Gas chromatography vs. quantum cascade laser-based N₂O flux measurements using a novel chamber design

Recent advances in laser spectrometry offer new opportunities to investigate the soil–atmosphere exchange of nitrous oxide. During two field campaigns conducted at a grassland site and a willow field, we tested the performance of a quantum cascade laser (QCL) connected to a newly developed automated chamber system against a conventional gas chromatography (GC) approach using the same chambers plus an automated gas sampling unit with septum capped vials and subsequent laboratory GC analysis. Through its high precision and time resolution, data of the QCL system were used for quantifying the commonly observed nonlinearity in concentration changes during chamber deployment, making the calculation of exchange fluxes more accurate by the application of exponential models. As expected, the curvature values in the concentration increase was higher during long (60 min) chamber closure times and under high-flux conditions (FN2O > 150 µg N m−2 h−1) than those values that were found when chambers were closed for only 10 min and/or when fluxes were in a typical range of 2 to 50 µg N m−2 h−1. Extremely low standard errors of fluxes, i.e., from  ∼  0.2 to 1.7 % of the flux value, were observed regardless of linear or exponential flux calculation when using QCL data. Thus, we recommend reducing chamber closure times to a maximum of 10 min when a fast-response analyzer is available and this type of chamber system is used to keep soil disturbance low and conditions around the chamber plot as natural as possible. Further, applying linear regression to a 3 min data window with rejecting the first 2 min after closure and a sampling time of every 5 s proved to be sufficient for robust flux determination while ensuring that standard errors of N2O fluxes were still on a relatively low level. Despite low signal-to-noise ratios, GC was still found to be a useful method to determine the mean the soil–atmosphere exchange of N2O on longer timescales during specific campaigns. Intriguingly, the consistency between GC and QCL-based campaign averages was better under low than under high N2O efflux conditions, although single flux values were highly scattered during the low efflux campaign. Furthermore, the QCL technology provides a useful tool to accurately investigate the highly debated topic of diurnal courses of N2O fluxes and its controlling factors. Our new chamber design protects the measurement spot from unintended shading and minimizes disturbance of throughfall, thereby complying with high quality requirements of long-term observation studies and research infrastructures

Strong radiative effect induced by clouds and smoke on forest net ecosystem productivity in central Siberia

Aerosols produced by wildfires are a common phenomenon in boreal regions. For the Siberian taiga, it is still an open question if the effects of aerosols on atmospheric conditions increase net CO2 uptake or photosynthesis. We investigated the factors controlling forest net ecosystem productivity (NEP) and explored how clouds and smoke modulate radiation as a major factor controlling NEP during fire events in the years 2012 and 2013. To characterize the underlying mechanisms of the NEP response to environmental drivers, Artificial Neural Networks (ANNs) were trained by eddy covariance flux measurements nearby the Zotino Tall Tower Observatory (ZOTTO). Total photosynthetically active radiation, vapour pressure deficit, and diffuse fraction explain at about 54-58% of NEP variability. NEP shows a strong negative sensitivity to VPD, and a small positive to f(dlf). A strong diffuse radiation fertilization effect does not exist at ZOTTO forest due to the combined effects of low light intensity, sparse canopy and low leaf area index. Results suggests that light intensity and canopy structure are important factors of the overall diffuse radiation fertilization effect.Peer reviewe

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.Peer reviewe

Biophysical controls on evapotranspiration and water use efficiency in natural, semi-natural and managed African ecosystems

The effects of climatic factors and vegetation type on evapotranspiration (E) and water use efficiency (WUE) were analyzed using tower-based eddy-covariance (EC) data of eleven African sites (22 site years) located across a continental-scale transect. The seasonal pattern of E was closely linked to growing-season length and rainfall distribution. Although annual precipitation (P) was highly variable among sites (290 to 1650 mm), minimum annual E was not less than 250 mm and reached a maximum of 900 mm where annual P exceeded 1200 mm. Site-specific interannual variability in E could be explained by either changes in total P or variations in solar irradiance. At some sites, a highly positive linear correlation was found between monthly sums of E and net radiation (Rn), whereas a hysteretic relationship at other sites indicated that E lagged behind the typical seasonal progression of Rn. Results of a cross-correlation analysis between daily (24-h) E and Rn revealed that site-specific lag times were between 0 days and up to a few weeks depending on the lag of vapor pressure deficit (D) behind Rn and vegetation type. Physiological parameters (e.g. mean dry-foliage Priestley-Taylor alpha) implied that stomatal limitation to transpiration prevailed. During the rainy season, a strong linear correlation between monthly mean values of gross primary production (GPP) and E resulted in water use efficiency being constant with lower values at grass-dominated sites (~2 to ~3.5 g C kg-1 H2O) than at natural woodland sites and plantations (~4.5 to ~6 g C kg-1 H2O)

Comprehensive comparison of gap-filling techniques for eddy covariance net carbon fluxes

We review 15 techniques for estimating missing values of net ecosystem CO2 exchange (NEE) in eddy covariance time series and evaluate their performance for different artificial gap scenarios based on a set of 10 benchmark datasets from six forested sites in Europe. The goal of gap filling is the reproduction of the NEE time series and hence this present work focuses on estimating missing NEE values, not on editing or the removal of suspect values in these time series due to systematic errors in the measurements (e.g., nighttime flux, advection). The gap filling was examined by generating 50 secondary datasets with artificial gaps (ranging in length from single half-hours to 12 consecutive days) for each benchmark dataset and evaluating the performance with a variety of statistical metrics. The performance of the gap filling varied among sites and depended on the level of aggregation (native half-hourly time step versus daily), long gaps were more difficult to fill than short gaps, and differences among the techniques were more pronounced during the day than at night. The non-linear regression techniques (NLRs), the look-up table (LUT), marginal distribution sampling (MDS), and the semi-parametric model (SPM) generally showed good overall performance. The artificial neural network based techniques (ANNs) were generally, if only slightly, superior to the other techniques. The simple interpolation technique of mean diurnal variation (MDV) showed a moderate but consistent performance. Several sophisticated techniques, the dual unscented Kalman filter (UKF), the multiple imputation method (MIM), the terrestrial biosphere model (BETHY), but also one of the ANNs and one of the NLRs showed high biases which resulted in a low reliability of the annual sums, indicating that additional development might be needed. An uncertainty analysis comparing the estimated random error in the 10 benchmark datasets with the artificial gap residuals suggested that the techniques are already at or very close to the noise limit of the measurements. Based on the techniques and site data examined here, the effect of gap filling on the annual sums of NEE is modest, with most techniques falling within a range of ±25 g C m−2 year−1

Net ecosystem exchange

agricultural and forest meteorology 148 (2008) 821–838 available at www.sciencedirect.com journal homepage: www.elsevier.com/locate/agrformet Cross-site evaluation of eddy covariance GPP and RE decomposition technique

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Abstract The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible