Statistical uncertainty of eddy flux–based estimates of gross ecosystem carbon exchange at Howland Forest, Maine

We present an uncertainty analysis of gross ecosystem carbon exchange (GEE) estimates derived from 7 years of continuous eddy covariance measurements of forest-atmosphere CO2fluxes at Howland Forest, Maine, USA. These data, which have high temporal resolution, can be used to validate process modeling analyses, remote sensing assessments, and field surveys. However, separation of tower-based net ecosystem exchange (NEE) into its components (respiration losses and photosynthetic uptake) requires at least one application of a model, which is usually a regression model fitted to nighttime data and extrapolated for all daytime intervals. In addition, the existence of a significant amount of missing data in eddy flux time series requires a model for daytime NEE as well. Statistical approaches for analytically specifying prediction intervals associated with a regression require, among other things, constant variance of the data, normally distributed residuals, and linearizable regression models. Because the NEE data do not conform to these criteria, we used a Monte Carlo approach (bootstrapping) to quantify the statistical uncertainty of GEE estimates and present this uncertainty in the form of 90% prediction limits. We explore two examples of regression models for modeling respiration and daytime NEE: (1) a simple, physiologically based model from the literature and (2) a nonlinear regression model based on an artificial neural network. We find that uncertainty at the half-hourly timescale is generally on the order of the observations themselves (i.e., ∼100%) but is much less at annual timescales (∼10%). On the other hand, this small absolute uncertainty is commensurate with the interannual variability in estimated GEE. The largest uncertainty is associated with choice of model type, which raises basic questions about the relative roles of models and data

Near-surface remote sensing of spatial and temporal variation in canopy phenology

There is a need to document how plant phenology is responding to global change factors, particularly warming trends. “Near-surface” remote sensing, using radiometric instruments or imaging sensors, has great potential to improve phenological monitoring because automated observations can be made at high temporal frequency. Here we build on previous work and show how inexpensive, networked digital cameras (“webcams”) can be used to document spatial and temporal variation in the spring and autumn phenology of forest canopies. We use two years of imagery from a deciduous, northern hardwood site, and one year of imagery from a coniferous, boreal transition site. A quantitative signal is obtained by splitting images into separate red, green, and blue color channels and calculating the relative brightness of each channel for “regions of interest” within each image. We put the observed phenological signal in context by relating it to seasonal patterns of gross primary productivity, inferred from eddy covariance measurements of surface–atmosphere CO2 exchange. We show that spring increases, and autumn decreases, in canopy greenness can be detected in both deciduous and coniferous stands. In deciduous stands, an autumn red peak is also observed. The timing and rate of spring development and autumn senescence varies across the canopy, with greater variability in autumn than spring. Interannual variation in phenology can be detected both visually and quantitatively; delayed spring onset in 2007 compared to 2006 is related to a prolonged cold spell from day 85 to day 110. This work lays the foundation for regional- to continental-scale camera-based monitoring of phenology at network observatory sites, e.g., National Ecological Observatory Network (NEON) or AmeriFlux

Nitrogen cycling, forest canopy reflectance, and emergent properties of ecosystems

In Ollinger et al. (1), we reported that mass-based concentrations of nitrogen in forest canopies (%N) are positively associated with whole-canopy photosynthetic capacity and canopy shortwave albedo in temperate and boreal forests, the latter result stemming from a positive correlation between %N and canopy near infrared (NIR) reflectance. This finding is intriguing because a functional link between %N and NIR reflectance could indicate an influence of nitrogen cycling on surface energy exchange, and could provide a means for estimating %N using broad-band satellite sensors

Characteristics of Magnetoplasmas Semiannual Status Report No. 12, May 1 - Oct. 31, 1965

Magnetoplasma characteristics - anomalous diffusion across magnetic field, heat conduction in plasma, cesium plasma generator, and electron velocity distribution function in magnetoplasma

Direct Observation of Site-specific Valence Electronic Structure at Interface: SiO2/Si Interface

Atom specific valence electronic structures at interface are elucidated successfully using soft x-ray absorption and emission spectroscopy. In order to demonstrate the versatility of this method, we investigated SiO2/Si interface as a prototype and directly observed valence electronic states projected at the particular atoms of the SiO2/Si interface; local electronic structure strongly depends on the chemical states of each atom. In addition we compared the experimental results with first-principle calculations, which quantitatively revealed the interfacial properties in atomic-scale.Comment: 4 pages, 3 figure

Reply to Fisher: Nitrogen–albedo relationship in forests remains robust and thought-provoking

Fisher’s primary concerns have overlooked important methodological aspects of our study, whereas other concerns are consistent with our own presentation of the findings. We did not exclude photosynthetically active radiation (PAR) wavelengths, as Fisher states

Canopy nitrogen, carbon assimilation, and albedo in temperate and boreal forests: Functional relations and potential climate feedbacks

The availability of nitrogen represents a key constraint on carbon cycling in terrestrial ecosystems, and it is largely in this capacity that the role of N in the Earth\u27s climate system has been considered. Despite this, few studies have included continuous variation in plant N status as a driver of broad-scale carbon cycle analyses. This is partly because of uncertainties in how leaf-level physiological relationships scale to whole ecosystems and because methods for regional to continental detection of plant N concentrations have yet to be developed. Here, we show that ecosystem CO2 uptake capacity in temperate and boreal forests scales directly with whole-canopy N concentrations, mirroring a leaf-level trend that has been observed for woody plants worldwide. We further show that both CO2 uptake capacity and canopy N concentration are strongly and positively correlated with shortwave surface albedo. These results suggest that N plays an additional, and overlooked, role in the climate system via its influence on vegetation reflectivity and shortwave surface energy exchange. We also demonstrate that much of the spatial variation in canopy N can be detected by using broad-band satellite sensors, offering a means through which these findings can be applied toward improved application of coupled carbon cycle–climate models

Detecting long-term changes in stomatal conductance: challenges and opportunities of tree-ring δ18 O proxy

N/An/

Forest Drought Response Index (ForDRI): A New Combined Model to Monitor Forest Drought in the Eastern United States

Monitoring drought impacts in forest ecosystems is a complex process because forest ecosystems are composed of different species with heterogeneous structural compositions. Even though forest drought status is a key control on the carbon cycle, very few indices exist to monitor and predict forest drought stress. The Forest Drought Indicator (ForDRI) is a new monitoring tool developed by the National Drought Mitigation Center (NDMC) to identify forest drought stress. ForDRI integrates 12 types of data, including satellite, climate, evaporative demand, ground water, and soil moisture, into a single hybrid index to estimate tree stress. The model uses Principal Component Analysis (PCA) to determine the contribution of each input variable based on its covariance in the historical records (2003–2017). A 15-year time series of 780 ForDRI maps at a weekly interval were produced. The ForDRI values at a 12.5km spatial resolution were compared with normalized weekly Bowen ratio data, a biophysically based indicator of stress, from nine AmeriFlux sites. There were strong and significant correlations between Bowen ratio data and ForDRI at sites that had experienced intense drought. In addition, tree ring annual increment data at eight sites in four eastern U.S. national parks were compared with ForDRI values at the corresponding sites. The correlation between ForDRI and tree ring increments at the selected eight sites during the summer season ranged between 0.46 and 0.75. Generally, the correlation between the ForDRI and normalized Bowen ratio or tree ring increment are reasonably good and indicate the usefulness of the ForDRI model for estimating drought stress and providing decision support on forest drought management