Showing papers by "Stefan Metzger published in 2017"

Strong geologic methane emissions from discontinuous terrestrial permafrost in the Mackenzie Delta, Canada.

Abstract: Arctic permafrost caps vast amounts of old, geologic methane (CH4) in subsurface reservoirs. Thawing permafrost opens pathways for this CH4 to migrate to the surface. However, the occurrence of geologic emissions and their contribution to the CH4 budget in addition to recent, biogenic CH4 is uncertain. Here we present a high-resolution (100 m × 100 m) regional (10,000 km²) CH4 flux map of the Mackenzie Delta, Canada, based on airborne CH4 flux data from July 2012 and 2013. We identify strong, likely geologic emissions solely where the permafrost is discontinuous. These peaks are 13 times larger than typical biogenic emissions. Whereas microbial CH4 production largely depends on recent air and soil temperature, geologic CH4 was produced over millions of years and can be released year-round provided open pathways exist. Therefore, even though they only occur on about 1% of the area, geologic hotspots contribute 17% to the annual CH4 emission estimate of our study area. We suggest that this share may increase if ongoing permafrost thaw opens new pathways. We conclude that, due to permafrost thaw, hydrocarbon-rich areas, prevalent in the Arctic, may see increased emission of geologic CH4 in the future, in addition to enhanced microbial CH4 production.

Detecting impacts of extreme events with ecological in situ monitoring networks

Abstract: . Extreme hydrometeorological conditions typically impact ecophysiological processes on land. Satellite-based observations of the terrestrial biosphere provide an important reference for detecting and describing the spatiotemporal development of such events. However, in-depth investigations of ecological processes during extreme events require additional in situ observations. The question is whether the density of existing ecological in situ networks is sufficient for analysing the impact of extreme events, and what are expected event detection rates of ecological in situ networks of a given size. To assess these issues, we build a baseline of extreme reductions in the fraction of absorbed photosynthetically active radiation (FAPAR), identified by a new event detection method tailored to identify extremes of regional relevance. We then investigate the event detection success rates of hypothetical networks of varying sizes. Our results show that large extremes can be reliably detected with relatively small networks, but also reveal a linear decay of detection probabilities towards smaller extreme events in log–log space. For instance, networks with ≈ 100 randomly placed sites in Europe yield a ≥ 90 % chance of detecting the eight largest (typically very large) extreme events; but only a ≥ 50 % chance of capturing the 39 largest events. These findings are consistent with probability-theoretic considerations, but the slopes of the decay rates deviate due to temporal autocorrelation and the exact implementation of the extreme event detection algorithm. Using the examples of AmeriFlux and NEON, we then investigate to what degree ecological in situ networks can capture extreme events of a given size. Consistent with our theoretical considerations, we find that today's systematically designed networks (i.e. NEON) reliably detect the largest extremes, but that the extreme event detection rates are not higher than would be achieved by randomly designed networks. Spatio-temporal expansions of ecological in situ monitoring networks should carefully consider the size distribution characteristics of extreme events if the aim is also to monitor the impacts of such events in the terrestrial biosphere.

eddy4R 0.2.0: a DevOps model for community-extensible processing and analysis of eddy-covariance data based on R, Git, Docker, and HDF5

Abstract: . Large differences in instrumentation, site setup, data format, and operating system stymie the adoption of a universal computational environment for processing and analyzing eddy-covariance (EC) data. This results in limited software applicability and extensibility in addition to often substantial inconsistencies in flux estimates. Addressing these concerns, this paper presents the systematic development of portable, reproducible, and extensible EC software achieved by adopting a development and systems operation (DevOps) approach. This software development model is used for the creation of the eddy4R family of EC code packages in the open-source R language for statistical computing. These packages are community developed, iterated via the Git distributed version control system, and wrapped into a portable and reproducible Docker filesystem that is independent of the underlying host operating system. The HDF5 hierarchical data format then provides a streamlined mechanism for highly compressed and fully self-documented data ingest and output. The usefulness of the DevOps approach was evaluated for three test applications. First, the resultant EC processing software was used to analyze standard flux tower data from the first EC instruments installed at a National Ecological Observatory (NEON) field site. Second, through an aircraft test application, we demonstrate the modular extensibility of eddy4R to analyze EC data from other platforms. Third, an intercomparison with commercial-grade software showed excellent agreement (R2 = 1.0 for CO2 flux). In conjunction with this study, a Docker image containing the first two eddy4R packages and an executable example workflow, as well as first NEON EC data products are released publicly. We conclude by describing the work remaining to arrive at the automated generation of science-grade EC fluxes and benefits to the science community at large. This software development model is applicable beyond EC and more generally builds the capacity to deploy complex algorithms developed by scientists in an efficient and scalable manner. In addition, modularity permits meeting project milestones while retaining extensibility with time.

VOC emission rates over London and South East England obtained by airborne eddy covariance

Abstract: Volatile organic compounds (VOCs) originate from a variety of sources, and play an intrinsic role in influencing air quality. Some VOCs, including benzene, are carcinogens and so directly affect human health, while others, such as isoprene, are very reactive in the atmosphere and play an important role in the formation of secondary pollutants such as ozone and particles. Here we report spatially-resolved measurements of the surface-to-atmosphere fluxes of VOCs across London and SE England made in 2013 and 2014. High-frequency 3-D wind velocities and VOC volume mixing ratios (made by proton transfer reaction - mass spectrometry) were obtained from a low-flying aircraft and used to calculate fluxes using the technique of eddy covariance. A footprint model was then used to quantify the flux contribution from the ground surface at spatial resolution of 100 m, averaged to 1 km. Measured fluxes of benzene over Greater London showed positive agreement with the UK’s National Atmospheric Emissions Inventory, with the highest fluxes originating from central London. Comparison of MTBE and toluene fluxes suggest that petroleum evaporation is an important emission source of toluene in central London. Outside London, increased isoprene emissions were observed over wooded areas, at rates greater than those predicted by a UK regional application of the European Monitoring and Evaluation Programme model (EMEP4UK). This work demonstrates the applicability of the airborne eddy covariance method to the determination of anthropogenic and biogenic VOC fluxes and the possibility of validating emission inventories through measurements.

eddy4R: A community-extensible processing, analysis and modeling framework for eddy-covariance data based on R, Git, Docker and HDF5

Abstract: This study presents the systematic development of an open-source, flexible and modular eddy-covariance (EC) data processing framework. This is achieved through adopting a Development and Systems Operation (DevOps) philosophy, building on the eddy4R family of EC code packages in the R Language for Statistical Computing as foundation. These packages are community-developed via the GitHub distributed version control system and wrapped into a portable and reproducible Docker filesystem that is independent of the underlying host operating system. The HDF5 hierarchical data format then provides a streamlined mechanism for highly compressed and fully self-documented data ingest and output. This framework is applicable beyond EC, and more generally builds the capacity to deploy complex algorithms developed by scientists in an efficient and scalable manner. In addition, modularity permits meeting project milestones while retaining extensibility with time. The efficiency and consistency of this framework is demonstrated in the form of three application examples. These include tower EC data from first instruments installed at a National Ecological Observatory (NEON) field site, aircraft flux measurements in combination with remote sensing data, as well as a software intercomparison. In conjunction with this study, the first two eddy4R packages and simple NEON EC data products are released publicly. While this proof-of-concept represents a significant advance, substantial work remains to arrive at the automated framework needed for the streaming generation of science-grade EC fluxes.