy Observation, Photochemistry and Energy fluxes, SCOPE). We simulated the effect of VPD on SIF with two different simulation modes of fluorescence emission representing contrasting moisture conditions, ‘Moderate’ and ‘Soil Moisture (SM) Stress’ modes. The decreasing SIF to rising VPD was only found in the SM Stress mode, implying that the SIF-VPD relationship depends on soil moisture conditions. Furthermore, we observed a similar response of SIF to VPD at hourly and daily scales, indicating that satellite measurements can be used to study the effects of environmental drivers other than light conditions. Finally, the definition of aPAR emphasizes the importance of canopy structure research to interpret remote sensing observations properly. 

Abstract Trees regulate canopy temperature (T_c) via transpiration to maintain an optimal temperature range. In diverse forests such as those of the eastern United States, the sensitivity ofT_cto changing environmental conditions may differ across species, reflecting wide variability in hydraulic traits. However, these links are not well understood in mature forests, whereT_cdata have historically been difficult to obtain. Recent advancement of thermal imaging cameras (TICs) enablesT_cmeasurement of previously inaccessible tall trees. By leveraging TIC and sap flux measurements, we investigated how co‐occurring trees (Quercus alba,Q. falcata, andPinus virginiana) change theirT_cand vapor pressure deficit near the canopy surface (VPD_c) in response to changing air temperature (T_a) and atmospheric VPD (VPD_a). We found a weaker cooling effect for the species that most strongly regulates stomatal function during dry conditions (isohydric;P. virginiana). Specifically, the pine had higherT_c(up to 1.3°C) and VPD_c(up to 0.3 kPa) in the afternoon and smaller sensitivity of both∆T(=T_c − T_a) and∆VPD (=VPD_c − VPD_a) to changing conditions. Furthermore, significant differences inT_cand VPD_cbetween sunlit and shaded portions of a canopy implied a non‐evaporative effect onT_cregulation. Specifically,T_cwas more homogeneous within the pine canopy, reflecting differences in leaf morphology that allow higher canopy transmittance of solar radiation. The variability ofT_camong species (up to 1.3°C) was comparable to the previously reported differences in surface temperature across land cover types (1°C to 2°C), implying the potential for significant impact of species composition change on local/regional surface temperature.